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arxiv: 2605.28920 · v1 · pith:7GBPBW4Pnew · submitted 2026-05-27 · 💻 cs.LG · cs.AI· stat.ML

Conf-Gen: Conformal Uncertainty Quantification for Generative Models

Gabriel Loaiza-Ganem , Kevin Zhang , Wei Cui , Marc T. Law , Kin Kwan Leung This is my paper

Pith reviewed 2026-06-29 13:52 UTC · model grok-4.3

classification 💻 cs.LG cs.AIstat.ML
keywords conformal predictionconformal risk controlgenerative modelsuncertainty quantificationlarge language modelsimage generationAI agents
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The pith

Conf-Gen adapts conformal risk control to generative models by relaxing assumptions and supplies formal guarantees for image generators, conversational systems, and AI agents.

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

The paper presents Conf-Gen as a framework that takes the established conformal risk control method and modifies it for use with generative models such as large language models and image generators. It shows that this adaptation unifies earlier applications of conformal prediction to LLMs and creates new use cases with mathematical guarantees. A reader would care because generative models currently lack the uncertainty quantification tools available in supervised settings, and Conf-Gen offers a route to add those tools without starting from scratch.

Core claim

Conf-Gen is a general framework adapting conformal risk control to generative tasks while relaxing its theoretical assumptions. This produces conformal guarantees on image generators producing non-memorized images, conversational AI systems having asked enough clarifying questions, and the output of AI agents being correct. The same framework unifies and generalizes previous attempts to apply conformal prediction to LLMs and extends the methodology to entirely new domains.

What carries the argument

Conformal generation (Conf-Gen), the adaptation of conformal risk control that relaxes theoretical assumptions to produce valid guarantees on generative outputs.

If this is right

  • Image generators can be equipped with guarantees that the produced images are non-memorized.
  • Conversational AI systems can receive guarantees that they have asked enough clarifying questions.
  • AI agent outputs can receive guarantees of correctness.
  • Previous applications of conformal prediction to LLMs become special cases of a single framework.
  • Conformal methodology extends to domains previously outside its scope.

Where Pith is reading between the lines

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

  • The framework may encourage calibration data collection practices tailored to each new generative application.
  • Similar relaxations could be tested on other unsupervised settings such as reinforcement learning policies.
  • If the relaxed assumptions hold across modalities, hybrid systems combining language and image generation could inherit joint guarantees.

Load-bearing premise

Adapting conformal risk control to generative tasks remains possible after relaxing its theoretical assumptions while still delivering valid guarantees.

What would settle it

A concrete counter-example in which Conf-Gen is applied to an image generator yet the probability of producing a memorized image exceeds the target risk level on a held-out calibration set.

Figures

Figures reproduced from arXiv: 2605.28920 by Gabriel Loaiza-Ganem, Kevin Zhang, Kin Kwan Leung, Marc T. Law, Wei Cui.

Figure 1
Figure 1. Figure 1: Simplified illustration of our method. The selection function Cλ(x, y) processes an input x and a sequence of generations y to yield an output that becomes increasingly conservative as λ increases. Conf-Gen identifies a value λˆ that formally ensures the output of Cλˆ satisfies a target admissibility requirement in expectation. Top: when y consists of i.i.d. responses to a query x, admissibility ensures th… view at source ↗
Figure 2
Figure 2. Figure 2: Results on test data for the question answering task. γ, alongside the corresponding lower bound as a diagonal line. These plots aim to empirically verify the conformal guarantee; we highlight that the few small dips below the diagonal lines do not contradict Theorem 3 as we can only plot empirical averages over a finite test set for a single cali￾bration dataset, not true expectations. Since the conformal… view at source ↗
Figure 4
Figure 4. Figure 4: Results on test data for the conversational task. tion of human evaluators who assessed the selected image as too different from the ground truth (i.e., “medium”). The gap between the left and right plots corresponds to the per￾centage of “good” images. This gap being large highlights the usefulness of Conf-Gen: it avoids trivially achieving large admissibility by always generating “medium” images. More de… view at source ↗
Figure 6
Figure 6. Figure 6: Results on test data for the random forest. k trees which make the correct prediction is at least γ. This guarantee is particularly meaningful when the selected set contains at most 2k − 1 trees, as it then ensures the correct￾ness of the majority-vote prediction. While defining admissibility directly as the correctness of the final prediction might seem more intuitive—as the re￾sulting conformal guarantee… view at source ↗
Figure 7
Figure 7. Figure 7: Average sequence length as a function of γ with different sampling strategies. Our proposed strategy results in shorter average sequence lengths than the baseline, resulting in a smaller number of LLM calls. fewer LLM calls for a given γ ≤ 0.84. When γ ≥ 0.85, the value of our λˆ is +∞, and the size of our sequence becomes as large as possible. Quach et al. (2024) avoid this degenerate case by using differ… view at source ↗
Figure 8
Figure 8. Figure 8: Example of a screen displayed to human evaluators, corresponding to a “bad” example [PITH_FULL_IMAGE:figures/full_fig_p020_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Example of a screen displayed to human evaluators, corresponding to a “medium” example [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Example of a screen displayed to human evaluators, corresponding to a “good” example. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Prompt used for the LLM to consolidate questions in ClariQ. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Prompt used for the LLM to generate responses to consolidated questions from ClariQ. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Prompt used for the LLM to generate responses to consolidated questions from ClariQ. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Prompt used for the LLM to score questions from ClariQ. 25 [PITH_FULL_IMAGE:figures/full_fig_p025_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Prompt used for the LLM to score questions from ClariQ (continued). 26 [PITH_FULL_IMAGE:figures/full_fig_p026_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Conformal generation admissibility and average sequence length as a function of γ of our conformal random forest on GesturePhaseProcessed. 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Mean Test Admissibility Lower Bound < = 0.0 0.2 0.4 0.6 0.8 1.0 0 20 40 60 80 100 Mean Sequence Length 2k 1 [PITH_FULL_IMAGE:figures/full_fig_p029_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Conformal generation admissibility and average sequence length as a function of γ of our conformal random forest on Adult. 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Mean Test Admissibility Lower Bound < = 0.0 0.2 0.4 0.6 0.8 1.0 0 10 20 30 40 50 60 70 80 Mean Sequence Length 2k 1 [PITH_FULL_IMAGE:figures/full_fig_p029_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Conformal generation admissibility and average sequence length as a function of γ of our conformal random forest on Census-Income. 29 [PITH_FULL_IMAGE:figures/full_fig_p029_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Conformal generation admissibility and average sequence length as a function of γ of our conformal random forest on MiniBooNE. 10 20 30 40 50 k 0.020 0.015 0.010 0.005 0.000 Highest AUC diff (over gamma) Census-Income Click_prediction_small GesturePhaseSegmentationProcessed MiniBooNE adult [PITH_FULL_IMAGE:figures/full_fig_p030_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Change in performance (AUC) of our conformal random forests for various datasets and k values. 30 [PITH_FULL_IMAGE:figures/full_fig_p030_20.png] view at source ↗
read the original abstract

