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
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“…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
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MethodA General Inductive Approach for Analyzing Qualitative Evaluation Data
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MethodThe Computer for the 21st Century
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“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
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Items 1,401–1,500 of 2,105
Vidler, A., & Walsh, T. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-16328-8_8
Pérez-Elvira, R., Oltra‐Cucarella, J., Juan, M. A., Polo-Ferrero, L., Díaz, M. Q., Bosch‐Bayard, J., Ruíz, A. S., Rashid, A. N. M. M. O., & Juárez‐Vela, R. (2026) · AI · doi:10.3390/ai7020069 · Cited by 1
“Beyond their role as conversational assistants, these systems have demonstrated a substantial capacity to behave linguistically as “complex persons,” maintaining stylistic, emotional, and biographical coherence across extended interactions [1–5].”
Characterizing User Needs for GenAI Incorporation in Educational Games
Goldshtein, M., Ahmed, I., Yu, F., Verma, V., McNamara, D. S., & Arner, T. (2026) · Education Sciences · doi:10.3390/educsci16020300 · Cited by 1
Teamwork in Adversarial Video Games
Dunin-Kȩplicz, B., & Tyl, R. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-18011-7_11
Debnath, A., Cranefield, S., Lorini, E., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-17542-7_6
Encounters between Artificial and Human Intelligences in Early‐Stage Innovation Work
Notini, W., Kochoska, A., & Perkel, D. (2025) · Ethnographic Praxis in Industry Conference Proceedings · doi:10.1111/epic.70015
MRAFnd: Multimodal Retrieval-Augmented Framework for Zero-Shot Fake News Detection
Zhang, L., Cheng, Y., Hu, S., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-6950-2_41
“LLMs have evolved into autonomous agents capable of complex reasoning and planning [23], leading to the development of multi-agent systems where agents collaborate to solve complex problems [16].”
Yue, F., Zhang, Y., Liu, Y., et al. (2026) · Image and Vision Computing · doi:10.1016/j.imavis.2026.105938
Give: A Multi-Agent Framework for Generating Immersive Multi-Modal Virtual Environments for 3D Games
Vyas, S., Venkatesh, S. B., & Su, G. (2025) · doi:10.1109/icipw68931.2025.11386126
“These systems have been used for applications ranging from simulating social behaviors in virtual worlds [12] to building LLM-based operating systems [13] and task oriented tools [2].”
Gao, D., Zhang, H., Liu, Y., & Qi, Z. (2026) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2026.103262 · Cited by 1
Se, W., Li, Z., & Wu, G. (2025) · International Conference on Big Data and Information Analytics (BigDIA) · doi:10.1109/bigdia68682.2025.11382737
Complexity in the humanities and the humanities in complexity
Chen, S. (2026) · Frontiers in Social Psychology · doi:10.3389/frsps.2026.1746969
Yin, J., Xie, X., Chen, J., Guo, S., & Cui, J. (2026) · Electronics · doi:10.3390/electronics15040870
“Park et al. [36] introduced a generative agent architecture that extends LLMs to enable credible simulation of human behaviors, including memory storage, reflective induction, and dynamic retrieval.”
Generative Agents as Reliable Proxies for Human Evaluation of AI-Generated Content
Vu, T., Nayak, R., & Balasubramaniam, T. (2026) · Communications in computer and information science · doi:10.1007/978-981-95-6888-8_1
Keyword-Centered Rescheduling for LLM Agents
Ji, Y., Lee, I., & Sung, Y. (2026) · Cognitive Computation · doi:10.1007/s12559-026-10561-2
“Controllable text generation (CTG) steers LLMs toward target attributes while preserving semantics [23, 33] LLM agents motivate reliable control because their memory, planning, and reflection amplify behavior-level effects [ 34, 35].”
Pandora: A Large Language Model-Driven Self-Evolving Agent for On-Campus Task Execution
Zeng, W., Xie, X., Yan, X., Li, Z., Wang, Y.-F., Luo, G.-F., Wang, C., & Chen, L.-B. (2025) · 2025 IEEE Smart World Congress (SWC) · doi:10.1109/swc65939.2025.00200
“In urban studies, Stanford’s “Stanford Town” simulated multi-agent social interactions [16], while CityGPT targeted diverse city-level tasks [17].”
