Search: “governance”

Jigsaw-CRL: Recovering Global Latent Causal Order from Fragmented Multi-Client Interventions

arXiv:2608.28991v1 Announce Type: cross Abstract: Causal representation learning (CRL) aims to recover latent causal variables and their structural relations from high-dimensional observations. Existi

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Hardware

Korea’s Trillion-Dollar Sovereign AI Investment: Nvidia Wins, Hynix Loses

DGX agent

South Korea announced a sovereign AI investment exceeding one trillion dollars, awarding most of the contracts to Nvidia while sidelining competitors such as Hynix due to policy alignment criteria. Th

hardwaresemianalysis

1 Sept 2026

Learning Representations through Token Prediction: Geometry, Approximation, and Downstream Guarantees

arXiv:2608.30072v1 Announce Type: cross Abstract: Token prediction is a central pre-training objective for modern language models. Despite its empirical success, why token prediction learns broadly us

Learning the Geometry of Admissible Hypotheses through Inductive Bias in Training Distributions

arXiv:2608.31028v1 Announce Type: cross Abstract: Scientific discovery often requires reasoning over competing hypotheses that are consistent with experimental observations. For mixed-variable and com

LLM Post-Training as Brownfield Maintenance: An Industrial Perspective on Dataware Engineering

model-releasesarxiv-cs-ai

arXiv:2608.31102v1 Announce Type: cross Abstract: Industrial post-training is a brownfield regime. Teams inherit a deployed checkpoint and must land targeted improvements under fixed compute and mixtu

Mamba-Assisted Non-Markovian Closure for Reduced-Order Modeling

arXiv:2606.05371v2 Announce Type: replace Abstract: Reduced-order modeling of high-dimensional dynamical systems is often hindered by closure effects arising from unresolved variables, which can intro

Measuring Memory and Generalization as Separable Geometric Channels: The Topo^2 Framework

arXiv:2608.30487v1 Announce Type: cross Abstract: Deep networks trained on noisy labels simultaneously generalize on clean data and memorize flipped labels. These are usually conflated as pressures on

researcharxiv-cs-ai

Normalized Low-Rank Adaptation

arXiv:2608.31036v1 Announce Type: new Abstract: While low-rank adaptation (LoRA) is widely used for parameter-efficient model adaptation, how to regularize its training dynamics for stable and effecti

Industry

OpenAI says it plans to publicly release a version of Astra 'soon' but will make its advanced cyber capabilities available only to select partners (Wired)

Wired: OpenAI says it plans to publicly release a version of Astra “soon” but will make its advanced cyber capabilities available only to select partners — The company will give select partners early

industrytechmeme

Paper Pilot: A Human-in-the-Loop Expert System for Evidence-Traceable Scientific Manuscript Generation in Applied Sciences

model-releasesarxiv-cs-ai

arXiv:2608.28596v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly embedded in scientific workflows for literature analysis, drafting, and review. Existing systems adva

Reference-Grafting Matches Fine-Tuning at Eliciting Sandbagged Capabilities

arXiv:2608.29458v1 Announce Type: cross Abstract: Sandbagging, in which a model deliberately underperforms on an evaluation despite retaining the underlying capability, threatens the safety evaluation

safetyarxiv-cs-ai

Responsible Integration of AI in Cancer Genomics: Barriers, Risks, and Pathways to Trustworthy Clinical Translation

arXiv:2608.30912v1 Announce Type: new Abstract: Artificial intelligence (AI) and natural language processing (NLP) are increasingly used to extract, integrate, and interpret biomedical knowledge relev

safetyarxiv-cs-ai

Taking the Whys Seriously: Limitations of Counterfactual Explanations in Justification and Recourse

arXiv:2608.30956v1 Announce Type: cross Abstract: Counterfactual explanations (CEs) are widely used in explainable artificial intelligence (AI) to show how a model's outputs would change if the input

researcharxiv-cs-ai

The Double-Edged Nature of the Rashomon Set for Trustworthy Machine Learning

arXiv:2511.21799v2 Announce Type: replace Abstract: Real-world machine learning (ML) pipelines rarely produce a single model; instead, they produce a Rashomon set of many near-optimal ones. We show th

applicationsarxiv-cs-lg

The Value of Depth in Message Passing on Sparse Graphs: A Kesten-Stigum Dichotomy

arXiv:2607.16676v2 Announce Type: replace-cross Abstract: How deep does a graph neural network need to be on a sparse graph? We study its purest statistical form: node classification on the sparse con

TrainSDC: Characterizing and Mitigating Silent Data Corruption in Large Language Model Training

arXiv:2608.30769v1 Announce Type: new Abstract: LLM training is increasingly vulnerable to silent data corruption (SDC), yet existing protection methods largely treat Transformer computations uniforml

