Search: “context”

arXiv:2607.04443v1 Announce Type: cross Abstract: We present Wan-Streamer v0.2, a latency-preserving upgrade of the native-streaming, end-to-end audio-visual interaction model. v0.2 keeps the v0.1 mod

hardwarearxiv-cs-ai

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When Does Small Data Work? Accuracy and Efficiency Trade-offs Between Tabular Foundation Models and Conventional Methods for Crowd-State Classification at Hajj and Umrah

DGX agent

arXiv:2607.04013v1 Announce Type: cross Abstract: Learning from few labeled examples is a central challenge in tabular machine learning, and it becomes the binding constraint in domains where labeling

researcharxiv-cs-ai

7 Jul 2026

WorldBagel: Uncovering the Power of Unified Multimodal Models for Vision-Language-Action-World Modeling

model-releasesarxiv-cs-cv

arXiv:2607.03461v1 Announce Type: new Abstract: World models aim to capture environment dynamics in ways that support perception, reasoning, and action, and have recently become a central direction in

You Frame It: How Conceptual Representations Shape LLM Detection and Reasoning about Antisemitism

arXiv:2607.04945v1 Announce Type: new Abstract: LLMs enable the integration of external conceptual resources at inference time, creating new opportunities for detecting ideologically and historically

researcharxiv-cs-cl

Your Agent's Memories Are Not Its Own: Forged Reasoning Attacks on LLM Agent Memory and Defenses

arXiv:2607.05029v1 Announce Type: cross Abstract: Persistent memory has enabled large language model (LLM) agents to store factual knowledge, prior decisions, reasoning histories, tool usage informati

A Hippocampus for Linear Attention: An Exact Memory for What the Recurrent State Forgets

arXiv:2607.02303v1 Announce Type: new Abstract: Linear-attention and state-space language models compress the prefix into a fixed-size recurrent state, yielding O(1) memory at the cost of a lossy exac

A Practice Auditing Framework for Large Language Model Use: Collective Empiricism, Pseudo-Rational Cognition, and Governance of AI-Generated Content

arXiv:2607.01248v1 Announce Type: cross Abstract: Large language models are increasingly used for knowledge acquisition, code generation, academic writing, and agent-based automation. In these setting

A Storm-Centric 250 m NEXRAD Level-II Dataset for High-Resolution ML Nowcasting

arXiv:2510.16031v2 Announce Type: replace-cross Abstract: Machine learning-based precipitation nowcasting relies on high-fidelity radar reflectivity sequences to model the short-term evolution of conv

Tutorials

A Survey of Circuit Foundation Model: Foundation AI Models for VLSI Circuit Design and EDA

arXiv:2504.03711v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI)-driven electronic design automation (EDA) techniques have been extensively explored for VLSI circuit design appli

tutorialsarxiv-cs-lg

Adaptively trained Physics-informed Radial Basis Function Neural Networks for Solving Multi-asset Option Pricing Problems

arXiv:2601.12704v2 Announce Type: replace Abstract: The present study investigates the numerical solution of Black-Scholes partial differential equation (PDE) for option valuation with multiple underl

ADMC: Attention-based Diffusion Model for Missing Modalities Feature Completion

arXiv:2507.05624v2 Announce Type: replace Abstract: Multimodal emotion and intent recognition is essential for automated human-computer interaction, It aims to analyze users' speech, text, and visual

AnyGroundBench: A Specialized-Domain Benchmark for Video Grounding in Vision-Language Models

arXiv:2607.02269v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have demonstrated immense promise in Spatio-Temporal Video Grounding (STVG). However, current evaluation protocols are l

Assessing VLM Reliability for Medical Image Quality Evaluation Under Corruption and Bias

arXiv:2607.01973v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are increasingly applied in medical tasks such as pathology description, report generation, and visual question answerin

Autorelevance function and other feature relevance measures for univariate time series

arXiv:2607.01959v1 Announce Type: cross Abstract: We propose a model agnostic methodology to measure lag relevance in machine learning forecasting models applied to univariate time series. Particularl

BamiBERT: A New BERT-based Language Model for Vietnamese

arXiv:2607.02259v1 Announce Type: new Abstract: In this paper, we introduce BamiBERT, a new BERT-based pre-trained language model for Vietnamese that addresses key limitations of PhoBERT -- the curren

researcharxiv-cs-cl

Beyond Detection: Redesigning Assessment and Governande of Generative AI at the Universidad Politecnica de Madrid (UPM)

arXiv:2607.01255v1 Announce Type: cross Abstract: Universities have responded to generative artificial intelligence (GenAI) in noticeably different ways, both internationally and within Spain. So far,

