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45 papers in the last four weeks, up 137% on the four weeks before. 0.4% of all new papers.

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Latest papers 254

Aug 14, 2026eess.AS

A Parameter-Free Few-Shot Evaluation for Elephant Vocalisation Classification

We present a parameter-free episodic evaluation of nearest-centroid classification of elephant vocalisations on fixed pretrained embeddings, for the Elephant Voices (EV) and Linguistic Data Consortium (LDC) datasets. We ask not which embedding yields the best classifier trained on all labelled data, but how the simplest classifier performs as the number of exemplars per class varies. There are no learnable parameters, because each class is modelled as the mean of its support embeddings and each query is assigned to the nearest centroid under squared Euclidean distance. Evaluation covers the fixed Perch (ver. 1), Perch (ver. 2) and HuBERT (base, layer 2) embeddings, alongside mel frequency cepstral coefficient (MFCC) features,

N

-way

k

-shot, under the same stratified

K

-fold cross-validation protocol as the trained classifiers. None of these embedding models was trained to distinguish elephant call types. On the smaller EV dataset the centroid classifier is markedly data-efficient. Using Perch (ver. 1) or Perch (ver. 2) embeddings it overtakes in mean average precision (mAP) the fully-trained logistic regression (LR) baseline from one or two exemplars and the recurrent baseline from two. Over the reduced set of call types on which the strongly-supervised end-to-end baseline was trained, the centroid classifier using Perch (ver. 2) embeddings overtakes that baseline in mAP as well, from two exemplars. On the larger LDC dataset the recurrent baselines retain their advantage for all considered values of

k

. Only LR is overtaken, and only in mAP. Nearest-centroid classification is therefore preferable precisely when exemplars are few and the fixed embedding already separates the call types.

Christiaan M. Geldenhuys, Thomas R. Niesler

Department of Electrical and Electronic Engineering University of Stellenbosch

Few-Shot Audio Classification Prototype-Based Classification Classification Audio Classification

Aug 13, 2026stat.ML

Bagging Robustly Learns VC Classes with Linear Sample Complexity

We revisit the problem of learning predictors robust to adversarial examples at test-time. We prove that VC classes are adversarially robustly learnable with sample complexity linear in the VC dimension

d

, providing an exponential improvement over the previous upper bound of Montasser, Hanneke, and Srebro (2019). Remarkably, this result is achieved with a simple improper algorithm that combines the classic heuristic bagging (bootstrap aggregation) of Breiman (1996) with robust empirical risk minimization (RERM). Our algorithm computes RERMs on

O(d^\star)

independent bootstrap samples and outputs their majority vote, where

d^\star

denotes the dual VC dimension. We complement this result with a lower bound showing that this is unavoidable: in general, any learner in this oracle model requires

Ω(d^\star)

calls to an RERM oracle, even when given arbitrarily many training examples.

Omar Montasser

Yale University

Adversarial Robustness Classification Statistical Learning Theory

Aug 12, 2026cs.CL

When Explanations Betray Backdoors: Black-Box Auditing for Language Model Classifiers

Language model classifiers with explanations are used for moderation, routing, topic triage, and low-resource annotation. We study black-box auditing when the defender has only clean calibration data without trigger information but can ask the classifier for a label plus a short rationale or quoted evidence. We introduce Groundedness Drift, a lightweight score measuring whether the answer summary remains grounded in the input. Across two 7B backbones, five datasets, and four common non-adaptive OpenBackdoor-style attack families, Groundedness Drift achieves higher AUROC and lower residual target ASR than every compared detector in all cases at a nominal 5% clean-FPR budget. We then evaluate Unsupported Groundedness, a multi-probe escalation for explanation-camouflage stress cases. Unsupported Groundedness improves signals but does not close the adaptive gap.

Yang Liu, Ran Zou

Department of Statistics and Operations Research, University of North Carolina at Chapel Hill · Department of Statistics, University of California, Irvine

LLM Backdoor Attacks Backdoor Detection Backdoor Defense in LLMs Classification Explainability Evaluation Black-Box Model Auditing+1

Aug 12, 2026cs.LG

Robust and Efficient Noisy-Label Time-Series Classification via Dynamic Time Warping Based Granular Ball Computing

Dynamic Time Warping (DTW)-based Nearest-Neighbor (NN) classifiers are effective for time-series classification but are vulnerable to mislabeled training samples and require numerous DTW computations during inference. We propose DTW-based Granular Ball Computing (DTW-GBC), which organizes temporally similar training samples into granular balls and performs classification at the granule level. We further develop two granular-ball construction strategies for DTW-GBC. Experiments on four benchmark datasets with symmetric label noise show that the two DTW-GBC variants generally mitigate the performance degradation caused by label noise while requiring substantially fewer comparisons than DTW-based 1-NN during inference. These findings suggest that DTW-GBC provides a favorable balance between classification robustness and inference efficiency.

