All entries

iPhoneme: Brain-to-Text Communication for ALS Using ConformerXL Decoding

arXiv:2604.16441v1 Announce Type: cross Abstract: Brain-computer interfaces (BCIs) for speech restoration hold transformative potential for the approximately 173,000--232,500 individuals worldwide wit

researcharxiv-cs-cl

Agents

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Is Agentic RAG worth it? An experimental comparison of RAG approaches

DGX agent

arXiv:2601.07711v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) systems are usually defined by the combination of a generator and a retrieval component that extracts textual c

agentsarxiv-cs-cl

21 Apr 2026

Is SAM3 ready for pathology segmentation?

arXiv:2604.18225v1 Announce Type: new Abstract: Is Segment Anything Model 3 (SAM3) capable in segmenting Any Pathology Images? Digital pathology segmentation spans tissue-level and nuclei-level scales

IYKYK (But AI Doesn't): Automated Content Moderation Does Not Capture Communities' Heterogeneous Attitudes Towards Reclaimed Language

arXiv:2604.16654v1 Announce Type: new Abstract: Reclaimed slur usage is a common and meaningful practice online for many marginalized communities. It serves as a source of solidarity, identity, and sh

safetyarxiv-cs-cl

J-PARSE: Jacobian-based Projection Algorithm for Resolving Singularities Effectively in Inverse Kinematic Control of Serial Manipulators

arXiv:2505.00306v5 Announce Type: replace Abstract: J-PARSE is an algorithm for smooth first-order inverse kinematic control of a serial manipulator near kinematic singularities. The commanded end-eff

safetyarxiv-cs-ro

Jailbreaking Large Language Models with Morality Attacks

arXiv:2604.17053v1 Announce Type: new Abstract: Pluralism alignment with AI has the sophisticated and necessary goal of creating AI that can coexist with and serve morally multifaceted humanity. Resea

safetyarxiv-cs-cl

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

Judge a Book by its Cover: Investigating Multi-Modal LLMs for Multi-Page Handwritten Document Transcription

arXiv:2502.20295v2 Announce Type: replace-cross Abstract: Handwriting text recognition (HTR) remains a challenging task. Existing approaches require fine-tuning on labeled data, which is impractical t

JudgeMeNot: Personalizing Large Language Models to Emulate Judicial Reasoning in Hebrew

arXiv:2604.18041v1 Announce Type: new Abstract: Despite significant advances in large language models, personalizing them for individual decision-makers remains an open problem. Here, we introduce a s

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

KaLDeX: Kalman Filter based Linear Deformable Cross Attention for Retina Vessel Segmentation

arXiv:2410.21160v2 Announce Type: replace-cross Abstract: Background and Objective: In the realm of ophthalmic imaging, accurate vascular segmentation is paramount for diagnosing and managing various

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

Knowing When to Quit: A Principled Framework for Dynamic Abstention in LLM Reasoning

arXiv:2604.18419v1 Announce Type: cross Abstract: Large language models (LLMs) using chain-of-thought reasoning often waste substantial compute by producing long, incorrect responses. Abstention can m

Knowledge without Wisdom: Measuring Misalignment between LLMs and Intended Impact

model-releasesarxiv-cs-lg

arXiv:2603.00883v2 Announce Type: replace Abstract: LLMs increasingly excel on AI benchmarks, but doing so does not guarantee validity for downstream tasks. This study contrasts LLM alignment on bench

L1 Regularization Paths in Linear Models by Parametric Gaussian Message Passing

arXiv:2604.16949v1 Announce Type: new Abstract: The paper considers the computation of L1 regularization paths in a state space setting, which includes L1 regularized Kalman smoothing, linear SVM, LAS

Applications

LAGS: Low-Altitude Gaussian Splatting with Groupwise Heterogeneous Graph Learning

arXiv:2604.16910v1 Announce Type: new Abstract: Low-altitude Gaussian splatting (LAGS) facilitates 3D scene reconstruction by aggregating aerial images from distributed drones. However, as LAGS priori

applicationsarxiv-cs-cv

Language Models Don't Know What You Want: Evaluating Personalization in Deep Research Needs Real Users

arXiv:2603.16120v2 Announce Type: replace Abstract: Deep Research (DR) systems help researchers cope with ballooning publishing counts. Such tools synthesize scientific papers to answer research queri

Applications

Large Language Models Are Bad Dice Players: LLMs Struggle to Generate Random Numbers from Statistical Distributions

arXiv:2601.05414v2 Announce Type: replace Abstract: As large language models (LLMs) transition from chat interfaces to integral components of stochastic pipelines and systems approaching general intel

applicationsarxiv-cs-cl

Large Language Models Are Still Misled by Simple Bias Ensembles

arXiv:2505.16522v3 Announce Type: replace Abstract: With the evolution of large language models (LLMs), their robustness against individual simple biases has been enhanced. However, we observe that th

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

Late Fusion Neural Operators for Extrapolation Across Parameter Space in Partial Differential Equations

model-releasesarxiv-cs-lg

arXiv:2604.16721v1 Announce Type: new Abstract: Developing neural operators that accurately predict the behavior of systems governed by partial differential equations (PDEs) across unseen parameter re

