Researchers Propose CausalNeg to Improve LLM-Generated Hard Negatives in Retrieval Training
Researchers have proposed FineGen, a vision-language model (VLM)-based multi-agent framework that automatically constructs fine-grained image-text datasets with hard negative samples. Current vision-language datasets lack sufficient hard negatives—examples that are semantically plausible but visually contradictory—which limits models' fine-grained perception abilities. Applied to ImageNet, the resulting FineGen-100K dataset yielded a 14.4% accuracy improvement on hard samples in downstream benchmarks, outperforming existing state-of-the-art methods.
FineGen is a newly proposed automated dataset construction framework designed to address a key gap in vision-language research: the scarcity of hard negative samples that challenge models to distinguish subtle visual-semantic differences. The system employs a three-stage Generation-Verification-Correction pipeline with a closed-loop feedback mechanism, ensuring that synthesized negatives are both semantically valid and strictly contradictory to the visual content they are paired with. Using this framework on ImageNet, the authors produced FineGen-100K, a hierarchical dataset containing over 147,000 attribute-specific hard negatives at a 1:10 positive-to-negative ratio. Quality evaluations found a 96.7% attribute validity rate, suggesting the automated pipeline produces reliable training data. Downstream fine-tuning experiments on the FG-OVD benchmark demonstrated a +14.4% accuracy gain on hard samples, substantially outperforming prior state-of-the-art approaches. The work was submitted to arXiv in June 2026 and spans 15 pages with 2 figures, targeting the computer vision and AI communities.
What's missing
The paper has not yet undergone formal peer review, as it is a preprint.
What different sources said
- arXiv cs.AICenter
FineGen: A VLM-based Multi-Agent Framework for Fine-Grained Image-Text Dataset Construction
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