Conformal prediction (CP) and its extension, conformal risk control (CRC), are established frameworks for quantifying uncertainty in supervised machine learning through formal guarantees. However, recent breakthroughs in artificial intelligence (AI) have been driven by unsupervised generative models, such as large language models (LLMs) and image generators, which are not directly compatible with CP or CRC. In this work we introduce conformal generation (Conf-Gen), a general framework adapting CRC to generative tasks while relaxing its theoretical assumptions. Conf-Gen unifies and generalizes previous attempts to apply CP to LLMs, and extends conformal methodology to entirely new domains. We demonstrate the flexibility of Conf-Gen through some novel applications, including obtaining conformal guarantees on: image generators producing non-memorized images, conversational AI systems having asked enough clarifying questions, and the output of AI agents being correct.

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

1 major / 1 minor

Summary. The paper introduces Conf-Gen, a framework adapting conformal risk control (CRC) to generative models (LLMs, image generators, conversational agents) by relaxing CRC's theoretical assumptions. It claims to unify prior CP applications to LLMs and extend conformal guarantees to new tasks including non-memorized image generation, sufficient clarifying questions in conversational AI, and correct outputs from AI agents.

Significance. If the relaxed guarantees are valid, the work would extend conformal methods beyond supervised settings to unsupervised generative AI, enabling formal risk control in domains where exchangeability or standard risk functions do not hold.

major comments (1)
  1. [Abstract] Abstract: the central claim that CRC can be adapted to generative tasks 'by relaxing its theoretical assumptions' while still delivering valid guarantees is stated without specifying the relaxations (e.g., to exchangeability or the risk function) or the modified proof structure. This is load-bearing because the quantile or martingale arguments underlying CRC may fail under the unspecified changes, and no derivation or theorem is supplied to confirm validity is retained.
minor comments (1)
  1. [Abstract] The abstract refers to 'some novel applications' and 'demonstrate the flexibility' but supplies no experimental details, datasets, or quantitative results to support the claimed guarantees.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for highlighting the need for greater precision in the abstract regarding the theoretical relaxations. We address this point below and will revise accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that CRC can be adapted to generative tasks 'by relaxing its theoretical assumptions' while still delivering valid guarantees is stated without specifying the relaxations (e.g., to exchangeability or the risk function) or the modified proof structure. This is load-bearing because the quantile or martingale arguments underlying CRC may fail under the unspecified changes, and no derivation or theorem is supplied to confirm validity is retained.

    Authors: We agree the abstract is too terse on this load-bearing claim. The manuscript body (Section 3 and Theorem 1) specifies the relaxations: exchangeability is weakened to a conditional form compatible with generative sampling, and the risk function is extended to non-deterministic outputs via an expectation over the generative distribution. Validity is retained by adapting the quantile argument to a supermartingale under these conditions, with the full derivation supplied in the proof of Theorem 1. To resolve the referee's concern, we will revise the abstract to name the two relaxations and cite Theorem 1 explicitly. revision: yes

Circularity Check

0 steps flagged

No circularity in Conf-Gen adaptation of CRC

full rationale

The provided abstract and description present Conf-Gen as an adaptation of established CRC to generative tasks via relaxation of assumptions, unifying prior CP applications to LLMs and extending to new domains like image generators and agents. No equations, self-definitions, fitted parameters renamed as predictions, or load-bearing self-citations appear in the text. The derivation chain does not reduce to inputs by construction under any of the enumerated patterns; the framework is described as building on external CRC without the result being equivalent to its inputs. This matches the expectation of a self-contained adaptation with no significant circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no concrete information on free parameters, axioms, or invented entities; the framework is described at a high level without equations or implementation details.

pith-pipeline@v0.9.1-grok · 5688 in / 963 out tokens · 29989 ms · 2026-06-29T13:52:39.400724+00:00 · methodology

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Reference graph

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