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A Proof-of-Concept Simulation-Driven Digital Twin Framework for Decision-Aware Diabetes Modeling

model-releasesarxiv-cs-lg

arXiv:2605.11247v1 Announce Type: new Abstract: This paper presents a proof-of-concept digital twin framework for simulation-driven diabetes modeling using benchmark clinical data, synthetic temporal

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Model Releases

A Proprioceptive-Only Benchmark for Quadruped State Estimation: ATE, RPE, and Runtime Trade-offs Between Filters and Smoothers

DGX agent

arXiv:2605.11674v1 Announce Type: new Abstract: We compare three state-of-the-art proprioceptive state estimators for quadruped robots: MUSE [1], the Invariant Extended Kalman Filter (IEKF) [2], and t

model-releasesarxiv-cs-ro

13 May 2026

A proximal gradient algorithm for composite log-concave sampling

arXiv:2605.12461v1 Announce Type: cross Abstract: We propose an algorithm to sample from composite log-concave distributions over R^d, i.e., densities of the form pipropto e^{-f-g}, assuming access to

A Semi-Supervised Framework for Speech Confidence Detection using Whisper

arXiv:2605.12387v1 Announce Type: cross Abstract: Automatic detection of speaker confidence is critical for adaptive computing but remains constrained by limited labelled data and the subjectivity of

A Study on Hidden Layer Distillation for Large Language Model Pre-Training

arXiv:2605.11513v1 Announce Type: new Abstract: Knowledge Distillation (KD) is a critical tool for training Large Language Models (LLMs), yet the majority of research focuses on approaches that rely s

A Survey of On-Policy Distillation for Large Language Models

arXiv:2604.00626v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) continue to grow in both capability and cost, transferring frontier capabilities into smaller, deployable stud

safetyarxiv-cs-cl

A survey predicted that 43% of companies would be using AI “as scale” by now. In reality, the figure is just 19%.

A survey predicted that 43% of companies would be deploying AI at scale by the current date, but actual adoption has reached only 19%, indicating a significant gap between forecasted and realized AI i

safetygary-marcus--x

A Switching System Theory of Q-Learning with Linear Function Approximation

model-releasesarxiv-cs-lg

arXiv:2605.11021v1 Announce Type: new Abstract: This paper develops a switching-system interpretation of Q-learning with linear function approximation (LFA) based on the joint spectral radius (JSR). W

A Theoretical Analysis of Why Masked Diffusion Models Mitigate the Reversal Curse

arXiv:2602.02133v2 Announce Type: replace-cross Abstract: Autoregressive language models (ARMs) suffer from the reversal curse: after learning ''A is B,'' they often fail on the reverse query ''B is A

A Theory of Time-Sensitive Language Generation: Sparse Hallucination Beats Mode Collapse

arXiv:2605.11302v1 Announce Type: cross Abstract: We study language generation in the limit under a global preference ordering on strings, as introduced by Kleinberg and Wei. As in [arXiv:2504.14370,

safetyarxiv-cs-cl

Tutorials

A Transfer Learning Evaluation of Deep Neural Networks for Image Classification

arXiv:2605.11989v1 Announce Type: new Abstract: Transfer learning is a machine learning technique that uses previously acquired knowledge from a source domain to enhance learning in a target domain by

tutorialsarxiv-cs-cv

A Unified Graph Language Model for Multi-Domain Multi-Task Graph Alignment Instruction Tuning

arXiv:2605.12197v1 Announce Type: new Abstract: Leveraging Graph Neural Networks (GNNs) as graph encoders and aligning the resulting representations with Large Language Models (LLMs) through alignment

Industry

ABAC row filtering and column masking policies, governed tags, and data classification are now generally available in Unity Catalog

Databricks has released general availability for attribute-based access control (ABAC) features in Unity Catalog, including row filtering and column masking policies that dynamically restrict data acc

industrydatabricks

ABRA: Agent Benchmark for Radiology Applications

model-releasesarxiv-cs-cv

arXiv:2605.11224v1 Announce Type: new Abstract: Existing medical-agent benchmarks deliver imaging as pre-selected samples, never as an environment the agent must navigate. We introduce ABRA, a radiolo

Hardware

Accelerated X-Ray Analysis for Nanoscale Imaging (XANI) of Novel Materials

XANI (X-ray Analysis for Nanoscale Imaging) is an accelerated computing pipeline developed by NVIDIA engineers for rapid X-ray data analysis. NVIDIA's accelerated computing enables real-time experimen

hardwarenvidia-developer

Acceleration of horizontal numerical advection for atmospheric modeling through surrogate modeling with temporal coarse-graining

arXiv:2605.10956v1 Announce Type: cross Abstract: Machine-learned surrogate modeling of advection may accelerate geoscientific models, but existing approaches have either achieved limited speedup or h

