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
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Items 601–700 of 2,105
Gamoura, S. C., Koruca, H. İ., & Urgancı, K. B. (2023) · Lecture notes in mechanical engineering · doi:10.1007/978-981-99-6062-0_34 · Cited by 5
Human vs. LMMs: Exploring the Discrepancy in Emoji Interpretation and Usage in Digital Communication
Lyu, H., Qi, W., Wei, Z., & Luo, J. (2024) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v18i1.31453 · Cited by 7
Foundation Models for the Digital Twins Creation of Cyber-Physical Systems
Ali, S., Arcaini, P., & Arrieta, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-75390-9_2 · Cited by 14
“For example, Part et al. [37] presented an agent architecture for generating believable behavior using LLMs.”
BuildingSage: A safe and secure AI copilot for smart buildings
Dedeoglu, V., Zhang, Q., Li, Y., et al. (2024) · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys) · doi:10.1145/3671127.3699677 · Cited by 5
“…application in smart building contexts: Multi-agent framework: Recent studies show that large foundational models, when deployed as agents with complementary goals, outperform single agents in handling complex tasks collectively [15, 16], as demonstrated in AI-agent frameworks like AutoGen [24].”
Is ChatGPT Humanly Irrational?Preprint
Ma, D., Zhang, T., & Saunders, M. A. (2023) · Research Square · doi:10.21203/rs.3.rs-3220513/v1 · Cited by 7
Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scale
Vaithilingam, P., Kim, M. G., Acosta-Parenteau, F.-C., Lee, D. W., Mhedhbi, A., Glassman, E. L., & Arawjo, I. (2025) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3746059.3747778 · Cited by 23
Large Language Models as Biomedical Hypothesis Generators: A Comprehensive Evaluation
Qi, B., Zhang, K., Tian, K., Li, H., Chen, Z.-R., Zeng, S., Hua, E., Jinfang, H., & Zhou, B. (2024) · arXiv · doi:10.48550/arxiv.2407.08940 · Cited by 5
“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).”
BackgroundHindsight: Structured Agent Memory that Retains, Recalls, and Reflects
Latimer, C., Boschi, N., Neeser, A., Bartholomew, C., Srivastava, G., Wang, X., & Ramakrishnan, N. (2026) · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · doi:10.18653/v1/2026.acl-demo.27 · Cited by 5
Episodic Memory in AI Agents Poses Risks that Should be Studied and Mitigated
DeChant, C. (2025) · 2025 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml64287.2025.00024 · Cited by 5
“…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].”
Co-Creating Question-and-Answer Style Articles with Large Language Models for Research Promotion
Lim, H., Cho, J. Y., Kim, T., Park, J., Shin, H., Choi, S., Park, S., Lee, K., Kim, J., Lee, M., et al. (2024) · Designing Interactive Systems Conference · doi:10.1145/3643834.3660705 · Cited by 7
“The capability of LLMs to simulate specifc personas can provide a novel approach for authors to craft QA-style articles [49], taking into account a variety of perspectives and needs, including those of their target audiences in research communication context [4].”
BackgroundSelf-citationHarnessing the Power of LLMs for Normative Reasoning in MASs
Savarimuthu, B. T. R., Ranathunga, S., & Cranefield, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-82039-7_9 · Cited by 11
Piao, J., Lu, Z., Gao, C., & Li, Y. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3696410.3714537 · Cited by 4
Xiong, X., Sun, D., Wong, I. A., & Lian, Q. L. (2025) · Journal of Travel Research · doi:10.1177/00472875251332953 · Cited by 4
“5 can act as generative proxies that mimic human interactive behaviors (Park et al., 2023, Xiong et al., 2024), showcasing preferences consistent with those of ordinary humans in classic cognitive and risk preference tasks (Binz & Schulz, 2023; Suri et al., 2024).”
Treanor, M., Samuel, B., & Nelson, M. J. (2025) · Proceedings of the 20th International Conference on the Foundations of Digital Games · doi:10.1145/3723498.3723806 · Cited by 4
“Finally, we make the opposite representational choice from the otherwise related Smallville social simulation system [18].”
Policies for Refugee Mental Health Using Generative Agents
Khatua, A. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3701716.3715580 · Cited by 4
Actions Speak Louder than Words: Agent Decisions Reveal Implicit Biases in Language Models
Li, Y., Shirado, H., & Das, S. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732212 · Cited by 25
“Studies demonstrate that these simulations can approximate human behavior in artificial social environments and economic games [28, 29, 75, 76, 107].”
