RICE-PO is a policy optimization framework that converts retrieval interactions into credit signals for latent reasoning steps in agents by selecting high-uncertainty actions as anchors and propagating credit based on influence strength and residual stability, outperforming baselines on BRIGHT and B
ConvGQR: Generative query reformulation for con- versational search
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A unified evaluation finds LLM query reformulation gains are strongly conditioned on retrieval paradigm, do not consistently transfer to neural retrievers, and are not uniformly improved by larger LLMs.
BRIDGE reaches 29.7 nDCG@10 on MM-BRIGHT by RL-aligning multimodal queries to text and using a reasoning retriever, beating multimodal encoders and, when combined with Nomic-Vision, exceeding the best text-only retriever at 33.3.
citing papers explorer
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RICE-PO: Turning Retrieval Interactions into Credit Signals for Reasoning Agents
RICE-PO is a policy optimization framework that converts retrieval interactions into credit signals for latent reasoning steps in agents by selecting high-uncertainty actions as anchors and propagating credit based on influence strength and residual stability, outperforming baselines on BRIGHT and B
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A Reproducibility Study of LLM-Based Query Reformulation
A unified evaluation finds LLM query reformulation gains are strongly conditioned on retrieval paradigm, do not consistently transfer to neural retrievers, and are not uniformly improved by larger LLMs.
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BRIDGE: Multimodal-to-Text Retrieval via Reinforcement-Learned Query Alignment
BRIDGE reaches 29.7 nDCG@10 on MM-BRIGHT by RL-aligning multimodal queries to text and using a reasoning retriever, beating multimodal encoders and, when combined with Nomic-Vision, exceeding the best text-only retriever at 33.3.