The paper introduces a risk‑controlled framework for using large language models (LLMs) as judges in tasks without reference answers. By calibrating uncertainty thresholds on a held‑out set, the method ensures that the false discovery rate of accepted verdicts stays below a user‑specified level α with high probability, using finite‑sample Clopper–Pearson intervals. When the parametric judge lacks confidence, the instance is routed to a retrieval‑augmented mode with a second calibrated threshold, preserving the error guarantee while achieving higher coverage than single‑mode baselines.
arXiv:2609.35815v1 Announce Type: cross
Abstract: Researchers across academia increasingly base significance claims on LLM judge scores and small-sample AI evaluations. Yet without well-calibrated co...
By Ian Arawjo
The paper investigates how AI systems that perform best‑of‑n search require different validation strategies as the search width changes. It shows that auditing only small search widths leaves a gap in reliability estimates for larger widths, and proposes retaining candidate ranks and truth labels to estimate reliability across all widths up to N. The authors derive theoretical bounds on the minimax mean‑squared error, design procedures that achieve these bounds, and demonstrate that a shared audit can significantly reduce maximum error across many widths in practical CodeRM pools.
By Ricardo Fitas
The paper investigates how large language models (LLMs) used as judges in absolute scoring tasks exhibit systematic biases that compromise reliability. It shows that a judge’s task accuracy strongly predicts both its judging accuracy and its directional bias, yet more capable examinee models consistently receive more lenient judgments. To mitigate these biases, the authors propose a calibrated weighted majority voting (WMV) ensemble that estimates judges’ error rates from inter-judge agreement patterns, achieving near-oracle performance without labeled data and improving both accuracy and fairness.
By Gemma Zhang, Prachi Badarayani, Asmi Kumar, Sadid Hasan, Sulaiman Vesal
arXiv:2608.22483v1 Announce Type: new
Abstract: Large Language Models (LLMs) increasingly support decision-making in high-stakes domains, but they often hallucinate and express confidence that is mis...
By Toghrul Abbasli, Kentaroh Toyoda, Yuan Wang, Li Chen
arXiv:2608. 14425v1 Announce Type: new Abstract: LLM evaluations often use fixed sampling budgets, testing every item the same number of times even after estimates are precise.
By Toby D. Pilditch
arXiv:2604. 11996v2 Announce Type: replace-cross Abstract: Should we trust Large Language Models (LLMs) with high accuracy?
By Manas Pathak, Xingyao Chen, Shuozhe Li, Amy Zhang, Liu Leqi
The paper introduces VTC-Bench, a five‑domain benchmark designed to evaluate multiple outputs from large language models (LLMs) by measuring Validated Task Coverage (VTC). VTC quantifies how many distinct, useful results are produced within a set number of attempts, using real‑data tasks that allow automatic, reproducible checks of output quality and task‑relevant distinctness without relying on model‑based judges. Experiments show that models which perform best on single‑draw quality do not always achieve the highest coverage, and simple output‑variation metrics fail to capture task‑relevant diversity, highlighting the importance of evaluating finite candidate sets directly.
By Florian Le Bronnec, Rio Yokota
Test-time scaling often uses an external verifier, such as compilers and test cases in coding or trained value functions in robotics applications, to obtain high-quality rollouts. Verifier-free test-time scaling (or VF-TTS) is gaining extensive attention as a mechanism to enhance Large Language Model (LLM) reasoning, primarily because we do not have access to such high-quality verifiers in many real-world applications.
arXiv:2603. 05167v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used as judges of chain-of-thought (CoT) reasoning, yet it remains unclear whether they can reliably assess process faithfulness rather than merely answer plausibility.
By Avni Mittal, Rauno Arike
arXiv:2602. 15327v2 Announce Type: replace-cross Abstract: Machine learning model performance improvements tend to arise from competition and application.
By Hanlin Zhang, Jikai Jin, Vasilis Syrgkanis, Sham Kakade
The paper investigates how to improve confidence calibration for large language models (LLMs) used in automated code revision (ACR). It proposes applying local Platt-scaling to three fine-grained confidence scores, rather than the conventional global method, and demonstrates that this approach consistently reduces calibration error across multiple tasks, metrics, and model sizes. The study shows that fine-grained calibration, especially when combined with global scaling, yields more reliable confidence estimates for ACR tasks.
By Hong Yi Lin, Chunhua Liu, Haoyu Gao, Patanamon Thongtanunam, Christoph Treude