ircot-8725c098·2 events·first seen Aliases: IRCoT
GRADRAG is a new framework that treats a multi-agent RAG pipeline as a computational graph and propagates structured evaluator feedback upstream to iteratively update retriever, graph constructor, and answerer prompts. Unlike prior work that optimizes components in isolation, GRADRAG coordinates improvements across the full pipeline via a Prompt Optimizer and an early-stopping Evaluator. Evaluated on SQUALITY and QMSUM benchmarks under both flat chunk-based and graph-based retrieval paradigms, GRADRAG achieves a 12–15 percentage point net preference margin over one-step refinement baselines in LLM-judged pairwise comparisons.
RASER introduces a family of lightweight routers that decide whether to escalate retrieval complexity for multi-hop QA without making additional LLM calls. Built on top of one-shot RAG using six derived features, RASER-2 and RASER-3 route queries to progressively more expensive retrieval strategies (PRUNE, IRCoT) only when needed. Across six LLMs and three benchmarks, the routers match SOTA F1 while consuming only 41-49% of the tokens required by always-escalating baselines.