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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

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

DGX agent

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

14 Apr 2026

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs

arXiv:2604.10480v1 Announce Type: new Abstract: Post-training data plays a pivotal role in shaping the capabilities of Large Language Models (LLMs), yet datasets are often treated as isolated artifact

Training-Free Object-Background Compositional T2I via Dynamic Spatial Guidance and Multi-Path Pruning

model-releasesarxiv-cs-cv

arXiv:2604.09850v1 Announce Type: new Abstract: Existing text-to-image diffusion models, while excelling at subject synthesis, exhibit a persistent foreground bias that treats the background as a pass

Triviality Corrected Endogenous Reward

arXiv:2604.11522v1 Announce Type: new Abstract: Reinforcement learning for open-ended text generation is constrained by the lack of verifiable rewards, necessitating reliance on judge models that requ

safetyarxiv-cs-cl

Tuning Qwen2.5-VL to Improve Its Web Interaction Skills

arXiv:2604.09571v1 Announce Type: cross Abstract: Recent advances in vision-language models (VLMs) have sparked growing interest in using them to automate web tasks, yet their feasibility as independe

Uncertainty-quantified Pulse Signal Recovery from Facial Video using Regularized Stochastic Interpolants

model-releasesarxiv-cs-cv

arXiv:2604.10777v1 Announce Type: new Abstract: Imaging Photoplethysmography (iPPG), an optical procedure which recovers a human's blood volume pulse (BVP) waveform using pixel readout from a camera,

Unified Graph Prompt Learning via Low-Rank Graph Message Prompting

arXiv:2604.11257v1 Announce Type: new Abstract: Graph Data Prompt (GDP), which introduces specific prompts in graph data for efficiently adapting pre-trained GNNs, has become a mainstream approach to

Unifying Ontology Construction and Semantic Alignment for Deterministic Enterprise Reasoning at Scale

arXiv:2604.09608v1 Announce Type: new Abstract: While enterprises amass vast quantities of data, much of it remains chaotic and effectively dormant, preventing decision-making based on comprehensive i

UniPROT: Uniform Prototype Selection via Partial Optimal Transport with Submodular Guarantees

arXiv:2604.10952v1 Announce Type: new Abstract: Selecting prototypical examples from a source distribution to represent a target data distribution is a fundamental problem in machine learning. Existin

researcharxiv-cs-lg

Unsupervised Detection of Spatiotemporal Anomalies in PMU Data Using Transformer-Based BiGAN

arXiv:2509.25612v2 Announce Type: replace-cross Abstract: Ensuring power grid resilience requires the timely and unsupervised detection of anomalies in synchrophasor data streams. We introduce T-BiGAN

Unsupervised Domain Adaptation for Binary Classification with an Unobservable Source Subpopulation

arXiv:2509.20587v3 Announce Type: replace-cross Abstract: We study an unsupervised domain adaptation problem where the source domain consists of subpopulations defined by the binary label Y and a bina

Variable Selection Using Relative Importance Rankings

arXiv:2509.10853v2 Announce Type: replace-cross Abstract: Although conceptually related, variable selection and relative importance (RI) analysis have been treated quite differently in the literature.

Variance-Aware Prior-Based Tree Policies for Monte Carlo Tree Search

arXiv:2512.21648v2 Announce Type: replace-cross Abstract: Monte Carlo Tree Search (MCTS) has profoundly influenced reinforcement learning (RL) by integrating planning and learning in tasks requiring l

safetyarxiv-cs-ai

Virtual Smart Metering in District Heating Networks via Heterogeneous Spatial-Temporal Graph Neural Networks

arXiv:2604.10166v1 Announce Type: cross Abstract: Intelligent operation of thermal energy networks aims to improve energy efficiency, reliability, and operational flexibility through data-driven contr

Visual Enhanced Depth Scaling for Multimodal Latent Reasoning

arXiv:2604.10500v1 Announce Type: new Abstract: Multimodal latent reasoning has emerged as a promising paradigm that replaces explicit Chain-of-Thought (CoT) decoding with implicit feature propagation

safetyarxiv-cs-cv

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

VP-VLA: Visual Prompting as an Interface for Vision-Language-Action Models

model-releasesarxiv-cs-ro

arXiv:2603.22003v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models typically map visual observations and linguistic instructions directly to robotic control signals. This 'black-b

WaveMoE: A Wavelet-Enhanced Mixture-of-Experts Foundation Model for Time Series Forecasting

arXiv:2604.10544v1 Announce Type: cross Abstract: Time series foundation models (TSFMs) have recently achieved remarkable success in universal forecasting by leveraging large-scale pretraining on dive

When Meaning Isn't Literal: Exploring Idiomatic Meaning Across Languages and Modalities

model-releasesarxiv-cs-cl

arXiv:2604.10787v1 Announce Type: new Abstract: Idiomatic reasoning, deeply intertwined with metaphor and culture, remains a blind spot for contemporary language models, whose progress skews toward su

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

Applications

Why Don't You Know? Evaluating the Impact of Uncertainty Sources on Uncertainty Quantification in LLMs

arXiv:2604.10495v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly deployed in real-world applications, reliable uncertainty quantification (UQ) becomes critical for safe

applicationsarxiv-cs-cl

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

model-releasesarxiv-cs-cl

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

Zero-shot World Models Are Developmentally Efficient Learners

arXiv:2604.10333v1 Announce Type: new Abstract: Young children demonstrate early abilities to understand their physical world, estimating depth, motion, object coherence, interactions, and many other

researcharxiv-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

researcharxiv-cs-ai

A Compact Hybrid Convolution--Frequency State Space Network for Learned Image Compression

model-releasesarxiv-cs-cv

arXiv:2511.20151v2 Announce Type: replace Abstract: Learned image compression (LIC) has recently benefited from Transformer- and state space models (SSM)- based backbones for modeling long-range depen

