All entries

Fast and Featureless Node Representation Learning with Partial Pairwise Supervision

arXiv:2605.19916v1 Announce Type: cross Abstract: We introduce Contrastive FUSE, a fast and unified framework for scalable node representation learning in graphs with partially available pairwise node

Source

Human DGX agent

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

Fast and Lightweight Backdoor Detection via Head Random Probing

DGX agent

arXiv:2605.18908v1 Announce Type: cross Abstract: Deep neural networks (DNNs) remain critically vulnerable to backdoor attacks. Existing post-training detectors often require clean or surrogate data,

model-releasesarxiv-cs-ai

20 May 2026

Fast-BEV++: Fast by Algorithm, Deployable by Design

model-releasesarxiv-cs-cv

arXiv:2512.08237v3 Announce Type: replace Abstract: The advancement of vision-only Bird's-Eye-View (BEV) perception, a core paradigm for cost-effective autonomous driving, is hindered by the long-stan

Fast Tensorization of Neural Networks via Slice-wise Feature Distillation

arXiv:2605.19842v1 Announce Type: new Abstract: We propose a scalable tensorization framework for neural network compression based on slice-wise feature distillation. Unlike conventional tensor decomp

Faster-GCG: Efficient Discrete Optimization Jailbreak Attacks against Aligned Large Language Models

arXiv:2410.15362v2 Announce Type: replace-cross Abstract: Aligned Large Language Models (LLMs) have attracted significant attention for their safety, particularly in the context of jailbreak attacks t

safetyarxiv-cs-ai

Tutorials

FAWN: A MultiEncoder Fusion-Attention Wave Network for Integrated Sensing and Communication Indoor Scene Inference

arXiv:2509.14968v3 Announce Type: replace Abstract: The upcoming generations of wireless technologies promise an era where everything is interconnected and intelligent. As the need for intelligence gr

tutorialsarxiv-cs-lg

Feature-Space Smoothing: Certified Robustness of Deep Representations

arXiv:2601.16200v3 Announce Type: replace-cross Abstract: Modern deep learning models exhibit strong capabilities across diverse applications, yet remain vulnerable to malicious inputs that induce err

safetyarxiv-cs-cv

Features have life history. And we should care

arXiv:2605.18789v1 Announce Type: cross Abstract: Features in language models have life history: they emerge, persist, and die during training, yet the importance of that history remains largely unexp

researcharxiv-cs-ai

FedBiCross: A Bi-Level Optimization Framework to Tackle Non-IID Challenges in Data-Free One-Shot Federated Learning on Medical Data

arXiv:2601.01901v2 Announce Type: replace Abstract: Data-free knowledge distillation-based one-shot federated learning (OSFL) trains a model in a single communication round without sharing raw data, m

Federated Learning for ICD Classification with Lightweight Models and Pretrained Embeddings

model-releasesarxiv-cs-cl

arXiv:2507.03122v2 Announce Type: replace-cross Abstract: This study investigates the feasibility and performance of federated learning (FL) for multi-label ICD code classification using clinical note

Federated Learning with Nonvacuous Generalisation Bounds

arXiv:2310.11203v2 Announce Type: replace Abstract: We introduce a novel strategy to train randomised predictors in federated learning, where each node of the network aims at preserving its privacy by

FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Data

model-releasesarxiv-cs-cl

arXiv:2605.18936v1 Announce Type: cross Abstract: Social media text data are often used to train Machine Learning (ML) models to identify users exhibiting high-risk mental health behaviors. However, s

Applications

Feed-Forward Gaussian Splatting from Sparse Aerial Views

arXiv:2605.19949v1 Announce Type: new Abstract: Reconstructing large-scale urban scenes from sparse aerial views is a crucial yet challenging task. Due to biased top-down and shallow-oblique camera po

applicationsarxiv-cs-cv

FGSVQA: Frequency-Guided Short-form Video Quality Assessment

arXiv:2605.20016v1 Announce Type: cross Abstract: Short-form video poses new challenges to the quality assessment of user-generated content (UGC) due to its complex generation pipeline, rapid content

researcharxiv-cs-cv

Local Ai

FieldFormer: Locality-Aware Transformers for Spatio-Temporal Modeling on Sparse Sensor Networks

arXiv:2510.03589v2 Announce Type: replace Abstract: Spatio-temporal sensor data in real-world systems is often sparse, noisy, and irregular, making latent field reconstruction fundamentally underconst

local-aiarxiv-cs-lg

Hardware

FiLark: a streaming-first software framework for end-to-end exploration, annotation, and algorithm integration in distributed acoustic sensing

arXiv:2605.20132v1 Announce Type: cross Abstract: Distributed acoustic sensing (DAS) systems generate continuous, ultra-high-channel-count data streams at rates that exceed the capabilities of convent

hardwarearxiv-cs-lg

Finding Structure in Continual Learning

model-releasesarxiv-cs-lg

arXiv:2602.04555v2 Announce Type: replace Abstract: Learning from a stream of tasks usually pits plasticity against stability: acquiring new knowledge often causes catastrophic forgetting of past info

