arXiv AI
Jun 17
Detail++: Training-Free Detail Enhancer for Text-to-Image Diffusion Models
arXiv:2507. 17853v2 Announce Type: replace-cross Abstract: Recent advances in text-to-image (T2I) generation have led to impressive visual results.
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
Reinforcing Dual-Path Reasoning in Spatial Vision Language Models
arXiv:2606. 17539v1 Announce Type: cross Abstract: Spatial VLMs have made substantial progress in geometric perception, yet complex spatial reasoning requiring multi-step inference over depth, distance, and scene relations remains challenging.
arXiv Machine Learning
Jun 17
Stable and Steerable Sparse Autoencoders with Weight Regularization
arXiv:2603. 04198v2 Announce Type: replace-cross Abstract: Sparse autoencoders (SAEs) are widely used to extract human-interpretable features from neural network activations, but their learned features can vary substantially across random seeds and training choices.
arXiv AI
Jun 17
AIPatient Arena: EHR-grounded evaluation of large language models in end-to-end clinical consultation workflows
arXiv:2606. 17474v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly considered for use in clinical consultation tasks, yet most medical evaluations remain static, single-turn, or narrowly outcome-based, limiting their ability to reflect the sequential, uncertain, and interactive nature of real-world care.
arXiv Machine Learning
Jun 17
Asynchronous Decentralized Federated Learning over Lossy Wireless Links via Reception- and Age-Aware Aggregation
arXiv:2606. 10774v2 Announce Type: replace Abstract: Decentralized Federated Learning(DFL) enables collaborative model training across wireless edge nodes, including IoT deployments, autonomous vehicles, UAV swarms, and satellite constellations.
arXiv AI
Jun 17
C2FL: Clustered Continual Federated Learning under Spatial and Temporal Drift
arXiv:2606. 18003v1 Announce Type: cross Abstract: Collective Adaptive Systems (CAS) increasingly rely on machine learning to let each node learn from locally sensed data, aligning its behavior with the surrounding environment.
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 Machine Learning
Jun 17
Searching Neural Architectures for Sensor Nodes on IoT Gateways
arXiv:2505. 23939v2 Announce Type: replace Abstract: This paper presents an automatic method for the design of Neural Networks (NNs) at the edge, enabling Machine Learning (ML) access even in privacy-sensitive Internet of Things (IoT) applications.
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
Fast Nonparametric Conditional Independence Testing via Two-Stage Regression
arXiv:2606. 18011v1 Announce Type: cross Abstract: Constraint-based causal discovery relies on repeated conditional independence tests, but fast nonparametric tests often sacrifice calibration, especially when variables depend on the conditioning set through nonlinear relationships.
arXiv Machine Learning
Jun 17
Maximin Relative Improvement: Fair Learning as a Bargaining Problem
arXiv:2602. 04155v2 Announce Type: replace-cross Abstract: When deploying a single predictor across multiple subpopulations, we propose a fundamentally different approach: interpreting group fairness as a bargaining problem among subpopulations.
arXiv AI
Jun 17
FinAcumen: Financial Multimodal Reasoning via Self-Evolving Experience Memory Harness
arXiv:2606. 17642v1 Announce Type: new Abstract: Financial multimodal reasoning requires agents to coordinate numerical computation, retrieval, visual interpretation, and temporal grounding across heterogeneous evidence sources.
arXiv AI
Jun 17
Any2Any: Efficient Cross-Embodiment Transfer for Humanoid Whole-Body Tracking
arXiv:2605. 23733v2 Announce Type: replace-cross Abstract: Whole-body tracking (WBT) models have become a key foundation for humanoid robots, enabling them to imitate diverse motions with high fidelity.
arXiv Machine Learning
Jun 17
Beyond Independent Genes: Learning Module-Inductive Representations for Single-Cell Gene Perturbation Prediction
arXiv:2602. 04901v2 Announce Type: replace-cross Abstract: Predicting transcriptional responses to genetic perturbations is a central problem in functional genomics.
arXiv AI
Jun 17
AnalogFed: Privacy-Preserving Discovery of Analog Circuits at Scale with Federated Generative AI
arXiv:2507. 15104v2 Announce Type: replace-cross Abstract: Recent advances in generative AI (GenAI) have shown transformative potential for modern hardware design.
arXiv Machine Learning
Jun 17
Provably Efficient Regularized Online RLHF with Generalized Bilinear Preferences
arXiv:2602. 23116v3 Announce Type: replace Abstract: We consider the problem of regularized best-response max-regret minimization in online RLHF under general preferences and bandit feedback.
arXiv AI
Jun 17
Pulling The REINS: Training-Free Safety Alignment of Video Diffusion Models via Representation Steering
arXiv:2606. 17257v1 Announce Type: cross Abstract: Open-weight video diffusion models can generate photorealistic unsafe content, from violence to misinformation, yet existing defenses either require expensive safety fine-tuning that degrades general capability, or apply external filters that are trivially bypassed by adversarial prompts.
arXiv AI
Jun 17
CMIP-Forge: An Agentic System that Retrieves, Computes, and Self-Reviews Climate Science
arXiv:2606. 17076v1 Announce Type: cross Abstract: The Coupled Model Intercomparison Project Phase 6 (CMIP6) has generated thousands of peer-reviewed publications documenting model configurations, evaluation procedures, emergent constraints, and projection uncertainties.
arXiv AI
Jun 17
TrustErase: Auditable Instant Machine Unlearning with Passport-Embedded Representations
arXiv:2606. 17122v1 Announce Type: cross Abstract: The demand for privacy-compliant AI has amplified the need for machine unlearning; yet, existing retraining or distillation-based methods remain unverifiable and computationally costly.
arXiv AI
Jun 17
MODE-RAG: Manifold Outlier Diagnosis and Energy-based Retrieval-Augmented Generation Evaluation
arXiv:2606. 17449v1 Announce Type: cross Abstract: While Multimodal Retrieval-Augmented Generation (M-RAG) enhances Large Vision-Language Models, it remains highly susceptible to cross-modal hallucinations, causal fabrications, and sycophancy.