BOUNDARY_SYNC: Measuring Communication-Induced Representational Coupling in Multi-Agent LLM Systems

model-releasesarxiv-cs-cl

arXiv:2607.01600v1 Announce Type: cross Abstract: As large language models (LLMs) are deployed as communicating agents, does inter-agent communication cause outputs to converge? We introduce BOUNDARY_

Controllable Sim Agents with Behavior Latents

model-releasesarxiv-cs-lg

arXiv:2607.02496v1 Announce Type: cross Abstract: Realistic traffic simulation requires agents that imitate logged behavior and can also be steered along interpretable axes. Such controllability enabl

DiPS: Dialogue Policy Selection for High-Stakes Persuasion Agents

arXiv:2607.01557v1 Announce Type: cross Abstract: Large Language Models (LLMs) often struggle with persuasion in high-stakes scenarios. People's individual personalities and concerns require tailored

Do LLMs Truly Generalize in the Molecular Domain? A Perturbation-Based Analysis

arXiv:2607.01800v1 Announce Type: cross Abstract: Large Language Models (LLMs) have recently shown promise in molecular discovery, yet a gap remains between their probabilistic nature over discrete se

researcharxiv-cs-cl

DRIFTLENS: Measuring Memory-Induced Reasoning Drift in Personalized Language Models

arXiv:2607.02374v1 Announce Type: new Abstract: Personalization changes what a model says to a user; we show that it can also change the reasoning trajectory used to justify the response. Modern LLMs

Hardware

EHHN: An Event-driven Heterogeneous Hypergraph Network for Object-Centric Next Activity Prediction

arXiv:2607.01785v1 Announce Type: new Abstract: Next activity prediction helps service-oriented processes anticipate upcoming steps before delays, exceptions, or service-level risks occur. Most existi

hardwarearxiv-cs-lg

Embedding Inference Attack

arXiv:2607.01276v1 Announce Type: cross Abstract: Embedding models are essential components of modern Information Retrieval (IR) systems, yet they are typically hidden behind APIs. Recent works have s

Episodic-to-Semantic Consolidation Without Identity Drift

arXiv:2607.01988v1 Announce Type: new Abstract: Long-running adaptive intelligent agents face a structural tension between knowledge consolidation and information integrity. Memory consolidation is co

Evolutionary Feature Engineering for Structured Data

arXiv:2607.01548v1 Announce Type: cross Abstract: Large language models are increasingly used as open-ended search operators in evolutionary optimization. We introduce Evolutionary Feature Engineering

ExPerT: Personalizing LLM Responses to Users' Domain Expertise via Query-Wise Semantic and Keystroke Behavioral Cues

arXiv:2607.01242v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used by end users, yet existing personalization methods relying on static profiles or text-only signals

From Approximation to Emergence: A Theory of Deep Learning

arXiv:2607.01311v1 Announce Type: new Abstract: Deep learning has outgrown any single mathematical explanation. From Approximation to Emergence develops a unified, proof-oriented account of modern dee

safetyarxiv-cs-lg

Grounded Optimization: A Layered Engineering Framework for Reducing LLM Hallucination in Automated Personal Document Rewriting

arXiv:2607.01457v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly applied to resume optimization for applicant tracking systems, introducing hallucination failures distin

Applications

Hawk: Harnessing Hardware-Aware Knowledge for High-Performance NPU Kernel Generation

arXiv:2607.01590v1 Announce Type: new Abstract: Developing high-performance kernels for Neural Processing Units (NPUs) is a critical industry bottleneck, requiring developers to manually navigate impl

applicationsarxiv-cs-ai

Iextsuperscript{2}RiMA: Spectral Riemannian Representation with Temporal Attention for Mental Stress Detection based on EEG Signals

arXiv:2607.01279v1 Announce Type: new Abstract: Cross-subject EEG stress detection remains challenging because discriminative stress-related patterns are both subject-dependent and frequency-specific.

