Search: “framework”

Learning Class Difficulty in Imbalanced Histopathology Segmentation via Dynamic Focal Attention

arXiv:2604.13479v1 Announce Type: cross Abstract: Semantic segmentation of histopathology images under class imbalance is typically addressed through frequency-based loss reweighting, which implicitly

safetyarxiv-cs-cv

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Model Releases

LiveClawBench: Benchmarking LLM Agents on Complex, Real-World Assistant Tasks

DGX agent

arXiv:2604.13072v1 Announce Type: new Abstract: LLM-based agents are increasingly expected to handle real-world assistant tasks, yet existing benchmarks typically evaluate them under isolated sources

model-releasesarxiv-cs-cl

16 Apr 2026

Tutorials

Nonparametric Sparse Online Learning of the Koopman Operator

arXiv:2405.07432v4 Announce Type: replace-cross Abstract: The Koopman operator provides a powerful framework for representing the dynamics of general nonlinear dynamical systems. However, existing dat

tutorialsarxiv-cs-lg

Out of Context: Reliability in Multimodal Anomaly Detection Requires Contextual Inference

arXiv:2604.13252v1 Announce Type: new Abstract: Anomaly detection aims to identify observations that deviate from expected behavior. Because anomalous events are inherently sparse, most frameworks are

Agents

pi-Play: Multi-Agent Self-Play via Privileged Self-Distillation without External Data

arXiv:2604.14054v1 Announce Type: cross Abstract: Deep search agents have emerged as a promising paradigm for addressing complex information-seeking tasks, but their training remains challenging due t

agentsarxiv-cs-cl

Rare Event Analysis via Stochastic Optimal Control

arXiv:2604.13213v1 Announce Type: cross Abstract: Rare events such as conformational changes in biomolecules, phase transitions, and chemical reactions are central to the behavior of many physical sys

Local Ai

Rethinking Image-to-3D Generation with Sparse Queries: Efficiency, Capacity, and Input-View Bias

arXiv:2604.13905v1 Announce Type: new Abstract: We present SparseGen, a novel framework for efficient image-to-3D generation, which exhibits low input-view bias while being significantly faster. Unlik

local-aiarxiv-cs-cv

RobotPan: A 360^irc Surround-View Robotic Vision System for Embodied Perception

arXiv:2604.13476v1 Announce Type: cross Abstract: Surround-view perception is increasingly important for robotic navigation and loco-manipulation, especially in human-in-the-loop settings such as tele

Robust Ultra Low-Bit Post-Training Quantization via Stable Diagonal Curvature Estimate

arXiv:2604.13806v1 Announce Type: new Abstract: Large Language Models (LLMs) are widely used across many domains, but their scale makes deployment challenging. Post-Training Quantization (PTQ) reduces

Safe and Nonconservative Contingency Planning for Autonomous Vehicles via Online Learning-Based Reachable Set Barriers

arXiv:2509.07464v2 Announce Type: replace Abstract: Autonomous vehicles must navigate dynamically uncertain environments while balancing safety and efficiency. This challenge is exacerbated by unpredi

safetyarxiv-cs-ro

SEDTalker: Emotion-Aware 3D Facial Animation Using Frame-Level Speech Emotion Diarization

arXiv:2604.13335v1 Announce Type: new Abstract: We introduce SEDTalker, an emotion-aware framework for speech-driven 3D facial animation that leverages frame-level speech emotion diarization to achiev

SSD-GS: Scattering and Shadow Decomposition for Relightable 3D Gaussian Splatting

arXiv:2604.13333v1 Announce Type: new Abstract: We present SSD-GS, a physically-based relighting framework built upon 3D Gaussian Splatting (3DGS) that achieves high-quality reconstruction and photore

Synthetic Tabular Generators Fail to Preserve Behavioral Fraud Patterns: A Benchmark on Temporal, Velocity, and Multi-Account Signals

arXiv:2604.13125v1 Announce Type: new Abstract: We introduce behavioral fidelity -- a third evaluation dimension for synthetic tabular data that measures whether generated data preserves the temporal,

The cognitive companion: a lightweight parallel monitoring architecture for detecting and recovering from reasoning degradation in LLM agents

arXiv:2604.13759v1 Announce Type: cross Abstract: Large language model (LLM) agents on multi-step tasks suffer reasoning degradation, looping, drift, stuck states, at rates up to 30% on hard tasks. Cu

