Search: “framework”

Primitive Representation Learning for Unsupervised Dynamic Contrast Enhanced MRI Reconstruction

arXiv:2608.18055v1 Announce Type: cross Abstract: Reliable quantitative analysis of dynamic contrast-enhanced MRI requires high-quality spatiotemporal reconstructions at high undersampling rates. Scan

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Agents

PrivAct: Internalizing Contextual Privacy Preservation via Multi-Agent Preference Training

DGX agent

arXiv:2602.13840v2 Announce Type: replace Abstract: Large language model (LLM) agents are increasingly deployed in personalized tasks involving sensitive, context-dependent information, where privacy

agentsarxiv-cs-cl

19 Aug 2026

Proactive Road Safety Intervention in Australia: Predicting Risky Driving Hotspots from Connected Vehicle Data

arXiv:2608.16913v1 Announce Type: new Abstract: Road safety monitoring has historically been reactive, relying on crash-record analysis after fatalities and injuries have already occurred. Proactive i

safetyarxiv-cs-lg

PROBE: Manipulation-Grounded Visual Question Answering with VLM Agents

arXiv:2608.17129v1 Announce Type: new Abstract: Vision-language Models (VLMs) excel at 2D grounding, spatial reasoning and agentic tool-based planning in static scenes. However, consider asking a home

agentsarxiv-cs-cv

Q-Learning With World Models

arXiv:2608.17163v1 Announce Type: cross Abstract: Off-policy reinforcement learning (RL) has become increasingly sample-efficient, enabling applications such as RL fine-tuning of Vision-Language-Actio

safetyarxiv-cs-ai

QuantumNovelty: A Skill-Orchestrating Language Agent for Referee-Style Review and Patentability Screening of Quantum Papers and Patents

arXiv:2608.16900v1 Announce Type: cross Abstract: Language-model agents increasingly produce quantum-science results; we ask whether the same agentic paradigm can also scrutinize them in an auditable,

agentsarxiv-cs-ai

REChart: Reasoning-Efficient Chart Editing with Large Reasoning Models

arXiv:2608.17414v1 Announce Type: new Abstract: Chart editing requires inferring and modifying visualization code from a reference chart image based on an editing instruction, challenging fine-grained

agentsarxiv-cs-cv

Reconfiguration-Complete Motion Primitives with Constructive Planning for Deformable Planar Modular Robots

arXiv:2608.17324v1 Announce Type: new Abstract: The continuously deformable geometry of modular robots makes it difficult to define a fixed representation for reconfiguration planning and analysis. Th

Reinforcement Learning as (Discrete) Potential Theory

arXiv:2608.17181v1 Announce Type: new Abstract: Reinforcement learning (RL) theory fundamentally depends on probability theory through the Markov chain. There is a deep connection between probability

safetyarxiv-cs-lg

Hardware

rl-triton: High-Performance Triton GPU Kernels for Reinforcement Learning Credit Assignment

arXiv:2608.17641v2 Announce Type: new Abstract: We present rl-triton, an open-source library of high-performance GPU kernels for reinforcement learning credit assignment, implemented in Triton. The co

hardwarearxiv-cs-lg

Robust Brachiation on a Life-Sized Dual-Arm Robot Using Waypoint-Guided Reinforcement Learning

arXiv:2608.17320v1 Announce Type: new Abstract: Brachiation is a form of locomotion in which primates move primarily using their arms, enabling traversal in environments without footholds. However, th

SAGE: Self-Evolving Storyboard Skills via Attribution-Guided Rule Evolution

arXiv:2608.17468v1 Announce Type: new Abstract: Storyboards turn screenplays into visual shot plans for automated short drama production. Professional storyboarding relies on tacit directorial experti

applicationsarxiv-cs-ai

Scalix: Uncertainty-Aware Scale-Consistent Monocular SLAM

arXiv:2608.17553v1 Announce Type: new Abstract: Cameras are ubiquitous sensors in robotics due to their compact form factor and the perceptual richness captured through visual information. Monocular S

SCRIBES: Web-Scale Script-Based Semi-Structured Data Extraction with Reinforcement Learning

arXiv:2510.01832v2 Announce Type: replace Abstract: Semi-structured content in HTML tables, lists, and infoboxes accounts for a substantial share of factual data on the web, yet the formatting complic

researcharxiv-cs-cl

SE-MoLoRA: Shared-Expert LoRA Adapters for Domain-Specific Photographic Assessment

model-releasesarxiv-cs-cv

arXiv:2608.17514v1 Announce Type: new Abstract: Vision-language models can describe images fluently, but they often fail to provide actionable photographic critique because semantic content and aesthe

