Search: “framework”

Greedy Kalman-Swarm: Improving State Estimation in Robot Swarms in Harsh Environments

arXiv:2604.16868v1 Announce Type: new Abstract: State estimation is a fundamental requirement in robotics, where the accurate determination of a robot's state is essential for stable operation despite

agentsarxiv-cs-ro

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HalluSAE: Detecting Hallucinations in Large Language Models via Sparse Auto-Encoders

DGX agent

arXiv:2604.16430v1 Announce Type: new Abstract: Large Language Models (LLMs) are powerful and widely adopted, but their practical impact is limited by the well-known hallucination phenomenon. While re

model-releasesarxiv-cs-cl

21 Apr 2026

Hard to Be Heard: Phoneme-Level ASR Analysis of Phonologically Complex, Low-Resource Endangered Languages

arXiv:2604.18204v1 Announce Type: new Abstract: We present a phoneme-level analysis of automatic speech recognition (ASR) for two low-resourced and phonologically complex East Caucasian languages, Arc

HEALing Entropy Collapse: Enhancing Exploration in Few-Shot RLVR via Hybrid-Domain Entropy Dynamics Alignment

arXiv:2604.17928v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Reward (RLVR) has proven effective for training reasoning-oriented large language models, but existing methods la

safetyarxiv-cs-lg

HeteroCache: A Dynamic Retrieval Approach to Heterogeneous KV Cache Compression for Long-Context LLM Inference

arXiv:2601.13684v2 Announce Type: replace Abstract: The linear memory growth of the KV cache poses a significant bottleneck for LLM inference in long-context tasks. Existing static compression methods

Heterogeneous Self-Play for Realistic Highway Traffic Simulation

arXiv:2604.16406v1 Announce Type: cross Abstract: Realistic highway simulation is critical for scalable safety evaluation of autonomous vehicles, particularly for interactions that are too rare to stu

safetyarxiv-cs-lg

HiP-LoRA: Budgeted Spectral Plasticity for Robust Low-Rank Adaptation

arXiv:2604.17751v1 Announce Type: cross Abstract: Adapting foundation models under resource budgets relies heavily on Parameter-Efficient Fine-Tuning (PEFT), with LoRA being a standard modular solutio

HopRank: Self-Supervised LLM Preference-Tuning on Graphs for Few-Shot Node Classification

arXiv:2604.17271v1 Announce Type: new Abstract: Node classification on text-attributed graphs (TAGs) is a fundamental task with broad applications in citation analysis, social networks, and recommenda

applicationsarxiv-cs-cl

How to Ground a Korean AI Agent in Real Demographics with Synthetic Personas

This article explains how to ground Korean AI agents in realistic demographic data by utilizing synthetic personas, likely leveraging NVIDIA's Nemotron models available on Hugging Face. The approach e

agentshugging-face

HQA-VLAttack: Towards High Quality Adversarial Attack on Vision-Language Pre-Trained Models

arXiv:2604.16499v1 Announce Type: new Abstract: Black-box adversarial attack on vision-language pre-trained models is a practical and challenging task, as text and image perturbations need to be consi

Hybrid-Vector Retrieval for Visually Rich Documents: Combining Single-Vector Efficiency and Multi-Vector Accuracy

arXiv:2510.22215v2 Announce Type: replace-cross Abstract: Retrieval over visually rich documents is essential for tasks such as legal discovery, scientific search, and enterprise knowledge management.

Hyperbolic Enhanced Representation Learning for Incomplete Multi-view Clustering

arXiv:2604.16959v1 Announce Type: cross Abstract: Incomplete Multi-View Clustering (IMVC) faces the challenge of learning discriminative representations from fragmentary observations while maintaining

safetyarxiv-cs-cv

I think the CoALA paper's classification system of semantic/episodic/procedural is maybe the closest thing we have to a standard for agent m…

The CoALA paper proposes a classification system for agent memory that distinguishes between semantic memory (facts and concepts), episodic memory (specific experiences and events), and procedural mem

agentsharrison-chase--x

ICLAD: In-Context Learning with Comparison-Guidance for Audio Deepfake Detection

arXiv:2604.16749v1 Announce Type: cross Abstract: Audio deepfakes pose a significant security threat, yet current state-of-the-art (SOTA) detection systems do not generalize well to realistic in-the-w

Improving Radio Interferometry Imaging by Explicitly Modeling Cross-Domain Consistency in Reconstruction

arXiv:2604.16794v1 Announce Type: new Abstract: Radio astronomy plays a crucial role in understanding the universe, particularly within the realm of non-thermal astrophysics. Images of celestial objec

researcharxiv-cs-cv

Incentivizing Parametric Knowledge via Reinforcement Learning with Verifiable Rewards for Cross-Cultural Entity Translation

arXiv:2604.16881v1 Announce Type: new Abstract: Cross-cultural entity translation remains challenging for large language models (LLMs) as literal or phonetic renderings are usually yielded instead of

