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This paper introduces MESA, a framework that dynamically selects and fuses a query-adaptive subset of multiple structural memory views for long-horizon agents, achieving 8.5% accuracy improvement over the strongest baseline while using 41% fewer evidence tokens.
The paper investigates whether retrieving more evidence helps visual retrieval-augmented generation with diffusion language models, finding that unconditionally expanding evidence hurts accuracy due to semantic conflict, and proposes a training-free Entropy-Based Candidate Filter (ECF) to selectively admit evidence, improving accuracy across benchmarks.
This paper proposes a QUBO-based method for selecting evidence passages in retrieval-augmented question answering, achieving competitive performance with LLM-based selectors while enabling the use of unconventional solvers like quantum annealers.
This paper introduces EP-HUBO, a quantum-inspired method that treats evidence selection in chain-of-thought reasoning as a combinatorial optimization problem, significantly improving performance on legal reasoning benchmarks like MMLU-Pro law and LEXam by allowing minority-but-correct hypotheses to override noisy majorities.
The article explores reinforcement learning fine-tuning of small (4B) recursive language models (RLMs) to perform evidence selection from scientific documents, showing that RL-trained 4B models match Claude Sonnet 4.6 performance at a fraction of the size and cost.
AdaGATE is a training-free evidence controller for multi-hop RAG that uses entity-centric gap tracking, micro-query generation, and utility-based selection to improve robustness under noisy retrieval, achieving state-of-the-art evidence F1 with fewer input tokens.