SIGMA: SHAP-Guided Implicit-Trajectory Generation for Metadata-Free LLM-Based AutoFE

arXiv:2608.17948v1 Announce Type: cross Abstract: Recent research has leveraged Large Language Models (LLMs) to enhance Automated Feature Engineering (AutoFE) through semantic descriptions and traject

Sparse Coverage: Semantic Center Representations for Patent Prior-Art Retrieval

arXiv:2608.16918v1 Announce Type: cross Abstract: Patent prior-art retrieval is a recall-oriented search task over long and highly structured technical documents. Dense retrieval improves semantic mat

Speak in Context: Multilingual ASR with Speech Context Alignment via Contrastive Learning

arXiv:2603.06505v2 Announce Type: replace Abstract: Automatic speech recognition (ASR) has benefited from advances in pretrained speech and language models, yet most systems remain constrained to mono

safetyarxiv-cs-cl

SpurCon: Weighted Supervised Contrastive Learning for Mitigating Spurious Cues in Medical Imaging

arXiv:2608.17598v1 Announce Type: new Abstract: Despite the rapid progress of deep neural networks in visual recognition, their adoption in high-risk medical applications remains limited due to reliab

SPVC: Structured and Panoptic Video Fixing for Cross-Dataset Driving Scene Rendering

arXiv:2608.17420v1 Announce Type: new Abstract: Driving scene reconstruction and rendering, especially with 3D Gaussian Splatting, has become an important component of autonomous driving simulation. H

agentsarxiv-cs-cv

TabNSM: Neural Sparse Mixer for Tabular Regression

arXiv:2608.18026v1 Announce Type: new Abstract: Large-scale, high-dimensional tabular regression remains challenging: tree-based models are robust but lack end-to-end representation learning, while de

applicationsarxiv-cs-lg

TabularQGAN: A quantum generative model for tabular data synthesis

arXiv:2505.22533v2 Announce Type: replace-cross Abstract: In this paper, we introduce a novel quantum generative model for synthesizing tabular data. Synthetic data is valuable in scenarios where real

applicationsarxiv-cs-ai

Task Specialization Fine-Tuning for Contextual Reinforcement Learning

arXiv:2608.17180v1 Announce Type: cross Abstract: Contextual Reinforcement Learning (CRL) seeks to generalize classical RL by maximizing task coverage across a context space of related tasks. While pr

safetyarxiv-cs-ai

TEAMS: Text-prompted spatiotEmporal dual-heAd Mamba Snake

arXiv:2608.17421v1 Announce Type: new Abstract: Deep snake is a promising family of instance segmentation methods that accurately predicts object-level contours, thereby overcoming common pixel-level

Terrain-Aware Local Path Planning with Global DEM Data Integration for Autonomous UGV Navigation

arXiv:2608.17038v1 Announce Type: new Abstract: Autonomous navigation in complex outdoor terrains presents critical challenges for unmanned ground vehicles (UGVs) due to the inherent disconnect betwee

safetyarxiv-cs-ro

To Remove or Not to Remove Clouds: A Comparative Analysis and Fusion of Raw SAR and Synthetic NDWI for Overcast Water Segmentation

arXiv:2608.17398v1 Announce Type: new Abstract: Persistent clouds blind optical satellites during floods. While Synthetic Aperture Radar (SAR) penetrates clouds, its raw data is noisy and lacks clear

Token Optimization and Context Window Management in Multi-Agent AI Workflows

arXiv:2608.17188v1 Announce Type: cross Abstract: Multi-agent AI workflows are limited not only by model quality but by token cost, latency, and context-window quality. This paper presents a practitio

TokEval: A Tokenizer Evaluation Suite

arXiv:2608.18062v1 Announce Type: new Abstract: Language model tokenizers are typically selected with minimal evaluation, despite the fact that their design choices directly impact model capabilities.

safetyarxiv-cs-cl

Toward Personal Intelligence Through Cooperative Observation

arXiv:2608.17128v1 Announce Type: new Abstract: A personal AI system needs a model of the user's goals, constraints, and ongoing commitments to plan and act on their behalf, and the quality of that mo

Training-Free Human-in-the-Loop Anomaly Detection via Memory Bank Correction

arXiv:2608.17775v1 Announce Type: new Abstract: Anomaly detectors are hardest to deploy exactly where training data is scarcest: a newly commissioned production line has a handful of verified 'golden'

applicationsarxiv-cs-lg

TRUSS: Towards Task-Reliable and User-Safe Automated Agent Skill Generation

arXiv:2608.17588v1 Announce Type: new Abstract: Agent Skills package reusable natural language procedures with executable resources, enabling software agents to acquire task specific capabilities with

