Search: “framework”

Self-DACE++: Robust Low-Light Enhancement via Efficient Adaptive Curve Estimation

model-releasesarxiv-cs-cv

arXiv:2604.25367v1 Announce Type: new Abstract: In this paper, we present Self-DACE++, an improved unsupervised and lightweight framework for Low-Light Image Enhancement (LLIE), building upon our prev

29 Apr 2026

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

Sensitivity-Based Tube NMPC for Cooperative Aerial Structures Under Parametric Uncertainty

DGX agent

arXiv:2604.25766v1 Announce Type: new Abstract: This paper presents a sensitivity-based tube Nonlinear Model Predictive Control (NMPC) framework for cooperative aerial chains under bounded parametric

researcharxiv-cs-ro

29 Apr 2026

Soft-TransFormers for Continual Learning

model-releasesarxiv-cs-cv

arXiv:2411.16073v3 Announce Type: replace-cross Abstract: Inspired by the Well-initialized Lottery Ticket Hypothesis (WLTH), we introduce Soft-Transformer (Soft-TF), a parameter-efficient framework fo

29 Apr 2026

A Divergence-Based Method for Weighting and Averaging Model Predictions

arXiv:2604.24172v1 Announce Type: cross Abstract: This paper uses a minimum divergence framework to introduce a new way of calculating model weights that can be used to average probabilistic predictio

Active Inference: A method for Phenotyping Agency in AI systems?

arXiv:2604.23278v1 Announce Type: new Abstract: The proliferation of agentic artificial intelligence has outpaced the conceptual tools needed to characterize agency in computational systems. Prevailin

AdaMamba: Adaptive Frequency-Gated Mamba for Long-Term Time Series Forecasting

arXiv:2604.23239v1 Announce Type: new Abstract: Accurate long-term time series forecasting (LTSF) requires the capture of complex long-range dependencies and dynamic periodic patterns. Recent advances

applicationsarxiv-cs-ai

AI Identity: Standards, Gaps, and Research Directions for AI Agents

arXiv:2604.23280v1 Announce Type: new Abstract: AI agents are now running real transactions, workflows, and sub-agent chains across organizational boundaries without continuous human supervision. This

AI Safety Training Can be Clinically Harmful

arXiv:2604.23445v1 Announce Type: cross Abstract: Large language models are being deployed as mental health support agents at scale, yet only 16% of LLM-based chatbot interventions have undergone rigo

AlphaFold's Bayesian Roots in Probability Kinematics

arXiv:2505.19763v3 Announce Type: replace Abstract: The seminal breakthrough of AlphaFold in protein structure prediction relied on a learned potential energy function parameterized by deep models, in

BSViT: A Burst Spiking Vision Transformer for Expressive and Efficient Visual Representation Learning

arXiv:2604.23165v1 Announce Type: new Abstract: Spiking Vision Transformers (S-ViTs) offer a promising framework for energy-efficient visual learning. However, existing designs remain limited by two f

CA-IDD: Cross-Attention Guided Identity-Conditional Diffusion for Identity-Consistent Face Swapping

arXiv:2604.24493v1 Announce Type: new Abstract: Face swapping aims to optimize realistic facial image generation by leveraging the identity of a source face onto a target face while preserving pose, e

safetyarxiv-cs-cv

CAPSULE: Control-Theoretic Action Perturbations for Safe Uncertainty-Aware Reinforcement Learning

arXiv:2604.23576v1 Announce Type: cross Abstract: Ensuring safe exploration in high-dimensional systems with unknown dynamics remains a significant challenge. Existing safe reinforcement learning meth

Comparative Study of Weighted and Coupled Second- and Fourth-Order PDEs for Image Despeckling in Grayscale, Color, SAR, and Ultrasound

model-releasesarxiv-cs-cv

arXiv:2604.23612v1 Announce Type: new Abstract: Partial Differential Equation (PDE)-based approaches have gained significant attention in image despeckling due to their strong capability to preserve s

Computational Design and Co-Robotic Fabrication for Material Reuse in Architecture

arXiv:2604.24648v1 Announce Type: new Abstract: Climate change and resource depletion demand a shift from the dominant linear 'take-make-use-dispose' paradigm of construction toward circular, low-wast

applicationsarxiv-cs-ro

CorpusQA: A 10 Million Token Benchmark for Corpus-Level Analysis and Reasoning

model-releasesarxiv-cs-ai

arXiv:2601.14952v2 Announce Type: replace-cross Abstract: While large language models now handle million-token contexts, their capacity for reasoning across entire document repositories remains largel

Credal Concept Bottleneck Models for Epistemic-Aleatoric Uncertainty Decomposition

arXiv:2604.24170v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) predict through human-interpretable concepts, but they typically output point concept probabilities that conflate epist

CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoning

arXiv:2601.13262v2 Announce Type: replace Abstract: While large language models (LLMs) have shown to perform well on monolingual mathematical and commonsense reasoning, they remain unreliable for mult

Diagnostic-Driven Layer-Wise Compensation for Post-Training Quantization of Encoder-Decoder ASR Models

arXiv:2601.02455v2 Announce Type: replace-cross Abstract: Deploying Automatic Speech Recognition (ASR) models on memory-constrained edge devices requires aggressive low-bit weight quantization. Layer-

researcharxiv-cs-cl

Discovering Failure Modes in Vision-Language Models using RL

arXiv:2604.04733v2 Announce Type: replace-cross Abstract: Vision-language Models (VLMs), despite achieving strong performance on multimodal benchmarks, often misinterpret straightforward visual concep

EL3DD: Extended Latent 3D Diffusion for Language Conditioned Multitask Manipulation

arXiv:2511.13312v2 Announce Type: replace-cross Abstract: Acting in human environments is a crucial capability for general-purpose robots, necessitating a robust understanding of natural language and

Flexible Deep Neural Networks for Partially Linear Survival Data: Estimation and Survival Inference

arXiv:2512.10570v2 Announce Type: replace-cross Abstract: We propose a flexible deep neural network (DNN) framework for modeling survival data within a partially linear regression structure. The appro

From Noisy Historical Maps to Time-Series Oil Palm Mapping Without Annotation in Malaysia and Indonesia (2020-2024)

arXiv:2604.23776v1 Announce Type: cross Abstract: Accurate monitoring of oil palm plantations is critical for balancing economic development with environmental conservation in Southeast Asia. However,

GLIER: Generative Legal Inference and Evidence Ranking for Legal Case Retrieval

arXiv:2604.23779v1 Announce Type: cross Abstract: The semantic gap between colloquial user queries and professional legal documents presents a fundamental challenge in Legal Case Retrieval (LCR). Exis

applicationsarxiv-cs-ai

Governing What You Cannot Observe: Adaptive Runtime Governance for Autonomous AI Agents

arXiv:2604.24686v1 Announce Type: new Abstract: Autonomous AI agents can remain fully authorized and still become unsafe as behavior drifts, adversaries adapt, and decision patterns shift without any

GTAC: A Generative Transformer for Approximate Circuits

arXiv:2601.19906v2 Announce Type: replace-cross Abstract: Targeting error-tolerant applications, approximate computing relaxes rigid functional equivalence to significantly improve power, performance,

Identifiability and Stability of Generative Drifting with Companion-Elliptic Kernel Families

arXiv:2604.24196v1 Announce Type: cross Abstract: This paper analyzes identifiability and stability for the drifting field underlying distributional matching in the Generative Drifting framework of De

Implicit Framing in Obstetric Counseling Notes: A Grounded LLM Pipeline on a VBAC-Eligible Cohort

arXiv:2604.23059v1 Announce Type: new Abstract: Clinical framing -- the linguistic manner in which clinical information is presented -- can influence patient understanding and decision-making, with im

applicationsarxiv-cs-cl

Hardware

JigsawRL: Assembling RL Pipelines for Efficient LLM Post-Training

arXiv:2604.23838v1 Announce Type: new Abstract: We present JigsawRL, a cost-efficient framework that explores Pipeline Multiplexing as a new dimension of RL parallelism. JigsawRL decomposes each pipel

hardwarearxiv-cs-lg

LLM-Assisted Op-Amp Behavioral-Level Design via Agentic Human-Mimicking Reasoning

model-releasesarxiv-cs-ai

arXiv:2601.21321v2 Announce Type: replace Abstract: This paper proposes White-Op, an operational amplifier (op-amp) behavioral-level parameter design framework assisted by the human-mimicking reasonin

Machine-Learning-Based Classification of Radio Frequency Building Loss

arXiv:2604.24143v1 Announce Type: new Abstract: Accurate modeling of outdoor-to-indoor (O2I) and indoor-to-indoor (I2I) signal loss is important for improving indoor wireless network performance in de

applicationsarxiv-cs-lg

MegaScale-Data: Scaling Dataloader for Multisource Large Foundation Model Training

arXiv:2504.09844v4 Announce Type: replace-cross Abstract: Modern frameworks for training large foundation models (LFMs) employ dataloaders in a data-parallel manner, with each loader processing a disj

MeshLAM: Feed-Forward One-Shot Animatable Textured Mesh Avatar Reconstruction

arXiv:2604.22865v1 Announce Type: new Abstract: We introduce MeshLAM, a feed-forward framework for one-shot animatable mesh head reconstruction that generates high-fidelity, animatable 3D head avatars

