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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. TOOL · arXiv cs.MA (Multiagent) English(EN) ·

    RACL: Reasoning-Agent Control Layers for Continuous Metaheuristic Learning

    This paper introduces RACL, a Reasoning-Agent Control Layer designed to enhance metaheuristic learning. RACL operates by placing a reasoning agent above an existing optimizer, influencing its search behavior through observation, hypothesis formulation, and policy consolidation. Experiments using the vehicle routing problem demonstrate RACL's effectiveness, showing improvements over existing policies and minimal computational overhead. The system utilized Codex as an in-the-loop reasoning agent during its proof-of-concept phase. AI

    RACL: Reasoning-Agent Control Layers for Continuous Metaheuristic Learning

    IMPACT Introduces a novel method for improving optimization algorithms through agent-based reasoning, potentially impacting fields requiring complex problem-solving.

  2. RESEARCH · arXiv cs.AI English(EN) · · [2 sources]

    Repair Before Veto: Repair-Augmented Constraint Learning for Contextual Decisions

    Researchers have introduced Repair-Augmented Constraint Learning (RACL), a new framework for contextual decision-making. RACL integrates repair operators directly into the classifier's semantics, allowing systems to learn when and how to modify a candidate decision before outright rejection. This approach aims to reduce false vetoes by prioritizing affordable repairs that make a candidate feasible and preferred, offering a structured rejection credit and repair plan when necessary. AI

    Repair Before Veto: Repair-Augmented Constraint Learning for Contextual Decisions

    IMPACT This framework could improve decision-making systems by allowing them to intelligently modify options rather than immediately rejecting them, potentially leading to more efficient and accurate outcomes.