12 papers · 1 filter
Trajectory2Gym: Decision-Grounded Compositional Interaction Synthesis for Tool-Use Agents
Yihao Hu, Zhihao Wen, Yitong Lin +5
Successful tool-use trajectories are not necessarily useful supervision. Even successful interactions may underrepresent multi-step dependencies or contain actions that are unjusti…
Rotochemical heating of neutron stars in 2D
Mikhail V. Beznogov, Jerome Novak, Dany Page +1
The matter inside a spinning-down neutron star (NS) undergoes a slow compression that may drive it out of chemical equilibrium and, consequently, trigger non-equilibrium nuclear re…
Grounded Skill Synthesis from Code at Scale for Agentic Intelligence
Yongqi Tong, Pan Wang, Hang Wang +4
Reusable skills give agents transferable procedural knowledge, making scalable acquisition essential for extending agents beyond prior experience. Existing methods face two limitat…
Towards Better Agents for Multi-Turn User Interaction: The Next User Turn Is More Than Context
Yiwen Zhao, Zhihao Wen, Yuchen Mao +5
User-facing tool agents must coordinate dialogue and tool use as user goals unfold over multiple turns. Yet interactive reinforcement learning typically reduces each rollout to a t…
Diagnosis Before Recovery: Turning Agent Failures into Selective Self-Correction
Pan Wang, Yihao Hu, Hang Wang +6
Self-correction is particularly useful when a failure constrains the next repair. Coding agents benefit from this property because compilers, tests, and execution traces turn many…
Learning Preference Adaptation for Large Language Model Personalization via Verbal Reinforcement Learning
Yuting Liu, Wei Wu, Jianzhe Zhao +1
Natural language user preferences provide an interpretable interface for LLM personalization. However, universal preference summaries often contain information irrelevant to a part…
How to Train a Real-World Silicon Concierge? Internalizing Complex Business Workflow to Only OneModel
Chang Liu, Chaoyang Ning, Dayi Jiang +32
Traditional industrial agents rely on modular pipelines, including Router, Retriever, Planner, Executor, Responder, Reviewer, and other components. These systems often fracture int…
SEAL: Synergistic Co-Evolution of Agents and Learning Environments
Yihao Hu, Zhihao Wen, Xiujin Liu +3
Large Language Model (LLM) agents are increasingly improved through interaction, yet most self-evolution methods adapt either the policy or the learning environment in isolation. W…
DelTA: Discriminative Token Credit Assignment for Reinforcement Learning from Verifiable Rewards
Kaiyi Zhang, Wei Wu, Yankai Lin
Reinforcement learning from verifiable rewards (RLVR) has emerged as a central technique for improving the reasoning capabilities of large language models. Despite its effectivenes…
WorldArena 2.0: Extending Embodied World Model Benchmarking on Modality, Functionality and Platform
Yu Shang, Yinzhou Tang, Yiding Ma +26
World models have emerged as a central paradigm for embodied intelligence, enabling agents to predict action-conditioned future and reason about environmental dynamics. However, ex…
MemForest: An Efficient Agent Memory System with Hierarchical Temporal Indexing
Han Chen, Zining Zhang, Wenqi Pei +6
Memory is a fundamental component for long-context LLM agents, supporting persistent state across interactions through a continuous serve-and-update lifecycle. Despite substantial…
Text as a Universal Interface for Transferable Personalization
Yuting Liu, Jian Guan, Jia-Nan Li +4
We study the problem of personalization in large language models (LLMs). Prior work predominantly represents user preferences as implicit, model-specific vectors or parameters, yie…