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Partial-Revelation Panel Forecasting: Predictive Information Gain, Allocation, and Coarsening-Consistent Operators

Bin Wen, Dai-Qiao Zhang, Tien-Ping Tan

arXiv:2610.22831v1cs.LG

TL;DR

The paper asks how to forecast a future dialogue panel while only some votes on the previous turn are revealed, a question distinct from valuing another vote on the same item. It defines coarsening-consistent panel operators and evaluates their predictive and allocation behavior. Results show that same-item gains are measurable, while consistency is an auditable property rather than a guarantee of better prediction.

  • Problem

    Annotation budgets for subjective tasks are often set by convention, while forecasting the next panel from a partially revealed previous panel is distinct from same-item annotation value.

    Source: §1 · p. 1

  • Method

    The paper defines coarsening-consistent panel operators and studies target-free allocation using predicted row-level marginal gains.

    Source: §4 · p. 6, §6.12 · p. 14

  • Results

    Held-out same-item votes reduce NLL by 0.583/0.221 nats on IEMOCAP, 0.578/0.184 on M3ED, and 0.180/0.069 on EmoWOZ; target-free adaptive allocation helps only where row-level signal is predictable.

    Source: §10 · p. 18

  • Takeaways & Limitations

    Cross-turn and same-item gains are not interchangeable, and target-free allocation benefits depend on predictable row-level signal.

    Source: §10 · p. 18

    CCAR masking is a controlled intervention rather than an empirically verified arrival process, and consistency does not guarantee predictive improvement.

    Source: §9 · pp. 17–18

Abstract

from arXiv · show

We study partial-revelation panel forecasting: given $k$ of $n$ annotator votes on the previous dialogue turn, predict the full count distribution of the next turn's panel. This cross-turn predictive information differs from same-item annotation value. Under any nested revelation mechanism, the population decrement satisfies $V(k)-V(k+1)=I(Y;R_{k+1}\mid X_k)$; a fitted model also contributes adjacent excess-risk differences. Coarsened-completely-at-random (CCAR) masking supplies the exchangeable random-rater interpretation used in our operators. We define coarsening-consistent panel operators with a closed-form Dirichlet-multinomial realization, learned completion, and rater-count transfer. Against per-level lookup, cold-start count negative log-likelihood differences are -0.422/-0.166/-0.014 nats on IEMOCAP/M3ED/EmoWOZ. A held-out rater experiment measures same-item value: first and second additional votes reduce NLL by 0.583/0.221, 0.578/0.184, and 0.180/0.069 nats, respectively. Cross-turn and same-item gains are not interchangeable, and CCAR masks are controlled interventions rather than observed arrival processes. Consistency is auditable but does not guarantee predictive improvement; per-level marginalization can outperform a shared table. Target-free allocation beats uniform only where row-level signal is predictable: at budget one it recovers 83--93% of retrospective feature class headroom on IEMOCAP/M3ED but does not improve EmoWOZ. Gains are modest and corpus-dependent, with several small or null partial-revelation differences. Code and aggregate result artifacts accompany the preprint.

1 Introduction

The paper frames forecasting a future dialogue panel from a partially revealed previous panel as a distinct task from predicting another vote for the same item. It develops consistent panel operators and examines their predictive, allocation, and empirical scope.

1 Introduction

  • The task predicts the next turn’s full panel count distribution from partially revealed votes on the previous turn, distinct from same-item annotation value.The paper evaluates the cross-turn and same-item estimands separately.Source: §1 · pp. 1–2

1.1 The Cross-Turn Gain Has an Information-Theoretic Identity

  • For the true conditional family, each cross-turn revelation decrement equals conditional mutual information, while fitted decrements also reflect adjacent excess risk.A separate held-out rater experiment measures same-item predictive value.Source: §1.1 · p. 2

1.2 Operators That Behave Sensibly Across Revelation Levels

  • A Dirichlet-multinomial completion model yields coarsening-consistent forecasts by averaging full-panel forecasts over hidden-vote completions.The paper also introduces learned completion and rater-count transfer.Source: §1.2 · p. 2, §1.4 · pp. 2–3

1.3 What We Found, Including What We Did Not Expect

  • Consistency is auditable but does not itself drive predictive accuracy; a per-level marginalizer that violates it can match or beat the consistent operator.The paper also reports that exact-marginal completion-head training underperforms its cross-entropy surrogate.Source: §1.3 · p. 2

