1 citations · 1 across the 12 of their papers we have counts for
18 papers
The Weight of Teamwork: Does Communication Structure Help LLM Scientists Search for Scientific Laws?
Jianing Shi, Zaibin Zhang, Shijun Lei +1
Team performance is often reported as one delivered score, even though that score is produced in two stages: a system generates candidate answers and a selection rule returns one o…
SocioVerse2: A Longitudinal Dynamic Social Simulation Framework under a Human-AI Co-evolutionary Paradigm
Xinnong Zhang, Jiayu Lin, Jia Wang +19
Social simulation offers the social sciences an experimental instrument that the real world cannot supply, and generative agents have transformed it by acting as silicon samples th…
JEPA-Anything: Learning Predictive Models across Different Worlds
Taoyong Cui, Zhongyao Wang, Xinyue Xu +10
World modeling enables intelligence to anticipate consequences, guide interventions, and learn from interaction. Yet predictive models remain domain-specific: can a common learning…
ScienceBuddy: Recursive-in-Recursive Self-Improvement for Interactive Scientific Agents
Shuhan Xue, Jianyuan Zhong, Ziyuan Nan +10
We introduce and release ScienceBuddy, an interactive scientific research workspace that brings continually improving scientific agents into researchers' everyday workflows. Scienc…
Discovery Foundation Models: Toward Open-Ended Discovery Intelligence
Ling Yang, Zhenfei Yin, Yingcheng Wu
Foundation models have progressed from learning and reasoning over existing knowledge, to increasingly learning through action, tool use, and outcome feedback. We argue that the ne…
An immune world model for multiscale forecasting and therapeutic hypothesis generation
Taoyong Cui, Xi Wang, Zonghang Li +11
Immune therapies act across cell-intrinsic programs, tissue ecosystems, and patient-specific immune states, yet most predictors address these scales separately. We used a governed…
RAP: Research Attention Prediction Reveals Target-Conditioned Evidence Acquisition Biases
Yingqian Wu, Jingcong Liang, Siyuan Wang +4
Large language models (LLMs) increasingly act as research agents, yet their ability to track shifts in research attention is difficult to evaluate because reviews and research idea…
Recursive Experiential-Working Memory Evolution for Long-Horizon Agent Harnesses
Zhaochen Yu, Yingcheng Wu, Zhenfei Yin +5
Recursive self-improvement (RSI) remains hard in long-horizon tasks, where growing histories obscure the task state and misalign skill invocation. We introduce Recuris, a recursive…
Toward Skill-Native LLMs: Skill Entropy for Benchmarking and Training Long-Horizon Reasoning
Yinghui He, Ling Yang, Jiarui Liu +6
Long-horizon reasoning in recent LLMs demands that the model switch between distinct skills inside a reasoning chain, such as first doing a math derivation, then using the result t…
PAST-Bench: Benchmarking the Foundations of Recursive Self-Improvement in Personal Agents
Shuhan Xue, Zixin Ding, Yichen Shen +6
Recursive self-improvement requires agents to turn accumulated experience into better future behavior. Personal AI agents offer a concrete setting for studying this capability beca…
CreFlow: Corrective Reflow for Sparse-Reward Embodied Video Diffusion RL
Zhenyang Ni, Yijiang Li, Ruochen Jiao +7
Video generation models trained on heterogeneous data with likelihood-surrogate objectives can produce visually plausible rollouts that violate physical constraints in embodied man…
Strategic Exploitation in LLM Agent Markets: A Simulation Framework for E-Commerce Trust
Shijun Lei, Quang Nguyen, Swapneel S Mehta +7
Agent-based modeling (ABM) has long been used in economics to study human behavior, and large language model (LLM) agents now enable new forms of social and economic simulation. Wh…
Behavioral Consistency Validation for LLM Agents: An Analysis of Trading-Style Switching through Stock-Market Simulation
Zeping Li, Guancheng Wan, Keyang Chen +6
Recent works have increasingly applied Large Language Models (LLMs) as agents in financial stock market simulations to test if micro-level behaviors aggregate into macro-level phen…
Rethinking the Role of Entropy in Optimizing Tool-Use Behaviors for Large Language Model Agents
Zeping Li, Hongru Wang, Yiwen Zhao +7
Tool-using agents based on Large Language Models (LLMs) excel in tasks such as mathematical reasoning and multi-hop question answering. However, in long trajectories, agents often…
LatentChem: From Textual CoT to Latent Thinking in Chemical Reasoning
Xinwu Ye, Yicheng Mao, Yuxuan Liao +16
Current chemical large language models (LLMs) predominantly rely on explicit Chain-of-Thought (CoT) to solve complex reasoning problems. However, forcing nonverbal tacit chemical l…
Why Reasoning Fails to Plan: A Planning-Centric Analysis of Long-Horizon Decision Making in LLM Agents
Zehong Wang, Fang Wu, Hongru Wang +8
Large language model (LLM)-based agents exhibit strong step-by-step reasoning capabilities over short horizons, yet often fail to sustain coherent behavior over long planning horiz…
From Word to World: Can Large Language Models be Implicit Text-based World Models?
Yixia Li, Hongru Wang, Jiahao Qiu +7
Agentic reinforcement learning increasingly relies on experience-driven scaling, yet real-world environments remain non-adaptive, limited in coverage, and difficult to scale. World…
Depicting Beyond Scores: Advancing Image Quality Assessment through Multi-modal Language Models
Zhiyuan You, Zheyuan Li, Jinjin Gu +3
We introduce a Depicted image Quality Assessment method (DepictQA), overcoming the constraints of traditional score-based methods. DepictQA allows for detailed, language-based, hum…