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Research

PLUS: A Detector-Agnostic Subspace Signal for Near-OOD Detection

The paper introduces PLUS, a post-hoc signal that complements existing out-of-distribution (OOD) detectors to improve near-OOD detection. PLUS builds per-class low-dimensional subspaces from sparse autoencoder decoder directions and scores inputs via whitened squared Mahalanobis distance. Fused additively with any calibrated base score at a fixed global weight, it improves near-OOD AUROC across all 126 detector-backbone combinations tested, while largely maintaining far-OOD performance.

arxiv13h agoScore 78

out-of-distribution detectiondeep learninganomaly detection

Research

Task-Efficient Agent Evaluation Using Cached Traces

This paper introduces a method to estimate an agentic system's benchmark performance from a small number of task executions by leveraging rich execution traces. The approach extracts structured summaries with an LLM and embeds them to create representations that preserve task-related signal while suppressing identity-related noise. On SWE-bench Verified and Terminal-Bench 2.0, combining this geometry-based prediction with an item-response model reduces mean absolute error significantly compared to baselines.

arxiv13h agoScore 75

AI evaluationagentic systemsefficient benchmarking

Research

TAPES: Task-Aligned Joint Predictive Pretraining for Transaction Event Sequences

TAPES is a pretraining framework for transaction event sequences that selects predictive target sets based on downstream utility and models joint likelihood via a shared discrete latent variable. It outperforms baselines on real-world and public financial benchmarks, with ablations confirming the value of target selection and joint modeling.

arxiv1d agoScore 78

transaction event sequencespredictive pretrainingfinancial benchmarks

Research

EEG Foundation Models Show Convergent Representations with Decodability

This paper tests the Platonic Representation Hypothesis on EEG foundation models, finding that models with better EEG decodability show stronger cross-family alignment in local and global structure, though not consistently with parameter count. Cross-modal convergence is modality-dependent: stronger global alignment to language but not vision, with local alignment showing no consistent association. The results suggest a dissociation between local sensory features and global semantic structure.

arxiv1d agoScore 78

EEG foundation modelsrepresentation learningcross-modal alignment

Research Editor selected

DINOFAS: Bonafide-Centric Representation Learning for Face Anti-Spoofing

DINOFAS is a DINOv3-based framework for face anti-spoofing that learns bonafide-centric representations rather than attack-specific cues. It uses LoRA for parameter-efficient adaptation, an attention style-content disentanglement module to reduce domain variations, and a dual-branch architecture separating prototype-based liveness learning from attack supervision. The model achieves strong cross-domain generalization, with an average HTER of 9.09% on the OCIM protocol and 21.41% on synthetic-to-real transfer. Evaluations on multiple benchmarks show robustness to unseen acquisition conditions and presentation attacks.

Why it matters: Presents a novel representation learning approach for face anti-spoofing that significantly improves generalization across domains and attack types.

arxiv2d agoScore 88

face anti-spoofingrepresentation learningdomain generalization

Research

Aligning MLLMs with Human Cognition via Behavior-Derived Semantic Dimensions

Researchers propose a framework to align multimodal large language models with human cognitive structure by using a behavior-derived semantic embedding space of 66 dimensions from 4.7 million human Odd-One-Out judgments. The method infers salient decision dimensions for each triplet and generates dimension-grounded rationales, improving consistency with human similarity judgments and increasing representational alignment in fMRI data. The approach retains competitive performance on standard multimodal benchmarks.

arxiv3d agoScore 78

multimodal large language modelshuman-AI alignmentcognitive science

Research Editor selected

Berkeley AI Research: Closed-Form Theory Explains What Word2vec Learns

Researchers at Berkeley AI Research have developed a quantitative theory showing that, under mild approximations, word2vec's learning problem reduces to unweighted least-squares matrix factorization. They prove that the learned embeddings are given by PCA on a specific matrix defined by corpus statistics and hyperparameters, and that training proceeds in discrete rank-incrementing steps. The theory predicts interpretable latent features, such as celebrity, government, and geography topics, and m

Why it matters: Readers gain a rigorous theoretical understanding of how word2vec learns interpretable embeddings, bridging classical NLP and modern representation learning.

Berkeley AI ResearchSep 1, 2025Score 80

word2vecrepresentation learningembedding theory