Ziqiang Li, Yun Liu, Gouhei Tanaka

Department of Computer Science, Graduate School of Engineering, Nagoya Institute of Technology, Nagoya, 466-8555, Japan · International Research Center for Neurointelligence, The University of Tokyo, Tokyo, 113-0033, Japan. · Faculty of Information and Human Sciences, Kyoto Institute of Technology, Kyoto, 606-8585, Japan

Dynamic Time Warping Time Series Classification Noisy-Label Learning Classification

Aug 11, 2026cs.CV

CLEAR: Class-wise Expert Aggregation with Structured Sampling for Long-Tailed Classification

Long-tailed classification poses a reliability challenge because models trained on imbalanced data are unevenly reliable across frequent and underrepresented classes. While existing methods address imbalance through re-balancing, adjustment, representation learning, or multi-expert modeling, they rarely estimate which expert should be trusted for each class. This paper proposes CLEAR (Class-wise reLiability-aware Expert Aggregation for long-tailed Recognition), a modular ensemble framework for long-tailed classification. CLEAR generates diverse experts through threshold-based structured sampling while preserving the full label space, then estimates a class-wise trust score for each expert using a smoothed class-wise precision formulation. During inference, expert predictions are combined through class-wise generalized product-of-experts aggregation, allowing different experts to be emphasized for different classes. Experiments on CIFAR-100-LT, ImageNet-LT, and Places-LT across multiple backbones show that CLEAR achieves competitive overall accuracy and particularly strong few-shot performance. These results support class-wise expert reliability as a useful design principle for long-tailed ensemble learning.

Gawon Lim

School of Information Sciences University of Illinois Urbana-Champaign

Long-Tailed Classification Ensemble Learning Classification Mixture-of-Experts Models

Aug 11, 2026cs.LG

Optimistic Rates for Multiclass PAC Learning

Worst-case multiclass bounds do not become smaller when the best classifier is already nearly correct: what is missing is an optimistic rate, a guarantee whose fluctuation scales with the oracle risk itself. For a class of Natarajan dimension

d_N

and Daniely-Shalev-Shwartz dimension

d_{DS}

, the optimal excess risk is known at the two endpoints (

d_{DS}/n

realizable,

\sqrt{d_N/n}+d_{DS}/n

agnostic [HMZ24, CEH+26, Pab26]) and open in between. We close the gap: at every fixed oracle risk

L^\star

, the optimal excess risk is

\widetildeΘ(\sqrt{L^\star d_N/n}+d_{DS}/n)

, uniformly in the alphabet size, attained by a learner that knows neither

L^\star

nor the confidence level. The upper bound composes the cover-menu-compression architecture of [CEH+26], at the realizable rate of [Pab26], with a new comparator-facing relative compression theorem: a size-

k

compression rule that empirically dominates a comparator

h

has population risk at most

L(h)+O(\sqrt{L(h)Γ}+Γ)

with

Γ=(k\log n+\log(1/δ))/n

, without stability; this transfers the comparison principle of the sharp binary theory [MQZ26] while discarding its Boolean-cube geometry, which does not lift to multiclass labels. The lower bound forces both terms using one class and one distribution at every fixed

L^\star

, by a pair-Assouad scheme calibrated to

L^\star

and a fiber argument on the pseudo-cubes underlying the Natarajan-versus-DS separation of [BCD+22]. Both theorems extend to list learning: against the best

r

-tuple of hypotheses, the same architecture and the same two engines yield an optimistic rate and a lower bound of the same shape, forcing the fluctuation term that [Pab26] expected to be necessary against list comparators, and removing the factor

r

from the known realizable list lower bound.

Xiaoyu Li, Andi Han, Jiaojiao Jiang +1

University of New South Wales · University of Sydney

Multiclass Classification Classification PAC Learning Generalization Bounds

Aug 11, 2026cs.CL

MD-ProTector: Positioning Multiple Data-Driven Prototypes for LLM-Generated Text Detection

As LLM-generated content becomes more sophisticated, detection systems for distinguishing those texts from human-written text must operate at scale while handling diverse writing styles, domains, languages, and generator models. Input-only encoder detectors are suitable for practical deployment setting, but standard binary classification supplies only the class label and does not explicitly organize the substantial variation within either class. We propose MD-ProTector, which represents each class with multiple trainable reference vectors in the encoder embedding space, referred to as prototypes. These prototypes provide separate decision boundaries for different groups of texts within the same class. However, adding multiple prototypes alone does not determine which variation each prototype should represent. MD-ProTector addresses this problem with Prototype Positioning loss, which separates class-level structure from the within-class variation that differentiates individual prototypes. Evaluated across five settings from three large-scale benchmarks covering domain, generator, language, and adversarial variation, MD-ProTector achieves the highest AvgRec on MAGE CDCM and RAID and the highest AUROC and lowest FPR95 on RAID among the compared encoder-based methods.

Jinmo Han, Jimin Hong, Chanyeong Moon +3

Department of Electrical and Computer Engineering and INMC Seoul National University, Seoul, Republic of Korea

AI-Generated Text Detection Prototype Learning Classification Language Model Robustness

Aug 10, 2026cs.LG

ReliableNet: A Chance-Constrained Approach to Trustworthy Classification in Deep Learning

A prediction that is both confident and wrong is a critical reliability failure because it can bypass abstention and human review precisely when the model is mistaken. Empirical risk minimization (ERM) controls average loss but not this failure directly, while calibration, uncertainty estimation, conformal risk control, and selective prediction methods target related reliability properties rather than bounding the joint failure event during training. We propose ReliableNet, which constrains the Joint Confident-Wrong (JCW) probability, the probability that a prediction is simultaneously confident and incorrect, below a user-specified risk budget

α\in(0,1)

. We formulate this as a chance-constrained ERM problem, use a conservative smooth inner approximation whose population feasibility implies the original JCW constraint. Across four tabular and two image datasets, ReliableNet is the only method certified within the JCW budget for every dataset and seed in distribution, when compared against baselines spanning ERM, post-hoc calibration, conformal risk control, and selective prediction. Under demographic, ambiguity, spurious-correlation, novel-class, and covariate shifts, it achieves the lowest empirical JCW among the compared methods while remaining very competitive in accuracy, coverage, calibration, and selective prediction. Risk-coverage results further indicate that ReliableNet achieves better selective ranking than the benchmark methods on most datasets. Overall, ReliableNet provides a principled approach to trustworthy classification.

