Papers with Adaptive-RAG

2 papers
Accelerating Adaptive Retrieval Augmented Generation via Instruction-Driven Representation Reduction of Retrieval Overlaps (2025.findings-acl)

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Challenge: Existing approaches to augmented generation ignore the overlap in retrieval results . overlapping content is redundantly represented, affecting the overall efficiency.
Approach: They propose a model-agnostic approach to re-augmented generation that speeds up prefilling and decoding . they propose an instruction-driven module to guide the model to more suitable ways for LLMs .
Outcome: The proposed approach achieves 2.79 and 2.33 times significant acceleration on average for prefilling and decoding respectively while maintaining equal generation quality.
RAG-on-a-Diet: A Reinforcement Learning-Based Dynamic Resource Optimization Framework for RAG (2026.acl-long)

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Challenge: Existing frameworks for knowledge-intensive multi-hop question answering do not adapt to how a trajectory unfolds.
Approach: They propose a lightweight reinforcement-learning agent that treats each reasoning hop as an independent decision and selects the smallest model sufficient for it.
Outcome: The proposed agent cuts Monetary Inference Cost by 60.07% against IRCoT with only a 3.7% F1 drop and matches Adaptive-RAG’s F1 at 37.30% lower cost.

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