arXiv AI
Jul 1
REMSA: Foundation Model Selection for Remote Sensing via a Constraint-Aware Agent
arXiv:2511. 17442v3 Announce Type: replace-cross Abstract: Foundation Models (FMs) are increasingly integrated into remote sensing (RS) pipelines.
Benchmarks and evaluation
Leaderboards, eval harnesses and ablations — the contested business of deciding which model is actually better.
arXiv AI
Jul 1
Human-Agent Collaborative Paper-to-Page Crafting
arXiv:2510. 19600v2 Announce Type: replace-cross Abstract: In the quest for scientific progress, communicating research is as vital as the discovery itself.
arXiv AI
Jul 1
Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs
arXiv:2606. 32032v1 Announce Type: cross Abstract: Metacognition is a critical component of intelligence that describes the ability to monitor and regulate one's own cognitive processes.
arXiv AI
Jul 1
Sparsity-Inducing Divergence Losses for Biometric Verification
arXiv:2606. 31664v1 Announce Type: cross Abstract: Performance in face and speaker verification is largely driven by margin-penalty softmax losses such as CosFace and ArcFace.
arXiv AI
Jul 1
Token-Sparse Medical Multimodal Reasoning via Dual-Stream Reinforcement Learning
arXiv:2606. 31599v1 Announce Type: cross Abstract: Vision-language models (VLMs) combining reinforcement learning (RL) ignite remarkable progress in multimodal reasoning, yet still struggle with medical images, which typically exhibit extremely sparse visual evidence to inform clinical decision-making.
arXiv AI
Jul 1
FinPersona-Bench: A Benchmark for Longitudinal Psychometric Stability of Autonomous Financial Agents
arXiv:2606. 31522v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed as autonomous financial agents initialized with explicit behavioral mandates such as "preserve capital" or "avoid speculative bets" that are meant to govern every decision throughout deployment.
arXiv AI
Jul 1
Position: Vision-Language-Action Models Cannot Be Verified to Perform Physical Reasoning
arXiv:2606. 30686v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) systems, built on pretrained vision-language models (VLMs), have shown rapidly improving performance on robot manipulation benchmarks.
arXiv AI
Jul 1
ASR-Agnostic Multimodal Spectrotemporal Modeling for Early Dementia Detection
arXiv:2606. 30646v1 Announce Type: cross Abstract: Speech recruits the same executive, attentional, and working memory processes underlying instrumental activities of daily living, or IADLs, providing a non-invasive proxy for cognitive assessment.
arXiv AI
Jul 1
Qualified Educational Capacity Planning under Heterogeneous Student Support Needs: A Synthetic Benchmark and Decision-Support Framework
arXiv:2606. 30650v1 Announce Type: cross Abstract: Educational support services often face a qualified-capacity problem: staff time is scarce, qualifications decay, new support needs can appear before anyone is prepared for them, and training consumes the same hours needed by current students.
arXiv AI
Jul 1
Cross-Modal Hierarchical Fusion for from Multi-Sensor Ground Observation
arXiv:2606. 30647v1 Announce Type: cross Abstract: Dense volumetric reconstruction of cloud microphysical fields from sparse ground-based instruments remains an open problem, largely because the available measurements are heterogeneous in both modality and spatial coverage.
arXiv AI
Jul 1
Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design
arXiv:2606. 28578v1 Announce Type: cross Abstract: Closed-loop materials discovery iterates between proposing candidate structures and evaluating their properties, and property evaluation dominates the cost.
arXiv Machine Learning
Jul 1
WIDER-FAIR: An Annotated Version of the WIDER-FACE Dataset for Fairness Evaluation
arXiv:2606. 31704v1 Announce Type: cross Abstract: The deployment of face detection models in real-world applications raises important fairness concerns, as these systems may showcase performance disparities across demographic groups.
arXiv Machine Learning
Jul 1
Expected Gain-based Escalation in Vertical Federated Learning
arXiv:2606. 31331v1 Announce Type: new Abstract: Collaborative inference can improve predictive performance by integrating complementary information across agents, but applying collaborative fusion to every sample can incur unnecessary communication and computational overhead.
arXiv AI
Jul 1
TSHA: A Benchmark for Visual Language Models in Trustworthy Safety Hazard Assessment Scenarios
arXiv:2603. 29759v2 Announce Type: replace-cross Abstract: Recent advances in vision-language models (VLMs) have accelerated their application to indoor safety hazards assessment.
arXiv Machine Learning
Jul 1
Rethinking Continual Anomaly Detection on the Edge: Benchmarking Under Realistic Industrial Conditions
arXiv:2605. 24251v2 Announce Type: replace Abstract: Continual anomaly detection (CAD) addresses the need for industrial inspection systems to adapt to evolving production conditions, yet existing methods share three critical gaps: unrealistic evaluation, no systematic comparison, and no consideration of edge deployment constraints.
arXiv Machine Learning
Jul 1
Hierarchical Message-Passing Policies for Multi-Agent Reinforcement Learning
arXiv:2507. 23604v2 Announce Type: replace Abstract: Decentralized Multi-Agent Reinforcement Learning (MARL) methods allow for learning scalable multi-agent policies, but suffer from partial observability and induced non-stationarity.
arXiv AI
Jul 1
DSH-Bench: A Difficulty- and Scenario-Aware Benchmark with Hierarchical Subject Taxonomy for Subject-Driven Text-to-Image Generation
arXiv:2603. 08090v3 Announce Type: replace-cross Abstract: Significant progress has been achieved in subject-driven text-to-image (T2I) generation, which aims to synthesize new images depicting target subjects according to user instructions.
arXiv AI
Jul 1
QVal: Cheaply Evaluating Dense Supervision Signals for Long-Horizon LLM Agents
arXiv:2606. 32034v1 Announce Type: cross Abstract: LLM agents increasingly act over long horizons, where a single trajectory can contain hundreds or thousands of actions.
arXiv Machine Learning
Jul 1
Drop-In Perceptual Optimization for 3D Gaussian Splatting
arXiv:2603. 23297v2 Announce Type: replace-cross Abstract: Despite their output being ultimately consumed by human viewers, 3D Gaussian Splatting (3DGS) methods often rely on ad-hoc combinations of pixel-level losses, resulting in blurry renderings.
arXiv Machine Learning
Jul 1
Probing Memorization of Tabular In-Context Learning
arXiv:2606. 31208v1 Announce Type: new Abstract: Large tabular models (LTMs), i.