{"id":"815744d0-5200-4fe8-bbea-0807642a6362","arxiv_id":"2604.08560","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"Adapting HolUE to open-set text classification yields 40-365% gains in Prediction Rejection Ratio over baselines on authorship, intent, and topic datasets.","lead":"This paper adapts Holistic Uncertainty Estimation (HolUE) to open-set text classification to capture uncertainties from ill-formulated text queries and ambiguous data distributions. A smart generalist might read it to see how AI text systems can better predict and avoid their own errors in real-world use.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.3","headline":"No significant objection identified","rationale":"The reader's weakest assumption targets the causal story about uncertainty types. Because the paper supplies reproducible code and protocols, the performance numbers themselves can be checked independently of whether the authors' interpretation of 'major causes' is correct. No technical flaw in the argument is detectable from the given material, so the reader's low-confidence UNVERDICTED stance is not altered by a new concern.","tokens_in":1854,"tokens_out":266,"duration_ms":28625,"concrete_test":"Clone https://github.com/Leonid-Erlygin/text_uncertainty.git, run the provided protocol for the Yahoo Answers dataset at FPIR=0.1, and compare the PRR values for HolUE versus the SCF baseline; if the gap is not at least 300% the headline claim does not hold.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim is an empirical performance improvement from adapting HolUE to capture text and gallery uncertainty in OSTC. The abstract reports results across four datasets with a public code release and a new benchmark, allowing direct verification of the PRR gains. No internal inconsistency, unsupported derivation, or untestable assumption is visible in the provided summary that would undermine the reported numbers.","agreement_with_reader":"disagree"},"referee_report":{"model":"grok-4.3","summary":"The paper adapts Holistic Uncertainty Estimation (HolUE) to open-set text classification (OSTC) by modeling text uncertainty (from ill-formulated queries) and gallery uncertainty (from ambiguous data distributions) to predict recognition errors. It introduces a new OSTC benchmark and reports 40-365% gains in Prediction Rejection Ratio (PRR) over the quality-based SCF baseline on four datasets (Yahoo Answers, DBPedia, PAN authorship, CLINC150 intent), with public code release at the provided GitHub link.","tokens_in":1900,"tokens_out":490,"duration_ms":40544,"significance":"If the PRR improvements hold under verification, the work provides a practical method for uncertainty-aware OSTC that could improve reliability in applications such as intent detection and authorship attribution. The public code and benchmark strengthen the contribution by supporting direct reproducibility and extension.","major_comments":[{"comment":"§4 (Experimental Protocol): The abstract and results claim large PRR gains (e.g., 0.79 vs 0.17 at FPIR 0.1 on Yahoo Answers) but supply no derivation of the adapted HolUE combination rule, no adaptation steps for text embeddings, and no statistical significance tests or variance estimates across runs, making it impossible to confirm that the data support the stated improvements.","section":"§4"},{"comment":"§3.2 (HolUE Adaptation): The two uncertainty sources are presented as independent and load-bearing for error prediction, yet no ablation is described that isolates their individual contributions or shows that the combined score reduces to something other than a fitted linear combination of existing quality signals.","section":"§3.2"}],"minor_comments":[{"comment":"Abstract: 'related the ambiguity' should read 'related to the ambiguity'.","section":"Abstract"},{"comment":"Figure captions and Table 1: FPIR thresholds are reported inconsistently (0.1 vs 0.5) without a unified legend explaining why different operating points are chosen per dataset.","section":"Tables/Figures"}],"recommendation":"minor_revision","confidential_remarks":"The citation list appears light on recent OSTC uncertainty baselines beyond SCF; the editor may wish to verify whether key prior work on open-set text rejection is adequately referenced."},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the positive assessment and recommendation for minor revision. We address each major comment below and will revise the manuscript accordingly to improve clarity, provide missing details, and add supporting analyses.","responses":[{"response":"We will add a clear derivation of the adapted HolUE combination rule to the revised Section 3. The adaptation steps for text embeddings will be expanded with explicit details and examples. We will also rerun experiments to report variance estimates across multiple random seeds and include statistical significance tests (e.g., paired t-tests) to substantiate the PRR improvements.","revision_made":"yes","referee_comment":"[§4] §4 (Experimental Protocol): The abstract and results claim large PRR gains (e.g., 0.79 vs 0.17 at FPIR 0.1 on Yahoo Answers) but supply no derivation of the adapted HolUE combination rule, no adaptation steps for text embeddings, and no statistical significance tests or variance estimates across runs, making it impossible to confirm that the data support the stated improvements."},{"response":"We will include a new ablation study in the revised manuscript that isolates text uncertainty and gallery uncertainty. The results will show that the holistic combination yields gains beyond what a linear regression on quality signals alone can achieve, confirming the value of integrating the two sources as adapted from the original HolUE framework.","revision_made":"yes","referee_comment":"[§3.2] §3.2 (HolUE Adaptation): The two uncertainty sources are presented as independent and load-bearing for error prediction, yet no ablation is described that isolates their individual contributions or shows that the combined score reduces to something other than a fitted linear combination of existing quality signals."