VFR-Audit: Verdict-Level Reliability for Fairness Audits in Hospital Length-of-Stay Prediction

arXiv:2608.30846v1 Announce Type: new Abstract: Fairness audits in clinical Artificial Intelligence convert continuous fairness metrics into binary pass-or-fail verdicts against operational thresholds

safetyarxiv-cs-ai

Whatever you may think about anthropomorphic language, the most immediate “house that is on fire” is the combination of OpenAI’s lax securit…

Whatever you may think about anthropomorphic language, the most immediate “house that is on fire” is the combination of OpenAI’s lax security procedures and the complete lack of appropriate federal st

safetygary-marcus--x

Agents

When agents move at machine speed, security teams lose their lag time

The agentic AI attack surface is less a matter of new territory than of new velocity, as autonomous software now reads, writes and moves corporate content faster than any human adversary could. That s

agentssiliconangle

Which LLM for Which Work? Budgeted Model Allocation under Uncertain Evaluation

arXiv:2608.29560v1 Announce Type: new Abstract: A company with a fixed artificial intelligence (AI) budget must decide which large language model (LLM) handles each recurring workload. What it lacks i

safetyarxiv-cs-lg

A Formal Limitation on Learning Human Language From Textual Corpora

arXiv:2608.28560v1 Announce Type: new Abstract: Can a listener recover what a speaker means from the form of an utterance alone? We answer this question information-theoretically, and for a listener g

researcharxiv-cs-cl

Climate Physics Dynamic Matching

model-releasesarxiv-cs-cv

arXiv:2608.26907v1 Announce Type: cross Abstract: Deep generative models such as flow matching and diffusion models have shown potential for learning complex dynamical systems, but typically act as bl

Agents

Code as Worlds: Agentic Discovery of Executable World Representations for Physical Reasoning

arXiv:2608.27549v1 Announce Type: new Abstract: Physical understanding and reasoning depend on forming compact and generalizable representations of the world. While modern vision-language models can r

agentsarxiv-cs-cv

DisCTI: Who Needs to Know Timely? Automated Sector-Aware Cyber Threat Intelligence Dissemination

arXiv:2608.27967v1 Announce Type: cross Abstract: The timely dissemination of cyber threat intelligence (CTI) is critical for organizations to mount swift and effective incident response. When valid C

applicationsarxiv-cs-cl

Fully Unleashing the Multimodal Attacker: Meta-Adaptive Jailbreaking of Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2608.27531v1 Announce Type: cross Abstract: The safety of large vision-language models is increasingly stress-tested by multimodal jailbreaks, yet existing attacks remain largely static at the m

Learning between the peaks: sharp asymptotics for kernel ridge regression under power-law anisotropy

arXiv:2608.28564v1 Announce Type: cross Abstract: We study kernel ridge regression under anisotropic Gaussian data, where the input covariance decays as a power law with exponent alphageq 0 for polyno

safetyarxiv-cs-lg

Hardware

Not to Break, but to Attest: Adversarial Probes for Privacy-Preserving LLM Verification

arXiv:2608.27954v1 Announce Type: cross Abstract: Post-deployment changes to large language models can alter behavior while leaving routine outputs largely unchanged, creating a challenge for AI gover

hardwarearxiv-cs-lg

QGPINNs: A Physics-Informed Neural Network Framework for Nonlocal Differential Equations on Quantum Graphs

arXiv:2608.28589v1 Announce Type: new Abstract: We propose QGPINNs, a physics-informed neural network framework developed in PyTorch for the numerical solution of nonlocal differential equations on qu

RecourseBench: A Modular Framework for Reproducible Algorithmic Recourse Evaluation

arXiv:2606.16113v2 Announce Type: replace-cross Abstract: Algorithmic recourse methods provide counterfactual explanations that inform individuals of the actions required to overturn an unfavorable mo

Remote Human and Robot Interaction for Greenhouse Gardening Using Virtual Reality

arXiv:2608.27545v1 Announce Type: new Abstract: This study evaluates the effectiveness of remote human-robot interaction using virtual reality for leaf inspection and soil moisture assessment in a gre

researcharxiv-cs-ro

Shift Before You Learn: Enabling Low-Rank Representations in Reinforcement Learning

arXiv:2509.05193v3 Announce Type: replace Abstract: Low-rank structure is a common implicit assumption in many modern reinforcement learning (RL) algorithms. For instance, reward-free and goal-conditi