Ange-Clément Akazan, Ineza Remy Mugenga, Abebe Geletu +2

School of Agriculture and Science, University of KwaZulu-Natal, Pietermaritzburg, KwaZulu-Natal, South Africa · 2AIMS Research and Innovation Centre, African Institute for Mathematical Sciences, Kigali, Rwanda · 3African Institute for Mathematical Sciences, Kigali, Rwanda

Chance-Constrained Optimization Classification Empirical Risk Minimization

Aug 10, 2026cs.CL

UNMASK: Discovering and Causally Verifying Spurious Shortcuts in Text Classifiers

Neural language models trained on large crowdsourced corpora frequently exploit spurious surface patterns tied to target labels without true linguistic or causal relevance, boosting benchmark performance while failing on adversarial or out-of-distribution inputs. Existing approaches either require manual specification of the feature vocabulary or automate discovery only partially, leaving the gap between dataset-level correlation and model-level exploitation unaddressed. We present U N M ASK, a fully automated pipeline that discovers, causally verifies, and mitigates spurious correlations in text classifiers without additional human annotation. Given unlabeled training examples, U N M ASK generates candidate surface patterns as executable boolean expressions, filters them through a statistical validation protocol with independent replication, and establishes causal model dependence via verified counterfactual interventions. Causally confirmed features then serve as annotation-free group definitions for Deep Feature Reweighting, eliminating the group labels that standard DFR requires. Applied to BERT and RoBERTa trained on MNLI, our pipeline independently rediscovers established lexical-overlap and negation biases, verifying 9 of 10 features on BERT and 6 on RoBERTa, and improving HANS accuracy by up to 12.58 pp. On CivilComments-WILDS, programmatic groups match the 70.1% worst- group accuracy of hand-labeled DFR (Kirichenko et al., 2023) without demographic annotation. We further demonstrate that the discovery and validation stages generalize to reward model preference data, surfacing interpretable spurious correlations in RewardBench2.

Chidaksh Ravuru, Shashank Srivastava

Department of Computer Science University of North Carolina, Chapel Hill

Classification Spurious Correlation Robustness Language Model Robustness

Aug 9, 2026cs.CL

Are LLMs Positionally Consistent Ordinal Classifiers? A Systematic Evaluation

Large language models are increasingly used for ordinal classification, yet semantically equivalent changes to prompt organization can alter their predictions. We conduct systematic experiments to characterize positional bias from label order, demonstration order, and demonstration placement. First, we apply the three probes to ten frontier LLMs on a common ordinal-classification task; every model is sensitive to all three positional sources, showing that the problem is pervasive. Second, we vary eight prompt-, task-, and model-level factors across five datasets; accuracy and stability are often misaligned, and only lower scale cardinality consistently improves both. Third, we compare pointwise, pairwise, and listwise inference, alternative aggregation and debiasing methods, and joint configurations; the tested corrections do not provide a reliable remedy, while a comparison-based listwise formulation offers the best balance but transfers unevenly across models and bias sources. These findings show that positional robustness depends on the full system configuration rather than the model alone. Ordinal-classification systems should therefore be selected jointly for predictive performance and stability.

Yu Wang, Zhe Zhou, Menglin Liu +1

Cornell University · University of Washington · The Chinese University of Hong Kong, Shenzhen +1

Language Model Bias Evaluation Classification Language Model Robustness

Aug 6, 2026stat.ML

Minimax Optimal Early-Stopped Gradient Descent for Gaussian Mixture Classification

In overparameterised classification, training data can be linearly separable even when the underlying distribution is not. In this setting, gradient descent (GD) on the logistic loss diverges in norm while converging in direction to a max-margin interpolating classifier, whose implicit bias can be statistically suboptimal. In this work, we show that early stopping can overcome this suboptimality: in a Gaussian mixture model with label-flipping noise, GD stopped at an appropriate oracle time achieves minimax-optimal excess zero-one risk for covariance spectra with fast and continuous decay, including polynomial and exponential spectral decays. Our analysis combines a sharp upper bound for the early-stopped iterate with a matching statistical lower bound over arbitrary classifiers, yielding optimal rates that are validated by experiments. A central technical contribution is a new calibration result that converts excess logistic risk into excess zero-one risk; it handles the model misspecification induced by the label-flipping noise, and removes the square-root rate in standard bounds. We also establish a lower bound for linear interpolators, showing that interpolation can require exponentially more samples than early stopping to achieve the same excess risk.

Alex Buna, Shirley Xiaoqi Liu, Patrick Rebeschini

Department of Statistics, University of Oxford

Early Stopping Gradient Descent Gaussian Mixture Models Minimax Estimation Classification

Aug 6, 2026cs.LG

Evidential Rule Learning for Interpretable Classification with Abstention

Interpretable classification often requires more than accurate predictions for real-life deployment: models should be transparent about the evidence behind their decisions and abstain when they cannot decide reliably. We introduce Fast Evidential Rule Learning (FERL), a method that learns interpretable, accurate fuzzy rule models whose outputs are evidential. Unlike post-hoc calibration, FERL's belief, plausibility, and abstention capabilities arise directly from the fuzzy memberships in a single deterministic pass, with no auxiliary head, held-out set, or repeated inference. Our theoretical analysis further shows that FERL is Lipschitz stable, which means that its evidential outputs vary smoothly with the input. Against state-of-the-art rule learners, FERL is statistically significantly more accurate across a 30 tabular-dataset benchmark (

+2.6\%

average accuracy over the second best). Its native set predictions attain the best utility-discounted accuracy among credal classifiers (

u_{65}/u_{80}=0.80/0.83

vs.\

0.79/0.80

for the naive credal classifier), at higher set coverage (

0.92

vs.\

\le0.82

). FERL also matches dedicated out-of-distribution detectors on tabular near-OOD detection (

77.7

vs.\

77.4

AUROC for the strongest baseline). Under detector-class-disjoint concept-bottleneck evaluation, its it is within

2.3

AUROC points of the strongest dedicated detector on both CUB and AwA2, while attaining the best AwA2 AUPR-Out (

68.3

) and novel-class rejection (

57.2

), while being able to name which attributes are anomalous.

