Search: “context”

Who Gets Which Message? Auditing Demographic Bias in LLM-Generated Targeted Text

arXiv:2601.17172v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly capable of generating personalized, persuasive text at scale, raising new questions about bias a

14 Apr 2026

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ZARA: Training-Free Motion Time-Series Reasoning via Evidence-Grounded LLM Agents

DGX agent

arXiv:2508.04038v2 Announce Type: replace Abstract: Motion sensor time-series are central to Human Activity Recognition (HAR), yet conventional approaches are constrained to fixed activity sets and ty

model-releasesarxiv-cs-cl

14 Apr 2026

2D or 3D: Who Governs Salience in VLA Models? -- Tri-Stage Token Pruning Framework with Modality Salience Awareness

arXiv:2604.09244v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have emerged as the mainstream of embodied intelligence. Recent VLA models have expanded their input modalities fr

Across the Levels of Analysis: Explaining Predictive Processing in Humans Requires More Than Machine-Estimated Probabilities

arXiv:2604.09466v1 Announce Type: new Abstract: Under the lens of Marr's levels of analysis, we critique and extend two claims about language models (LMs) and language processing: first, that predicti

researcharxiv-cs-cl

AlphaCast: A Human Wisdom-LLM Intelligence Co-Reasoning Framework for Interactive Time Series Forecasting

arXiv:2511.08947v5 Announce Type: replace Abstract: Time series forecasting plays a crucial role in decision-making across many real-world applications. Despite substantial progress, most existing met

agentsarxiv-cs-ai

Arbitration Failure, Not Perceptual Blindness: How Vision-Language Models Resolve Visual-Linguistic Conflicts

arXiv:2604.09364v1 Announce Type: cross Abstract: When a Vision-Language Model (VLM) sees a blue banana and answers 'yellow', is the problem of perception or arbitration? We explore the question in te

researcharxiv-cs-cl

B-MoE: A Body-Part-Aware Mixture-of-Experts 'All Parts Matter' Approach to Micro-Action Recognition

arXiv:2603.24245v3 Announce Type: replace Abstract: Micro-actions, fleeting and low-amplitude motions, such as glances, nods, or minor posture shifts, carry rich social meaning but remain difficult fo

Beyond Relevance: Utility-Centric Retrieval in the LLM Era

arXiv:2604.08920v1 Announce Type: cross Abstract: Information retrieval systems have traditionally optimized for topical relevance-the degree to which retrieved documents match a query. However, relev

agentsarxiv-cs-ai

Constraint-Aware Corrective Memory for Language-Based Drug Discovery Agents

arXiv:2604.09308v1 Announce Type: new Abstract: Large language models are making autonomous drug discovery agents increasingly feasible, but reliable success in this setting is not determined by any s

safetyarxiv-cs-ai

DDSP-QbE++: Improving Speech Quality for Speech Anonymisation for Atypical Speech

arXiv:2604.09246v1 Announce Type: cross Abstract: Differentiable Digital Signal Processing (DDSP) pipelines for voice conversion rely on subtractive synthesis, where a periodic excitation signal is sh

Differentially Private and Federated Structure Learning in Bayesian Networks

arXiv:2512.01708v2 Announce Type: replace-cross Abstract: Learning the structure of a Bayesian network from decentralized data poses two major challenges: (i) ensuring rigorous privacy guarantees for

Distributed Online Convex Optimization with Compressed Communication: Optimal Regret and Applications

arXiv:2604.09276v1 Announce Type: new Abstract: Distributed online convex optimization (D-OCO) is a powerful paradigm for modeling distributed scenarios with streaming data. However, the communication

Do Vision Language Models Need to Process Image Tokens?

arXiv:2604.09425v1 Announce Type: new Abstract: Vision Language Models (VLMs) have achieved remarkable success by integrating visual encoders with large language models (LLMs). While VLMs process dens

Dual Mamba for Node-Specific Representation Learning: Tackling Over-Smoothing with Selective State Space Modeling

arXiv:2511.06756v3 Announce Type: replace Abstract: Over-smoothing remains a fundamental challenge in deep Graph Neural Networks (GNNs), where repeated message passing causes node representations to b

eBandit: Kernel-Driven Reinforcement Learning for Adaptive Video Streaming

arXiv:2604.08791v1 Announce Type: cross Abstract: User-space Adaptive Bitrate (ABR) algorithms cannot see the transport layer signals that matter most, such as minimum RTT and instantaneous delivery r

applicationsarxiv-cs-ai

EfficientSign: An Attention-Enhanced Lightweight Architecture for Indian Sign Language Recognition

arXiv:2604.08694v1 Announce Type: new Abstract: How do you build a sign language recognizer that works on a phone? That question drove this work. We built EfficientSign, a lightweight model which take

