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Neural-Network Solutions to Real-Space Charge Density and Generalization

arXiv:2609.14906v1 Announce Type: cross Abstract: The Hohenberg-Kohn theorem establishes that, in principle, the ground state (GS) charge density contains all GS information of a many-electron system,

Neural Operators for Nonlinear Functionals on RKHS

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DGX agent

arXiv:2403.12187v2 Announce Type: replace-cross Abstract: Motivated by the abundance of functional data, such as time series and images, we study the approximation and statistical learning of nonlinea

model-releasesarxiv-cs-lg

15 Sept 2026

NeuroActiSep: Detecting Factual Hallucinations from Feed-Forward Neurons in a Single Pass

arXiv:2609.14448v1 Announce Type: cross Abstract: Hallucination in large language models reduces their reliability and slows adoption. Various white-box studies have used internal representations to d

NeuroFlex: Lossless Element-Level ANN-SNN Co-Execution for Efficient Sparse Inference

arXiv:2609.14092v1 Announce Type: cross Abstract: Sparse DNN accelerators specialize in ANN or SNN execution, leaving energy or latency on the table when workload characteristics vary within a layer.

Neuron Activation-based Computation of Logical Explanations for Deep Neural Networks

arXiv:2609.14099v1 Announce Type: cross Abstract: Formal explainability of classifying neural networks (NNs) is an active area of research, providing explanations with provable guarantees of the class

NeuroProlog: Multi-Task Fine-Tuning for Neurosymbolic Mathematical Reasoning via the Cocktail Effect

arXiv:2603.02504v3 Announce Type: replace Abstract: Large Language Models (LLMs) achieve strong performance on natural language tasks but remain unreliable in mathematical reasoning, frequently genera

safetyarxiv-cs-ai

New Conditions for Philosophers to Catch the Wave of Citizen Deliberation in the Age of Artificial Intelligence in advance

arXiv:2609.15707v1 Announce Type: new Abstract: Powerful technologies labeled ``AI''-without sufficient epistemic caution-are already reshaping political and private life, bringing both new dangers an

safetyarxiv-cs-ai

New York-based AI marketing startup Profound raised a 180M Series D led by Sequoia and Kleiner Perkins at a 1.8B valuation, taking its total funding to $335M+ (Rebecca Torrence/Bloomberg)

Rebecca Torrence / Bloomberg: New York-based AI marketing startup Profound raised a 180M Series D led by Sequoia and Kleiner Perkins at a 1.8B valuation, taking its total funding to 335M+ — The artifi

industrytechmeme

Newton Deep Unfolding for Compressed Sensing

arXiv:2609.14391v1 Announce Type: new Abstract: Compressed sensing (CS) reconstructs images from highly limited measurements, but existing deep unfolding methods are typically driven by first-order op

researcharxiv-cs-cv

Neyshekar: An Open Persian Read-Speech Corpus for Automatic Speech Recognition

model-releasesarxiv-cs-cl

arXiv:2609.14542v1 Announce Type: new Abstract: Neyshekar is presented as an open Persian read-speech corpus designed for coverage of both formal and informal language, named entities, and longer utte

Applications

Noise-Adaptive Conformal Classification with Marginal Coverage

arXiv:2501.18060v2 Announce Type: replace-cross Abstract: Conformal inference provides a rigorous statistical framework for uncertainty quantification in machine learning, enabling well-calibrated pre

applicationsarxiv-cs-lg

Local Ai

Noise-Aware and Dynamically Adaptive Federated Defense Framework for SAR Image Target Recognition

arXiv:2601.00900v2 Announce Type: replace-cross Abstract: As a critical application of computational intelligence in remote sensing, deep learning-based synthetic aperture radar (SAR) image target rec

local-aiarxiv-cs-cv

Non-Asymptotic Global Convergence of PPO-Clip

arXiv:2512.16565v2 Announce Type: replace-cross Abstract: Reinforcement learning has gained attention for modern Large Language Model post-training. The actor-only variants of Proximal Policy Optimiza

Nonparametric Variance-Penalized Actor-Critic: Statistical Inference for Risk-Sensitive Reinforcement Learning

arXiv:2609.14327v1 Announce Type: new Abstract: Variance penalization is a principled approach to risk-sensitive reinforcement learning (RL) that explicitly trades expected return for policy stability

North Small Translate: Advanced Cost-Effective Translation (Cohere CAT+)

arXiv:2609.13916v1 Announce Type: new Abstract: We present North Small Translate, an open-weight, LLM-based machine translation (MT) model with instruction-following capabilities built on the same fou

agentsarxiv-cs-cl

Not All Duplicates Are Coordination: Generic vs. Non-Generic Duplicate Campaigns in Information Operations

arXiv:2609.13671v1 Announce Type: cross Abstract: Duplicate content is widely used to study coordinated behavior in social media information operations (IOs), but not all repetition provides equally m

