11 papers
EvoHarness-RL: Learning Self-Evolving Runtime Harness for Long-Horizon LLM Agents
Xuying Ning, Dongqi Fu, Tianxin Wei +13
Long-horizon LLM agents increasingly rely on external execution support to maintain state, track progress, invoke tools, verify outcomes, and reuse experience across interactions.…
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…
Aligning Language Models with Selective Prediction
Gaoxiang Luo, Yifan Wu, Sinian Zhang +2
Large language models (LLMs) are increasingly deployed as critical decision-making components in high-stakes real-world AI systems, rendering LLM reliability a foremost practical c…
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…
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…
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…