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Evidence record 17977 · automatically gathered

Evidence-RL: Towards Evidence-intensive Visual Reasoning

Vision-Language Models (VLMs) should answer from concrete image evidence rather than language priors, dataset shortcuts, or irrelevant visual context. Existing perception-aware post-training methods encourage image use through global perturbations or attention proxies, but they do not test whether a sampled answer causally depends on the local evidence that supports it. We propose Counterfactual Evidence Disentanglement (CED), a training-time evidence audit for VLM grounding. For each response,

Record details

Published: 7 August 2026
Source: HuggingFace Daily Papers
Category: Research
Topics: Transparency
Retrieved: 11 August 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (7 August 2026), “Evidence-RL: Towards Evidence-intensive Visual Reasoning,” evidence record 17977, https://ethics.ai/record/17977 (originally published by HuggingFace Daily Papers).

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