All entries

arXiv:2310.19043v3 Announce Type: replace-cross Abstract: Recent years have witnessed growing concerns about the privacy of sensitive data. In response to these concerns, differential privacy has emer

applicationsarxiv-cs-lg

DIRECT: Direct Decoding for Efficient and Aligned Sequence Labeling with Large Language Models

arXiv:2607.26891v1 Announce Type: new Abstract: Sequence labeling is a fine-grained information extraction task, yet existing large language model-based approaches suffer from insufficient domain alig

safetyarxiv-cs-cl

30 Jul 2026

Dissecting Sensitivity to Training Language in Self-Supervised Speech Learning Using Neural Audio Codec Tokens

arXiv:2607.26350v1 Announce Type: cross Abstract: Neural audio codecs (NACs) have become popular for obtaining speech representations as discrete tokens. Beyond compression, discrete tokens can be use

researcharxiv-cs-cl

DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation

arXiv:2607.26811v1 Announce Type: new Abstract: Existing autoregressive video distillation methods commonly adopt a Distribution Matching Distillation (DMD)-based multi-stage pipeline. However, they t

Local Ai

DLAM: Distributional Latent Actions with Temporal Constraints

arXiv:2607.27138v1 Announce Type: cross Abstract: Vision-language-action (VLA) models remain constrained by scarce action-labeled robot data, whereas action-free videos offer abundant observations of

local-aiarxiv-cs-cv

Do Methods Support the Claims? Intra-Paper Verification for Peer Review

arXiv:2607.26066v1 Announce Type: new Abstract: The growing volume of scientific submissions has motivated interest in using large language models (LLMs) to assist peer review. Existing automated nove

safetyarxiv-cs-cl

Do more with less: How GKE can reduce your cost per agent by 75%

In today’s agentic era, modern cloud applications are evolving from a set of passive tools to fleets of autonomous digital workers that reason, plan, and take action across a wide range of tasks. For

agentsgoogle-cloud-ai

Do Unified Multimodal Models Think in One Space? A Lens Through Cross-Branch Steering

arXiv:2607.26411v1 Announce Type: new Abstract: Unified multimodal models (UMMs) aim to integrate understanding and generation within a single architecture, yet it remains unclear whether these capabi

Do You Really Need to Pretrain Q-Functions for Online RL Fine-Tuning?

arXiv:2607.27203v1 Announce Type: new Abstract: Pre-training followed by fine-tuning has become the dominant recipe for learning performant policies, and in value-based reinforcement learning (RL) thi

safetyarxiv-cs-lg

Does MTP head get loaded in VRAM by default?

model-releasesr-localllama

I ran into a doubt when using the following command. It seems that the System RAM usage keeps increasing even though there is >10GB of space left in VRAM while using the MTP mode. Does the MTP head lo

Domain adaptation for handwriting trajectory reconstruction from IMU sensors

arXiv:2607.26736v1 Announce Type: new Abstract: Digital pens are commonly used to write on digital devices, providing the handwriting trace and enhancing human-computer interation. This study focuses

DP-MGTD: Privacy-Preserving Machine-Generated Text Detection via Adaptive Differentially Private Entity Sanitization

arXiv:2601.04641v2 Announce Type: replace-cross Abstract: The deployment of Machine-Generated Text (MGT) detection systems necessitates processing sensitive user data, creating a fundamental conflict

researcharxiv-cs-cl

DREvo: Distilling Recalibrated Historical Experience for Harness Self-Evolution

arXiv:2607.26722v1 Announce Type: cross Abstract: Harness plays a critical role in large language model agent performance, and building a high-performing harness requires substantial expert effort. Th

agentsarxiv-cs-lg

Dual Inversion for Text-to-Image Diffusion Models: From Both Prompt and Noise Perspectives

arXiv:2607.26735v1 Announce Type: new Abstract: Prompt inversion, as a typical reverse engineering technique, enables text-to-image (T2I) diffusion models to generate the desired target images without

Applications

Dual-Path LLM Reasoning for Multimodal Few-Shot Knowledge Graph Completion

arXiv:2607.26909v1 Announce Type: new Abstract: Knowledge graph completion (KGC) aims to infer missing facts in knowledge graphs (KGs), thereby improving their completeness and supporting downstream i

applicationsarxiv-cs-cl

DuplexGen: Adaptive Synthesis of Human-AI Turn-Taking Dialogues

arXiv:2607.26178v1 Announce Type: new Abstract: Turn-taking is a central component of full-duplex interaction. Which turn-taking behaviors are appropriate varies with the scenario, yet current models

researcharxiv-cs-cl

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving

arXiv:2607.26165v1 Announce Type: new Abstract: Safe autonomous navigation requires a holistic understanding of dynamic environments, necessitating the simultaneous estimation of metric depth, semanti

agentsarxiv-cs-cv

Dynamic Parameterization Is Not Dynamic Inference

arXiv:2607.26192v1 Announce Type: new Abstract: Input-dependent controller coefficients are often treated as evidence of dynamic inference or computational savings. This interpretation conflates three

