11 papers
Ask, Condition or Abstain: Reinforcement Learning for Missing-Premise Reasoning
Yongqi Tong, Zhenyu Zhang, Zimi Liu +8
Answer-only reinforcement learning (RL) trains reasoning models to solve fully specified problems, but many realistic queries omit a premise needed for a unique answer. In this set…
STAGE: Controlled Objective Admission for Multi-Preference LLM Alignment
Yongqi Tong, Zhenyu Zhang, Ruirui Wang +6
Multi-preference alignment is often framed as scalarization: combine reward dimensions, then optimize. This leaves a temporal decision underspecified: when should each preference d…
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…
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…
Optimal Transport for LLM Reward Modeling from Noisy Preference
Licheng Pan, Haochen Yang, Haoxuan Li +8
Reward models are fundamental to Reinforcement Learning from Human Feedback (RLHF), yet real-world datasets are inevitably corrupted by noisy preference. Conventional training obje…
Dingtalk DeepResearch: A Unified Multi Agent Framework for Adaptive Intelligence in Enterprise Environments
Mengyuan Chen, Chengjun Dai, Xinyang Dong +7
We present Dingtalk DeepResearch, a unified multi agent intelligence framework for real world enterprise environments, delivering deep research, heterogeneous table reasoning, and…