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arxiv: 2605.19779 · v1 · pith:A7YV4GCEnew · submitted 2026-05-19 · 💻 cs.AI · cs.LG

Distribution-Free Uncertainty Quantification for Continuous AI Agent Evaluation

Yuxuan Gao , Megan Wang , Yi Ling Yu This is my paper

Pith reviewed 2026-05-20 06:19 UTC · model grok-4.3

classification 💻 cs.AI cs.LG
keywords conformal predictionuncertainty quantificationAI agent evaluationdistribution-free methodscoverage guaranteesadaptive conformal inferenceranking abstention
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The pith

Split conformal prediction and adaptive conformal inference adapt to continuous AI agent evaluation to provide distribution-free coverage guarantees for forecasted quality scores.

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

This paper adapts split conformal prediction and adaptive conformal inference to continuous AI agent evaluation. It supplies distribution-free coverage guarantees for the forecasted quality scores of agents. Tests on hourly signals from 50 agents show calibration error below 0.02 at the 24-hour horizon and proper interval widening after releases. The work also supplies compositional bounds for multi-agent pipelines and abstention rules that control false-ranking rates in pairwise comparisons and leaderboards.

Core claim

We adapt split conformal prediction and adaptive conformal inference (ACI) to continuous AI agent evaluation, providing distribution-free coverage guarantees for forecasted quality scores. Conformal intervals achieve calibration error below 0.02 across all nominal levels at the 24h horizon, while ACI correctly widens intervals by 35% following agent releases then reconverges. We further develop compositional uncertainty bounds for multi-agent pipelines, a conformal abstention rule for pairwise rankings with controlled false-ranking rate, and FDR-corrected abstention for leaderboard-scale multiple testing.

What carries the argument

The adaptation of split conformal prediction and adaptive conformal inference (ACI) to continuous agent evaluation signals under exchangeability, which delivers the distribution-free coverage guarantees.

If this is right

  • Conformal intervals achieve calibration error below 0.02 across all nominal levels at the 24h horizon.
  • ACI widens intervals by 35% following agent releases then reconverges.
  • Compositional uncertainty bounds for multi-agent pipelines hold across inter-stage correlations in the range [-0.5, 0.9].
  • A conformal abstention rule controls the false-ranking rate for pairwise rankings.
  • FDR-corrected abstention manages multiple testing on leaderboard-scale evaluations.

Where Pith is reading between the lines

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

  • The framework could support safer ongoing monitoring of deployed AI agents by flagging when forecasts are unreliable without distributional assumptions.
  • The link between cross-source sentiment divergence and ranking instability offers a practical signal for anticipating when agent rankings may shift.
  • Similar adaptations might apply to continuous performance tracking in other domains such as robotics or financial forecasting.

Load-bearing premise

The observations satisfy the exchangeability condition that underpins the coverage guarantees of split conformal prediction and ACI.

What would settle it

A dataset of AI agent quality scores where the coverage rate falls materially below the nominal level would show that the exchangeability assumption does not hold and the guarantees do not apply.

Figures

Figures reproduced from arXiv: 2605.19779 by Megan Wang, Yi Ling Yu, Yuxuan Gao.