Utilizing Inpainting for Keypoint Detection for Vision-Based Control of Robotic Manipulators

arXiv:2604.13309v1 Announce Type: new Abstract: In this paper we present a novel visual servoing framework to control a robotic manipulator in the configuration space by using purely natural visual fe

researcharxiv-cs-ro

Agents

WebXSkill: Skill Learning for Autonomous Web Agents

arXiv:2604.13318v1 Announce Type: cross Abstract: Autonomous web agents powered by large language models (LLMs) have shown promise in completing complex browser tasks, yet they still struggle with lon

agentsarxiv-cs-cl

Zero-Shot Function Encoder-Based Differentiable Predictive Control

arXiv:2511.05757v3 Announce Type: replace-cross Abstract: We introduce a differentiable framework for zero-shot adaptive control over parametric families of nonlinear dynamical systems. Our approach i

A General Model for Deepfake Speech Detection: Diverse Bonafide Resources or Diverse AI-Based Generators

arXiv:2603.27557v2 Announce Type: replace-cross Abstract: In this paper, we analyze two main factors of Bonafide Resource (BR) or AI-based Generator (AG) which affect the performance and the generalit

A Workflow to Efficiently Generate Dense Tissue Ground Truth Masks for Digital Breast Tomosynthesis

arXiv:2604.11927v1 Announce Type: new Abstract: Digital breast tomosynthesis (DBT) is now the standard of care for breast cancer screening in the USA. Accurate segmentation of fibroglandular tissue in

AdaMCoT: Rethinking Cross-Lingual Factual Reasoning through Adaptive Multilingual Chain-of-Thought

arXiv:2501.16154v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown impressive multilingual capabilities through pretraining on diverse corpora. Although these models sho

researcharxiv-cs-ai

Adaptive Budget Allocation in LLM-Augmented Surveys

arXiv:2604.12497v1 Announce Type: new Abstract: Large language models (LLMs) can generate survey responses at low cost, but their reliability varies substantially across questions and is unknown befor

Local Ai

Agentic Control in Variational Language Models

arXiv:2604.12513v1 Announce Type: new Abstract: We study whether a variational language model can support a minimal and measurable form of agentic control grounded in its own internal evidence. Our mo

local-aiarxiv-cs-lg

AlphaEval: Evaluating Agents in Production

model-releasesarxiv-cs-cl

arXiv:2604.12162v1 Announce Type: new Abstract: The rapid deployment of AI agents in commercial settings has outpaced the development of evaluation methodologies that reflect production realities. Exi

Causal Fingerprints of AI Generative Models

arXiv:2509.15406v2 Announce Type: replace Abstract: AI generative models leave implicit traces in their generated images, which are commonly referred to as model fingerprints and are exploited for sou

CBAM-Enhanced DenseNet121 for Multi-Class Chest X-Ray Classification with Grad-CAM Explainability

arXiv:2604.12305v1 Announce Type: cross Abstract: Pneumonia remains a leading cause of childhood mortality worldwide, with a heavy burden in low-resource settings such as Bangladesh where radiologist

Applications

Characterizing Human Semantic Navigation in Concept Production as Trajectories in Embedding Space

arXiv:2602.05971v2 Announce Type: replace Abstract: Semantic representations can be framed as a structured, dynamic knowledge space through which humans navigate to retrieve and manipulate meaning. To

applicationsarxiv-cs-cl

Clustering-Enhanced Domain Adaptation for Cross-Domain Intrusion Detection in Industrial Control Systems

arXiv:2604.12183v1 Announce Type: cross Abstract: Industrial control systems operate in dynamic environments where traffic distributions vary across scenarios, labeled samples are limited, and unknown

Dataset Safety in Autonomous Driving: Requirements, Risks, and Assurance

arXiv:2511.08439v2 Announce Type: replace Abstract: Dataset integrity is fundamental to the safety and reliability of AI systems, especially in autonomous driving. This paper presents a structured fra

safetyarxiv-cs-ai

Decidable By Construction: Design-Time Verification for Trustworthy AI

arXiv:2603.25414v2 Announce Type: replace-cross Abstract: A prevailing assumption in machine learning is that model correctness must be enforced after the fact. We observe that the properties determin

researcharxiv-cs-ai

DecompSR: A dataset for decomposed analyses of compositional multihop spatial reasoning

arXiv:2511.02627v2 Announce Type: replace Abstract: We introduce DecompSR, decomposed spatial reasoning, a large benchmark dataset (over 5m datapoints) and generation framework designed to analyse com

