The DOI matches its Crossref record; 90 of the 91 references it cites were found in a registry or an index; 11 of the 15 sources its claims cite were checked for support.
Generative Agents: Interactive Simulacra of Human Behavior
Open accessAbstract
Introduces generative agents that store experiences in natural language, synthesize reflections, and retrieve memories dynamically for planning and social behavior.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The paper introduces generative agents powered by an LLM architecture with memory, reflection, and planning, instantiated as 25 characters in a Sims-like sandbox. In controlled and end-to-end evaluations, the full architecture produced more believable behavior than ablations and supported emergent social phenomena such as information spread, relationship formation, and party coordination.
Key findings
- In a controlled believability ranking with 100 evaluators, the full architecture scored highest (TrueSkill mean 29.89, SD 0.72), ahead of no-reflection (26.88, 0.69), no-reflection/planning (25.64, 0.68), human crowdworker (22.95, 0.69), and fully ablated (21.21, 0.70); Kruskal-Wallis χ²(4)=150.29, p<0.001; all pairwise Dunn differences were significant at p<0.001 except crowdworker vs. fully ablated; Cohen’s d=8.16 versus the prior-work baseline.
- Over two game days with 25 agents, knowledge of Sam’s mayoral run rose from 1 agent (4%) to 8 (32%), and knowledge of Isabella’s Valentine’s party rose from 1 (4%) to 13 (52%); none who claimed knowledge of this information had hallucinated it.
- Relationship network density increased from 0.167 to 0.74; among 453 awareness responses, 1.3% (n=6) were hallucinated; five of twelve invited agents attended the Valentine’s party.
- Common failures included retrieving wrong or incomplete memories, embellishing memories, and inheriting overly formal or overly cooperative language from the underlying model.
Methods
- Architecture: an LLM (gpt3.5-turbo ChatGPT) augmented with a natural-language memory stream, retrieval scoring by recency (decay 0.995), importance (LLM-rated 1–10), and relevance (embedding cosine similarity), periodic reflection, and recursive planning/reacting/dialogue.
- Controlled evaluation: within-subjects, 100 Prolific participants ranked believability of five conditions (full architecture, three ablations, human crowdworker) across five interview question categories; analysis used TrueSkill, Kruskal-Wallis, Dunn post-hoc with Holm-Bonferroni, plus inductive qualitative coding.
- End-to-end evaluation: 25 agents in Smallville over two game days; measured information diffusion via interviews, relationship formation via mutual-knowledge network density, and coordination via party attendance.
Limitations
- Stated: evaluation was short (two game days) and the human baseline was crowdworker-authored, not maximal human performance; simulation cost thousands of dollars in token credits and multiple days; robustness to prompt hacking, memory hacking, and hallucination is largely untested.
- Stated: agents inherit underlying LLM imperfections, including potential bias/stereotypes and weaker believability for marginalized subpopulations due to limited data.
- Evident from the text: agents showed erratic location choices, misapplied physical/social norms (e.g., one-person bathroom, after-hours stores), and instruction-tuning effects toward formality and excessive agreeableness; the environment was manually authored and not the focus.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
11 of 15 cited sources checked · 5 supported · 0 flagged · accuracy not yet measured
Every source the paper cites for this claim was checked and supports it.
In the paper
“Challenges with long-term planning and coherence remain [19] even with today's most performant models such as GPT-4.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 4 GENERATIVE AGENT ARCHITECTURE
What the sources say
- Supportedcites [19] 19. Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. 2023. Sparks of artifcial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712 (2023). source
Section 1.2 says the paper provides an in-depth analysis of GPT-4’s lack of planning. Section 10.2 also says the model can produce inconsistent content and cannot reliably check whether its output is self-consistent. Together, these support the claim that planning and coherence remain challenges for GPT-4. (LLM, conf=0.90)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The no observation, no refection, no planning condition efectively represents the previous state of the art for agents created through large language models [12,46,80].”
§ Generative Agents: Interactive Simulacra of Human Behavior > 6.2 Conditions
What the sources say
- Could not verifycites [12] 12. Marcel Binz and Eric Schulz. 2023. Using cognitive psychology to under- stand GPT-3. Proceedings of the National Academy of Sciences 120, 6 (2023), e2218523120. sourceExperimental — accuracy not yet measured
The Discussion (page not provided) describes GPT-3 as learning passively from text and recommends that future agents interact with the world. The Results (page not provided) examine observations and decision-making in psychological tasks. Neither section identifies a condition without observations, reflection, and planning as the previous state of the art for LLM agents; searches for that condition and for a prior agent baseline found no such passage. (LLM, conf=0.76)
- Could not verifycites [46] 46. John J. Horton. 2023. Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus? arXiv:2301.07543 [econ.GN] sourceExperimental — accuracy not yet measured
Section 2.4 describes creating LLM agents through prompts, but does not characterize a condition without observations, reflections, and planning. Searches for that combination and for the previous state of the art found no passage in our copy addressing the comparison. (LLM, conf=0.73)
- Partly supportedcites [80] 80. Joon Sung Park, Lindsay Popowski, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein. 2022. Social Simulacra: Creating Populated Prototypes for Social Computing Systems. In In the 35th Annual ACM Symposium on User Interface Software and Technology (UIST '22) (Bend, OR, USA) (UIST '22). Association for Computing Machinery, New York, NY, USA. https://doi.org/10. 1145/3526113.3545616 sourceExperimental — accuracy not yet measured
Section 4.2 describes a prior GPT-3 system that generates social participants through persona-based prompt chains, so this paper is an example of an earlier language-model agent approach. But §4.2.3 says replies are prompted with the prior conversation as context; the paper does not identify its system as a no-observation, no-reflection, no-planning condition or establish that condition as the previous state of the art. (LLM, conf=0.78)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Could not verify
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Comparing the condition representing prior work (with no memory, planning, or refection [12,46,80]) to the full architecture produces a standardized efect size of = 8.16, or eight standard deviations.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 6.5.1 The Full Architecture Bests Other Conditions.
What the sources say
- Could not verifycites [12] 12. Marcel Binz and Eric Schulz. 2023. Using cognitive psychology to under- stand GPT-3. Proceedings of the National Academy of Sciences 120, 6 (2023), e2218523120. sourceExperimental — accuracy not yet measured
Two searches of Binz and Schulz’s “Using cognitive psychology to understand GPT-3” found no relevant passage; the closest snippets concern GPT-3’s exploration behavior (scores 0.179 and 0.127), not an architecture ablation or an effect size of 8.16. An unscoped search found the specific 8.16 comparison in “Generative Agents: Interactive Simulacra of Human Behavior,” but its match score was 0.428, below the rubric’s 0.70 threshold for a misattribution verdict. The cited paper does not make a contrary claim, so the prior disputed verdict is unsupported; the exact-match finding warrants a manual citation check. (LLM, conf=0.55)
- Supportedcites [46] 46. John J. Horton. 2023. Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus? arXiv:2301.07543 [econ.GN] source
The citation is attached to the description of prior work, not to the citing paper’s reported effect size. The Introduction (page unavailable) describes Horton’s GPT-3 agents answering economic decision scenarios, and §3.3 describes supplying beliefs through prompts; this supports citing the paper as an example of earlier agent work rather than the full architecture. Horton’s paper does not report the 8.16 comparison. (LLM, conf=0.88)
- Could not verifycites [80] 80. Joon Sung Park, Lindsay Popowski, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein. 2022. Social Simulacra: Creating Populated Prototypes for Social Computing Systems. In In the 35th Annual ACM Symposium on User Interface Software and Technology (UIST '22) (Bend, OR, USA) (UIST '22). Association for Computing Machinery, New York, NY, USA. https://doi.org/10. 1145/3526113.3545616 sourceExperimental — accuracy not yet measured
Section 5.5 reports error rates for SimReddit and its ablations, but not a standardized effect size of 8.16. Searches for that value and for a prior-work condition without memory, planning, or reflection found no matching passage in our copy; page numbers were unavailable. (LLM, conf=0.76)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: The five conditions adequately isolate the contributions of memory, planning, and reflection to believability.
- Could not verify
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Extending prior work [80], these social behaviors are emergent rather than pre-programmed.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 3.4 Emergent Social Behaviors
What the sources say
- Partly supportedcites [80] 80. Joon Sung Park, Lindsay Popowski, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein. 2022. Social Simulacra: Creating Populated Prototypes for Social Computing Systems. In In the 35th Annual ACM Symposium on User Interface Software and Technology (UIST '22) (Bend, OR, USA) (UIST '22). Association for Computing Machinery, New York, NY, USA. https://doi.org/10. 1145/3526113.3545616 sourceExperimental — accuracy not yet measured
Section 3 describes SimReddit generating users and their interactions with GPT-3, supporting the prior-work premise that social behaviors can be generated rather than individually pre-programmed. The paper does not examine Smallville, so it cannot establish how Smallville’s particular behaviors arose. (LLM, conf=0.82)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Partly supported
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For example, starting with only a single user-specifed notion that one agent wants to throw a Valentine's Day party, the agents autonomously spread invitations to the party over the next two”
§ Generative Agents: Interactive Simulacra of Human Behavior > Abstract
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We demonstrate that, with generative agents, it is sufcient to simply tell one agent that she wants to throw a party.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 1 INTRODUCTION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We observe that each of these components is critical to strong performance across these interview tasks.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 1 INTRODUCTION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: Natural language interviews can validly assess an agent's knowledge and behavior, including its ability to stay in character, remember, plan, react, and reflect accurately.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Generative agents draw a wide variety of inferences about themselves, other agents, and their environment;”
§ Generative Agents: Interactive Simulacra of Human Behavior > 1 INTRODUCTION
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The architecture of generative agents operates using natural language.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 5.1 From Structured World Environments to Natural Language, and Back Again
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Out of the 453 agent responses regarding their awareness of other agents, 1.3% (n=6) were found to be hallucinated.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 7.1.2 Results.
Strength:plain· not yet calibratedn = 6Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Rests on an assumption: The agents' memory streams accurately record the events and dialogues that ground their interview responses.
- Rests on an assumption: A two-day simulation with 25 agents is sufficient to observe emergent social behaviors such as information diffusion, relationship formation, and coordination.
- Probably not a substantial claim. 36
- It may state two separate claims as one. 42
- The quote only partly states this claim. 1
Every source the paper cites for this claim was checked and supports it.
