Search: “framework”

Fast-SegSim: Real-Time Open-Vocabulary Segmentation for Robotics in Simulation

arXiv:2604.10951v1 Announce Type: new Abstract: Open-vocabulary panoptic reconstruction is crucial for advanced robotics and simulation. However, existing 3D reconstruction methods, such as NeRF or Ga

researcharxiv-cs-ro

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FedKLPR: KL-Guided Pruning-Aware Federated Learning for Person Re-Identification

DGX agent

arXiv:2508.17431v3 Announce Type: replace-cross Abstract: Person re-identification (re-ID) is a fundamental task in intelligent surveillance and public safety. Federated learning (FL) provides a priva

model-releasesarxiv-cs-ai

14 Apr 2026

FedQUIT: On-Device Federated Unlearning via a Quasi-Competent Virtual Teacher

arXiv:2408.07587v4 Announce Type: replace Abstract: Federated Learning (FL) enables the collaborative training of machine learning models without requiring centralized collection of user data. To comp

local-aiarxiv-cs-lg

FF3R: Feedforward Feature 3D Reconstruction from Unconstrained views

arXiv:2604.09862v1 Announce Type: new Abstract: Recent advances in vision foundation models have revolutionized geometry reconstruction and semantic understanding. Yet, most of the existing approaches

FGML-DG: Feynman-Inspired Cognitive Science Paradigm for Cross-Domain Medical Image Segmentation

arXiv:2604.10524v1 Announce Type: new Abstract: In medical image segmentation across multiple modalities (e.g., MRI, CT, etc.) and heterogeneous data sources (e.g., different hospitals and devices), D

safetyarxiv-cs-cv

FlashMem: Distilling Intrinsic Latent Memory via Computation Reuse

model-releasesarxiv-cs-cl

arXiv:2601.05505v2 Announce Type: replace Abstract: The stateless architecture of Large Language Models inherently lacks the mechanism to preserve dynamic context, compelling agents to redundantly rep

Flow-Controlled Scheduling for LLM Inference with Provable Stability Guarantees

model-releasesarxiv-cs-lg

arXiv:2604.11001v1 Announce Type: new Abstract: Large language models (LLMs) have been widely adopted due to their great performance across a wide range of applications. ChatGPT and Gemini now serve h

FlowBind: Efficient Any-to-Any Generation with Bidirectional Flows

arXiv:2512.15420v2 Announce Type: replace Abstract: Any-to-any generation seeks to translate between arbitrary subsets of modalities, enabling flexible cross-modal synthesis. Despite recent success, e

researcharxiv-cs-lg

FlowCoMotion: Text-to-Motion Generation via Token-Latent Flow Modeling

arXiv:2604.11083v1 Announce Type: cross Abstract: Text-to-motion generation is driven by learning motion representations for semantic alignment with language. Existing methods rely on either continuou

safetyarxiv-cs-ai

FlowPalm: Optical Flow Driven Non-Rigid Deformation for Geometrically Diverse Palmprint Generation

arXiv:2604.09989v1 Announce Type: cross Abstract: Recently, synthetic palmprints have been increasingly used as substitutes for real data to train recognition models. To be effective, such synthetic d

FREE-Switch: Frequency-based Dynamic LoRA Switch for Style Transfer

arXiv:2604.10023v1 Announce Type: cross Abstract: With the growing availability of open-sourced adapters trained on the same diffusion backbone for diverse scenes and objects, combining these pretrain

safetyarxiv-cs-ai

Applications

FreeScale: Scaling 3D Scenes via Certainty-Aware Free-View Generation

arXiv:2604.10512v1 Announce Type: new Abstract: The development of generalizable Novel View Synthesis (NVS) models is critically limited by the scarcity of large-scale training data featuring diverse

applicationsarxiv-cs-cv

From Perception to Planning: Evolving Ego-Centric Task-Oriented Spatiotemporal Reasoning via Curriculum Learning

arXiv:2604.10517v1 Announce Type: new Abstract: Modern vision-language models achieve strong performance in static perception, but remain limited in the complex spatiotemporal reasoning required for e

researcharxiv-cs-ai

From Redaction to Restoration: Deep Learning for Medical Image Anonymization and Reconstruction

arXiv:2604.11376v1 Announce Type: cross Abstract: Removing patient-specific information from medical images is crucial to enable sharing and open science without compromising patient identities. Howev

researcharxiv-cs-ai

From Scalars to Tensors: Declared Losses Recover Epistemic Distinctions That Neutrosophic Scalars Cannot Express

arXiv:2604.09602v1 Announce Type: new Abstract: Leyva-Vazquez and Smarandache (2025) demonstrated that neutrosophic T/I/F evaluation, where Truth, Indeterminacy, and Falsity are independent dimensions