Uncertainty-Aware Decision Making in Multimodal Large Language Models

arXiv:2608.17084v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) increasingly answer questions whose correctness depends on visual, textual, temporal, acoustic, document, chart

safetyarxiv-cs-cl

Tutorials

Understanding the Surprising Generalization Properties of Tabular Foundation Models

arXiv:2608.17957v1 Announce Type: new Abstract: Tabular Foundation Models (TFMs) increasingly rely on in-context learning, where a model receives labelled examples at inference time and predicts label

tutorialsarxiv-cs-lg

UniQuery4R: Unified 4D Scene Reconstruction from a Single Query

arXiv:2608.17283v1 Announce Type: new Abstract: Reconstructing dynamic 4D scenes requires jointly estimating correspondence, geometry, object motion, and camera motion. Existing feed-forward methods t

UniReflex: Plug-and-Play Force Control for Pretrained Generative Policies via Fast-Slow Reflex

arXiv:2608.17432v1 Announce Type: new Abstract: Generative imitation learning policies excel at trajectory planning but lack closed-loop force regulation, while directly incorporating force modalities

applicationsarxiv-cs-ro

Unsupervised Deep Generative Models for Anomaly Detection in Neuroimaging: A Systematic Scoping Review

arXiv:2510.14462v3 Announce Type: replace Abstract: Unsupervised anomaly detection (UAD) based on deep generative modelling has been increasingly explored for identifying pathological brain abnormalit

Vision Transformers for End-to-End Quark-Gluon Jet Classification from Calorimeter Images

arXiv:2506.14934v2 Announce Type: replace Abstract: Distinguishing between quark- and gluon-initiated jets is a critical and challenging task in high-energy physics, pivotal for improving new physics

Tutorials

Wasted large language models: A life cycle thinking approach

arXiv:2608.17055v1 Announce Type: cross Abstract: Large Language Models (LLMs) are machine learning (ML) models that have an increasingly large carbon footprint through their development and use. Effo

tutorialsarxiv-cs-lg

When Personalization Becomes Bias: Structural and Discursive Religious Framing in AI-Generated Financial Advice

arXiv:2608.16909v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly integrated into financial advisory systems, yet their role in reproducing religious bias remains underex

When Writing Style Drifts: Benchmarking Authorship Verification under Distribution Shifts in Genre, Time and the AI-Era

model-releasesarxiv-cs-cl

arXiv:2608.17979v1 Announce Type: new Abstract: Authorship verification (AV) assumes that an author's writing style remains sufficiently stable to distinguish it from that of other writers. In practic

Why GPT-Style Models Do Not Directly Transfer to Symbolic Music: Compression in the Wrong Coordinate System

arXiv:2608.18025v1 Announce Type: cross Abstract: GPT-style models achieve strong performance by representing language with finite vocabularies of reusable discrete tokens. This success has motivated

WorldBench: Benchmarking Physical Understanding of World Models by Isolating Physics Concepts

model-releasesarxiv-cs-cv

arXiv:2601.21282v2 Announce Type: replace Abstract: Recent advances in generative foundational models, often termed 'world models,' have propelled interest in applying them to critical tasks like robo

Write, Execute, Refine: From Skill Followers to Skill Optimizers via Reinforcement Learning from Execution Feedback

arXiv:2608.17587v1 Announce Type: new Abstract: Expert-written natural language skills can improve tool-using agents, yet agent-authored skills perform 8-11 points worse than using no skill. This gap

agentsarxiv-cs-cl

Wuying-Browser-Agent: Real-World Centric Fundamental Long-Horizon Browser Agents

arXiv:2608.17319v1 Announce Type: new Abstract: Browser agents perform well on short, clean demonstrations, but real deployment is fundamentally different: agents must sustain dozens of decisions on l

Tutorials

A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing

arXiv:2607.16183v2 Announce Type: replace Abstract: To help address the escalating energy and latency demands of machine-learning workloads, we introduce a blueprint for an energy-efficient and fast t

tutorialsarxiv-cs-lg

A Declarative-Procedural Perspective on Expert Routing in Bilingual Mixture-of-Experts Language Models

arXiv:2608.15102v1 Announce Type: new Abstract: We investigate whether Mixture-of-Experts (MoE) language models develop linguistically structured expert routing during bilingual language acquisition.

researcharxiv-cs-cl

A validity-guided workflow for robust large language model research in psychology

arXiv:2507.04491v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are rapidly being integrated into psychological and behavioral research as research tools, evaluation targets, hu

Accelerating Mixed Discrete-Continuous Motion Planning via Neural Graphs of Convex Sets

arXiv:2608.15440v1 Announce Type: new Abstract: Motion planning problems such as collision-free navigation and contact-rich manipulation can be naturally formulated as optimization problems that coupl