1.4 Contributions

  • The paper contributes a measurement framework, operator variants, and audits across four corpora, while making no state-of-the-art claim.Its evidence chain includes held-out splits, cluster bootstraps, and deterministic numeric, logic, and data-quality audits.Source: §1.4 · pp. 2–3

2.1 Learning from Disagreement

  • Prior disagreement-aware models estimate a current item’s label distribution, whereas this paper forecasts a future panel conditional on a partially revealed previous one.The cited representative approaches include soft-label prediction, evidential estimation, Dirichlet-prior models, and incomplete-label learning.Source: §2.1 · p. 3, §2.3 · p. 3

2.2 How Many Annotators Are Worth It

  • Kohli [8] measures metric-dependent saturation as annotators are subsampled; this paper characterizes revelation increments as conditional mutual information and predicts curves per row.The paper also derives allocation consequences from that characterization.Source: §2.2 · p. 3

2.3 Repeated Labeling and Crowd Allocation

  • Repeated-labeling and active-learning work studies acquisition objectives different from sequentially forecasting the next item’s full panel in an existing dialogue.The paper uses uncertainty-style ideas as target-free baselines, not as reproductions of those acquisition objectives.Source: §2.3 · p. 3

3 Partial-Revelation Panel Forecasting

The forecasting object is a full conditional count distribution for the next panel given the partially revealed previous panel and a transcript-derived condition. The section defines consistency through completion marginalization and distinguishes cross-turn forecasting from same-item value.

3.1 Notation

  • The forecast is the next turn’s full conditional count distribution given revealed votes from the previous panel and a transcript-derived condition, scored by count NLL.Count NLL is strictly proper for full-support distributions over count vectors.Source: §3.1 · p. 4

3.2 Worked Example

  • With K = 2, n = 3, and two revealed votes (1, 1), the consistent forecast averages the two possible full-panel completions using their posterior weights.The example illustrates finite marginalization over hidden votes.Source: §3.2 · p. 4

3.3 Coarsening Consistency

  • Coarsening consistency defines a partial-panel forecast as the completion-posterior average of the full-panel operator; separately fitted lookup tables need not satisfy this condition.At k = 0, lookup collapses to a relation-conditioned marginal panel distribution and discards transition structure.Source: §3.3 · p. 4

3.4 Estimands and Masking Interpretation

  • Cross-turn revelation measures prediction of the next turn’s panel, while same-item value measures the NLL reduction for an item held fixed.CCAR masks are controlled interventions, not claims about historical annotation arrival, which the corpora do not fully timestamp.Source: §3.4 · pp. 4–5

4 Population and Fitted Predictive Information

For nested revelations, the population reduction in expected count NLL equals the conditional mutual information of the newly revealed information about the target panel. Fitted curves add adjacent excess-risk differences, so their behavior is not itself a population information estimate.

Monotonicity and fitted curves

  • The Bayes expected loss is non-increasing under nested revelation, but finite-sample fitted curves may violate monotonicity or diminishing returns because of adjacent excess-risk differences.Such deviations are fitted-risk diagnostics, not failures of the population information identity.Source: §4 · p. 5

Population revelation information

  • At each reveal, V(k) − V(k + 1) = I(Y; R_k+1 | X_k); across all reveals, V(0) − V(n) = I(Y; X_n | X_0).These identities apply to the true conditional family under nested revelation.Source: §4 · p. 5

Fitted-risk decomposition

  • V_q(k) − V_q(k + 1) equals the population information increment plus ε_k − ε_k+1, so a fitted decrement is not automatically a CMI estimate.The fitted curve matches the population increment when adjacent excess risks are equal.Source: §4 · p. 5

Allocation

  • Greedy water-filling minimizes total expected loss when each row’s expected marginal gains are non-increasing; individual rows are noisy, so policies use bin-averaged curves.This diminishing-return condition is an assumption, not a property guaranteed by fitted curves.Source: §4 · p. 6

5 Coarsening-Consistent Operator Families

The paper builds panel operators around completion of hidden votes, using a Dirichlet-multinomial realization and variants for deployment, rater-count transfer, and sparse-cell shrinkage. The variants trade exact consistency against other modeling choices, with guarantees varying by design.