Kara: Efficient Reasoning LLM Serving via Sliding-Window KV Cache Compression

arXiv:2607.01237v1 Announce Type: cross Abstract: Reasoning language models often generate long chain-of-thought (CoT), which accumulates a massive KV cache during the decoding phase and incurs high d

LLM Priors for ERM over Programs

arXiv:2510.14331v3 Announce Type: replace Abstract: We study program-learning methods that are efficient in both samples and computation. Classical learning theory suggests that when the target admits

Locality-Aware Continual Unlearning for Diffusion Models

arXiv:2512.02657v2 Announce Type: replace-cross Abstract: Real-world deployment of text-to-image diffusion models requires continual concept removal as new privacy, copyright, or safety obligations ar

Mastermind: Strategy-grounded Learning for Repository-Scale Vulnerability Reproduction

arXiv:2607.01764v1 Announce Type: new Abstract: Repository-level vulnerability reproduction is a demanding software engineering (SE) task: an agent must inspect a codebase, infer the input grammar tha

MKGR: Multimodal Knowledge-Graph Representation Learning for Cold-Start Protein-Protein Interaction Prediction

arXiv:2607.01627v1 Announce Type: cross Abstract: Accurate protein-protein interaction (PPI) prediction is central to functional genomics, disease mechanism discovery, and drug development. A difficul

Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning

arXiv:2604.02091v2 Announce Type: replace-cross Abstract: Rerankers play a pivotal role in refining retrieval results for Retrieval-Augmented Generation. However, current reranking models are typicall

Applications

Profit-Based Counterfactual Explanations for Product Improvement: A Case Study of Manga Sales in Japan

arXiv:2607.01610v1 Announce Type: new Abstract: Counterfactual explanation (CE) is widely used to enhance the interpretability of machine learning models and support data-driven decision-making based

applicationsarxiv-cs-ai

Prompt Coverage Adequacy

arXiv:2607.02057v1 Announce Type: cross Abstract: In recent years, it has become increasingly evident that large language models (LLMs) and autonomous agents raise the level of abstraction in software

Psychological Imagination Networks Show Cross-Population Centrality and Clustering Alignment in Humans That Large Language Models Fail to Replicate

arXiv:2510.04391v5 Announce Type: replace Abstract: Mental imagery vividness is a stable individual trait, yet whether imagined scenarios share relational structure across human and synthetic large la

Local Ai

Rethinking Generic Object Tracking Toward Human-Level Perceptual Intelligence

arXiv:2607.01395v1 Announce Type: cross Abstract: At the heart of human visual perception lies the ability to maintain a continuous and coherent understanding of the external world. By integrating obs

local-aiarxiv-cs-ai

SABER: A Semantic-Aligned Brain Network Analysis Framework via Multi-scale Hypergraphs

arXiv:2607.01901v1 Announce Type: cross Abstract: Effective brain disease diagnosis requires the synergy of brain connectivity patterns and high-level semantic knowledge. Existing methods, however, la

Safe and Adaptive Cloud Healing: Verifying LLM-Generated Recovery Plans with a Neural-Symbolic World Model

arXiv:2607.01595v1 Announce Type: new Abstract: As the scale and complexity of cloud-based AI systems continue to escalate, ensuring service reliability through rapid fault detection and adaptive reco

Safeguarding LLM Agents from Misalignment through Provenance Analysis

arXiv:2607.01236v1 Announce Type: cross Abstract: As LLM agents gain increasing access to powerful tools, ensuring that their actions are aligned with the user's intent becomes critical. When an agent

Simulation Based Reward Function Validation for Multi-Agent On Orbit Inspection

arXiv:2607.01367v1 Announce Type: cross Abstract: A proposed method for the control of groups of inspection spacecraft is Multi-Agent Reinforcement Learning (MARL). While MARL has already been employe

agentsarxiv-cs-ro

StatEval: A Comprehensive Benchmark for Large Language Models in Statistics

model-releasesarxiv-cs-cl

arXiv:2510.09517v2 Announce Type: replace Abstract: Despite rapid advances in large language models (LLMs), statistical reasoning remains underrepresented in existing LLM benchmarks, which often do no

Structured Gaussian Processes for Uncertainty-Aware Classification of High-Dimensional, Small-Sampled Omics Data

model-releasesarxiv-cs-lg

arXiv:2607.02103v1 Announce Type: cross Abstract: Classifying heterogeneous omics data remains a fundamental challenge in computational biology, particularly in high-dimensional, small-sample settings

SUNTA: Hierarchical Video Prediction with Surprise-based Chunking

arXiv:2607.02087v1 Announce Type: new Abstract: Hierarchical state-space models (HSSMs) offer a promising approach to long-horizon prediction by segmenting sequences into temporal chunks. However, the

Verifiable Knowledge Expansion through Retrieval-Grounded Formal Concept Analysis

arXiv:2607.01773v1 Announce Type: new Abstract: Ontology construction requires deciding which objects, attributes, and structural relations should be accepted as valid knowledge. Language models can p