Search: “framework”

Cornfigurator: Automated Planning for Any-to-Any Multimodal Model Serving

arXiv:2512.14098v3 Announce Type: replace Abstract: Any-to-Any models are an emerging class of multimodal models that accept combinations of text and multimodal data as input and generate them as outp

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

Cross Paradigm Representation and Alignment Transformer for Image Deraining

DGX agent

arXiv:2504.16455v2 Announce Type: replace Abstract: Transformer-based networks have achieved strong performance in low-level vision tasks like image deraining by utilizing spatial or channel-wise self

model-releasesarxiv-cs-cv

17 Apr 2026

Data Synthesis Improves 3D Myotube Instance Segmentation

arXiv:2604.14720v1 Announce Type: new Abstract: Myotubes are multinucleated muscle fibers serving as key model systems for studying muscle physiology, disease mechanisms, and drug responses. Mechanist

researcharxiv-cs-cv

Local Ai

Deepfake Detection Generalization with Diffusion Noise

arXiv:2604.14570v1 Announce Type: new Abstract: Deepfake detectors face growing challenges in generalization as new image synthesis techniques emerge. In particular, deepfakes generated by diffusion m

local-aiarxiv-cs-cv

DPQuant: Efficient and Differentially-Private Model Training via Dynamic Quantization Scheduling

arXiv:2509.03472v2 Announce Type: replace Abstract: Differentially-Private SGD (DP-SGD) and its adaptive variant DP-Adam are powerful techniques to protect user privacy when using sensitive data to tr

Edge-preserving noise for diffusion models

arXiv:2410.01540v4 Announce Type: replace Abstract: Classical diffusion models typically rely on isotropic Gaussian noise, treating all regions uniformly and overlooking structural information importa

researcharxiv-cs-cv

Agents

Empowerment Gain and Causal Model Construction: Children and adults are sensitive to controllability and variability in their causal interventions

arXiv:2512.08230v2 Announce Type: replace Abstract: Learning about the causal structure of the world is a fundamental problem for human cognition. Causal models and especially causal learning have pro

agentsarxiv-cs-ai

Applications

Energy-based Regularization for Learning Residual Dynamics in Neural MPC for Omnidirectional Aerial Robots

arXiv:2604.14678v1 Announce Type: cross Abstract: Data-driven Model Predictive Control (MPC) has lately been the core research subject in the field of control theory. The combination of an optimal con

applicationsarxiv-cs-ro

Exploration and Exploitation Errors Are Measurable for Language Model Agents

arXiv:2604.13151v1 Announce Type: new Abstract: Language Model (LM) agents are increasingly used in complex open-ended decision-making tasks, from AI coding to physical AI. A core requirement in these

safetyarxiv-cs-ai

Fact4ac at the Financial Misinformation Detection Challenge Task: Reference-Free Financial Misinformation Detection via Fine-Tuning and Few-Shot Prompting of Large Language Models

arXiv:2604.14640v1 Announce Type: new Abstract: The proliferation of financial misinformation poses a severe threat to market stability and investor trust, misleading market behavior and creating crit

Faithfulness Serum: Mitigating the Faithfulness Gap in Textual Explanations of LLM Decisions via Attribution Guidance

arXiv:2604.14325v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong performance and have revolutionized NLP, but their lack of explainability keeps them treated as black boxes,

researcharxiv-cs-cl

Feature Extraction in the Remote Sensing Data Value Chain: A Systematic Review of Methods and Applications

arXiv:2510.18935v3 Announce Type: replace Abstract: Earth observation involves collecting, analyzing, and processing an ever-growing mass of data. This planetary data is crucial for addressing relevan

tutorialsarxiv-cs-cv

FRAGATA: Semantic Retrieval of HPC Support Tickets via Hybrid RAG over 20 Years of Request Tracker History

arXiv:2604.13721v1 Announce Type: cross Abstract: The technical support team of a supercomputing centre accumulates, over the course of decades, a large volume of resolved incidents that constitute cr

tutorialsarxiv-cs-ai

Applications

From Black Box to Glass Box: Cross-Model ASR Disagreement to Prioto Review in Ambient AI Scribe Documentation

arXiv:2604.14152v1 Announce Type: cross Abstract: Ambient AI 'scribe' systems promise to reduce clinical documentation burden, but automatic speech recognition (ASR) errors can remain unnoticed withou

applicationsarxiv-cs-cl

Generalization in LLM Problem Solving: The Case of the Shortest Path

arXiv:2604.15306v1 Announce Type: cross Abstract: Whether language models can systematically generalize remains actively debated. Yet empirical performance is jointly shaped by multiple factors such a