5.1 Response Table and DM Closure

  • Under shared Dirichlet label probabilities and ignorable CCAR revelation, the hidden votes have a Dirichlet-multinomial completion posterior, yielding a finite-mixture response operator.The full-panel operator is a Dirichlet-multinomial response table.Source: §5.1 · pp. 6–7

5.2 CCPR-J (Exact Consistency)

  • CCPR-J estimates one shared response table across revelation levels with a posterior-weighted iteration, but its moment update is not an exact likelihood maximizer and convergence to an EM optimum is not guaranteed.Per-level empirical completion tables can replace the conjugate posterior; the development-selected neural blends are not claimed to inherit exact consistency.Source: §5.2 · p. 7, §5.1 · p. 7

5.3 CCPR-D (Deployment Variant)

  • CCPR-D uses a dedicated full-panel table at k = n and shared completion machinery elsewhere, making consistency approximate; its audited gap is 0.003–0.008 on IEMOCAP.The reported comparison is ≈10−17 for CCPR-J and 0.02–0.05 for per-level lookup.Source: §5.3 · p. 7

5.4 CCPR-N (Rater-Count Invariance)

  • CCPR-N uses a per-vote distribution over a coarser summary of the previous panel, enabling zero-shot transfer from n = 3 to n = 5 and mixed-rater-count deployment with one table.The summary key trades away the full-composition key; its effect is measured on the n = 3 rows of Table 11.Source: §5.4 · p. 7

5.5 CCPR-S (Cell Density Shrinkage)

  • CCPR-S interpolates between per-level flexibility in dense cells and the shared table in sparse cells, but its level-specific weights have no corresponding analytic consistency-gap guarantee.It is a development-selected heuristic relaxation of the exact operator family.Source: §5.5 · pp. 7–8

6 Results

Across the evaluated corpora, the consistent operator improves cold-start count NLL over per-level lookup, while partial-revelation results vary. Further tests show that consistency does not ensure predictive gains, and that learned completion and target-free allocation have corpus- and setting-dependent outcomes.

6.2 Main Results vs. Per-Level Lookup

  • At partial revelation, ccprJ significantly wins at k = 1/2 on IEMOCAP and k = 1/2 on EmoWOZ, while M3ED differences are at most 0.007 nats and nonsignificant.At IEMOCAP k = 3, ccprJ is 0.021 nats worse; the paper does not claim wins at every level.Source: §6.2 · p. 8
  • Cold-start count NLL differences for ccprJ −lookup are −0.422 nats on IEMOCAP, −0.166 on M3ED, and −0.014 on EmoWOZ, with confidence intervals excluding zero.Table 1 reports differences by corpus and revelation level; negative values favor ccprJ.Source: §6.2 · p. 8, Table 1 · p. 9

6.3 Same-Item Held-out Rater Validation

  • The held-out-rater experiment separately measures same-item NLL reductions, and the low cross-turn/same-item correlations on IEMOCAP mean the two curves are not interchangeable.The reported correlations are ρ = 0.266 at k = 0 and ρ = 0.011 at k = 1.Source: §6.3 · p. 9, Table 2 · p. 9

6.4 Affective Inertia and Speaker Transition Controls

  • Full partial-composition controls retain later gains on M3ED and IEMOCAP, whereas majority-only controls have negative final increments on all three corpora.The authors interpret this as evidence that majority-only compression can discard composition information; fitted negative increments do not imply negative population information.Source: §6.4 · pp. 9–10, Table 3 · p. 10

6.5 Synthetic Validation of Fitted-Risk and Masking Sensitivity

  • Synthetic population references remain monotone across 39 settings, while finite-sample fitted curves can violate monotonicity; consistency has no significant full-data predictive improvement and can cost up to 0.048 nats at low k.The cost of sharing shrinks with less training data, while sparse cells can favor sharing, including IEMOCAP k = 2 and EmotionLines n = 5, k = 4.Source: §6.5 · p. 11, §6.6 · p. 11, §6.8 · p. 12

6.7 Learned Completion: CCPR-L

  • CCPR-L’s context contribution is significant in eight of nine partial-revelation cells, while CCPR-S stays within 0.002 nats of the better endpoint and beats both endpoints at the sparsest cells.The CCPR-L context term dominates cold-start gains; its k = 3 losses are attributed to the residual table gap, not to CCPR-L.Source: §6.7 · p. 12, §6.8.1 · p. 13

6.9 Rater-Count Invariance

  • The per-vote operator transfers from n = 3 to n = 5 without seeing n = 5 training rows, but stable identity-aware routing is not validated by the available corpus files.In neural-context comparisons, results vary by corpus and revelation level; IEMOCAP wins at every level, while M3ED partial levels favor lookup+head.Source: §6.9 · p. 13, §6.10 · p. 14, §6.11 · p. 14