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

researcharxiv-cs-lg

A Little Rank Goes a Long Way: Random Scaffolds with LoRA Adapters Are All You Need

arXiv:2604.08749v1 Announce Type: new Abstract: How many of a neural network's parameters actually encode task-specific information? We investigate this question with LottaLoRA, a training paradigm in

Accelerating Transformer-Based Monocular SLAM via Geometric Utility Scoring

arXiv:2604.08718v1 Announce Type: cross Abstract: Geometric Foundation Models (GFMs) have recently advanced monocular SLAM by providing robust, calibration-free 3D priors. However, deploying these mod

researcharxiv-cs-ai

Applications

Adaptive Candidate Point Thompson Sampling for High-Dimensional Bayesian Optimization

arXiv:2604.08891v1 Announce Type: new Abstract: In Bayesian optimization, Thompson sampling selects the evaluation point by sampling from the posterior distribution over the objective function maximiz

applicationsarxiv-cs-lg

Adaptive Simulation Experiment for LLM Policy Optimization

arXiv:2604.08779v1 Announce Type: new Abstract: Large language models (LLMs) have significant potential to improve operational efficiency in operations management. Deploying these models requires spec

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

Agents

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

Applications

Artificial intelligence can persuade people to take political actions

arXiv:2604.09200v1 Announce Type: cross Abstract: There is substantial concern about the ability of advanced artificial intelligence to influence people's behaviour. A rapidly growing body of research

applicationsarxiv-cs-ai

ASTRA: Adaptive Semantic Tree Reasoning Architecture for Complex Table Question Answering

arXiv:2604.08999v1 Announce Type: cross Abstract: Table serialization remains a critical bottleneck for Large Language Models (LLMs) in complex table question answering, hindered by challenges such as

safetyarxiv-cs-ai

Attention-Based Sampler for Diffusion Language Models

arXiv:2604.08564v1 Announce Type: new Abstract: Auto-regressive models (ARMs) have established a dominant paradigm in language modeling. However, their strictly sequential decoding paradigm imposes fu

researcharxiv-cs-cl

Automated Batch Distillation Process Simulation for a Large Hybrid Dataset for Deep Anomaly Detection

arXiv:2604.09166v1 Announce Type: new Abstract: Anomaly detection (AD) in chemical processes based on deep learning offers significant opportunities but requires large, diverse, and well-annotated tra

AVA-VLA: Improving Vision-Language-Action models with Active Visual Attention

arXiv:2511.18960v3 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have shown remarkable progress in embodied tasks recently, but most methods process visual observations in

safetyarxiv-cs-cv

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

researcharxiv-cs-cv

BadSkill: Backdoor Attacks on Agent Skills via Model-in-Skill Poisoning

arXiv:2604.09378v1 Announce Type: cross Abstract: Agent ecosystems increasingly rely on installable skills to extend functionality, and some skills bundle learned model artifacts as part of their exec

Agents

Bandwidth-constrained Variational Message Encoding for Cooperative Multi-agent Reinforcement Learning

arXiv:2512.11179v3 Announce Type: replace Abstract: Graph-based multi-agent reinforcement learning (MARL) enables coordinated behavior under partial observability by modeling agents as nodes and commu

agentsarxiv-cs-lg

Better Eyes, Better Thoughts: Why Vision Chain-of-Thought Fails in Medicine

arXiv:2603.06665v2 Announce Type: replace-cross Abstract: Large vision-language models (VLMs) often benefit from chain-of-thought (CoT) prompting in general domains, yet its efficacy in medical vision

safetyarxiv-cs-ai

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

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

Beyond Flicker: Detecting Kinematic Inconsistencies for Generalizable Deepfake Video Detection

arXiv:2512.04175v2 Announce Type: replace Abstract: Generalizing deepfake detection to unseen manipulations remains a key challenge. A recent approach to tackle this issue is to train a network with p

researcharxiv-cs-cv

Bias-Constrained Diffusion Schedules for PDE Emulations: Reconstruction Error Minimization and Efficient Unrolled Training

arXiv:2604.08357v2 Announce Type: replace Abstract: Conditional Diffusion Models are powerful surrogates for emulating complex spatiotemporal dynamics, yet they often fail to match the accuracy of det

safetyarxiv-cs-lg

Biologically-Grounded Multi-Encoder Architectures as Developability Oracles for Antibody Design

arXiv:2604.09369v1 Announce Type: cross Abstract: Generative models can now propose thousands of de novo antibody sequences, yet translating these designs into viable therapeutics remains constrained

Boosting Brain-inspired Path Integration Efficiency via Learning-based Replication of Continuous Attractor Neurodynamics

arXiv:2511.17687v2 Announce Type: replace Abstract: The brain's Path Integration (PI) mechanism offers substantial guidance and inspiration for Brain-Inspired Navigation (BIN). However, the PI capabil

Breaking Block Boundaries: Anchor-based History-stable Decoding for Diffusion Large Language Models

model-releasesarxiv-cs-cl

arXiv:2604.08964v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have recently become a promising alternative to autoregressive large language models (ARMs). Semi-autoregressive