AgentSME for Simulating Diverse Communication Modes in Smart Education
Yang, W.-X., & Zhao, T.-F. (2025) · IEEE Smart World Congress (SWC) · doi:10.1109/swc65939.2025.00119
“The entities of generative agents powered by state-of-the-art generative models have demonstrated remarkable capabilities to simulate human-like behaviors [11] and generate context-aware interactions [12].”
Towards Autonomous Building Construction: A Multi-agent Framework Leveraging Large Multimodal Models
Soma, W., Ding, D., & Ito, T. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-7081-2_17
Magnol.AI Copilot: Multimodal LLMs for Natural, Accessible Data Interaction
Ruan, G., Zhang, H., Xiao, H., & Yang, J. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00237 · Cited by 1
“The foundation of this tool is a robust integration of large language models (LLMs) [1, 3, 11, 12] and a multi-agent architecture [8, 10, 13].”
GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzles
Shan, M., Curless, B., Kemelmacher-Shlizerman, I., & Seitz, S. M. (2025) · 2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw69036.2025.00212
Generative AI and organisational collective intelligence: A dependency-structured framework
Patel, P., & Dey, M. (2026) · Business Information Review · doi:10.1177/02663821261429686 · Cited by 6
“It can support deliberation, from moderating discussion and sur-facing alignment or disagreement to simulating stakeholder perspectives (Argyle et al., 2023; Park et al., 2023).”
Application of Large Language Models in the Problem of Event Forecasting
Dakhnovich, A. D., Богина, В. М., & Makeeva, A. A. (2025) · Automatic Control and Computer Sciences · doi:10.3103/s0146411625701111
ForexAgent: Identifying Trading Strategies in Forex Markets with Large Language Models
Shu, X., Ju, M., Chen, Z., Ding, Y., Zhang, W., Wen, D., & Yang, Z. (2026) · IEEE International Conference on Big Data and Smart Computing (BigComp) · doi:10.1109/bigcomp68355.2026.00019 · Cited by 1
“Recent studies in related domains, such as agent modeling [37] and game-theoretic reasoning [38], suggest that LLMs can infer latent goals and strategies from observed actions, providing a promising foundation for financial behavioral inference.”
Jury, Judge, and Executioner: Using LLMs to Steer Gamified Multi-Agent Decision-Making
Raka, L. D., & Lhaksmana, K. M. (2025) · Beyond Technology Summit on Informatics International Conference (BTS-I2C) · doi:10.1109/bts-i2c67944.2025.11399472
“We adopt a two-store memory design inspired by the work of Park et al. [11].”
InfluentialSchück, F. (2026) · Frontiers in Water · doi:10.3389/frwa.2026.1749745
“So far, LLMs in ABMs have been used to explore echo chambers (Törnberg et al., 2023), human-like interactions (Kaiya et al., 2023; Park et al., 2023), and people’s attitudes to gender discrimination and nuclear energy (Gao et al., 2023).”
Meng, X., Wang, T., Dong, Z., Li, X., Cui, X., & Wang, L. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.25.707926
Doropoulos, S., Vologiannidis, S. D., & Magnisalis, I. D. (2026) · Information and Software Technology · doi:10.1016/j.infsof.2026.108078 · Cited by 2
Javadi, V. S., Róg, F., Aksa, A., Trippas, J. R., Vakulenko, S., & Flek, L. (2026) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3786304.3787921 · Cited by 5
“Frame-works such as Generative Agents [36], SOTOPIA-𝑆 4 [49, 56], Social-Sim [8], and AutoGen [53] have demonstrated emergent behaviors, open-ended multi-agent interactions, and task-oriented reasoning.”
Tian, W. (2025) · International Conference on Machine Learning, Computational Intelligence and Pattern Recognition (MLCIPR) · doi:10.1109/mlcipr68329.2025.11407347 · Cited by 1
Singh, R. K., & Patil, S. D. (2026) · IEEE Transactions on Games · doi:10.1109/tg.2026.3669660
A Standardized Framework for Agent-to-Agent Communication in Distributed AI Ecosystems
Kota, S. K. (2025) · European Modern Studies Journal · doi:10.59573/emsj.9(3).2025.57
An LLM-Driven Multi-Agent Simulation Framework for Coupled Epidemic–Economic Dynamics
Wang, S., Liu, H., Zhang, S., et al. (2026) · Information · doi:10.3390/info17030259
[Withdrawn] AI Agents Need Memory Control Over More ContextPreprint
Bousetouane, F. (2026) · Qeios · doi:10.32388/mzqb3t
Large language models for urban transportation
Ma, Z., Wang, L., Qin, Z., & Ling, Y. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-26789-5.00011-0
SAMAG: Structure-Aware Multi-Agent Graph Generation with Large Language Models
Cen, J., Ji, J., Wang, Z., Wei, Z., Li, Y., & Ding, B. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11402080
“By endowing agents with capabilities for reasoning, memory, and planning, these systems can simulate believable human behaviors and emergent social phenomena [15].”