6.12.1 What the Oracle Can and Cannot Bound

  • At budget one, CCPR-P recovers 83%/93% of retrospective feature-class headroom on IEMOCAP/M3ED, but loses to uniform on EmoWOZ, where feature-bound headroom is only 0.007 nats.The retrospective reference is a diagnostic, not a deployment-time bound; the authors recommend calibration-only cross-validation as a pre-deployment check.Source: §6.12 · p. 14, §6.12.1 · p. 15

7 Negative Results and Corrections

The section corrects three claims whose apparent advantages did not survive controls, and reports two additional null or limited findings. The corrections narrow what the experiments support about completion training, likelihood families, allocation benchmarks, and identity-aware modeling.

7 Negative Results and Corrections

  • The section groups three claims that failed their controls because their corrections change how apparently favorable numbers should be interpreted.The corrected conclusions are narrower than the original claims.Source: §7 · p. 15

7.1 Exact-Marginal Training of the Completion Head Fails

  • +0.064/+0.043/+0.015 nats at k = 0/1/2 on IEMOCAP: exact-marginal completion training performed worse than surrogate training, and selection discarded it on four of five folds.The authors attribute this to completion weights overfitting when freed from the surrogate’s semantic anchor.Source: §7.1 · p. 16

7.2 The Dirichlet-Versus-Cross-Entropy Comparison Was a Likelihood-Family Artifact

  • ≤0.02 nats separated the matched Dirichlet-multinomial and cross-entropy heads, after the likelihood-family mismatch was removed.The family alone accounted for 1.09 nats on M3ED and 0.39 on EmoWOZ in the original comparison.Source: §7.2 · p. 16

7.3 The Usual Allocation Oracle Bounds Nothing Deployable

  • 83–93% of retrospective feature-class headroom, versus 35–41% of the clairvoyant oracle, was recovered on IEMOCAP/M3ED; EmoWOZ showed no positive headroom result.The retrospective reference uses evaluation targets and is not a deployable upper bound; the clairvoyant oracle includes target randomness.Source: §7.3 · p. 16

7.4 Two Further Nulls, Reported for Completeness

  • Consistency cost +0.0085 nats against a per-level marginalizer, halving to +0.0044 when that baseline lacked oracle panel-size training data.The identity-aware extension was not supported as a general empirical contribution: IEMOCAP’s original columns were not stable IDs, and the stable-subset check was compatible with no identity effect.Source: §7.4 · p. 16

8 Discussion: What This Means for Annotation Projects

The discussion favors measuring annotation-budget effects rather than assuming a universal curve shape or allocation rule. Its practical guidance is bounded by corpus-specific results, the predictability of item-level signal, and the available identity-resolved data.

8 Discussion: What This Means for Annotation Projects

  • The section frames its practical consequences in decreasing order of confidence.Source: §8 · p. 16

8.1 Spread the Budget; Measure the Gain Rather than Assume Its Shape

  • Uniform allocation produced positive estimated savings over concentrated allocation in all 15 held-out budget cells and significant savings in 14.The authors prefer this operational result to a universal diminishing-return claim because fitted curves can violate the population shape at sparse levels.Source: §8.1 · p. 17

8.2 Practitioner Takeaway

  • The first/second additional votes reduced NLL by 0.583/0.221 nats on IEMOCAP, 0.578/0.184 on M3ED, and 0.180/0.069 on EmoWOZ; broad panel coverage instead favored spreading a fixed budget.These are protocol- and corpus-specific findings, not a universal rule for annotation projects.Source: §8.2 · p. 17

8.3 Predicting Per-Item Value Can Help, but the Reference Must Be Honest

  • Target-free adaptive allocation recovered much of retrospective feature-class headroom where item-level signal was predictable, but lost where it was not.Because the reference uses evaluation targets, the authors recommend a calibration-only cross-validation diagnostic before deployment.Source: §8.3 · p. 17

8.4 Identity-Aware Routing Remains an Open Problem

  • Identity-aware routing remains unevaluated cleanly because the corpora lack timestamped, identity-resolved arrival logs.The IEMOCAP stable-subset comparison was only +0.0074 nats versus a pooled table, with a confidence interval crossing zero.Source: §8.4 · p. 17

8.5 Modeling Guidance

  • Choose the likelihood family carefully, calibrate concentration post-hoc, and prefer shared consistent tables when levels or panel sizes are numerous relative to data.Per-level fitting is recommended when cells are dense.Source: §8.5 · p. 17

9 Limitations

The reported evidence has scope and design boundaries: gains vary by corpus, consistency need not improve prediction, and CCAR masking is an intervention rather than an observed arrival process. The evaluations are concentrated in conversational emotion data and small panel sizes, with no leaderboard claim.

