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arxiv: 2605.01346 · v1 · submitted 2026-05-02 · 💻 cs.CV

CHASE: Competing Hypotheses for Ambiguity-Aware Selective Prediction

Kartik Jhawar , Yuhao Geng , Atul N. Parikh , Lipo Wang This is my paper

Pith reviewed 2026-05-09 14:09 UTC · model grok-4.3

classification 💻 cs.CV
keywords selective predictioncompeting hypothesesambiguity-aware abstentionhidden connectivity inferencetemporal explanationsranking-aware selectorGUV dynamicsuncertainty estimation
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The pith

Reasoning over competing hypotheses yields better selective prediction under ambiguity than single-branch uncertainty estimates.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces CHASE as a selective prediction framework that generates multiple structured temporal explanations for an input and compares them to decide whether to commit to an answer or abstain. Standard methods estimate uncertainty from one predictive output, but this often misleads when local evidence supports contradictory interpretations. CHASE instead tracks the score gap between hypotheses, because genuine ambiguity shrinks that gap, and trains a ranking-aware selector on the margins to separate reliable cases from uncertain ones. The approach is tested on inferring hidden connectivity in a controlled vesicle simulator plus zero-shot transfer to real videos. A reader would care if the method makes abstention decisions more aligned with actual ambiguity levels, reducing errors in partial-observability settings.

Core claim

CHASE is 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. On hidden connectivity inference using a GUV-inspired simulator and real videos, the method delivers statistically significant gains in no-abstain accuracy, three-way accuracy, and ambiguity-aligned abstention at 80 percent coverage, while reducing overall risk by 9.9 percent at 90 percent coverage.

What carries the argument

The CHASE framework, which generates competing structured temporal explanations and feeds the margins between their scores into a ranking-aware selector that decides abstention.

If this is right

  • CHASE yields up to an 11.0 percent relative mean improvement in overall alignment compared with canonical uncertainty baselines.
  • It produces up to an 8.8 percent relative boost in three-way accuracy inside the very-high ambiguity regime.
  • At 80 percent coverage the selective risk boundary stays at parity with the strongest baselines while other alignment metrics improve.
  • At 90 percent coverage overall risk drops by 9.9 percent relative to the same baselines.
  • The framework supports zero-shot qualitative transfer to real GUV videos without retraining or fine-tuning.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same hypothesis-margin mechanism could be applied to other temporal computer-vision tasks where partial views create structured ambiguity, such as multi-object tracking or action recognition.
  • If the selector generalizes, hybrid models might run a fast single branch most of the time and invoke hypothesis comparison only on borderline inputs to control compute cost.
  • Testing the method on datasets with externally labeled ambiguity levels would reveal whether the observed risk reductions hold when ground-truth uncertainty is measured independently of the hypotheses.
  • The ranking-aware training objective might inspire new loss terms for any multi-explanation learning setting that needs explicit abstention.

Load-bearing premise

That genuine ambiguity reliably causes the score gap between competing hypotheses to collapse and that a ranking-aware selector trained on these margins can globally separate safe commitments from uncertain cases without introducing new biases.

What would settle it

A controlled test on new high-ambiguity GUV videos or an extended simulator where CHASE shows no gain in three-way accuracy or an increase in selective risk relative to single-branch baselines would falsify the central claim.

Figures

Figures reproduced from arXiv: 2605.01346 by Atul N. Parikh, Kartik Jhawar, Lipo Wang, Yuhao Geng.

Figure 1
Figure 1. Figure 1: Single-branch confidence versus hypothesis-driven selective prediction. classifiers are forced to make confident binary decisions from this incomplete evidence, selective prediction is crucial: a model must learn to commit only when evidence is sufficient, and abstain otherwise. Existing approaches usually estimate uncertainty from a single predictive branch, either by threshold￾ing confidence, measuring p… view at source ↗
Figure 2
Figure 2. Figure 2: Task setup and simulator regimes. The model predicts whether a vesicle pair is connected, not connected, or should be abstained on because the evidence is insufficient. The simulator generates four physical regimes across varying degrees of temporal and local ambiguity, allowing us to test whether abstentions align with genuinely ambiguous cases rather than transient noise view at source ↗
Figure 3
Figure 3. Figure 3: CHASE pipeline. Two competing hypotheses are evaluated, ensembled, and fused with an auxiliary classifier. A cost-aware selector balances wrong-prediction and ambiguity costs via a pairwise ranking objective to output a final accept score. 3 Method The pipeline ( view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative zero-shot transfer to real GUVs. 6 Discussion and Limitations Our results demonstrate that ambiguity-aware selective prediction requires explicitly comparing competing hypotheses to reliably identify contradictory evidence. However, we note two primary limitations. First, while CHASE achieves strong mean improvements across all metrics, its most robust, statistically significant gains are conce… view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript introduces CHASE, a selective prediction framework for ambiguity-aware decision making that explicitly compares structured temporal hypotheses (rather than relying on a single predictive branch) to decide whether to commit or abstain. It is evaluated on hidden connectivity inference using a physically grounded GUV-inspired simulator, with additional zero-shot qualitative transfer to real GUV videos, and reports statistically significant gains over canonical uncertainty baselines including up to 11.0% relative improvement in overall alignment, 8.8% boost in three-way accuracy under very-high ambiguity, and 9.9% risk reduction at 90% coverage.

