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MedCAGD: Context-Aware Gated Decoder for Efficient Medical Image Segmentation

arXiv:2607.00409v1 Announce Type: new Abstract: Medical image segmentation relies on the ability of encoder-decoder architectures to translate rich feature representations into accurate pixel-level pr

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Meta-Transfer Learning for mmWave Beam Alignment

DGX agent

arXiv:2607.00860v1 Announce Type: cross Abstract: Millimeter-wave (mmWave) beam alignment plays a critical role in next-generation wireless systems, yet its efficient implementation remains challengin

safetyarxiv-cs-ai

2 Jul 2026

Mirror-Fusion Attention for Reflection-Aware Self-Supervised Representation Learning

arXiv:2607.00850v1 Announce Type: new Abstract: Most self-supervised learning (SSL) methods encourage invariance across augmentations, but strict flip invariance can suppress informative left--right c

Mnemosyne: Agentic Transaction Processing for Validating and Repairing AI-generated Workflows

arXiv:2607.00269v1 Announce Type: new Abstract: LLMs, solvers, and agent teams increasingly generate workflow actions, repairs, and plans, but a generated action may be syntactically valid yet stale,

MoVA: Learning Asymmetric Dual Projections for Modular Long Video-Text Alignment

arXiv:2607.00858v1 Announce Type: new Abstract: Contrastive pre-training has propelled video-text alignment, yet models often inherit the critical limitations of their image-text predecessors like CLI

Multi-scale Mixture of World Models for Embodied Agents in Evolving Environments

arXiv:2607.00457v1 Announce Type: new Abstract: Embodied agents operating in the real world require multi-scale reasoning and knowledge adaptation as conditions change. We identify two challenges in a

Multiplicity is an Inevitable and Inherent Challenge in Multimodal Learning

arXiv:2505.19614v2 Announce Type: replace-cross Abstract: Multimodal learning has seen remarkable progress, particularly with large-scale pre-training across various modalities. Most current approache

NeHMO: Neural Hamilton-Jacobi Reachability Learning for Decentralized Safe Multi-Arm Motion Planning

arXiv:2607.00326v1 Announce Type: new Abstract: Safe multi-arm motion planning is a challenging problem in robotics due to its high dimensionality, coupled configuration space, and complex collision c

NeuroCogMap Reveals Cognitive Organization of Large Language Models

arXiv:2607.00397v1 Announce Type: cross Abstract: Understanding how complex cognitive functions are organized within artificial systems is central to interpreting large language models (LLMs) and rela

NEW paper from NVIDIA. They discuss robot programming that compounds experience instead of throwing it away. Traditional robot programming f…

NEW paper from NVIDIA. They discuss robot programming that compounds experience instead of throwing it away. Traditional robot programming forces you to orchestrate perception, contact dynamics, diver

safetydair-ai--x

OmniView-Space: Reinforcing Spatial Reasoning via Multi-Perspective Spatial Mapping

arXiv:2607.00881v1 Announce Type: new Abstract: Spatial intelligence remains a persistent challenge for Multimodal Large Language Models (MLLMs), as it requires coherent spatial scene representations

OpenReward: Learning to Reward Long-form Agentic Tasks via Reinforcement Learning

arXiv:2510.24636v3 Announce Type: replace Abstract: Reward models (RMs) have become essential for aligning large language models (LLMs), serving as scalable proxies for human evaluation in both traini

PAPA: Online Personalized Active Preference Alignment

arXiv:2607.00486v1 Announce Type: cross Abstract: Diffusion models are highly effective at modeling complex data distributions, including images and text. However, in applications like personalized re

Personalization as Inverse Planning: Learning Latent Design Intents for Agentic Slide Generation via Structural Denoising

arXiv:2607.00407v1 Announce Type: new Abstract: Slide design requires personalizing both deck themes and page layouts. Yet, current AI agent-based methods struggle with fine-grained, page-level design

Predicting LLM Reasoning Performance with Small Proxy Model

arXiv:2509.21013v4 Announce Type: replace-cross Abstract: Given the prohibitive cost of pre-training large language models, it is essential to leverage smaller proxy models to optimize datasets before