Digital moral systems in the game and in reality
Bylieva, D. S. (2025) · Vestnik of Minin University · doi:10.26795/2307-1281-2025-13-2-15 · Cited by 11
Exploring Human-Like Thinking in Search Simulations with Large Language Models
Zhang, E., Wang, X., Gong, P., Yang, Z., & Mao, J. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730193 · Cited by 6
Multi-sensor fusion for AI-driven behavior planning in medical applications
Jianming, C., Yuanyuan, Q., Yan-ling, X., Li, L., Wu, M., & Wang, L. (2025) · Frontiers in Physics · doi:10.3389/fphy.2025.1588715 · Cited by 3
Let It Go or Control It All? The Dilemma of Prompt Engineering in Generative Agent‐Based Models
Li, Z.-W., & Wu, Q. (2025) · System Dynamics Review · doi:10.1002/sdr.70008 · Cited by 7
A replica for our democracies? On using digital twins to enhance deliberative democracy
Novelli, C., Sánchez–Vaquerizo, J. A., Helbing, D., Rotolo, A., & Floridi, L. (2025) · AI & Society · doi:10.1007/s00146-025-02511-7 · Cited by 11
Human-Centered Agentic Framework for Machine Learning Modeling in Finance
Okpala, I., Golgoon, A., & Kannan, A. R. (2025) · IEEE International Symposium on Service-Oriented System Engineering (SOSE) · doi:10.1109/sose67019.2025.00030 · Cited by 46
“Improving on [3]’s metrics for recency , relevancy , and importance , [6] models a hierarchical arrangement of events within each memory layer and within an agent’s memory.”
CFA-Bench: Cybersecurity Forensic Llm Agent Benchmark and Testing
Santis, F. D., Huang, K., Valentim, R., et al. (2025) · IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) · doi:10.1109/eurospw67616.2025.00031 · Cited by 4
“Armed with the benchmark, test cases, and tasks, we compare the performance of three different LLM-agent architectures, from the simple ReACT [1], Re-ACT+Summary [2], to the Decoupled ReACT+Summary.”
Construction, Validation, and Generalization of Digital Consumption Value Scale
Tripathi, S., Jain, V., Pandey, J., Ford, J. B., & Gupta, D. G. (2025) · Journal of Consumer Behaviour · doi:10.1002/cb.70042 · Cited by 4
Never Start from Scratch: Expediting On-Device LLM Personalization via Explainable Model Selection
Wang, H., Yang, B., Yin, X., & Gao, W. (2025) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3711875.3729132 · Cited by 9
AI Alignment: The Case for Including Animals
Tse, Y. F., Moret, A., Ziesche, S., & Singer, P. (2025) · Philosophy & Technology · doi:10.1007/s13347-025-00979-1 · Cited by 4
“These agen - tic AI systems can autonomously decide how to act by using external tools such as the internet and software, by generating and distributing contents autonomously, and interacting with users through chat interfaces (Park et al., 2023 ).”
Tsai, C.-N., Xie, J., Lai, C.-M., & Lin, C.-S. (2025) · Cluster Computing · doi:10.1007/s10586-025-05721-2 · Cited by 6
“Trained on extensive and diverse datasets, large language models (LLMs) have demonstrated impressive capabilities across numerous domains [14]–[15].”
Leveraging Large Language Models for Tacit Knowledge Discovery in Organizational Contexts
Zuin, G. L., Mastelini, S. M., Loures, T. C., & Veloso, A. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227259 · Cited by 5
“Park et al. [16] proposed generative agents that simulate human behavior through dynamic memory mechanisms, enabling decision-making in a sandbox setting inspired by the game The Sims.”
An Agent-Based RAG Architecture for Intelligent Tourism Assistance: The Valencia Case Study
Bonetti, A., Salcedo-Puche, A., Vila‐Francés, J., García, X. B., Fernández-Vargas, E., Magdalena‐Benedito, R., & Soria‐Olivas, E. (2025) · Tourism and Hospitality · doi:10.3390/tourhosp6050266 · Cited by 5
“Complex applications often employ multi-agent frameworks, where specialized components collaborate to solve multi-step queries (Park et al., 2023).”