Dynamic Modeling and Robust Gait Optimization of a Compliant Worm Robot

arXiv:2604.12031v1 Announce Type: new Abstract: Worm-inspired robots provide an effective locomotion strategy for constrained environments by combining cyclic body deformation with alternating anchori

researcharxiv-cs-ro

E2E-Fly: An Integrated Training-to-Deployment System for End-to-End Quadrotor Autonomy

arXiv:2604.12916v1 Announce Type: new Abstract: Training and transferring learning-based policies for quadrotors from simulation to reality remains challenging due to inefficient visual rendering, phy

safetyarxiv-cs-ro

EvoSpark: Endogenous Interactive Agent Societies for Unified Long-Horizon Narrative Evolution

arXiv:2604.12776v1 Announce Type: new Abstract: Realizing endogenous narrative evolution in LLM-based multi-agent systems is hindered by the inherent stochasticity of generative emergence. In particul

safetyarxiv-cs-cl

FlowBoost Reveals Phase Transitions and Spectral Structure in Finite Free Information Inequalities

arXiv:2604.11922v1 Announce Type: cross Abstract: Using FlowBoost, a closed-loop deep generative optimization framework for extremal structure discovery, we investigate ell^p-generalizations of the fi

FRTSearch: Unified Detection and Parameter Inference of Fast Radio Transients using Instance Segmentation

arXiv:2604.12344v1 Announce Type: cross Abstract: The exponential growth of data from modern radio telescopes presents a significant challenge to traditional single-pulse search algorithms, which are

Local Ai

GigaCheck: Detecting LLM-generated Content via Object-Centric Span Localization

arXiv:2410.23728v3 Announce Type: replace Abstract: With the increasing quality and spread of LLM assistants, the amount of generated content is growing rapidly. In many cases and tasks, such texts ar

local-aiarxiv-cs-cl

INFORM-CT: INtegrating LLMs and VLMs FOR Incidental Findings Management in Abdominal CT

arXiv:2512.14732v2 Announce Type: replace-cross Abstract: Incidental findings in CT scans, though often benign, can have significant clinical implications and should be reported following established

Applications

INTARG: Informed Real-Time Adversarial Attack Generation for Time-Series Regression

arXiv:2604.11928v1 Announce Type: new Abstract: Time-series forecasting aims to predict future values by modeling temporal dependencies in historical observations. It is a critical component of many r

applicationsarxiv-cs-lg

LASA: Language-Agnostic Semantic Alignment at the Semantic Bottleneck for LLM Safety

arXiv:2604.12710v1 Announce Type: cross Abstract: Large language models (LLMs) often demonstrate strong safety performance in high-resource languages, yet exhibit severe vulnerabilities when queried i

Leveraging Weighted Syntactic and Semantic Context Assessment Summary (wSSAS) Towards Text Categorization Using LLMs

arXiv:2604.12049v1 Announce Type: cross Abstract: The use of Large Language Models (LLMs) for reliable, enterprise-grade analytics such as text categorization is often hindered by the stochastic natur

Mobile GUI Agents under Real-world Threats: Are We There Yet?

arXiv:2507.04227v2 Announce Type: replace-cross Abstract: Recent years have witnessed a rapid development of mobile GUI agents powered by large language models (LLMs), which can autonomously execute d

Narrative-Driven Paper-to-Slide Generation via ArcDeck

arXiv:2604.11969v1 Announce Type: new Abstract: We introduce ArcDeck, a multi-agent framework that formulates paper-to-slide generation as a structured narrative reconstruction task. Unlike existing m

Applications

One Model for All: Unified Try-On and Try-Off in Any Pose via LLM-Inspired Bidirectional Tweedie Diffusion

arXiv:2508.04559v3 Announce Type: replace Abstract: Recent diffusion-based approaches have made significant advances in image-based virtual try-on, enabling more realistic and end-to-end garment synth

applicationsarxiv-cs-cv

PAINT: Partner-Agnostic Intent-Aware Cooperative Transport with Legged Robots

arXiv:2604.12852v1 Announce Type: new Abstract: Collaborative transport requires robots to infer partner intent through physical interaction while maintaining stable loco-manipulation. This becomes pa

safetyarxiv-cs-ro

Parametric Interpolation of Dynamic Mode Decomposition for Predicting Nonlinear Systems

arXiv:2604.12103v1 Announce Type: cross Abstract: We present parameter-interpolated dynamic mode decomposition (piDMD), a parametric reduced-order modeling framework that embeds known parameter-affine

PrivEraserVerify: Efficient, Private, and Verifiable Federated Unlearning

arXiv:2604.12348v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training without sharing raw data, offering a promising path toward privacy preserving artificial in

Rapid LoRA Aggregation for Wireless Channel Adaptation in Open-Set Radio Frequency Fingerprinting

arXiv:2604.12834v1 Announce Type: cross Abstract: Radio frequency fingerprints (RFFs) enable secure wireless authentication but struggle in open-set scenarios with unknown devices and varying channels

Retrievals Can Be Detrimental: Unveiling the Backdoor Vulnerability of Retrieval-Augmented Diffusion Models

arXiv:2501.13340v4 Announce Type: replace Abstract: Diffusion models (DMs) have recently demonstrated remarkable generation capability. However, their training generally requires huge computational re