MetaErr: Towards Predicting Error Patterns in Deep Neural Networks

model-releasesarxiv-cs-ai

arXiv:2604.23289v1 Announce Type: cross Abstract: Due to the unprecedented success of deep learning, it has become an integral component in several multimedia computing applications in todays world. U

Tutorials

Motion-aware Contrastive Learning for Temporal Panoptic Scene Graph Generation

arXiv:2412.07160v3 Announce Type: replace Abstract: To equip artificial intelligence with a comprehensive understanding towards a temporal world, video and 4D panoptic scene graph generation abstracts

tutorialsarxiv-cs-cv

Move-Then-Operate: Behavioral Phasing for Human-Like Robotic Manipulation

model-releasesarxiv-cs-ro

arXiv:2604.23620v1 Announce Type: new Abstract: We present Move-Then-Operate, a Vision language action framework that explicitly decouples robotic manipulation into two distinct behavioral phases: coa

Multi-Robot Motions in Milliseconds: Vector-Accelerated Primitives for Sampling-Based Planning

arXiv:2604.23960v1 Announce Type: new Abstract: In this paper, we extend the recent Vector-Accelerated Motion Planning (VAMP) framework to multi-robot motion planning (MRMP). We develop two vector-acc

researcharxiv-cs-ro

Optimal Experimental Design for Reliable Learning of History-Dependent Constitutive Laws

model-releasesarxiv-cs-lg

arXiv:2603.12365v2 Announce Type: replace-cross Abstract: History-dependent constitutive models serve as macroscopic closures for the aggregated effects of micromechanics. Their parameters are typical

PARASITE: Conditional System Prompt Poisoning to Hijack LLMs

arXiv:2505.16888v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed via third-party system prompts downloaded from public marketplaces. We identify a criti

Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface

arXiv:2604.24415v1 Announce Type: cross Abstract: Quantitative analysis of the kinematic chain in sports motion is essential for performance evaluation and injury prevention. Conventional methods such

Poster: ClawdGo: Endogenous Security Awareness Training for Autonomous AI Agents

arXiv:2604.24020v1 Announce Type: cross Abstract: Autonomous AI agents deployed on platforms such as OpenClaw face prompt injection, memory poisoning, supply-chain attacks, and social engineering, yet

Primitive Recursion without Composition: Dynamical Characterizations, from Neural Networks to Polynomial ODEs

model-releasesarxiv-cs-lg

arXiv:2604.24356v1 Announce Type: cross Abstract: What do recurrent neural networks, polynomial ODEs, and discrete polynomial maps each bring to computation, and what do they lack? All three operate o

Quasi-Quadratic Gradient: A New Direction for Accelerating the BFGS Method in Quasi-Newton Optimization

arXiv:2604.23922v1 Announce Type: cross Abstract: In this paper, we introduce the Quasi-Quadratic Gradient (QQG), a novel search direction designed to accelerate the BFGS method within the quasi-Newto

Radiomics- and Clinical Feature-Driven Prediction of Volumetric Response in Skull-Base Meningioma after CyberKnife Radiosurgery

arXiv:2604.24230v1 Announce Type: new Abstract: Skull-base meningiomas are often characterized by favorable long-term prognosis, yet their anatomical complexity and proximity to critical neurovascular

Local Ai

Reliable Microservice Tail Latency Prediction via Decoupled Dual-Stream Learning and Gradient Modulation

arXiv:2508.01635v2 Announce Type: replace-cross Abstract: Microservice architectures enable scalable cloud-native applications; however, the distributed nature of these systems complicates the mainten

local-aiarxiv-cs-ai

RoboECC: Multi-Factor-Aware Edge-Cloud Collaborative Deployment for VLA Models

arXiv:2603.20711v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models are mainstream in embodied intelligence but face high inference costs. Edge-Cloud Collaborative (ECC) depl

RouteNLP: Closed-Loop LLM Routing with Conformal Cascading and Distillation Co-Optimization

model-releasesarxiv-cs-cl

arXiv:2604.23577v1 Announce Type: new Abstract: Serving diverse NLP workloads with large language models is costly: at one enterprise partner, inference costs exceeded $200K/month despite over 70% of

SAGE: Sparse Adaptive Guidance for Dependency-Aware Tabular Data Generation

arXiv:2604.24368v1 Announce Type: new Abstract: Generating high-fidelity synthetic tabular data remains a critical challenge for enhancing data availability in privacy-sensitive and low-resource domai

safetyarxiv-cs-lg

SceneSelect: Selective Learning for Trajectory Scene Classification and Expert Scheduling

arXiv:2604.24514v1 Announce Type: new Abstract: Accurate trajectory prediction is fundamentally challenging due to high scene heterogeneity - the severe variance in motion velocity, spatial density, a

safetyarxiv-cs-lg