Designing AI-Agents With Personalities: A Psychometric Approach
Huang, M., Zhang, X., Soto, C. J., & Evans, J. A. (2026) · Personality Science · doi:10.1177/27000710251406471 · Cited by 10
LMAgent: A Large-Scale Multimodal Agents Society for Multi-User Simulation
Liu, Y., Liu, W. J., Gu, X., He, X., Rui, Y., & Zhang, Y. (2026) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2026.3651078 · Cited by 23
“Various proof-of-concept AI agents such as GenerativeAgent [2], AgentVerse [8], and ChatDEV [9] have showcased their application potential in respective domains.”
InfluentialToward Complementary Intelligence: Integrating Cognitive and Machine AI
González, C., & Malloy, T. (2026) · Current Directions in Psychological Science · doi:10.1177/09637214251407571 · Cited by 4
“They optimize prediction, recognition, and control through large-scale computation and have become the foundation of modern assistive technologies such as recommendation systems, classification tools, and generative models (Park et al., 2023).”
InfluentialXu, T., Wen, Z., Zhao, X., Wang, J., Li, Y., & Liu, C. (2025) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom66490.2025.00256 · Cited by 5
“The latest evolution of this trend is the development of “generative agents”—LLM-powered autonomous entities capable of complex, interactive behavior and planning [22].”
Yuan, C., Wei, T., Li, C., Yi, X., Liu, S., Zhang, Z., Cai, Y., & Du, X. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00708-4 · Cited by 6
From MOOC to MAIC: Reimagine Online Teaching and Learning Through LLM-Driven Agents
Yu, J.-F., Zhang-Li, D., Zhang, Z.-Y., Wang, Y., Li, H.-X., Lim, J. J. Y., Hao, Z.-X., Tu, S.-Q., Zhang, L., Dai, X.-S., et al. (2026) · Journal of Computer Science and Technology · doi:10.1007/s11390-025-6000-0 · Cited by 63
Qi, J., Li, M., Liu, J., Shu, Y., Yu, D. S. F., Ma, S., Cui, W., Zhao, Y., Chen, Y., Jiang, R., et al. (2026) · Academia AI and Applications · doi:10.20935/acadai8260 · Cited by 20
“RL [23–25] and is instantiated in recent LLM-based agents that interleave reasoning and acting [26, 27].”
GenAI and language learning motivation: Fundamental questions and a research agenda
Al‐Hoorie, A. H., Hiver, P. V., Liu, M., & Shlowiy, A. A. (2026) · Annual Review of Applied Linguistics · doi:10.1017/s0267190526100282 · Cited by 7
“In simulations like Stanford’s Smallville virtual town, GenAI agents with independent goals and memories form relationships and display emergent behaviors (Park et al., 2023).”
Interaction with AI companions and psychological well-being
Zhang, Y., Zhao, D., Hancock, J. T., Kraut, R. E., & Yang, D. (2026) · Nature Human Behaviour · doi:10.1038/s41562-026-02516-2 · Cited by 194
Agentic profiles for effective AI governance
Kasirzadeh, A., & Gabriel, I. (2026) · Nature · doi:10.1038/s41586-026-10805-z · Cited by 80
“Meanwhile, general high-autonomy agents, running in simulation, as anticipated by Park et al. 53 , do not need to be logged, audited and made amenable to authorization protocols in the same manner.”
InfluentialNarrativePlay: An Automated System for Crafting Visual Worlds in Novels for Role-Playing
Zhao, R., Zhang, W., Li, J., Zhu, L., Li, Y., He, Y., & Gui, L. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i21.30589 · Cited by 6
Wang, T., Ramanujan, R., Lu, Y., Mao, C., Chen, Y., & Brown, C. (2024) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3657604.3664663 · Cited by 5
“Existing research demonstrates these models’ capabilities in understanding human language, performing coding tasks, and simulating human behaviors [6, 9, 11, 13, 14].”