Applications

Early Failure Prediction from Near-Anomaly Detection: A Proactive Approach

arXiv:2607.26704v1 Announce Type: cross Abstract: Anomaly detection methods often have uncertain behavior with respect to samples near the distribution boundary, limiting their ability to anticipate f

applicationsarxiv-cs-lg

Early Verdicts, Better Budgets: Sequential Adaptive Rollout Allocation for Compute-Efficient RLVR

arXiv:2607.26253v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) is bottlenecked by rollout generation, yet many sampled prompts produce saturated groups (all resp

safetyarxiv-cs-lg

Echoverse: Deep, evolving environments for computer-use agents

researchmicrosoft-research

Computer-use AI agents struggle with multi-step workflows like email and customer support. Echoverse trains agents in realistic environments rather than simply providing more training tasks, helping t

Eddeep: a deep-learning framework for fast eddy-current distortion correction in diffusion MRI

arXiv:2607.26292v1 Announce Type: new Abstract: Diffusion MRI (dMRI) relies on diffusion-weighted echo-planar imaging, which is highly susceptible to eddy-current-induced geometric distortions. These

Efficient Heteroscedastic Bayesian Optimization for Risk-Aware AutoRL

arXiv:2607.26680v1 Announce Type: new Abstract: Reinforcement learning (RL) has shown remarkable success across a wide range of complex tasks. However, RL outcomes can be highly stochastic, and both e

EgoSafe: A First-Person Mobile-Captured Benchmark for Visual Safety Understanding

model-releasesarxiv-cs-cv

arXiv:2607.26518v1 Announce Type: new Abstract: Reliable visual safety understanding in real-world scenarios demands more than just object recognition; it requires causal reasoning under epistemic unc

Embodied Agents Take Control: Minimal-Interface Zero-Shot Agents Rival Industrial-Scale Policies in Vision-and-Language Navigation

arXiv:2607.26148v1 Announce Type: new Abstract: Autonomous embodied agents must sustain a long decision-making loop that involves perceiving, acting, verifying, and self-correcting over many steps. Cu

agentsarxiv-cs-ro

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning

arXiv:2607.26059v1 Announce Type: new Abstract: We report a striking phenomenon: deep reinforcement learning agents trained with frozen, randomly initialized CNN feature extractors spontaneously devel

Enfold: Folding World-Generator Computation into Predictive Representations for Efficient Embodied Control

arXiv:2607.26657v1 Announce Type: new Abstract: World generative models are typically used through what they produce: a rendered future, a video-conditioned action, or latent context computed by a cos

researcharxiv-cs-ro

Enhancing Automated Machine Learning via Homogeneous Train-Test Splitting Methods

arXiv:2607.26625v1 Announce Type: new Abstract: Accurate model evaluation in machine learning depends critically on how datasets are split into training and testing subsets. Standard random splitting

Enhancing Generative Information Extraction with Two-step Validation: A Product Attribute Use Case

model-releasesarxiv-cs-cl

arXiv:2607.26780v1 Announce Type: new Abstract: The ability of large language models (LLMs) to process and generate text has introduced potential for applications in information extraction (IE). While

Entity Resolution in Practice: Lessons from a Self-Serve Pipeline

arXiv:2607.26298v1 Announce Type: new Abstract: We built and evaluated a self-serve entity resolution (ER) system on six benchmarks spanning 864 to 5M records, and three lessons emerged that are absen

Equilibrium Training of Energy-Based Models with Parallel Trajectory Tempering

arXiv:2607.27077v1 Announce Type: new Abstract: Energy-Based Models (EBMs) provide an interpretable framework for generative modeling of scientific data, but poor Markov Chain Monte Carlo mixing often

Equivariant Eikonal Neural Networks: Grid-Free, Scalable Travel-Time Prediction on Homogeneous Spaces

arXiv:2505.16035v3 Announce Type: replace Abstract: We introduce Equivariant Neural Eikonal Solvers, a novel framework that integrates Equivariant Neural Fields (ENFs) with Neural Eikonal Solvers. Our