IncreFA: Breaking the Static Wall of Generative Model Attribution

arXiv:2604.17736v1 Announce Type: new Abstract: As AI generative models evolve at unprecedented speed, image attribution has become a moving target. New diffusion, adversarial and autoregressive gener

Inflated Excellence or True Performance? Rethinking Medical Diagnostic Benchmarks with Dynamic Evaluation

arXiv:2510.09275v2 Announce Type: replace Abstract: Medical diagnostics is a high-stakes and complex domain that is critical to patient care. However, current evaluations of large language models (LLM

Instinct vs. Reflection: Unifying Token and Verbalized Confidence in Multimodal Large Models

arXiv:2604.17274v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in various perception and reasoning tasks. Despite this success, ens

safetyarxiv-cs-cv

Instruction-as-State: Environment-Guided and State-Conditioned Semantic Understanding for Embodied Navigation

arXiv:2604.18223v1 Announce Type: new Abstract: Vision-and-Language Navigation requires agents to follow natural-language instructions in visually changing environments. A central challenge is the dyn

agentsarxiv-cs-cv

Integrating Feature Selection and Machine Learning for Nitrogen Assessment in Grapevine Leaves using In-Field Hyperspectral Imaging

arXiv:2507.17869v3 Announce Type: replace-cross Abstract: Nitrogen (N) is one of the most critical nutrients in winegrape production, influencing vine vigor, fruit composition, and wine quality. Becau

applicationsarxiv-cs-cv

InternScenes: A Large-scale Simulatable Indoor Scene Dataset with Realistic Layouts

arXiv:2509.10813v3 Announce Type: replace Abstract: The advancement of Embodied AI heavily relies on large-scale, simulatable 3D scene datasets characterized by scene diversity and realistic layouts.

Interrupt is 3 weeks away really excited for our 2nd agent conference. great lineup including @AndrewYNg, @Cisco, @box, @clay, @MongoDB, + m…

Interrupt is 3 weeks away really excited for our 2nd agent conference. great lineup including @AndrewYNg, @Cisco, @box, @clay, @MongoDB, + more. tickets are going fast! we sold out last year, and it w

agentsharrison-chase--x

Introducing the O-Value: A Universal Standardization for Confusion-Matrix-Based Classification Performance Metrics

arXiv:2505.07033v2 Announce Type: replace-cross Abstract: Many classification performance metrics exist, each suited to a specific application. However, these metrics often differ in scale and can exh

applicationsarxiv-cs-lg

Joint Distillation for Fast Likelihood Evaluation and Sampling in Flow-based Models

arXiv:2512.02636v3 Announce Type: replace-cross Abstract: Log-likelihood evaluation enables important capabilities in generative models, including model comparison, certain fine-tuning objectives, and

researcharxiv-cs-cv

Jupiter-N Technical Report

arXiv:2604.17429v1 Announce Type: new Abstract: We present Jupiter-N, a hybrid reasoning model post-trained from Nemotron 3 Super, a fully open-source 120 billion parameter LLM. We target three object

KIRA: Knowledge-Intensive Image Retrieval and Reasoning Architecture for Specialized Visual Domains

arXiv:2604.16915v1 Announce Type: new Abstract: Retrieval augmented generation (RAG) has transformed text based question answering, yet its extension to visual domains remains hindered by fundamental

LASER: Low-Rank Activation SVD for Efficient Recursion

arXiv:2604.17224v1 Announce Type: new Abstract: Recursive architectures such as Tiny Recursive Models (TRMs) perform implicit reasoning through iterative latent computation, yet the geometric structur

Latent Abstraction for Retrieval-Augmented Generation

arXiv:2604.17866v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become a standard approach for enhancing large language models (LLMs) with external knowledge, mitigating hallu

LatentMimic: Terrain-Adaptive Locomotion via Latent Space Imitation

arXiv:2604.16440v1 Announce Type: new Abstract: Developing natural and diverse locomotion controllers for quadruped robots that can adapt to complex terrains while preserving motion style remains a si

safetyarxiv-cs-ro

LayerCache: Exploiting Layer-wise Velocity Heterogeneity for Efficient Flow Matching Inference

arXiv:2604.16492v1 Announce Type: new Abstract: Flow Matching models achieve state-of-the-art image generation quality but incur substantial inference cost due to iterative denoising through large Tra