Agents

Synthetic Linguistic Agency: How an Embodied Mortal Agent Learns Linguistic Affordances through Consequential Social Experience

arXiv:2608.27843v1 Announce Type: new Abstract: Contemporary language models can converse fluently and influence human decisions, yet their exchanges do not enter a continuing, vulnerable life of thei

agentsarxiv-cs-cl

This is a watershed moment for cybersecurity, but the implications go much further than that. When agents go out into the world and are put …

applicationsgary-marcus--x

This is a watershed moment for cybersecurity, but the implications go much further than that. When agents go out into the world and are put in users’ hands, they will be tasked with actions that requi

14/ If frontier labs are serious about defense, they should offer cyber models to defenders at cost and stop fighting open weights through t…

14/ If frontier labs are serious about defense, they should offer cyber models to defenders at cost and stop fighting open weights through the regulatory channel. They created the problem, and for muc

safetygary-marcus--x

30 Aug 2026

AdaThinking-E: One-Token Entropy Regulation for Adaptive Thinking

model-releasesarxiv-cs-cl

arXiv:2608.26141v1 Announce Type: new Abstract: Multimodal large language models have demonstrated strong document reasoning capabilities by incorporating explicit thinking processes. While this capab

CLAP: Cross-Embodiment Video World Models are Zero-Shot Physical Simulators

arXiv:2608.27406v1 Announce Type: cross Abstract: State-of-the-art action-conditioned video models are typically restricted to a single robot embodiment, preventing them from leveraging the vast corpu

applicationsarxiv-cs-ai

Constraint-Aware Physics-Informed Neural Networks for Static Shape Estimation of Co-Manipulative Continuum Robots

arXiv:2608.26273v1 Announce Type: cross Abstract: Static shape estimation of co-manipulative continuum robots (CCRs) is challenging because the continuum arms and manipulated flexible object form a cl

Data Science Approaches to Evaluating Honours Candidates

arXiv:2608.26135v1 Announce Type: new Abstract: We present a modular data-science pipeline for estimating public sentiment towards individuals from fragmented, unstructured open-source intelligence (O

researcharxiv-cs-cl

DeflectBench: A Benchmark for Evaluating Rhetorical Fallacy Generation in LLMs

model-releasesarxiv-cs-cl

arXiv:2608.26119v1 Announce Type: new Abstract: Whether large language models can be prompted to generate rhetorical fallacies on demand, and whether current safety post-training constrains this behav

Disentangling Optimization Scale from Preference Scale in DPO

arXiv:2608.27032v1 Announce Type: new Abstract: Direct Preference Optimization (DPO) is a widely used objective for aligning language models from preference data, with the coefficient eta commonly int

safetyarxiv-cs-lg

Dynamical phase selection controls compute scaling in looped transformers

arXiv:2608.26556v1 Announce Type: cross Abstract: A looped transformer performs inference by iterating a weight-tied map, making its computation a dynamical process whose cost is set by the resulting

Exploring the Role of LLMs in HPC Programming: A Survey

arXiv:2608.26110v1 Announce Type: cross Abstract: Large Language Models (LLMs) are emerging as promising assistants in High-Performance Computing (HPC), where programming remains complex and expertise

applicationsarxiv-cs-ai

Local Ai

Fairness Invariants: A Relational Approach to Explaining and Mitigating Fairness Bugs

arXiv:2608.26209v1 Announce Type: cross Abstract: Data-driven software systems are increasingly deployed in high-stakes socio-economic domains, from criminal justice to financial lending. However, the

local-aiarxiv-cs-ai

Instruction Quality Matters: Refining Instructions for Effective Preference Learning

arXiv:2608.26779v1 Announce Type: new Abstract: Preference learning optimizes models using response pairs, yet the informativeness of these pairs is fundamentally shaped by the instructions from which

safetyarxiv-cs-cl

LeVJEPA: Efficient & Scalable Video Pretraining without the Heuristics

arXiv:2608.27395v1 Announce Type: cross Abstract: Video carries the temporal structure of the physical world, yet learning representations from it has remained computationally expensive: prevailing se

researcharxiv-cs-ai

Private and interpretable clinical prediction with quantum-inspired tensor train models

arXiv:2602.06110v2 Announce Type: replace Abstract: Publicly available clinical machine learning models pose an underappreciated privacy risk: their parameters or outputs can be exploited to recover i

QuantumBoostNet: A Hybrid Classical-Quantum Architecture for Enhanced Accuracy in Cardiac Ultrasound View Identification

arXiv:2608.27302v1 Announce Type: new Abstract: Accurate identification of the correct view or angle in cardiac ultrasound (echocardiogram) is a critical component of cardiologic imaging. This step is

TabuLM: Morphology-Aware Tabular Pre-training for Low-Resource Languages

model-releasesarxiv-cs-cl

arXiv:2608.26923v1 Announce Type: new Abstract: We present TabuLM, the first language model pre-trained on Kinyarwanda tabular data. Kinyarwanda is a morphologically rich Bantu language spoken by over