6 citations · 6 across the 13 of their papers we have counts for
13 papers
AgentRewind: Recoverable Execution for Long-Horizon LLM Agents
Yu Zhuang, Kefei Chen, Yitong Duan +3
Many real-world tasks require LLM agents to interact with their environments over long execution horizons. Errors that occur early in execution may propagate through both the agent…
Hierarchical Graph Memory for LLM Agents with Path-level Localization and Rewrite
Xiawei Yue, Boran Wang, Xiaoqing Zhang +2
Agents for long term reasoning require a memory that can be efficiently and effectively updated over time, as new facts and external feedback continue to arrive. Recently, graph me…
OrchBench: Evaluating Multi-Agent Orchestration Plans in Isolation via Deterministic Simulation
Zhenzhen Ren, Jiyan He, Xinpeng Zhang +5
Complex tasks often decompose into parallelizable yet interdependent subtasks, making orchestration critical to the performance of multi-agent systems (MAS). Existing evaluations t…
APS: Bias-Controlled Adaptive Prototype Simulation for Population-Scale LLM Agents
Quan Zheng, Yan Gao, Shaobin He +6
LLM-agent simulation offers a flexible computational tool for studying population response trajectories that depend on scenario events, memory, demographics, and evolving social co…
FutureWorld: A Live Reinforcement Learning Environment for Predictive Agents with Real-World Outcome Rewards
Zhixin Han, Yanzhi Zhang, Chuyang Wei +11
Live future prediction refers to the task of making predictions about real-world events before they unfold. This task is increasingly studied using large language model-based agent…
Harnessing Pre-Resolution Signals for Future Prediction Agents
Chuyang Wei, Maohang Gao, Zhixin Han +12
Many high-stakes decisions depend on forecasts made before outcomes are known. In this future prediction setting, the central challenge is that public evidence evolves over time, w…
GUIGuard-Bench: Toward a General Evaluation for Privacy-Preserving GUI Agents
Yanxi Wang, Zhiling Zhang, Wenbo Zhou +6
As GUI agents increasingly rely on screenshots to perceive and operate digital environments, they may inadvertently expose sensitive information such as identities, accounts, locat…
Population-Evolve: a Parallel Sampling and Evolutionary Method for LLM Math Reasoning
Yanzhi Zhang, Yitong Duan, Zhaoxi Zhang +2
Test-time scaling has emerged as a promising direction for enhancing the reasoning capabilities of Large Language Models in last few years. In this work, we propose Population-Evol…
GTM: Simulating the World of Tools for AI Agents
Zhenzhen Ren, Xinpeng Zhang, Zhenxing Qian +4
The integration of external tools is pivotal for empowering Large Language Model (LLM) agents with real-world capabilities. However, training these agents through direct, continuou…
No Free Lunch: Rethinking Internal Feedback for LLM Reasoning
Yanzhi Zhang, Zhaoxi Zhang, Haoxiang Guan +6
Reinforcement learning has emerged as a powerful paradigm for post-training large language models (LLMs) to improve reasoning. Approaches like Reinforcement Learning from Human Fee…
Modeling Earth-Scale Human-Like Societies with One Billion Agents
Haoxiang Guan, Jiyan He, Liyang Fan +10
Understanding the dynamic evolution of complex social phenomena requires both high-fidelity modeling of human behavior and large-scale simulations. Traditional agent-based models (…
Towards Generalist Prompting for Large Language Models by Mental Models
Haoxiang Guan, Jiyan He, Shuxin Zheng +3
Large language models (LLMs) have demonstrated impressive performance on many tasks. However, to achieve optimal performance, specially designed prompting methods are still needed.…
Controlling risks of AI in chemical science with agents
Jiyan He, Weitao Feng, Haoxiang Guan +11
Artificial intelligence is rapidly advancing scientific discovery, but this progress carries risks of misuse, such as the creation of harmful substances, or circumvention of establ…