Fine-Grained Benchmark Generation for Comprehensive Evaluation of Foundation Models

arXiv:2605.18824v1 Announce Type: cross Abstract: Evaluation of foundation models often rely on aggregate scores from benchmarks that lack comprehensive coverage and metadata for a fine-grained evalua

Fine-tuning language encoding models on slow fMRI improves prediction for fast ECoG

arXiv:2605.19224v1 Announce Type: new Abstract: Neuroscientists have recently turned to intracranial brain recording methods, like electrocorticography (ECoG), for human experiments because of the fin

Fine-tuning Large Language Model for Automated Algorithm Design

arXiv:2507.10614v2 Announce Type: replace-cross Abstract: The integration of large language models (LLMs) into automated algorithm design has shown promising potential. A prevalent approach embeds LLM

Fine-Tuning Without Forgetting via Loss-Adaptive Learning Rates

arXiv:2605.20005v1 Announce Type: new Abstract: Fine-tuning large language models on new data improves task performance but degrades capabilities learned during pretraining, a phenomenon known as cata

FineBench: Benchmarking and Enhancing Vision-Language Models for Fine-grained Human Activity Understanding

arXiv:2605.19846v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have demonstrated remarkable capabilities in general video understanding, yet they often struggle with the fine-grained

Fingerprinting LLMs via Prompt Injection

arXiv:2509.25448v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are often modified after release through post-processing such as post-training or quantization, which makes it ch

First-Passage Prediction of Grokking Delay: ACalibrated Law under AdamW with Causal Validation

arXiv:2605.18845v1 Announce Type: cross Abstract: We give the first quantitative prediction of grokking delay under AdamW. Treating the delay as a first-passage time, we derive a closed-form law T_gro

Flash PD-SSM: Memory-Optimized Structured Sparse State-Space Models

arXiv:2605.19150v1 Announce Type: cross Abstract: State-space models (SSMs) face a fundamental trade-off between efficiency and expressivity that is mainly dictated by the structure of the model's tra

researcharxiv-cs-ai

FlexDraft: Flexible Speculative Decoding via Attention Tuning and Bonus-Guided Calibration

arXiv:2605.20022v1 Announce Type: new Abstract: Speculative decoding accelerates memory-bound LLM inference without quality degradation by using a fast drafter to propose multiple candidate tokens and

FlowErase-RL: Rethinking Concept Erasure as Reward Optimization in Flow Matching Models

arXiv:2605.19739v1 Announce Type: new Abstract: Recent advances in flow matching models have significantly improved text-to-image generation quality, but also introduce growing safety risks due to the

safetyarxiv-cs-cv

FLUIDSPLAT: Reconstructing Physical Fields from Sparse Sensors via Gaussian Primitives

arXiv:2605.18866v1 Announce Type: cross Abstract: Reconstructing continuous flow fields from sparse surface-mounted sensors is central to aerodynamic design, flow control, and digital-twin instrumenta

FLUXtrapolation: A benchmark on extrapolating ecosystem fluxes

arXiv:2605.19812v1 Announce Type: cross Abstract: We introduce FLUXtrapolation, a benchmark for extrapolating ecosystem fluxes under progressively harder distribution shifts. Ecosystem fluxes are cent

Applications

FlyMirage: A Fully Automated Generation Pipeline for Diverse and Scalable UAV Flight Data via Generative World Model

arXiv:2605.19600v1 Announce Type: new Abstract: In the field of Vision-Language Navigation (VLN), aerial datasets remain limited in their ability to combine scale, diversity, and realism, often relyin

applicationsarxiv-cs-ro

Formal Skill: Programmable Runtime Skills for Efficient and Accurate LLM Agents

arXiv:2605.19604v1 Announce Type: new Abstract: Large Language Model (LLM) agents increasingly act inside real workspaces, where tools and skills determine whether model reasoning becomes reliable act

safetyarxiv-cs-ai

Local Ai

FormalASR: End-to-End Spoken Chinese to Formal Text

arXiv:2605.19266v1 Announce Type: cross Abstract: Automatic speech recognition (ASR) systems are typically optimized for verbatim transcription, which preserves disfluencies, filler words, and informa

local-aiarxiv-cs-ai

FPED: A Functional-Network Prior-Guided Mixture-of-Experts Framework for Interpretable Brain Decoding