From Topology to Trajectory: LLM-Driven World Models For Supply Chain Resilience

arXiv:2604.11041v1 Announce Type: new Abstract: Semiconductor supply chains face unprecedented resilience challenges amidst global geopolitical turbulence. Conventional Large Language Model (LLM) plan

From Translation to Superset: Benchmark-Driven Evolution of a Production AI Agent from Rust to Python

arXiv:2604.11518v1 Announce Type: cross Abstract: Cross-language migration of large software systems is a persistent engineering challenge, particularly when the source codebase evolves rapidly. We pr

From UAV Imagery to Agronomic Reasoning: A Multimodal LLM Benchmark for Plant Phenotyping

arXiv:2604.09907v1 Announce Type: cross Abstract: To improve crop genetics, high-throughput, effective and comprehensive phenotyping is a critical prerequisite. While such tasks were traditionally per

Gait Recognition with Temporal Kolmogorov-Arnold Networks

arXiv:2604.09990v1 Announce Type: new Abstract: Gait recognition is a biometric modality that identifies individuals from their characteristic walking patterns. Unlike conventional biometric traits, g

local-aiarxiv-cs-cv

Generalizable Deepfake Detection Based on Forgery-aware Layer Masking and Multi-artifact Subspace Decomposition

arXiv:2601.01041v3 Announce Type: replace Abstract: Deepfake detection remains highly challenging, particularly in cross-dataset scenarios and complex real-world settings. This challenge mainly arises

Hardware

Generative Design for Direct-to-Chip Liquid Cooling for Data Centers

arXiv:2604.10941v1 Announce Type: cross Abstract: Rapid growth in artificial intelligence (AI) workloads is driving up data center power densities, increasing the need for advanced thermal management.

hardwarearxiv-cs-lg

GenProve: Learning to Generate Text with Fine-Grained Provenance

arXiv:2601.04932v2 Announce Type: replace Abstract: Large language models (LLM) often hallucinate, and while adding citations is a common solution, it is frequently insufficient for accountability as

safetyarxiv-cs-cl

GeoArena: Evaluating Open-World Geographic Reasoning in Large Vision-Language Models

arXiv:2509.04334v4 Announce Type: replace Abstract: Geographic reasoning is a fundamental cognitive capability that requires models to infer plausible locations by synthesizing visual evidence with sp

GeoFormer: A Lightweight Swin Transformer for Joint Building Height and Footprint Estimation from Sentinel Imagery

arXiv:2602.09932v2 Announce Type: replace Abstract: Building height (BH) and footprint (BF) are fundamental urban morphological parameters required by climate modelling, disaster-risk assessment, and

Global Geometry of Orthogonal Foliations of Signed-Quadratic Systems

arXiv:2604.01912v2 Announce Type: replace-cross Abstract: This work formalizes the differential topology of redundancy resolution for systems governed by signed-quadratic actuation maps. By analyzing

researcharxiv-cs-ro

Agents

Governance by Design: A Parsonian Institutional Architecture for Internet-Wide Agent Societies

arXiv:2604.11337v1 Announce Type: cross Abstract: The dominant paradigm of local multi-agent systems -- orchestrated, enterprise-bounded pipelines -- is being superseded by internet-wide agent societi

agentsarxiv-cs-ai

Gradient-based Optimisation of Modulation Effects

arXiv:2601.04867v2 Announce Type: replace-cross Abstract: Modulation effects such as phasers, flangers and chorus effects are heavily used in conjunction with the electric guitar. Machine learning bas

local-aiarxiv-cs-lg

Graph-RHO: Critical-path-aware Heterogeneous Graph Network for Long-Horizon Flexible Job-Shop Scheduling

arXiv:2604.10073v1 Announce Type: cross Abstract: Long-horizon Flexible Job-Shop Scheduling~(FJSP) presents a formidable combinatorial challenge due to complex, interdependent decisions spanning exten

researcharxiv-cs-ai

GrOCE:Graph-Guided Online Concept Erasure for Text-to-Image Diffusion Models

arXiv:2511.12968v2 Announce Type: replace Abstract: Concept erasure aims to remove harmful, inappropriate, or copyrighted content from text-to-image diffusion models while preserving non-target semant

Grounded Forcing: Bridging Time-Independent Semantics and Proximal Dynamics in Autoregressive Video Synthesis

arXiv:2604.06939v2 Announce Type: replace Abstract: Autoregressive video synthesis offers a promising pathway for infinite-horizon generation but is fundamentally hindered by three intertwined challen

H-SPAM: Hierarchical Superpixel Anything Model

arXiv:2604.11218v1 Announce Type: new Abstract: Superpixels offer a compact image representation by grouping pixels into coherent regions. Recent methods have reached a plateau in terms of segmentatio

