Search: “models”

Polyphony: Diffusion-based Dual-Hand Action Segmentation with Alternating Vision Transformer and Semantic Conditioning

arXiv:2605.31115v1 Announce Type: new Abstract: Dual-hand action segmentation, densely predicting actions for both hands from untrimmed videos, is essential for understanding complex bimanual activiti

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Post-Training Neural Network Pruning using Graph Curvature

DGX agent

arXiv:2601.16366v2 Announce Type: replace Abstract: This paper provides a fresh view of the neural network (NN) pruning problem through the lens of graph theory. To achieve effective pruning, we aim t

researcharxiv-cs-lg

1 Jun 2026

ProofWala: A Framework for Multilingual Proof Data Synthesis and Theorem-Proving

arXiv:2502.04671v3 Announce Type: replace Abstract: Neural approaches to theorem proving require robust infrastructure for interfacing with interactive theorem provers (ITPs), extracting structured pr

RayDer: Scalable Self-Supervised Novel View Synthesis from Real-World Video

arXiv:2605.31535v1 Announce Type: cross Abstract: Self-supervised novel view synthesis (NVS) remains challenging to scale, despite the abundance of video data, largely due to the brittleness of traini

applicationsarxiv-cs-ai

Reasoning-Aware Multimodal Fusion for Hateful Video Detection

arXiv:2512.02743v2 Announce Type: replace-cross Abstract: Hate speech in online videos is posing an increasingly serious threat to digital platforms, especially as video content becomes increasingly m

local-aiarxiv-cs-ai

Refining Word-Based Grammatical Error Annotation for L2 Korean

arXiv:2605.30545v1 Announce Type: new Abstract: Korean grammatical error correction (K-GEC) presents a structural mismatch between word-based evaluation and the morpheme-level locus of many learner er

researcharxiv-cs-cl

// Reusable Context Engineering // Context bloat quietly kills long-horizon runs, but you can fix it from the outside without fine-tuning th…

// Reusable Context Engineering // Context bloat quietly kills long-horizon runs, but you can fix it from the outside without fine-tuning the underlying agent. (bookmark this) Context management is us

safetydair-ai--x

Separating Secrets from Placeholders: A Hybrid CNN-CodeBERT Framework for Three-Class Credential Leakage Detection

arXiv:2605.31520v1 Announce Type: cross Abstract: Credential leakage in public source code repositories poses a critical security threat, with over 23.8 million secrets exposed in 2024 alone. Existing

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense

arXiv:2506.08255v4 Announce Type: replace-cross Abstract: Continual learning under adversarial conditions remains an open problem, as existing methods often compromise either robustness, scalability,

SKETCH: Semantic Key-Point Conditioning for Long-Horizon Vessel Trajectory Prediction

arXiv:2601.18537v3 Announce Type: replace-cross Abstract: Accurate long-horizon vessel trajectory prediction remains challenging due to compounded uncertainty from complex navigation behaviors and env

applicationsarxiv-cs-ai

Skin Lesion Classification Based on ResNet-50 Enhanced With Adaptive Spatial Feature Fusion

arXiv:2510.03876v2 Announce Type: replace Abstract: Skin cancer classification is challenging due to high inter-class similarity, intra-class variability, and artifacts in dermoscopic images. To addre

SpecDB: LLM-Generated Customized Databases via Feature-Oriented Decomposition

arXiv:2605.31097v1 Announce Type: cross Abstract: Mainstream relational databases ship a uniform feature set across deployments, although individual workloads exercise only a fraction of the available

Tutorials

Text-guided Feature Disentanglement for Cross-modal Gait Recognition

arXiv:2605.30784v1 Announce Type: new Abstract: Gait recognition is a biometric technique that identifies individuals based on their walking patterns, offering advantages in long-range, non-intrusive

tutorialsarxiv-cs-cv

Toward Identifiable Sparse Autoencoders

arXiv:2605.31245v1 Announce Type: new Abstract: Recently, sparse autoencoders (SAEs) have emerged as an attractive tool for interpreting and interacting with representations in practical neural networ

applicationsarxiv-cs-lg

TTE-CAM: Self-Explainable Class Activation Maps for Pretrained Black-Box CNNs

arXiv:2603.26885v2 Announce Type: replace Abstract: Convolutional neural networks (CNNs) achieve state-of-the-art performance in medical image analysis yet remain opaque, limiting adoption in high-sta

Wall-Clock Complexity for Zeroth-Order Optimization with Tunable Oracle Fidelity

arXiv:2605.31346v1 Announce Type: cross Abstract: Zeroth-order (black-box) optimization is applied when gradients are unavailable and objective evaluations rely on expensive simulations. In many such