Evaluating Prompt Scope and Demonstration Similarity in Local LLM Machine Translation

model-releasesarxiv-cs-cl

arXiv:2607.26286v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as general-purpose translation systems, but their behavior is usually evaluated under a single prompt

Evaluating Regional Bias in LLMs From Abstract Stereotype to Concrete Social Decision-Making

arXiv:2607.27022v1 Announce Type: new Abstract: Regional bias in large language models (LLMs) may shape both perceptions of regional groups and decisions about individuals from different regions. Yet

safetyarxiv-cs-cl

Everyone is a real-world AI engineer. They don’t just make cars, but autonomous robots with frontier capabilities. Thanks for their hard wor…

Everyone is a real-world AI engineer. They don’t just make cars, but autonomous robots with frontier capabilities. Thanks for their hard work keeping the line smooth and running. It’s my pleasure work

agentselon-musk--x

EvoPINN: Agentic Discovery of Executable Algorithms for Physics-Informed Neural Networks

arXiv:2607.26490v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful paradigm for solving partial differential equations (PDEs), yet their performance

Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts

arXiv:2607.26404v1 Announce Type: new Abstract: Graph Neural Networks (GNN) facilitate effective prediction on graph data such as molecules, media networks and neural network blueprints. GNNs facilita

Tools

Excited to be on the CNBC live show!

Excited to be on the CNBC live show! Back from vacation and LIVE at 12pm PT / 3pm ET Is AI’s easy-money era ending? We’ll unpack a wild week for the AI trade—big tech earnings, Leopold Aschenbrenner’s

toolsfireworks-ai--x

Local Ai

Excited to work with the @IntelBusiness team on enabling open models with Intel Core Ultra Series 3.

Excited to work with the @IntelBusiness team on enabling open models with Intel Core Ultra Series 3. Built to bring open-source LLMs to private machines, @Ollama uses Intel Core Ultra Series 3 to run

local-aiollama--x

Exclusive: Upwind adds context scanning for AI agents as AI DR hits general availability

Cloud security startup Upwind Security Inc. today unveiled AI Agent Context Scanner, which inspects the instructions, tools and connections feeding artificial intelligence agents before those agents a

agentssiliconangle

Existence-Field Diffusion Model for Spatial Point Processes with Variable Cardinality

arXiv:2607.26428v1 Announce Type: new Abstract: We study generative modeling of spatial point processes (SPP), where both the number of points and their spatial configuration are governed by a joint d

Explainable and Resource-Efficient Spatial Reasoning in Multimodal LLMs for Decision-Critical Applications

arXiv:2607.27145v1 Announce Type: new Abstract: As Multimodal Large Language Models (MLLMs) are increasingly deployed in decision-critical pipelines such as robotics, embodied AI, and safety monitorin

Tutorials

Explicit Kinematic Guidance from Analytic Concepts for Vision-Language-Action Models

arXiv:2607.26513v1 Announce Type: new Abstract: Current Vision-Language-Action (VLA) models rely mainly on 2D inputs, neglecting the rich object structural information and commonsense knowledge inhere

tutorialsarxiv-cs-ro

exttt{AMEND++}: Benchmarking Eligibility Criteria Amendments in Clinical Trials

model-releasesarxiv-cs-cl

arXiv:2601.06300v2 Announce Type: replace Abstract: Clinical trial amendments frequently introduce delays, increased costs, and administrative burden, with eligibility criteria being the most commonly

Local Ai

FakeIDet3-DB: Refining Digital Attacks and Patch Extraction for Secure ID Benchmarking

arXiv:2607.26641v1 Announce Type: new Abstract: Identity document (ID) authentication relies on the structural integrity of complex, high-frequency security patterns. However, advanced Generative AI m

local-aiarxiv-cs-cv

FAS-R1: A Unified Multi-Task MLLM for Reasoning Face Anti-Spoofing

model-releasesarxiv-cs-cv

arXiv:2607.26432v1 Announce Type: new Abstract: Face anti-spoofing (FAS) is increasingly expected to provide not only bona fide/spoof decisions, but also attack semantics and image-grounded evidence f

Feature Bagging Provides Stability

arXiv:2607.26964v1 Announce Type: cross Abstract: We study feature bagging through the lens of algorithmic stability. Feature bagging is an ensemble strategy that aggregates base learners trained on r

FedTopo: Relation-Level Topology Sharing for Model-Heterogeneous Federated Learning

arXiv:2607.26801v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative learning over decentralized data silos without centralizing raw data. However, heterogeneous local archite