ACSAC: Adaptive Chunk Size Actor-Critic with Causal Transformer Q-Network

arXiv:2605.11009v1 Announce Type: new Abstract: Long-horizon, sparse-reward tasks pose a fundamental challenge for reinforcement learning, since single-step TD learning suffers from bootstrapping erro

Action Hallucination in Generative Vision-Language-Action Models

arXiv:2602.06339v2 Announce Type: replace Abstract: Robot Foundation Models, such as VLAs, promise end-to-end generative robot policies with broad generalization. Yet it remains unclear whether they f

researcharxiv-cs-ro

Active inference as a unified model of collision avoidance behavior in human drivers

arXiv:2506.02215v5 Announce Type: replace Abstract: Collision avoidance -- involving a rapid threat detection and quick execution of the appropriate evasive maneuver -- is a critical aspect of driving

researcharxiv-cs-ro

AcuityBench: Evaluating Clinical Acuity Identification and Uncertainty Alignment

arXiv:2605.11398v1 Announce Type: cross Abstract: We introduce AcuityBench, a benchmark for evaluating whether language models identify the appropriate urgency of care from user medical presentations.

Ada-MK: Adaptive MegaKernel Optimization via Automated DAG-based Search for LLM Inference

arXiv:2605.11581v1 Announce Type: new Abstract: When large language models (LLMs) serve real-time inference in commercial online advertising systems, end-to-end latency must be strictly bounded to the

Adaption, co-founded by ex-Cohere VP of AI research Sara Hooker, unveils AutoScientist, which can automate the research loop behind model training and alignment (Russell Brandom/TechCrunch)

Russell Brandom / TechCrunch: Adaption, co-founded by ex-Cohere VP of AI research Sara Hooker, unveils AutoScientist, which can automate the research loop behind model training and alignment — For yea

safetytechmeme

Adaptive Calibration in Non-Stationary Environments

arXiv:2605.11490v1 Announce Type: new Abstract: Making calibrated online predictions is a central challenge in modern AI systems. Much of the existing literature focuses on fully adversarial environme

Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness Predictions

arXiv:2211.03524v2 Announce Type: replace Abstract: Modern Review Helpfulness Prediction systems are dependent upon multiple modalities, typically texts and images. Unfortunately, those contemporary a

Adaptive Policy Learning Under Unknown Network Interference

arXiv:2605.11191v1 Announce Type: cross Abstract: Adaptive experimentation under unknown network interference requires solving two coupled problems: (i) learning the underlying dynamics of interferenc

Adaptive TD-Lambda for Cooperative Multi-agent Reinforcement Learning

arXiv:2605.11880v1 Announce Type: new Abstract: TD(lambda) in value-based MARL algorithms or the Temporal Difference critic learning in Actor-Critic-based (AC-based) algorithms synergistically integra

Adaptive Teacher Exposure for Self-Distillation in LLM Reasoning

arXiv:2605.11458v1 Announce Type: cross Abstract: On-policy self-distillation has become a strong recipe for LLM reasoning, where a privileged teacher supervises the student's own rollouts while condi

safetyarxiv-cs-cl

Local Ai

ADMM-Q: An Improved Hessian-based Weight Quantizer for Post-Training Quantization of Large Language Models

arXiv:2605.11222v1 Announce Type: new Abstract: Quantization is an effective strategy to reduce the storage and computation footprint of large language models (LLMs). Post-training quantization (PTQ)

local-aiarxiv-cs-lg

Adversarial Causal Tuning for Realistic Time-series Generation

arXiv:2506.02084v2 Announce Type: replace Abstract: We address the problem of generating simulated, yet realistic, time-series data from a causal model with the same observational and interventional d

Adversarial Effects on Expressibility and Trainability in Distributed Variational Quantum Algorithms

arXiv:2605.03629v1 Announce Type: cross Abstract: Distributed quantum algorithms offer a promising pathway to scale variational quantum algorithms beyond the constraints of noisy intermediate-scale qu

Applications

AESOP: Adversarial Execution-path Selection to Overload Deep Learning Pipelines

arXiv:2605.10987v1 Announce Type: new Abstract: Modern machine learning deployments increasingly compose specialized models into dynamic inference pipelines, where upstream components produce intermed

applicationsarxiv-cs-lg

After months in stealth, my co-founder @helloericsf and I are finally sharing @cimentoai with the world. 🌎 AI changed social engineering. A…