Javier Fumanal-Idocin, Javier Andreu-Perez

School of Computer Science and Electronic Engineering University of Essex, Colchester CO4 3SQ, UK

Selective Prediction Interpretable ML Fuzzy Logic Classification

Aug 5, 2026cs.LG

Perturbation Sensitivity at Convergence: A Simple Signal for Identifying Spuriously Correlated Samples

Models trained by empirical risk minimization on data containing spurious correlations achieve high average accuracy while failing on subpopulations where the correlation does not hold. Existing methods for identifying the affected samples without group annotations rely on signals from early training, which requires locating the epoch at which to intervene, a hyperparameter typically selected using group-labeled validation data. We show that a usable signal is available after convergence, when loss no longer distinguishes the two populations. Samples consistent with the spurious correlation are classified by a shared rule, while the remaining samples are fit through configurations specific to individual inputs and are correspondingly more fragile. Applying a fixed perturbation to a converged model's inputs flips the predictions of the latter far more often than the former. The resulting procedure requires two forward passes per training sample, no group annotations at any stage, and no early-stopping epoch. Using the detected samples to rebalance training raises worst-group accuracy on Waterbirds from 57.3% to 80.8%, against 85.8% with ground-truth group labels.

Nilesh Kumar

Training Data Selection Classification Spurious Correlation Robustness

Aug 4, 2026cs.LG

SpecDrop: Parameter-Free Category-Conditioned Routing for Modular Specialization

Mixture-of-experts (MoE) networks pursue specialization through learned routers, gates, and load-balancing losses, yet at matched total-parameter budgets learned routers can underperform equal-weight No-Routing baselines. Is the bottleneck the routing algorithm, or the alignment between training-signal granularity and the target categories? We probe the question with SpecDrop, a fixed parameter-free routing scheme: each of

K

branches receives weight

p_a

for its assigned category and a small leakage

p_i > 0

otherwise, merged through a category-independent fixed denominator, with no learned routing parameters and no auxiliary losses; the category label is required at inference. On vision tasks where each image has one superclass label (CIFAR-100 on ResNet-110; ImageNet-1K on ViT-S/16), SpecDrop reaches 79.23% on CIFAR-100 and 79.89% on ImageNet-1K, exceeding parameter-matched baselines that do not use the label (+4.75 over dense on CIFAR-100; +6.53 over the No-Routing+SE control on ImageNet-1K). These gains quantify what category supervision buys when deployed through routing -- not an advantage over label-aware deployments of the baselines: given the same label, masking a dense model's outputs is stronger for accuracy alone (85.2 / 83.7). SpecDrop's contribution is converting the label into trained-in modular structure: 58%/100% branch-category alignment, and masking gains of 0.00 (CIFAR) / +1.06 (ImageNet) -- the output-space restriction is largely internalized during training. On fuzzy partitions, where training units span multiple categories (SlimPajama-6B language modeling with a 30M Transformer; SuperNI instruction tuning over Llama-3.2-1B with LoRA), the routing mechanism reduces to the matched No-Routing controls within seed noise, the null our thesis predicts. Granularity alignment, not algorithm choice, localizes when routing helps. Code: https://github.com/Beryex/SpecDrop

Boyao Wang, Zhihan Lei

Machine Learning Department Carnegie Mellon University

Parameter-Free Mixture-of-Experts Routing Adaptive Model Routing Classification Mixture-of-Experts Models Modular Neural Networks

Aug 3, 2026cs.CL

Training-Free versus Training-Based Intent Classification in LLMs: Accuracy, Robustness, and Failure Modes

Intent classification in Large Language Models (LLMs) involves categorizing user prompts into predefined classes. For instance, given a user prompt, the system must determine whether it primarily concerns mathematics, coding, or general text processing. Such classification enables routing prompts to specialized models optimized for specific domains, improving both accuracy and computational efficiency. In this work, we conduct a systematic study comparing training-free vs training-based approaches for intent classification. For this purpose, we consider two lightweight, training-free methods based on statistics of internal representations and compare them against MLP classifiers and linear probes. Our comprehensive empirical evaluation reveals that 1) Both training-free and training-based methods saturate easy benchmarks (mathematics vs. coding vs. natural language), 2) Training-based classifiers have an advantage on harder classification tasks (e.g. Java vs Python), and 3) Training-free methods are generally more robust to mixed-intent and adversarial prompts.