EvoRL: Evolution of Agentic Workflows Using Reinforcement Learning
Attota, D. C., & Xie, Y. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11401641
“Studies on frameworks like ReAct [5] and Reflexion [6] have demonstrated that encouraging models to engage in a cyclical process of “thinking” and “acting” fosters advanced problem-solving capabilities, transforming them into dynamic contributors [7] rather than passive generators [8].”
Irony in Emojis: A Comparative Study of Human and LLM Interpretation
Zheng, Y., Lyu, H., & Luo, J. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11401988
“The emergence and advancement of Large Language Models (LLMs) have enabled more sophisticated simulations of human behavior, including the actions of social media users, through generative agents [1].”
ABL: An LLM-Based Agentic Framework for Applying Black-Litterman Portfolio Optimization
Lorenzoni, G., Alencar, P. S. C., & Cowan, D. D. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11402167
“Recent research demonstrates that such agents can operate across multiple time horizons, using sequential decision-making to adjust allocations in response to volatility shocks [10], [11].”
A Multi-Agent Architecture for Governance and Security of LLM-Based Knowledge Access
Aguiar, V. A., Amorim, L. A., Novais, A. M. A., de Jesus, T. P., Soares, A. D. S., & de Oliveira, S. S. T. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11401633
“When combined with LLMs, MAS architectures benefit from enhanced semantic coordination and richer interfaces, but face challenges in hallucination control, context management, and factual consistency [22], [23].”
Sheikhi, S., Kostakos, P., & Lovén, L. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11401968 · Cited by 3
“Park et al. [26] demonstrate that collections of LLM-driven agents can exhibit emergent social behaviors, such as information diffusion, relationship formation, and coordinated group activities, in a shared environment.”
Aggarwal, A., & Ghalaty, N. F. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11401700
“Enterprise AI deployments increasingly rely on large language model (LLM)-based agents that interact with users, access databases, invoke external APIs, and maintain conversational context across extended sessions [1].”
Assessing LLM Reasoning with Subtask Variation: Chess and DREAMS
Olea, C., Karns, A., & White, J. (2025) · IEEE International Conference on Cognitive Machine Intelligence (CogMI) · doi:10.1109/cogmi67134.2025.00014
Interactive XAI through Dialogue: Leveraging Approximate Inverse Model Explanations and LLM Agents
Nakanishi, T. (2025) · IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) · doi:10.1109/iiai-aai-winter69777.2025.00049
“…[31], evaluation suites like AgentBench [32] and MINT [33], autonomous pipelines exemplified by AutoGPT [34], memory-driven Generative Agents [35], developer toolkits in LangChain [36], and the real-world challenge set WildBench [37]—highlight the importance of tightly coupling LLM reasoning…”
MobiTwin: Toward Building End-to-End Framework for User’s Mobile Twins
Venkatraman, S., Tran, T., Luo, S., Chen, D., Wu, Y., & Wei, K. (2025) · IEEE International Conference on Cognitive Machine Intelligence (CogMI) · doi:10.1109/cogmi67134.2025.00066
“Generative agent frameworks [16] and LLM-driven agent-based modeling [5] demonstrate potential for lifelike sim-ulacra but focus on open-ended or desktop settings rather than production-scale mobile environments.”
Meta-agentic Framework for Software bug detection using Large Language Models
Padmanabhuni, S., Karuturi, B., Indupalli, J. K., Chilla, S. R., & Yelleti, V. (2026) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets67989.2026.11418178 · Cited by 1
“LLM agents are AI-driven systems that employ LLMs as the engines to process the user-provided queries and generate humanlike responses [5].”
Persona-Driven LLM Interaction in Stock Market Simulations
Parhy, M., & Goldwasser, D. (2025) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw69685.2025.00378 · Cited by 1
“This suggests a promising direction for creating more interpretable and controllable agent behaviors in multi-agent systems, allowing us to simulate the outcomes of social scenarios [21].”