In the paper
“Finally, we observed possible efects of instruction tuning [79], which seemed to guide the behavior of the agents to be more polite and cooperative overall.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 7.2 Boundaries and Errors
What the sources say
- Supportedcites [79] 79. Long Ouyang, Jef Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2022. Training language models to follow instructions with human feedback. arXiv:2203.02155 [cs.CL] source
The Introduction describes fine-tuning GPT-3 to follow instructions and reports improved behavior across tasks. The labeling instructions call for helpful responses and for treating people with kindness and respect. In the citing sentence, the authors’ observation that their agents seemed more polite and cooperative is their own; the citation identifies the instruction-tuning approach, rather than attributing that observation to Ouyang et al. (LLM, conf=0.88)
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: A two-day simulation with 25 agents is sufficient to observe emergent social behaviors such as information diffusion, relationship formation, and coordination.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“While a large language model can generate plausible behavior in response to situational information (e.g., [46,80]), agents need to plan over a longer time horizon to ensure that their sequence of actions is coherent and believable.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 4.3 Planning and Reacting
What the sources say
- Supportedcites [46] 46. John J. Horton. 2023. Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus? arXiv:2301.07543 [econ.GN] source
The Introduction says LLMs can be placed in scenarios and respond realistically to textual inputs, producing responses similar to those expected from humans. The citation is attached to that first clause, not to the citing authors’ subsequent claim about long-term planning. (LLM, conf=0.92)
- Supportedcites [80] 80. Joon Sung Park, Lindsay Popowski, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein. 2022. Social Simulacra: Creating Populated Prototypes for Social Computing Systems. In In the 35th Annual ACM Symposium on User Interface Software and Technology (UIST '22) (Bend, OR, USA) (UIST '22). Association for Computing Machinery, New York, NY, USA. https://doi.org/10. 1145/3526113.3545616 source
The citation is attached to the first clause, about generating plausible behavior from situational information. Section 3.3 describes SimReddit generating different responses to a specified post and moderator intervention; the Abstract also reports realistic interactions that shift in response to design changes. The later statement about longer-horizon planning is not attributed to this source by the citation placement. (LLM, conf=0.90)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Supported
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For instance, generative agents turn of the stove when they see that their breakfast is burning, wait outside the bathroom if it is occupied, and stop to chat when they meet another agent they want to talk to.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 1 INTRODUCTION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“they respond when the end user changes their environment or commands them in natural language.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 1 INTRODUCTION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“they create daily plans that refect their characteristics and experiences, act out those plans, react, and re-plan when appropriate;”
§ Generative Agents: Interactive Simulacra of Human Behavior > 1 INTRODUCTION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A society full of generative agents is marked by emergent social dynamics where new relationships are formed, information difuses, and coordination arises across agents.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 1 INTRODUCTION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Across the technical and end-to-end evaluation, the most common errors arose when the agent failed to retrieve relevant memories, fabricated embellishments to the agent's memory, or inherited overly formal speech or behavior from the language model.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 1 INTRODUCTION
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Despite many potential points of failure-the party planner must remember to invite other agents to the party, attendees must remember the invitation, those who remember must decide to actually show up, and moreour agents succeed.They spread the word about the party and then”
- Also stated in: § 1 INTRODUCTION · plain
“show up, with one agent even asking another on a date to the party, all from a single user-generated seed suggestion.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 1 INTRODUCTION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Also stated in: § 1 INTRODUCTION · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In an evaluation, these generative agents produce believable individual and emergent social behaviors.”
§ Generative Agents: Interactive Simulacra of Human Behavior > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In this scenario, the end user only set Isabella's initial intent to throw a party and Maria's crush on Klaus: the social behaviors of spreading the word, decorating, asking each other out, arriving at the party, and interacting with each other at the party were initiated by the agent architecture.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 3.4.3 Coordination.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It was rare for the agents to completely fabricate their knowledge: they may fail to recall certain events having taken place and respond by acknowledging their lack of memory.However, they did not afrmatively claim to have experienced something they had not.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 6.5.2 Generative Agents Remember, but With Embellishments.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The agents' memory streams accurately record the events and dialogues that ground their interview responses.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“These retrieved memories are also used to form longer-term plans and create higher-level refections, both of which are entered into the memory stream for future use.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 3.4.3 Coordination.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For instance, when asked what she might get Wolfgang Schulz for his birthday, Maria Lopez, with no access to refection, responded by acknowledging her uncertainty, stating that she did not know what Wolfgang likes, despite having had many interactions with him.However, with access to refection memories, Maria answered confdently,”
§ Generative Agents: Interactive Simulacra of Human Behavior > 6.5.3 Reflection Is Required for Synthesis.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The agents' memory streams accurately record the events and dialogues that ground their interview responses.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Records are recursively synthesized into higher-and higher-level refections that guide behavior.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 4 GENERATIVE AGENT ARCHITECTURE
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Agents build individual tree representations of the environment as they navigate it -subgraphs of the overall sandbox environment tree.”
- Also stated in: § 5.1 From Structured World Environments to Natural Language, and Back Again · hedged
“Agents are not omniscient: their tree may get out of date as they leave an area, and is updated when they re-enter the area.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 5.1 From Structured World Environments to Natural Language, and Back Again
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Also stated in: § 5.1 From Structured World Environments to Natural Language, and Back Again · hedged
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Two evaluations, a controlled evaluation and an end-to-end evaluation, that establish causal efects of the importance of components of the architecture, as well as identify breakdowns arising from, e.g., improper memory retrieval.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 1 INTRODUCTION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: Natural language interviews can validly assess an agent's knowledge and behavior, including its ability to stay in character, remember, plan, react, and reflect accurately.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The sandbox server is also responsible for sending all agents and objects that are within a preset visual range for each agent to that agent's memory, so the agent can react appropriately.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 5 SANDBOX ENVIRONMENT IMPLEMENTATION
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The present study required substantial time and resources to simulate 25 agents for two days, costing thousands of dollars in token credits and taking multiple days to complete.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 8.2 Future Work and Limitations
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This generates a rough sketch of the agent's plan for a day, divided into fve to eight chunks:”
§ Generative Agents: Interactive Simulacra of Human Behavior > 4.3 Planning and Reacting
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“When an agent executes an action on an object, we prompt the language model to ask what happens to the state of the object.”
§ Generative Agents: Interactive Simulacra of Human Behavior > This outputs
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This prompt returns an integer value of 2 for”
§ Generative Agents: Interactive Simulacra of Human Behavior > 4.1 Memory and Retrieval
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“As agents notice each other, they may engage in dialogue-as they do so, information can spread from agent to agent.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 3.4.1 Information Difusion.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In a later interaction, Sam's interactions with Latoya indicate a memory of that interaction, as he asks”
§ Generative Agents: Interactive Simulacra of Human Behavior > 3.4.2 Relationship Memory.
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“From the memory stream, records are retrieved as relevant to plan the agent's actions and react appropriately to the environment.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 4 GENERATIVE AGENT ARCHITECTURE
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Based on their perceptions, the architecture retrieves relevant memories and uses those retrieved actions to determine an action.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 3.4.3 Coordination.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“They are aware of other agents in their local area, and the generative agent architecture determines whether they walk by or engage in conversation.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 3.1.1 Inter-Agent Communication.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“At the center of our architecture is the memory stream, a database that maintains a comprehensive record of an agent's experience.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 4 GENERATIVE AGENT ARCHITECTURE
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Moreover, we observed that the instruction tuning also seemed to make the agents overly cooperative with one another.For example, Isabella received a wide range of suggestions and ideas from other agents for the Valentine's Day party from other agents, such as hosting a Shakespearean reading session or a professional networking event.Despite these ideas not aligning with her own interests and”
§ Generative Agents: Interactive Simulacra of Human Behavior > 7.2 Boundaries and Errors
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: A two-day simulation with 25 agents is sufficient to observe emergent social behaviors such as information diffusion, relationship formation, and coordination.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The output suggests that John could consider asking Eddy about his music composition project.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 4.3.1 Reacting and Updating Plans.
Strength:hedged· not yet calibratedTests✗ Asserted: fails✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The authors may not put it forward as true. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In practice, our agents refected roughly two or three times a day.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 4.2 Refection
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Gradually, Sam's candidacy becomes the talk of the town, with some supporting him and others remaining undecided.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 3.4.1 Information Difusion.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Agents may change their plans midstream if needed.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 4.3 Planning and Reacting
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In our context, we focus on three main components that, together, produce efective results.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 4.1 Memory and Retrieval
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“There are many possible implementations of an importance score; we fnd that directly asking the language model to output an integer score is efective.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 4.1 Memory and Retrieval
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Performance degraded with the removal of each component in the ablation conditions: the ablated architecture with no access to refection was the next best ( = 26.88;= 0.69), followed by no access to refection or planning ( = 25.64;= 0.68), and then the crowdworker condition ( = 22.95; = 0.69).”
§ Generative Agents: Interactive Simulacra of Human Behavior > 6.5.1 The Full Architecture Bests Other Conditions.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The five conditions adequately isolate the contributions of memory, planning, and reflection to believability.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Without access to her observational memory, Abigail denied awareness of Rajiv Patel, an acquaintance in the sandbox world.But with the aid of memory, she could successfully recall Rajiv and describe him:”
§ Generative Agents: Interactive Simulacra of Human Behavior > 6.5.2 Generative Agents Remember, but With Embellishments.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The agents' memory streams accurately record the events and dialogues that ground their interview responses.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“By interacting with each other, generative agents in Smallville exchange information, form new relationships, and coordinate joint activities.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 3.4 Emergent Social Behaviors
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Our architecture implements a retrieval function that takes the agent's current situation as input and returns a subset of the memory stream to pass on to the language model.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 4.1 Memory and Retrieval
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Agents in Smallville form new relationships over time and remember their interactions with other agents.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 3.4.2 Relationship Memory.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The agents interact with the world by their actions, and with each other through natural language.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 3.1.1 Inter-Agent Communication.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This generates Eddy's response:”
§ Generative Agents: Interactive Simulacra of Human Behavior > 4.3.2 Dialogue.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“With the approach described below, Klaus's afternoon plan is less gluttonous: he has lunch at Hobbs Cafe while reading at 12pm, works on his research paper at the school library at 1pm, and takes a break for a walk in the park at 3pm.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 4.3 Planning and Reacting
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We further inspected the seven agents who were invited to the party but did not attend by engaging them in an interview.Three cited conficts that prevented them from joining the party.For example, Rajiv, a painter, explained that he was too busy:”
§ Generative Agents: Interactive Simulacra of Human Behavior > 7.1.2 Results.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one. The quote only partly states this claim.
- Rests on an assumption: A two-day simulation with 25 agents is sufficient to observe emergent social behaviors such as information diffusion, relationship formation, and coordination.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Despite many possible points of failure in the ensuing chain of events-agents might not act on that intent, might forget to tell others, might not remember to show up-the Valentine's Day party does, in fact, occur, with a number of agents gathering and interacting.”
§ Generative Agents: Interactive Simulacra of Human Behavior > B.5 Refections
Strength:plain· not yet calibratedSingle caseTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A refection tree for Klaus Mueller.The agent's observations of the world, represented in the leaf nodes, are recursively synthesized to derive Klaus's self-notion that he is highly dedicated to his research.”
§ Generative Agents: Interactive Simulacra of Human Behavior > B.5 Refections
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“John wakes up around 6 am and completes his morning routine, which includes brushing his teeth, taking a shower, and eating breakfast.He briefy catches up with his wife, Mei, and son, Eddy, before heading out to begin his workday.”
§ Generative Agents: Interactive Simulacra of Human Behavior > B.5 Refections
Strength:plain· not yet calibratedSingle caseTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The full generative agent architecture produces more believable behavior than the ablated architectures and the human crowdworkers.”
