Empirical study finds isolation drives gains for weak models in multi-agent RAG while scoring matters for strong ones, enabling MADARA for cost-efficient adaptive assessment.
DRAGged into conflicts: Detecting and addressing conflicting sources in search- augmented LLMs
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CPT is introduced as a pairwise reasoning-trace comparison stage that improves the reasoning-metacognition trade-off over standard SFT+RL pipelines across model scales.
This survey categorizes agentic environments for LLMs by eight attributes and domains, introduces symbolic and neural synthesis paradigms with evaluation, and outlines four agent evolution pathways plus three environment evolution paradigms.
ConflictRAG introduces a conflict-aware RAG pipeline with two-stage detection (MLP + selective LLM), Entropy-TOPSIS credibility assessment, and a new CARS metric, reporting 88.7% F1 and 5.3-6.1% gains on benchmarks.