All entries

Large Language Models Should Learn Personalized Rather Than Aggregated Human Preferences

arXiv:2606.07629v1 Announce Type: cross Abstract: Current approaches to aligning large language models (LLMs) aggregate diverse human preferences into a single reward signal, effectively optimizing fo

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Applications

Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook

DGX agent

arXiv:2310.10196v3 Announce Type: replace-cross Abstract: Temporal data, including time series and spatio-temporal data, are pervasive in real-world applications. Generated in massive volumes by physi

applicationsarxiv-cs-ai

9 Jun 2026

Large-scale empirical tuning and comparison of default optimizers for variational inference

arXiv:2606.07841v1 Announce Type: cross Abstract: Black-box variational inference (BBVI) is a methodology for posterior approximation that relies on stochastic optimization. In practice, the stochasti

LargeMonitor: Monitoring Online Task-Free Continual Learning via Large Pretrained Models

model-releasesarxiv-cs-ai

arXiv:2606.09430v1 Announce Type: cross Abstract: Online task-free continual learning (TFCL) requires intelligent agents to sequentially accumulate knowledge from an unbounded, non-stationary data str

LARP: Learner-Agnostic Robust Data Prefiltering

arXiv:2506.20573v4 Announce Type: replace-cross Abstract: Public datasets, crucial for modern machine learning and statistical inference, often contain low-quality or contaminated samples that can har

researcharxiv-cs-lg

Late-Layer Fusion is Enough: Dual-Path Vision Token Routing for Multimodal Large Language Models under Visual Saturation

arXiv:2606.09131v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) commonly inherit the deep, symmetric Transformer backbone designed for unimodal text modeling, and apply the sa

Latent Diffusion Policy: Shaping Latent Spaces for Diffusion-Based Robotic Manipulation

arXiv:2606.08657v1 Announce Type: cross Abstract: Diffusion-based visuomotor policies operating directly in raw action spaces conflate scene comprehension with trajectory generation within a single de

Latent Spatial Memory for Video World Models

arXiv:2606.09828v1 Announce Type: new Abstract: Video world models that maintain 3D spatial consistency across generated frames typically rely on explicit point cloud memory constructed in RGB space.

Latent Spherical Flow Policy for Reinforcement Learning with Combinatorial Actions

arXiv:2601.22211v2 Announce Type: replace Abstract: Reinforcement learning (RL) with combinatorial action spaces remains challenging because feasible action sets are exponentially large and governed b

safetyarxiv-cs-lg

Latent Structural Categorical Matrix Completion with Application to Quasispecies Analysis

arXiv:2606.08188v1 Announce Type: cross Abstract: Matrix completion has been extensively studied for real-valued data, but existing methods are often limited in handling categorical variables. We prop

applicationsarxiv-cs-lg

LATTEArena: An Evaluation Framework for LLM-powered Tabular Feature Engineering (Extended Version)

arXiv:2606.09004v1 Announce Type: new Abstract: Feature engineering remains essential for tabular data analysis, and Large Language Models (LLMs) have emerged as a promising paradigm for automating th

Lattice: A Confidence-Gated Hybrid System for Uncertainty-Aware Sequential Prediction with Behavioral Archetypes

arXiv:2601.15423v2 Announce Type: replace Abstract: We introduce Lattice, a hybrid sequential prediction system that conditionally activates learned behavioral structure using binary confidence gating

applicationsarxiv-cs-lg

Layer-wise Derivative Controlled Networks Achieve Competitive Accuracy and Gradient Stability Across Data Regimes

arXiv:2606.07908v1 Announce Type: new Abstract: Derivative-controlled networks based on ChainzRule (CR) combine cubic polynomial layers with a lightweight forward-mode per-layer Jacobian penalty (DREG

safetyarxiv-cs-lg

LCAM: A Framework for Diagnosing Interactional Alignment Failures in Con-versational AI

arXiv:2606.08131v1 Announce Type: cross Abstract: Conversational AI is increasingly used for advice, interpretation, reassurance, and decision support in contexts where users may be vulnerable, uncert

LEAF: A Learning-Enabled ADMM Framework for Accelerated Convex Optimization

arXiv:2606.08993v1 Announce Type: new Abstract: We propose LEAF, a learning-enabled ADMM framework for accelerated convex optimization. The key idea is to approximate the Moreau envelope of the object

LEAF: Growing Trees Without Branching for Speech-Aware Large Language Model Post-Training

model-releasesarxiv-cs-ai

arXiv:2606.07610v1 Announce Type: cross Abstract: State-of-the-art GRPO-style methods for speech-aware large language model post-training suffer from coarse credit assignment, broadcasting the same te

