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JOIN: Anchor-Grasp-Conditioned Joining via Opposition, Inference, and Navigation for Bimanual Assistive Manipulation

arXiv:2606.11151v1 Announce Type: new Abstract: Assistive mobility and manipulation platforms have received increasing attention as a means of restoring independence to individuals with disabilities.

researcharxiv-cs-ro

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K-Forcing: Joint Next-K-Token Decoding via Push-Forward Language Modeling

DGX agent

arXiv:2606.10820v1 Announce Type: cross Abstract: Autoregressive (AR) language modeling is the dominant paradigm for text generation, yet its sequential token-by-token decoding makes inference memory-

applicationsarxiv-cs-ai

10 Jun 2026

k-Nearest Neighbors in Gromov--Wasserstein Space

arXiv:2606.10295v1 Announce Type: cross Abstract: The Gromov--Wasserstein (GW) distance provides a framework for comparing metric measure spaces, regardless of their underlying structure or geometry.

KCSAT-ML: Probing Reasoning Models with Nationwide-Cohort Human Difficulty

model-releasesarxiv-cs-cl

arXiv:2606.10403v1 Announce Type: new Abstract: Math reasoning benchmarks have proliferated, yet most lack a per-item difficulty signal grounded in actual human performance. We introduce KCSAT-ML, a d

KG-SoftMAP: Soft Knowledge-Graph Priors for Bayesian Network Structure Learning from Sparse Discrete Data

arXiv:2606.10358v1 Announce Type: cross Abstract: Learning Bayesian network (BN) structure from sparse discrete data is hard: when each instance records only a few variables, most variable pairs lack

Kwai Keye-VL-2.0 Technical Report

model-releasesarxiv-cs-cv

arXiv:2606.10651v1 Announce Type: new Abstract: We introduce Kwai Keye-VL-2.0-30B-A3B, an open-source Mixture-of-Experts (MoE) multimodal foundation model designed to advance long-video understanding

LAFP: Preserving Latent Action Structure in Latent Policy Learning via Flow Matching

arXiv:2606.10517v1 Announce Type: new Abstract: Learning high-quality latent actions from large-scale unlabeled videos, coupled with limited real-world interaction data for training an action decoder,

LakeQA: An Exploratory QA Benchmark over a Million-Scale Data Lake

arXiv:2606.10460v1 Announce Type: cross Abstract: Recent large language models (LLMs) have shown rapid progress in reading-based question answering (QA), where evidence is explicitly provided or can b

Agents

Language-Driven Cost Optimization for Autonomous Driving

arXiv:2606.10974v1 Announce Type: new Abstract: The driving behavior of autonomous vehicles is typically governed by the cost function of their motion planner, which encodes objectives such as speed t

agentsarxiv-cs-ro

Large Language Models as Modal Models in Linguistics

arXiv:2606.10467v1 Announce Type: new Abstract: The rapid advancement of large language models (LLMs) has intensified debates about their significance for linguistic theory. These debates are commonly

researcharxiv-cs-cl

Large-scale semantic mapping of learner agency and autonomy reveals what measurement and generative AI research overlook

arXiv:2606.10881v1 Announce Type: new Abstract: Learner agency and autonomy are foundational to personal development, yet a pervasive 'jingle-jangle' fallacy (i.e. identical terms denoting different c

applicationsarxiv-cs-ai

LaRI: Layered Ray Intersections for Single-view 3D Geometric Reasoning

arXiv:2504.18424v2 Announce Type: replace Abstract: We present Layered Ray Intersections (LaRI), a fully supervised method for occluded geometry reasoning from a single image. Unlike conventional dept

applicationsarxiv-cs-cv

Latent Guided Sampling for Combinatorial Optimization

model-releasesarxiv-cs-lg

arXiv:2506.03672v2 Announce Type: replace-cross Abstract: Combinatorial Optimization problems are widespread in domains such as logistics, manufacturing, and drug discovery, yet their NP-hard nature m

LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization

arXiv:2606.10531v1 Announce Type: cross Abstract: Quantization-aware training (QAT) is essential for extremely low-bit large language models (LLMs). Current QAT methods are mainly based on scalar quan

Learning Doubly Sparse Explicitly Conditioned Transforms

arXiv:2606.10975v1 Announce Type: new Abstract: Finding convenient spaces in which certain hypotheses regarding an assumed sparse structure of natural signals hold true has become a desirable result i

Learning Entropy and Spatial Adaptation Dynamics of Multilayer Perceptrons for Structural Point Extraction

arXiv:2606.10170v1 Announce Type: new Abstract: This paper extends the concept of Learning Entropy (LE) from temporal adaptive systems to spatial learning in multilayer perceptron networks (MLPs) appl

applicationsarxiv-cs-lg

Tutorials

Learning-Guided Integration Contours Construction for Fast Large-Scale Generalized Eigensolvers

arXiv:2511.01927v2 Announce Type: replace-cross Abstract: Solving large-scale Generalized Eigenvalue Problems (GEPs) is a fundamental yet computationally prohibitive task in science and engineering. A

tutorialsarxiv-cs-ai

Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions

arXiv:2606.10023v1 Announce Type: cross Abstract: Accurate posterior estimation is central to scientific inference, as uncertainties determine what can be reliably learned from observational data. Whi

applicationsarxiv-cs-lg

Local Ai

Learning What to Remember: Observability-Safe Memory Retention via Constrained Optimization for Long-Horizon Language Agents

arXiv:2606.10616v1 Announce Type: new Abstract: Long-horizon language agents accumulate observations, reasoning traces, and retrieved facts that exceed their finite context windows, making memory rete

local-aiarxiv-cs-ai

Learning Where to Simulate: Generative Active Sampling for Online PDE Surrogate Training

arXiv:2606.09949v1 Announce Type: cross Abstract: Data-driven PDE surrogates are trained with data produced by numerical PDE solvers. However, when the surrogate's goal is to generalize across a wide