§ Generative Agents: Interactive Simulacra of Human Behavior > B.5 Refections
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Generative agents, believable simulacra of human behavior that are dynamically conditioned on agents' changing experiences and environment.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 1 INTRODUCTION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A novel architecture that makes it possible for generative agents to remember, retrieve, refect, interact with other agents, and plan through dynamically evolving circumstances.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 1 INTRODUCTION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We synthesize this research to suggest that large language models, though not sufcient by themselves, open up a new angle for creating believable agents when leveraged using the appropriate architecture.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 2 RELATED WORK
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our argument is that large language models ofer an opportunity to re-examine these questions, provided that we can craft an efective architecture to synthesize memories into believable behavior.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 2.2 Believable Proxies of Human Behavior
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Refection explicitly allows the agents to refect not only on their observations but also on other refections: for example, the second statement about Klaus Mueller above is a refection that Klaus previously had, not an observation from his environment.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 4.2 Refection
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The architecture leverages the powerful prompting capabilities of large language models and supplements those capabilities to support longer-term agent coherence, the ability to manage dynamically evolving memory, and recursively produce higher-level refections.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 1 INTRODUCTION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Lastly, the robustness of generative agents is still largely unknown.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 8.2 Future Work and Limitations
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The study followed a within-subjects design, where 100 participants compared interview responses generated by four diferent agent architectures and a human-authored condition for the same agent.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 6.1 Evaluation Procedure
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Moreover, like many large language models, generative agents may struggle to generate believable behavior for certain subpopulations, particularly marginalized populations, due to limited data availability.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 8.2 Future Work and Limitations
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In general, any imperfections in the underlying large language models will be inherited by generative agents.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 8.2 Future Work and Limitations
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“In terms of evaluation, the assessment of generative agents' behavior in this study was limited to a relatively short timescale and a baseline human crowdworker condition.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 8.2 Future Work and Limitations
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To ensure that the crowdworker-authored responses met at least a baseline expectation of quality, the frst author manually inspected the workers' responses to the question”
§ Generative Agents: Interactive Simulacra of Human Behavior > 6.2 Conditions
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Additionally, for every response that confrmed the agents' knowledge of the information, we verifed that the agents did not hallucinate their responses by locating the specifc dialogue in their memory stream that provided them with the information.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 7.1.1 Measurements.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Rests on an assumption: The agents' memory streams accurately record the events and dialogues that ground their interview responses.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Without these mechanisms, large language models can output behavior, but the resulting agents may not react based on the agent's past experiences, may not make important inferences, and may not maintain long-term coherence.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 4 GENERATIVE AGENT ARCHITECTURE
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Four sets of crowdworker-authored responses did not meet these criteria and were re-generated by other workers.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 6.2 Conditions
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Architectures were given equivalent access to all memories accrued by the agent up until the moment of the interview, so the diferences observed here likely represent a conservative estimate of the true diferences: in reality, the ablated architectures would not have followed the same path as the full architecture through the two-day simulation.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 6.2 Conditions
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“They may be vulnerable to prompt hacking, memory hacking-where a carefully crafted conversation could convince an agent of the existence of a past event that never occurred-and hallucination, among other issues.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 8.2 Future Work and Limitations
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Given the known biases of language models, generative agents may potentially exhibit biased behavior or stereotypes.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 8.2 Future Work and Limitations
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We chose to design the experiment this way as re-simulating for each architecture would cause the simulations to diverge into diferent states, making comparison challenging.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 6.2 Conditions
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These serve as the initial memories that determine the agent's behavior.These memories are initial starting points: as the agents gain more experience in the sandbox world, and as more records saturate the memory stream, the agent's summary and behavior will evolve.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 5 SANDBOX ENVIRONMENT IMPLEMENTATION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Furthermore, the frst author conducted an inductive analysis [95] to study the qualitative distinctions between the responses produced in each condition.We employed qualitative open coding [33] in two phases.”
§ Generative Agents: Interactive Simulacra of Human Behavior > 6.4 Analysis
What the sources say
- Not checked — no full text of the sourcecites [95] 95. David R. Thomas. 2006. A General Inductive Approach for Analyzing Qualitative Evaluation Data. American Journal of Evaluation 27, 2 (2006), 237-246. https: //doi.org/10.1177/1098214005283748 source
- Not matchedcites [33] 33. Uwe Flick. 2009. An Introduction to Qualitative Research. SAGE.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
Natural language interviews can validly assess an agent's knowledge and behavior, including its ability to stay in character, remember, plan, react, and reflect accurately.
“In the technical evaluation, we leverage a methodological opportunity to evaluate an agent's knowledge and behavior by”
The paper relies on this without support.
The language model can reliably produce coherent and contextually appropriate natural language for agent behavior.
“Underlying this behavior is a novel agent architecture that combines a large language model with mechanisms for synthesizing and retrieving relevant information to condition the language model's output.”
The paper relies on this without support.
The retrieval and synthesis mechanisms are sufficient to maintain long-term coherence.
“Because generative agents produce large streams of events and memories that must be retained, a core challenge of our architecture is to ensure that the most relevant pieces of the agent's memory are retrieved and synthesized when needed.”
The paper relies on this without support.
Human evaluators' believability rankings are a valid measure of behavioral believability.
“Our dependent variable is the believability of the behavior, a central dependent variable in prior work on agents (e.g., [10]).”
The paper relies on this without support.
The crowdworker-authored responses provide a meaningful human baseline for behavioral competency.
“In addition to the ablation conditions, we added a condition with human crowdworker-authored behavior intended to provide a human baseline.”
The paper relies on this without support.
Human evaluators' rankings of believability provide a valid measure of the believability of agent behavior.
“Our experiment produced 100 sets of rank data, where each participant ranked the fve conditions by believability.”
“Our fndings suggest that the full architecture of generative agents generates the most believable behavior among all the conditions.”
The paper relies on this without support.
The five conditions adequately isolate the contributions of memory, planning, and reflection to believability.
“Performance degraded with the removal of each component in the ablation conditions: the ablated architecture with no access to refection was the next best ( = 26.88;= 0.69), followed by no access to refection or planning ( = 25.64;= 0.68), and then the crowdworker condition ( = 22.95; = 0.69).”
The paper relies on this without support.
The agents' memory streams accurately record the events and dialogues that ground their interview responses.
“we verifed that the agents did not hallucinate their responses by locating the specifc dialogue in their memory stream that provided them with the information.”
The paper relies on this without support.
A two-day simulation with 25 agents is sufficient to observe emergent social behaviors such as information diffusion, relationship formation, and coordination.
“we allowed 25 agents to interact with each other continuously over two full game days in Smallville.”
“We observed evidence of emergent outcomes across all three cases.”
The paper relies on this without support.
The crowdworker condition is a meaningful baseline for human believability.
“the crowdworker condition ( = 22.95; = 0.69).”
“While the crowdworker condition provided a helpful comparison point, it did not represent the maximal human performance that could serve as the gold standard in terms of believability.”
The paper relies on this without support.
The sandbox game world is a valid testbed for simulating human behavior.
“We then demonstrate the potential of generative agents by manifesting them as non-player characters in a Sims-style game world and simulating their lives within it.”
The paper relies on this without support.
For agents to coordinate, they must hear about an event and decide to attend by planning to arrive at the correct time and place.
“To coordinate their behavior, agents need to hear about the event and choose to act on it by planning to show up at the right time and location.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source doesn’t support the claim as written.
In the citing paper
“Agent memory systems do the same.”
From Is Agent Memory a Database? Rethinking Data Foundations for Long-Term AI Agent Memory (2026) · cites this paper as [32]
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Usually, there three commonly used criteria for information extraction, that is, the recency, relevance, and importance [20].”
From A survey on large language model based autonomous agents (2024) · cites this paper as [20]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Multi-agent simulations show that LLM agents exhibit key social traits like trust and coordination54,55;”
From Understanding large language models demands distinguishing human projection from machine cognition (2026) · cites this paper as [55]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“During decision-making, it retrieves relevant reflections from semantic memory, then reasons to make a high-level plan of the day.”
From Cognitive Architectures for Language Agents (2023) · cites this paper as Park et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Its action space also has all four kinds of actions: grounding, reasoning, retrieval, and learning.”
From Cognitive Architectures for Language Agents (2023) · cites this paper as Park et al., 2023
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“These agents use retrieval and reasoning to generate reflections on their episodic memory (e.g., 'I like to ski now. ') which are then written to long-term semantic memory.”
From Cognitive Architectures for Language Agents (2023) · cites this paper as Park et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“While this approach can generate seemingly believe-able activities to external observers, this process does not fully resemble how humans think.”
From Humanoid Agents: Platform for Simulating Human-like Generative Agents (2023) · cites this paper as Park et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Generative Agents focus on System 2 thinking at the cost of System 1.”
From Humanoid Agents: Platform for Simulating Human-like Generative Agents (2023) · cites this paper as Park et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“GenerativeAgents [26], AgentSociety [27], OpenClaw [24], and MoltBook [23] show that many real agent workloads run in explicit rounds with a shared scheduler.”
From TokenDance: Scaling Multi-Agent LLM Serving via Collective KV Cache Sharing (2026) · cites this paper as [26]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Prior work demonstrates that LLMs can synthesize characters, quests, narratives, and dialogue with high linguistic fluency and diversity, supporting applications ranging from conceptual game design to LLM-driven NPCs and open-ended simulations [1-4].”
From Game Knowledge Management System: Schema-Governed LLM Pipeline for Executable Narrative Generation in RPGs (2026) · cites this paper as [4]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In contrast, proactive GenAI agents are more engaging and interactive (Oertel et al., 2020; Park et al., 2023).”
From The effects of generative AI agents and scaffolding on enhancing students’ comprehension of visual learning analytics (2025) · cites this paper as Park et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Park et al.'s agents live in a text-based simulation called 'Smallville'.”
From A Case for AI Consciousness: Language Agents and Global Workspace Theory (2024) · cites this paper as Park et al. (2023)
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“At the end of each day, every agent in Smallville calls on the LLM (in this case, gpt3.5-turbo) to generate a plan for the next day in light of the contents of their memory stream .”
From A Case for AI Consciousness: Language Agents and Global Workspace Theory (2024) · cites this paper as Park et al. (2023)
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Memory and reflection systems such as MemAgent (Yu et al., 2025a), Reflexion (Shinn et al., 2023), and Generative Agents (Park et al., 2023) improve retrieval and episodic memory, but do not perform context shaping, state restructuring, or nextinstruction refinement.”
From PAACE: A Plan-Aware Automated Agent Context Engineering Framework (2025) · cites this paper as Park et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Effective agents (Park et al., 2023) must depend on persis-tent and updatable long-term memory mechanisms, rather than simply increasing context length.”
From From Recall to Forgetting: Benchmarking Long-Term Memory for Personalized Agents (2026) · cites this paper as Park et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“RAISE Shao et al. [2023] and Generative Agents Park et al. [2023] implement memory retrieval with recency, importance, and relevance scoring but lack consolidation, forgetting, and reconsolidation mechanisms.”
From Human-Inspired Memory Architecture for LLM Agents (2026) · cites this paper as Park et al. [2023]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The biggest experiments to date have simulated 25-50 agents (39), which is not close to the scale of a civilization.”
From Project Sid: Many-agent simulations toward AI civilization (2024) · cites this paper as [39]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In the Generative Agents experiment [Park et al., 2023], removing the reflection component caused agent behavior to degenerate from coherent multi-day planning to repetitive, context-free responses within 48 simulated hours.”
From Memory for Autonomous LLM Agents:Mechanisms, Evaluation, and Emerging Frontiers (2026) · cites this paper as Park et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In practice, naive accumulation progressively contaminates the retrievable pool: entangled units, redundant variants, and superseded versions increasingly occupy a fixed budget, yielding diffuse or even misleading contexts for downstream reasoning (Park et al., 2023; Liu et al., 2024; Wu et al., 2025).”
