Evaluating Confidence-Gated Retrieval with Matched Trajectory Replay

arXiv:2608.26846 · cs.CL, cs.AI · Submitted 2026-08-27 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-08-27

Updated: 2026-08-27

Code: https://github.com/hotpotqa/hotpot

Terminology

• Calibration — 78× in this paper · explained in 7 episodes
• RAG — 25× in this paper · explained in 3 episodes
• Accuracy — 24× in this paper · explained in 1 episode
• Trace — 12× in this paper · explained in 2 episodes
• Abstention — 4× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• Deferral — 2× in this paper · explained in 2 episodes
• LLM judge — 2× in this paper · explained in 2 episodes
• Retrieval-Augmented Generation — 2× in this paper
• Transformer — 2× in this paper · explained in 2 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Commentary — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Expected Calibration Error — 1× in this paper
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Information Gain — 1× in this paper · explained in 2 episodes
• Metacognition — 1× in this paper · explained in 5 episodes
• Retrieval-Augmented Generation (RAG) — 1× in this paper · explained in 16 episodes
• Self-consistency — 1× in this paper · explained in 2 episodes
• Sparse Attention — 1× in this paper · explained in 5 episodes

Sources

• AutoMix: Automatically Mixing Language Models
• FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance
• Mind the Confidence Gap: Overconfidence, Calibration, and Distractor Effects in Large Language Models
• Language Models (Mostly) Know What They Know

Related papers

• Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
• Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements
• Subliminal Steering: Stronger Encoding of Hidden Signals
• MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
• The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures
• Untangling the Mechanisms of Misleading Context in Medical Question Answering