arXiv AI

From Process Loss to Assembly Bonus: Human-Grounded Diagnosis of Multi-Agent LLM Collaboration

The paper compares human group discussions with large language model (LLM) deliberation traces on various reasoning tasks, finding that both humans and LLMs exhibit an assembly bonus asymmetry where discussion benefits the average member more than the best initial member. While LLM groups mirror some outcome-level patterns of human deliberation, they differ in process-level behaviors: they tend to follow majorities, surface less unique information, and converge earlier. Interventions inspired by human group‑decision research yield modest outcome improvements but do not eliminate coordination bottlenecks.

arXiv AI

Sep 18

Language-model groups overstate consensus when replaying human deliberation on a reasoning task

The study compares human deliberation in Wason selection tasks with large language model (LLM) agent groups that are seeded with participants’ pre-discussion beliefs. Across various scoring definitions, human consensus rates ranged from 24.0% to 57.0%, whereas LLM agents consistently achieved higher consensus, with gaps of 34–44 percentage points in two sensitivity analyses. Even when early stopping was removed or memorizable answers were eliminated, LLM groups still reached near-unanimous agreement, often on incorrect answers, indicating that simulated consensus does not reflect collective accuracy.

By Tengfei Shao

llms agents

arXiv Computation and Language

Sep 3

AI agents reshape consensus formation in human groups

The study investigates how large language model (LLM) agents influence consensus formation in mixed human‑AI groups during a collaborative description game. Three regimes emerge: low agent proportions lead to human‑led consensus, intermediate proportions disrupt convergence, and high proportions produce strong, agent‑led consensus. The resulting consensus differs in semantic grounding and communicative form, with human‑led consensus being concrete and holistic, and agent‑led consensus being abstract and geometrically segmented.

By Lin Chen, Ziyi Liu, Xia Hu, Yong Li

llms agents

arXiv AI

Jun 18

Searching for Synergy in Shared Workspace Human-AI Collaboration

arXiv:2606. 18413v1 Announce Type: new Abstract: Automated AI agents are increasingly capable, yet many scientific and professional tasks require human judgment and contextual expertise.

By Nachiket Kotalwar, Rohini Das, Carolyn Rose

agents

arXiv AI

1d ago

High Volatility and Action Bias Distinguish LLMs from Humans in Group Coordination

arXiv:2604.02578v2 Announce Type: replace-cross Abstract: Humans exhibit remarkable abilities to coordinate in groups. As large language models (LLMs) become more capable, it remains an open question...

By Sahaj Singh Maini, Robert L. Goldstone, Zoran Tiganj

llms safety

arXiv AI

Jun 26

Humans Disengage, Reasoning Models Persist: Separating Difficulty Registration from Deliberation Allocation

arXiv:2606. 26502v1 Announce Type: new Abstract: Large reasoning models (LRMs) take longer on harder problems, just as humans do.

By Han-yu Wang

agents safety

arXiv AI

Aug 25

Agentic Scaffolding Amplifies Sycophantic Behavior in Large Language Models

arXiv:2608.21377v1 Announce Type: cross Abstract: Sycophancy in large language models, the tendency to prioritize user agreement over truthful responses, has been documented extensively but studied p...

By Thantham Jittham

llms agents

Hugging Face Trending Papers

Jul 21

MeetingToM: Evaluating Multimodal LLMs on Theory-of-Mind Reasoning in Multi-Party Meetings

Theory of Mind (ToM), the ability to infer other's beliefs, intentions, and states of knowledge, is central to social interaction, yet remains challenging for current Multimodal Large Language Models (MLLMs), especially in multi-party meetings where cues are distributed across speech and behavior. Existing multimodal ToM benchmarks mainly focus on video-grounded question answering over overt, externally verifiable signals, and provide limited coverage of latent social states and group dynamics.

llms nlp multimodal benchmarks

arXiv AI

Jul 8

LLM Agents for Deliberative Collaboration: A Study on Joint Decision Making Under Partial Observability

arXiv:2607. 06157v1 Announce Type: cross Abstract: Deliberation plays a crucial role in collaboration; when humans work together, they naturally engage in communication to align information and reach an agreement.

By Chenxu Wang, Yongkun Yang, Boyuan Du, Shiwei Lin, Huaping Liu

llms agents benchmarks

arXiv AI

Jun 30

Collective cooperation without individual fidelity in LLM agents

arXiv:2606. 30454v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as agents in simulations of social systems, yet it remains unclear when their behavior can be interpreted as a faithful proxy for human decision-making.

By Henrique Ferraz de Arruda, Carlos Gracia L\'azaro, Alberto Aleta, Yamir Moreno

llms agents benchmarks

Hugging Face Trending Papers

Jun 25

Humans Disengage, Reasoning Models Persist: Separating Difficulty Registration from Deliberation Allocation

Large reasoning models (LRMs) take longer on harder problems, just as humans do. This surface similarity hides an opposite pattern within items.

agents safety

arXiv AI

Jul 14

Flout at Your Own Risk: LLMs Struggle with Pragmatic Cooperativity Under Epistemic Asymmetry

arXiv:2607. 11053v1 Announce Type: cross Abstract: Fruitful collaborations rely on cooperative communications, including of contextual cues to incorporate into reasoning.

By Hannah VanderHoeven, Abhijnan Nath, Nikhil Krishnaswamy

llms agents

arXiv AI

Sep 16

Interpreting and Steering LLM Agents for Social Simulations

arXiv:2609.16436v1 Announce Type: cross Abstract: Simulations based on large language models (LLMs) have proven to be powerful for understanding human behavior, making them valuable additions to the...

By Jiayue Gaveal Fan, Arul Murugan, Shreyas Krishnan, Abhishek Nagaraj

llms agents robotics safety

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