EgoTL: Egocentric Think-Aloud Chains for Long-Horizon Tasks

model-releasesarxiv-cs-cv

arXiv:2604.09535v1 Announce Type: new Abstract: Large foundation models have made significant advances in embodied intelligence, enabling synthesis and reasoning over egocentric input for household ta

EthicMind: A Risk-Aware Framework for Ethical-Emotional Alignment in Multi-Turn Dialogue

arXiv:2604.09265v1 Announce Type: new Abstract: Intelligent dialogue systems are increasingly deployed in emotionally and ethically sensitive settings, where failures in either emotional attunement or

safetyarxiv-cs-cl

EXAONE 4.5 Technical Report

model-releasesarxiv-cs-cl

arXiv:2604.08644v1 Announce Type: new Abstract: This technical report introduces EXAONE 4.5, the first open-weight vision language model released by LG AI Research. EXAONE 4.5 is architected by integr

Gen-n-Val: Agentic Image Data Generation and Validation

arXiv:2506.04676v2 Announce Type: replace-cross Abstract: The data scarcity, label noise, and long-tailed category imbalance remain important and unresolved challenges in many computer vision tasks, s

agentsarxiv-cs-ai

HiFloat4 Format for Language Model Pre-training on Ascend NPUs

arXiv:2604.08826v1 Announce Type: cross Abstract: Large foundation models have become central to modern machine learning, with performance scaling predictably with model size and data. However, traini

LLM4Delay: Flight Delay Prediction via Cross-Modality Adaptation of Large Language Models and Aircraft Trajectory Representation

arXiv:2510.23636v3 Announce Type: replace-cross Abstract: Flight delay prediction has become a key focus in air traffic management (ATM), as delays reflect inefficiencies in the system. This paper pro

Modality-Aware Zero-Shot Pruning and Sparse Attention for Efficient Multimodal Edge Inference

arXiv:2604.08971v1 Announce Type: new Abstract: Edge devices increasingly run multimodal sensing pipelines that must remain accurate despite fluctuating power budgets and unpredictable sensor dropout.

Music Audio-Visual Question Answering Requires Specialized Multimodal Designs

arXiv:2505.20638v2 Announce Type: replace-cross Abstract: While recent Multimodal Large Language Models exhibit impressive capabilities for general multimodal tasks, specialized domains like music nec

NOMAD: Generating Embeddings for Massive Distributed Graphs

arXiv:2604.09419v1 Announce Type: new Abstract: Successful machine learning on graphs or networks requires embeddings that not only represent nodes and edges as low-dimensional vectors but also preser

applicationsarxiv-cs-lg

OpenKedge: Governing Agentic Mutation with Execution-Bound Safety and Evidence Chains

arXiv:2604.08601v1 Announce Type: new Abstract: The rise of autonomous AI agents exposes a fundamental flaw in API-centric architectures: probabilistic systems directly execute state mutations without

safetyarxiv-cs-ai

Practical Bayesian Inference for Speech SNNs: Uncertainty and Loss-Landscape Smoothing

arXiv:2604.08624v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) are naturally suited for speech processing tasks due to their specific dynamics, which allows them to handle temporal d

PS-TTS: Phonetic Synchronization in Text-to-Speech for Achieving Natural Automated Dubbing

arXiv:2604.09111v1 Announce Type: cross Abstract: Recently, artificial intelligence-based dubbing technology has advanced, enabling automated dubbing (AD) to convert the source speech of a video into

Retrieval Augmented Classification for Confidential Documents

arXiv:2604.08628v1 Announce Type: cross Abstract: Unauthorized disclosure of confidential documents demands robust, low-leakage classification. In real work environments, there is a lot of inflow and

Revisiting Anisotropy in Language Transformers: The Geometry of Learning Dynamics

arXiv:2604.08764v1 Announce Type: new Abstract: Since their introduction, Transformer architectures have dominated Natural Language Processing (NLP). However, recent research has highlighted an inhere

researcharxiv-cs-cl

RIRF: Reasoning Image Restoration Framework

arXiv:2604.09511v1 Announce Type: new Abstract: Universal image restoration (UIR) aims to recover clean images from diverse and unknown degradations using a unified model. Existing UIR methods primari

agentsarxiv-cs-cv

Robust Reasoning Benchmark

arXiv:2604.08571v1 Announce Type: cross Abstract: While Large Language Models (LLMs) achieve high performance on standard mathematical benchmarks, their underlying reasoning processes remain highly ov