Wall-OSS-0.5 Technical Report

arXiv:2605.30877v1 Announce Type: new Abstract: Large-scale Vision-Language-Action (VLA) pretraining is increasingly adopted as the foundation for robot policies, yet the evidence for pretrained VLAs

safetyarxiv-cs-ro

What is Missing? Explaining Neurons Activated by Absent Concepts

arXiv:2603.09787v2 Announce Type: replace Abstract: Explainable artificial intelligence (XAI) aims to provide human-interpretable insights into the behavior of deep neural networks (DNNs), typically b

Wind Turbine Maintenance Log Labelling Framework: LLM-Driven Data Correction and Enrichment via Semantic Extraction of Reliability Intelligence

arXiv:2605.31281v1 Announce Type: new Abstract: As wind turbine fleets age, data-driven reliability engineering is essential to optimise their operation and maintenance for service life extension and

researcharxiv-cs-cl

b9441

local-aillama-cpp-releases

B9441 is a release from llama.cpp, which provides LLM inference in C/C++ . The release follows the project's versioning pattern used by the ggml-org/llama.cpp repository on GitHub, typically containin

31 May 2026

A Fully Convolutional Approach to Denoising Structural Dynamics Data from X-Ray Photon Correlation Spectroscopy

arXiv:2605.29975v1 Announce Type: new Abstract: We present a fully convolutional denoising autoencoder (FC-DAE) for denoising two-time intensity-intensity correlation functions (C_2) in X-ray photon c

safetyarxiv-cs-lg

Assessing Dutch Syllabification Algorithms and Improving Accuracy by Combining Phonetic and Orthographic Information through Deep Learning

arXiv:2605.28834v1 Announce Type: cross Abstract: Syllabification describes the task of dividing words into syllables. Due to many rules and exceptions, training an algorithm to perform syllabificatio

Behavior-Aware Auxiliary Corrections for Off-Policy Temporal-Difference Prediction

arXiv:2605.28855v1 Announce Type: new Abstract: Temporal-difference learning with function approximation can be unstable under off-policy sampling. TDC stabilizes off-policy TD through an auxiliary co

local-aiarxiv-cs-ai

Beyond Consensus: Trace-Level Synthesis in Mixture of Agents

arXiv:2605.29116v1 Announce Type: new Abstract: When multiple LLM agents solve the same problem, standard practice compresses each agent's reasoning into a majority vote or layered synthesis, treating

Bosses, Kings, and the Commons: Cooperation Under Power Asymmetry in LLM Societies

arXiv:2605.29062v1 Announce Type: new Abstract: Communities can sustainably manage shared resources (commons) through self-governance and cooperative norms, a central finding of Ostrom's theory of sel

agentsarxiv-cs-cl

CapTalk: Text-Guided Stylization and Speech-Driven 3D Head Animation

arXiv:2605.29316v1 Announce Type: new Abstract: Audio-driven 3D facial animation aims to generate synchronized lip movements and vivid facial expressions from arbitrary audio clips. While existing met

Computationally Efficient Replicable Learning of Parities and Applications

arXiv:2602.09499v2 Announce Type: replace Abstract: We study the computational relationship between replicability (Impagliazzo et al. [STOC `22], Ghazi et al. [NeurIPS `21]) and other stability notion

CRITIC-R1: Learning Structured Critics for Retrieval-Augmented Generation

arXiv:2605.29886v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) improves knowledge-intensive question answering by incorporating external evidence. However, existing RAG methods

safetyarxiv-cs-ai

Cycle Consistency in Video Object-Centric Learning

arXiv:2605.30211v1 Announce Type: new Abstract: Self-supervised video Object-Centric Learning (OCL) aims to discover distinct objects and associate them across time, whereas self-supervised Multi-Obje

safetyarxiv-cs-cv

Do not be greedy, Think Twice: Sampling and Selection for Document-level Information Extraction

arXiv:2601.18395v2 Announce Type: replace Abstract: Document-level Information Extraction (DocIE) aims to produce an output template with the entities, relations, and events of interest occurring in t

researcharxiv-cs-cl

Dynamics Within Latent Chain-of-Thought: An Empirical Study of Causal Structure

arXiv:2602.08783v3 Announce Type: replace Abstract: Latent or continuous chain-of-thought methods replace explicit textual rationales with a number of internal latent steps, but these intermediate com

safetyarxiv-cs-ai

E-valuator: Reliable Agent Verifiers with Sequential Hypothesis Testing

arXiv:2512.03109v2 Announce Type: replace-cross Abstract: Agentic AI systems execute a sequence of actions, such as reasoning steps or tool calls, in response to a user prompt. To evaluate the success