Nan Chen, Zhouhao Yang, Soufiane Hayou

Department of Applied Mathematics and Statistics Johns Hopkins University

Zero-Shot Learning LLM Evaluation Linear Probing Intent Classification Classification Language Model Robustness

Aug 3, 2026cs.SD

Can Foundation Models Hear What Made That Sound? A Tiered Benchmark of Audio-Language Models and Traditional Classifiers for Closed-Set Sound Source Identification

We benchmark eleven audio classification methods: five task-aware closed-set LLMs (four Gemini models plus open-weight Kimi-Audio-7B-Instruct), four fixed-vocabulary taggers (YAMNet, PANNs, Whisper-AT, and SSLAM), a zero-shot audio-text model (CLAP), and an audio-grounded LLM (BAT). We evaluate them on a closed-set sound-source identification task over 2,242 clips spanning 23 fine-grained classes and 11 categories. Since these methods differ fundamentally in how they receive the task and how outputs are scored, we group them into four evaluation tiers rather than one leaderboard, reporting macro Precision, Recall, F1, and false-negative rate per tier. The best model, Gemini-3.1-Pro-Preview, reaches 85.6 percent category-level F1 and 56.7 percent fine-grained F1. Kimi-Audio is competitive for its size, reaching 67.5 percent category-level F1 and 32.9 percent fine-grained F1, but fails to answer 1.6 percent of samples. SSLAM and CLAP match or exceed the best closed-set model at the category level without seeing the candidate list, but fall behind at the fine-grained level. Analyzing the Gemini models' chain-of-thought across 8,968 responses, we find that response length does not predict accuracy, an apparent "holistic judgment beats detailed analysis" effect is better explained as a difficulty confound, and wrong answers are stated confidently 92 to 100 percent of the time. We report full per-class confusion matrices and metrics for all eleven methods, identify the structural error modes behind most of the accuracy loss between granularities, and give practical guidance for choosing among these method families.

Sajjad Abdoli, Ghassan Al-Sumaidaee, Ahmad ElShiekh +1

Perle

Audio-Language Model Evaluation Audio-Language Models Classification Audio Classification

Aug 3, 2026cs.LG

BRiG-AFA: Bellman Risk-to-Go Learning for Non-Myopic Active Feature Acquisition

Active feature acquisition (AFA) asks which unobserved feature to measure next for each test instance under a budget. Greedy rules are easy to train but can overlook context features whose value is realized only through later acquisitions, while reinforcement-learning and generative approaches introduce difficult optimization or conditional-density estimation. We introduce \method, a deployable, supervised alternative that learns a separate candidate-conditioned risk-to-go function for every remaining budget. Starting from the one-step terminal classification risk, the functions are fitted backward with Bellman targets; inference greedily minimizes the learned terminal risk using only observed values, the mask, candidate identity, and remaining budget. A controlled non-myopic benchmark shows the expected mechanism: at budgets two and three, \method improves accuracy over its one-step ablation by

4.84\pm2.17

and

4.39\pm1.10

percentage points (mean

\pm

standard error over five seeds). On Fashion-MNIST with 20 candidate pixels, it improves accuracy at every nontrivial reported budget on average, including

10.20\pm0.74

points at four acquisitions; its mean paired gain across budgets

\{2,4,8,12,16\}

is

3.50\pm0.37

points. A three-seed MiniBooNE study is mixed at small budgets but positive at 8 and 16 acquisitions, identifying a current boundary rather than supporting a universal claim. These results establish a reproducible mechanism-level case for direct Bellman risk regression and delimit the experiments still needed for state-of-the-art comparison.

Jiaorong Feng, Qian Li, Ying Li

Curtin Business School, Curtin University, Perth, Western Australia, Australia · †Present affiliation: Independent Researcher. · School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, Western Australia, Australia

Cost-Aware Inference Active Learning Active Feature Acquisition Classification

Aug 3, 2026cs.AI

Cross-Fitted Residual Utility for Primary-Preserving Cognitive Decision Correction in Automatic Modulation Classification

Automatic modulation classification research has largely emphasized representation accuracy, but a cognitive receiver must also decide when heterogeneous evidence justifies overriding a trusted default prediction. We study this post-inference problem through cross-fitted residual utility and a primary-preserving cognitive decision policy. A structured KAN-Fourier classifier supplies the default probability, while neural and non-neural candidates provide observable evidence. Candidate-specific residual utility is learned from train-split out-of-fold predictions, and a disjoint validation split freezes action thresholds, approved transitions, conditional routes, and a unified risk mask before held-out evaluation. On RMLA, RMLB, and HISAR, the complete system improves overall accuracy from 63.632% to 66.332%, 65.161% to 66.168%, and 77.769% to 79.867%, respectively. Controlled comparisons show that the isolated utility target does not uniformly dominate alternative out-of-fold meta-learners; the consistent gain comes from the complete evidence-and-action policy. Paired bootstrap and Holm-corrected McNemar analyses support the controlled gains. A frozen-policy stress test under carrier-frequency offset, I/Q imbalance, and synthetic Rayleigh/Rician fading yields positive gains in all 11 conditions, with every paired 95% confidence interval above zero.

Linzhuo Han, Zongyong Cui, Houbiao Li

School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, China · School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China

Automatic Modulation Classification Classification

Aug 3, 2026stat.ME

Statistical comparisons of time-series feature sets on classification tasks

In recent years, numerous open-source software libraries have been developed for computing sets of features from univariate time series. The type and number of features vary across these feature sets, which have been constructed with varying disciplinary perspectives on quantifying structure in time-series data. To date, the relative strengths and weaknesses of these feature sets on time-series classification problems remains largely unexplored. Here we aimed to understand the relative performance of six open-source feature sets and three baseline feature sets (based on distributional and/or basic spectral structure) across 124 univariate time-series classification problems using a normalization-based approach to problem-level benchmarking that better indexes the relative strengths and weaknesses of different algorithms compared to prior rank-based approaches. Despite their dramatic differences in size, composition, and computation time, we found that feature sets performed relatively similarly overall (85.3% of pairwise comparisons resulted in ties), with the largest feature set, tsfresh, exhibiting the strongest overall performance (29.03% wins across all pairwise comparisons against other feature sets). We also highlighted specific problems on which the specific composition of a given feature set gave it a substantial performance advantage or disadvantage, and problems where simple baselines comprised of Fourier coefficients and quantiles were sufficient to achieve strong performance. Our results demonstrate the need to consider problem-level performance when benchmarking time-series feature sets, and highlight the importance of feature make-up in driving relative classification performance.

