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 ...
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...
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...
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...
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...
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...
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...
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...
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...
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...
Algorithm Selection with Zero Domain Knowledge via Text Embeddings
arXiv:2604.19753v1 Announce Type: new
Abstract: We propose a feature-free approach to algorithm selection that replaces hand-crafted instance features with pretrained text embeddings. Our method, ZeroFolio, proceeds in three steps: it reads the raw instance file as plain text, embeds it with a pret...
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...
On-Meter Graph Machine Learning: A Case Study of PV Power Forecasting for Grid Edge Intelligence
arXiv:2604.19800v1 Announce Type: new
Abstract: This paper presents a detailed study of how graph neural networks can be used on edge intelligent meters in a microgrid to forecast photovoltaic power generation. The problem background and the adopted technologies are introduced, including ONNX and O...
Accelerating PayPal's Commerce Agent with Speculative Decoding: An Empirical Study on EAGLE3 with Fine-Tuned Nemotron Models
arXiv:2604.19767v1 Announce Type: new
Abstract: We evaluate speculative decoding with EAGLE3 as an inference-time optimization for PayPal's Commerce Agent, powered by a fine-tuned llama3.1-nemotron-nano-8B-v1 model. Building on prior work (NEMO-4-PAYPAL) that reduced latency and cost through domain...
On Solving the Multiple Variable Gapped Longest Common Subsequence Problem
arXiv:2604.18645v1 Announce Type: new
Abstract: This paper addresses the Variable Gapped Longest Common Subsequence (VGLCS) problem, a generalization of the classical LCS problem involving flexible gap constraints between consecutive solutions' characters. The problem arises in molecular sequence c...
The Cost of Relaxation: Evaluating the Error in Convex Neural Network Verification
arXiv:2604.18728v1 Announce Type: new
Abstract: Many neural network (NN) verification systems represent the network's input-output relation as a constraint program. Sound and complete, representations involve integer constraints, for simulating the activations. Recent works convexly relax the integ...
Apple is advancing AI and ML with fundamental research, much of which is shared through publications and engagement at conferences in order to accelerate progress in this important field and support the broader community. This week, the Fourteenth International Conference on Learning Representations...