Hierarchical Retrieval Augmented Generation for Adversarial Technique Annotation in Cyber Threat Intelligence Text

arXiv:2604.14166v1 Announce Type: new Abstract: Mapping Cyber Threat Intelligence (CTI) text to MITRE ATT&CK technique IDs is a critical task for understanding adversary behaviors and automating threa

Hoi! -- A Multimodal Dataset for Force-Grounded, Cross-View Articulated Manipulation

arXiv:2512.04884v3 Announce Type: replace Abstract: We present a dataset for force-grounded, cross-view articulated manipulation that couples what is seen with what is done and what is felt during rea

researcharxiv-cs-ro

Hybrid Decision Making via Conformal VLM-generated Guidance

arXiv:2604.14980v1 Announce Type: cross Abstract: Building on recent advances in AI, hybrid decision making (HDM) holds the promise of improving human decision quality and reducing cognitive load. We

tutorialsarxiv-cs-cl

Integrating Object Detection, LiDAR-Enhanced Depth Estimation, and Segmentation Models for Railway Environments

arXiv:2604.14781v1 Announce Type: new Abstract: Obstacle detection in railway environments is crucial for ensuring safety. However, very few studies address the problem using a complete, modular, and

safetyarxiv-cs-cv

IROSA: Interactive Robot Skill Adaptation using Natural Language

arXiv:2603.03897v3 Announce Type: replace-cross Abstract: Foundation models have demonstrated impressive capabilities across diverse domains, while imitation learning provides principled methods for r

Local Ai

Large Vision Model-Guided Masked Low-Rank Approximation for Ground-Roll Attenuation

arXiv:2604.00998v2 Announce Type: replace Abstract: Ground roll is a common type of coherent noise in seismic records, and its attenuation remains challenging due to its substantial overlap with usefu

local-aiarxiv-cs-cv

Layered Mutability: Continuity and Governance in Persistent Self-Modifying Agents

arXiv:2604.14717v1 Announce Type: cross Abstract: Persistent language-model agents increasingly combine tool use, tiered memory, reflective prompting, and runtime adaptation. In such systems, behavior

safetyarxiv-cs-lg

LLM Predictive Scoring and Validation: Inferring Experience Ratings from Unstructured Text

arXiv:2604.14321v1 Announce Type: new Abstract: We tasked GPT-4.1 to read what baseball fans wrote about their game-day experience and predict the overall experience rating each fan gave on a 0-10 sur

LongAct: Harnessing Intrinsic Activation Patterns for Long-Context Reinforcement Learning

arXiv:2604.14922v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has emerged as a critical driver for enhancing the reasoning capabilities of Large Language Models (LLMs). While recent ad

MARCA: A Checklist-Based Benchmark for Multilingual Web Search

arXiv:2604.14448v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as sources of information, yet their reliability depends on the ability to search the web, select rel

Mechanistic Decoding of Cognitive Constructs in LLMs

arXiv:2604.14593v1 Announce Type: new Abstract: While Large Language Models (LLMs) demonstrate increasingly sophisticated affective capabilities, the internal mechanisms by which they process complex

Agents

MIND: AI Co-Scientist for Material Research

arXiv:2604.13699v1 Announce Type: cross Abstract: Large language models (LLMs) have enabled agentic AI systems for scientific discovery, but most approaches remain limited to textbased reasoning witho

agentsarxiv-cs-ai

MinShap: A Modified Shapley Value Approach for Feature Selection

arXiv:2604.15107v1 Announce Type: cross Abstract: Feature selection is a classical problem in statistics and machine learning, and it continues to remain an extremely challenging problem especially in

ML-based approach to classification and generation of structured light propagation in turbulent media

arXiv:2604.14208v1 Announce Type: cross Abstract: This work develops machine learning approaches to classify structured light wave beams developing random speckle disturbances as they propagate throug

Modeling LLM Unlearning as an Asymmetric Two-Task Learning Problem

arXiv:2604.14808v1 Announce Type: new Abstract: Machine unlearning for large language models (LLMs) aims to remove targeted knowledge while preserving general capability. In this paper, we recast LLM

NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results

arXiv:2604.14816v1 Announce Type: new Abstract: This paper presents an overview of the NTIRE 2026 Challenge on Video Saliency Prediction. The goal of the challenge participants was to develop automati

researcharxiv-cs-cv

On the Expressive Power and Limitations of Multi-Layer SSMs

arXiv:2604.14501v1 Announce Type: new Abstract: We study the expressive power and limitations of multi-layer state-space models (SSMs). First, we show that multi-layer SSMs face fundamental limitation

