CHASE: Competing Hypotheses for Ambiguity-Aware Selective Prediction

• URL: http://arxiv.org/abs/2605.01346v1
• Date: Sat, 02 May 2026 09:39:48 GMT
• Title: CHASE: Competing Hypotheses for Ambiguity-Aware Selective Prediction
• Abstract summary: CHASE is a selective prediction framework that explicitly compares structured temporal explanations to determine whether to commit to a decision or abstain.<n>Because genuine ambiguity causes the score gap between competing hypotheses to collapse, CHASE prioritizes a ranking-aware selector over these hypothesis margins.<n>Compared to canonical uncertainty baselines, CHASE achieves significant gains in overall no-abstain accuracy, three-way accuracy, and overall ambiguity-aligned abstention.
• Score: 2.1707529464094555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Standard selective prediction methods typically estimate uncertainty from the output of a single predictive branch. While effective for general uncertainty estimation, these approaches often struggle under partial observability, where local temporal evidence can be contradictory and standard confidence scores become misleading. We introduce CHASE (Competing Hypotheses for Ambiguity-Aware Selective Prediction), a selective prediction framework that explicitly compares structured temporal explanations to determine whether to commit to a decision or abstain. Because genuine ambiguity causes the score gap between competing hypotheses to collapse, CHASE optimizes a ranking-aware selector over these hypothesis margins to globally separate safe commitments from fundamentally uncertain ones. We evaluate this framework on the problem of hidden connectivity inference, utilizing a controlled, physically grounded simulator inspired by the dynamics of giant unilamellar vesicles (GUVs), alongside zero-shot qualitative transfer (without retraining or fine tuning) to representative real GUV videos. Our experiments demonstrate that explicitly reasoning over competing hypotheses provides a superior balance of metrics. Compared to canonical uncertainty baselines, CHASE achieves statistically significant gains in overall no-abstain accuracy, three-way accuracy, and overall ambiguity-aligned abstention (at 80% coverage). Specifically, it yields up to an 11.0% relative mean improvement in overall alignment, alongside up to an 8.8% relative boost in three-way accuracy in the very-high ambiguity regime. By maintaining a selective risk boundary strictly at par with the best baselines at 80% coverage, and reducing overall risk by 9.9% at 90% coverage, this framework offers a more reliable approach to decision-making under structured ambiguity.