Tutorials

Sample-Efficient Neurosymbolic Deep Reinforcement Learning

arXiv:2601.02850v2 Announce Type: replace Abstract: Reinforcement Learning (RL) is a well-established framework for sequential decision-making in complex environments. However, state-of-the-art Deep R

tutorialsarxiv-cs-ai

SatQNet: Satellite-assisted Quantum Network Entanglement Routing Using Directed Line Graph Neural Networks

arXiv:2604.09306v1 Announce Type: cross Abstract: Quantum networks are expected to become a key enabler for interconnecting quantum devices. In contrast to classical communication networks, however, i

applicationsarxiv-cs-ai

ScheMatiQ: From Research Question to Structured Data through Interactive Schema Discovery

arXiv:2604.09237v1 Announce Type: new Abstract: Many disciplines pose natural-language research questions over large document collections whose answers typically require structured evidence, tradition

applicationsarxiv-cs-cl

Screen, Cache, and Match: A Training-Free Causality-Consistent Reference Frame Framework for Human Animation

arXiv:2601.22160v2 Announce Type: replace-cross Abstract: Human animation aims to generate temporally coherent and visually consistent videos over long sequences, yet modeling long-range dependencies

See, Hear, and Understand: Benchmarking Audiovisual Human Speech Understanding in Multimodal Large Language Models

arXiv:2512.02231v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) are expected to jointly interpret vision, audio, and language, yet existing video benchmarks rarely a

SPASM: Stable Persona-driven Agent Simulation for Multi-turn Dialogue Generation

model-releasesarxiv-cs-cl

arXiv:2604.09212v1 Announce Type: new Abstract: Large language models are increasingly deployed in multi-turn settings such as tutoring, support, and counseling, where reliability depends on preservin

'Take Me Home, Wi-Fi Drone': A Drone-based Wireless System for Wilderness Search and Rescue

arXiv:2604.09115v1 Announce Type: cross Abstract: Wilderness Search and Rescue (WiSAR) represents a longstanding and critical societal challenge, demanding innovative and automatic technological solut

agentsarxiv-cs-ro

Local Ai

Tango: Taming Visual Signals for Efficient Video Large Language Models

arXiv:2604.09547v1 Announce Type: new Abstract: Token pruning has emerged as a mainstream approach for developing efficient Video Large Language Models (Video LLMs). This work revisits and advances th

local-aiarxiv-cs-cv

Temporal Dropout Risk in Learning Analytics: A Harmonized Survival Benchmark Across Dynamic and Early-Window Representations

arXiv:2604.08870v1 Announce Type: cross Abstract: Student dropout is a persistent concern in Learning Analytics, yet comparative studies frequently evaluate predictive models under heterogeneous proto

Hardware

TensorHub: Scalable and Elastic Weight Transfer for LLM RL Training

arXiv:2604.09107v1 Announce Type: cross Abstract: Modern LLM reinforcement learning (RL) workloads require a highly efficient weight transfer system to scale training across heterogeneous computationa

hardwarearxiv-cs-ai

V-CAGE: Vision-Closed-Loop Agentic Generation Engine for Robotic Manipulation

arXiv:2604.09036v1 Announce Type: new Abstract: Scaling Vision-Language-Action (VLA) models requires massive datasets that are both semantically coherent and physically feasible. However, existing sce

agentsarxiv-cs-ro

WAND: Windowed Attention and Knowledge Distillation for Efficient Autoregressive Text-to-Speech Models

arXiv:2604.08558v1 Announce Type: cross Abstract: Recent decoder-only autoregressive text-to-speech (AR-TTS) models produce high-fidelity speech, but their memory and compute costs scale quadratically

Which Pieces Does Unigram Tokenization Really Need?

model-releasesarxiv-cs-cl

arXiv:2512.12641v2 Announce Type: replace Abstract: The Unigram tokenization algorithm offers a probabilistic alternative to the greedy heuristics of Byte-Pair Encoding. Despite its theoretical elegan

A systematic framework for generating novel experimental hypotheses from language models

arXiv:2408.05086v3 Announce Type: replace Abstract: Neural language models (LMs) have been shown to capture complex linguistic patterns, yet their utility in understanding human language and more broa

safetyarxiv-cs-cl

10 Apr 2026

Action Without Interaction: Probing the Physical Foundations of Video LMMs via Contact-Release Detection

model-releasesarxiv-cs-cv

arXiv:2511.20162v2 Announce Type: replace Abstract: Large multi-modal models (LMMs) show increasing performance in realistic visual tasks for images and, more recently, for videos. For example, given

10 Apr 2026

Adversarial Evasion Attacks on Computer Vision using SHAP Values

arXiv:2601.10587v2 Announce Type: replace Abstract: The paper introduces a white-box attack on computer vision models using SHAP values. It demonstrates how adversarial evasion attacks can compromise