Tutorials

Early Detection of Misinformation for Infodemic Management: A Domain Adaptation Approach

arXiv:2406.10238v2 Announce Type: replace Abstract: An infodemic refers to an enormous amount of true information and misinformation disseminated during a disease outbreak. Detecting misinformation at

tutorialsarxiv-cs-cl

Efficient, Validation-Free Intrinsic Quality Estimation for Large-Scale Face Recognition Datasets

arXiv:2605.29720v1 Announce Type: new Abstract: We propose Intrinsic Quality (IQ), a validation-free metric designed to estimate the inherent potential of face recognition (FR) datasets to produce hig

Enhancing LLM Training via Spectral Clipping

arXiv:2603.14315v2 Announce Type: replace Abstract: While spectral-based optimizers like Muon operate directly on the spectrum of updates, standard adaptive methods such as AdamW do not account for th

EXACT-MPPI: Exact Signed-Distance Navigation for Arbitrary-Footprint Robots from Point Clouds via Path Integral Control

arXiv:2605.29663v1 Announce Type: new Abstract: Ground robots often carry payloads, implements, or other attachments that turn their effective footprint into complex, non-convex shapes. Navigating saf

local-aiarxiv-cs-ro

Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Non-Conservative Forces

arXiv:2602.14975v3 Announce Type: replace-cross Abstract: Following our previous work (J. Phys. Chem. Lett., 2026, 17, 5, 1288-1295), we propose the DMTS-NC approach, a distilled multi-time-step (DMTS

applicationsarxiv-cs-lg

Fewer Steps, Better Performance: Efficient Cross-Modal Clip Trimming for Video Moment Retrieval Using Language

arXiv:2605.29793v1 Announce Type: new Abstract: Given an untrimmed video and a sentence query, video moment retrieval using language (VMR) aims to locate a target query-relevant moment. Since the untr

safetyarxiv-cs-cv

Tutorials

Financially Guided Deep Portfolio Optimization

arXiv:2605.28853v1 Announce Type: cross Abstract: Portfolio optimization in real-world financial markets is notoriously difficult due to non-stationarity, noisy data, and high transaction costs. Stand

tutorialsarxiv-cs-lg

Follow Everything: A Leader-Following and Obstacle Avoidance Framework with Goal-Aware Adaptation

arXiv:2504.19399v4 Announce Type: replace Abstract: Robust and flexible leader-following is a critical capability for robots to integrate into human society. While existing methods struggle to general

applicationsarxiv-cs-ro

From Meta-Thought to Execution: Cognitively Aligned Post-Training for Generalizable and Reliable LLM Reasoning

arXiv:2601.21909v2 Announce Type: replace Abstract: Current LLM post-training methods optimize complete reasoning trajectories through Supervised Fine-Tuning (SFT) followed by outcome-based Reinforcem

From Prompts to Context: An Ontology-Driven Framework for Human-Generative AI Collaboration

arXiv:2605.29675v1 Announce Type: cross Abstract: Collaborations with Generative AI often begin with a short prompt and end with an opaque output, leaving implicit who was involved, what task was bein

From Short Histories to Long Futures: Horizon-Aware Graph Neural Networks for Long Horizon Forecasting

arXiv:2605.29952v1 Announce Type: new Abstract: Accurate long-range prediction of geophysical systems is difficult due to strongly nonlinear dynamics, the high computational cost of full-physics simul

local-aiarxiv-cs-lg

Future Forcing: Future-aware Training-free KV Cache Policy for Autoregressive Video Generation

arXiv:2605.30083v1 Announce Type: new Abstract: Autoregressive (AR) video generation has emerged as a promising paradigm for long-horizon video synthesis, where each frame is generated conditioned on

safetyarxiv-cs-cv

Gaga: Group Any Gaussians via 3D-aware Memory Bank

arXiv:2404.07977v4 Announce Type: replace Abstract: We introduce Gaga, a framework that reconstructs and segments open-world 3D scenes by leveraging inconsistent 2D masks predicted by zero-shot class-

applicationsarxiv-cs-cv

Hardware

i warned these guys of exactly this problem - no moat because everyone is training on same data - two years ago. and warned them that AI wou…

i warned these guys of exactly this problem - no moat because everyone is training on same data - two years ago. and warned them that AI would become a commodity. now they think they have made some bi

hardwaregary-marcus--x

Inspectorch: Efficient rare event exploration in solar observations

arXiv:2602.20316v2 Announce Type: replace-cross Abstract: The Sun is observed in unprecedented detail, enabling studies of its activity on very small spatiotemporal scales. However, the large volume o

Leave a Window Out: Modifying the Jackknife for Predictive Inference in Time Series

arXiv:2605.30292v1 Announce Type: cross Abstract: Conformal prediction methods enjoy strong theoretical and empirical predictive inference performance, provided the data is exchangeable, and predictor