Not all Negation Cues are Equal: Affixal Negations Yield Better Negation Understanding

arXiv:2609.13685v1 Announce Type: cross Abstract: Negation remains a longstanding challenge for both language models (LMs) and large language models (LLMs). Prior work mainly focuses on a small set of

Not All Prompts Are Equal: Exploration-Guided Prompt Scaffolding for Multimodal Reinforcement Post-Training

arXiv:2609.15051v1 Announce Type: cross Abstract: Training prompts in online reinforcement learning (RL) differ substantially in how informative they are for the current policy: some are already satur

safetyarxiv-cs-ai

NoteVQA: Benchmarking VLMs on Real-Life Questions from Human Communities

arXiv:2609.15695v1 Announce Type: new Abstract: Vision-language models (VLMs) increasingly power consumer-facing AI search, yet evaluating them on the diversity of everyday visual questions remains ch

agentsarxiv-cs-ai

Novel Ex-vivo Calf Brain Model with Integrated Sub-Skull Force Sensors to Access Simulated Neurosurgical Procedures

arXiv:2609.14198v1 Announce Type: new Abstract: Surgical tissue manipulation demands precision; however, tool-tissue manipulation force magnitudes under realistic conditions are rarely quantified. To

researcharxiv-cs-ro

‘Now We Can Know Everything and Do Anything,’ Jensen Huang Says at Dreamforce

model-releasesnvidia-blog

Know everything. Do anything. That was the message NVIDIA founder and CEO Jensen Huang brought to Salesforce Dreamforce Tuesday, joining CEO Marc Benioff onstage in an appearance that coincided with t

Nuance Labs bags $50M in funding to fix the awkwardness of AI avatars

Nuance Labs, a Seattle-based startup that’s trying to make artificial intelligence models better at face-to-face conversations, said today it has closed on a 50 million early-stage funding round to ma

industrysiliconangle

Occlusal Geometry in Closed Form for Orthodontic Report Generation

model-releasesarxiv-cs-cl

arXiv:2609.13237v1 Announce Type: cross Abstract: Orthodontic report generation from intraoral data is normally cast as multimodal captioning, yet the released Bite2Text scan pairs are supplied alread

OCT-FedSIR: Toward Trustworthy Federated Ophthalmic Learning under Annotation Noise

arXiv:2609.14734v1 Announce Type: cross Abstract: Federated learning enables collaborative model development without centralizing patient data, but annotation reliability at participating institutions

Odds-Shift Slippage in One-vs-Rest Rankers: Diagnosing and Repairing Reweighting-Induced Top-K Errors

model-releasesarxiv-cs-lg

arXiv:2609.13810v1 Announce Type: cross Abstract: One-vs-rest rankers that show each user the top-K of many rare labels usually counter imbalance with a per-label positive-class weight, scale_pos_weig

Omni-Streaming Thinking

arXiv:2609.15128v1 Announce Type: new Abstract: Streaming omni-modal models must decide what and when to answer from the video chunks and synchronized audio observed so far. Visual cues often support

On-Demand Human Assistance for Task Continuation under Physical Action Failures in LLM-based Planning

arXiv:2603.28156v2 Announce Type: replace Abstract: While robot action planning based on large language models (LLMs) has advanced remarkably, continuing a task after a physical action failure during

agentsarxiv-cs-ro

On the Equivalence of Stochastic Control and Path Space Formulations for Schrodinger Bridges over Compact Connected Lie Groups

arXiv:2609.13758v1 Announce Type: cross Abstract: We establish the equivalence between the stochastic optimal control and path space formulations of the Schrodinger bridge problem (SBP) for the kinema

On the Potential of Multi-Task Learning in Predictive Process Monitoring

arXiv:2609.13477v1 Announce Type: new Abstract: Predictive Process Monitoring (PPM) forecasts how ongoing organizational processes unfold, enabling information systems to move beyond execution support

On the role of the tokenizer in ECG transformer models

arXiv:2609.15433v1 Announce Type: cross Abstract: Tokenization determines both the physiological content presented to an ECG Transformer and the sequence over which attention operates. We compare eigh

One Example Is Enough to Pass Fairness Benchmarks: Rethinking Fairness Evaluation for Aligned LLMs

model-releasesarxiv-cs-ai

arXiv:2609.14860v1 Announce Type: cross Abstract: Warning: This submission studies stereotypes and biases, and contains toxic and offensive examples, used for illustration purposes only. Fairness benc