From All-Mem: Agentic Lifelong Memory via Dynamic Topology Evolution (2026) · cites this paper as Park et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In sandboxes of society based on LLMs, agents equipped with long-term memory can better simulate human social behaviors, making them valuable subjects for sociological and psychological research [61, 330, 331].”
From Human-inspired Perspectives: A Survey on AI Long-term Memory (2024) · cites this paper as [330]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“We adopted the same GPT-3.5-turbo version of ChatGPT used in Park et al. (2023) to generate agent behaviors. This approach ensures that the comparison of memory retrieval performance is not influenced by variations in the capabilities of the large language models.”
From Enhancing memory retrieval in generative agents through LLM-trained cross attention networks (2025) · cites this paper as Park et al. (2023)
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Existing memory retrieval methods for agents primarily focus on relevance and temporal validity.”
From Enhancing memory retrieval in generative agents through LLM-trained cross attention networks (2025) · cites this paper as Park et al. (2023)
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“the baseline memory retrieval method we used Park et al. (2023) represents one of the most state-of-the-art approaches currently available for comparison in agent memory retrieval.”
From Enhancing memory retrieval in generative agents through LLM-trained cross attention networks (2025) · cites this paper as Park et al. (2023)
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Existing work manages agent state through reflection, memory, skill libraries, and execution traces (Shinn et al., 2023; Park et al., 2023; Packer et al., 2023; Wang et al., 2024; Hu et al., 2025).”
From Multi-agent Collaboration with State Management (2026) · cites this paper as Park et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Existing mitigation strategies, such as sliding context windows, heuristic truncation, and summary-based memory mechanisms (Park et al., 2023), as well as explicit long-term structures such as user profiles or persona memories (Xu et al., 2022a), improve scalability but often degrade recall reliability.”
From A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents (2026) · cites this paper as Park et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Each agent's perceptual observations are stored in a text file called the memory stream along with other beliefs, including those comprising their text backstory, which specifies their long-term goals and relationships with other agents.”
From A Case for AI Consciousness: Language Agents and Global Workspace Theory (2024) · cites this paper as Park et al. (2023)
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Given an input circumstance, the retrieval function produces a list of entries from the memory stream ordered by taking a weighted sum of their importance (as described above), their recency, and their relevance to the input circumstance (this is again calculated by the LLM).”
From A Case for AI Consciousness: Language Agents and Global Workspace Theory (2024) · cites this paper as Park et al. (2023)
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Inefficient Communication Interfaces: Most existing systems use natural language as the primary communication medium, typically relying on message histories or an unstructured pool of information for decision-making (Park et al., 2023; Qian et al., 2024). This approach often results in a 'telephone effect', where details are lost, and states become corrupted as conversations lengthen.”
From TradingAgents: Multi-Agents LLM Financial Trading Framework (2024) · cites this paper as Park et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Agents are increasingly governed by in-context constraints-organizational policies, standing instructions, and loaded 'memory' that specify what the agent must and must not do (Xiang et al. 2024; Debenedetti et al. 2024; Park et al. 2023).”
From Governance Decay: How Context Compaction Silently Erases Safety Constraints in Long-Horizon LLM Agents (2026) · cites this paper as Park et al. 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“25 simulated characters autonomously organize a Valen- tine's party via observation-reflection-planning cycles.”
From Memory for Autonomous LLM Agents:Mechanisms, Evaluation, and Emerging Frontiers (2026) · cites this paper as Park et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In contrast, many systems use repeated large-model calls for online memory operations, improving accuracy but accumulating latency over long interactions.”
From Lightweight LLM Agent Memory with Small Language Models (2026) · cites this paper as Park et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“finally, they internalize interactions into persistent traits of the persona to ensure uniformity in behavior (Park et al., 2023; Westhäußer et al., 2025; Liang et al., 2025).”
From From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms (2026) · cites this paper as Park et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“LLM-based agents couple large language models with planning, memory retrieval, tool invocation and execution, enabling temporally extended, partially autonomous collaboration [1-3].”
From Caring Without Feeling: Affective Dynamics as the Control Layer of Human-AI Agent Collaboration (2026) · cites this paper as [1]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“These processes are especially consequential in LLM-based agents, whose natural-language fluency, memory and tool-use capabilities can make affective expression appear continuous with competence, intention and social presence [1, 2].”
From Caring Without Feeling: Affective Dynamics as the Control Layer of Human-AI Agent Collaboration (2026) · cites this paper as [1]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Multi-agent architectures [1] make this challenge collective, requiring oversight of emergent group-level affective patterns.”
From Caring Without Feeling: Affective Dynamics as the Control Layer of Human-AI Agent Collaboration (2026) · cites this paper as [1]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Furthermore, multi-agent collaboration plays a significant role in LAA applications, allowing agents to collaborate and interact to solve problems through various role-playing scenarios (Park et al., 2023;Fu et al., 2023;Gong et al., 2023;Li et al., 2023).”
From Large Language Models as Biomedical Hypothesis Generators: A Comprehensive Evaluation (2024) · cites this paper as Park et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The weights were fixed a priori and were not tuned: they encode a prior, drawn from the memorystream and forgetting-curve literature [Park et al., 2023, Zhong et al., 2024], that access-driven signals (recency, frequency) should dominate structural and age-based ones.”
From Selective Forgetting: A Graph-Based Memory Framework for Long-Term LLM Agents (2026) · cites this paper as Park et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“For example, Park et al. [83] first calculate the relevance between the current context and memory entries by cosine similarity, and obtain the importance and recency according to auxiliary information.”
From A Survey on the Memory Mechanism of Large Language Model-based Agents (2025) · cites this paper as [83]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Generative Agents maintains a memory stream over observations, retrieves memories by relevance, recency, and importance, and uses reflection to synthesize higher-level inferences from accumulated experience [20].”
From Oracle Agent Memory as an Enterprise Memory Substrate for Long-Horizon AI Agents (2026) · cites this paper as [20]
The cited source says what the claim attributes to it.
In the citing paper
“More specifically, in Generative Agent [20], the agent has the capability to summarize its past experiences stored in memory into broader and more abstract insights.”
From A survey on large language model based autonomous agents (2024) · cites this paper as [20]
The cited source says what the claim attributes to it.
In the citing paper
“Agent profiles typically encompass basic information such as age, gender, and career [20], as well as psychology information, reflecting the personalities of the agent, and social information, detailing the relationships between agents [21].”
From A survey on large language model based autonomous agents (2024) · cites this paper as [20]
The cited source says what the claim attributes to it.
In the citing paper
“In this strategy, the action is generated by extracting information from the agent memory according to the current task.The task and the extracted memories are used as prompts to trigger the agent actions.”
From A survey on large language model based autonomous agents (2024) · cites this paper as [20]
The cited source says what the claim attributes to it.
In the citing paper
“Actions taken by the agent can also change the agent itself, including updating memories, forming new plans, acquiring novel knowledge, and more.”
From A survey on large language model based autonomous agents (2024) · cites this paper as [20]
The cited source says what the claim attributes to it.
In the citing paper
“A pivotal application of LLM-based autonomous agents lies in simulating diverse real-world human behaviors [20].”
From A survey on large language model based autonomous agents (2024) · cites this paper as [20]
The cited source says what the claim attributes to it.
In the citing paper
“Generative Agents (Park et al., 2023) are language agents grounded to a sandbox game affording interaction with the environment and other agents.”
From Cognitive Architectures for Language Agents (2023) · cites this paper as Park et al., 2023
The cited source says what the claim attributes to it.
In the citing paper
“While executing the plan, the agent receives a stream of grounding observations; it can reason over these to maintain or adjust the plan.”
From Cognitive Architectures for Language Agents (2023) · cites this paper as Park et al., 2023
The cited source says what the claim attributes to it.
In the citing paper
“Each agent has a long-term episodic memory that stores events in a list.”
From Cognitive Architectures for Language Agents (2023) · cites this paper as Park et al., 2023
The cited source says what the claim attributes to it.
In the citing paper
“Generative Agents plan activities over a day, execute them at each time-step and adapt their plans based on observations of their environment.”
From Humanoid Agents: Platform for Simulating Human-like Generative Agents (2023) · cites this paper as Park et al., 2023
The cited source says what the claim attributes to it.
In the citing paper
“Humanoid Agents directly build upon Generative Agents, which aim to model believe-able human behavior (Park et al., 2023).”
From Humanoid Agents: Platform for Simulating Human-like Generative Agents (2023) · cites this paper as Park et al., 2023
The cited source says what the claim attributes to it.
In the citing paper
“Existing studies remain narrowly focused on isolated problems and rely on simplified environments, such as text-based [4] or simplistic game environments [80], with limited attention to real-world environment fidelity, revealing notable limitations.”
From AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society (2025) · cites this paper as [80]
The cited source says what the claim attributes to it.
In the citing paper
“Figure 3 confirms this quantitatively: in an 8-agent GenerativeAgents [26] round, pairwise block similarity ranges from 91% to 97%, meaning the vast majority of cached data is duplicated across agents.”
From TokenDance: Scaling Multi-Agent LLM Serving via Collective KV Cache Sharing (2026) · cites this paper as [26]
The cited source says what the claim attributes to it.
In the citing paper
“Further, IT artifacts were considered passive as they were used directly by humans. With the advent of agentic IT artifacts (Baird and Maruping 2021) powered by LLMs (Park et al. 2023), this human agency primacy assumption needs to be revisited and impacts how we devise the relation between human and AI based on their potency.”
From Generative AI (2024) · cites this paper as Park et al. 2023
The cited source says what the claim attributes to it.
In the citing paper
“Agentic systems (Park et al., 2023) enable GenAI agents to address irrelevant responses generated by incomplete user prompts (White et al., 2023).”
From The effects of generative AI agents and scaffolding on enhancing students’ comprehension of visual learning analytics (2025) · cites this paper as Park et al., 2023
The cited source says what the claim attributes to it.
In the citing paper
“These plans flexibly shape how an agent acts the following day.”
From A Case for AI Consciousness: Language Agents and Global Workspace Theory (2024) · cites this paper as Park et al. (2023)
The cited source says what the claim attributes to it.
In the citing paper
“During reflection, agents draw on the LLM to first pose and then answer a series of questions about how to generalize from their recent experiences.”
From A Case for AI Consciousness: Language Agents and Global Workspace Theory (2024) · cites this paper as Park et al. (2023)
The cited source says what the claim attributes to it.
In the citing paper
“Timestampaware retrieval down-weights older memories as new ones accumulate [31, 52].”
From Transferable End-to-End Optimization for Indirect Long-Term Memory Poisoning in LLM Agents (2026) · cites this paper as [31]
The cited source says what the claim attributes to it.
In the citing paper
“Common memory retrieval methods include temporal decay ranking, evaluation of memory importance, vector similarity matching, and combinations of these techniques (Park et al., 2023).”
From Enhancing memory retrieval in generative agents through LLM-trained cross attention networks (2025) · cites this paper as Park et al., 2023
The cited source says what the claim attributes to it.
In the citing paper
“Generative Agents [Park et al., 2023] demonstrate how persistent memory enables believable social simulation.”
From MemX: A Local-First Long-Term Memory System for AI Assistants (2026) · cites this paper as Park et al., 2023
The cited source says what the claim attributes to it.
