Search: “framework”

Egocentric Tactile and Proximity Sensors as Observation Priors for Humanoid Collision Avoidance

arXiv:2604.25554v1 Announce Type: cross Abstract: Collision-free motion is often aided by tactile and proximity sensors distributed on the body of the robot due to their resistance to occlusion as opp

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Emergent Self-Attention from Astrocyte-Gated Associative Memory Dynamics

DGX agent

arXiv:2604.25481v1 Announce Type: cross Abstract: We introduce a Hopfield-type associative memory in which effective connectivity is multiplicatively modulated by astrocytic gains evolving under an en

researcharxiv-cs-lg

29 Apr 2026

Explainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Plane

arXiv:2604.25885v1 Announce Type: cross Abstract: Graph neural networks such as ParticleNet and transformer based networks on point clouds such as ParticleTransformer achieve state-of-the-art performa

Tutorials

Exploring Time Conditioning in Diffusion Generative Models from Disjoint Noisy Data Manifolds

arXiv:2604.25289v1 Announce Type: cross Abstract: Practically, training diffusion models typically requires explicit time conditioning to guide the network through the denoising sampling process. Espe

tutorialsarxiv-cs-cv

From Syntax to Emotion: A Mechanistic Analysis of Emotion Inference in LLMs

arXiv:2604.25866v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in emotionally sensitive human-AI applications, yet little is known about how emotion recognition is

researcharxiv-cs-cl

GEGLU-Transformer for IMU-to-EMG Estimation with Few-Shot Adaptation

arXiv:2604.25670v1 Announce Type: new Abstract: Reliable estimation of neuromuscular activation is a key enabler for adaptive and personalized control in wearable robotics. However, surface electromyo

applicationsarxiv-cs-ro

Tutorials

Generative diffusion models for spatiotemporal influenza forecasting

arXiv:2604.24913v1 Announce Type: new Abstract: Forecasting infectious disease incidence can provide important information to guide public health planning, yet is difficult because epidemic dynamics a

tutorialsarxiv-cs-lg

GS-Playground: A High-Throughput Photorealistic Simulator for Vision-Informed Robot Learning

arXiv:2604.25459v1 Announce Type: new Abstract: Embodied AI research is undergoing a shift toward vision-centric perceptual paradigms. While massively parallel simulators have catalyzed breakthroughs

researcharxiv-cs-ro

HANDFUL: Sequential Grasp-Conditioned Dexterous Manipulation with Resource Awareness

model-releasesarxiv-cs-ro

arXiv:2604.25126v1 Announce Type: new Abstract: Dexterous robot hands offer rich opportunities for multifunctional manipulation, where a robot must execute multiple skills in sequence while maintainin

Heterogeneous Variational Inference for Markov Degradation Hazard Models: Discretized Mixture with Interpretable Clusters

arXiv:2604.24818v1 Announce Type: new Abstract: Bayesian finite mixture models can identify discrete risk clusters (low-risk vs. high-risk equipment), but face three critical bottlenecks: (1) insuffic

applicationsarxiv-cs-lg

Hidden States as Early Signals: Step-level Trace Evaluation and Pruning for Efficient Test-Time Scaling

arXiv:2601.09093v2 Announce Type: replace Abstract: Large Language Models (LLMs) can enhance reasoning capabilities through test-time scaling by generating multiple traces. However, the combination of

How RL Unlocks the Aha Moment in Geometric Interleaved Reasoning

arXiv:2603.01070v2 Announce Type: replace Abstract: Solving complex geometric problems inherently requires interleaved reasoning: a tight alternation between constructing diagrams and performing logic

safetyarxiv-cs-cl

I-INR: Iterative Implicit Neural Representations

arXiv:2504.17364v4 Announce Type: replace Abstract: Implicit Neural Representations (INRs) have revolutionized signal processing and computer vision by modeling signals as continuous, differentiable f

safetyarxiv-cs-cv

IAM: Identity-Aware Human Motion and Shape Joint Generation

arXiv:2604.25164v1 Announce Type: new Abstract: Recent advances in text-driven human motion generation enable models to synthesize realistic motion sequences from natural language descriptions. Howeve

researcharxiv-cs-cv

Images Amplify Misinformation Sharing in Vision-Language Models

arXiv:2505.13302v2 Announce Type: replace Abstract: As language and vision-language models (VLMs) become central to information access and online interaction, concerns grow about their potential to am

Improving LLM Predictions via Inter-Layer Structural Encoders

arXiv:2603.22665v2 Announce Type: replace Abstract: The standard practice in Large Language Models (LLMs) is to base predictions on final-layer representations. However, intermediate layers encode com

