Search: “context”

Synthius-Mem: Brain-Inspired Hallucination-Resistant Persona Memory Achieving 94.4% Memory Accuracy and 99.6% Adversarial Robustness on LoCoMo

arXiv:2604.11563v1 Announce Type: cross Abstract: Providing AI agents with reliable long-term memory that does not hallucinate remains an open problem. Current approaches to memory for LLM agents -- s

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Model Releases

TAG-Head: Time-Aligned Graph Head for Plug-and-Play Fine-grained Action Recognition

DGX agent

arXiv:2604.11498v1 Announce Type: new Abstract: Fine-grained human action recognition (FHAR) is challenging because visually similar actions differ by subtle spatio-temporal cues. Many recent systems

model-releasesarxiv-cs-cv

14 Apr 2026

TaleDiffusion: Multi-Character Story Generation with Dialogue Rendering

arXiv:2509.04123v2 Announce Type: replace Abstract: Text-to-story visualization is challenging due to the need for consistent interaction among multiple characters across frames. Existing methods stru

researcharxiv-cs-cv

Teaching Language Models How to Code Like Learners: Conversational Serialization for Student Simulation

arXiv:2604.10720v1 Announce Type: new Abstract: Artificial models that simulate how learners act and respond within educational systems are a promising tool for evaluating tutoring strategies and feed

The Blind Spot of Agent Safety: How Benign User Instructions Expose Critical Vulnerabilities in Computer-Use Agents

arXiv:2604.10577v1 Announce Type: cross Abstract: Computer-use agents (CUAs) can now autonomously complete complex tasks in real digital environments, but when misled, they can also be used to automat

Towards Green Wearable Computing: A Physics-Aware Spiking Neural Network for Energy-Efficient IMU-based Human Activity Recognition

arXiv:2604.10458v1 Announce Type: cross Abstract: Wearable IMU-based Human Activity Recognition (HAR) relies heavily on Deep Neural Networks (DNNs), which are burdened by immense computational and buf

Tutorials

TRACE: An Experiential Framework for Coherent Multi-hop Knowledge Graph Question Answering

arXiv:2604.11193v1 Announce Type: new Abstract: Multi-hop Knowledge Graph Question Answering (KGQA) requires coherent reasoning across relational paths, yet existing methods often treat each reasoning

tutorialsarxiv-cs-cl

TRACE: Thermal Recognition Attentive-Framework for CO2 Emissions from Livestock

arXiv:2604.09648v1 Announce Type: new Abstract: Quantifying exhaled CO2 from free-roaming cattle is both a direct indicator of rumen metabolic state and a prerequisite for farm-scale carbon accounting

researcharxiv-cs-cv

Turning Generators into Retrievers: Unlocking MLLMs for Natural Language-Guided Geo-Localization

arXiv:2604.10721v1 Announce Type: cross Abstract: Natural-language Guided Cross-view Geo-localization (NGCG) aims to retrieve geo-tagged satellite imagery using textual descriptions of ground scenes.

Applications

Understanding Generalization in Role-Playing Models via Information Theory

arXiv:2512.17270v2 Announce Type: replace-cross Abstract: Role-playing models (RPMs) are widely used in real-world applications but underperform when deployed in the wild. This degradation can be attr

applicationsarxiv-cs-ai

VeriInteresting: An Empirical Study of Model Prompt Interactions in Verilog Code Generation

model-releasesarxiv-cs-cl

arXiv:2603.08715v2 Announce Type: replace-cross Abstract: Rapid advances in language models (LMs) have created new opportunities for automated code generation while complicating trade-offs between mod

VLMaterial: Vision-Language Model-Based Camera-Radar Fusion for Physics-Grounded Material Identification

arXiv:2604.11671v1 Announce Type: cross Abstract: Accurate material recognition is a fundamental capability for intelligent perception systems to interact safely and effectively with the physical worl

safetyarxiv-cs-ro

VS-Bench: Evaluating VLMs for Strategic Abilities in Multi-Agent Environments

arXiv:2506.02387v3 Announce Type: replace Abstract: Recent advancements in Vision Language Models (VLMs) have expanded their capabilities to interactive agent tasks, yet existing benchmarks remain lim

Weird Generalization is Weirdly Brittle

arXiv:2604.10022v1 Announce Type: new Abstract: Weird generalization is a phenomenon in which models fine-tuned on data from a narrow domain (e.g. insecure code) develop surprising traits that manifes

safetyarxiv-cs-cl

What Users Leave Unsaid: Under-Specified Queries Limit Vision-Language Models

arXiv:2601.06165v2 Announce Type: replace-cross Abstract: Current vision-language benchmarks predominantly feature well-structured questions with clear, explicit prompts. However, real user queries ar