Corpus-dependent gains

  • Cold-start gains replicate across corpora, but mid-revelation gains are significant on IEMOCAP and EmoWOZ and indistinguishable from zero on M3ED.Some neural mixtures show slightly negative mid-revelation differences.Source: §9 · p. 17

Consistency does not guarantee predictive improvement

  • A per-level inconsistent marginalizer matches or beats the consistent operator, while consistency provides single-table auditability at a quantified cost.In mixed-{3, 5} deployment, the oracle per-(n, k) refit beats the shared table by +0.0085 nats overall; the shared table wins the sparsest cells.Source: §9 · p. 17

CCAR is an assumption

  • CCAR is a controlled masking intervention, not an empirically verified arrival process; the raw corpora lack timestamps to reconstruct arrival order.Identity- and label-dependent masking results are synthetic sensitivity analyses, not measurements of the original workflow.Source: §9 · p. 18

Small and domain-specific evidence

  • The real-data evaluations cover conversational emotion datasets, mostly panels of n = 3 and n = 5, while substantially larger panels remain unestablished.IEMOCAP has only five independent sessions; same-item validation is setting-specific rather than a universal budgeting law.Source: §9 · p. 18

No leaderboard comparison

  • The baselines are same-protocol, capacity-matched internal methods, and the paper makes no state-of-the-art claim.Source: §9 · p. 18

10 Conclusions

The conclusion separates cross-turn forecasting information from same-item annotation value, presents coarsening consistency as a coherence and deployment property, and reports protocol-bounded allocation findings. It also notes that the accompanying artifacts support inspection but do not certify complete reproducibility.

10 Conclusions

  • For the cross-turn task, the population decrement is conditional mutual information, while a fitted forecaster’s decrement also contains adjacent excess predictive risk.The held-out rater experiment measures same-item value directly, rather than treating it as interchangeable with cross-turn gain.Source: §10 · p. 18
  • The first/second additional votes reduced NLL by 0.583/0.221 nats on IEMOCAP, 0.578/0.184 on M3ED, and 0.180/0.069 on EmoWOZ.The results support additional-label informativeness in this setting, not a universal equivalence between cross-turn gain and annotation value.Source: §10 · p. 18
  • Uniform allocation had positive estimated savings over the tested concentrated policy in all 15 held-out cells and significant savings in 14; adaptive allocation helped only where row-level signal was predictable.The 83–93% figure is relative to retrospective feature-class headroom, not a deployable or population upper bound.Source: §10 · p. 18

Data and code availability

  • The ancillary artifact includes implementation and validation scripts, aggregate summaries, and an artifact manifest, but does not certify bit-identical reproduction or rule out every data-processing error.The original corpora are not redistributed, and full training reproduction requires the original datasets and, for multimodal experiments, source features.Source: §10 · p. 18, Data and code availability · p. 19

Funding

Ethics statement

  • The study analyzed previously collected, publicly documented corpora and involved no new human or animal data collection.Source: Ethics statement · p. 19

A Proofs

The proofs connect nested revelation to predictive-risk reduction and establish closure and consistency properties for panel operators. The full NLL tables report count-NLL differences across revelation levels, though the supplied values do not identify their table columns.

A Proofs

  • With ignorable CCAR revelation, the unobserved counts follow a Dirichlet-multinomial distribution with parameters β + the observed partial counts, independent of which subset was revealed.This follows from Dirichlet conjugacy and exchangeability.Source: A Proofs · p. 19
  • A fixed-weight mixture of consistent operator families sharing a completion posterior remains consistent, while its KL consistency gap is bounded by the weighted endpoint gaps.The bound does not cover level-specific mixture weights or differing completion kernels.Source: A Proofs · pp. 19–20
  • Under nested revelation, the finer-information forecast has no greater expected strictly proper score, and the logarithmic score is strictly proper for full-support count distributions.Summing the population decrements gives V(0) − V(n) = I(Y; X_n | X_0); the unconditional form requires I(Y; X_0) = 0.Source: A Proofs · pp. 19–20
  • Per-level lookup is consistent only when each level table equals the completion-marginalized full-panel table; its measured gap is 0.02–0.05 nats on IEMOCAP at k = 1/2.CCPR-J's reported gap is approximately 10^-17, compared with 0.003–0.008 for CCPR-D.Source: A Proofs · p. 20

C Operator Ablation Details

These appendices describe the consistency-ablation and training-data-scan comparisons, alongside table scopes for rater-count transfer and allocation controls. The supplied passages specify comparison layouts and evaluation procedures but do not provide most table outcomes.