Instruction-Evidence Contrastive Dual-Stream Decoding for Grounded Vision-Language Reasoning

arXiv:2604.25809v1 Announce Type: new Abstract: Vision-Language Models (VLMs) exhibit strong performance in instruction following and open-ended vision-language reasoning, yet they frequently generate

researcharxiv-cs-cv

Investigation into In-Context Learning Capabilities of Transformers

arXiv:2604.25858v1 Announce Type: new Abstract: Transformers have demonstrated a strong ability for in-context learning (ICL), enabling models to solve previously unseen tasks using only example input

Learning with Embedded Linear Equality Constraints via Variational Bayesian Inference

arXiv:2604.24911v1 Announce Type: new Abstract: Machine Learning is becoming more prevalent in science and engineering, but many approaches do not provide meaningful uncertainty estimates and predicti

LegalMidm: Use-Case-Driven Legal Domain Specialization for Korean Large Language Model

arXiv:2604.25297v1 Announce Type: new Abstract: In recent years, the rapid proliferation of open-source large language models (LLMs) has spurred efforts to turn general-purpose models into domain spec

applicationsarxiv-cs-cl

Local Ai

Leveraging Previous-Traversal Point Cloud Map Priors for Camera-Based 3D Object Detection and Tracking

arXiv:2604.25405v1 Announce Type: new Abstract: Camera-based 3D object detection and tracking are central to autonomous driving, yet precise 3D object localization remains fundamentally constrained by

local-aiarxiv-cs-cv

Lightweight Real-Time Rendering Parameter Optimization via XGBoost-Driven Lookup Tables

model-releasesarxiv-cs-cv

arXiv:2604.25178v1 Announce Type: new Abstract: Achieving a desirable balance between rendering quality and real-time performance is a long-standing challenge in modern game and rendering engines, par

Logic of Fuzzy Paths

arXiv:2604.24907v1 Announce Type: cross Abstract: We introduce a new family of temporal logics intended for specifications in motion planning (MP). It builds upon the signal temporal logic (STL), whic

researcharxiv-cs-ro

minAction.net: Energy-First Neural Architecture Design -- From Biological Principles to Systematic Validation

arXiv:2604.24805v1 Announce Type: new Abstract: Modern machine learning optimizes for accuracy without explicitly accounting for internal computational cost, even though physical and biological system

Modeling Human-Like Color Naming Behavior in Context

arXiv:2604.25674v1 Announce Type: new Abstract: Modeling the emergence of human-like lexicons in computational systems has advanced through the use of interacting neural agents, which simulate both le

researcharxiv-cs-cl

MolReFlect: Towards In-Context Fine-grained Alignments between Molecules and Texts

arXiv:2411.14721v2 Announce Type: replace Abstract: Molecule discovery is a pivotal research field, impacting everything from medicine to materials. Recently, Large Language Models (LLMs) have been wi

safetyarxiv-cs-cl

Multi-layer Cross-Attention is Provably Optimal for Multi-modal In-context Learning

arXiv:2602.04872v2 Announce Type: replace-cross Abstract: Recent progress has rapidly advanced our understanding of the mechanisms underlying in-context learning in modern attention-based neural netwo

OMHBench: Benchmarking Balanced and Grounded Omni-Modal Multi-Hop Reasoning

arXiv:2508.16198v3 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have increasingly supported omni-modal processing across text, vision, and speech. However, existing evalua

Local Ai

OmniAlpha: Aligning Transparency-Aware Generation via Multi-Task Unified Reinforcement Learning

arXiv:2511.20211v2 Announce Type: replace Abstract: Transparency-aware generation requires modeling not only RGB appearance but also alpha-based opacity and cross-layer composition, which are essentia

local-aiarxiv-cs-cv

One Refiner to Unlock Them All: Inference-Time Reasoning Elicitation via Reinforcement Query Refinement

arXiv:2604.25444v1 Announce Type: new Abstract: Large Language Models (LLMs) often fail to utilize their latent reasoning capabilities due to a distributional mismatch between ambiguous human inquirie

safetyarxiv-cs-cl

Optimization-Free Topological Sort for Causal Discovery via the Schur Complement of Score Jacobians

arXiv:2604.25295v1 Announce Type: new Abstract: Continuous causal discovery typically couples representation learning with structural optimization via non-convex acyclicity penalties, which subjects s

Origin-Destination Demand Prediction: An Urban Radiation and Attraction Perspective

arXiv:2412.00167v2 Announce Type: replace Abstract: In recent years, origin-destination (OD) demand prediction has gained significant attention for its profound implications in urban development. Exis

