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NeurIPS 2026Umass AmherstU ConnecticutEmoryUMass LowellRetrieval-augmented generation

In-Context Optimization for Retrieval-Augmented Generation: A Gradient-Descent Perspective

Retrieval-augmented generation is framed as in-context optimization via linear self-attention gradient descent, yielding a frozen-model forward-only interface update that improves QA with low per-query cost.

mingchen li, Jiatan Huang, Chuxu Zhang, Liang Zhao and 1 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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lenient 4/5
medium 8/10
strict 1/5
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NeurIPS 2026Umass AmherstU Massachusetts at AmherstTexas TechU ConnecticutU Massachusetts, AmherstRL for LLMs

RICE-PO: Turning Retrieval Interactions into Credit Signals for Reasoning Agents

RICE-PO turns retrieval interactions into localized credit signals to train reasoning agents, outperforming RL baselines on BRIGHT and BEIR.

mingchen li, Hansi Zeng, Zhuo Qian, Jiatan Huang and 3 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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AI panel: 12 of 20 reviewers recommend it
lenient 4/5
medium 8/10
strict 0/5