In the citing paper
“Loaded memories are then ranked or refined using semantic similarity, heuristic constraints, or budget-aware selection strategies before being injected into the model's prompt or working context, forming the primary interface between external memory and the LLM's inference process (Park et al., 2023; Wang et al., 2025q).”
From A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents (2026) · cites this paper as Park et al., 2023
The cited source says what the claim attributes to it.
In the citing paper
“To solve this problem, Park et al. introduce a function that enables agents to freely draw inferences from their existing beliefs.”
From A Case for AI Consciousness: Language Agents and Global Workspace Theory (2024) · cites this paper as Park et al. (2023)
The cited source says what the claim attributes to it.
In the citing paper
“Since an agent's memory stream is unmanageably long, in planning action is it necessary to select an action-relevant subset of entries.”
From A Case for AI Consciousness: Language Agents and Global Workspace Theory (2024) · cites this paper as Park et al. (2023)
The cited source says what the claim attributes to it.
In the citing paper
“In deciding how to act in a given circumstance, each language agent considers only those entries from its memory stream which rank highly enough according to the retrieval function.”
From A Case for AI Consciousness: Language Agents and Global Workspace Theory (2024) · cites this paper as Park et al. (2023)
The cited source says what the claim attributes to it.
In the citing paper
“In Park et al.'s agents, the memory stream is implicated in all important cognitive processes. It is where perceptions are recorded, as well as where non-perceptual beliefs, desires, and plans are stored.”
From A Case for AI Consciousness: Language Agents and Global Workspace Theory (2024) · cites this paper as Park et al. (2023)
The cited source says what the claim attributes to it.
In the citing paper
“Each entry in the memory stream comes along with a timestamp, and each is assigned an importance score by the LLM as it is recorded.”
From A Case for AI Consciousness: Language Agents and Global Workspace Theory (2024) · cites this paper as Park et al. (2023)
The cited source says what the claim attributes to it.
In the citing paper
“These importance scores play a key role in determining how each entry shapes the future behavior of the agent: entries with higher importance scores are more likely to influence an agent's other beliefs and plans.”
From A Case for AI Consciousness: Language Agents and Global Workspace Theory (2024) · cites this paper as Park et al. (2023)
The cited source says what the claim attributes to it.
In the citing paper
“The results of reflection are then stored in the memory stream, where they are accessible to the retrieval function.”
From A Case for AI Consciousness: Language Agents and Global Workspace Theory (2024) · cites this paper as Park et al. (2023)
The cited source says what the claim attributes to it.
In the citing paper
“As an early example, successful (pro-social) cooperation and coordination can emerge as a by-product of different language model instances that simulate characters in a social environment [Park et al., 2023].”
From The alignment problem from a deep learning perspective (2022) · cites this paper as Park et al., 2023
The cited source says what the claim attributes to it.
In the citing paper
“Retrieval scores memories by a weighted mix of recency (exponential decay), relevance (embedding similarity), and importance (a self-assessed integer).”
From Memory for Autonomous LLM Agents:Mechanisms, Evaluation, and Emerging Frontiers (2026) · cites this paper as Park et al., 2023
The cited source says what the claim attributes to it.
In the citing paper
“For instance, a group of 25 generative agents in a dynamic sandbox environment successfully organised and conducted a Valentine's Day celebration based on a single user input [54].”
From Promises and challenges of generative artificial intelligence for human learning (2024) · cites this paper as [54]
The cited source says what the claim attributes to it.
In the citing paper
“while Park et al. (2023) retrieves memories based on a weighted combination of relevance, recency, and importance.”
From From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms (2026) · cites this paper as Park et al., 2023
The cited source says what the claim attributes to it.
In the citing paper
“For instance, Park et al. [74] observed generative agents in a continuously running simulated town environment to study emergent behaviors across multi-day interactions.”
From Evaluation and Benchmarking of LLM Agents: A Survey (2025) · cites this paper as [74]
The cited source says what the claim attributes to it.
In the citing paper
“• GAMemory (Generative Agents [7]): A pioneer memory model with weighted retrieval combination and self-reflection mechanism.”
From MemEngine: A Unified and Modular Library for Developing Advanced Memory of LLM-based Agents (2025) · cites this paper as [7]
The cited source says what the claim attributes to it.
In the citing paper
“AGENTREVIEW is built upon the capabilities of LLMs to perform realistic simulations of societal environments (Wu et al., 2023a;Chen et al., 2024a;Park et al., 2023) and provide high-quality feedback on academic literature comparable to or exceeds human levels”
From AgentReview: Exploring Peer Review Dynamics with LLM Agents (2024) · cites this paper as Park et al., 2023
The cited source says what the claim attributes to it.
In the citing paper
“Generative Agents [32] rank observations at retrieval by recency, importance, and relevance but never attenuate them from the store.”
From Is Agent Memory a Database? Rethinking Data Foundations for Long-Term AI Agent Memory (2026) · cites this paper as [32]
The cited source says what the claim attributes to it.
In the citing paper
“Generative Agents [32] update a memory's recency on access, but only as an embedded heuristic with no correctness condition.”
From Is Agent Memory a Database? Rethinking Data Foundations for Long-Term AI Agent Memory (2026) · cites this paper as [32]
The cited source says what the claim attributes to it.
In the citing paper
“They combine an importance score with recency decay to rank observations at retrieval.”
From Is Agent Memory a Database? Rethinking Data Foundations for Long-Term AI Agent Memory (2026) · cites this paper as [32]
The cited source says what the claim attributes to it.
In the citing paper
“In real-world applications, however, existing systems (Park et al., 2023; Zhuge et al., 2023; Cai et al., 2023; Wang et al., 2023c; Li et al., 2023; Du et al., 2023; Liang et al., 2023; Hao et al., 2023; Zhou et al., 2023b) tend to oversimplify the complexities.”
From MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework (2023) · cites this paper as Park et al., 2023
The cited source says what the claim attributes to it.
In the citing paper
“Researchers have begun directing more attention to developing memory abilities in AI models.”
From Episodic Memory in AI Agents Poses Risks that Should be Studied and Mitigated (2025) · cites this paper as [3]
The cited source says what the claim attributes to it.
In the citing paper
“The existing work which comes closest to our conceptualization of the role of memory introduces a virtual environment of interacting LLM-based agents that record and later consult natural language records of their actions, using these to better understand their environment and make coherent and relevant plans [3].”
From Episodic Memory in AI Agents Poses Risks that Should be Studied and Mitigated (2025) · cites this paper as [3]
The cited source says what the claim attributes to it.
In the citing paper
“Maintaining an up-to-date memory is essential for human-like long-term interactions [4, 12, 44].”
From CAIM: Development and Evaluation of a Cognitive AI Memory Framework for Long-Term Interaction with Intelligent Agents (2026) · cites this paper as [44]
The cited source says what the claim attributes to it.
In the citing paper
“They employed periodic reflections to summarize prior events, synthesizing memories into high-level knowledge and storing them for future use. This produced believable agents but at computational costs unsuitable for real-time interactions.”
From Post-Thinking in NPC Dialogue: A Paradigm for Reflective Character Models (2026) · cites this paper as [4]
The cited source says what the claim attributes to it.
In the citing paper
“in Generative Agents, each character starts from a set of seed memories, and the reflections synthesized from its memory stream inform how it plans and responds [44].”
From Agentic Memory Management Is the Next Frontier of Information Retrieval (2026) · cites this paper as [44]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“The All-Gather pattern is a natural starting point because it already appears across many agent workloads.”
From TokenDance: Scaling Multi-Agent LLM Serving via Collective KV Cache Sharing (2026) · cites this paper as [26]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“While these systems demonstrate the power of LLMs for complex medical simulations and text-based dialogue training, many remain primarily confined to textual interactions.”
From LLM-Powered Virtual Patient Agents for Interactive Clinical Skills Training with Automated Feedback (2025) · cites this paper as [11]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“This design is motivated by the need to monitor emotional states, particularly frustration with complex queries and satisfaction with search results, which play important roles in shaping search experiences and decision-making processes [25].”
From Generative Agents Navigating Digital Libraries (2025) · cites this paper as [25]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“PANGeA's novel validation system is a key feature supporting these features, as it addresses the challenges behind processing free-form text input and maintaining narrative coherence (Kumaran et al. 2023; Garavaglia et al. 2022; Park et al. 2023).”
From PANGeA: Procedural Artificial Narrative Using Generative AI for Turn-Based, Role-Playing Video Games (2024) · cites this paper as Park et al. 2023
Experimental — accuracy not yet measuredIn the citing paper
“LLM agents [19] serve as intelligent systems that bridge the gap between LLMs and practical applications, making significant impacts in various fields like human behavior simulation [20], chemistry [21–24], bioinformatics [25] and paper searching [26].”
From Automating structure-activity analysis for electrochemical nitrogen reduction catalyst design through multi-agent collaborations (2025) · cites this paper as [20]
In the citing paper
“The architecture facilitates transparency and fast integration by structuring memory as human-readable summaries and lists (Yue et al., 2025; Park et al., 2023).”
From A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents (2026) · cites this paper as Park et al., 2023
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
John's wake-up time differs between the text and figure caption
MinorThe account of John Lin's morning says he wakes at 7 a.m., while the caption for the same morning says he wakes around 6 a.m.
“In the Lin family, John is the frst to wake up at 7 a.m.”
“John wakes up around 6 am and completes his morning routine”
The party scenario is described as having both one and two user-set premises
MinorThe paper attributes the party scenario to a single user-generated seed, but its detailed account says the user also set Maria's crush on Klaus. That second premise bears directly on the reported date invitation.
“all from a single user-generated seed suggestion.”
“In this scenario, the end user only set Isabella's initial intent to throw a party and Maria's crush on Klaus:”
Citations
Cites
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
A Simple Sequentially Rejective Multiple Test Procedure
(1979) · Scandinavian Journal of Statistics · doi:10.2307/4615733 · Cited by 21,526 · cited 1×
“…memories accrued by the agent up until the moment of the interview, so the differences observed here likely represent a conservative estimate of the true differences: in reality, the ablated architectures would not have followed the same path as the full architecture through the two-day simulation.”
BackgroundUse of Ranks in One-Criterion Variance Analysis
Kruskal, W. H., & Wallis, W. A. (1952) · Journal of the American Statistical Association · doi:10.1080/01621459.1952.10483441 · Cited by 13,912 · cited 1×
“Separately, to investigate the statistical significance of these re-sults, we applied the Kruskal-Wallis test [56], a non-parametric alternative to the one-way ANOVA, to the raw rank data.”
MethodA General Inductive Approach for Analyzing Qualitative Evaluation Data
Thomas, D. R. (2006) · American Journal of Evaluation · doi:10.1177/1098214005283748 · Cited by 10,858 · cited 1×
“Furthermore, the first author conducted an inductive analysis [95] to study the qualitative distinctions between the responses produced in each condition.”
MethodThe Computer for the 21st Century
Weiser, M. (1991) · Scientific American · doi:10.1038/scientificamerican0991-94 · Cited by 10,017
Understanding and Using Context
Dey, A. K. (2001) · Personal and Ubiquitous Computing · doi:10.1007/s007790170019 · Cited by 5,035
ELIZA—a computer program for the study of natural language communication between man and machine
Weizenbaum, J. (1966) · Communications of the ACM · doi:10.1145/365153.365168 · Cited by 4,452 · cited 1×
“Creating NPCs with believable behavior, if possible, could enhance player experiences in games and interactive fictions by enabling emergent narratives [8, 16, 49, 93] and social interactions with the agents [109].”
