2 citations · 3 across the 20 of their papers we have counts for
12 papers · 1 filter
OR-License: Evaluating LLM Agents as Real-World Operations Research Practitioners
Peisong Wang, Feixue Liu, Yize Meng +16
Can large language model (LLM) agents deliver expert-level operations research (OR) solutions directly from original client materials, without prior expert reformulation? Existing…
EMRB: A Multi-Level Benchmark for Evaluating LLM Reasoning over Raw Electromagnetic Signals
Mingxu Zhang, Ying Sun, Yuhan Li +4
Large language models (LLMs) are increasingly used as code agents for scientific and engineering analysis, but their ability to analyze raw physical-layer measurements remains unte…
-Bench: LLMs Struggle with Resource-Rational Reasoning under Shared Budgets
Peisong Wang, Zhiwei Ma, Bowen Liu +6
In cognitive science, resource rationality asks how an agent should allocate limited computation to maximize expected value. Most reasoning and agent benchmarks use independent per…
PHF: Privileged Hidden Flow for On-Policy Self-Distillation
Yuhan Li, Mingxu Zhang, Dazhong Shen +1
On-policy self-distillation (OPSD) trains a reasoning model on rollouts sampled from its own policy by matching a privileged teacher that also sees verified reference solutions. Ex…
EvoMemBench: Benchmarking Agent Memory from a Self-Evolving Perspective
Yuyao Wang, Zhongjian Zhang, Mo Chi +7
Recent benchmarks for Large Language Model (LLM) agents mainly evaluate reasoning, planning, and execution. However, memory is also essential for agents, as it enables them to stor…
IRDS: Interpretable RLVR Data Selection via Verifier-Coupled Sparse Autoencoder Coverage
Yuhan Li, Mingxu Zhang, Dazhong Shen +1
Reinforcement learning with verifiable rewards (RLVR) has become a key technique for en- hancing LLM reasoning, yet its data ineffi- ciency remains a major bottleneck. Existing met…
SAE-FD: Sparse Autoencoder Feature Distillation for Continual Learning of Large Language Models
Mingxu Zhang, Yuhan Li, Lujundong Li +3
Continual learning enables large language models to adapt to evolving tasks without retraining from scratch, yet catastrophic forgetting remains a central obstacle. Among continual…
CP-Agent: A Calibrated Risk-Controlled Agent for Feedback-Driven Competitive Programming
Peisong Wang, Bowen Liu, Zehua Li +4
Large language models still struggle with contest-level programming, while many agentic remedies rely on massive inference-time sampling or expensive multi-stage post-training. We…
CoLVR: Enhancing Exploratory Latent Visual Reasoning via Contrastive Optimization
Ziyang Ding, Linjian Meng, Yiming Wu +3
Due to the potential for exploratory reasoning of Latent Visual Reasoning, recent works tend to enable MLLMs (Multimodal Large Language Models) to perform visual reasoning by propa…
SLIM: Sparse Latent Steering for Interpretable and Property-Directed LLM-Based Molecular Editing
Mingxu Zhang, Yuhan Li, Lujundong Li +3
Large language models possess strong chemical reasoning capabilities, making them effective molecular editors. However, property-relevant information is implicitly entangled across…
LoopRPT: Reinforcement Pre-Training for Looped Language Models
Guo Tang, Shixin Jiang, Heng Chang +7
Looped language models (LoopLMs) perform iterative latent computation to refine internal representations, offering an alternative to explicit chain-of-thought (CoT) reasoning. Howe…
Unlocking the Potentials of Retrieval-Augmented Generation for Diffusion Language Models
Chuanyue Yu, Jiahui Wang, Yuhan Li +6
Diffusion Language Models (DLMs) have recently demonstrated remarkable capabilities in natural language processing tasks. However, the potential of Retrieval-Augmented Generation (…