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Learning from Failures: Retrieval-Centric CoT via Hard Negatives for Unified Multimodal Retrieval

Unified multimodal retrieval aims to identify candidates that satisfy complex user intent expressed through heterogeneous inputs. Although Large Vision-Language Model (LVLM)-based retrievers are efficient and scalable, directly encoding raw multimodal inputs often misses fine-grained discriminative cues, leading to confusion among semantically similar candidates. Recent methods mitigate this limitation by generating Chain-of-Thought (CoT) rationales to enrich the query representation. However, s

Record details

Published: 5 August 2026
Source: HuggingFace Daily Papers
Category: Research
Topics: Bias & fairness
Retrieved: 7 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.

Shared topic: Bias & fairness

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Shared topic: Bias & fairness

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Shared topic: Bias & fairness

Manipulation-Proof Oblivious Audits against Deceptive Model Providers

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How to cite this record

ethics.ai (5 August 2026), “Learning from Failures: Retrieval-Centric CoT via Hard Negatives for Unified Multimodal Retrieval,” evidence record 17036, https://ethics.ai/record/17036 (originally published by HuggingFace Daily Papers).

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