Trent Henderson, Ben D. Fulcher

Dynamics and Neural Systems Lab, The University of Sydney, Camperdown, 2006, NSW, Australia.

Time Series Classification Classification Automated Feature Engineering

Aug 2, 2026cs.LG

Factorized AdaBoost.MH Achieves the Same Convergence Rate as AdaBoost.MH

{AdaBoost.MH} reduces multi-class classification to a collection of binary subproblems and enjoys the classical boosting-type convergence guarantee under a weak learning condition. A more structured variant, Factorized {AdaBoost.MH}, uses base classifiers of the form

\mathbf{h}(x)=α\mathbf{v} \bm{\varphi}(x)

, where a single binary classifier

\bm{\varphi}

is shared across all classes and the label dependence is carried by a vote vector

\mathbf{v} \in\{\pm1\}^K

. This factorization is algorithmically attractive and achieves better performance in practice, but its convergence depends on whether one can always choose a vote vector with sufficiently large induced binary weight mass. Previous work resolved this question with a lower bound

\max\{1/n,1/\sqrt{2K}\}

, which still leaves a dimension-dependent slowdown relative to the original {AdaBoost.MH} analysis. In this paper, we sharpen this combinatorial step. For the minimax quantity

\mathfrak{W}_{n,K}

governing the factorized edge, we prove

\mathfrak{W}_{n,K} = C_{\min\{n+1,K\}}

, where

C_q=1

for

q=1

,

C_q=q/(3q-4)

for even

q\ge2

, and

C_q=(q+1)/(3q-1)

for odd

q\ge2

. Since

C_q\downarrow 1/3

, our bounds show that

\mathfrak{W}_{n,K}=Θ(1)

uniformly over

n

and

K

. Consequently, Factorized {AdaBoost.MH} achieves the same boosting-type convergence rate as {AdaBoost.MH} up to a universal constant factor, removing the previously suggested additional dependence on

n

or

K

in the number of boosting rounds.

Xin Zou, Jingyuan Xu

Ensemble Learning Multiclass Classification Classification Statistical Learning Theory

Aug 2, 2026cs.LG

Subtype Robustness Is Not Just Accuracy: Calibration Under Unseen Subtype Shift

Subtype robustness asks whether a model keeps the correct coarse prediction when test examples come from fine-grained subtypes absent from training but still inside a known coarse category. Prior work studies this almost entirely through accuracy. We ask whether the model also stays calibrated. We present the first systematic study of the question across ImageNet, BREEDS, iNaturalist and CIFAR-100 with five architectures. Calibration breaks down on unseen subtypes, where accuracy drops while confidence barely follows, leaving the model systematically overconfident exactly where it has become less accurate. At matched accuracy loss, generic image corruption causes a much larger drop in confidence, so the effect is not a general consequence of losing accuracy. The model reacts to visible degradation but not to in-taxonomy novelty. Recalibration tuned on seen subtypes narrows the gap but does not close it, and out-of-distribution scores flag the affected inputs only weakly. Subtype robustness should therefore be evaluated through calibration, not accuracy alone.

Hanyu Su, Carlota Julbe i Juanola, Yibo Hu

Illinois Institute of Technology

Distribution Shift Robustness Neural Network Robustness Model Calibration Classification

Aug 1, 2026cs.CL

Loanword or Switch? The Annotation Boundary, Not the Model, Drives Kazakh-Russian Code-Switching Identification

Off-the-shelf LID and letter heuristics over-label Kazakh-Russian social text as mixed: Russian loanwords inside Kazakh look like code-switching under a shared Cyrillic script. We release a document-level gold LID set whose guideline keeps integrated borrowings as Kazakh and reserves mixed for clause-level switches, plus a mixed-only sentiment pool used after LID in a filter-first cascade. On a shared LID test, FastText, Lingua, raw and windowed HeLI, character-trigram NB, and XLM-R range from weak to strong performance. The gap shows the bottleneck is the loanword-vs-switch annotation boundary, not model class alone.

Bogdan Savelyev

Independent Researcher Kazakhstan

Code-Switching Language Identification Text Classification Classification Low-Resource Language Processing

Jul 31, 2026cs.CL

FriendBench: Benchmarking Dyadic Familiarity Inference in Humans and Multimodal Large Language Models

Reading a social situation often depends on behavior, not words alone. We introduce FriendBench, a benchmark for inferring whether two people are already familiar or are meeting as strangers, from a 20-second clip of a dyadic ice-breaker conversation. Every pair answers the same type of prompt, so only the manner of interaction can reveal the answer. Across text, audio, and video, we compare 26 models from seven companies against matched human panels over 96 balanced dyads. The best model and the human crowd are statistically indistinguishable on accuracy in every modality, but reach it differently: humans stay balanced across the two answers, while the strongest models favor ``stranger.'' This is a difference in effective prior, not in discrimination. Richer channels help both unequally, and only humans gain from visible behavior on top of speech. We release the stimuli, human ratings, and model predictions.

