works on

From the 1 of 23 linked papers with an AI index.

collaborators

23 papers

cs.AI2026

SVR-R1: Bootstrapping Multi-modal Reasoning with Self-verification in Reinforcement Learning

Mingyuan Wu, Jingcheng Yang, Shengyi Qian +11

The paper introduces SVR-R1, a reinforcement learning framework that lets a multimodal model generate an answer and then self‑verify it with a binary verdict, allowing a second‑cha…

cs.AI2026

Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents

Yifan Wu, Lizhu Zhang, Yuhang Zhou +5

In long-horizon tasks, decision-relevant state is often scattered across an expanding trajectory, while the action agent must surface it and act. As trajectories grow, task require…

cs.SE2026

SWE-Together: Evaluating Coding Agents in Interactive User Sessions

Yifan Wu, Zhuokai Zhao, Songlin Li +8

Most coding-agent benchmarks are static: an agent receives a complete task description up front and is judged only by its final code. Real coding assistance is interactive, with us…

cs.CL2026

SAGE-OPD: Selective Agent-Guided Intervention for Multi-Turn On-Policy Distillation

Yuhang Zhou, Lizhu Zhang, Yifan Wu +5

On-policy distillation (OPD) improves student models by training them on trajectories induced by their own policy, making it a promising approach for mitigating exposure bias in ag…

cs.AI2026

Token-Level LLM Collaboration via FusionRoute

Nuoya Xiong, Yuhang Zhou, Hanqing Zeng +5

Large language models (LLMs) exhibit strengths across diverse domains. However, achieving strong performance across these domains with a single general-purpose model typically requ…

cs.LG2026

OmniOPD: Logit-Free On-Policy Distillation via Speculative Verification

Yuhang Zhou, Lizhu Zhang, Yifan Wu +5

On-Policy Distillation (OPD) trains a student model on its own generative trajectories under dense token-level feedback from a stronger teacher, mitigating both the off-policy dist…