Evaluating the Effectiveness of Persona Simulation in Opinion Prediction with GPT-4.1
Li, S. W. L., & Yao, Z. (2025) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw69685.2025.00377
“Promise has also been shown by Park et al. in simulating human conversations and interactions in a virtual town environment [4] and Yue et al. in an educational setting [5].”
Modeling Network Formation with LLM Agents: The Role of Demographics and Personality
Gkartzios, C., Pitoura, E., & Tsaparas, P. (2025) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw69685.2025.00022
“Recent systems pair this ABM design with language model agents that perceive context, store short memories, and act via text, yielding multi step social behavior in open environments [12]–[14].”
Zhang, G., Wu, J., Jeon, G., Wang, P., & Camacho, D. (2026) · Cognitive Computation · doi:10.1007/s12559-025-10534-x · Cited by 1
Wang, W., Yang, Y., & Luan, Z. (2026) · Design for Augmented Humanity · doi:10.1177/29776481261427591
“Furthermore, generative agents are instantiated to populate an interactive environment, Smallville, a virtual town where 25 AI agents live, displaying realistic individual and social behaviors (Park et al., 2023).”
No skin in the game: why agentic AI requires principal-agent governance
Prause, M. (2026) · AI and Ethics · doi:10.1007/s43681-026-01067-6
Метод реализации псевдореалистичного перемещения неигровых персонажей в открытых виртуальных мирах
Шутов, К. И., & Лобанов, А. Ю. (2026) · Modelirovanie, optimizaciâ i informacionnye tehnologii. · doi:10.26102/2310-6018/2026.54.3.006
Liu, Y., Liao, X., Ma, H., et al. (2025) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc60802.2025.11423465
“Recent work has explored using LLMs to create agents capable of planning and reflection, enabling more realistic responses to dynamic conditions [15], [16], [17], [18].”
Embodied Virtual Agents: Expected Impact of Labor Relations
Анатольевна, Ф. И. (2026) · Lex Russica · doi:10.17803/1729-5920.2026.231.2.024-038
Algorithmic Market Orientation: The Shift from Retrospective Insight to Agentic IntelligencePreprint
Yi, Z., & Fu, Y. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6431442
Exploring user representation learning from interaction data: a data-centric survey
Zhong, S., Guo, S.-Y., & Liu, C. (2026) · International Journal of Data Science and Analysis · doi:10.1007/s41060-026-01076-y
Accelerating Language Model Workflows with Prompt Choreography
Bai, T., & Eisner, J. M. (2026) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.643
Metacognitive Multi-agent Retrieval-Augmented Generation for Multi-hop Reasoning
Iturra-Bocaz, G. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-21324-2_42
Sun, S., & Fu, Y. (2026) · Buildings · doi:10.3390/buildings16061265
Generative AI for climate governance and acceptability-constrained policy design
Manivannan, A., Spaiser, V., Cann, T. J. B., Evans, J., Everall, J., Falkenberg, M., García, D., Guo, W., Herzog, R., Otto, I. M., et al. (2026) · npj Climate Action · doi:10.1038/s44168-026-00362-6 · Cited by 1
“Unlikeequation-based models that simulate aggregate system changes (top-down), ABMs simulate the system from the bottom-up: global phenomena (like polarization or market crashes) emerge from the local interactions of thousands ofindividualagents.”
InfluentialChen, Y., Luan, Z., Tang, L., et al. (2025) · doi:10.1109/eccst68196.2025.11441180
“The “Planning–Memory–Reflection” closed-loop has gradually emerged as a representative architecture for enabling long-horizon reasoning, behavioral consistency, and iterative self-improvement [3]–[5], [14].”
Aligning Agents With Experts in Material Selection: Prompting, Size, and Reasoning
Ying, M. Y., Grandi, D., Groom, A., & McComb, C. (2026) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4071467
Shatdarsanam, C. N., Kundiladi, M. S., Jonnalagadda, D. R., et al. (2026) · doi:10.1109/icauc68182.2026.11441048
“However, these models required extensive labeled training data that proved difficult to obtain at scale, and their performance degraded significantly when applied to codebases with different characteristics than training data [13].”
RetractedRole-Aware Context Compression in Agentic AI Pipelines for Question Answering
Vasumathi, M. T., Asha, V., Dhiman, D., Harsha, Y., A, K. G., & Adheeksha, P. (2026) · International Conference on AI-Driven Smart Systems and Ubiquitous Computing (ICAUC) · doi:10.1109/icauc68182.2026.11441156
“Retrieval-Augmented Generation [10] fetches relevant context dynamically from external knowledge bases and hence reduces the burden on context windows.”
