Search: “context”

Efficient DP-SGD for LLMs with Randomized Clipping

model-releasesarxiv-cs-lg

arXiv:2605.24879v1 Announce Type: new Abstract: Large language models (LLMs) are trained on vast datasets that may contain sensitive information. Differential privacy (DP), the de facto standard for f

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Efficient Long-Horizon Vision-Language-Action Models via Static-Dynamic Disentanglement

DGX agent

arXiv:2602.03983v3 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have recently emerged as a promising paradigm for generalist robotic control. Built upon vision-language model (

model-releasesarxiv-cs-ro

26 May 2026

Factorize to Generalize: Retrieval-Guided Invariant-Dynamic Decomposition for Time Series Forecasting

arXiv:2605.24911v1 Announce Type: cross Abstract: Time series foundation models (TSFMs) have recently achieved strong zero-shot forecasting performance through large-scale pretraining and retrieval-au

tutorialsarxiv-cs-ai

High-Risk AI Systems and the Problem of Identity in the European AI Act

arXiv:2605.23922v1 Announce Type: cross Abstract: The EU Artificial Intelligence Act (AIA) establishes a lifecycle governance regime for high-risk AI systems built around ex-ante conformity assessment

Interdomain Attention: Beyond Token-Level Key-Value Memory

arXiv:2605.24330v1 Announce Type: new Abstract: Transformers and deep state space models (SSMs) sit at opposite ends of a basic design choice: attention routes each query through a growing key-value (

Is GPT-4o mini Blinded by its Own Safety Filters? Exposing the Multimodal-to-Unimodal Bottleneck in Hate Speech Detection

arXiv:2509.13608v2 Announce Type: replace Abstract: As Large Multimodal Models (LMMs) become integral to daily digital life, understanding their safety architectures is a critical problem for AI Align

safetyarxiv-cs-lg

// Language Models Need Sleep // Let your agents 'sleep', folks. On a serious note, this is a fascinating paper on getting the most from lon…

// Language Models Need Sleep // Let your agents 'sleep', folks. On a serious note, this is a fascinating paper on getting the most from long-horizon agents. Here is the problem with agents today: Att

tutorialsdair-ai--x

Locality Matters for Training-Free Audio Token Compression in Audio-Language Models

arXiv:2605.25179v1 Announce Type: new Abstract: Audio-language models (ALMs) are increasingly used for audio captioning, question answering, and open-ended audio understanding, but their inference cos

safetyarxiv-cs-cl

Machine Intelligence that Understands Visual and Linguistic Information and Interacts with Humans and Environments

arXiv:2605.24020v1 Announce Type: cross Abstract: Advancements at the intersection of computer vision and natural language processing are crucial for applications like assistive tech, multimedia query

Memory-Induced Tool-Drift in LLM Agents

model-releasesarxiv-cs-lg

arXiv:2605.24941v1 Announce Type: cross Abstract: Modern LLM agents combine long-term memory for personalization with tool-calling interfaces for taking actions in the world -- a combination underpinn

Neural Router: Semantic Content Matching for Agentic AI

arXiv:2605.25701v1 Announce Type: cross Abstract: Large language models (LLMs) can serve as the semantic-matching engine of a content-based publish/subscribe broker for agentic AI across the edge-clou

New study: Securing AI in the browser is a top priority for IT Leaders

The way we work has fundamentally changed. From automated agents to sophisticated AI services, Generative AI (GenAI) has become a daily tool for a vast majority of employees. But with this rapid adopt

tutorialsgoogle-cloud-ai

PathMem: Toward Cognition-Aligned Memory Transformation for Pathology MLLMs

arXiv:2603.09943v2 Announce Type: replace Abstract: Computational pathology demands both visual pattern recognition and dynamic integration of structured domain knowledge, including taxonomy, grading

ran my first benchmark this weekend (longmemeval) mostly to test activegraph, learned a lot! - this is a stepping stone to show the event ba…

model-releasesyohei-nakajima--x

ran my first benchmark this weekend (longmemeval) mostly to test activegraph, learned a lot! - this is a stepping stone to show the event based agent system works. the AI convinced me not to start wit

Re-defining Humor Data Objects for AI Humor Research

arXiv:2605.25171v1 Announce Type: new Abstract: In most existing AI humor research, humor was treated as either 'present' or 'not present.' We explore the concept of humor as a social interaction with

researcharxiv-cs-cl

SpecPrune-VLA: Accelerating Vision-Language-Action Models via Action-Aware Self-Speculative Pruning

arXiv:2509.05614v3 Announce Type: replace-cross Abstract: Pruning is a typical acceleration technique for compute-bound models by removing computation on unimportant values. Recently, it has been appl

local-aiarxiv-cs-ai

System scaling is the next real bottleneck in agentic AI. If you build agent orchestration layers, this is a clean map of where the engineer…

