Search: “models”

arXiv:2506.17633v2 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection attempts to distinguish outlier samples to prevent models trained on the in-distribution (ID) dataset from

applicationsarxiv-cs-ai

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

Advancing Metallic Surface Defect Detection via Anomaly-Guided Pretraining on a Large Industrial Dataset

DGX agent

arXiv:2509.18919v2 Announce Type: replace Abstract: The pretraining-finetuning paradigm is a crucial strategy in metallic surface defect detection for mitigating the challenges posed by data scarcity.

researcharxiv-cs-cv

27 May 2026

Approximate Equivariance via Projection-based Regularisation

arXiv:2601.05028v2 Announce Type: replace Abstract: Equivariance is a powerful inductive bias in neural networks, improving generalisation and physical consistency. Recently, however, non-equivariant

Assessing Per-Sample Membership Inference Vulnerability without Retraining

arXiv:2602.15919v2 Announce Type: replace-cross Abstract: Recent work in the privacy literature shows that sample-targeted membership inference attacks (MIAs) significantly outperform untargeted appro

ATOM: Instantiating Budget-Controllable Multi-Agent Collaboration via Nucleus-Electron Hierarchy

arXiv:2605.26178v1 Announce Type: cross Abstract: Large Language Model (LLM)-based multi-agent systems rely on optimized collaboration topologies to balance performance and communication costs. Howeve

agentsarxiv-cs-lg

BASIS: Batchwise Advantage Estimation from Single-Rollout Information Sharing for LLM Reasoning

arXiv:2605.27293v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards has become a standard recipe for improving the reasoning abilities of large language models. Existing alg

Beyond Self-Talk: A Communication-Centric Survey of LLM-Based Multi-Agent Systems

arXiv:2502.14321v3 Announce Type: replace-cross Abstract: Large language model-based multi-agent systems have recently gained significant attention due to their potential for complex, collaborative, a

agentsarxiv-cs-cl

Beyond Trajectory-Level Attribution: Graph-Based Credit Assignment for Agentic Reinforcement Learning

arXiv:2605.26684v1 Announce Type: cross Abstract: Group-based reinforcement learning (RL) methods have achieved remarkable success in improving the performance of large language models (LLMs) and have

Bilevel Optimization over Saddle Points of Zero-Sum Markov Games

arXiv:2605.26654v1 Announce Type: cross Abstract: Reinforcement learning (RL) often has a hierarchical structure, where an upper-level (UL) learner selects model parameters and a lower-level (LL) deci

Local Ai

Bounded Path Context: A Controlled Study of Visible Path History in LLM-Based Knowledge Graph Question Answering

arXiv:2605.26645v1 Announce Type: new Abstract: LLM-based knowledge-graph question answering (KGQA) delegates graph traversal to language models, turning each question into a sequence of local relatio

local-aiarxiv-cs-cl

Convergence of Spectral Descent for Non-smooth Optimization

arXiv:2605.26977v1 Announce Type: new Abstract: The Muon optimizer has recently demonstrated remarkable empirical success in training large language models. However, the theoretical understanding of i

Tutorials

CRoFT: Robust Fine-Tuning with Concurrent Optimization for OOD Generalization and Open-Set OOD Detection

arXiv:2405.16417v2 Announce Type: replace Abstract: Recent vision-language pre-trained models (VL-PTMs) have shown remarkable success in open-vocabulary tasks. However, downstream use cases often invo

tutorialsarxiv-cs-cv

CSV-ViT: A Vision Transformer with the Variable-sized Cortical Supervertices for Detection of Alzheimer's Disease Pathologies

arXiv:2605.26514v1 Announce Type: cross Abstract: Confirming Alzheimer's disease (AD) typically relies on positron emission tomography (PET), which remains costly and invasive, motivating the use of s

Data-driven sparse identification of governing PDEs via knockoff filters and multi-criteria trade-offs

arXiv:2605.26631v1 Announce Type: cross Abstract: We propose KO-PDE-IDENT, a data-driven framework for identifying parsimonious partial differential equations (PDEs) with false discovery rate (FDR) co

Deep Learning-based Algebraic Reynolds Stress Closures for RANS Simulations of Turbulent Flows

arXiv:2605.26358v1 Announce Type: cross Abstract: Turbulence is ubiquitous in engineering and science, yet direct simulation is prohibitively expensive. The Reynolds-averaged Navier-Stokes (RANS) equa

Detached Skip-Links and R-Probe: Decoupling Feature Aggregation from Gradient Propagation for MLLM OCR

arXiv:2603.20020v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) excel at high-level reasoning yet fail on OCR tasks where fine-grained visual details are compromised

