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20242026
most citedResearchTown: Simulator of Human Research Community

1 citations · 2 across the 19 of their papers we have counts for

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9 papers · 1 filter

cs.AI2026

Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory

Minghao Guo, Qingyue Jiao, Xiujin Liu +77

Multimodal models and agents have emerged to enable AI systems to learn from and interact with the real world more like humans by integrating information across multiple modalities…

cs.AI2026

ReSolve: Reusing Candidate Reasoning through Selective Generative Moderation

Bangji Yang, Jiajun Fan, Hongba Ma +7

Sampling multiple solutions spends computation on intermediate deductions and unfinished arguments as well as final answers. We introduce ReSolve, a training-free inference procedu…

cs.AI2026

RINI: Seeing the Prior Is Not Enough

Hongyi Du, Tianyi Zhang, Heng Wang +7

A research proposal can describe an established mechanism correctly while claiming to introduce it. We study whether providing the earlier paper corrects such contribution claims.…

cs.AI2026

Relic: From Multi-Agent Collaboration to Persistent Organizational Capability

Hongyi Du, Tianyi Zhang, Weijia Zhang +9

Multiple agents may often conflict in an organization: for example, one coding agent changes an interface in a repository, but another continues to develop on the old version where…

cs.AI2026

Interactive Evaluation Requires a Design Science

Keyang Xuan, Peiyang Song, Pan Lu +10

AI evaluation is undergoing a structural change. Large language models (LLMs) are increasingly deployed as systems that act over time through tools, environments, users, and other…

cs.AI2026

SocialVeil: Probing Social Intelligence of Language Agents under Communication Barriers

Keyang Xuan, Pengda Wang, Chongrui Ye +3

Large language models (LLMs) are increasingly evaluated in interactive environments to test their social intelligence. However, existing benchmarks often assume idealized communica…

cs.AI2025

ConsistencyChecker: Tree-based Evaluation of LLM Generalization Capabilities

Zhaochen Hong, Haofei Yu, Jiaxuan You

Evaluating consistency in large language models (LLMs) is crucial for ensuring reliability, particularly in complex, multi-step interactions between humans and LLMs. Traditional se…

cs.AI2025★ 1 cited

SafeScientist: Toward Risk-Aware Scientific Discoveries by LLM Agents

Kunlun Zhu, Jiaxun Zhang, Ziheng Qi +6

Recent advancements in large language model (LLM) agents have significantly accelerated scientific discovery automation, yet concurrently raised critical ethical and safety concern…

cs.AI2025

Table as Thought: Exploring Structured Thoughts in LLM Reasoning

Zhenjie Sun, Naihao Deng, Haofei Yu +1

Large language models' reasoning abilities benefit from methods that organize their thought processes, such as chain-of-thought prompting, which employs a sequential structure to g…