arXiv:2603.20990v2 Announce Type: replace-cross Abstract: Hard-negative source selection for dense retrieval is usually decided only after fine-tuning and downstream evaluation. We propose Effective Contrastive Information (ECI), a training-free diagnostic that ranks candidate negative sources using frozen target-encoder embeddings. ECI is training-free, not label-free: each scored example requires a query, a labeled positive, and an explicit candidate negative. $\mathrm{ECI}_{\mathrm{sem}}$ builds a weighted residual information matrix from target consistency, semantic locality, lexical resid
The proliferation of dense retrieval systems necessitates more efficient and accurate methods for selecting hard negatives to improve AI model performance without excessive computational overhead.
This development offers a training-free diagnostic for evaluating hard-negative sources, significantly reducing the cost and time associated with fine-tuning and downstream evaluation in information retrieval and AI agent training.
AI developers can now use ECI to pre-select superior negative examples, leading to more robust and efficient dense retrieval models and potentially accelerating the development of more capable AI agents.
- · AI developers
- · Information retrieval systems
- · AI agent platforms
- · Cloud computing providers (due to efficiency gains)
- · Inefficient hard-negative selection methods
- · Organizations with high compute budgets for model tuning
Improved accuracy and efficiency for dense retrieval models are achieved through better hard-negative selection.
This efficiency gain could accelerate the training and deployment of advanced AI agents, making them more practical for real-world applications.
More sophisticated and computationally cheaper AI agents could automate complex white-collar tasks, further impacting labor markets and SaaS layers.
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Read at arXiv cs.AI