BackgroundChain-of-Thought Prompting Elicits Reasoning in Large Language Models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022) · arXiv · doi:10.48550/arxiv.2201.11903 · Cited by 4,263
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208
Grandmaster level in StarCraft II using multi-agent reinforcement learning
Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., Choi, D. H., Powell, R., Ewalds, T., Georgiev, P., et al. (2019) · Nature · doi:10.1038/s41586-019-1724-z · Cited by 3,455 · cited 1×
“On the other hand, prevalent learning-based approaches for creating believable agents, such as reinforcement learning, have overcome the challenge of manual authoring by letting the agents learn their behavior, and have achieved superhuman performance in recent years in games such as AlphaStar for Starcraft [99] and O…”
Background
Flagged first, then by citations
Research works printed but not linked: 19
- John R. Anderson. 1993. Rules of the Mind. Lawrence Erlbaum Associates, Hillsdale, NJ.
- Ruth Aylett. 1999. Narrative in virtual environments—towards emergent narrative. In Narrative Intelligence: Papers from the AAAI Fall Symposium (Technical Report FS-99-01). AAAI Press 83–86.
- 10.5555/7900.16137 · DOI
- Kevin Dill and L Martin. 2011. A Game AI Approach to Autonomous Control of Virtual Characters. In Proceedings of the Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC’11). Orlando, FL, USA.
- Arpad E Elo. 1967. The Proposed USCF Rating System, Its Development, Theory, and Applications. Chess Life XXII, 8 (August 1967), 242–247.
- Rebecca Fiebrink and Perry R Cook. 2010. The Wekinator: a system for real-time, interactive machine learning in music. In Proceedings of The Eleventh International Society for Music Information Retrieval Conference (ISMIR 2010)(Utrecht), Vol. 3. Citeseer, 2–1.
- Uwe Flick. 2009. An Introduction to Qualitative Research. SAGE.
- 10.1609/aimag.v22i2.1558 · DOI
- John E. Laird. 2000. It Knows What You’re Going To Do: Adding Anticipation to a QUAKEBOT. In Papers from the AAAI 2000 Spring Symposium on Artificial Intelligence and Interactive Entertainment(Technical Report SS-00-02). AAAI Press 41–50.
- Josh McCoy, Michael Mateas, and Noah Wardrip-Fruin. 2009. Comme il Faut: A System for Simulating Social Games Between Autonomous Characters. In Proceedings of the 7th International Conference on Digital Arts and Culture. 87–94.
- Marvin Minsky and Seymour Papert. 1970. Draft of a proposal to ARPA for research on artificial intelligence at MIT, 1970–71.
- 10.1109/SNPD.2017.8023884 · DOI
- Kyle Orland. 2021. So what is ’the metaverse’, exactly?Ars Technica (7 November 2021). arxiv:2111.04169https://arstechnica.com/gaming/2021/11/so-what-is-the-metaverse-exactly/
- Richard W. Pew and Ann S. Mavor (Eds.). 1998. Modeling Human and Organizational Behavior: Applications to Military Simulations. National Academy Press Washington D.C.
- Prolific. 2022. Prolific: Quickly Find Research Participants You Can Trust. https://www.prolific.co/
- Mark O. Riedl. 2012. Interactive narrative: A novel application of artificial intelligence for computer games. In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI’12). 2160–2165.
- Ho Chit Siu, Jaime Peña, Edenna Chen, Yutai Zhou, Victor Lopez, Kyle Palko, Kimberlee Chang, and Ross Allen. 2021. Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi. In Advances in Neural Information Processing Systems, M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan (Eds.). Vol. 34. Curran Associates, Inc., 16183–16195. https://proceedings.neurips.cc/paper_files/paper/2021/file/86e8f7ab32cfd12577bc2619bc635690-Paper.pdf
- Graham Upton and Ian Cook. 2006. A Dictionary of Statistics (2 ed.). Oxford University Press, Oxford, United Kingdom.
- Robert Zubek. 2002. Towards implementation of social interaction. In AAAI Spring Symposium on Artificial Intelligence and Interactive Entertainment. AAAI Press. https://www.aaai.org/Papers/Symposia/Spring/2002/SS-02-01/SS02-01-003.pdf
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Sorted by citations
A survey on large language model based autonomous agents
Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., Chen, Z., Tang, J., Chen, X., Lin, Y., et al. (2024) · Frontiers of Computer Science · doi:10.1007/s11704-024-40231-1 · Cited by 1,941
“Usually, there three commonly used criteria for information extraction, that is, the recency, relevance, and importance [121].”
InfluentialA Survey of Large Language Models
Zhao, W. X., Zhou, K., Li, J., Tang, T., Dong, Z., Hou, Y., Zhang, B., Min, Y., Zhang, J., Liu, P., et al. (2026) · Frontiers of Computer Science · doi:10.1007/s11704-026-60308-3 · Cited by 2,093
Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024) · Business & Information Systems Engineering · doi:10.1007/s12599-023-00834-7 · Cited by 2,267
“…methods such as language models (e.g., Teubner et al. 2023; Dwivedi et al. 2023; Scho¨bel et al. 2023) or specific applications such as marketing (e.g., Peres et al. 2023), innovation management (Burger et al. 2023), scholarly research (e.g., Susarla et al. 2023; Davison et al. 2023), and education…”
The Rise and Potential of Large Language Model Based Agents: A Survey
Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., et al. (2023) · arXiv · doi:10.48550/arxiv.2309.07864 · Cited by 972
The rise and potential of large language model based agents: a survey
Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., et al. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4222-0 · Cited by 970
“However, these efforts have predominantly focused on enhancing specific skills (such as symbolic reasoning or quick responsiveness) or mastering particular tasks (such as Go or Chess) [13–15].”
InfluentialCan Large Language Models Transform Computational Social Science?
Ziems, C., Held, W., Shaikh, O., Chen, J., Zhang, Z., & Yang, D. (2024) · Computational Linguistics · doi:10.1162/coli_a_00502 · Cited by 585
“, 2022), community and their members’ interactions (Park et al., 2023), public opinion (Argyle et al.”
BackgroundGreshake, K., Abdelnabi, S., Mishra, S., Endres, C., Holz, T., & Fritz, M. (2023) · Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security · doi:10.1145/3605764.3623985 · Cited by 667
Mathematical discoveries from program search with large language models
Romera-Paredes, B., Barekatain, M., Novikov, A., Balog, M., Kumar, M. P., Dupont, E., Ruiz, F. J. R., Ellenberg, J. S., Wang, P., Fawzi, O., et al. (2024) · Nature · doi:10.1038/s41586-023-06924-6 · Cited by 544
Promises and challenges of generative artificial intelligence for human learning
Yan, L., Greiff, S., Teuber, Z., & Gašević, D. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-02004-5 · Cited by 499
“For instance, a group of 25 generative agents in a dynamic sandbox environment successfully organized and conducted a Valentine’s Day celebration based on a single user input 54 .”
Role play with large language models
Shanahan, M., McDonell, K., & Reynolds, L. (2023) · Nature · doi:10.1038/s41586-023-06647-8 · Cited by 491
ChatDev: Communicative Agents for Software Development
Qian, C., Liu, W., Liu, H., Chen, N., Dang, Y., Li, J., Yang, C., Chen, W., Su, Y., Cong, X., et al. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.810 · Cited by 490
A survey on LLM-based multi-agent systems: Workflow, infrastructure, and challenges
Li, X., Wang, S., Zeng, S., Wu, Y., & Yang, Y. (2024) · Vicinagearth · doi:10.1007/s44336-024-00009-2 · Cited by 404
“For example, Generative Agent [28], immersed in the context of a software company, utilizes authored natural language descriptions to define each agent’s identity, encompassing their occupation and inter-agent relationships, serving as seed memories.”
InfluentialMore human than human: measuring ChatGPT political bias
Motoki, F., Pinho Neto, V., & Rodrigues, V. (2024) · Public Choice · doi:10.1007/s11127-023-01097-2 · Cited by 329
“Finally, Park et al. (2023) document that ChatGPT can simulate human behavior, taking actions that vary with the agents’ experiences and environment.”
Empowering biomedical discovery with AI agents
Gao, S., Fang, A., Huang, Y., Giunchiglia, V., Noori, A., Schwarz, J. R., Ektefaie, Y., Kondic, J., & Zitnik, M. (2024) · Cell · doi:10.1016/j.cell.2024.09.022 · Cited by 315
Can Generative AI improve social science?
Bail, C. A. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2314021121 · Cited by 369
Reflexion: Language Agents with Verbal Reinforcement Learning
Shinn, N., Cassano, F., Berman, E., Gopinath, A., Narasimhan, K., & Yao, S. (2023) · arXiv · doi:10.48550/arxiv.2303.11366 · Cited by 1,087
“Recent works such as ReAct [30], SayCan [1], Toolformer [22], HuggingGPT [23], generative agents [19], and WebGPT [17] have demonstrated the feasibility of autonomous decision-making agents that are built on top of a large language model (LLM) core.”
BackgroundEncouraging Divergent Thinking in Large Language Models through Multi-Agent Debate
Liang, T., He, Z., Jiao, W., Wang, X., Wang, Y., Wang, R., Yang, Y., Shi, S., & Tu, Z. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2024.emnlp-main.992 · Cited by 1,062
A review of large language models and autonomous agents in chemistry
Ramos, M. C., Collison, C. J., & White, A. D. (2025) · Chemical Science · doi:10.1039/d4sc03921a · Cited by 274
Large language models empowered agent-based modeling and simulation: a survey and perspectives
Gao, C., Lan, X., Li, N., Yuan, Y., Ding, J., Zhou, Z., Xu, F., & Li, Y. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03611-3 · Cited by 294
AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges
Sapkota, R., Roumeliotis, K. I., & Karkee, M. (2026) · Information Fusion · doi:10.1016/j.inffus.2025.103599 · Cited by 671
“…serve to not only reinforce the structural differences between Generative AI, AI Agents, and Agentic AI(as explained in Table 4), but also introduce an emergent category of Generative Agents representing modular agents designed for embedded subtask-level generation within broader workflows [154].”
Zhang, R., Du, H., Liu, Y., Niyato, D. T., Kang, J., Xiong, Z., Jamalipour, A., & Kim, D. I. (2024) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2024.3459037 · Cited by 277
“To address Challenge I, an interactive generative artificial intelligent (AI) agent has been introduced [11].”
BackgroundFoundation models in robotics: Applications, challenges, and the future
Firoozi, R., Tucker, J., Tian, S., Majumdar, A., Sun, J., Liu, W., Zhu, Y., Song, S., Kapoor, A., Hausman, K., et al. (2025) · The International Journal of Robotics Research · doi:10.1177/02783649241281508 · Cited by 238
“For example, Generative Agents [203] discusses how generative agents can produce realistic imitations of human behavior for interactive applications, creating a miniature community of agents similar to those found in games like The Sims.”