After months in stealth, my co-founder @helloericsf and I are finally sharing @cimentoai with the world. 🌎 AI changed social engineering. Attacks are now personalized, convincing, and cheap to generat

agentsyohei-nakajima--x

Agent-Based Post-Hoc Correction of Agricultural Yield Forecasts

model-releasesarxiv-cs-lg

arXiv:2605.12375v1 Announce Type: new Abstract: Accurate crop yield forecasting in commercial soft fruit production is constrained by the data available in typical commercial farm records, which lack

Agent-BRACE: Decoupling Beliefs from Actions in Long-Horizon Tasks via Verbalized State Uncertainty

arXiv:2605.11436v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed on long-horizon tasks in partially observable environments, where they must act while inferring a

Agent observability is more than just logging and viewing traces. You need agents sitting on top of your traces to proactively identify issu…

Agent observability is more than just logging and viewing traces. You need agents sitting on top of your traces to proactively identify issues. We're launching Engine. Think of it as an always-on AI e

agentsharrison-chase--x

AgentDisCo: Towards Disentanglement and Collaboration in Open-ended Deep Research Agents

arXiv:2605.11732v1 Announce Type: cross Abstract: In this paper, we present AgentDisCo, a novel Disentangled and Collaborative agentic architecture that formulates deep research as an adversarial opti

AgentShield: Deception-based Compromise Detection for Tool-using LLM Agents

arXiv:2605.11026v1 Announce Type: cross Abstract: Defenses against indirect prompt injection (IPI) in tool-using LLM agents share two structural weaknesses. First, they all attempt to prevent attacks

agentsarxiv-cs-cl

agreed. RL is not (at least by itself) the way to alignment

agreed. RL is not (at least by itself) the way to alignment Yoshua Bengio says Reinforcement Learning is a dangerous path for building superintelligence It can create systems with hidden goals, reward

safetygary-marcus--x

Applications

AI ambition is crashing into a decade of deferred IT maintenance, says Red Hat CEO

Enterprise AI infrastructure modernization has reached a critical crossroads as organizations grapple with decades of technical debt while facing intense pressure to deploy AI — and returning to IT fu

applicationssiliconangle

AI chatbots are giving out people’s real phone numbers

People report that their personal contact info was surfaced by Google AI—and there’s apparently no easy way to prevent it. A Redditor recently wrote that he was “desperate for help”: for about a month

researchmit-tech-review

Tools

AI powered creativity on full display. Vibecon transforms NYC into a living gallery of code-driven installations, June 17–18. Two days where…

AI powered creativity on full display. Vibecon transforms NYC into a living gallery of code-driven installations, June 17–18. Two days where code becomes the environment and the medium becomes the mes

toolsreplit--x

AIA: Rethinking Architecture Decoupling Strategy In Unified Multimodal Model

arXiv:2511.22663v5 Announce Type: replace Abstract: Unified multimodal models for image generation and understanding represent a significant step toward AGI and have attracted widespread attention fro

safetyarxiv-cs-cv

Tools

[AINews] The End of Finetuning

This article likely discusses how advances in large language models, prompt engineering, and in-context learning are making traditional finetuning less necessary for many applications. It probably exp

toolslatent-space

Industry

Alexa is moving into Amazon․com

Amazon is bringing Alexa Plus to Amazon.com, integrating its LLM-powered AI assistant directly into the company's shopping experience. Beginning today, when you type a query into Amazon, you'll be tal

industrythe-verge-ai

.@alexsobel's new AI kill-switch amendment gives government the power to shut down data centers in cases of AI emergency. It's also the firs…

.@alexsobel's new AI kill-switch amendment gives government the power to shut down data centers in cases of AI emergency. It's also the first piece of proposed UK law recognizing ASI as the national s

safetyconnor-leahy--x

Industry

Alibaba reports Q4 revenue up 3% YoY to ~35.8B, below ~36.3B est., and net income up ~100% to ~$3.7B, in part due to investments, as it seeks to monetize AI (Luz Ding/Bloomberg)

Luz Ding / Bloomberg: Alibaba reports Q4 revenue up 3% YoY to ~35.8B, below ~36.3B est., and net income up ~100% to ~$3.7B, in part due to investments, as it seeks to monetize AI — Alibaba Group Holdi

industrytechmeme

Aligning Flow Map Policies with Optimal Q-Guidance

arXiv:2605.12416v1 Announce Type: new Abstract: Generative policies based on expressive model classes, such as diffusion and flow matching, are well-suited to complex control problems with highly mult

All of this aligns with METR’s results as well. Report: https://www.aisi.gov.uk/blog/how-fast-is-autonomous-ai-cyber-capability-advancing

This post references METR's research findings on the advancement rate of autonomous AI capabilities in cybersecurity, as discussed in an AISI report examining how rapidly AI systems are developing ind

agentsethan-mollick--x