New paper probes agentic RAG trajectories to predict partial answer quality and enable early stopping
_reachsumit · x · 2026-09-16
Researchers Fangzheng Tian, Debasis Ganguly and Craig Macdonald introduce an in-trajectory probing framework for Agentic RAG on arXiv.
- Method: after each retrieval-reasoning iteration, the agentic model is forced to stop and emit an intermediate answer, defining two iteration-level measures: partial answer quality and partial utility (the change in quality across iterations).
- Finding: on multi-hop QA benchmarks, partial answer quality often plateaus before natural termination, with many later iterations adding only marginal measurable gains.
- Contribution: they formulate partial answer quality and partial utility prediction tasks, using trajectory-derived signals from intra-iteration, inter-iteration and query-iteration perspectives, enabling early stopping to save compute.
More from coding & agent
- Jason Kneen burns 104.56B tokens and $24.7K in 304 days of AI coding on Codex and Claude Code — jasonkneen · 2026-10-23
- Med student builds RAG book library with Cognee for cross-book concept retrieval — shrike293103 · 2026-10-23
- Benchmarking 56 fresh dependency bugs: MCP routing lifts Claude Code accuracy 68% to 91% — Available_Side_7134 · 2026-10-23
- Swapping only the eval harness moves results 30%: designing agent evals like a scientist — AI Engineer · 2026-10-23
- Med student builds a whole-library RAG with Cognee; single-book ingestion still freezes his machine — shrike293103 · 2026-10-23
- Prompt injection scanners should be positional, not phrasal: same string, different blast radius — mrpkk · 2026-10-23