Dynamic Adaptation-Based Multi-Agent Emotional Regulation Mechanism in Public Opinion
Wei, Q., Rao, N., Wen, X., & Xiao, H. (2026) · International Conference on Embedded Systems, Mobile Communication and Computing (EMC²) · doi:10.1109/emc68537.2026.11441766
“In the Perception layer, agents invoke the proposed Gestalt-based calculation to interpret information; in the Memory layer, LSTM networks are employed to facilitate dynamic updates of cognitive states rather than static storage [10].”
Agentic AI as the Next Paradigm in PHM
Naderkhani, F. (2026) · IEEE reliability magazine · doi:10.1109/mrl.2026.3654129
Smart Contracts as Semantic Signalling for AI Agents on Blockchains
Pakana, F. (2026) · doi:10.1145/3793638.3793651
“Recent work on autonomous and generative agents highlights how large language models enable adaptive, context-aware decision making, raising new security concerns when such agents operate in open, adversarial environments [4, 6, 14, 15, 19].”
Compiler.next: A Search-Based Compiler to Power the AI-Native Future of Software Engineering
Côgo, F., Oliva, G., & Hassan, A. E. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3802581 · Cited by 3
“…is characterized by using prompts (typically written in natural language) to interact with the FMs, whereas Agentware is characterized by the usage of FMs to implement autonomous agents [27, 28] that are capable of decision-making and interaction with the environment or with other agents.”
Wang, X., Gu, J., Qiu, J., Liu, G., Cai, X., Zhu, J., Liu, Y., Dong, Z. Y., & Zhao, J. (2026) · Engineering · doi:10.1016/j.eng.2026.02.031
DHR-LLM: A Dynamic Heterogeneous Redundancy Framework for Resilient Large Language Model Inference
Chen, P., Yu, Z., & Zhang, S. (2025) · International Conference on Cyber Resilience and Endogenous Safety & Security (CRESS) · doi:10.1109/cress68073.2025.11452519
“Collaborative paradigms such as Model Soups [34] and Generative Agents [35] demonstrate that diversity across checkpoints or behaviors can improve generalization without retraining.”
Look2React: Making VR NPCs Come Alive with Dynamic Vision-Guided Reactions
Vatsal, R., Huang, X., & Xiao, R. (2026) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2026.3679121
“Park et. al. also show how LLM-driven action selection and memory recall yield NPC-like behaviors that feel intentional and consistent over time [38].”
Conclusion and the Road Ahead: Open Challenges and Responsibility
Nagasubramanian, D. (2026) · Apress eBooks · doi:10.1007/979-8-8688-2361-9_14
Collaborative Agents (Multi-agent Systems, Human–AI Teaming)
Nagasubramanian, D. (2026) · Apress eBooks · doi:10.1007/979-8-8688-2361-9_12
Yıldız, Z. Ç., Baytak, F., & Ozkaya, F. (2026) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2026.3679468
Balsa-Barreiro, J., Rabbani, S., Difallah, D. E., et al. (2026) · Discover Data · doi:10.1007/s44248-026-00105-2
Anywhere Academy — Avatar-Based Collaborative Teaching and Learning in VR
Querl, P., Chandra, R. L., Castermans, K., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-20129-4_23
Agentic AI Meets RAG: A Framework for Domain-Specific Knowledge Evaluation
Vijay Kumar, K., M, A. A., Uma, K. V., & S, V. V. (2025) · IEEE International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW) · doi:10.1109/hipcw66559.2025.00044
“It has led to the emergence of the AI Agent paradigm, where grounding and retrieval are integrated into autonomous, task-oriented systems [3].”
Towards AI-Mediated Bidirectional Communication Support for Cross-Neurotype Interactions
Haroon, R. (2026) · ACM SIGACCESS Accessibility and Computing · doi:10.1145/3803767.3803779
From Simulation to Strategy: Automating Personalized Interaction Planning for Conversational Agents
Chang, W.-Y., Huang, T., Chen, C.-D., & Chen, Y.-N. (2025) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru65441.2025.11434776
“The role-playing capabilities of large language models (LLMs) have attracted growing attention in recent research [6]–[8].”
