arXiv:2605.02907v1 Announce Type: new
Abstract: Softmax attention maps every query--key interaction into a probability distribution, but the underlying structure remains largely unexplored. We define the \emph{energy field}, the row-centered attention logit, and show that it exhibits invariant prop...
Computing Thiele Rules on Interval Elections and their Generalizations
arXiv:2605.03067v1 Announce Type: new
Abstract: Approval-based committee voting has received significant attention in the social choice community. Among the studied rules, Thiele rules, and especially Proportional Approval Voting (PAV), stand out for desirable properties such as proportional repres...
Making the Invisible Visible: Understanding the Mismatch Between Organizational Goals and Worker Experiences in AI Adoption
arXiv:2605.03078v1 Announce Type: new
Abstract: While AI is often introduced into organizations to drive innovation and efficiency, many adoption efforts fail as workers resist and struggle to integrate these systems. These failures point to a deeper issue: workers, the very people expected to coll...
SpecMD: A Comprehensive Study on Speculative Expert Prefetching
Mixture-of-Experts (MoE) models enable sparse expert activation, meaning that only a subset of the model’s parameters is used during each inference. However, to translate this sparsity into practical performance, an expert caching mechanism is required. Previous works have proposed hardware-centric ...
Sparse Regression under Correlation and Weak Signals: A Reproducible Benchmark of Classical and Bayesian Methods
arXiv:2605.00835v1 Announce Type: new
Abstract: Choosing between classical and Bayesian sparse regression methods involves a real trade-off: penalized estimators like Lasso run in milliseconds but give no uncertainty estimates,while Horseshoe and Spike-and-Slab priors produce full posteriors but ne...
ClinicBot: A Guideline-Grounded Clinical Chatbot with Prioritized Evidence RAG and Verifiable Citations
arXiv:2605.00846v1 Announce Type: new
Abstract: Clinical diagnosis requires answers that are accurate, verifiable, and explicitly grounded in official guidelines. While large language models excel at natural language processing, their tendency to hallucinate undermines their utility in high-stakes ...
Accelerating battery research with an AI interface between FINALES and Kadi4Mat
arXiv:2605.00909v1 Announce Type: new
Abstract: The time-consuming formation process critically impacts the longevity of sodium-ion coin cells and End Of Life (EOL) performance. This study aims to optimize formation protocols for duration efficiency, targeting high-performance outcomes while minimi...
FedACT: Concurrent Federated Intelligence across Heterogeneous Data Sources
arXiv:2605.00011v1 Announce Type: new
Abstract: Federated Learning (FL) enables collaborative intelligence across decentralized data source devices in a privacy-preserving way. While substantial research attention has been drawn to optimizing the learning process for an individual task, real-world ...
Learning physically grounded traffic accident reconstruction from public accident reports
arXiv:2605.00050v1 Announce Type: new
Abstract: Traffic accidents are routinely documented in textual reports, yet physically grounded accident reconstruction remains difficult because detailed scene measurements and expert reconstructions are scarce, costly and hard to scale. Here we formulate acc...
Stanford Merges AI and Data Science Efforts Under Single Institute
The combined institute will retain the Stanford HAI name and be helmed by computer scientist James Landay. Co-founder Fei-Fei Li takes on a new university-wide role as Special Advisor on AI and joins John Hennessy as co-chair of the advisory council.
As artificial intelligence transforms society, Stanford HAI’s James Landay, Fei-Fei Li, and John Hennessy explain why they’re merging HAI with the Stanford Data Science initiative, mobilizing “team science at scale,” and betting that academic openness will shape AI’s future.
Monitoring Neural Training with Topology: A Footprint-Predictable Collapse Index
arXiv:2604.26984v1 Announce Type: new
Abstract: Representational collapse, where embeddings become anisotropic and lose multi-scale structure, can erode downstream performance long before performance metrics react. We propose an online, topology-aware monitor for evolving neural representations tha...
When Continual Learning Moves to Memory: A Study of Experience Reuse in LLM Agents
arXiv:2604.27003v1 Announce Type: new
Abstract: Memory-augmented LLM agents offer an appealing shortcut to continual learning: rather than updating model parameters, they accumulate experience in external memory, seemingly sidestepping the stability-plasticity dilemma of parametric learning. We sho...
Automatic Causal Fairness Analysis with LLM-Generated Reporting
arXiv:2604.27011v1 Announce Type: new
Abstract: AutoML, intended as the process of automating the application of machine learning to real-world problems, is a key step for AI popularisation. Most AutoML frameworks are not accounting for the potential lack of fairness in the training data and in the...
Compositional Meta-Learning for Mitigating Task Heterogeneity in Physics-Informed Neural Networks
arXiv:2604.26999v1 Announce Type: new
Abstract: Physics-informed neural networks (PINNs) approximate solutions of partial differential equations (PDEs) by embedding physical laws into the loss function. In parameterized PDE families, variations in coefficients or boundary/initial conditions define ...
Mini-Batch Class Composition Bias in Link Prediction
arXiv:2604.25978v1 Announce Type: new
Abstract: Prior work on node classification has shown that Graph Neural Networks (GNNs) can learn representations that transfer across graphs, when underlying graph properties are shared. For a fixed graph, one would then expect GNNs trained for link prediction...
International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2026
Apple is presenting new research at the annual International Conference on Acoustics, Speech and Signal Processing (ICASSP), which takes place in person in Barcelona, Spain, from May 4 to 8. We are proud to again sponsor the conference, which brings together the scientific and industrial research co...
Automated detection of pediatric congenital heart disease from phonocardiograms using deep and handcrafted feature fusion
arXiv:2604.24767v1 Announce Type: new
Abstract: Congenital heart disease (CHD) is the most common type of birth defect, impacting about 1% of live births worldwide. Echocardiography, the gold-standard diagnostic method, is costly and inaccessible in low-resource settings. Diagnosis is delayed due t...
Comparative Study of Bending Analysis using Physics-Informed Neural Networks and Numerical Dynamic Deflection in Perforated nanobeam
arXiv:2604.24768v1 Announce Type: new
Abstract: In this chapter, we investigate the bending behavior of a perforated nanobeam subjected to sinusoidal loading using an efficient and computationally robust Physics-Informed Functional Link Constrained Framework with Domain Mapping (DFL-TFC) method. Ou...
Performance Anomaly Detection in Athletics: A Benchmarking System with Visual Analytics
arXiv:2604.21953v1 Announce Type: new
Abstract: Anti-doping programs rely on biological testing to detect performance-enhancing drugs, but such testing costs over $800 per sample and is limited by short detection windows for many prohibited substances. These constraints leave large portions of athl...
arXiv:2604.21991v1 Announce Type: new
Abstract: Multi-task optimization is a powerful approach for solving a large number of tasks in parallel. However, existing algorithms face distinct limitations: Population-based methods scale poorly and remain underexplored for large task sets. Approaches that...
Architecture of an AI-Based Automated Course of Action Generation System for Military Operations
arXiv:2604.20862v1 Announce Type: new
Abstract: The automation system for Course of Action (CoA) planning is an essential element in future warfare. As maneuver speeds increase, surveillance ranges extend, and weapon ranges grow, the operational area expands, making traditional manned-based CoA pla...
The Tool-Overuse Illusion: Why Does LLM Prefer External Tools over Internal Knowledge?
arXiv:2604.19749v1 Announce Type: new
Abstract: Equipping LLMs with external tools effectively addresses internal reasoning limitations. However, it introduces a critical yet under-explored phenomenon: tool overuse, the unnecessary tool-use during reasoning. In this paper, we first reveal this phen...