What's In My Human Feedback? Learning Interpretable Descriptions of Preference Data

arXiv:2510.26202v2 Announce Type: replace-cross Abstract: Human feedback can alter language models in unpredictable and undesirable ways, as practitioners lack a clear understanding of what feedback d

safetyarxiv-cs-ai

When simulations look right but causal effects go wrong: Large language models as behavioral simulators

arXiv:2604.02458v2 Announce Type: replace-cross Abstract: Behavioral simulation is increasingly used to anticipate responses to interventions. Large language models (LLMs) enable researchers to specif

safetyarxiv-cs-ai

When Verification Fails: How Compositionally Infeasible Claims Escape Rejection

arXiv:2604.10990v1 Announce Type: cross Abstract: Scientific claim verification, the task of determining whether claims are entailed by scientific evidence, is fundamental to establishing discoveries

Tutorials

Who Handles Orientation? Investigating Invariance in Feature Matching

arXiv:2604.11809v1 Announce Type: new Abstract: Finding matching keypoints between images is a core problem in 3D computer vision. However, modern matchers struggle with large in-plane rotations. A st

tutorialsarxiv-cs-cv

ZoomR: Memory Efficient Reasoning through Multi-Granularity Key Value Retrieval

arXiv:2604.10898v1 Announce Type: new Abstract: Large language models (LLMs) have shown great performance on complex reasoning tasks but often require generating long intermediate thoughts before reac

safetyarxiv-cs-ai

3D-VCD: Hallucination Mitigation in 3D-LLM Embodied Agents through Visual Contrastive Decoding

arXiv:2604.08645v1 Announce Type: cross Abstract: Large multimodal models are increasingly used as the reasoning core of embodied agents operating in 3D environments, yet they remain prone to hallucin

A fast and Generic Energy-Shifting Transformer for Hybrid Monte Carlo Radiotherapy Calculation

arXiv:2604.09157v1 Announce Type: cross Abstract: We introduce a novel learning framework for accelerated Monte Carlo (MC) dose calculation termed Energy-Shifting. This approach leverages deep learnin

ActionNex: A Virtual Outage Manager for Cloud Computing

arXiv:2604.03512v2 Announce Type: replace Abstract: Outage management in large-scale cloud operations remains heavily manual, requiring rapid triage, cross-team coordination, and experience-driven dec

ActivityEditor: Learning to Synthesize Physically Valid Human Mobility

arXiv:2604.05529v2 Announce Type: replace Abstract: Human mobility modeling is indispensable for diverse urban applications. However, existing data-driven methods often suffer from data scarcity, limi

Adaptive Dual Residual U-Net with Attention Gate and Multiscale Spatial Attention Mechanisms (ADRUwAMS)

arXiv:2604.08893v1 Announce Type: cross Abstract: Glioma is a harmful brain tumor that requires early detection to ensure better health results. Early detection of this tumor is key for effective trea

Adaptor: Advancing Assistive Teleoperation with Few-Shot Learning and Cross-Operator Generalization

arXiv:2604.09462v1 Announce Type: new Abstract: Assistive teleoperation enhances efficiency via shared control, yet inter-operator variability, stemming from diverse habits and expertise, induces high

safetyarxiv-cs-ro

AI-Induced Human Responsibility (AIHR) in AI-Human teams

arXiv:2604.08866v1 Announce Type: cross Abstract: As organizations increasingly deploy AI as a teammate rather than a standalone tool, morally consequential mistakes often arise from joint human-AI wo

Aligned Agents, Biased Swarm: Measuring Bias Amplification in Multi-Agent Systems

arXiv:2604.08963v1 Announce Type: cross Abstract: While Multi-Agent Systems (MAS) are increasingly deployed for complex workflows, their emergent properties-particularly the accumulation of bias-remai

Applications

An Open-Source, Open Data Approach to Activity Classification from Triaxial Accelerometry in an Ambulatory Setting

arXiv:2604.09451v1 Announce Type: cross Abstract: The accelerometer has become an almost ubiquitous device, providing enormous opportunities in healthcare monitoring beyond step counting or other aver

applicationsarxiv-cs-lg

Are Independently Estimated View Uncertainties Comparable? Unified Routing for Trusted Multi-View Classification

arXiv:2604.09288v1 Announce Type: new Abstract: Trusted multi-view classification typically relies on a view-wise evidential fusion process: each view independently produces class evidence and uncerta

safetyarxiv-cs-lg

Local Ai

Bayesian Ego-graph Inference for Networked Multi-Agent Reinforcement Learning

arXiv:2509.16606v5 Announce Type: replace-cross Abstract: In networked multi-agent reinforcement learning (Networked-MARL), decentralized agents must act under local observability and constrained comm

local-aiarxiv-cs-lg

Beyond Augmented-Action Surrogates for Multi-Expert Learning-to-Defer

model-releasesarxiv-cs-lg

arXiv:2604.09414v1 Announce Type: cross Abstract: Learning-to-Defer routes each input to the expert that minimizes expected cost, but it assumes that the information available to every expert is fixed