Vella, S., & Shariah, S. (2024) · Interdisciplinary Conference on Electrics and Computer (INTCEC) · doi:10.1109/intcec61833.2024.10602875 · Cited by 4
Estuary: A Framework For Building Multimodal Low-Latency Real-Time Socially Interactive Agents
Lin, S., Rizk, B., Jun, M., Artze, A., Sullivan, C., Mozgai, S., & Fisher, S. (2024) · ACM International Conference on Intelligent Virtual Agents (IVA) · doi:10.1145/3652988.3696198 · Cited by 5
Cooperative Scheduling and Hierarchical Memory Model for Multi-Agent Systems
Zou, H., Li, R., Sun, T., Wang, F., Li, T., & Liu, K. (2024) · 2024 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA) · doi:10.1109/ispce-asia64773.2024.10756271 · Cited by 4
“By calculating the cosine similarity [26] between memory entries and the user input, the system evaluates the relevance of memory entries to the current task and returns the most suitable memory information.”
Butlin, P. (2024) · Inquiry · doi:10.1080/0020174x.2024.2439995 · Cited by 10
A Multi-Agent Collaborative Framework for Constructing Knowledge Graphs from Text
Chen, G., & Liu, X. (2024) · IEEE International Conference on Knowledge Graph (ICKG) · doi:10.1109/ickg63256.2024.00010 · Cited by 5
Integrating generative AI with neurophysiological methods in psychiatric practice
Feng, Y., Zhou, Y., Xu, J., Lu, X., Gu, R., & Qiao, Z. (2025) · Asian Journal of Psychiatry · doi:10.1016/j.ajp.2025.104499 · Cited by 5
Hwang, A. H., Bernstein, M. S., Sundar, S. S., Zhang, R., Ribeiro, M. H., Lu, Y., Chang, S. Y., Wu, T., Yang, A., Williams, D. C., et al. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3716299 · Cited by 16
“Joon Sung Park (Stanford University) works first-hand on various highly recognized studies of generative agents and LLM simulations work [14–16].”
Self-citationRedefining Research Crowdsourcing: Incorporating Human Feedback with LLM-Powered Digital Twins
Chan, A., Di, C., Rupertus, J., Smith, G. D., Rao, V. N., Ribeiro, M. H., & Monroy‐Hernández, A. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3720269 · Cited by 4
“By prompting LLMs to emulate human preferences, personalized human DTs, or generative agents, have shown significant promise in accurately replicating human attitudes and behaviors [21, 22].”
Envisioning Recommendations on an LLM-Based Agent Platform
Zhang, J., Bao, K., Wang, W., Zhang, Y., Shi, W., Xu, W., Feng, F., & Chua, T. (2025) · Communications of the ACM · doi:10.1145/3699952 · Cited by 40
“28 Previous work showed that individual agents can be optimized for specific tasks 5,11 and simulate human behaviors, 23 and that collaborative groups of agents can achieve higher levels of intelligence.”
Disentangling Planning, Driving and Rendering for Photorealistic Avatar Agents
Wang, D., Dai, B., Deng, Y., & Wang, B. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91578-9_8 · Cited by 9
Teaming in the AI Era: AI-Augmented Frameworks for Forming, Simulating, and Optimizing Human Teams
Almutairi, M. (2025) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3699682.3727574 · Cited by 4
Tu, G., Wang, B., Cambria, E., Li, W., & Xu, R. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3701716.3715200 · Cited by 4
“Workflow of Supporter’s Memory Learning: the supporter analyzes the seeker’s evaluation, identifies successful and problematic responses, generates reflective questions as per [11], and updates the seeker’s memory.”
MatchNav: LLM-based enhanced description and instruction matching in vision-and-language navigation
Wang, C., Wei, S., & Juntong, Q. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103444 · Cited by 5
Barbera, D. L., Lunardi, R., Zhuang, M., & Roitero, K. (2025) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3731120.3744581 · Cited by 5
“The personas used to prompt LLMs typically encompass a set of traits such as personality characteristics [4, 5, 17, 22, 25, 40], behaviors such as lifestyle [32], habits [11, 34, 40, 42, 47], perspectives such as political views [2], and demographic attributes such as educational background,…”
Haroon, R., Wigdor, K., Yang, K., Toumanios, N., Crehan, E. T., & Dogar, F. R. (2025) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3663547.3746337 · Cited by 8
“We believe this capability, if utilized responsibly, can be used to engage neurotypical individuals in interactive, personalized learning experiences that cultivate empathy and appreciation for autistic communication styles.”
ArchiConnect: Supporting Architects’ Design Drafting with Dynamic Demands from Multi-Stakeholders
Wang, X., Liu, C., Wei, L., Fang, C., Fang, L., & Wang, S. J. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2560514 · Cited by 3
“Given their robust language understanding, reasoning, and generative abilities, LLMs have been employed to simulate human-like behaviors, interactions, and decision-making processes (Park et al., 2023; Wang et al., 2024).”