arXiv:2605.19279v1 Announce Type: new Abstract: Visual image reconstruction from functional Magnetic Resonance Imaging (fMRI) is a fundamental task in brain decoding, providing a crucial pathway for u

researcharxiv-cs-cv

Tutorials

FreeOrbit4D: Training-Free Arbitrary Camera Redirection for Monocular Videos via Foreground-Complete 4D Reconstruction

arXiv:2601.18993v2 Announce Type: replace-cross Abstract: Camera redirection aims to replay a dynamic scene from a single monocular video under a user-specified camera trajectory. However, large-angle

tutorialsarxiv-cs-ai

From Cumulative Constraints to Adaptive Runtime Safety Control for Nonstationary Reinforcement Learning

arXiv:2605.18841v1 Announce Type: new Abstract: Safety in reinforcement learning is often specified through cumulative cost constraints, but these trajectory-level guarantees do not directly prevent u

safetyarxiv-cs-lg

From Division to Decision: Leveraging Temporal Cell-Stage Segmentation for Embryo Transferability Prediction

arXiv:2605.18923v1 Announce Type: cross Abstract: Accurate selection of bovine embryos is a challenging task, as current practice relies on a single expert assessment on the seventh day after insemina

researcharxiv-cs-cv

Agents

From Intent to AI Pipelines: A Controlled Agentic Framework for Non-AI Expert Scientists

arXiv:2605.18764v1 Announce Type: cross Abstract: Artificial Intelligence (AI) pipelines have become integral to modern research, supporting fields such as Medical Sciences, Agriculture, and Social Sc

agentsarxiv-cs-ai

From Llama to Cria: Scaling Down Neural Networks via Neuron-Level Spectral Structural Importance Evaluation

model-releasesarxiv-cs-cv

arXiv:2605.18860v1 Announce Type: cross Abstract: This paper proposes a neuron pruning framework based on neuron-level spectral structural importance evaluation. Given a trained neural network, we rec

From Prompts to Pavement Through Time: Temporal Grounding in Agentic Scene-to-Plan Reasoning

arXiv:2605.19824v1 Announce Type: new Abstract: Recent attempts to support high-level scene interpretation and planning in Autonomous Vehicles (AVs) using ensembles of Large Language Models (LLMs) and

From Refusal to Recovery: A Control-Theoretic Approach to Generative AI Guardrails

arXiv:2510.13727v2 Announce Type: replace Abstract: Generative AI systems are increasingly assisting and acting on behalf of end users in practical settings, from digital shopping assistants to next-g

safetyarxiv-cs-ai

From Score Matching to Diffusion: A Fine-Grained Error Analysis in the Gaussian Setting

arXiv:2503.11615v3 Announce Type: replace Abstract: Sampling from an unknown distribution, accessible only through discrete samples, is a fundamental problem at the core of generative AI. The current

From Seeing to Thinking: Decoupling Perception and Reasoning Improves Post-Training of Vision-Language Models

arXiv:2605.20177v1 Announce Type: new Abstract: Recent advances in vision-language models (VLMs) emphasize long chain-of-thought reasoning; yet, we find that their performance on visual tasks is prima

From SGD to Muon: Adaptive Optimization via Schatten-p Norms

arXiv:2605.19781v1 Announce Type: new Abstract: Modern optimizers, like Muon, impose matrix-wise geometry constraints on their updates. These matrix-wise constraints can be unified under Linear Minimi

From Simple to Complex: Curriculum-Guided Physics-Informed Neural Networks via Gaussian Mixture Models

model-releasesarxiv-cs-lg

arXiv:2605.19263v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) offer a mesh-free framework for solving partial differential equations (PDEs), yet training often suffers from

From Sparsity to Simplicity: Enabling Simpler Sequential Replacements via Sparse Attention Distillation

arXiv:2605.18865v1 Announce Type: cross Abstract: Self-attention serves as the core foundation of large-scale transformer pretraining, but its quadratic token interaction cost makes inference expensiv

GAE Falls Short in Imperfect-Information Self-Play Reinforcement Learning

arXiv:2605.19235v1 Announce Type: new Abstract: Competitive multi-agent reinforcement learning in imperfect-information games requires agents to act under partial observability and against adversarial

safetyarxiv-cs-lg

Gauge-Equivariant Graph Networks via Self-Interference Cancellation

arXiv:2511.16062v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) excel on homophilous graphs but often fail under heterophily due to self-reinforcing and phase-inconsistent signals. We

Local Ai

Gaussian Approximation and Multiplier Bootstrap for Federated Linear Stochastic Approximation

arXiv:2605.19629v1 Announce Type: cross Abstract: In this paper, we establish Berry-Esseen-type bounds for federated linear stochastic approximation (LSA). Our results provide the first federated Gaus

local-aiarxiv-cs-lg