most citedWhere LLM Agents Fail and How They can Learn From Failures

3 citations · 3 across the 10 of their papers we have counts for

collaborators

15 papers

cs.CL2026

Reliable Use of Lemmas via Eligibility Reasoning and SectionAware Reinforcement Learning

Zhikun Xu, Xiaodong Yu, Ben Zhou +6

Recent large language models (LLMs) perform strongly on mathematical benchmarks yet often misapply lemmas, importing conclusions without validating assumptions. We formalize lemma$…

cs.CL2026

CD4LM: Consistency Distillation and aDaptive Decoding for Diffusion Language Models

Yihao Liang, Ze Wang, Hao Chen +7

Autoregressive large language models achieve strong results on many benchmarks, but decoding remains fundamentally latency-limited by sequential dependence on previously generated…

cs.CL2025

Instella: Fully Open Language Models with Stellar Performance

Jiang Liu, Jialian Wu, Xiaodong Yu +10

Large language models (LLMs) have demonstrated remarkable performance across a wide range of tasks, yet the majority of high-performing models remain closed-source or partially ope…

cs.CV2025

Learning from Online Videos at Inference Time for Computer-Use Agents

Yujian Liu, Ze Wang, Hao Chen +7

Computer-use agents can operate computers and automate laborious tasks, but despite recent rapid progress, they still lag behind human users, especially when tasks require domain-s…

cs.CV2025

Latent Visual Reasoning

Bangzheng Li, Ximeng Sun, Jiang Liu +7

Multimodal Large Language Models (MLLMs) have achieved notable gains in various tasks by incorporating Chain-of-Thought (CoT) reasoning in language spaces. Recent work extends this…

cs.CV2025

ImageDoctor: Diagnosing Text-to-Image Generation via Grounded Image Reasoning

Yuxiang Guo, Jiang Liu, Ze Wang +7

The rapid advancement of text-to-image (T2I) models has increased the need for reliable human preference modeling, a demand further amplified by recent progress in reinforcement le…