Papers
Research papers from arXiv and related sources
Phase-Aware Mixture of Experts for Agentic Reinforcement Learning
Reinforcement learning (RL) has equipped LLM agents with a strong ability to solve complex tasks. However, existing RL methods normally use a \emph{single} policy network, causing \emph{simplicity ...
Shengtian Yang, Yu Li, Shuo He, Yewen Li, Qingpeng Cai, Peng Jiang, Lei Feng
Wink: Recovering from Misbehaviors in Coding Agents
Autonomous coding agents, powered by large language models (LLMs), are increasingly being adopted in the software industry to automate complex engineering tasks. However, these agents are prone to ...
Rahul Nanda, Chandra Maddila, Smriti Jha, Euna Mehnaz Khan, Matteo Paltenghi, Satish Chandra
Patch-Based Spatial Authorship Attribution in Human-Robot Collaborative Paintings
As agentic AI becomes increasingly involved in creative production, documenting authorship has become critical for artists, collectors, and legal contexts. We present a patch-based framework for sp...
Eric Chen, Patricia Alves-Oliveira