Probability Contracts: Accuracy, Coherence, and Decisions Across LLM Interfaces
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Abstract (arXiv)
Equivalent probability requests can lead to different decisions even when both reports are valid. We introduce probability contracts, a benchmark that connects exact finite-world posteriors, validated event alignment, interface coherence, and failure-aware decision evaluation. Across four model-interface configurations on 1,000 worlds, Kev has lower aggregate canonical posterior error than Jev but larger complement and coarsening residuals; accuracy ordering varies by stratum. Jev's Event and Choice interfaces change the binary action on 32.8% of valid pairs at defer cost 0.10. Post-hoc analyses show that disagreement certifies only 11-52% of mean binary pair error and does not consistently outperform confidence for selection. An action-region characterization and a standard scoring-rule identity explain averaging's expected Brier guarantee relative to random interface selection, but not a decision-loss guarantee at each cost. The loss contrast takes both signs on a 99-cost grid for every configuration; small penalties where both policies beat deferral have pointwise intervals containing zero. Secondary checks specified before collection include a separate 400-root cohort, where Event/Choice effects remain configuration-dependent. A joint surface-order and answer-ID intervention shifts posttrained probabilities. Both bounded reasoning arms yield no valid probability reports, leaving their probability accuracy undefined. Probability contracts make these distinctions measurable by evaluating event semantics, posterior error, coverage, and decision cost together.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 45 references that are in the map. First posted on arXiv 27 Sep 2026.
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References · 26
- 2017On Calibration of Modern Neural Networkscited by 4,860
- 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,371
- 2019Evaluating model calibration in classificationcited by 165
- 2021Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibrationcited by 79
- 2024Factual Confidence of LLMs: on Reliability and Robustness of Current Estimatorscited by 59
- 2022Beyond calibration: estimating the grouping loss of modern neural networkscited by 32
- 2024Incoherent Probability Judgments in Large Language Modelscited by 29
- 2015Of Quantiles and Expectiles: Consistent Scoring Functions, Choquet Representations, and Forecast Rankingscited by 28
- 2024Consistency Checks for Language Model Forecasterscited by 27
- 2024What Are the Odds? Language Models Are Capable of Probabilistic Reasoningcited by 25
- 2024Language Model Probabilities are Not Calibrated in Numeric Contextscited by 18
- 2026Type-Safe Is Not Error-Free: Typed Decision Models Follow the Option Name, Not the Definition Bound to Itcited by 13
14 more
- 2026Calibrated Decisions at Scale: Converting Police Crash Narratives into Probabilistic Crash Variables with a System One Model (Jev)cited by 11
- 2026Jev for Scientific Decisions: Evaluating Semantic Choices and Their Consequencescited by 10
- 2026When Agents Say One Thing and Do Another: Validating Elicited Beliefs from LLMscited by 8
- 2026Evaluating Decision Models for Text Annotation in Computational Social Sciencecited by 8
- 2026Can Jev Judge Radiology Reports? Evaluating a System One Model for Clinical Factualitycited by 8
- 2026Same Scores, Different Decisions: Evaluating JEV and Language Models for Legal Document Understandingcited by 8
- 2026Same Answer, Different Confidence: Protocol Sensitivity in LLM Confidence Calibrationcited by 6
- 2026Just Ask Jev: Reinforcement Learning for Calibrated Decisions as a Zero-Shot Detector of AI Alignment Failurescited by 4
- 2026JevOut: Natural Context Can Flip Decision Modelscited by 4
- 2024CONTESTS: a Framework for Consistency Testing of Span Probabilities in Language Modelscited by 3
- 2026Revealed Rationality: Label-Free Evaluation and Regularization from Representation Theoremscited by 3
- 2026Dutch Books for Language Modelscited by 3
- 2026ForecastBench-Sim: A Simulated-World Forecasting Benchmarkcited by 2
- 2026When Policies Change Probabilities: Modular Decision-Making for LLM Code Reviewcited by 1
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