Jeffrey M. Girard, Jason Z. Zheng, Jacqueline R. Vertino +2

Fluid Concepts Research

Classification Multimodal Model Evaluation Multimodal Understanding

Jul 31, 2026cs.CV

Explaining AI-Image Detection: What the Heatmap Actually Shows

A marketplace review photograph is a document: platforms approve refunds on it, and generative models drove the cost of forging one to zero. We study that detection problem, so we build a detector and attach an attribution map as its evidence, then measure what that pair delivers on 186,527 images under controls designed to change our conclusions when something is wrong. Compression history, not synthesis, drives naive evaluation: our strongest model reaches 0.9999 PR-AUC (area under the precision-recall curve) on a product-disjoint split, yet falls to 0.7254 once we re-encode synthetics into the real class's format, while five public detectors move by at most 0.07. Aligning one class relocates the cue rather than removing it, and the repaired model then assigns native files a median probability of synthesis of 0.0004. One identical final encode for both classes repairs that, and a three-seed factorial credits the encoding change with the whole gain (+0.176 +- 0.009 PR-AUC). That encode equalises the last stage only: forensic features alone still separate the classes at 0.7145 against a base rate of 0.254. For evidence we test maps causally, against controls that never consult the detector. Whether an attribution ranking exists at all depends on whether the detector reacts to the image. On our first-fix detector, which calls 96 of 100 edited frames real, no map beats a random one. On the detector we selected, twelve of seventeen maps clear that control on edited images and eight on generated ones; perturbation leads both axes and no gradient-CAM variant shows a positive advantage. The trivial controls never clear it, and on generated images the centre prior is worse than random. Our ensembled regional map clears both axes and takes the top pixel AP at 12.4 s per map against 44.9 for occlusion. Clearing a detector-blind control is not yet a faithful explanation, and we demonstrate none.

Leonid Kuturin, Ilya Sotnikov, Mark Khusnutdinov +4

Sirius Educational Centre, Big Challenges programme · HSE University, Faculty of Computer Science

Image Forgery Detection Digital Image Forensics Saliency Map Evaluation Explainable Image Classification Classification AI-Generated Image Detection

Jul 30, 2026cs.LG

Search Strategies for Optimal Classification and Regression Trees

Optimal decision trees (ODTs) are compact, interpretable machine learning models that globally optimize a given objective, but their scalability remains challenging. While recent work has proposed a variety of search strategies to improve scalability, the precise contribution of each strategy remains unclear. To address this gap, we introduce a general algorithmic framework for ODTs that instantiates previously used search strategies and enables the definition of new ones. This provides a common lens through which to understand and compare different strategies, which we use to empirically investigate the effect of 18 search strategies. Compared to the state of the art, the best strategy in our evaluation achieves significantly better anytime performance for classification, and improves runtime by more than an order of magnitude for regression.

Jacobus G. M. van der Linden, Mim van den Bos, Emir Demirović

Delft University of Technology

Decision Tree Learning Classification

Jul 30, 2026cs.LG

Contrastive Concept Importance: Explaining Pairwise Class Decisions Through Automatically Extracted Concept Representations

Concept-based explanations are a prevalent way to explain the decisions of complex black-box methods through semantically meaningful, human-interpretable concepts. To attribute the contribution of such concepts to a model's decisions, feature attribution methods are used to quantify how strongly each concept contributes to a model output. These attributions are typically computed for a single output class and therefore answer a non-contrastive "why P?" question. In many situations, however, such as cases of misclassification, class confusion, and low-margin predictions, the more natural question to ask is "why P rather than Q?". We introduce contrastive concept importance (CCI), which attributes the logit margin between a target class and a contrast, or foil, class to concepts in an automatically extracted visual concept basis. The resulting scores are signed, indicating whether a concept supports the target over the foil or the foil over the target, and can be decomposed into target-logit and foil-logit effects. This makes it possible to distinguish globally important concepts from concepts that specifically influence a class-pair distinction, including whether their effect is shared, one-sided, or directly contrastive. We evaluate the method on ImageNet class pairs using CRAFT-style concept bases, insertion and deletion curves, logit-wise decomposition analysis, and semantic class hierarchy. The results show that contrastive concept importance reveals class-pair-specific model behavior that is not captured by ordinary concept importance alone, and that highly contrastive concepts can be evaluated against semantic superclass structure to assess whether they affect fine-grained distinctions rather than broad category evidence.

Roel Visser, Isaac Roberts, Barbara Hammer

Bielefeld University, Inspiration 1, 33619 Bielefeld, Germany

Feature Attribution Pairwise Comparison Concept-Based Explanations Explainable Image Classification Classification Explainability Evaluation

Jul 29, 2026cs.CL

Knowledge before Reasoning: EC-Reason-Bench, a Training-Free Diagnostic Benchmark for LLM Enzyme Classification

Enzyme function prediction is a hierarchical, knowledge-intensive form of protein function classification. Existing benchmarks expose an anomaly: general LLMs often get the coarse first level right, yet once asked for a complete EC number their accuracy at levels two through four drops to almost zero, while specialized models and tools stay usable. We propose EC-Reason-Bench, a training-free, diagnostic evaluation protocol built to answer two questions: why general LLMs score close to nothing on EC number prediction, and how much of that loss can be recovered without updating a single weight. We break enzyme classification ability into four orthogonal levers that can each be measured on their own: output structure, external knowledge, reasoning structure, and reasoning robustness. We test each lever with an inference-time method against a shared zero-shot baseline reproducing previously reported near-zero performance. Experiments with several strong reasoning LLMs yield four main findings. First, external knowledge is decisive and must precede reasoning: uniformly low closed-book performance rises sharply with open-book access, narrowing model gaps. Second, in closed-book settings, whether cascading and chain-of-thought help or hurt depends on a model's tendency to abstain. Third, once evidence is available the aggregate score of the best LLM setting is indistinguishable from simply voting the EC numbers of the nearest retrieved neighbors; that tie is an artifact of averaging, and it hides a large gain on adversarial evidence set against an equally large loss on multi-functional enzymes. Reasoning over evidence therefore acts as an arbiter of conflicting neighbors rather than as a source of knowledge, and no single-number leaderboard can see it. Fourth, accuracy obeys a law of homology availability.