C Operator Ablation Details

  • The consistency-ablation table reports ccprJ-eq minus the per-level inconsistent marginalizer, with positive values indicating the shared table is worse.The accompanying text identifies the table as the full consistency-ablation values.Source: Table 8 · p. 21, C Operator Ablation Details · p. 21

D Data-Amount Scan Details

  • The data-amount scan subsamples training rows by fraction, repeats dev selection and posterior-weighted fits, and evaluates on the same held-out rows as the full-data run.For n = 3 corpora it compares ccprJ-eq with the per-level inconsistent marginalizer; EmotionLines uses the shared CCPR-N summary table.Source: D Data-Amount Scan Details · p. 21, Table 9 · p. 21

E Rater-Count Transfer and Budget Tables

  • The transfer and budget tables cover mixed-3/5 deployment, EmotionLines CCPR-N versus in-corpus lookup, concentrated versus uniform allocation, and target-free CCPR-P versus uniform.The CCPR-P comparison uses per-row mean count NLL; retrospective feature-class and realized-curve oracle references are not deployable policies.Source: Table 10 · p. 22, Table 11 · p. 22, Table 12 · p. 22

F Standard Allocation Heuristics

  • Mean-label uncertainty controls compare policy-minus-uniform mean count NLL, with negative values better; predictive entropy is exactly uniform at budget B = 1.The listed scores use predicted mean single-rater label distributions, not full panel composition distributions.Source: F Standard Allocation Heuristics · p. 23, Table 14 · p. 23

G CCPR-S Per-Level Table

CCPR-S adapts the shared-versus-per-level trade-off with a scalar per revelation level, while CCPR-L reports a decomposition of its gains. In mixed-rater-count deployment, the shared table trades a small overall loss for simpler deployment and wins the sparsest cells.

G CCPR-S Per-Level Table

  • The per-level CCPR-S table reports NLL and deltas in nats, with positive deltas indicating CCPR-S is worse; its shared comparator is ccprJ-eq for n = 3 and ccprN for n = 5.Bold entries indicate 95% confidence intervals excluding zero.Source: Table 15 · p. 23

H CCPR-L Decomposition with Confidence Intervals

I CCPR-S Details

  • CCPR-S stays within 0.002 nats of the better endpoint everywhere and matches the per-level marginalizer overall, while significantly beating the shared table on IEMOCAP, M3ED, and EmotionLines.Its differences versus the shared table are −0.029/−0.015/−0.012, respectively; it beats both endpoints at the sparsest cells, including M3ED k = 2/3 and n = 5, k = 4 (−0.007).Source: Table 15 · p. 23, I CCPR-S Details · p. 24

J Mixed Rater-Count Head-to-Head

  • Against the per-(n, k) inconsistent marginalizer, the shared table costs +0.0085 nats overall (CI [0.005, 0.012]) but wins the sparsest cells.It wins at n = 5, k = 4 (−0.002) and n = 3, k = 2 (−0.003); the comparison's positive values mean the shared table is worse.Source: J Mixed Rater-Count Head-to-Head · p. 24

K Experimental Details and Audit

The appendix gives the CCPR-J posterior-weighted moment-estimation algorithm and implementation-audit details. The reported diagnostic shows fitted-risk nonmonotonicity at high revelation levels, while a concentration probe did not improve test NLL.

K Experimental Details and Audit

  • The implementation fixes random seeds and uses CPU computations; archived audit scripts are implementation checks, not independent verification of datasets, estimators, or inferential assumptions.Probability normalization and exact completion marginalization can be checked separately from predictive performance.Source: K Experimental Details and Audit · p. 25
  • At n = 5, both models violate V(k + 1) ≤ V(k) at high k; a per-level concentration probe never improves test NLL and does not establish a unique cause.In 21 of 22 cells the shared κ is selected, while the exception is worse.Source: K Experimental Details and Audit · p. 25
  • CCPR-J initializes its response table from smoothed full-panel training rows and fits empirical completion tables from partial counts and relation.The algorithm then accumulates posterior-weighted contributions over training rows and completions, smoothing each response row when its denominator is positive.Source: K Experimental Details and Audit · p. 25
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