Related papers

• Cross-Fitted Residual Utility for Primary-Preserving Cognitive Decision Correction in Automatic Modulation Classification [1.547549252134621]
  We study a post-inference problem through cross-fitted residual utility and a primary-preserving cognitive decision policy.<n>A structured KAN-Fourier classifier supplies the default probability, while neural and non-neural candidates provide observable evidence.<n>On RMLA, RMLB, and HISAR, the complete system improves overall accuracy from 63.632% to 66.332%, 65.161% to 66.168%, and 77.769% to 79.867%.
  arXiv  Detail & Related papers  (2026-08-03T11:07:37Z)
• Prediction Sets for Counterfactual Decisions: Coverage, Optimality, and Conformal Prediction [7.899668963928307]
  We develop a decision-theoretic framework for uncertainty-informed counterfactual decisions.<n>We identify a novel notion of emphpolicy-coupled coverage -- namely, coverage of the realized outcome under the action induced by the prediction sets themselves.
  arXiv  Detail & Related papers  (2026-07-02T14:13:08Z)
• Towards Anytime-Valid Statistical Watermarking [63.02116925616554]
  We develop the first e-value-based watermarking framework, Anchored E-Watermarking, that unifies optimal sampling with anytime-valid inference.<n>Our framework can significantly enhance sample efficiency, reducing the average token budget required for detection by 13-15% relative to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-02-19T18:32:26Z)
• The Evaluation Gap in Medicine, AI and LLMs: Navigating Elusive Ground Truth & Uncertainty via a Probabilistic Paradigm [49.287792149338976]
  We introduce a probabilistic paradigm to theoretically explain how high certainty in ground truth answers is almost always necessary for even an expert to achieve high scores.<n>We thus bring forth the concepts of expected accuracy and expected F1 to estimate the score an expert human or system can achieve given ground truth answer variability.
  arXiv  Detail & Related papers  (2026-01-09T03:19:37Z)
• Adaptive Conformal Prediction via Bayesian Uncertainty Weighting for Hierarchical Healthcare Data [2.922743999325622]
  We present a hybrid Bayesian-conformal framework that addresses the fundamental limitation in healthcare predictions.<n>Our approach integrates Bayesian hierarchical random forests with group-aware conformal calibration, using posterior uncertainties to weight conformity scores.<n>We evaluate our method on 61,538 admissions across 3,793 U.S. hospitals and 4 regions.
  arXiv  Detail & Related papers  (2026-01-03T16:06:37Z)
• Calibrating Uncertainty for Zero-Shot Adversarial CLIP [33.707647228637114]
  We propose a novel adversarial fine-tuning objective for CLIP considering both prediction accuracy and uncertainty alignments.<n>Our objective aligns these distributions holistically under perturbations, moving beyond single-logit anchoring and restoring uncertainty.
  arXiv  Detail & Related papers  (2025-12-15T05:41:08Z)
• Calibrated Decomposition of Aleatoric and Epistemic Uncertainty in Deep Features for Inference-Time Adaptation [3.018583625592182]
  Most estimators collapse all uncertainty modes into a single confidence score, preventing reliable reasoning about when to allocate more compute or adjust inference.<n>We introduce Uncertainty-Guided Inference-Time Selection, a lightweight inference time framework that disentangles aleatoric (data-driven) and model-driven uncertainty directly in deep feature space.
  arXiv  Detail & Related papers  (2025-11-15T23:47:30Z)
• COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees [51.5976496056012]
  COIN is an uncertainty-guarding selection framework that calibrates statistically valid thresholds to filter a single generated answer per question.<n>COIN estimates the empirical error rate on a calibration set and applies confidence interval methods to establish a high-probability upper bound on the true error rate.<n>We demonstrate COIN's robustness in risk control, strong test-time power in retaining admissible answers, and predictive efficiency under limited calibration data.
  arXiv  Detail & Related papers  (2025-06-25T07:04:49Z)
• SConU: Selective Conformal Uncertainty in Large Language Models [59.25881667640868]
  We propose a novel approach termed Selective Conformal Uncertainty (SConU)<n>We develop two conformal p-values that are instrumental in determining whether a given sample deviates from the uncertainty distribution of the calibration set at a specific manageable risk level.<n>Our approach not only facilitates rigorous management of miscoverage rates across both single-domain and interdisciplinary contexts, but also enhances the efficiency of predictions.
  arXiv  Detail & Related papers  (2025-04-19T03:01:45Z)
• Beyond Confidence: Adaptive Abstention in Dual-Threshold Conformal Prediction for Autonomous System Perception [0.4124847249415279]
  Safety-critical perception systems require reliable uncertainty quantification and principled abstention mechanisms to maintain safety.<n>We present a novel dual-threshold conformalization framework that provides statistically-guaranteed uncertainty estimates while enabling selective prediction in high-risk scenarios.
  arXiv  Detail & Related papers  (2025-02-11T04:45:31Z)
• Conformal Prediction Sets with Improved Conditional Coverage using Trust Scores [52.92618442300405]
  It is impossible to achieve exact, distribution-free conditional coverage in finite samples.<n>We propose an alternative conformal prediction algorithm that targets coverage where it matters most.
  arXiv  Detail & Related papers  (2025-01-17T12:01:56Z)
• Equal Opportunity of Coverage in Fair Regression [50.76908018786335]
  We study fair machine learning (ML) under predictive uncertainty to enable reliable and trustworthy decision-making. We propose Equal Opportunity of Coverage (EOC) that aims to achieve two properties: (1) coverage rates for different groups with similar outcomes are close, and (2) the coverage rate for the entire population remains at a predetermined level.
  arXiv  Detail & Related papers  (2023-11-03T21:19:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.