Local Ai

Case-Grounded Evidence Verification: A Framework for Constructing Evidence-Sensitive Supervision

arXiv:2604.09537v1 Announce Type: cross Abstract: Evidence-grounded reasoning requires more than attaching retrieved text to a prediction: a model should make decisions that depend on whether the prov

local-aiarxiv-cs-ai

CausalVAD: De-confounding End-to-End Autonomous Driving via Causal Intervention

arXiv:2603.18561v2 Announce Type: replace Abstract: Planning-oriented end-to-end driving models show great promise, yet they fundamentally learn statistical correlations instead of true causal relatio

safetyarxiv-cs-cv

CERBERUS: A Three-Headed Decoder for Vertical Cloud Profiles

arXiv:2604.08772v1 Announce Type: cross Abstract: Atmospheric clouds exhibit complex three-dimensional structure and microphysical details that are poorly constrained by the predominantly two-dimensio

Constraining Sequential Model Editing with Editing Anchor Compression

arXiv:2503.00035v2 Announce Type: replace-cross Abstract: Large language models (LLMs) struggle with hallucinations due to false or outdated knowledge. Given the high resource demands of retraining th

Contribution of task-irrelevant stimuli to drift of neural representations

arXiv:2510.21588v2 Announce Type: replace-cross Abstract: Biological and artificial learners are inherently exposed to a stream of data and experience throughout their lifetimes and must constantly ad

agentsarxiv-cs-lg

Cross-Modal Knowledge Distillation from Spatial Transcriptomics to Histology

arXiv:2604.09076v1 Announce Type: new Abstract: Spatial transcriptomics provides a molecularly rich description of tissue organization, enabling unsupervised discovery of tissue niches -- spatially co

researcharxiv-cs-cv

dnaHNet: A Scalable and Hierarchical Foundation Model for Genomic Sequence Learning

arXiv:2602.10603v3 Announce Type: replace Abstract: Genomic foundation models have the potential to decode DNA syntax, yet face a fundamental tradeoff in their input representation. Standard fixed-voc

EchoTrail-GUI: Building Actionable Memory for GUI Agents via Critic-Guided Self-Exploration

arXiv:2512.19396v3 Announce Type: replace Abstract: Contemporary GUI agents, while increasingly capable due to advances in Large Vision-Language Models (VLMs), often operate with a critical limitation

EvoLen: Evolution-Guided Tokenization for DNA Language Model

arXiv:2604.08698v1 Announce Type: new Abstract: Tokens serve as the basic units of representation in DNA language models (DNALMs), yet their design remains underexplored. Unlike natural language, DNA

safetyarxiv-cs-lg

Local Ai

FaceLiVTv2: An Improved Hybrid Architecture for Efficient Mobile Face Recognition

arXiv:2604.09127v1 Announce Type: new Abstract: Lightweight face recognition is increasingly important for deployment on edge and mobile devices, where strict constraints on latency, memory, and energ

local-aiarxiv-cs-cv

Fast-dVLM: Efficient Block-Diffusion VLM via Direct Conversion from Autoregressive VLM

arXiv:2604.06832v2 Announce Type: replace Abstract: Vision-language models (VLMs) predominantly rely on autoregressive decoding, which generates tokens one at a time and fundamentally limits inference

agentsarxiv-cs-cl

Few-Shot Personalized Age Estimation

model-releasesarxiv-cs-cv

arXiv:2604.09125v1 Announce Type: new Abstract: Existing age estimation methods treat each face as an independent sample, learning a global mapping from appearance to age. This ignores a well-document

FP8-RL: A Practical and Stable Low-Precision Stack for LLM Reinforcement Learning

arXiv:2601.18150v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) for large language models (LLMs) is increasingly bottlenecked by rollout (generation), where long output sequence

safetyarxiv-cs-cl

Gated-SwinRMT: Unifying Swin Windowed Attention with Retentive Manhattan Decay via Input-Dependent Gating

arXiv:2604.06014v2 Announce Type: replace Abstract: We introduce Gated-SwinRMT, a family of hybrid vision transformers that combine the shifted-window attention of the Swin Transformer with the Manhat

safetyarxiv-cs-lg

Hierarchical Alignment: Enforcing Hierarchical Instruction-Following in LLMs through Logical Consistency

arXiv:2604.09075v1 Announce Type: new Abstract: Large language models increasingly operate under multiple instructions from heterogeneous sources with different authority levels, including system poli

safetyarxiv-cs-cl

Imitation Learning for Combinatorial Optimisation under Uncertainty

arXiv:2601.05383v4 Announce Type: replace Abstract: Imitation learning (IL) provides a data-driven framework for approximating policies for large-scale combinatorial optimisation problems formulated a