Figure 1
Figure 1. Figure 1: shows calibration across nominal levels. Confor￾mal achieves the nominal coverage level (calibration error < 0.02) while parametric systematically exceeds it, mean￾ing parametric intervals are wider than necessary [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: shows the distribution: mean coverage is 80.4% (matching nominal), with 90% of agents within [72%, 90%]. Five volatile agents (high σcross) have cover￾age below 75%. This motivates a Mondrian extension. Mondrian conformal with σcross stratification. Standard conformal calibrates a single quantile across all agents. Mondrian conformal (Vovk et al., 2005) maintains sepa￾rate calibration sets per group, provi… view at source ↗
Figure 4
Figure 4. Figure 4: Standard vs. Mondrian conformal coverage. Under standard conformal (left), volatile agents (red) cluster well below the 80% nominal level (mean 64.6%, 11/15 below 75%). Mondrian conformal (right) stratifies by σcross and calibrates per-group quantiles, lifting volatile coverage to 80.4% (2/15 below 75%) at the cost of wider intervals for that group. 1h 6h 24h 48h 72h Stable ( <0.04) Medium (0.04-0.07) Vola… view at source ↗
Figure 5
Figure 5. Figure 5: Conformal coverage by forecast horizon and agent volatility class. Volatile agents degrade substantially at long hori￾zons (68% at 72h vs. nominal 80%), motivating Mondrian strati￾fication. 0.5 0.0 0.5 Inter-stage correlation 0.06 0.07 0.08 0.09 pipeline Compositional Bound Tightness Between bounds True pipeline Independence bound Worst-case bound [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Compositional bound tightness. True pipeline σ (black) falls between independence (blue) and worst-case (red) bounds for ρ > 0. At ρ < 0, the independence bound is anti-conservative. 0.072 (independence) to 0.096 (worst-case). 4.5. Conformal Selective Abstention For agents a, b, we construct a conformal interval for the score difference ∆ab = AP(a) − AP(b), calibrated from historical score-difference resid… view at source ↗
Figure 7
Figure 7. Figure 7: Benchmark-only vs. composite ranking (n=11). Blue: adoption-driven; red: closed-source penalty. 7. Discussion What’s new about conformal here. The contribution is not split conformal itself but its application to a domain with four specific challenges: (a) structured exchange￾ability violations (agent releases) addressed via ACI; (b) conditional coverage failure for volatile agents, addressed via Mondrian … view at source ↗
read the original abstract

We adapt split conformal prediction and adaptive conformal inference (ACI) to continuous AI agent evaluation, providing distribution-free coverage guarantees for forecasted quality scores. Conformal intervals achieve calibration error below 0.02 across all nominal levels at the 24h horizon, while ACI correctly widens intervals by 35% following agent releases then reconverges. We further develop compositional uncertainty bounds for multi-agent pipelines (validated via simulation across inter-stage correlations rho in [-0.5, 0.9]), a conformal abstention rule for pairwise rankings with controlled false-ranking rate, and FDR-corrected abstention for leaderboard-scale multiple testing. Evaluating 50 agents via 18 real-time signals collected hourly, we show that per-agent conditional coverage is well-concentrated around the nominal level (mean 80.4%, 90% of agents within [72%, 90%]), and that cross-source sentiment divergence predicts ranking instability (r=0.64, p<0.01). A circularity-controlled validation confirms the framework captures signal beyond benchmarks (rho_s=0.52, p<0.01, n=35). Code and data are released under CC BY 4.0.

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

2 major / 2 minor

Summary. The paper adapts split conformal prediction and adaptive conformal inference (ACI) to continuous AI agent evaluation, providing distribution-free coverage guarantees for forecasted quality scores. It reports calibration error below 0.02 at the 24h horizon, ACI intervals widening 35% after agent releases then reconverging, compositional uncertainty bounds validated in simulation for inter-stage correlations in [-0.5, 0.9], a conformal abstention rule for pairwise rankings, FDR-corrected abstention for leaderboards, and evaluation on 50 agents using 18 hourly real-time signals showing per-agent coverage concentrated around nominal levels (mean 80.4%) plus cross-source sentiment divergence predicting instability (r=0.64). A circularity-controlled validation is included (rho_s=0.52, n=35), with code and data released.

Significance. If the distribution-free guarantees hold under the observed temporal structure, the work supplies a practical, reproducible toolkit for uncertainty-aware continuous evaluation of AI agents, including handling of distribution shifts and multi-agent pipelines. Strengths include the explicit circularity-controlled validation, simulation-based checks on compositional bounds, and public release of code and data under CC BY 4.0, which support auditability and extension.

