arXiv Machine Learning
Jun 18
Lifecycle-Aware Dynamic Analysis for Secure ML Model Execution
arXiv:2606. 19023v1 Announce Type: cross Abstract: The growing reliance on pre-trained Machine Learning (ML) models has introduced new attack surfaces.
Benchmarks and evaluation
Leaderboards, eval harnesses and ablations — the contested business of deciding which model is actually better.
arXiv Machine Learning
Jun 18
MOLAR: Learning Multimodal Molecular Representations from Noisy Labels
arXiv:2606. 18390v1 Announce Type: new Abstract: Motivation: Noisy labels are a common challenge in molecular property prediction because molecular annotations are often obtained from assays, curated databases, or weak annotation pipelines rather than directly observed clean biological states.
arXiv Machine Learning
Jun 18
Benchmarking Physics-Informed Time-Series Models for Operational Global Station Weather Forecasting
arXiv:2406. 14399v4 Announce Type: replace Abstract: The development of Time-Series Forecasting (TSF) models is often constrained by the lack of comprehensive datasets, especially in Global Station Weather Forecasting (GSWF), where existing datasets are small, temporally short, and spatially sparse.
arXiv AI
Jun 18
Sparsity Curse: Understanding RLVR Model Parameter Space from Model Merging
arXiv:2606. 18521v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Reward (RLVR) has emerged as a powerful post-training paradigm that surpasses Supervised Fine-Tuning (SFT) in eliciting reasoning intelligence and resisting catastrophic forgetting.
arXiv AI
Jun 18
Notation Matters: A Benchmark Study of Token-Optimized Formats in Agentic AI Systems
arXiv:2605. 29676v2 Announce Type: replace Abstract: Large language models in Agentic AI systems consume tool schemas and execution results and emit tool invocations as structured data.
arXiv Machine Learning
Jun 18
InTrain: Intrinsic Trainability for Zero-Cost Neural Architecture Search
arXiv:2606. 18676v1 Announce Type: new Abstract: Training-free neural architecture search promises efficient discovery of high-performance networks without costly training.
arXiv Machine Learning
Jun 18
Task-Restricted Symmetries in Recurrent Weight Space
arXiv:2606. 18457v1 Announce Type: new Abstract: Recurrent networks can contain substantial functional redundancy in weight space: changing a recurrent matrix may leave the input-output rollout nearly unchanged on a task distribution, while similar-scale changes can destroy the same behavior.
arXiv Machine Learning
Jun 18
DIPHINE: Diffusion-based $\Phi$-ID Neural Estimator
arXiv:2606. 18997v1 Announce Type: new Abstract: Uncovering the true informational architecture of real-world complex systems requires disentangling how their components uniquely store, redundantly share, and synergistically integrate information over time.
arXiv Machine Learning
Jun 18
Formalizing and Mitigating Structural Distortion in LLM Attention for Graph Reasoning
arXiv:2606. 15633v2 Announce Type: replace Abstract: Large Language Models (LLMs) have shown promise for reasoning over Text-Attributed Graphs (TAGs).
arXiv Machine Learning
Jun 18
SCAN: Enhance Time Series Anomaly Detection via Multi-Scale Neighborhood-Centered Clustering
arXiv:2606. 19255v1 Announce Type: new Abstract: Time series anomaly detection plays a crucial role in a wide range of real-world applications.
arXiv Machine Learning
Jun 18
Where Will They Go? Modelling Multimodal Pedestrian Manoeuvres from Ego-centric Videos
arXiv:2606. 18824v1 Announce Type: cross Abstract: Pedestrian trajectory prediction from an ego-centric camera is challenging since it depends on complex interactions with vehicles and scene context, as well as the intention of the pedestrian.
arXiv AI
Jun 18
Neural Phase Correlation
arXiv:2606. 18496v1 Announce Type: cross Abstract: Correspondence is fundamentally relational: it seeks the unknown transformation between two observations of a common scene, not the content of either.
arXiv AI
Jun 18
Do Neural Networks Lose Plasticity in a Gradually Changing World?
arXiv:2602. 09234v2 Announce Type: replace-cross Abstract: Continual learning has become a trending topic in machine learning.
arXiv AI
Jun 18
Structured Representation Learning with Locally Linear Embeddings and Adaptive Feature Fusion
arXiv:2606. 18469v1 Announce Type: cross Abstract: Neuroscientific research has revealed that the brain encodes complex behaviors by leveraging structured, low-dimensional manifolds and dynamically fusing multiple sources of information through adaptive gating mechanisms.
arXiv AI
Jun 18
LLM-Evolved Domain-Independent Heuristics for Symbolic AI Planning
arXiv:2605. 29649v2 Announce Type: replace Abstract: Heuristic search is the dominant paradigm in symbolic AI planning, and the strongest heuristics are the result of decades of work by planning researchers.
arXiv Machine Learning
Jun 18
Acceleration of an algebraic multigrid pressure solver using graph neural networks
arXiv:2606. 19251v1 Announce Type: cross Abstract: Solving the pressure-Poisson equation remains the primary computational bottleneck in incompressible unstructured flow solvers primarily due to the inherent sensitivity of traditional linear solvers to mesh irregularities.
arXiv AI
Jun 18
EffiNav: Fusing Depth and Vision-Language for Efficient Object Goal Navigation
arXiv:2606. 18634v1 Announce Type: cross Abstract: To locate a target object while exploring the unknown environment is a fundamental capability for autonomous agents, with applications ranging from search-and-rescue to field robots.
arXiv AI
Jun 18
Target-confidence Recourse Using tSeTlin machines: TRUST
arXiv:2606. 18832v1 Announce Type: cross Abstract: Counterfactual explanations are widely used to provide algorithmic recourse in high-stakes decision-making systems.
arXiv AI
Jun 18
Revisiting Active Speaker Detection: An In-the-Wild Benchmark for Generalization and Robustness
arXiv:2505. 21954v2 Announce Type: replace-cross Abstract: We present UniTalk, a novel dataset emphasizing challenging scenarios to enhance model generalization for the task of active speaker detection (ASD).
arXiv AI
Jun 18
Narrative Theory-Driven LLM Methods for Automatic Story Generation and Understanding: A Survey
arXiv:2602. 15851v2 Announce Type: replace-cross Abstract: Applications of narrative theories using large language models (LLMs) deliver promising methods in automatic story generation and understanding tasks.