Agentic AI vs ML-based Autotuning: A Comparative Study for Loop Reordering Optimization
Rosas, M. R., Eigenmann, R., & Ibrahim, K. Z. (2025) · doi:10.1145/3731599.3767401 · Cited by 4
GenSwarm: Scalable Multi-Robot Code-Policy Generation and Deployment via Language Models
Ji, W., Chen, H., Chen, M., Zhu, G., Xu, L., Gros, R., Zhou, R., Cao, M., & Zhao, S. (2026) · npj Robotics · doi:10.1038/s44182-025-00065-w · Cited by 12
Blake, A., Carter, M., & Velloso, E. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790508 · Cited by 7
“Instead of only asking how to protect youth from AI, we must also ask how to empower them to co-design systems that support their flourishing [31, 61]”
Intelligent Recognition of Tool Wear with Artificial Intelligence Agent
Gao, J., Qiao, H., & Zhang, Y. (2024) · Coatings · doi:10.3390/coatings14070827 · Cited by 4
Effective Tool Augmented Multi-Agent Framework for Data Analysis
Zhang, X., Mao, Z., Chen, Z., & Gao, S. (2024) · Data Intelligence · doi:10.3724/2096-7004.di.2024.0013 · Cited by 7
“Recently, LLM-based multi-agent frameworks, e.g., Generative Agents [41], CAMEL [42], Multi-Agent Debate [43], have attracted tremendous attention for achieving human-level intelligence in diverse domains.”
BackgroundRobust and Communication-Efficient Federated Domain Adaptation via Random Features
Feng, Z., Wang, Y., Li, J., Yang, F., Lou, J., Mi, T., Qiu, R. C., & Liao, Z. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3510296 · Cited by 6
“…learning (ML) and artificial intelligence (AI), increasingly larger ML models [1], [2] are being trained to address challenges across various fields, ranging from game [3], [4], text-to-image synthesis [5], natural language processing [1], [6], to robotics [7] and intelligent decision-making [8].”
Serverless Ai Agents in the Cloud
Chaplia, O., Klym, H., & Elsts, E. (2024) · Advances in Cyber-Physical Systems · doi:10.23939/acps2024.02.115 · Cited by 4
LLM Augmentations to support Analytical Reasoning over Multiple Documents
Yousuf, R. B., Defelice, N., Sharma, M., Xu, S., & Ramakrishnan, N. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10826114 · Cited by 6
“LLMs are also given external memory module to drive simulation [16].”
Tutorial: Artificial Neural Networks for Discrete-Event Simulation
Haas, P. J. (2024) · Winter Simulation Conference · doi:10.1109/wsc63780.2024.10838940 · Cited by 4
“During the simulation, each agents then chooses the action that yields the highest reward as estimated by the MLP. Recently, Park et al. (2023) tried representing each agent by an LLM, resulting in plausible individual and emergent social behaviors.”
Cheng, Z., Lin, Z., Yang, Y., Wei, Z., & Chen, S. (2025) · Visual Informatics · doi:10.1016/j.visinf.2025.100260 · Cited by 6
Multimodal Fusion with LLMs for Engagement Prediction in Natural Conversation
Ma, C., Joo, H. K., Vail, A. K., Bhattacharya, S., García, Á. F., Baker-Matsuoka, K., Mathew, S., Holt, L. L., & la Torre, F. D. (2025) · Companion Publication of the International Conference on Multimodal Interaction (ICMI Companion) · doi:10.1145/3747327.3764904 · Cited by 11
“Park et al. introduce generative agents powered by LLMs that simulate believable human behavior in a virtual environment [50], also similarly seen in [79].”
The Puzzle of Evaluating Moral Cognition in Artificial Agents
Reinecke, M. G., Mao, Y., Kunesch, M., Duéñez‐Guzmán, E. A., Haas, J., & Leibo, J. Z. (2023) · Cognitive Science · doi:10.1111/cogs.13315 · Cited by 7
McDonald, C., Malloy, T., Nguyen, T. N., & González, C. (2024) · Proceedings of the AAAI Symposium Series · doi:10.1609/aaaiss.v2i1.27697 · Cited by 5
“To the latter point, Park et al. (2023) develop a social simulation that relies on LLM prompting for agents to select actions and communicate with one another.”