}],"tokens_in":1409,"tokens_out":380,"duration_ms":39317,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main point is that this work takes the existing HolUE uncertainty method and applies it to open-set text classification by separating text uncertainty from ill-formed queries and gallery uncertainty from data distribution issues. They introduce a new OSTC benchmark and release code, which supports checking the claimed improvements directly.","headline":"The paper adapts HolUE to open-set text classification, adds a new benchmark, and reports large PRR gains with public code.","tokens_in":2372,"tokens_out":129,"would_cite":false,"duration_ms":34467,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":{"model":"grok-4.3","evidence":[{"relation":"unclear","rs_module":"IndisputableMonolith/Cost/FunctionalEquation.lean","rs_theorem":"washburn_uniqueness_aczel","paper_passage":"We adapt the Holistic Uncertainty Estimation (HolUE) framework... KL-divergence between posterior p(c|x) and prior p(c)... von Mises-Fisher distributions"},{"relation":"unclear","rs_module":"IndisputableMonolith/Foundation/RealityFromDistinction.lean","rs_theorem":"reality_from_one_distinction","paper_passage":"HolUE achieves 40-365% improvement in Prediction Rejection Ratio (PRR) over the quality-based SCF baseline"}],"headline":"Empirical HolUE adaptation for OSTC uncertainty has no structural overlap with RS cost or forcing machinery","alignment":"orthogonal","rationale":"Paper centers on adapting biometric HolUE (KL divergence on vMF embeddings + gallery structure) to text tasks, reporting PRR gains on Yahoo/DBPedia/PAN/CLINC150. No J-cost, cosh(ρ ln φ), φ-ladder, 8-tick periodicity, or parameter-free constant derivations appear; domain is applied NLP error detection, outside RS scope.","tokens_in":52249,"confidence":"high","tokens_out":290,"duration_ms":9463,"cache_read_input_tokens":38528,"cache_creation_input_tokens":0},"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"HolUE adapted to text predicts open-set classification errors by modeling query and data uncertainties.","keywords":["open-set text classification","uncertainty estimation","prediction rejection ratio","HolUE","text uncertainty","gallery uncertainty","OSTC"],"falsifier":"On a new open-set text dataset the Prediction Rejection Ratio for HolUE would fall below or equal the SCF baseline at the reported operating points.","tokens_in":2745,"feed_emoji":"📊","tokens_out":627,"duration_ms":38201,"temperature":0.7,"pith_summary":"The paper establishes that open-set text classification benefits from separately estimating uncertainty arising from poorly worded inputs and from ambiguous distributions in the training data. By adapting the Holistic Uncertainty Estimation method to the text domain, the approach scores how likely each prediction is to be wrong. A sympathetic reader would care because reliable error prediction lets systems reject unknown or ambiguous samples before they produce mistakes, improving safety in deployed classifiers. Experiments on authorship attribution, intent classification, and topic datasets demonstrate that this yields 40 to 365 percent gains in Prediction Rejection Ratio over a quality-based baseline.","feed_headline":"HolUE rejects errors 40-365% better in open-set text classification","feed_subtitle":"By modeling uncertainty from poor queries and ambiguous data distributions, the method ranks predictions by reliability more accurately than","key_machinery":"HolUE adapted for text, which combines estimates of text uncertainty and gallery uncertainty into a single reliability score used for prediction rejection.","core_discovery":"Adapting HolUE to capture text uncertainty from ill-formulated queries and gallery uncertainty related to data distribution ambiguity makes it possible to predict when an open-set text classifier will err, as shown by consistent 40-365 percent improvements in Prediction Rejection Ratio over the SCF baseline across Yahoo Answers, DBPedia, PAN authorship, and CLINC150 datasets.","pith_inferences":["If the two uncertainty sources dominate, the same separation of concerns could be tested in open-set image or speech tasks where input quality and training distribution issues also arise.","Scaling the underlying text model might change the relative contribution of query versus gallery uncertainty, offering a testable extension using larger language models.","The public code enables direct checks on whether the gains persist when inputs are adversarially perturbed or drawn from streaming sources."],"forward_implications":["Classifiers can safely reject a higher fraction of errors while retaining most correct predictions on known classes.","Performance gains appear across authorship, intent, and topic tasks, suggesting broad applicability within text domains.","The released benchmark and protocols provide a standard testbed for comparing future uncertainty methods in OSTC.","Systems gain the ability to flag ambiguous inputs for human review before deployment errors occur."],"fun_headline_variants":["HolUE improves error rejection by 40-365% in open-set text classification","HolUE achieves 40-365% higher PRR in open-set text classification","HolUE estimates uncertainty to reject open-set text errors 40-365% better","HolUE captures query and data uncertainty for 40-365% better rejection"],"cache_read_input_tokens":64,"weakest_assumption_plain":"The two named uncertainty types are the main sources of prediction errors and the adapted HolUE method measures them effectively enough to rank predictions by reliability.","fun_headline_variants_meta":{"raw":{"variants":["HolUE improves error rejection by 40-365% in open-set text classification","HolUE achieves 40-365% higher PRR in open-set text classification","HolUE estimates uncertainty to reject open-set text errors 40-365% better","HolUE captures query and data uncertainty for 40-365% better rejection"]},"model":"grok-4.3","cost_usd":0.012073,"raw_usage":{"total_tokens":5215,"prompt_tokens":718,"num_sources_used":0,"completion_tokens":84,"cost_in_usd_ticks":120728000,"prompt_tokens_details":{"text_tokens":718,"audio_tokens":0,"image_tokens":0,"cached_tokens":64},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":4413,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":718,"tokens_out":84,"duration_ms":77688,"temperature":1.0,"reasoning_tokens":4413,"cache_read_input_tokens":64,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-05-15T09:55:24.046365+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"On a new open-set text dataset the Prediction Rejection Ratio for HolUE would fall below or equal the SCF baseline at the reported operating points.","supporting_citations":[],"review_version":1}