Detectability in Diversity: Improved Canary Crafting for Privacy Auditing in One Run

arXiv:2605.27292v1 Announce Type: new Abstract: Privacy auditing aims to empirically assess privacy leakage in machine learning models using membership inference attacks (MIAs), and to derive lower bo

Detecting Is Not Resolving: The Monitoring Control Gap in Retrieval Augmented LLMs

arXiv:2605.27157v1 Announce Type: new Abstract: Retrieval-augmented LLMs are deployed for tasks where evidence quality determines action safety, yet evaluation protocols assume that single-turn robust

DuoGesture: Neuro-Inspired and Biomechanically Informed Dual-Stream Co-Speech Gesture Generation

arXiv:2605.26236v1 Announce Type: new Abstract: Co-speech gesture generation requires both semantic expressivity and biomechanically plausible rhythmic motion. Existing holistic gesture models mix lex

safetyarxiv-cs-cv

DV-SFT: Direct Vision Supervision for Fine-Grained Visual Understanding

arXiv:2605.26656v1 Announce Type: new Abstract: Multimodal large language models are typically trained end-to-end to predict ground-truth answers, yet supervision signals are applied exclusively to te

safetyarxiv-cs-cv

Efficient Agentic Reinforcement Learning with On-Policy Intrinsic Knowledge Boundary Enhancement

arXiv:2605.26952v1 Announce Type: new Abstract: Agentic reinforcement learning (RL) has proven effective for training LLM-based agents with external tool-use capabilities. However, we identify that ag

safetyarxiv-cs-cl

Enabling Extensible Embodied Capabilities with Tools

arXiv:2605.26637v1 Announce Type: new Abstract: Most existing embodied intelligence methods formulate perception, reasoning, planning, and control within a unified parameterized policy. Yet these capa

safetyarxiv-cs-ro

Generalist Graph Anomaly Detection via Prototype-Based Distillation

arXiv:2605.26857v1 Announce Type: new Abstract: Driven by the pressing demand for graph anomaly detection (GAD) in high-stakes domains, the generalist GAD paradigm, which trains a single detector tran

High-Quality Synthetic Financial Time-Series using a GAN-Diffusion Framework

arXiv:2605.27113v1 Announce Type: cross Abstract: In recent years, financial institutions and firms have increasingly adopted synthetic data to address data scarcity and to generate counterfactual mar

Inference-Time Search Using Side Information for Diffusion-Based Image Reconstruction

arXiv:2510.03352v3 Announce Type: replace-cross Abstract: Diffusion models have been used as priors for solving inverse problems. However, existing approaches typically overlook side information that

Is an Image Also Worth 16x16=256 Superpixels? A Framework for Attentional Image Classification

arXiv:2605.27144v1 Announce Type: new Abstract: Superpixel-based image classification has traditionally leveraged graph neural networks (GNNs) for processing irregular image representations. Recent ad

researcharxiv-cs-cv

LATTE: Forecasting Peer Anchored Preference Trajectories for Personalized LLM Generation

arXiv:2605.26612v1 Announce Type: new Abstract: Personalized generation with frozen large language models requires a conditioning signal that is both compact and current. Existing personalization meth

researcharxiv-cs-cl

Local Ai

LDP-Slicing: Local Differential Privacy for Images via Randomized Bit-Plane Slicing

arXiv:2603.03711v3 Announce Type: replace Abstract: Local Differential Privacy (LDP) is the gold standard trust model for privacy-preserving machine learning by guaranteeing privacy at the data source

local-aiarxiv-cs-cv

LearnedCache: An eBPF-Integrated Perceptron-Based Eviction Policy for the Linux Page Cache

arXiv:2605.26168v1 Announce Type: cross Abstract: Linux is the foundation of the digital age, accounting for the majority of the cloud and mobile OS markets. Any device that runs Linux uses the Linux

Learning to Reason Efficiently with Discounted Reinforcement Learning

arXiv:2510.23486v2 Announce Type: replace Abstract: Large reasoning models (LRMs) often consume excessive tokens, inflating computational cost and latency. More broadly, in goal reaching sequential de

LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding

arXiv:2605.27365v1 Announce Type: cross Abstract: Vision-language models (VLMs) commonly formulate visual grounding and detection as a coordinate-token generation problem, serializing each 2D box into

Tutorials

MatFormBench: A Benchmarking Evaluation Framework for Target-Driven Materials Formulation

arXiv:2605.26741v1 Announce Type: cross Abstract: Inverse design of materials has significantly advanced target-driven formulation optimization, yet existing materials machine learning benchmarks rema

tutorialsarxiv-cs-ai

MechRL: Reinforcement Learning Agents Perform Circuit Discovery for Mechanistic Interpretability

arXiv:2605.26343v1 Announce Type: new Abstract: Mechanistic interpretability has identified small sets of attention heads that implement specific behaviours in transformer language models, but recover

MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation

arXiv:2605.27366v1 Announce Type: new Abstract: Large language model (LLM) agents rely on reusable skills to solve complex tasks. However, existing skill creation approaches treat skills as isolated a

agentsarxiv-cs-ai

On the Detection of Commutative Factors in Factor Graphs: Necessary and Sufficient Conditions

arXiv:2605.26908v1 Announce Type: new Abstract: Exploiting the indistinguishability of objects in a probabilistic graphical model such as a factor graph is key to lifted probabilistic inference algori

On the Error-Correcting Effects of Stochasticity in Discrete Diffusion

arXiv:2605.26582v1 Announce Type: cross Abstract: Discrete diffusion models achieve strong performance in text and image generation, but their inference remains slow and must inherently balance sampli

Tutorials

On the Generalization Capabilities, Design Choices and Limitations of Keypoint Imitation Learning

arXiv:2605.26649v1 Announce Type: new Abstract: RGB-based imitation learning requires many demonstrations to generalize to unseen objects or scenes, motivating research into intermediate representatio

tutorialsarxiv-cs-ro

Over-Alignment vs Over-Fitting: The Role of Feature Learning Strength in Generalization

arXiv:2602.00827v2 Announce Type: replace Abstract: Feature learning strength (FLS), i.e., the inverse of the effective output scaling of a model, plays a critical role in shaping the optimization dyn

Pair-In, Pair-Out: Latent Multi-Token Prediction for Efficient LLMs

arXiv:2605.27255v1 Announce Type: cross Abstract: Long chain-of-thought reasoning has made autoregressive decoding the dominant inference cost of modern large language models. Existing methods target

Persona Generators: Generating Diverse Synthetic Personas for Arbitrary Contexts

arXiv:2602.03545v2 Announce Type: replace Abstract: Evaluating AI systems that interact with humans requires understanding their behavior across diverse user populations, but collecting representative

agentsarxiv-cs-ai

PICACO: Pluralistic In-Context Value Alignment of LLMs via Total Correlation Optimization

arXiv:2507.16679v3 Announce Type: replace-cross Abstract: In-Context Learning has shown great potential for aligning Large Language Models (LLMs) with human values, helping reduce harmful outputs and

Probing Minimalist Phase Structure in LLMs: What Universal Dependencies Cannot Represent

arXiv:2605.26431v1 Announce Type: new Abstract: Structural probes train on Universal Dependencies (UD), which does not encode formal-syntactic abstractions such as phase boundaries or phase-internal c

researcharxiv-cs-cl

Local Ai

Prototyping an End-to-End Multi-Modal Tiny-CNN for Cardiovascular Sensor Patches

arXiv:2510.18668v2 Announce Type: replace-cross Abstract: The vast majority of cardiovascular diseases may be preventable if early signs and risk factors are detected. Cardiovascular monitoring with b

local-aiarxiv-cs-cv

Q-GeoMem: Question-Guided Geometric Memory for Video Spatial Reasoning

arXiv:2605.27318v1 Announce Type: new Abstract: Video spatial reasoning requires accumulating viewpoint-dependent evidence over time while retaining information useful to the question being asked. Exi

researcharxiv-cs-cv

QAM-W: Joint 2D Codebook Quantization for LLM Weights via Hadamard Rotation and Activation-Aware Scaling

arXiv:2605.26339v1 Announce Type: cross Abstract: Scalar post-training quantizers discard pairwise coordinate structure within weight rows. We introduce QAM-W (Quadrature Amplitude Modulation for Weig

researcharxiv-cs-cl

Hardware

Qrita: High-performance Top-k and Top-p using Pivot-based Truncation and Selection

arXiv:2602.01518v2 Announce Type: replace Abstract: Despite their importance in model sampling, efficient implementation of Top-k and Top-p algorithms for large vocabularies remains a significant chal

hardwarearxiv-cs-ai

Quantized Keys Steal Attention: Bias Correction for KV-Cache Compression in Video Diffusion

arXiv:2605.26266v1 Announce Type: cross Abstract: Chunk-wise autoregressive video diffusion models rely on a KV cache of previously generated chunks to avoid redundant computation, but this cache quic

Rethinking the Trust Region in LLM Reinforcement Learning

arXiv:2602.04879v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a cornerstone for fine-tuning Large Language Models (LLMs), with Proximal Policy Optimization (PPO) ser