BackgroundPrototyping Slice of Life: Social Physics with Symbolically Grounded LLM-based Generative Dialogue
Treanor, M., Samuel, B., & Nelson, M. (2024) · International Conference on the Foundations of Digital Games (FDG) · doi:10.1145/3649921.3656988 · Cited by 4
“Finally, we make the opposite representational choice from the otherwise related Smallville social simulation system [11].”
MethodUnderstanding Telecom Language Through Large Language ModelsPreprint
Bariah, L., Zou, H., Zhao, Q., Mouhouche, B., Bader, F., & Debbah, M. A. (2023) · doi:10.36227/techrxiv.23501271 · Cited by 4
Kereopa-Yorke, B. (2023) · doi:10.31234/osf.io/97fyg · Cited by 4
Replicating Reason: The Advent of Human-like Audit Judgment by Generative AIPreprint
Isack, M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4716860 · Cited by 7
Diversity of Thought Elicits Stronger Reasoning Capabilities in Multi-Agent Debate FrameworksPreprint
Hegazy, M. (2024) · Qeios · doi:10.32388/3y8v71 · Cited by 5
Navigating the Knowledge Problem in the Era of Open AI and Polycentric GovernancePreprint
Makridis, C. A., & Lazanski, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5382315 · Cited by 4
AI agents can coordinate beyond human scale
De Marzo, G., Castellano, C., & Garcia, D. (2024) · arXiv · doi:10.48550/arxiv.2409.02822 · Cited by 4
“The right graph displays the average opinion for the 25-agent subgroup, which quickly fully coordinates. less [25, 4, 31], mostly with a focus on social simulation of network structures [16, 33, 12], opinion and information spreading [34, 14, 11] and online interaction [41, 35, 38].”
TAKAGI, H., Moriya, S., Sato, T., Nagao, M., & Higuchi, K. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712119 · Cited by 6
StorySage: Conversational Autobiography Writing Powered by a Multi-Agent Framework
Talaei, S., Li, M., Grover, K., Hippler, J. K., Yang, D., & Saberi, A. (2025) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3746059.3747681 · Cited by 9
AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents
Anokhin, P., Semenov, N., Sorokin, A., Evseev, D., Kravchenko, A., Burtsev, M., & Burnaev, E. (2024) · arXiv · doi:10.48550/arxiv.2407.04363 · Cited by 8
“Experimental results demonstrate that our agent Ariadne can effectively learn through interactions with environment and significantly outperforms other memory approaches for LLMs such as full history, summarization, RAG, Simulacra [Park et al. , 2023] and Reflexion [Shinn et al. , 2023].”
AgentQuest: a modular benchmark framework to measure progress and improve LLM agents
Gioacchini, L., Siracusano, G., Sanvito, D., Gashteovski, K., Friede, D., Bifulco, R., & Lawrence, C. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations) · doi:10.18653/v1/2024.naacl-demo.19 · Cited by 4
How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following Behavior
Xiong, Z., Lin, Y., Xie, W., He, P., Liu, Z., Tang, J., Lakkaraju, H., & Xiang, Z. (2026) · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2026.acl-long.27 · Cited by 4
Xiao, B., Yin, Z., & Shan, Z. (2023) · arXiv · doi:10.48550/arxiv.2311.06957 · Cited by 4
“Limitations include memory degradation and excessive rigidity when fine-tuned [16, 45].”
BackgroundPersonalized Non-Player Characters: A Framework for Character-Consistent Dialogue Generation
Liu, X., Xie, Z., & Jiang, S. (2025) · AI · doi:10.3390/ai6050093 · Cited by 3
“Teams from Stanford University and DeepMind [18] demonstrated the ability of LLMs to simulate human memory, reflection, and dialogue through prompts.”
Accountability and Reliability in 6G O-RAN: Who is Responsible When it Fails?
He, Y., Yu, G., Wang, X., et al. (2025) · IEEE Wireless Communications · doi:10.1109/mwc.001.2400292 · Cited by 3
“Tools like ChatGPT and Copilot utilize LLMs to streamline task automation and optionally act as agents to engage domain-specifi c expert NNs for enhanced performance [12].”