Human mobility in the metaverse mirrors patterns in the physical world
Vasan, K., Karsai, M., & Barabási, A.-Ĺ. (2026) · Scientific Reports · doi:10.1038/s41598-026-45128-6
Wang, Z., Chen, W., Liu, R., Yang, K., Zhou, W., Lin, C., Liu, Q., Sim, M. Y., Zhang, G., Chen, X., et al. (2026) · Synthesis lectures on human language technologies · doi:10.1007/978-3-032-06264-2_13
Parihar, B. S., Singh, T. P., Gonnade, G., Dewangan, B. K., Shelke, N. M., & Pimpalkar, A. P. (2026) · Scientific Reports · doi:10.1038/s41598-026-47145-x
“GenAI agents suggested by Park et al. 9 demonstrated emergent social behaviours and also employed advanced architecture that enabled agents to produce relationships, organize actions and present patterns of complex actions with comparatively simple elements of architecture.”
LGA: lightweight design and privacy analysis of generative agents in social simulations
Guo, Y., Lu, X., Ma, W., Xiao, X., & Zhu, T. (2026) · International Journal of Information Security · doi:10.1007/s10207-026-01244-y
“To validate the practicality of the LGA framework, we conducted experiments based on two classic scenarios from prior work [13].”
InfluentialWang, Z., & Chen, W. (2026) · Synthesis lectures on human language technologies · doi:10.1007/978-3-032-06264-2_2
Wang, Z., & Lin, C. (2026) · Synthesis lectures on human language technologies · doi:10.1007/978-3-032-06264-2_1
Wang, Z., & Fu, J. (2026) · Synthesis lectures on human language technologies · doi:10.1007/978-3-032-06264-2_8
Wang, Z., Zhang, G., Shi, N., & Lin, C. (2026) · Synthesis lectures on human language technologies · doi:10.1007/978-3-032-06264-2_3
Wang, Z., Zhang, G., Chen, X., Hao, S., Li, Y., Xiong, G., Yang, Z., Yang, K., Sim, M. Y., & Xu, K. (2026) · Synthesis lectures on human language technologies · doi:10.1007/978-3-032-06264-2_11
Generative Agents for High-Fidelity Simulation of Community-Scale Mobility and Energy Behavior
Chen, Y., Yang, Z., & Ou, S. S. (2026) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2026-01-0465
The evolving study of political polarization
Erdogan, E., Jaworsky, B. N., Álvares, C., Bodroža, B., Butkevičienė, E., Cela, E., Dodeigne, J., Fahmy, S. S., Schäfer, S. H., Scott, G., et al. (2026) · Open Research Europe · doi:10.12688/openreseurope.23252.1
RS-IntentionAgent: A Semantic Intention-Adaptive Agent for Remote Sensing Image Interpretation
Kang, Z., & Su, X. (2026) · International Conference on Neural Networks, Information and Communication Engineering (NNICE) · doi:10.1109/nnice68970.2026.11466220
“Recent advancements in agent rameworks—often powered by large language models—have demonstrated strong potential in coordinating multiple tools, complex tasks, and flexibly adapting to diverse user goals through modular design[11][12][13].”
Damastuti, F. A., Firmansyah, K., Barakbah, A., & Hariadi, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6536138
Improving Cooperation in Multi-agent Deep Reinforcement Learning for Mobile Robotics
Toquebiau, M. (2024) · theses.fr (ABES) · doi:10.70675/3385d0bbzc66cz4fceza9e0z8696ff61ff4e
Tarmissi, K., & Marref, A. (2026) · Frontiers in Computer Science · doi:10.3389/fcomp.2026.1773479
Ran, Z., Ouyang, K., Ma, T., et al. (2025) · The Web Conference · doi:10.1145/3774904.3792901
“For example, AgentSociety [3] and Stanford AI Town [2] utilize LLM-driven agents to simulating agents v.s. agents and agents v.s. environment interactions, and observe various phenomena and mechanisms analogous to those found in human societies.”
From Symbols to Synapses: The Reemergence of Agency in the Large Language Model Era
An, B. (2026) · IEEE Intelligent Systems · doi:10.1109/mis.2026.3668397
“32 Building on this idea, several frameworks represent households, firms, or other actors with LLM-based agents whose micro-level decisions about work, consumption, saving, or communication collectively generate macro-level dynamics.”
AI Architectures for Intelligent Shopping Agents
Al-E’mari, S., Sanjalawe, Y. K., & Hajjaj, R. Y. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7330-0.ch004
Nan, H., Han, Y., Yu, P., et al. (2026) · Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3798665
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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