Significance. If the central claims hold after addressing verification gaps, the work provides a structured approach to selective prediction under partial observability by leveraging hypothesis-margin collapse, which could improve reliability in domains with contradictory local evidence. The zero-shot qualitative transfer to real videos without retraining is a clear strength that supports potential generalizability.

major comments (3)
  1. [Section 3] Section 3 (Method): The optimization of the ranking-aware selector over hypothesis margins is described qualitatively without an explicit objective function, loss, or derivation; this leaves open whether the reported metric improvements reduce to fitting simulator-specific regularities rather than detecting genuine ambiguity, which is load-bearing for the claim of a bias-free global selector.
  2. [Section 4] Section 4 (Experiments): No control or ablation isolates whether the observed collapse in score gaps between competing hypotheses is caused by ambiguity itself or by shared simulator artifacts such as vesicle geometry and temporal sampling; without this, the attribution of superior alignment and risk reduction to the competing-hypotheses mechanism cannot be verified.
  3. [Abstract and Section 4.3] Abstract and Section 4.3: The claims of statistically significant gains (e.g., 11.0% relative mean improvement in overall alignment) are presented without details on the exact statistical tests, sample sizes per regime, or multiple-comparison corrections, undermining the strength of the quantitative results.
minor comments (2)
  1. [Abstract] The abstract introduces the term 'overall alignment' without a brief definition or pointer to its precise formulation in the main text; adding this would improve standalone readability.
  2. [Figures] Figure captions throughout should explicitly state what error bars (if present) represent and the number of runs or seeds used, to aid reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed review. We address each major comment below and outline the revisions planned to enhance the manuscript's clarity and rigor.

read point-by-point responses

  1. Referee: [Section 3] Section 3 (Method): The optimization of the ranking-aware selector over hypothesis margins is described qualitatively without an explicit objective function, loss, or derivation; this leaves open whether the reported metric improvements reduce to fitting simulator-specific regularities rather than detecting genuine ambiguity, which is load-bearing for the claim of a bias-free global selector.

    Authors: We agree that an explicit formulation is needed for full transparency. In the revised manuscript, we will add the precise objective function, loss derivation, and optimization details for the ranking-aware selector. This will clarify how margin collapse is leveraged as a general signal of ambiguity, independent of simulator-specific regularities. revision: yes

  2. Referee: [Section 4] Section 4 (Experiments): No control or ablation isolates whether the observed collapse in score gaps between competing hypotheses is caused by ambiguity itself or by shared simulator artifacts such as vesicle geometry and temporal sampling; without this, the attribution of superior alignment and risk reduction to the competing-hypotheses mechanism cannot be verified.

    Authors: We acknowledge the need for stronger isolation of the mechanism. We will add controlled ablation experiments in the revised Section 4, varying ambiguity levels while holding simulator parameters (vesicle geometry, temporal sampling) fixed, and vice versa. These will be complemented by further discussion of the zero-shot real-video transfer results as evidence of generalizability beyond simulator artifacts. revision: yes

  3. Referee: [Abstract and Section 4.3] Abstract and Section 4.3: The claims of statistically significant gains (e.g., 11.0% relative mean improvement in overall alignment) are presented without details on the exact statistical tests, sample sizes per regime, or multiple-comparison corrections, undermining the strength of the quantitative results.

    Authors: We thank the referee for highlighting this omission. In the revision, we will expand Section 4.3 with full details on the statistical tests, exact sample sizes per ambiguity regime, p-values, and any multiple-comparison corrections. The abstract will be updated to reference these details for the reported gains. revision: yes

Circularity Check

0 steps flagged

No circularity detected; CHASE is an empirically evaluated framework with no derivations reducing to self-defined inputs or fitted quantities by construction.

full rationale

The provided abstract and context introduce CHASE as a selective prediction method that compares competing temporal hypotheses and optimizes a ranking-aware selector over their margins. No equations, derivations, or self-citations appear in the text. The claimed performance gains (e.g., 11.0% relative improvement in alignment) are presented as experimental outcomes from a GUV simulator and zero-shot real-video transfer, not as quantities forced by the training procedure or by renaming prior results. The central premise—that ambiguity collapses hypothesis score gaps—is stated as an empirical observation rather than a self-referential definition. This satisfies the default expectation of a non-circular paper whose central claim retains independent empirical content.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review is based solely on the abstract; no specific free parameters, axioms, or invented entities are detailed in the provided text.

axioms (1)
  • domain assumption Standard machine learning assumptions about uncertainty estimation and selective prediction hold in the temporal domain.
    The framework extends existing selective prediction ideas without stating new foundational axioms.

pith-pipeline@v0.9.0 · 5595 in / 1148 out tokens · 33218 ms · 2026-05-09T14:09:06.947786+00:00 · methodology

discussion (0)

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Reference graph

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