arXiv Machine Learning
Jul 3
From Approximation to Emergence: A Theory of Deep Learning
arXiv:2607. 01311v1 Announce Type: new Abstract: Deep learning has outgrown any single mathematical explanation.
Large language models
Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
arXiv AI
Jul 3
ContraFix: Skill-Enhanced Contrastive Runtime Analysis for Vulnerability Repair
arXiv:2605. 17450v2 Announce Type: replace-cross Abstract: As software systems grow increasingly complex, automated vulnerability repair (AVR) remains difficult because the materials available to a repair system are usually failure artifacts rather than repair guidance.
arXiv AI
Jul 3
Safety Testing LLM Agents at Scale: From Risk Discovery to Evidence-Grounded Verification
arXiv:2607. 01793v1 Announce Type: new Abstract: LLM agents increasingly perform autonomous actions through external tools, leading to complex and evolving safety risks.
arXiv AI
Jul 3
Evergreen: Efficient Claim Verification for Semantic Aggregates
arXiv:2604. 26180v2 Announce Type: replace-cross Abstract: With recent semantic query processing engines, semantic aggregation has become a primitive operator, enabling the reduction of a relation into a natural language aggregate using an LLM.
arXiv Machine Learning
Jul 3
Embedding Inference Attack
arXiv:2607. 01276v1 Announce Type: cross Abstract: Embedding models are essential components of modern Information Retrieval (IR) systems, yet they are typically hidden behind APIs.
arXiv AI
Jul 3
Hidden Forgetting in Continual Multimodal Learning: When Accuracy Survives but Grounding Fails
arXiv:2607. 02020v1 Announce Type: new Abstract: Multimodal large language models must continually adapt to evolving tasks and domains, yet standard continual learning metrics mainly measure whether old answers remain correct, leaving the stability of multimodal grounding largely unexamined.
arXiv AI
Jul 3
An Isotropic Approach to Efficient Uncertainty Quantification with Gradient Norms
arXiv:2603. 29466v2 Announce Type: replace-cross Abstract: Existing methods for quantifying predictive uncertainty in neural networks are either computationally intractable for large language models or require access to training data that is typically unavailable.
arXiv Machine Learning
Jul 3
Scaling to Multimodal and Multichannel Heart Sound Classification with Synthetic and Augmented Biosignals
arXiv:2509. 11606v4 Announce Type: replace-cross Abstract: Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for approximately 17.
arXiv AI
Jul 3
Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning
arXiv:2604. 02091v2 Announce Type: replace-cross Abstract: Rerankers play a pivotal role in refining retrieval results for Retrieval-Augmented Generation.
arXiv Machine Learning
Jul 3
Many Voices, One Reward: Multi-Role Rubric Generation for LLM Judging and Reward Modeling
arXiv:2607. 01830v1 Announce Type: new Abstract: Reliable reward and preference signals are critical for evaluating and optimizing large language models on open-ended tasks.
arXiv AI
Jul 3
Black-Box Inference of LLM Architectural Properties with Restrictive API Access
arXiv:2607. 01313v1 Announce Type: cross Abstract: In practice, most commercial LLM providers do not publicly release details of underlying LLM architectures.
arXiv Machine Learning
Jul 3
Do LLMs Truly Generalize in the Molecular Domain? A Perturbation-Based Analysis
arXiv:2607. 01800v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently shown promise in molecular discovery, yet a gap remains between their probabilistic nature over discrete sequential tokens and the rigid topological constraints of chemical space.
arXiv Machine Learning
Jul 3
WARP: Weight-Space Analysis for Recovering Training Data Portfolios
arXiv:2607. 01686v1 Announce Type: new Abstract: Foundation models are routinely released to the public, yet the data recipes used to train them -- such as domain mixture weights that determine how different sources are sampled -- are rarely disclosed.
arXiv AI
Jul 3
DemoPSD: Disagreement-Modulated Policy Self-Distillation
arXiv:2607. 02502v1 Announce Type: cross Abstract: On-policy self-distillation (OPSD) has emerged as a practical method for training large language models (LLMs) to reason, where a single model acts as both the teacher and the student with different levels of information access.
arXiv AI
Jul 3
What LLM Agents Say When No One Is Watching: Social Structure and Latent Objective Emergence in Multi-Agent Debates
arXiv:2607. 02507v1 Announce Type: new Abstract: LLM agents will increasingly act in socially structured settings where role, audience, and relational context can shape what is advantageous or costly to say.
arXiv Machine Learning
Jul 3
Dendritic In-Context Learning in a Single-Layer Spiking Neural Network
arXiv:2607. 02283v1 Announce Type: cross Abstract: In-context learning (ICL) operates via implicit gradient descent embedded in the forward pass of modern AI architectures -- Transformers, Mamba, state-space models, and MLPs.
arXiv Machine Learning
Jul 3
WattGPU: Predicting Inference Power and Latency on Unseen GPUs and LLMs
arXiv:2607. 02391v1 Announce Type: cross Abstract: Large Language Model (LLM) inference workloads are a rapidly growing contributor to data center energy consumption.
arXiv AI
Jul 3
Hawk: Harnessing Hardware-Aware Knowledge for High-Performance NPU Kernel Generation
arXiv:2607. 01590v1 Announce Type: new Abstract: Developing high-performance kernels for Neural Processing Units (NPUs) is a critical industry bottleneck, requiring developers to manually navigate implicit hardware constraints and strict memory hierarchies.
arXiv AI
Jul 3
Verifiable Knowledge Expansion through Retrieval-Grounded Formal Concept Analysis
arXiv:2607. 01773v1 Announce Type: new Abstract: Ontology construction requires deciding which objects, attributes, and structural relations should be accepted as valid knowledge.
arXiv Machine Learning
Jul 3
LLM Priors for ERM over Programs
arXiv:2510. 14331v3 Announce Type: replace Abstract: We study program-learning methods that are efficient in both samples and computation.