arXiv:2510.04584v2 Announce Type: replace Abstract: Recent advances in large audio language models (LALMs) have primarily been assessed using a multiple-choice question answering (MCQA) framework. However, subtle…
arXiv:2410.02587v2 Announce Type: replace Abstract: The total variation (TV) method is an image denoising technique that aims to reduce noise by minimizing the total variation of…
arXiv:2605.31220v2 Announce Type: replace-cross Abstract: Confidence estimation (CE), i.e., quantifying the reliability of a model's prediction, has attracted great interest in the context of large language…
arXiv:2606.26057v1 Announce Type: cross Abstract: AI agents are granted access to tools, APIs, and other infrastructure, making them active principals in those systems. The dominant approach…
arXiv:2606.25042v1 Announce Type: cross Abstract: We prove a single algebraic mixed coincidence identity that unifies a broad swath of information-theoretic variational results. For any family of…
arXiv:2506.23033v4 Announce Type: replace-cross Abstract: Fairness audits are a key component of responsible machine-learning deployment. Yet, audit-recommendation reliability under incomplete protected-label access is still poorly understood.…
arXiv:2606.25057v1 Announce Type: new Abstract: The rapid growth of scientific submissions has pushed traditional peer review toward its scalability limits, motivating the exploration of large language…
arXiv:2606.25253v1 Announce Type: new Abstract: Highlights provide a concise summary of the main contributions of an academic paper and help readers quickly understand its focus. However,…
arXiv:2606.25087v1 Announce Type: new Abstract: Neural network quantization aims to find a discrete representation of parameters that preserves the performance of a full-precision (FP) model as…
arXiv:2606.25532v1 Announce Type: cross Abstract: Artificial intelligence increasingly drives automated scientific discovery, yet contemporary generalist agents lack physical grounding, frequently hallucinating hardware-incompatible designs. Here, we present…
arXiv:2606.24956v1 Announce Type: new Abstract: Spectral graph neural networks (GNNs) interpret message passing as frequency-selective filtering. While low-order spectral filters are efficient, their limited selectivity often…
arXiv:2606.26053v1 Announce Type: new Abstract: Synthetic data augmentation is widely used to mitigate class imbalance, but its theoretical effects on score-based classification remain poorly understood. This…
arXiv:2606.25102v1 Announce Type: new Abstract: Large language models have transformed code generation, raising concerns around authorship, assessment integrity, and software trust. SemEval-2026 Task 13 Subtask A…
arXiv:2602.11933v2 Announce Type: replace Abstract: End-to-End Speech Translation (E2E-ST) has seen significant advancements, yet current models are primarily benchmarked on curated, "clean" datasets. This overlooks critical…
arXiv:2507.16863v2 Announce Type: replace Abstract: A common belief in multimodal research is that the perceptual weaknesses of vision--language models can be compensated by stronger language reasoning…
arXiv:2606.24947v1 Announce Type: new Abstract: The increasing integration of distributed energy resources (DERs) is crucial for power system decarbonization, yet unlocking DERs' flexibility is challenged by…
arXiv:2506.04281v3 Announce Type: replace Abstract: Compound flooding, driven by nonlinear interactions between multiple hydrometeorological factors, poses a significant challenge to hazard prevention. Existing forecasting approaches, whether…
arXiv:2606.24981v1 Announce Type: cross Abstract: We study linear TD(0) under Markovian sampling, where data are generated along a single trajectory. We provide high-probability guarantees for a…
arXiv:2606.24957v1 Announce Type: new Abstract: While speculative decoding improves inference throughput for multi-batch long-context Large Language Models (LLMs), its efficiency is often limited by a verification…
arXiv:2606.25579v1 Announce Type: cross Abstract: Background: Response to neoadjuvant imatinib in gastrointestinal stromal tumors (GISTs) is highly variable and cannot be reliably predicted using current clinical…
arXiv:2606.25215v1 Announce Type: new Abstract: Most vision-language-action (VLA) models are reactive: they predict the next action from the current instruction and observation, implicitly assuming that the…
arXiv:2606.24948v1 Announce Type: new Abstract: Knowledge graph embedding (KGE) models predict single-hop links well but have no mechanism for zero-shot compositional queries: multi-hop questions whose relation…
arXiv:2606.17775v2 Announce Type: replace-cross Abstract: Efficient processing of continuous audio streams remains a key challenge for real-time and resource-constrained systems. This paper introduces a neuromorphic trigger…
arXiv:2606.26037v1 Announce Type: new Abstract: Federated learning has emerged as the foremost approach for decentralized model training with privacy preservation. The global class imbalance and cross-client…