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cs.AI2026

When Self-Evolution Backfires: Pre-Commit Gating against Skill Contamination in LLM Agents

Linfang Shang, Ming Xu, Yiding Sun +4

Self-evolving agents accumulate capability by distilling reusable skills from their execution trajectories, but we find this process is not monotonic: past a critical pool size, ne…

cs.AI2026

SPARK: Susceptibility-Guided Profiling and Steering of Latent Reasoning States in Large Language Models

Dongxu Zhang, Yiding Sun, Zihao Guo +5

Reasoning failures in large language models (LLMs) are usually evaluated from final answers, but a wrong answer does not reveal why the model failed. The same incorrect output may…

cs.AI2026

FADE: Mitigating Hallucinations by Reducing Language-Prior Dominance in Large Vision-Language Models

Yichen Guo, Kai Tang, Fenglai Lin +5

Despite the impressive capabilities of Large Vision-Language Models (LVLMs), they remain susceptible to hallucination, generating content inconsistent with the input image. Recent…

cs.AI2026

CFMS: A Coarse-to-Fine Multimodal Synthesis Framework for Enhanced Tabular Reasoning

Qixian Huang, Hongqiang Lin, Tong Fu +5

Reasoning over tabular data is a crucial capability for tasks like question answering and fact verification, as it requires models to comprehend both free-form questions and semi-s…

cs.AI2026

PersonalQ: Select, Quantize, and Serve Personalized Diffusion Models for Efficient Inference

Qirui Wang, Qi Guo, Yiding Sun +4

Personalized text-to-image generation lets users fine-tune diffusion models into repositories of concept-specific checkpoints, but serving these repositories efficiently is difficu…