arXiv Machine Learning
Jun 17
Price of metric universality in vector quantization is at most 0.11 bit
arXiv:2602. 05790v2 Announce Type: replace-cross Abstract: Fast computation of a matrix product $W^\top X$ is a workhorse of modern LLMs.
AI safety and alignment
Alignment, interpretability, red-teaming, bias and privacy: the research on what these systems do when they misbehave.
arXiv AI
Jun 17
First, do NOHARM: towards clinically safe large language models
arXiv:2512. 01241v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are routinely used by physicians and patients for medical advice, yet their clinical safety profiles remain poorly characterized.
arXiv AI
Jun 17
SketchXplain: Intuitive Visual Explanations of Image Classifiers with Sketches
arXiv:2606. 17646v1 Announce Type: cross Abstract: Saliency map visualizations explain image-based AI predictions by pointing to regions, but these are often unintuitive and semantically unclear, leaving an interpretability gap.
arXiv AI
Jun 17
m2sv: A Scalable Benchmark for Map-to-Street-View Spatial Reasoning
arXiv:2601. 19099v2 Announce Type: replace-cross Abstract: Vision--language models (VLMs) achieve strong performance on many multimodal benchmarks but remain brittle on spatial reasoning tasks that require aligning abstract overhead representations with egocentric views.
arXiv Machine Learning
Jun 17
On Surjectivity of Neural Networks: Can you elicit any behavior from your model?
arXiv:2508. 19445v3 Announce Type: replace Abstract: Given a trained neural network, can any specified output be generated by some input?
arXiv Machine Learning
Jun 17
Multimodal Graph Negative Learning
arXiv:2606. 12863v2 Announce Type: replace Abstract: Multimodal attributed graphs (MAGs) integrate graph topology with heterogeneous modality attributes, such as text and images, thereby enabling richer modeling of complex relational systems.
arXiv AI
Jun 17
Rift: A Conflict Signature for Deception in Language Models
arXiv:2606. 17229v1 Announce Type: cross Abstract: A model that lies while knowing the truth is the central case ELK cannot handle with behavioral evaluation alone.
arXiv AI
Jun 17
Temporal Preference Optimization for Unsupervised Retrieval
arXiv:2606. 17664v1 Announce Type: cross Abstract: Unsupervised dense retrievers offer scalability by learning semantic similarity from unlabeled documents via contrastive learning, but they struggle to capture the temporal relevance, retrieving semantically related but temporally misaligned documents-an important aspect when a document collection spans multiple time periods (e.
arXiv Machine Learning
Jun 17
Evaluating Uplift Modeling under Structural Biases: Insights into Metric Stability and Model Robustness
arXiv:2603. 20775v2 Announce Type: replace Abstract: In personalized marketing, uplift models estimate the incremental effect of an intervention by modeling how customer behavior would change under alternative treatments using counterfactual analysis.
arXiv AI
Jun 17
Enhancing Pathological VLMs with Cross-scale Reasoning
arXiv:2606. 17412v1 Announce Type: cross Abstract: Pathological images are inherently multi-scale, requiring pathologists to integrate evidence from global tissue architecture at low magnification to cellular morphology at higher magnification for accurate diagnosis.
arXiv Machine Learning
Jun 17
Evaluating Open-Source LLMs for Multi-Label ATT&CK Technique Classification on CTI Reports
arXiv:2606. 18166v1 Announce Type: cross Abstract: Classifying Cyber Threat Intelligence (CTI) using MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) is essential for proactive defense, but historically required extensive human effort.
arXiv Machine Learning
Jun 17
Multi-Source Cybersecurity Logs: An ATT&CK-Labeled Dataset and SLM Evaluation
arXiv:2606. 18190v1 Announce Type: cross Abstract: Multi-stage cyberattacks span system, network, and browser logs.
arXiv Machine Learning
Jun 17
Monotonic Kolmogorov-Arnold Networks: A Theoretical and Empirical Study of Monotonicity as an Inductive Bias
arXiv:2606. 17886v1 Announce Type: new Abstract: Monotonicity has been a long-running architectural inductive bias for neural networks, motivated by tabular, scientific, and economic settings where outputs are known to respond monotonically to certain inputs.
arXiv AI
Jun 17
Retrofitters, pragmatists and activists: Public interest litigation for accountable automated decision-making
arXiv:2511. 03211v4 Announce Type: replace-cross Abstract: This paper examines the role of public interest litigation in promoting accountability for AI and automated decision-making (ADM) in Australia.
arXiv Machine Learning
Jun 17
The Implicit Bias of Steepest Descent with Mini-batch Stochastic Gradient
arXiv:2602. 11557v2 Announce Type: replace Abstract: A variety of widely used optimization methods like SignSGD and Muon can be interpreted as instances of steepest descent under different norm-induced geometries.
arXiv Machine Learning
Jun 17
Expanding SPHERE-JEPA: A Family of Statistical Regularizers for the Hypersphere
arXiv:2606. 17603v1 Announce Type: new Abstract: In Self-Supervised Learning (SSL), preventing representation collapse by explicitly enforcing a uniform distribution on the unit hypersphere has proven to be effective.
arXiv AI
Jun 17
Functional Equivalence in Attention: A Comprehensive Study with Applications to Linear Mode Connectivity
arXiv:2606. 17830v1 Announce Type: cross Abstract: Neural network parameter spaces are inherently non-injective, as distinct parameter configurations can realize identical functions through functional equivalence.
arXiv AI
Jun 17
Temporal Motif-aware Graph Test-time Adaptation for OOD Blockchain Anomaly Detection
arXiv:2605. 29526v2 Announce Type: replace-cross Abstract: Ever-evolving transaction patterns have significantly hindered anomaly detection on emerging cryptocurrency blockchains due to the vast number of addresses and diverse anomalous behaviors.
arXiv AI
Jun 17
Large Language Models for Agentic NetOps and AIOps: Architectures, Evaluation, and Safety
arXiv:2605. 12729v2 Announce Type: replace-cross Abstract: Large language models are increasingly being used to support network operations (NetOps) and artificial intelligence for IT operations (AIOps), including incident investigation, root-cause analysis, configuration synthesis, and limited self-healing.
arXiv AI
Jun 17
When LLMs Analyze Scars: From Images to Clinically-Meaningful Features
arXiv:2606. 18063v1 Announce Type: cross Abstract: Medical image classification faces a fundamental dilemma: while deep learning models achieve remarkable performance at scale, real-world clinical scenarios often suffer from severe data scarcity due to annotation costs, privacy constraints, and disease rarity.