System scaling is the next real bottleneck in agentic AI. If you build agent orchestration layers, this is a clean map of where the engineering leverage actually sits. The labs own the model. You own

model-releasesdair-ai--x

The Age of Curiosity Meets the Age of AI: Benchmarking Child Safety in Large Language Models

arXiv:2605.25510v1 Announce Type: new Abstract: Children increasingly have access to Large Language Models (LLMs), which may expose them to responses that are developmentally inappropriate or require

The Model Parking Tax: Quantifying the Hidden Energy Cost of Always-On GPU Model Deployment

arXiv:2605.23918v1 Announce Type: cross Abstract: The AI inference industry keeps models loaded in GPU memory around the clock to avoid cold-start latency, implicitly treating idle power as a fixed co

local-aiarxiv-cs-lg

They Are Not the Same: Direct Causes Are Not Grounded Emotion Explanations

arXiv:2605.25208v1 Announce Type: new Abstract: Emotion-Cause Pair Extraction (ECPE) was introduced to explain why an emotion occurs, but this goal is now often reduced to binary pair/non-pair predict

researcharxiv-cs-cl

Transformer-based few-shot learning for modeling Electricity Consumption Profiles with minimal data across thousands of domains

arXiv:2408.08399v3 Announce Type: replace Abstract: Electricity Consumption Profiles (ECPs) are crucial for operating and planning power distribution systems, especially with the increasing number of

UWM-JEPA: Predictive World Models That Imagine in Belief Space

model-releasesarxiv-cs-ai

arXiv:2605.25313v1 Announce Type: cross Abstract: World models for partially observed environments must imagine multiple compatible hidden futures and steer between them under counterfactual actions.

XRPO: Pushing the limits of GRPO with Targeted Exploration and Exploitation

arXiv:2510.06672v3 Announce Type: replace Abstract: Reinforcement learning algorithms such as GRPO have driven recent advances in large language model (LLM) reasoning. While scaling the number of roll

safetyarxiv-cs-lg

Building a privacy-preserving Federated Recommender system for mobile devices

arXiv:2605.22924v1 Announce Type: new Abstract: Serving personalized content on mobile devices has traditionally required pooling sensitive user data on centralized servers, a practice increasingly at

local-aiarxiv-cs-lg

Applications

Emotion Recognition in Sign Language Conversation

arXiv:2605.23328v1 Announce Type: new Abstract: Emotion Recognition in Conversation is a core component of affective computing, while current resources of sign language emotion datasets primarily focu

applicationsarxiv-cs-cl

Agents

From Residuals to Reasons: LLM-Guided Mechanism Inference from Tabular Data

arXiv:2605.22897v1 Announce Type: new Abstract: A persistent challenge in machine learning for scientific applications is jointly achieving prediction and understanding. Statistical models excel on st

agentsarxiv-cs-lg

General Hazard Detection

arXiv:2605.23304v1 Announce Type: new Abstract: Hazard, as an abstract concept, is typically defined through cognitive-level logical reasoning rather than concrete examples. In contrast, existing haza

safetyarxiv-cs-cv

Is TabPFN the Silver Bullet for Insurance Pricing?

arXiv:2605.22892v1 Announce Type: cross Abstract: Modelling claim frequency and severity for non-life insurance pricing predominantly relies on generalised linear models, with gradient-boosted machine

Applications

Learning Individual Dynamics from Sparse Cross-Sectional Snapshots

arXiv:2605.23470v1 Announce Type: cross Abstract: Predicting how a dynamical unit evolves over time - how an individual ages, an epidemic spreads, or a physical system degrades - typically requires de

applicationsarxiv-cs-ai

LFRAG: Layout-oriented Fine-grained Retrieval-Augmented Generation on Multimodal Document Understanding

model-releasesarxiv-cs-ai

arXiv:2605.22829v1 Announce Type: cross Abstract: Multimodal Retrieval-Augmented Generation (RAG) has emerged as an effective paradigm for enhancing Large Language Models (LLMs) with external knowledg

Memorization Dynamics of Fill-in-the-Middle Pretraining

model-releasesarxiv-cs-ai

arXiv:2605.22981v1 Announce Type: cross Abstract: Fill-in-the-middle (FIM) is a pretraining objective widely used to equip causal language models with infilling ability, yet its effect on verbatim mem

