1 citations · 4 across the 43 of their papers we have counts for
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Reels-RSI: Program-Level Self-Improvement for Code-Rendered Explainer Videos
Wei Lin
Explainer videos -- a worked geometry problem, why the sky is blue, how a dynasty fell -- are usually one more target for pixel video generation. We argue they are a different kind…
SAKI: Maximal-Coupling-Routed Teacher Supervision for On-Policy Distillation
Miteto Wei, Xiaohan Wang, Zehao Chen +7
On-policy distillation (OPD) reduces train-test state mismatch by training a student on its own generated trajectories, but weak students may visit teacher-misaligned prefixes wher…
ATLAS: Dual-Horizon Diagnostic Evaluation for Industrial Tool-Use Agents
Wei Chen, Peilun Zhou, Zhaoyu Hu +8
Large language model (LLM) agents are increasingly deployed in user-facing services that require iterative tool use under dynamic business conditions. Reliable evaluation is essent…
AMR-SD: Asymmetric Meta-Reflective Self-Distillation for Token-Level Credit Assignment
Zhenlin Wei, Pu Jian, Yingzhuo Deng +6
The alignment of Large Language Models (LLMs) for complex reasoning heavily relies on Reinforcement Learning with Verifiable Rewards (RLVR). However, standard algorithms like GRPO…
SPREG: Structured Plan Repair with Entropy-Guided Test-Time Intervention for Large Language Model Reasoning
Xuan Wang, Yu Ming, Xinhao Zhong +4
Large Language Models (LLMs) are prone to logical hallucinations and stochastic drifts during long-chain reasoning. While Classifier-Free Guidance (CFG) can improve instruction adh…
AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning
Jingbo Sun, Wenyue Chong, Songjun Tu +7
Agentic retrieval-augmented generation (RAG) systems enable large language models (LLMs) to solve complex tasks through multi-step interaction with external retrieval tools. Howeve…
CDRRM: Contrast-Driven Rubric Generation for Reliable and Interpretable Reward Modeling
Dengcan Liu, Fengkai Yang, Xiaohan Wang +7
Reward modeling is essential for aligning Large Language Models(LLMs) with human preferences, yet conventional reward models suffer from poor interpretability and heavy reliance on…
SAE as a Crystal Ball: Interpretable Features Predict Cross-domain Transferability of LLMs without Training
Qi Zhang, Yifei Wang, Xiaohan Wang +4
In recent years, pre-trained large language models have achieved remarkable success across diverse tasks. Besides the pivotal role of self-supervised pre-training, their effectiven…
Beyond Dialogue Time: Temporal Semantic Memory for Personalized LLM Agents
Miao Su, Yucan Guo, Zhongni Hou +8
Memory enables Large Language Model (LLM) agents to perceive, store, and use information from past dialogues, which is essential for personalization. However, existing methods fail…
ToolForge: A Data Synthesis Pipeline for Multi-Hop Search without Real-World APIs
Hao Chen, Zhexin Hu, Jiajun Chai +7
Training LLMs to invoke tools and leverage retrieved information necessitates high-quality, diverse data. However, existing pipelines for synthetic data generation often rely on te…
LocalSearchBench: Benchmarking Agentic Search in Real-World Local Life Services
Hang He, Chuhuai Yue, Chengqi Dong +12
Recent advances in large reasoning models LRMs have enabled agentic search systems to perform complex multi-step reasoning across multiple sources. However, most studies focus on g…
MTIR-SQL: Multi-turn Tool-Integrated Reasoning Reinforcement Learning for Text-to-SQL
Zekun Xu, Siyu Xia, Chuhuai Yue +6
As large language models (LLMs) are increasingly used in Text-to-SQL tasks, Reinforcement Learning (RL) has become a common method for improving performance. Existing methods prima…
Promoting Efficient Reasoning with Verifiable Stepwise Reward
Chuhuai Yue, Chengqi Dong, Yinan Gao +4
Large reasoning models (LRMs) have recently achieved significant progress in complex reasoning tasks, aided by reinforcement learning with verifiable rewards. However, LRMs often s…