Crawford, J., Allen, K.-A., Pani, B., & Cowling, M. (2024) · Studies in Higher Education · doi:10.1080/03075079.2024.2326956 · Cited by 224
MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework
Hong, S., Zhuge, M., Chen, J., Zheng, X., Cheng, Y., Zhang, C., Wang, J., Wang, Z., Yau, S. K. S., Lin, Z., et al. (2023) · arXiv · doi:10.48550/arxiv.2308.00352 · Cited by 214
“[10] constructed a town of 25 agents, realizing simple language interaction, social cognition, and memory of the agents, while there is a lack of cooperation and production.”
BackgroundAI agents under threat: A survey of key security challenges and future pathways
Deng, Z., Guo, Y., Han, C., Ma, W., Xiong, J., Wen, S., & Xiang, Y. (2025) · ACM Computing Surveys · doi:10.1145/3716628 · Cited by 210
Voyager: An Open-Ended Embodied Agent with Large Language Models
Wang, G., Xie, Y., Jiang, Y., Mandlekar, A., Xiao, C., Zhu, Y., Fan, L., & Anandkumar, A. (2023) · arXiv · doi:10.48550/arxiv.2305.16291 · Cited by 222
“Decision-making Agents in Minecraft.”
BackgroundRoCo: Dialectic Multi-Robot Collaboration with Large Language Models
Mandi, Z., Jain, S., & Song, S. (2023) · arXiv · doi:10.48550/arxiv.2307.04738 · Cited by 180
AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges
Sapkota, R., Roumeliotis, K. I., & Karkee, M. (2025) · SuperIntelligence - Robotics - Safety & Alignment · doi:10.70777/si.v2i3.15161 · Cited by 186
A Multiagent-Driven Robotic AI Chemist Enabling Autonomous Chemical Research On Demand
Song, T., Luo, M., Zhang, X., Chen, L., Huang, Y., Cao, J., Zhu, Q., Liu, D., Zhang, B., Zou, G., et al. (2025) · Journal of the American Chemical Society · doi:10.1021/jacs.4c17738 · Cited by 171
Drive Like a Human: Rethinking Autonomous Driving with Large Language Models
Fu, D., Li, X., Wen, L., Dou, M., Cai, P., Shi, B., & Qiao, Y. (2024) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw60836.2024.00102 · Cited by 251
“In the first case depicted in Fig.6(a), LLaMA-Adapter identified that the pickup truck in the photograph is carrying multiple traffic cones and inferred that it may be transporting them to their destination.”
BackgroundHuman-AI agency in the age of generative AI
Krakowski, S. M. (2025) · Information and Organization · doi:10.1016/j.infoandorg.2025.100560 · Cited by 223
RoCo: Dialectic Multi-Robot Collaboration with Large Language Models
Mandi, Z., Jain, S., & Song, S. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610855 · Cited by 316
“Outside of robotics, LLMs have been shown to possess the capability of representing agentic intentions [2] and behaviors, which enables multi-agent interactions in simulated environments such as text-based games [36], [3] and social sandbox [33], [22], [27].”
BackgroundExpel: LLM agents are experiential learners
Zhao, A., Huang, D., Xu, Q., Lin, M., Liu, Y.-J., & Huang, G. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29936 · Cited by 153
Learning theories for artificial intelligence promoting learning processes
Gibson, D., Kovanovic, V., Ifenthaler, D., Dexter, S., & Feng, S. (2023) · British Journal of Educational Technology · doi:10.1111/bjet.13341 · Cited by 155
“We can compare this to the recent paper on generative agents (Park et al., 2023) where the structure of agency uses the plan- do- study-act cycle and includes memory.”
Concepts and applications of digital twins in healthcare and medicine
Zhang, K., Zhou, H.-Y., Baptista‐Hon, D. T., Gao, Y., Liu, X., Oermann, E. K., Xu, S. H., Jin, S., Zhang, J., Sun, Z., et al. (2024) · Patterns · doi:10.1016/j.patter.2024.101028 · Cited by 185
“46 These unique characteristics will allow for customization and personalization, 47 which align well with the need to construct medical DT models.”
BackgroundMethodEvaluating large language models as agents in the clinic
Mehandru, N., Miao, B. Y., Almaraz, E. R., Sushil, M., Butte, A. J., & Alaa, A. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01083-y · Cited by 143
Large language models for robotics: Opportunities, challenges, and perspectives
Wang, J., Shi, E., Hu, H., Ma, C., Liu, Y., Wang, X., Yao, Y., Liu, X., Ge, B., & Zhang, S. (2024) · Journal of Automation and Intelligence · doi:10.1016/j.jai.2024.12.003 · Cited by 281
MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning
Tang, X., Zou, A., Zhang, Z., Li, Z., Zhao, Y., Zhang, X., Cohan, A., & Gerstein, M. (2024) · Findings of the Association for Computational Linguistics ACL 2024 · doi:10.18653/v1/2024.findings-acl.33 · Cited by 376
Yin, H., Zhang, Z., & Liu, Y. (2023) · Systems · doi:10.3390/systems11120566 · Cited by 131
“…computing, cultural computing, and other scientific and humanistic methods, one can study the human experience mode, expand the boundaries of human cognition, and lay the communication foundation for future interdisciplinary teams to carry out design and innovation cooperation as “lubricant” [40].”
BackgroundCharacter-LLM: A Trainable Agent for Role-Playing
Shao, Y., Li, L., Dai, J., & Qiu, X. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.814 · Cited by 140
Generative Echo Chamber? Effect of LLM-Powered Search Systems on Diverse Information Seeking
Sharma, N., Liao, Q. V., & Xiao, Z. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642459 · Cited by 132
AI for social science and social science of AI: A survey
Xu, R. F., Sun, Y., Ren, M., Guo, S., Pan, R., Lin, H., Sun, L., & Han, X. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103665 · Cited by 174
A Survey on the Memory Mechanism of Large Language Model-based Agents
Zhang, Z., Dai, Q., Bo, X., Ma, C., Li, R., Chen, X., Zhu, J., Dong, Z., & Wen, J.-R. (2025) · ACM Transactions on Information Systems · doi:10.1145/3748302 · Cited by 592
“In this survey, we provide a systematical review on the memory mechanism of LLM-based agents, where we focus on three key problems including “What is,” “Why do we need,” and “How to design and evaluate” the memory module in LLM-based agents.”
InfluentialWhen Embodied AI Meets Industry 5.0: Human-Centered Smart Manufacturing
Xu, J., Sun, Q., Han, Q., & Tang, Y. S. (2025) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2025.125327 · Cited by 141
GPTeach: Interactive TA Training with GPT-based Students
Markel, J. M., Opferman, S. G., Landay, J. A., & Piech, C. (2023) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3573051.3593393 · Cited by 187
The Butterfly Effect in artificial intelligence systems: Implications for AI bias and fairness
Ferrara, E. (2024) · Machine Learning with Applications · doi:10.1016/j.mlwa.2024.100525 · Cited by 178
Cui, C., Ma, Y., Cao, X., Ye, W., & Wang, Z. (2024) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw60836.2024.00101 · Cited by 156
“Secondly, these methodologies enable LLMs to continually adapt and align their decision-making processes with user preferences, enhancing personalization and trust in autonomous vehicles.”
MethodRecommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach
Zhang, J., Xie, R., Hou, Y., Zhao, W. X., Lin, L., & Wen, J.-R. (2024) · ACM Transactions on Information Systems · doi:10.1145/3708882 · Cited by 363
“Overall, we can flexibly integrate these four parts in the form of natural language text.”
S3: Social-network Simulation System with Large Language Model-Empowered Agents
Gao, C., Lan, X., Lu, Z., Mao, J., Piao, J., Wang, H., Jin, D., & Li, Y. (2023) · doi:10.2139/ssrn.4607026 · Cited by 113
On the creativity of large language models
Franceschelli, G., & Musolesi, M. (2025) · AI & SOCIETY · doi:10.1007/s00146-024-02127-3 · Cited by 113
“For example, in Park et al. (2023), multiple LLM-based agents interact through natural language in a sandbox environment inspired by The Sims .”
Wang, A., Morgenstern, J., & Dickerson, J. P. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-00986-z · Cited by 311
“These four reasons are R1-Contingent, R2-Relevant 1,4,74–79 , R3-Subjective 28–30 and R4-Coverage 1,54,55,77,80 .”
Empowering digital twins with large language models for global temporal feature learning
Sun, Y., Zhang, Q., Bao, J., Lu, Y., & Liu, S. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.02.015 · Cited by 107
LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression
Jiang, H., Wu, Q., Luo, X., Li, D., Lin, C.-Y., Yang, Y., & Qiu, L. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.91 · Cited by 400
Teach AI How to Code: Using Large Language Models as Teachable Agents for Programming Education
Jin, H., Lee, S., Shin, H., & Kim, J. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642349 · Cited by 99
“However, LLMs now allow the configuration of agents with natural languages [37, 56], opening new doors for learners to adjust teachable agents for their educational needs.”
Generative language models exhibit social identity biases
Hu, T., Kyrychenko, Y., Rathje, S., Collier, N., van der Linden, S., & Roozenbeek, J. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00741-1 · Cited by 100
“GPT-2-Large Positive .5106 .9375 2 Results”
Mustapha, K. B. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.103066 · Cited by 107
Exploring the role of large language models in the scientific method: from hypothesis to discovery
Zhang, Y., Khan, S. A., Mahmud, A., Yang, H., Lavin, A., Levin, M. G., Frey, J. G., Dunnmon, J., Evans, J., Bundy, A., et al. (2025) · npj Artificial Intelligence · doi:10.1038/s44387-025-00019-5 · Cited by 160
“Since such automation is not fully hard-coded and the core of this automation is also an LLM, they can exhibit some emergent behaviour 66,114 , adapting unexpected situations, which is vital for exploring new knowledge.”
Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization
Tseng, Y.-M., Huang, Y.-C., Hsiao, T.-Y., Chen, W.-L., Huang, C.-W., Meng, Y., & Chen, Y.-N. (2024) · Findings of the Association for Computational Linguistics: EMNLP 2024 · doi:10.18653/v1/2024.findings-emnlp.969 · Cited by 87
Simulating Opinion Dynamics with Networks of LLM-based Agents
Chuang, Y.-S., Goyal, A., Harlalka, N., Suresh, S., Hawkins, R., Yang, S., Shah, D., Hu, J., & Rogers, T. (2024) · Findings of the Association for Computational Linguistics: NAACL 2024 · doi:10.18653/v1/2024.findings-naacl.211 · Cited by 85
Xiao, T., & Xu, P. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114691 · Cited by 86
Hao, H., Wang, Y., & Chen, J. (2024) · Engineering · doi:10.1016/j.eng.2024.06.012 · Cited by 99
Theory of Mind for Multi-Agent Collaboration via Large Language Models
Li, H., Yu, C. H. A., Stepputtis, S., Campbell, J., Hughes, D., Lewis, C., & Sycara, K. P. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.13 · Cited by 192
“Park et al. (2023) utilized ChatGPT to operate a sandbox environment populated by generative agents, observing emergent social behaviors among LLM-based agents.”
MethodLarge Language Model-Powered Smart Contract Vulnerability Detection: New Perspectives
Hu, S., Huang, T., İlhan, F., Tekin, S. F., & Liu, L. (2023) · IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA) · doi:10.1109/tps-isa58951.2023.00044 · Cited by 134
“Integrating Generative AI agents: Generative AI agents [27], [50] employ LLMs as the core component and perform specific roles in various tasks like software development [30].”
