11 papers
False Frontiers: Diagnosing and Mitigating Co-Cheating in Self-Evolving Search Agents
Meijia Chen, Hao Li, Zheng Lu +12
Self-evolving search agents build their own training curricula by jointly optimizing a proposer that generates questions and a solver that answers them. This closed loop introduces…
Zero2Repo: Can Coding Agents Build Repositories from Scratch?
Pei Yang, Tianyu Shi, Yuhang Yao +23
Coding agents are increasingly asked to build software rather than patch it, yet benchmarks for from-scratch repository construction are mostly limited to a single language and dep…
MERA: Model Evolution and Routing with Skill Adaptation for Agentic Systems at Scale
Yuhang Yao, Zeyu Wang, Wanyi Chen +8
LLM agents execute heterogeneous sequences of model calls within a single task: some invocations require careful reasoning, while others are structured steps such as formatting or…
CARE: Context-Aware Ranking Evolution with Executable Scoring Programs for Budgeted Reaction Optimization
Guanyu Liu, Weiyi Kong, Chao Tang +5
High-throughput experimentation can evaluate many reaction conditions, yet combinatorial condition spaces still exceed the available experiment budget. This makes experiment select…
Organizational Control Layer: Governance Infrastructure at the Execution Boundary of LLM Agent Systems
Tianyu Shi, Yang Mo, Yiou Liu +6
LLM-based agents are increasingly deployed in workflows where generated outputs may trigger state-changing actions, such as price offers, refunds, payments, or tool calls. This cre…
See, Foresee, Act: Intent-Aware World Modeling for Proactive Retail Assistance
Honghui Zhang, Anna Min, Chenmeinian Guo +9
World models allow agents to anticipate action consequences, but proactive assistance also requires interpreting incomplete observations and deciding whether to intervene before a…
TwinRouterBench: Fast Static and Live Dynamic Evaluation for Realistic Agentic LLM Routing
Pei Yang, Wanyi Chen, Tongyun Yang +15
LLM routing matters most in long-horizon applications such as coding agents, deep research systems, and computer-use agents, where a single user request triggers many model calls.…
Meta-Learning Reinforcement Learning for Crypto-Return Prediction
Junqiao Wang, Zhaoyang Guan, Guanyu Liu +7
Predicting cryptocurrency returns is notoriously difficult: price movements are driven by a fast-shifting blend of on-chain activity, news flow, and social sentiment, while labeled…
GoalfyMax: A Protocol-Driven Multi-Agent System for Intelligent Experience Entities
Siyi Wu, Zeyu Wang, Xinyuan Song +3
Modern enterprise environments demand intelligent systems capable of handling complex, dynamic, and multi-faceted tasks with high levels of autonomy and adaptability. However, trad…
Gradientsys: A Multi-Agent LLM Scheduler with ReAct Orchestration
Xinyuan Song, Zeyu Wang, Siyi Wu +2
We present Gradientsys, a next-generation multi-agent scheduling framework that coordinates diverse specialized AI agents using a typed Model-Context Protocol (MCP) and a ReAct-bas…
MountainLion: A Multi-Modal LLM-Based Agent System for Interpretable and Adaptive Financial Trading
Siyi Wu, Junqiao Wang, Zhaoyang Guan +11
Cryptocurrency trading is a challenging task requiring the integration of heterogeneous data from multiple modalities. Traditional deep learning and reinforcement learning approach…