Prior-Anchored Debiasing for Long-Tailed Multi-Organ Pathology Report Generation

arXiv:2607.00499v1 Announce Type: new Abstract: Automated pathology report generation from Whole Slide Images (WSIs) has attracted increasing attention in digital pathology. However, existing methods

PRISM: Prioritized Channel Importance with Semi-supervised Domain Adaptation for Cross-Subject EEG Emotion Recognition

arXiv:2607.00358v1 Announce Type: new Abstract: Electroencephalogram (EEG) captures endogenous brain activity with high temporal fidelity and holds substantial promise for precise emotion decoding. Ho

safetyarxiv-cs-lg

Prompt Optimization for User Simulation in Conversational Recommender Systems: A Multi-Objective Framework

arXiv:2607.00010v1 Announce Type: cross Abstract: Conversational recommender systems (CRSs) are a core component of next-generation intelligent recommender systems because they enable users to activel

Prompt2Effect: Training-Free Image-to-Video Model Specialization via LoRA Generation

arXiv:2606.13971v2 Announce Type: replace Abstract: While personalizing Image-to-Video (I2V) diffusion models with specific visual effects is increasingly demanded for high-end generation, current pra

QuasiMoTTo: Quasi-Monte Carlo Test-Time Scaling

arXiv:2607.01179v1 Announce Type: cross Abstract: Scaling inference compute, by generating many parallel attempts per problem, is a costly but reliable lever for improving language model capabilities.

Real-Time Hard Negative Sampling via LLM-based Clustering for Large-Scale Two-Tower Retrieval

arXiv:2607.00448v1 Announce Type: cross Abstract: The two-tower model has been widely used for large-scale recommendation systems, particularly in the retrieval stage. Industry standards for training

Reasoning Up the Instruction Ladder for Controllable Language Models

arXiv:2511.04694v5 Announce Type: replace-cross Abstract: As large language model (LLM) based systems take on high-stakes roles in real-world decision-making, they must reconcile competing instruction

Revisiting Autoregressive Models for Generative Image Classification

arXiv:2603.19122v2 Announce Type: replace Abstract: Class-conditional generative models have emerged as accurate and robust classifiers, with diffusion models demonstrating clear advantages over other

Robots Ask the Way: Communication-Enabled Social Navigation

arXiv:2607.01044v1 Announce Type: new Abstract: Assistive autonomous robots operating in multi-agent environments require efficient strategies to locate specific individuals among multiple residents.

RoboWorld: Fast and Reliable Neural Simulators for Generalist Robot Policy Evaluation

arXiv:2607.01060v1 Announce Type: new Abstract: Video world models are emerging as a scalable alternative for evaluating generalist robot policies, bypassing the physical constraints and engineering b

Robust Operational Space Control with Conformal Disturbance Bounds for Safe Redundant Manipulation

arXiv:2607.00424v1 Announce Type: new Abstract: Redundant robotic manipulators operating in constrained and human-interactive environments require accurate task-space tracking together with rigorous s

Safe Alone, Unsafe Together: Safeguarding Against Implicit Toxicity When Benign Images Combine

arXiv:2607.00576v1 Announce Type: new Abstract: Multi-image content has become an increasingly prevalent form of visual communication in social media, giving rise to a new safety issue, multi-image im

Salt: Self-Consistent Distribution Matching with Cache-Aware Training for Fast Video Generation

arXiv:2604.03118v2 Announce Type: replace Abstract: Distilling video generation models to extremely low inference budgets (e.g., 2--4 NFEs) is crucial for real-time deployment, yet remains challenging

Sam Altman calls for US-led international forum to set global AI standards

Sam Altman is calling for a US-led international forum to set global safety standards for artificial intelligence, arguing that no single country should be left to dominate the technology. In an op-ed

safetysiliconangle

Selective Expert Guidance for Effective and Diverse Exploration in Reinforcement Learning of LLMs

arXiv:2510.04140v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a widely adopted technique for enhancing the reasoning ability of Large Language Mo

Selective Test-Time Debiasing for CLIP via Reward Gating

arXiv:2607.00423v1 Announce Type: new Abstract: Vision language models (VLMs) demonstrate strong zero-shot performance, but often perpetuate social stereotypes in person-centric queries, yielding skew

Self-Evolving Agents with Anytime-Valid Certificates

arXiv:2607.00871v1 Announce Type: new Abstract: Self-evolving agents violate the assumption behind most learning-theoretic guarantees: the data, evaluator, components, and hypothesis space are produce