Hardware

Hardware Utilization and Inference Performance of Edge Object Detection Under Fault Injection

arXiv:2604.09631v1 Announce Type: cross Abstract: As deep learning models are deployed on resource constrained edge platforms in autonomous driving systems, reli able knowledge of hardware behavior un

hardwarearxiv-cs-ai

Tutorials

HiddenObjects: Scalable Diffusion-Distilled Spatial Priors for Object Placement

arXiv:2604.10675v1 Announce Type: new Abstract: We propose a method to learn explicit, class-conditioned spatial priors for object placement in natural scenes by distilling the implicit placement know

tutorialsarxiv-cs-cv

HiEdit: Lifelong Model Editing with Hierarchical Reinforcement Learning

model-releasesarxiv-cs-cl

arXiv:2604.11214v1 Announce Type: new Abstract: Lifelong model editing (LME) aims to sequentially rectify outdated or inaccurate knowledge in deployed LLMs while minimizing side effects on unrelated i

HiPRAG: Hierarchical Process Rewards for Efficient Agentic Retrieval Augmented Generation

arXiv:2510.07794v2 Announce Type: replace-cross Abstract: Agentic RAG is a powerful technique for incorporating external information that LLMs lack, enabling better problem solving and question answer

Tutorials

HO-Flow: Generalizable Hand-Object Interaction Generation with Latent Flow Matching

arXiv:2604.10836v1 Announce Type: new Abstract: Generating realistic 3D hand-object interactions (HOI) is a fundamental challenge in computer vision and robotics, requiring both temporal coherence and

tutorialsarxiv-cs-cv

HOG-Layout: Hierarchical 3D Scene Generation, Optimization and Editing via Vision-Language Models

arXiv:2604.10772v1 Announce Type: new Abstract: 3D layout generation and editing play a crucial role in Embodied AI and immersive VR interaction. However, manual creation requires tedious labor, while

How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularities

arXiv:2603.02578v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed in socially sensitive domains, yet their unpredictable behaviors, ranging from misalign

Agents

HTAA: Enhancing LLM Planning via Hybrid Toolset Agentization & Adaptation

arXiv:2604.10917v1 Announce Type: new Abstract: Enabling large language models to scale and reliably use hundreds of tools is critical for real-world applications, yet challenging due to the inefficie

agentsarxiv-cs-cl

Human vs. Machine Deception: Distinguishing AI-Generated and Human-Written Fake News Using Ensemble Learning

arXiv:2604.09960v1 Announce Type: new Abstract: The rapid adoption of large language models has introduced a new class of AI-generated fake news that coexists with traditional human-written misinforma

researcharxiv-cs-cl

HumanVBench: Probing Human-Centric Video Understanding in MLLMs with Automatically Synthesized Benchmarks

arXiv:2412.17574v3 Announce Type: replace-cross Abstract: Evaluating the nuanced human-centric video understanding capabilities of Multimodal Large Language Models (MLLMs) remains a great challenge, a

Identity-Aware U-Net: Fine-grained Cell Segmentation via Identity-Aware Representation Learning

arXiv:2604.09702v1 Announce Type: cross Abstract: Precise segmentation of objects with highly similar shapes remains a challenging problem in dense prediction, especially in scenarios with ambiguous b

local-aiarxiv-cs-ai

Immune2V: Image Immunization Against Dual-Stream Image-to-Video Generation

arXiv:2604.10837v1 Announce Type: new Abstract: Image-to-video (I2V) generation has the potential for societal harm because it enables the unauthorized animation of static images to create realistic d

Immunizing 3D Gaussian Generative Models Against Unauthorized Fine-Tuning via Attribute-Space Traps

arXiv:2604.09688v1 Announce Type: new Abstract: Recent large-scale generative models enable high-quality 3D synthesis. However, the public accessibility of pre-trained weights introduces a critical vu

IMPLICITSTAINER: Resolution Agnostic Data-Efficient Virtual Staining Using Neural Implicit Functions

arXiv:2505.09831v2 Announce Type: replace-cross Abstract: Hematoxylin and eosin (H&E)-stained slides are central to cancer diagnosis and monitoring, visualizing tissue architecture and cellular morpho

local-aiarxiv-cs-cv

Applications

Improving Neutrino Oscillation Measurements through Event Classification

arXiv:2511.11938v2 Announce Type: replace-cross Abstract: Precise neutrino energy reconstruction is essential for next-generation long-baseline oscillation experiments, yet current methods remain limi

applicationsarxiv-cs-ai

Improving Pediatric Emergency Department Triage with Modality Dropout in Late Fusion Multimodal EHR Models

arXiv:2604.09905v1 Announce Type: new Abstract: Emergency department triage relies heavily on both quantitative vital signs and qualitative clinical notes, yet multimodal machine learning models predi

researcharxiv-cs-lg

Tutorials

Improving the Reasoning of Multi-Image Grounding in MLLMs via Reinforcement Learning

arXiv:2507.00748v3 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) perform well in single-image visual grounding but struggle with real-world tasks that demand cross-image re

tutorialsarxiv-cs-cv