Tutorials

Personalized Cross-Modal Emotional Correlation Learning for Speech-Preserving Facial Expression Manipulation

arXiv:2604.25255v1 Announce Type: new Abstract: Speech-preserving facial expression manipulation (SPFEM) aims to enhance human expressiveness without altering mouth movements tied to the original spee

tutorialsarxiv-cs-cv

Policy Improvement Reinforcement Learning

arXiv:2604.00860v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a central post-training paradigm for improving the reasoning capabilities of large

safetyarxiv-cs-lg

PortraVec: Image-Based Portrait Vectorization with Text-Guided Manipulation

model-releasesarxiv-cs-cv

arXiv:2410.04182v2 Announce Type: replace Abstract: While portrait sketch generation is a special task in sketch synthesis, most existing methods are pixel-based, limiting their interpretability and e

Prior-Aligned Data Cleaning for Tabular Foundation Models

arXiv:2604.25154v1 Announce Type: new Abstract: Tabular Foundation Models (TFMs) achieve state-of-the-art zero-shot accuracy on small tabular datasets by meta-learning over synthetic data-generating p

Quantifying and Mitigating Socially Desirable Responding in LLMs: A Desirability-Matched Graded Forced-Choice Psychometric Study

arXiv:2602.17262v2 Announce Type: replace Abstract: Human self-report questionnaires are increasingly used in NLP to benchmark and audit large language models (LLMs), from persona consistency to safet

Quantum Dynamics via Score Matching on Bohmian Trajectories

arXiv:2604.25137v1 Announce Type: cross Abstract: We solve the time-dependent Schrodinger equation by learning the score function, the gradient of the log-probability density, on Bohmian trajectories.

Agents

R^3-SQL: Ranking Reward and Resampling for Text-to-SQL

arXiv:2604.25325v1 Announce Type: cross Abstract: Modern Text-to-SQL systems generate multiple candidate SQL queries and rank them to judge a final prediction. However, existing methods face two limit

agentsarxiv-cs-cl

Rapid tracking through strongly scattering media with physics-informed neuromorphic speckle analysis

arXiv:2604.25310v1 Announce Type: new Abstract: This work addresses the critical problem of tracking fast-moving objects through strongly scattering media in a low-light environment. Different from ex

researcharxiv-cs-cv

Reference-Augmented Learning for Precise Tracking Policy of Tendon-Driven Continuum Robots

arXiv:2604.25698v1 Announce Type: new Abstract: Tendon-Driven Continuum Robots (TDCRs) pose significant control challenges due to their highly nonlinear, path-dependent dynamics and non-Markovian char

safetyarxiv-cs-ro

Reinforcement Learning for Testing Interdependent Requirements in Autonomous Vehicles: An Empirical Study

arXiv:2502.15792v2 Announce Type: replace-cross Abstract: Autonomous vehicles (AVs) make driving decisions without humans, making dependability assurance critical. Scenario-based testing is widely use

safetyarxiv-cs-lg

Reinforcement Learning Using known Invariances

arXiv:2511.03473v2 Announce Type: replace Abstract: In many real-world reinforcement learning (RL) problems, the environment exhibits inherent symmetries that can be exploited to improve learning effi

applicationsarxiv-cs-lg

RELIC: Evaluating Complex Reasoning via the Recognition of Languages In-Context

arXiv:2506.05205v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to solve complex tasks where they must retrieve and compose many pieces of in-context information

applicationsarxiv-cs-cl

ResetEdit: Precise Text-guided Editing of Generated Image via Resettable Starting Latent

arXiv:2604.25128v1 Announce Type: new Abstract: Recent advances in diffusion models have enabled high-quality image generation, leading to increasing demand for post-generation editing that modifies l

safetyarxiv-cs-cv

Rethinking Entropy Interventions in RLVR: An Entropy Change Perspective

arXiv:2510.10150v3 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) serves as a cornerstone technique for enhancing the reasoning capabilities of Large Language M

safetyarxiv-cs-lg

RISE: Self-Improving Robot Policy with Compositional World Model

arXiv:2602.11075v2 Announce Type: replace Abstract: Despite the sustained scaling on model capacity and data acquisition, Vision-Language-Action (VLA) models remain brittle in contact-rich and dynamic

safetyarxiv-cs-ro

Robust Deepfake Detection: Mitigating Spatial Attention Drift via Calibrated Complementary Ensembles

arXiv:2604.25889v1 Announce Type: new Abstract: Current deepfake detection models achieve state-of-the-art performance on pristine academic datasets but suffer severe spatial attention drift under rea

applicationsarxiv-cs-cv