MethodPerformance and biases of large language models in public opinion simulation.
Qu, Y., & Wang, J. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03609-x · Cited by 79
“Complementary to this, studies by Brand et al. (2023) and Park et al. (2023) highlighted ChatGPT ’ s skill in simulating consumer behavior and human actions in various scenarios.”
Yan, L., Martinez-Maldonado, R., Jin, Y., Echeverria, V., Milesi, M., Fan, J., Zhao, L., Alfredo, R., Li, X., & Gašević, D. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105322 · Cited by 85
Generating meaning: active inference and the scope and limits of passive AI
Pezzulo, G., Parr, T., Cisek, P., Clark, A., & Friston, K. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2023.10.002 · Cited by 80
Do LLMs Exhibit Human-like Response Biases? A Case Study in Survey Design
Tjuatja, L., Chen, V., Wu, T., Talwalkwar, A., & Neubig, G. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00685 · Cited by 171
VideoAgent: Long-Form Video Understanding with Large Language Model as Agent
Wang, X., Zhang, Y., Zohar, O., & Yeung, S. Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72989-8_4 · Cited by 196
Agentic Large Language Models, a Survey
Plaat, A., Van Duijn, M., Van Stein, N., Preuss, M., Van der Putten, P., & Batenburg, K. J. (2025) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.18675 · Cited by 75
Astronomia ex machina: a history, primer and outlook on neural networks in astronomy
Smith, M. J., & Geach, J. E. (2023) · Royal Society Open Science · doi:10.1098/rsos.221454 · Cited by 91
Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs
Smit, A., Duckworth, P., Grinsztajn, N., Barrett, T. D., & Pretorius, A. (2023) · arXiv · doi:10.48550/arxiv.2311.17371 · Cited by 73
Chan, A., Ezell, C., Kaufmann, M., Wei, K., Hammond, L., Bradley, H., Bluemke, E., Rajkumar, N., Krueger, D., Kolt, N., et al. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3658948 · Cited by 84
“Since such deployment may exacerbate existing risks and introduce new ones [35, 147], it is imperative to understand how to govern AI agents.”
BackgroundAnatomy of an AI-powered malicious social botnet
Yang, K., & Menczer, F. (2024) · Journal of Quantitative Description: Digital Media · doi:10.51685/jqd.2024.icwsm.7 · Cited by 71
“However, emerging research in-19 dicates that LLMs can facilitate the development of autonomous agents capable of independently processing exposed information, making autonomous decisions, and utilizing tools such as APIs and search engines (Li et al., 2023; Park et al., 2023; Wang et al., 2023).”
BackgroundDomain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
Ling, C., Zhao, X., Lu, J., Deng, C., Zheng, C., Wang, J., Chowdhury, T., Li, Y., Cui, H., Zhang, X., et al. (2025) · ACM Computing Surveys · doi:10.1145/3764579 · Cited by 385
Large Language Model based Multi-Agents: A Survey of Progress and Challenges
Guo, T., Chen, X., Wang, Y., Chang, R., Pei, S., Chawla, N. V., Wiest, O., & Zhang, X. (2024) · arXiv · doi:10.48550/arxiv.2402.01680 · Cited by 1,713
Emergent social conventions and collective bias in LLM populations
Ashery, A. F., Aiello, L. M., & Baronchelli, A. (2025) · Science Advances · doi:10.1126/sciadv.adu9368 · Cited by 81
“Unlike studies that use LLMs to predict human responses in social science experiments ( 37 ) or to simulate human societies ( 38 – 40 ), our work does not treat LLMs as proxies for human participants but rather investigates how conventions emerge organically within a population of communicating AI…”
Large Language Models Enable Few-Shot Clustering
Viswanathan, V., Gashteovski, K., Gashteovski, K., Lawrence, C., Wu, T., & Neubig, G. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00648 · Cited by 119
“Building on a body of recent work that uses Large Language Models (LLMs) as noisy simulations of human decision-making (Fu et al., 2023; Horton, 2023; Park et al., 2023), we propose a different approach for semi-supervised text clustering.”
BackgroundCognitive Architectures for Language Agents
Sumers, T. R., Yao, S., Narasimhan, K., & Griffiths, T. L. (2023) · arXiv · doi:10.48550/arxiv.2309.02427 · Cited by 72
RAH! RecSys–Assistant–Human: A Human-Centered Recommendation Framework With LLM Agents
Shu, Y., Zhang, H., Gu, H., Zhang, P., Lu, T., Li, D., & Gu, N. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3404039 · Cited by 66
“Inspired from [18], our reflec-tion mechanism addresses issues arising from duplication and conflicts in learned personalities (preferences and aversions).”
BackgroundMethodWang, Y., Pan, Y., Su, Z., Deng, Y., Zhao, Q., Du, L., Luan, T. H., Kang, J., & Niyato, D. T. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3576176 · Cited by 115
“Role-playing [60], [98] is a typical method for facilitating cooperation among LM agents.”
Can Generative AI Improve Social Science?
Bail, C. A. (2023) · doi:10.31235/osf.io/rwtzs · Cited by 369
Stance Detection with Collaborative Role-Infused LLM-Based Agents
Lan, X., Gao, C., Jin, D., & Li, Y. (2024) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v18i1.31360 · Cited by 69
Multi-Modal LLMs in Agriculture: A Comprehensive Review
Sapkota, R., Qureshi, R., Hadi, M. U., Hassan, S. Z., Sadak, F., Shoman, M., Sajjad, M., Dharejo, F. A., Paudel, A., Li, J., et al. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3612154 · Cited by 81
“By employing the advanced cognitive capabilities of MM-LLMs, such as their reasoning and memory functions [182], [183], the RL agent within this framework can comprehend complex aspects of crop growth and simulation environments more e ff ectively.”
Generative AIPreprint
Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4443189 · Cited by 65
Comprehensive review of Transformer‐based models in neuroscience, neurology, and psychiatry
Cong, S., Wang, H., Zhou, Y., Wang, Z., Yao, X., & Yang, C. (2024) · Brain‐X · doi:10.1002/brx2.57 · Cited by 75
AIGC-empowered smart manufacturing: Prospects and challenges
Leng, J., Zheng, K., Li, R. C., Chen, C., Wang, B., Liu, Q., Chen, X., & Shen, W. (2025) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2025.103076 · Cited by 74
The alignment problem from a deep learning perspective
Ngo, R., Chan, L., & Mindermann, S. (2022) · arXiv · doi:10.48550/arxiv.2209.00626 · Cited by 68
“As an early example, successful (pro-social) cooperation and coordination can emerge as a by-product of different language model instances that simulate characters in a social environment [Park et al., 2023].”
BackgroundEvaluation and Benchmarking of LLM Agents: A Survey
Mohammadi, M., Li, Y., Lo, J., & Yip, W. (2025) · Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 · doi:10.1145/3711896.3736570 · Cited by 74
EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities
Li, N., Gao, C., Li, M., Li, Y., & Liao, Q. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.829 · Cited by 59
“…for simulation, including autonomous but adaptive reactions (Team, 2022; Yoheinakajima, 2023), human-like intelligence for planning (Wang et al., 2023; Xi et al., 2023), and interaction and communication with other agents or human (Park et al., 2023; Gilbert and Troitzsch, 2005; Park et al., 2023).”
BackgroundFoundation models meet visualizations: Challenges and opportunities
Yang, W., Liu, M., Wang, Z., & Liu, S. (2024) · Computational Visual Media · doi:10.1007/s41095-023-0393-x · Cited by 77
AppAgent: Multimodal Agents as Smartphone Users
Zhang, C., Yang, Z., Liu, J., Li, Y., Han, Y., Chen, X., Huang, Z., Fu, B., & Yu, G. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713600 · Cited by 424
The Emerged Security and Privacy of LLM Agent: A Survey with Case Studies
He, F., Zhu, T., Ye, D., Liu, B., Zhou, W., & Yu, P. S. (2026) · ACM Computing Surveys · doi:10.1145/3773080 · Cited by 58
“When malicious agents are placed within systems that control critical infrastructure like industry, transportation, energy, and environmental monitoring [83], they can cause malfunctions in industrial control systems by tampering with critical operational data, such as temperature and pressure…”
Understanding World or Predicting Future? A Comprehensive Survey of World Models
Ding, J., Zhang, Y., Shang, Y., Zhang, Y., Zong, Z., Feng, J., Yuan, Y., Su, H., Li, N., Sukiennik, N., et al. (2026) · ACM Computing Surveys · doi:10.1145/3746449 · Cited by 169
“Recent studies suggest that theories of human behavior patterns and cognitive processes can inform the design of agentic workflows, which in turn enhance the human behavior simulation capabilities of LLMs [189, 229], representing an important direction for future research.”
InfluentialMemGPT: Towards LLMs as Operating Systems
Packer, C., Wooders, S., Lin, K., Fang, V., Patil, S. G., Stoica, I., & Gonzalez, J. E. (2023) · arXiv · doi:10.48550/arxiv.2310.08560 · Cited by 64
An, J., Huang, D., Lin, C., & Tai, M. (2025) · PNAS Nexus · doi:10.1093/pnasnexus/pgaf089 · Cited by 58
Language as Reality: A Co-Creative Storytelling Game Experience in 1001 Nights Using Generative AI
Sun, Y., Li, Z., Fang, K., Lee, C. H., & Asadipour, A. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment · doi:10.1609/aiide.v19i1.27539 · Cited by 63
More Human than Human: Measuring ChatGPT Political BiasPreprint
Motoki, F. Y. S., Neto, V. P., & Rodrigues, V. R. D. S. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4372349 · Cited by 55
ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate
Chan, C.-M., Chen, W., Su, Y., Yu, J., Xue, W., Zhang, S., Fu, J., & Liu, Z. (2023) · arXiv · doi:10.48550/arxiv.2308.07201 · Cited by 725
Sun, G., Zhan, X., & Such, J. (2024) · ACM Conversational User Interfaces 2024 · doi:10.1145/3640794.3665887 · Cited by 55
Empowering generative AI through mobile edge computing
Ale, L., Zhang, N., King, S. A., & Chen, D. (2024) · Nature Reviews Electrical Engineering · doi:10.1038/s44287-024-00053-6 · Cited by 61
“For example, Gen AI can offer tailored health and wellness advice based on the physiological data of the user 35 , navigate commuting using real-time location information 36,37 and enhance social interactions by observing communication patterns 38 .”
Metadata
- Authors
- Park, J. S., O'Brien, J., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S.
- Affiliations
- Stanford University (US)
- Google (United States) (US)
- ORCID
- Venue
- Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology
- Publisher
- ACM
- Published
- 2023-10-29
- Pages
- 1-22
- OpenAlex
- W4387835442
- Semantic Scholar
- 5278a8eb2ba2429d4029745caf4e661080073c81
- Cited by
- 2,187
- References
- 70
- Citations by year
- 2026: 961 · 2025: 745 · 2024: 380 · 2023: 101
- FWCI
- 386.60
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Symposium on User Interface Software and Technology (UIST) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Microsoft Research
- Center for Advanced Study in the Behavioral Sciences, Stanford University
- Center for Advanced Study, University of Illinois at Urbana-Champaign
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- generative AI agents, large language models, interactive simulation environment, natural language interaction, agent planning, interpersonal communication
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-09-28, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-09-28 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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