Semi-Bandit Learning for Monotone Stochastic Optimization

arXiv:2312.15427v3 Announce Type: replace Abstract: Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variabl

safetyarxiv-cs-lg

Sequentially-Controlled Interactive Multi-Particle Flow-Maps for Online Feedback-Driven Search

arXiv:2607.01144v1 Announce Type: cross Abstract: While generative models have enabled training-free reward alignment, current methods typically excel in local exploration within narrow regions of the

Should we be spending literally trillions on a speculative bet when we can’t even solidly document clear positive impact? Great analysis of …

Should we be spending literally trillions on a speculative bet when we can’t even solidly document clear positive impact? Great analysis of what we don’t yet know below, by @bencasselman drawing on a

safetygary-marcus--x

SleepLM: Natural-Language Intelligence for Human Sleep

arXiv:2602.23605v2 Announce Type: replace Abstract: We present SleepLM, a family of sleep-language foundation models that enable human sleep alignment, interpretation, and interaction with natural lan

Spatio-Temporal Gaussian Process for Building Terrain-Incorporating Wind Power Curves

arXiv:2607.00051v1 Announce Type: cross Abstract: Accurate modeling of wind turbine power curves is crucial for optimal wind farm operation. Nearly all existing power curve models focus on temporal va

safetyarxiv-cs-lg

Speech Playground: An Interactive Tool for Speech Analysis and Comparison

arXiv:2607.00418v1 Announce Type: new Abstract: This paper presents Speech Playground, an interactive speech visualization and comparison tool. While existing tools such as Praat are excellent, it can

Staleness-Learning Rate Scaling Laws for Asynchronous RLHF

arXiv:2607.01083v1 Announce Type: cross Abstract: High-throughput RLHF systems often decouple rollout generation from policy optimization, leading to the use of stale rollouts during learner updates.

Surfacing Variations to Calibrate Perceived Reliability of MLLM-generated Image Descriptions

arXiv:2507.15692v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) provide new opportunities for blind and low vision (BLV) people to access visual information in their daily l

Teaching AI to run with the turbines

Artificial intelligence may have captured the public imagination through chatbots and image generators, but some of its most consequential use cases are unfolding far from consumer-facing tools. In in

safetymit-tech-review

TetraSDF: Analytic Isosurface Extraction with Multi-resolution Tetrahedral Grid

arXiv:2511.16273v2 Announce Type: replace Abstract: Extracting an explicit surface that exactly matches the zero-level set of a neural signed distance function (SDF) remains challenging. Sampling-base

The Illusion of High Utility in Safety Alignment of Text-to-Image Diffusion Models

arXiv:2607.00402v1 Announce Type: cross Abstract: Safety alignment of text-to-image (T2I) diffusion models aims to suppress harmful generations while preserving utility on benign prompts. Recent metho

This is a very bold letter from Maria Ressa and Bengio! It is historic to see the chairs of the UN Independent International Scientific Pane…

Maria Ressa and Yann Bengio, prominent figures in technology and AI governance, co-authored a significant letter addressing concerns related to the UN Independent International Scientific Panel on AI.

safetygary-marcus--x

Towards an automated AI-based framework for floor plan compliance checks for residential buildings

arXiv:2607.00015v1 Announce Type: cross Abstract: To improve residents' well-being in Australia's urban areas, governments have introduced policy reforms such as SEPP65, BADS, and SPP7.3 to enhance ap

Training-Free Debiasing of Diffusion Models via CLIP-Guided Denoising Optimization

arXiv:2607.00817v1 Announce Type: new Abstract: Text-to-image diffusion models achieve impressive visual quality, yet demographic bias remains a challenge, as neutral prompts consistently produce ster

Two AI Metrics Diverged: Will it Make All the Difference?

arXiv:2607.00913v1 Announce Type: new Abstract: As exponential compute scaling continues, will the capabilities of frontier AI models outstrip what is accessible to developers on a small fixed budget?

Understanding Why Language Models Hallucinate: Testing Reasoning Against Priors

arXiv:2607.00447v1 Announce Type: new Abstract: Large language models often produce hallucinated answers that violate prompt-level constraints. A key diagnostic question is whether these failures refl