Tutorials

Learning Invariant Modality Representation for Robust Multimodal Learning from a Causal Inference Perspective

arXiv:2604.18460v1 Announce Type: new Abstract: Multimodal affective computing aims to predict humans' sentiment, emotion, intention, and opinion using language, acoustic, and visual modalities. Howev

tutorialsarxiv-cs-lg

Local Ai

Learning to Seek Help: Dynamic Collaboration Between Small and Large Language Models

arXiv:2604.17827v1 Announce Type: new Abstract: Large language models (LLMs) offer strong capabilities but raise cost and privacy concerns, whereas small language models (SLMs) facilitate efficient an

local-aiarxiv-cs-cl

Tutorials

Learning Whole-Body Humanoid Locomotion via Motion Generation and Motion Tracking

arXiv:2604.17335v1 Announce Type: new Abstract: Whole-body humanoid locomotion is challenging due to high-dimensional control, morphological instability, and the need for real-time adaptation to vario

tutorialsarxiv-cs-ro

LEPO: nderline{L}atent Rnderline{e}asoning nderline{P}olicy nderline{O}ptimization for Large Language~Models

arXiv:2604.17892v1 Announce Type: new Abstract: Recently, latent reasoning has been introduced into large language models (LLMs) to leverage rich information within a continuous space. However, withou

Less Noise, More Voice: Reinforcement Learning for Reasoning via Instruction Purification

arXiv:2601.21244v3 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has advanced LLM reasoning, but remains constrained by inefficient exploration under lim

safetyarxiv-cs-cl

LiFT: Does Instruction Fine-Tuning Improve In-Context Learning for Longitudinal Modelling by Large Language Models?

arXiv:2604.16382v1 Announce Type: new Abstract: Longitudinal NLP tasks require reasoning over temporally ordered text to detect persistence and change in human behavior and opinions. However, in-conte

Light-Adapted Electroretinogram and Oscillatory Potentials (LEOPs) Dataset for Autism Spectrum Disorder and Typically Developing Individuals

arXiv:2604.16981v1 Announce Type: cross Abstract: The LEOPs (Light-ERG-Oscillatory Potentials) dataset provides light-adapted (LA) electroretinogram (ERG) and Oscillatory Potentials (OPs) waveforms fo

Live Avatar: Streaming Real-time Audio-Driven Avatar Generation with Infinite Length

arXiv:2512.04677v5 Announce Type: replace Abstract: Audio-driven avatar interaction demands real-time, streaming, and infinite-length generation -- capabilities fundamentally at odds with the sequenti

LIVE: Leveraging Image Manipulation Priors for Instruction-based Video Editing

arXiv:2604.17021v1 Announce Type: new Abstract: Video editing aims to modify input videos according to user intent. Recently, end-to-end training methods have garnered widespread attention, constructi

LiveFact: A Dynamic, Time-Aware Benchmark for LLM-Driven Fake News Detection

arXiv:2604.04815v2 Announce Type: replace Abstract: The rapid development of Large Language Models (LLMs) has transformed fake news detection and fact-checking tasks from simple classification to comp

LLM as a Tool, Not an Agent: Code-Mined Tree Transformations for Neural Architecture Search

arXiv:2604.16555v1 Announce Type: cross Abstract: Neural Architecture Search (NAS) aims to automatically discover high-performing deep neural network (DNN) architectures. However, conventional algorit

agentsarxiv-cs-cv

Local Inconsistency Resolution: The Interplay between Attention and Control in Probabilistic Models

arXiv:2604.17140v1 Announce Type: cross Abstract: We present a generic algorithm for learning and approximate inference with an intuitive epistemic interpretation: iteratively focus on a subset of the

Local learning for stable backpropagation-free neural network training towards physical learning

arXiv:2603.24790v2 Announce Type: replace Abstract: While backpropagation and automatic differentiation have driven deep learning's success, the physical limits of chip manufacturing and rising enviro

applicationsarxiv-cs-lg

LOGICAL-COMMONSENSEQA: A Benchmark for Logical Commonsense Reasoning

arXiv:2601.16504v3 Announce Type: replace Abstract: Commonsense reasoning often involves evaluating multiple plausible interpretations rather than selecting a single atomic answer, yet most benchmarks

LoRA on the Go: Instance-level Dynamic LoRA Selection and Merging

arXiv:2511.07129v3 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) has emerged as a parameter-efficient approach for fine-tuning large language models. However, conventional LoRA adapters

Lower Bounds and Proximally Anchored SGD for Non-Convex Minimization Under Unbounded Variance

arXiv:2604.16620v1 Announce Type: new Abstract: Analysis of Stochastic Gradient Descent (SGD) and its variants typically relies on the assumption of uniformly bounded variance, a condition that freque

LTRR: Learning To Rank Retrievers for LLMs

arXiv:2506.13743v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) systems typically rely on a single fixed retriever, despite growing evidence that no single retriever performs