One Feedback System Does Not Fit All: Localising Data-to-Text Driver Coaching for the United Kingdom and Nigeria

arXiv:2609.14687v1 Announce Type: new Abstract: Data-to-text driver coaching is often presented as a generic pipeline from telematics events to advice. This paper argues that its content requires loca

safetyarxiv-cs-cl

Applications

One Model, Two Physical Stories: Auditing Misalignment in Multi-Modal World Modeling

arXiv:2609.14833v1 Announce Type: new Abstract: World models, systems that generate what happens next given current environmental conditions, are increasingly being implemented with multi-modal genera

applicationsarxiv-cs-ai

One Size Does Not Fit All: Setting Inference Depth from the Questions a Deployment Actually Asks

model-releasesarxiv-cs-cl

arXiv:2609.14144v1 Announce Type: new Abstract: A transformer language model is trained to respond to any prompt, but each deployment asks only a narrow range of questions: a support assistant sees de

One Spectrum, Two Resources: Data-Memory Scaling in Autoregressive Prediction

arXiv:2609.13500v1 Announce Type: cross Abstract: How much learned memory is needed to benefit from more data? We show that the two resources are governed by one predictive-energy spectrum in a positi

Online Bayesian Node Classification on Inductive Graphs under Distribution Shift

arXiv:2609.13655v1 Announce Type: new Abstract: On evolving graphs, node classifiers must satisfy two key requirements: inductive generalization to newly arriving nodes under distribution shift and ca

Online Language Adaptive Sampling for Better Distributed Cross-lingual Gains

model-releasesarxiv-cs-ai

arXiv:2609.14969v1 Announce Type: cross Abstract: Realignment is a promising approach for improving the cross-lingual transfer ability of multilingual language models, particularly for extremely low-r

Online local learning for generative thermodynamic computing

arXiv:2609.15439v1 Announce Type: cross Abstract: Generative thermodynamic computers turn thermal noise into structured data through Langevin dynamics. We train these systems with a local update at ea

Oops, Not Now: PEARL, a RAG-Based Support Agent for Gameplay and What Players Want from AI Help

arXiv:2609.13718v1 Announce Type: cross Abstract: AI-powered gameplay support agents hold promise for game-based learning, yet grounding generative models in structured game data remains an open chall

agentsarxiv-cs-ai

Open-UniMo: Towards Unified Motion-Language Understanding and Generation in the Open World

model-releasesarxiv-cs-ai

arXiv:2609.14615v1 Announce Type: cross Abstract: Unified motion generation and understanding is crucial for embodied AI systems that can both synthesize and interpret human actions in open-world envi

OpenAI backs a provision of the FRONTIER Act, a bipartisan House proposal, that would require AI companies to embed outside evaluators to ensure model safety (Brendan Bordelon/Politico)

Brendan Bordelon / Politico: OpenAI backs a provision of the FRONTIER Act, a bipartisan House proposal, that would require AI companies to embed outside evaluators to ensure model safety — Artificial

safetytechmeme

OpenAI says it has been working with Anthropic and Google for several weeks on AI safety, and it does not need an antitrust waiver to coordinate on safety (Maggie Eastland/Bloomberg Law)

Maggie Eastland / Bloomberg Law: OpenAI says it has been working with Anthropic and Google for several weeks on AI safety, and it does not need an antitrust waiver to coordinate on safety — OpenAI is

safetytechmeme

OpenAl4S: Code as Action, Science as Sessions

arXiv:2609.15096v1 Announce Type: new Abstract: AI co-scientists could accelerate computational research, but over a long-running study the workflow also has to stay inspectable, resumable and reprodu

agentsarxiv-cs-ai

OpenRouter users spent more on OpenAI's models than on Anthropic's in the week of September 7, the first time that happened since the week of February 26, 2024 (@openrouter)

@openrouter: OpenRouter users spent more on OpenAI's models than on Anthropic's in the week of September 7, the first time that happened since the week of February 26, 2024 — OpenRouter users spent mo

industrytechmeme

Operational Range Bounding in Spectroscopy: A Safety Cage Framework for Machine Learning Models

arXiv:2609.13514v1 Announce Type: new Abstract: Ensuring the reliability of black-box machine learning models in safety-critical space missions remains a significant challenge, particularly when groun

Tutorials

Optimal Learning Rate Schedules under Functional Scaling Laws: Power Decay and Warmup-Stable-Decay

arXiv:2602.06797v3 Announce Type: replace-cross Abstract: We study optimal learning rate (LR) schedules under the functional scaling law (FSL) framework (Li et al., 2025), which decomposes training dy

tutorialsarxiv-cs-lg

Optimal Transport for Efficient, Unsupervised Anomaly Detection on Industrial Data

model-releasesarxiv-cs-lg

arXiv:2609.13940v1 Announce Type: new Abstract: Effective anomaly detection frameworks are a central pillar of the Industry 4.0 paradigm. In this paper, we introduce an Optimal Transport (OT)-based fr

Optimizing cost and latency with Amazon Bedrock prompt caching

Prompt caching in Amazon Bedrock can cut input token costs by up to 90% when you repeatedly send the same context to foundation models. This post walks through six practical prompt caching scenarios u

industryaws-ml-blog