Learnable Token Sparsification for Efficient Gigapixel Whole Slide Image Reasoning

arXiv:2606.08641v1 Announce Type: new Abstract: The processing of gigapixel whole slide images within vision language models faces a major difficulty due to an excessive number of visual tokens. Exist

tutorialsarxiv-cs-cv

Learning a Semantic Calibration Network for Open-Vocabulary Semantic Segmentation

arXiv:2606.08001v1 Announce Type: new Abstract: Semantic image segmentation assigns a predefined category label to each pixel, has achieved significant progress lately. Open-Vocabulary Segmentation (O

Learning Dynamics Reveal a Hierarchy of Weight-Induced Layerwise Gram Metrics

arXiv:2606.09744v1 Announce Type: new Abstract: We study feed-forward ReLU networks with fixed readout and quadratic loss. The aim is to rewrite gradient descent not primarily as a dynamics in weight

researcharxiv-cs-lg

Learning from flowsheets: A generative transformer model for autocompletion of flowsheets

arXiv:2208.00859v2 Announce Type: replace Abstract: We propose a novel method enabling autocompletion of chemical flowsheets. This idea is inspired by the autocompletion of text. We represent flowshee

Learning from Human Driving: A Human-in-the-Loop Online Behavior Cloning Framework for Autonomous Driving

model-releasesarxiv-cs-ro

arXiv:2606.08170v1 Announce Type: new Abstract: With the evolution of large foundation models (LFMs), data-driven autonomous driving has made significant strides. However, existing paradigms still fac

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning

model-releasesarxiv-cs-ro

arXiv:2606.08136v1 Announce Type: new Abstract: Model Predictive Control (MPC) is widely used for autonomous-vehicle (AV) motion planning, but its real-time applicability is often limited by the need

Learning Quantized Continuous Controllers for Integer Hardware

arXiv:2511.07046v4 Announce Type: replace-cross Abstract: Deploying continuous-control reinforcement learning policies on embedded hardware requires meeting tight latency and power budgets. Small FPGA

Learning to Attack and Defend: Adaptive Red Teaming of Language Models via GRPO

arXiv:2606.09701v1 Announce Type: cross Abstract: AI red teaming must continually adapt to evolving attackers and defenders. Reinforcement learning offers a promising approach to discovering novel att

Learning to Optimize by Differentiable Programming

arXiv:2601.16510v3 Announce Type: replace-cross Abstract: Solving massive-scale optimization problems requires scalable first-order methods with low per-iteration cost. This tutorial highlights a shif

Learning to Solve Generative ODEs Beyond the Linear Span

arXiv:2606.08672v1 Announce Type: new Abstract: Diffusion and flow generative models sample by integrating a learned ODE, but high quality still requires many sequential model evaluations. Solver lear

Learning Transfers: Kan Extensions for Neural Invariants

arXiv:2606.07627v1 Announce Type: new Abstract: Transfer learning presumes that a representation learned on source tasks carries structure that remains usable on related target tasks. Standard evaluat

LEGS: Laplacian-Enhanced Gaussian Splatting with a Nonlinear Weighted Loss

arXiv:2606.07932v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has become an efficient explicit representation for radiance field reconstruction and real-time novel view synthesis. Howev

Local Ai

Less Is More: Training-Free Acceleration Framework of 3D Diffusion Models for Low-Count PET Denoising via Global-Local Trajectory Reduction

arXiv:2606.08751v1 Announce Type: new Abstract: Accurate quantification and uptake measurement in PET are critical for assessing disease progression and supporting clinical decision-making. While high

local-aiarxiv-cs-cv

Leveraging Morphology for Historical Script Metrological Analysis

arXiv:2606.09446v1 Announce Type: new Abstract: Advances in handwritten text recognition have enabled large-scale transcription of historical documents, but still provide limited access to interpretab

tutorialsarxiv-cs-cv

Leveraging NeRF-Rendered Images for 3D Gaussian Splatting

arXiv:2606.09034v1 Announce Type: new Abstract: Neural radiance field (NeRF) and 3D Gaussian splatting (3DGS) are two mainstream approaches for novel view synthesis. They often show complementary perf

Local Ai

Leveraging Structural Constraints for Diffusion-based Neural TSP Solvers

arXiv:2606.09343v1 Announce Type: new Abstract: Neural combinatorial optimization has recently achieved strong results on the Euclidean Traveling Salesman Problem (TSP) using generative models such as

local-aiarxiv-cs-ai

LFNO: Bridging Laplace and Fourier via Transient-Steady Decomposition

arXiv:2606.07601v1 Announce Type: cross Abstract: We introduce the Laplace-Fourier Neural Operator (LFNO), a unified framework for modeling dynamical systems across transient and steady-state regimes