major comments (2)
  1. [Abstract / §3] Abstract and §3 (Methods): The distribution-free coverage claims rest on exchangeability for split conformal prediction and the martingale property for ACI. The evaluation uses hourly signals across 50 agents with documented distribution shifts at releases and cross-source divergence (r=0.64), yet no theorem or proposition establishes that the online adaptation preserves exact or approximate coverage under serial correlation or non-stationarity beyond standard ACI assumptions. This assumption is load-bearing for the central guarantee.
  2. [§4] §4 (Experiments): The reported per-agent conditional coverage (mean 80.4%, 90% of agents in [72%, 90%]) and calibration error <0.02 are presented without error bars, explicit data-exclusion criteria, or sensitivity analysis to the temporal dependence structure; these details are needed to assess whether the empirical results support the theoretical claims under realistic violations of exchangeability.
minor comments (2)
  1. [Abstract] Abstract: The phrase 'circularity-controlled validation' is used without a brief parenthetical definition or pointer to the specific procedure in the main text.
  2. [§4 / Figures] Figure captions and §4: Axis labels and legends for the ACI widening/reconvergence plots could explicitly note the nominal coverage level and the exact post-release window used.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive report. We address each major comment below and indicate the revisions planned for the next version of the manuscript.

read point-by-point responses

  1. Referee: [Abstract / §3] Abstract and §3 (Methods): The distribution-free coverage claims rest on exchangeability for split conformal prediction and the martingale property for ACI. The evaluation uses hourly signals across 50 agents with documented distribution shifts at releases and cross-source divergence (r=0.64), yet no theorem or proposition establishes that the online adaptation preserves exact or approximate coverage under serial correlation or non-stationarity beyond standard ACI assumptions. This assumption is load-bearing for the central guarantee.

    Authors: We agree that the coverage guarantees rely on the standard exchangeability assumption for split conformal prediction and the martingale property for ACI. The manuscript applies ACI as introduced in the literature without deriving a new proposition that would guarantee exact or approximate coverage under arbitrary serial correlation or non-stationarity. In the revised manuscript we will add a dedicated paragraph in §3 that explicitly states these assumptions, notes that ACI provides asymptotic coverage under its original conditions, and cites relevant extensions of conformal prediction to dependent data. The empirical results (including the reported calibration error, ACI adaptation after releases, and circularity-controlled validation) are presented as supporting evidence rather than as a formal proof of robustness to all forms of dependence. revision: partial

  2. Referee: [§4] §4 (Experiments): The reported per-agent conditional coverage (mean 80.4%, 90% of agents in [72%, 90%]) and calibration error <0.02 are presented without error bars, explicit data-exclusion criteria, or sensitivity analysis to the temporal dependence structure; these details are needed to assess whether the empirical results support the theoretical claims under realistic violations of exchangeability.

    Authors: We acknowledge that the current version of §4 presents the coverage statistics and calibration error without accompanying uncertainty quantification, without stating the precise data-exclusion rules, and without a sensitivity check for temporal dependence. In the revised manuscript we will (i) add bootstrap-based error bars to the per-agent coverage figures, (ii) explicitly describe the data-exclusion criteria applied to the 50 agents, and (iii) include a sensitivity analysis that varies the block size in a time-blocked resampling procedure to examine robustness under serial correlation. revision: yes

Circularity Check

0 steps flagged

Adaptation of standard split-CP and ACI methods shows no derivation circularity

full rationale

The paper adapts established split conformal prediction and adaptive conformal inference (ACI) to AI agent evaluation, relying on the standard exchangeability assumption for distribution-free coverage guarantees. Empirical results such as calibration error <0.02, ACI interval widening by 35%, per-agent coverage concentration, and the reported rho_s=0.52 validation metric are presented as separate empirical findings rather than quantities forced by the paper's own equations or fitted parameters. No self-citations, self-definitional steps, or reductions of predictions to inputs by construction appear in the provided abstract or described claims. The central derivation chain draws from external statistical properties of conformal methods and remains self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on the standard exchangeability assumption of conformal prediction together with empirical validation on real-time signals; no new free parameters or invented entities are introduced in the abstract.

axioms (1)
  • domain assumption Observations are exchangeable
    This is the background condition required for the finite-sample coverage guarantees of split conformal prediction and ACI.

pith-pipeline@v0.9.0 · 5737 in / 1152 out tokens · 58635 ms · 2026-05-20T06:19:06.787121+00:00 · methodology

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Reference graph

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