47 citations · 165 across the 90 of their papers we have counts for
96 papers
From Sequence to Structure: Relational Uncertainty Propagation for LLM Agents
Zhengzhao Ma, Boxi Cao, Yaojie Lu +3
Reliable uncertainty quantification (UQ) is essential for deploying large language model (LLM) agents in complex interactive environments. Existing UQ methods largely rely on local…
ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation
Qingyu Zhang, Qianhao Yuan, Hongyu Lin +5
Structured pruning is a hardware-friendly way to compress LLMs, but it is mostly validated on multiple-choice recognition tasks, while the same compressed checkpoints can collapse…
Knowing When to Quit: Diagnosing and Training LLMs to Abort Futile Reasoning
Xinyan Guan, Jiali Zeng, Chunlei Xin +5
Large language models generate computationally expensive yet semantically void reasoning on beyond-capability tasks, creating risks where plausible-sounding but incorrect derivatio…
DocOps: A Verifiable Benchmark for Autonomous Agents in Complex Document Operations
Jiazhen Jiang, Boxi Cao, Lingyong Yan +6
As autonomous agents rapidly evolve, their ability to reliably manipulate ubiquitous digital documents has become critical for enabling general-purpose AI assistants and automating…
Before the Action: Benchmarking LLMs on Prospective Hypothesis Discovery
Tianyun Zhong, Wangyi Jiang, Wei Wang +15
Large language models (LLMs) excel at answering pre-specified questions, yet their ability to navigate the open-ended, pre-conclusion stage of discovery remains largely unmeasured.…
Inside the Skill Market: From Software Engineering Activities to Reusable Agent Skills
Jialun Cao, Xinru Yan, Songqiang Chen +3
Software engineering (abbrev. SE) has continuously evolved through increasingly powerful forms of reuse, from source code and libraries to components and services. Recent advances…
OmniFocus: Query-Guided Modality-Balanced Token Compression for Omni-Modal Large Language Models
Shijie Cao, Qingyu Zhang, Boxi Yu +6
Omni modal large language models (OmniLLMs) have attracted wide attention for their ability to jointly process audio and video, but they generate large token sequences under audio-…
PraMem: Practice-derived Experiential Memory for Long-horizon Behavior Prediction
Zhuoqun Li, Boxi Cao, Jiawei Chen +11
Long-horizon behavior prediction aims to infer a user's next action based on a lengthy historical sequence, playing a crucial role in artificial intelligence field. The rise of lar…
ReasoningLens: Hierarchical Visualization and Diagnostic Auditing for Large Reasoning Models
Jun Zhang, Jiasheng Zheng, Boxi Cao +5
The emergence of Large Reasoning Models has introduced exceptionally long Chain-of-Thought traces, creating a transparency burden where critical logic is often buried under massive…
Verifiable Environments Are LEGO Bricks: Recursive Composition for Reasoning Generalization
Hao Xiang, Qiaoyu Tang, Le Yu +8
Reinforcement Learning (RL) with verifiable environments has emerged as a powerful approach for enhancing the reasoning capabilities of Large Language Models (LLMs). While prior re…
The Meta-Agent Challenge: Are Current Agents Capable of Autonomous Agent Development?
Xinyu Lu, Tianshu Wang, Pengbo Wang +8
Current AI benchmarks evaluate agents on task execution within human-designed workflows. These evaluations fundamentally fail to measure a critical next-level capability: whether m…
Vision-OPD: Learning to See Fine Details for Multimodal LLMs via On-Policy Self-Distillation
Qianhao Yuan, Jie Lou, Xing Yu +4
Multimodal Large Language Models (MLLMs) still struggle with fine-grained visual understanding, where answers often depend on small but decisive evidence in the full image. We obse…
Combinatorial Synthesis: Scaling Code RLVR via Atomic Decomposition and Recombination
Jiasheng Zheng, Boxi Cao, Boxi Yu +6
Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as the cornerstone for shaping the remarkable coding abilities of Large Language Models (LLMs). However,…
Your Teacher Can't Help You Here: Combating Supervision Fidelity Decay in On-Policy Distillation
Yanjiang Liu, Jie Lou, Xinyan Guan +7
On-policy distillation transfers reasoning capabilities by training a student model on its own generated trajectories using token-level feedback from a teacher. However, we identif…
Planner-Centric Reinforcement Learning for Deep Research with Structure-Aware Reward
Mustafa Anis Hussain, Xinle Wu, Yao Lu
Deep research tasks require LLMs to plan what to investigate, retrieve evidence, and synthesize long-form answers across multiple branches of inquiry. Existing training paradigms e…
LiteCoder-Terminal: Scaling Long-Horizon Terminal Environments for Learning Language Agents
Xiaoxuan Peng, Kaiqi Zhang, Xinyu Lu +5
Mastering terminal environments requires language agents capable of multi-step planning, feedback-grounded execution, and dynamic state adaptation. However, training such agents is…
MetaphorVU: Towards Metaphorical Video Understanding
Zhuoqun Li, Boxi Cao, Guiping Jiang +13
Metaphorical videos are prevalent across various real-world scenarios to convey complex ideas, and understanding them typically requires high-order cognitive capabilities. The lack…
Towards Real-world Human Behavior Simulation: Benchmarking Large Language Models on Long-horizon, Cross-scenario, Heterogeneous Behavior Traces
Jiawei Chen, Ruoxi Xu, Boxi Cao +11
The emergence of Large Language Models (LLMs) has illuminated the potential for a general-purpose user simulator. However, existing benchmarks remain constrained to isolated scenar…
LiveFMBench: Unveiling the Power and Limits of Agentic Workflows in Specification Generation
Dong Xu, Jialun Cao, Guozhao Mo +9
Formal specification is essential for rigorous program verification, yet writing correct specifications remains costly and difficult to automate. Although large language models (LL…
ScaleBox: Enabling High-Fidelity and Scalable Code Verification for Large Language Models
Jiasheng Zheng, Xin Zheng, Boxi Cao +8
Code sandboxes have emerged as a critical infrastructure for advancing the coding capabilities of large language models, providing verifiable feedback for both RL training and eval…
All Languages Matter: Understanding and Mitigating Language Bias in Multilingual RAG
Dan Wang, Guozhao Mo, Yafei Shi +9
Multilingual Retrieval-Augmented Generation (mRAG) leverages cross-lingual evidence to ground Large Language Models (LLMs) in global knowledge. However, we show that current mRAG s…
Tackling Length Inflation Without Trade-offs: Group Relative Reward Rescaling for Reinforcement Learning
Zichao Li, Jie Lou, Fangchen Dong +8
Reinforcement learning significantly enhances LLM capabilities but suffers from a critical issue: length inflation, where models adopt verbosity or inefficient reasoning to maximiz…
P^2O: Joint Policy and Prompt Optimization
Xinyu Lu, Kaiqi Zhang, Jinglin Yang +6
Reinforcement Learning with Verifiable Rewards (RLVR) enhances Large Language Model (LLM) reasoning but is suffer from advantage collapse: when all rollouts of a query receive iden…
Decoupling Reasoning and Confidence: Resurrecting Calibration in Reinforcement Learning from Verifiable Rewards
Zhengzhao Ma, Xueru Wen, Boxi Cao +6
Reinforcement Learning from Verifiable Rewards (RLVR) significantly enhances large language models (LLMs) reasoning but severely suffers from calibration degeneration, where models…