Open-Set Vein Biometric Recognition with Deep Metric Learning

model-releasesarxiv-cs-cv

arXiv:2604.14874v1 Announce Type: new Abstract: Most state-of-the-art vein recognition methods rely on closed-set classification, which inherently limits their scalability and prevents the adaptive en

Physics-Informed Machine Learning for Pouch Cell Temperature Estimation

arXiv:2604.14566v1 Announce Type: new Abstract: Accurate temperature estimation of pouch cells with indirect liquid cooling is essential for optimizing battery thermal management systems for transport

Predictions of charge density distributions for nuclei with Z geq 8

arXiv:2604.05312v1 Announce Type: cross Abstract: A deep neural network (DNN) has been developed to accurately predict nuclear charge density distributions for nuclei with proton numbers Z geq 8. By i

Pushing the Boundaries of Multiple Choice Evaluation to One Hundred Options

arXiv:2604.14634v1 Announce Type: new Abstract: Multiple choice evaluation is widely used for benchmarking large language models, yet near ceiling accuracy in low option settings can be sustained by s

Quantization of Spiking Neural Networks Beyond Accuracy

arXiv:2604.14487v1 Announce Type: new Abstract: Quantization is a natural complement to the sparse, event-driven computation of Spiking Neural Networks, reducing memory bandwidth and arithmetic cost f

Regret Tail Characterization of Optimal Bandit Algorithms with Generic Rewards

model-releasesarxiv-cs-lg

arXiv:2604.14876v1 Announce Type: cross Abstract: We study the tail behavior of regret in stochastic multi-armed bandits for algorithms that are asymptotically optimal in expectation. While minimizing

Reward-Aware Trajectory Shaping for Few-step Visual Generation

arXiv:2604.14910v1 Announce Type: new Abstract: Achieving high-fidelity generation in extremely few sampling steps has long been a central goal of generative modeling. Existing approaches largely rely

safetyarxiv-cs-cv

RL-STPA: Adapting System-Theoretic Hazard Analysis for Safety-Critical Reinforcement Learning

arXiv:2604.15201v1 Announce Type: new Abstract: As reinforcement learning (RL) deployments expand into safety-critical domains, existing evaluation methods fail to systematically identify hazards aris

safetyarxiv-cs-lg

Sandwich: Joint Configuration Search and Hot-Switching for Efficient CPU LLM Serving

arXiv:2507.18454v2 Announce Type: replace-cross Abstract: CPUs are critical for LLM serving due to their availability, cost efficiency, and edge applicability. However, efficient CPU serving is hinder

researcharxiv-cs-ai

Shuffle the Context: RoPE-Perturbed Self-Distillation for Long-Context Adaptation

arXiv:2604.14339v1 Announce Type: new Abstract: Large language models (LLMs) increasingly operate in settings that require reliable long-context understanding, such as retrieval-augmented generation a

Stability and Generalization in Looped Transformers

arXiv:2604.15259v1 Announce Type: new Abstract: Looped transformers promise test-time compute scaling by spending more iterations on harder problems, but it remains unclear which architectural choices

Stateful Evidence-Driven Retrieval-Augmented Generation with Iterative Reasoning

arXiv:2604.14170v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) grounds Large Language Models (LLMs) in external knowledge but often suffers from flat context representations and

tutorialsarxiv-cs-cl

Structure as Computation: Developmental Generation of Minimal Neural Circuits

arXiv:2604.15143v1 Announce Type: cross Abstract: This work simulates the developmental process of cortical neurogenesis, initiating from a single stem cell and governed by gene regulatory rules deriv

The Code Whisperer: LLM and Graph-Based AI for Smell and Vulnerability Resolution

arXiv:2604.13114v1 Announce Type: cross Abstract: Code smells and software vulnerabilities both increase maintenance cost, yet they are often handled by separate tools that miss structural context and

researcharxiv-cs-ai

Local Ai

The Courtroom Trial of Pixels: Robust Image Manipulation Localization via Adversarial Evidence and Reinforcement Learning Judgment

arXiv:2604.14703v1 Announce Type: new Abstract: Although some existing image manipulation localization (IML) methods incorporate authenticity-related supervision, this information is typically utilize

local-aiarxiv-cs-cv

Agents

TopoDIM: One-shot Topology Generation of Diverse Interaction Modes for Multi-Agent Systems

arXiv:2601.10120v2 Announce Type: replace-cross Abstract: Optimizing communication topology in LLM-based multi-agent system is critical for enabling collective intelligence. Existing methods mainly re

agentsarxiv-cs-cl