Linyu Li, Zhi Jin, Yichi Zhang +6

School of Computer Science, Peking University, Beijing, China · College of Computer Science and Technology, Zhejiang University, Hangzhou, China · School of Computer Science, Tibet University, Lhasa, China

Retrieval-Augmented Generation LLM Evaluation Classification Molecular Property Prediction LLM Reasoning

Jul 28, 2026cs.CV

FunnelAL: Retrieve-then-Rank Active Learning for Single-Class Discovery

We present FunnelAL, a retrieve-then-rank active learning system for single-class discovery, which adapts the multi-stage funnel architecture of industrial recommender systems to data annotation. Large-scale supervised learning faces two challenges: efficiently finding relevant samples in a massive corpus, and distinguishing true positives from visually confusable negatives when embeddings do not cleanly separate classes. Conventional active learning offers a principled framework for reducing annotation cost, yet it treats sample selection as a single-stage process that addresses neither challenge efficiently. FunnelAL decomposes the problem into cascaded stages. Starting from a single positive and negative example, the system iterates through: (1) embedding-based retrieval scoring that narrows the corpus to a manageable candidate set; (2) a precision-triggered ranking stage that exploits a learned ranker (RankNet) while batch precision remains high, then automatically blends in committee-based exploration (QBC) once returns diminish; and (3) feedback from the annotator's labels that refines both stages in subsequent iterations. We evaluate on three diverse image classification benchmarks. With a perfect annotator, FunnelAL attains the best final F1 on all three benchmarks, the best annotation efficiency (first in AULC), and the fewest annotation rounds. The most recent single-class discovery methods (GAL, PF-MA) at best match its final quality, and only at consistently higher labeling cost. Under annotator labeling errors at realistic rates, FunnelAL remains first or statistically tied for first while classical uncertainty-based methods degrade two to three times faster. Our work provides a concrete bridge between multi-stage recommender systems and active learning.

Reihaneh Rostami, Brian Goodwin

RAIC Labs · RAIC Labs USA

Active Learning Classification

Jul 27, 2026cs.LG

Multiclass Classification without Labels via Posterior Simplex Geometry

In many classification problems, reliable instance-level labels are unavailable. However, it is often possible to construct weakly enriched unlabeled samples: datasets selected by different cuts, sources, populations, or experimental conditions that change latent class proportions without revealing them. Classification without Labels (CWoLa) shows that, in the binary case (

K=2

), a classifier trained to distinguish two impure mixtures with different class proportions can recover an optimal class discriminator without knowing the mixture proportions. We extend this principle to multiclass learning from several unlabeled mixtures (

K>2

), where the learner observes only mixture identity and neither latent class labels nor class-prior matrices. We prove that, for a multiclass mixture model, the Bayes-optimal mixture classifier

g^\star

maps data points into a

(K-1)

-simplex embedded in mixture-posterior space. The

K

vertices of this simplex are induced by the latent classes through the unknown mixing matrix. Leveraging this geometry, we propose prior-free procedures that train a standard classifier to distinguish mixture identities and then extract latent class structure using either post-hoc simplex fitting or a bottleneck architecture. Experiments on MNIST, CIFAR-10, and Galaxy10 DECaLS show that mixture identity alone can recover latent classes and their fractions in the mixture. By narrowing the gap between weakly supervised and fully supervised performance, we provide a mathematically grounded, scalable tool for multiclass discovery in label-scarce domains.

Raphaël Bonnet-Guerrini, Johann Ioannou-Nikolaides, Troels Petersen +1

Niels Bohr Institute University of Copenhagen Copenhagen, 2100 · Computer Science Department Università degli Studi di Milano Milan, MI, 20133

Multiclass Classification Weakly Supervised Learning Classification

Jul 27, 2026stat.ML

A Model for Imbalanced Label Aggregation: A Focus on Minority-Class Detection

We study imbalanced crowdsourcing with a focus on class-dependent annotator accuracy, a setting that, to the best of our knowledge, remains relatively underexplored despite its importance in real-world inspection systems where the labels of greatest operational importance are also the rarest ones. In this setting, annotators may be reliable on both classes, unreliable on both classes, majority-class specialists, or minority-class specialists. Existing models only partially address this problem: they either capture class-dependent errors but ignore item difficulty, or they model item difficulty without capturing class-dependent errors. To fill this gap for imbalanced datasets in crowdsourcing, we introduce a generative aggregation model combining item difficulty with class-dependent annotator competence. The model allows both annotator abilities and item difficulties to vary across classes. We then revisit Condorcet's Jury Theorem in the class-imbalanced setting. We also show that majority voting asymptotically preserves the underlying class proportion. We evaluate our model on

33

real-world crowdsourcing datasets, covering multiclass tasks such as images and text, as well as two large-scale regimes: large-scale annotation datasets, with many annotations per item, and large-scale item datasets, with a large number of annotated instances. Across these diverse settings, our model consistently achieves the highest minority recall while remaining competitive in balanced accuracy, making it particularly relevant when rare-label recovery is the primary objective.

Gabriel Singer, Samuel Gruffaz, Olivier Vo Van +2

SNCF, Paris, France · Université Paris-Saclay, ENS Paris-Saclay, Centre Borelli, France · 2Tampere University, Finland, Biostatistics group

Class-Imbalanced Learning Classification