Tools

Notes on Pope Leo XIV's encyclical on AI

Dropped this morning by the Vatican: Magnifica Humanitas of His Holiness Pope Leo XIV on Safeguarding the Human Person in the Time of Artificial Intelligence. This is a very interesting document. It's

toolssimon-willison

ObjectCache: Layerwise Object-Storage Retrieval for KV Cache Reuse

arXiv:2605.22850v1 Announce Type: cross Abstract: Prefix KV caching has become a key mechanism in LLM serving: it reduces time to first token (TTFT) by avoiding redundant computation across requests t

local-aiarxiv-cs-ai

OnePred: Next-Query Prediction via Recursive Intent Memory in Multi-Turn Conversations

arXiv:2605.23668v1 Announce Type: cross Abstract: Although large language model (LLM) conversational systems process millions of multi-turn dialogues daily, they remain fundamentally reactive: they re

RoboSurg-VQA: A Multimodal Benchmark for Surgical Segmentation-Aware Visual Question Answering

model-releasesarxiv-cs-cv

arXiv:2605.23068v1 Announce Type: new Abstract: Reliable visual understanding in robot-assisted and minimally invasive surgery (RMIS/MIS) demands more than accurate masks: in clinical practice, clinic

WeCon: An Efficient Weight-Conditioned Neural Solver for Multi-Objective Combinatorial Optimization Problems

arXiv:2605.22876v1 Announce Type: new Abstract: Existing neural solvers for Multi-Objective Combinatorial Optimization Problems (MOCOPs) commonly adopt decomposition-based strategies that scalarize an

World Machine: Towards Generative World Modeling for Time-Series

arXiv:2605.23025v1 Announce Type: new Abstract: World models represent a paradigm shift in generative AI, pursuing predictive understanding and controllable simulation of environments in a structured

PeakFocus: Bridging Peak Localization and Intensity Regression via a Unified Multi-Scale Framework for Electricity Load Forecasting

arXiv:2605.21550v1 Announce Type: new Abstract: Electricity load peak forecasting (ELPF), simultaneously predicting peak timing and intensity, is a prerequisite for effective grid scheduling and risk

23 May 2026

A Comparative Study of Language Models for Khmer Retrieval-Augmented Question Answering

arXiv:2605.22099v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has emerged as a promising paradigm for grounding large language model (LLM) outputs in retrieved evidence, thereby

AMEL: Accumulated Message Effects on LLM Judgments

arXiv:2605.22714v1 Announce Type: cross Abstract: Large language models are routinely used as automated evaluators: to review code, moderate content, or score outputs, often with many items passing th

Cross-Domain Human Action Recognition from Multiview Motion and Textual Descriptions

arXiv:2605.22697v1 Announce Type: new Abstract: Robustness to domain changes is a key capability for effective deployment of human action recognition systems in real-world scenarios, where action cate

tutorialsarxiv-cs-cv

MuKV: Multi-Grained KV Cache Compression for Long Streaming Video Question-Answering

arXiv:2605.22269v1 Announce Type: new Abstract: Long streaming video QA remains challenging due to growing visual tokens and limited reasoning length of large language models (LLMs). KV-caching stores

researcharxiv-cs-cv

Polite on the Surface, Wrong in Practice: A Curated Dataset for Fixing Honorific Failures in Multilingual Bangla Generation

arXiv:2605.22487v1 Announce Type: new Abstract: Recent advances in Multilingual Large Language Models (MLLMs) have significantly enhanced cross-lingual conversational capabilities, yet modeling cultur

Scene Abstraction for Lexical Semantics: Structured Representations of Situated Meaning

arXiv:2605.22542v1 Announce Type: new Abstract: Coffee and tea share many properties, yet they evoke strikingly different situations, atmospheres, and affective associations. These situated dimensions

researcharxiv-cs-cl

Video-o3: Native Interleaved Clue Seeking for Long Video Multi-Hop Reasoning

arXiv:2601.23224v2 Announce Type: replace Abstract: Existing multimodal large language models for long-video understanding predominantly rely on uniform sampling and single-turn inference, limiting th

researcharxiv-cs-cv

'Would You Want an AI Tutor?' Understanding Stakeholder Perceptions of LLM-based Systems in the Classroom

arXiv:2503.02885v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have gained traction in educational settings, often framed as virtual tutors or teaching assistants. Following ea

safetyarxiv-cs-cl

X-OmniClaw Technical Report: A Unified Mobile Agent for Multimodal Understanding and Interaction

arXiv:2605.05765v2 Announce Type: replace Abstract: Inspired by the development of OpenClaw, there is a growing demand for mobile-based personal agents capable of handling complex and intuitive intera

safetyarxiv-cs-cv

A Mechanistic Study of Tabular Foundation Models

arXiv:2605.21288v1 Announce Type: new Abstract: Tabular foundation models with different architectures converge in accuracy across a range of classification and regression tasks. This raises questions