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Yan-Yan Zhang

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#machine learning Preprint Sep 2026

Correcting WHERE, Preserving HOW: Compositional Generalization for Vision-Language-Action Models via Referential Guidance

While Vision-Language-Action (VLA) models enable flexible action generation, their generalization across diverse environmental elements, including manipulated objects, destinations, and backgrounds, is limited by the lack of diversity in robotic training data. Trained end-to-end on such data, VLAs tend to exploit visua...

Yan-Yan Zhang, Di-Sheng Liu, Xin-Peng Li et al. · 0 citations

Preprint Aug 2026

Test-Time Scaling in Reasoning LLMs: Inference Regimes, Evaluation, and Reproducibility

This empirical study covers broad knowledge, symbolic reasoning, and competition mathematics, and it introduces an evaluation profile whose coordinates and simple functionals recover or bound common repeated-sampling metrics, and require compute accounting and uncertainty estimates that match the protocol.

Mohsen Hariri, Weicong Chen, Nahal Shahini et al. · 5 citations · ⚡1

Preprint Aug 2026

Test-Time Scaling in Reasoning LLMs: Inference Regimes, Evaluation, and Reproducibility

Large language models can solve substantially harder reasoning problems with more inference-time compute. The term"test-time scaling,"however, now covers diverse inference algorithms that extend deliberation along a single trajectory, sample completed candidates and aggregate them through voting or verification, or sea...

Mohsen Hariri, Weicong Chen, Nahal Shahini et al. · 4 citations

Preprint Aug 2026

Imagining Recovery: Inference-Time Counterfactual Realignment for Vision-Language-Action Models

Counterfactual Realignment (CoRe), a training-free framework that recovers a frozen VLA at inference time without failure data, is proposed, a training-free framework that recovers a frozen VLA at inference time without policy fine-tuning or failure-specific recovery training.

Yan-Yan Zhang, Di-Sheng Liu, Kai Ye et al. · 1 citation