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

researcharxiv-cs-cl

Latent-Compressed Variational Autoencoder for Video Diffusion Models

arXiv:2604.16479v1 Announce Type: new Abstract: Video variational autoencoders (VAEs) used in latent diffusion models typically require a sufficiently large number of latent channels to ensure high-qu

Latent Phase-Shift Rollback: Inference-Time Error Correction via Residual Stream Monitoring and KV-Cache Steering

arXiv:2604.18567v1 Announce Type: cross Abstract: Large language models frequently commit unrecoverable reasoning errors mid-generation: once a wrong step is taken, subsequent tokens compound the mist

researcharxiv-cs-cl

Latent Preference Modeling for Cross-Session Personalized Tool Calling

arXiv:2604.17886v1 Announce Type: new Abstract: Users often omit essential details in their requests to LLM-based agents, resulting in under-specified inputs for tool use. This poses a fundamental cha

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

LBFTI: Layer-Based Facial Template Inversion for Identity-Preserving Fine-Grained Face Reconstruction

arXiv:2604.18358v1 Announce Type: new Abstract: In face recognition systems, facial templates are widely adopted for identity authentication due to their compliance with the data minimization principl

LEAF: Knowledge Distillation of Text Embedding Models with Teacher-Aligned Representations

arXiv:2509.12539v2 Announce Type: replace-cross Abstract: We present LEAF ('Lightweight Embedding Alignment Framework'), a knowledge distillation framework for text embedding models. A key distinguish

Learned Nonlocal Feature Matching and Filtering for RAW Image Denoising

arXiv:2604.17453v1 Announce Type: cross Abstract: Being one of the oldest and most basic problems in image processing, image denoising has seen a resurgence spurred by rapid advances in deep learning.

Learning-Based Sparsification of Dynamic Graphs in Robotic Exploration Algorithms

arXiv:2604.16509v1 Announce Type: cross Abstract: Many robotic exploration algorithms rely on graph structures for frontier-based exploration and dynamic path planning. However, these graphs grow rapi

safetyarxiv-cs-lg

Applications

Learning from Less: Measuring the Effectiveness of RLVR in Low Data and Compute Regimes

arXiv:2604.18381v1 Announce Type: cross Abstract: Fine-tuning Large Language Models (LLMs) typically relies on large quantities of high-quality annotated data, or questions with well-defined ground tr

applicationsarxiv-cs-lg

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

Learning residue level protein dynamics with multiscale Gaussians

arXiv:2509.01038v2 Announce Type: replace-cross Abstract: Many methods have been developed to predict static protein structures, however understanding the dynamics of protein structure is essential fo

Learning Stable Predictors from Weak Supervision under Distribution Shift

model-releasesarxiv-cs-lg

arXiv:2604.05002v2 Announce Type: replace Abstract: Learning from weak, proxy, or relative supervision is common when ground-truth labels are unavailable, but robustness under distribution shift remai

Learning the Riccati solution operator for time-varying LQR via Deep Operator Networks

arXiv:2604.18507v1 Announce Type: cross Abstract: We propose a computational framework for replacing the repeated numerical solution of differential Riccati equations in finite-horizon Linear Quadrati

Learning to Control Summaries with Score Ranking

arXiv:2604.17197v1 Announce Type: new Abstract: Recent advances in summarization research focus on improving summary quality across multiple criteria, such as completeness, conciseness, and faithfulne

Learning to Correct: Calibrated Reinforcement Learning for Multi-Attempt Chain-of-Thought

arXiv:2604.17912v1 Announce Type: new Abstract: State-of-the-art reasoning models utilize long chain-of-thought (CoT) to solve increasingly complex problems using more test-time computation. In this w

Learning to Retrieve User History and Generate User Profiles for Personalized Persuasiveness Prediction

arXiv:2601.05654v3 Announce Type: replace Abstract: Estimating the persuasiveness of messages is critical in various applications, from recommender systems to safety assessment of LLMs. While it is im

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

Agents

Learning to Trade Like an Expert: Cognitive Fine-Tuning for Stable Financial Reasoning in Language Models

arXiv:2604.16862v1 Announce Type: new Abstract: Recent deployments of large language models (LLMs) as autonomous trading agents raise questions about whether financial decision-making competence gener

agentsarxiv-cs-lg

Learning Unanimously Acceptable Lotteries via Queries

arXiv:2604.17505v1 Announce Type: cross Abstract: Many high-stakes AI deployments proceed only if every stakeholder deems the system acceptable relative to their own minimum standard. With randomizati

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

Leveraging Kernel Symmetry for Joint Compression and Error Mitigation in Edge Model Transfer

arXiv:2604.17371v1 Announce Type: cross Abstract: This paper investigates communication-efficient neural network transmission by exploiting structured symmetry constraints in convolutional kernels. In

Leveraging Large Language Models for Sarcastic Speech Annotation in Sarcasm Detection

arXiv:2506.00955v2 Announce Type: replace Abstract: Sarcasm fundamentally alters meaning through tone and context, yet detecting it in speech remains a challenge due to data scarcity. In addition, exi

Leveraging VR Robot Games to Facilitate Data Collection for Embodied Intelligence Tasks

arXiv:2604.16903v1 Announce Type: new Abstract: Collecting embodied interaction data at scale remains costly and difficult due to the limited accessibility of conventional interfaces. We present a gam

researcharxiv-cs-ro