Jiang, Y.-H., Chen, Z., Zhao, C., Tang, K., Duan, J., & Zhou, Y. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3721212 · Cited by 3
“Generative AI technologies, such as multi-agent systems, have enhanced interactive applications by simulating human behavior and generating credible individual and emergent social actions, as exemplified by Stanford’s Town model [39].”
Prompt. Exploit. Repeat: Automating Network Security Testing with LLMs
Rigaki, M., Lukáš, O., Catania, C. A., & García, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-87330-0_2 · Cited by 3
Lin, Z. (2026) · Annual Review of Psychology · doi:10.1146/annurev-psych-100925-034807 · Cited by 4
Extended reality technologies for applications in the metaverse
Mukawa, H., Hirota, Y., Mizuno, H., Murata, M., Homma, F., Mochizuki, K., Ogawa, R., Mamishin, Y., Aga, H., Yokono, J., et al. (2025) · Nature Reviews Electrical Engineering · doi:10.1038/s44287-025-00211-4 · Cited by 4
“Efforts to enhance dialogue capabilities include step-by-step reasoning 121 , memory stream modules for long-term memory 122 , external knowledge integration 123 and architectures that reduce the inference time 124 .”
PosterMate: Audience-driven Collaborative Persona Agents for Poster Design
Shin, D.-H., Lee, D. W., Hsieh, G., & Chan, G. Y.-Y. (2025) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3746059.3747769 · Cited by 15
Privacy Equilibrium: Balancing Privacy Needs in Dynamic Multi-User Augmented Reality Scenarios
Rajaram, S., Chen, J., & Nebeling, M. (2025) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3746059.3747783 · Cited by 3
Understanding Large Language Model Driven Social Bots: A Behavioral Analysis and Impact Assessment
Li, S., Yang, J., Zhao, K., & Jia, D. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3605278 · Cited by 61
“Park et al. [65] argued that GPT could simulate human behavior and function as social agents, experimentally establishing a societal sandbox with 25 GPT-driven agents, confirming LLMs’ viability for human behavior simulation.”
AI, Ethics, and Cognitive Bias: An LLM-Based Synthetic Simulation for Education and Research
Bertoncini, A. L., Matsushita, R. Y., & Silva, S. D. (2025) · AI in Education · doi:10.3390/aieduc1010003 · Cited by 6
“Consequently, their output, even when robustly controlled, inevitably carries implicit biases that could skew interpretations in sensitive areas like ethical decision-making, human interaction, and cognitive behavior (Park et al., 2023).”
Large language models for autism: evaluating theory of mind tasks in a gamified environment
Poglitsch, C., Reiss, A., Wriessnegger, S. C., & Pirker, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-18608-4 · Cited by 4
Artificial Creativity: from predictive AI to Generative System 3
Chávez-Autor, J. C. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1654716 · Cited by 3
How many digital minds can dance on the streaming multiprocessors of a GPU cluster?
Shiller, D. (2025) · Synthese · doi:10.1007/s11229-025-05310-1 · Cited by 8
“They can be hooked up to tools in feedback loops in ways that allow something like agency (Park et al., 2023) and be given text-based tools to operate in the external world.”
Tree-of-Reasoning: Towards Complex Medical Diagnosis via Multi-Agent Reasoning with Evidence Tree
Peng, Q., Cui, J., Xie, J.-Y., Cai, Y., & Li, Q. (2025) · ACM Multimedia · doi:10.1145/3746027.3755418 · Cited by 17
Improving online anti-phishing training using cognitive large language models
Malloy, T., Fang, F., & González, C. (2025) · Computers in Human Behavior · doi:10.1016/j.chb.2025.108760 · Cited by 6
Generative AI Agents for Hyper Predictive Maintenance of Solar Energy Systems
Haputhanthri, D., Samarajeewa, C. D., Silva, D. D., et al. (2025) · Proceedings of the Annual Conference of the IEEE Industrial Electronics Society · doi:10.1109/iecon58223.2025.11221450 · Cited by 3
“These capabilities create a foundation for hyper predictive maintenance, where intelligent agents act collaboratively, autonomously, and with contextual awareness, moving beyond passive monitoring to active, intelligent, and scalable system management [22].”
EvalAgent: Towards Evaluating News Recommender Systems with LLM-based Agents
Zhang, G., Zhang, P., Liu, J., Li, Z., Li, D., Gu, H., Lu, T., & Gu, N. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761127 · Cited by 3
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.
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