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Shuai Li

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Preprint Oct 2026

ScenePercept: Scenario-Conditioned Perceptual Assessment of Real-World User-Generated Content Images

The rapid development of mobile imaging and social platforms has led to explosive growth in user-generated content images, making their systematic and comprehensive perceptual assessment increasingly important. Existing image perception benchmarks are typically task-oriented, curating specific scenarios for quality or...

Shu-Han Xia, Yu Chen, Xiang Wang et al. · 0 citations
#artificial intelligence Preprint Sep 2026

RewardVerse: Rubric-Guided Policy Optimization for Video Reward Modeling

RewardVerse, a rubric-based video reward framework that introduces a dynamic rubric as an intermediate representation between the evaluation query and the scorer, mitigates scalar drift, and provides a robust and interpretable reward signal for RL in video generation.

Zhen-Chen Tang, Yang Li, Songlin Yang et al. · 1 citation
Preprint Aug 2026

One Ranking, Any Budget: Matryoshka Evidence-to-Context Frame Selection for Long-Video Understanding

Matryoshka Evidence-to-Context (MEC) Frame Selection is introduced, a training-free framework that builds a reusable sparse video index, discovers candidates through sparse probing and local zooming, and greedily constructs a position-adaptive ranking - early positions emphasize evidence; later positions progressively...

Wang Chen, Yu Chen, Xiang Wang et al. · 1 citation · ⚡1
Preprint Aug 2026

Measure, Don't Optimize: Forecasting Recovery in LLM Unlearning

J-Access is proposed, an inference-time audit that uses the Jacobian lens to map intermediate representations into vocabulary space and measures how often target concepts remain accessible along the model's output pathway, positioning J-Access as a model-level diagnostic for assessing residual susceptibility in unlearn...

Zirui Song, Hua-Xing Liu, Xiang Wang et al. · 1 citation
Preprint Aug 2026

Credit the Right Box: Marginal Contribution Assignment for Structured Visual Perception

This work proposes MCR-GRPO, a marginal contribution assignment framework that derives box-level credit directly from each sampled response, preserving GRPO's response-level comparison while enabling box-aware optimization of structured multi-object grounding.

Xinheng Han, Jianfeng Wang, Yu Chen et al. · 0 citations
Jul 2026

Inside the Unfair Judge: A Mechanistic Interpretability Account of LLM-as-Judge Bias

Existing studies of LLM-as-judge scoring bias work predominantly at the input-output level: they perturb inputs, measure score deltas, and propose prompt-level mitigations. We argue that the same biases admit a representation-level account in the judge's hidden state, complementary to the input-output view and operatio...

Zixiang Xu, Sixian Li, Hua-Xing Liu et al. · 2 citations

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