Liberating LLM Capabilities in Full-Duplex Speech Models

arXiv:2606.07547v1 Announce Type: cross Abstract: Speech-based large language models are typically constrained to spoken replies, which limits their user-facing outputs to what can be verbalized and s

Light-WAM: Efficient World Action Models with State-Fusion Action Decoding

arXiv:2606.08242v1 Announce Type: new Abstract: World Action Models (WAMs) extend robot policy learning by incorporating future prediction as an additional training objective, encouraging the policy t

safetyarxiv-cs-cv

LightTact: A Visual-Tactile Fingertip Sensor for Deformation-Independent Contact Sensing

arXiv:2512.20591v3 Announce Type: replace Abstract: Contact often occurs without macroscopic surface deformation, such as during interaction with liquids, semi-liquids, or ultra-soft materials. Howeve

researcharxiv-cs-ro

Liquid Neural Networks as a Drop-in Continuous-Time Deformation Field for Dynamic 3D Gaussian Splatting

arXiv:2606.07670v1 Announce Type: cross Abstract: Deformable 3D Gaussian Splatting (D-3DGS) re-constructs dynamic scenes from monocular video by deforming a canonical set of 3D Gaussians through a pos

LiteVSR: Lightweight Adaptation of Frozen Diffusion Transformers for Video Super-Resolution

arXiv:2606.09250v1 Announce Type: new Abstract: Adapting large-scale pre-trained video generators for Video Super-Resolution (VSR) in novel domains remains computationally prohibitive. Methods that re

safetyarxiv-cs-cv

LLM Inference at the Edge: Mobile, NPU, and GPU Performance Efficiency Trade-offs Under Sustained Load

arXiv:2603.23640v2 Announce Type: replace-cross Abstract: Deploying large language models on-device for always-on personal agents demands sustained inference from hardware tightly constrained in power

LLM-Orchestrated Conformance Checking in Stroke Care Without Computer-Interpretable Guidelines

arXiv:2606.09489v1 Announce Type: new Abstract: Objective: Conformance checking in healthcare seeks to assess whether patient care pathways adhere to clinical guidelines. However, its practical applic

applicationsarxiv-cs-ai

Local Ai

Locally Adaptive Conformal Inference for Operator Models

arXiv:2507.20975v5 Announce Type: replace-cross Abstract: Operator models are regression algorithms between Banach spaces of functions. They have become an increasingly critical tool for spatiotempora

local-aiarxiv-cs-lg

LogNEO: A GPT-Neo Reinforcement Learning Framework for Accurate Real-Time Log Anomaly Detection

arXiv:2606.08153v1 Announce Type: cross Abstract: Detecting anomalies in large-scale system logs is critical for the reliability and security of modern computing infrastructure. We present LogNEO, a l

Look Less, Reason More: Block-wise Attention Skipping for Efficient Multimodal LLMs

arXiv:2606.08511v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) face a significant inference bottleneck due to the quadratic computational cost of self-attention over long vis

Loss-Guided Adaptive Scale Refinement for Molecular Force Prediction

arXiv:2606.09480v1 Announce Type: new Abstract: Molecular systems involve interactions across multiple spatial scales, from local coordination and short-range perturbations to long-range electrostatic

researcharxiv-cs-lg

Lost in the Flow with Code Talkers: Unveiling the Instruction-Tuning Tax of Large Language Models in Code Tasks

arXiv:2606.08676v1 Announce Type: cross Abstract: AI coding assistants have significantly improved developer productivity by automatically suggesting code that aligns with user intent, and many of the

Lost in the Non-convex Loss Landscape: How to Fine-tune the Large Time Series Model?

arXiv:2606.08578v1 Announce Type: new Abstract: Recently, large time series models (LTSMs) have gained increasing attention due to their similarities to large language models, including flexible conte

LOTTERY: Learning from Reference-Only Samples in Two-Sample Testing under Size Asymmetry

arXiv:2606.08460v1 Announce Type: cross Abstract: Data-adaptive two-sample testing assesses if two samples come from the same distribution, using a discrepancy learned from the data (e.g., via kernel-

LoTUS: Large-Scale Machine Unlearning with a Taste of Uncertainty

arXiv:2503.18314v5 Announce Type: replace-cross Abstract: We present LoTUS, a novel Machine Unlearning (MU) method that eliminates the influence of training samples from pre-trained models, avoiding r

applicationsarxiv-cs-ai