arXiv Machine Learning
Jun 25
Communicability-Inspired Positional Encoding (CIPE)
arXiv:2606. 25293v1 Announce Type: new Abstract: Positional encodings (PEs) are essential for Transformers.
AI safety and alignment
Alignment, interpretability, red-teaming, bias and privacy: the research on what these systems do when they misbehave.
arXiv Machine Learning
Jun 25
Re-mixing Embeddings for Patient Augmentation in Data Scarce Multiple Instance Learning
arXiv:2606. 25770v1 Announce Type: new Abstract: Data scarcity is a major bottleneck in medical Multiple Instance Learning (MIL), especially for rare diseases or expensive modalities.
arXiv Machine Learning
Jun 25
Statistically Valid Hyperparameter Selection: From Tuning to Guarantees
arXiv:2606. 25601v1 Announce Type: cross Abstract: Hyperparameter selection is a critical step in the deployment of modern artificial intelligence systems, given the need to tune degrees of freedom such as inference-time parameters, implementation-level settings, and thresholds driving decision rules.
arXiv Machine Learning
Jun 25
Safe Learning Control with Optimality and Stability Guarantees
arXiv:2501. 15373v2 Announce Type: replace-cross Abstract: Merely pursuing performance may adversely affect safety, while a conservative policy for safe exploration will degrade the performance.
arXiv Machine Learning
Jun 25
Fourier Multi-Component and Multi-Layer Neural Networks: Unlocking High-Frequency Potential
arXiv:2502. 18959v4 Announce Type: replace Abstract: The architecture of a neural network and the choice of its activation function are both fundamental to its performance.
arXiv Machine Learning
Jun 25
Expresso-AI: Explainable Video-Based Deep Learning Models for Depression Diagnosis
arXiv:2606. 25606v1 Announce Type: cross Abstract: Given the widespread prevalence of depression and its consequential impact on individuals and society, it is crucial to obtain objective measures for early diagnosis and intervention.
arXiv Machine Learning
Jun 25
Kuramoto Oscillatory Phase Encoding: Neuro-inspired Synchronization for Improved Learning Efficiency
arXiv:2604. 07904v2 Announce Type: replace Abstract: Spatiotemporal neural dynamics and oscillatory synchronization are widely implicated in biological information processing and have been hypothesized to support flexible coordination such as feature binding.
arXiv Machine Learning
Jun 25
Narrative Feature or Structured Feature? A Study of Large Language Models to Identify Cancer Patients at Risk of Heart Failure
arXiv:2403. 11425v4 Announce Type: replace Abstract: Cancer treatments are known to introduce cardiotoxicity, negatively impacting outcomes and survivorship.
arXiv Machine Learning
Jun 25
The Hitchhiker's Guide to Agentic AI: From Foundations to Systems
arXiv:2606. 24937v1 Announce Type: cross Abstract: The Hitchhiker's Guide to Agentic AI is a comprehensive practitioner's reference for building autonomous AI systems.
arXiv Machine Learning
Jun 25
Contrastive Conditional-Unconditional Alignment for Long-tailed Diffusion Model
arXiv:2507. 09052v3 Announce Type: replace-cross Abstract: Training data for class-conditional image synthesis often exhibit a long-tailed distribution with limited amount of images for tail classes.
arXiv Machine Learning
Jun 25
Digital Twin-Driven Adaptive Sim-to-Real Alignment via Reinforcement Learning for Vibration-Based Bearing Health Monitoring Under Data Scarcity
arXiv:2606. 24954v1 Announce Type: new Abstract: Vibration-based health monitoring of rotating machinery requires reliable fault diagnosis under operational data constraints, yet condition assessment remains challenged by structural scarcity of fault events and heterogeneous sim-to-real gaps in digital twin-generated signals.
arXiv Machine Learning
Jun 25
TopoCast: A Topological Fidelity Framework for Evaluating Transformer-Based Time Series Forecasting
arXiv:2606. 25439v1 Announce Type: new Abstract: Deep learning-based models have achieved state-of-the-art performance in Time Series Forecasting (TSF), yet their evaluation remains dominated by pointwise error metrics such as Mean Squared Error (MSE), which quantify numerical accuracy but overlook structural properties of the forecast signal, including recurrent dynamics, oscillatory behavior, and phase alignment.
arXiv Machine Learning
Jun 25
Inverse Reinforcement Learning for Interpretable Keystroke Biomarkers in Parkinson's Disease
arXiv:2606. 25270v1 Announce Type: new Abstract: Keystroke dynamics have been explored extensively as a passive digital biomarker for Parkinson's disease (PD), typically by extracting summary statistics from typing timing and training a classifier to discriminate PD from healthy controls.
arXiv Machine Learning
Jun 25
Space-Efficient Language Generation in the Limit
arXiv:2606. 25777v1 Announce Type: cross Abstract: We initiate a resource-aware theory of \textit{language generation in the limit} under the minimal constraint of space efficiency.
arXiv Machine Learning
Jun 25
The 4/$\delta$ Bound: Designing Predictable LLM-Verifier Systems for Formal Method Guarantee
arXiv:2512. 02080v3 Announce Type: replace-cross Abstract: The integration of Formal Verification tools with Large Language Models (LLMs) offers a path to scale software verification beyond manual workflows.
arXiv Machine Learning
Jun 25
Learning Structural Hardness for Combinatorial Auctions: Instance-Dependent Algorithm Selection via Graph Neural Networks
arXiv:2602. 14772v2 Announce Type: replace Abstract: The Winner Determination Problem (WDP) in combinatorial auctions is NP-hard, and no existing method reliably predicts which instances will defeat fast greedy heuristics.
arXiv Machine Learning
Jun 25
Same Evidence, Different Answer: Auditing Order Sensitivity in Multimodal Large Language Models
arXiv:2606. 26079v1 Announce Type: cross Abstract: Standard benchmarks for multimodal large language models (MLLMs) score each item on one canonical ordering and miss whether order-irrelevant shuffling changes the answer, a baseline reliability property called for by emerging AI evaluation guidelines.
arXiv Machine Learning
Jun 25
Homogeneity Bias in Open-Weight LLMs Is Robust to Decoding Hyperparameters
arXiv:2501. 02211v2 Announce Type: replace-cross Abstract: Large language models (LLMs) reproduce homogeneity bias -- the tendency to portray marginalized groups as more internally similar than dominant groups -- but whether this bias is stable or an artifact of inference settings has only been studied in single proprietary models.
arXiv Machine Learning
Jun 25
When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification?
arXiv:2606. 26053v1 Announce Type: cross Abstract: Synthetic data augmentation is widely used to mitigate class imbalance, but its theoretical effects on score-based classification remain poorly understood.
Hugging Face Trending Papers
Jun 25
From Hallucination to Grounding: Diagnosing Visual Spatial Intelligence via CRISP
Current VLM evaluations often conflate language priors with genuine spatial reasoning. To address this, we introduce CRISP, a novel structural-diagnostic evaluation paradigm that assesses visual spatial intelligence through consistency, the alignment between implicit perception and explicit reasoning.