Less Context, Better Agents: Efficient Context Engineering for Long-Horizon Tool-Using LLM Agents

arXiv:2606.10209v1 Announce Type: new Abstract: Large language models deployed as autonomous agents for enterprise workflows face a key challenge: verbose tool responses from enterprise systems can ca

Less Context, More Accuracy: A Bi-Temporal Memory Engine for LLM Agents Where a Lean Retrieved Context Beats the Full History

arXiv:2606.09900v1 Announce Type: cross Abstract: Long-term memory is the missing layer for LLM agents: across sessions they forget, and the common workaround -- replaying the whole history into the p

Tutorials

Leveraging Metric Depth for Relative Depth Prediction

arXiv:2606.10628v1 Announce Type: new Abstract: We present our solution to the 2025 SoccerNet Monocular Depth Estimation Competition Challenge. Predicting the relative depth in football scenarios is c

tutorialsarxiv-cs-cv

Leveraging Social Media Data for COVID-19 Studies

arXiv:2606.10459v1 Announce Type: cross Abstract: Nowadays, social media networks have become widely preferred sources of information. Especially during the time of the Coronavirus disease 2019 COVID

researcharxiv-cs-cl

LIBERO-Occ: Evaluating and Improving Vision-Language-Action Models under Scene-Induced Occlusion via Viewpoint Imagination

arXiv:2606.10862v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models achieve strong performance on standard manipulation benchmarks, but most evaluations assume that task-relevant obj

LieIPM: Lie Group Interior Point Method for Direct Trajectory Optimization of Rigid Bodies

arXiv:2606.10579v1 Announce Type: new Abstract: Designing dynamically feasible trajectories for rigid bodies is a fundamental problem in robotics. While direct methods are widely used, the existing co

researcharxiv-cs-ro

Lightweight Latent Reasoning for Narrative Tasks

arXiv:2512.02240v2 Announce Type: replace Abstract: Large language models (LLMs) tackle complex tasks by generating long chains of thought or 'reasoning traces' that act as latent variables in the gen

safetyarxiv-cs-cl

Limitations of Learning Tanh Neural Networks with Finite Precision

arXiv:2606.11104v1 Announce Type: new Abstract: We investigate limitations of learning anh neural networks from point evaluations under finite-precision computations and L^p accuracy guarantees, build

Linguistically Augmented Audio Speech Data (LinguAS)

arXiv:2606.10246v1 Announce Type: cross Abstract: Maliciously-created fake speech, including deepfaked and spoofed audio, is proliferating at an alarming rate, and detection models are racing to stay

Lip Forcing: Few-Step Autoregressive Diffusion for Real-time Lip Synchronization

arXiv:2606.11180v1 Announce Type: new Abstract: Diffusion-based lip synchronization models achieve strong visual quality and audio-visual alignment, but full-sequence bidirectional attention and many

Tutorials

Listen, Look, and Learn: Learning Without Forgetting through SAM-Audio

arXiv:2606.10887v1 Announce Type: new Abstract: Class-Incremental Learning (CIL) aims to continuously learn new classes without forgetting previously acquired knowledge. While recent CIL advances have

tutorialsarxiv-cs-cv

LLM-Aided Joint Secrecy Precoding and Trajectory for RSMA-Based Heterogeneous UAV Networks

arXiv:2507.17188v2 Announce Type: replace-cross Abstract: This paper investigates secure communications in rate-splitting multiple access (RSMA) enabled heterogeneous UAV networks, where multiple UAVs

safetyarxiv-cs-ai

LLM-as-a-Discriminator: When Synthetic Tables Still Look Real

model-releasesarxiv-cs-lg

arXiv:2606.09865v1 Announce Type: new Abstract: Privacy and data sharing are often in tension. Many organizations use synthetic data to reduce privacy risk and still share useful data. For tabular dat

LLM-Based Code Documentation Generation and Multi-Judge Evaluation

arXiv:2606.09852v1 Announce Type: cross Abstract: High-quality source code documentation is vital yet often neglected, especially in critical domains like healthcare where reliability and maintainabil

LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networks

arXiv:2606.10294v1 Announce Type: cross Abstract: Deploying neural networks on unconventional hardware demands architectures that co-optimize task accuracy and platform-specific constraints such as en

LMT: A Bayesian Framework for Causal Discovery from Textual Alarm Records in Manufacturing Systems

arXiv:2606.09892v1 Announce Type: new Abstract: Textual event records, such as alarm logs, have become an increasingly common data source in engineering and manufacturing systems. Beyond identifying c

applicationsarxiv-cs-lg

Local Is Not a Sufficient Privacy Boundary: Governing OS-Integrated On-Device AI

arXiv:2606.10173v1 Announce Type: cross Abstract: As AI systems move into operating systems, privacy no longer turns only on whether a model runs locally. A local assistant may assemble email, calenda

Locomotion analysis of a quadruped interacting with the lunar granular surface

arXiv:2606.10273v1 Announce Type: new Abstract: Deploying legged robots in extra-terrestrial environments includes many challenges due to complex terrain interactions, energy, and thermal constraints.

safetyarxiv-cs-ro

LongMoE: Longitudinal Multimodal Learning via Trajectory-Aware Mixture-of-Experts

arXiv:2606.09907v1 Announce Type: cross Abstract: Multimodal clinical learning is increasingly important for integrating diverse patient data, including imaging, text, and personalised health records.

Lost in Serialization: Invariance and Generalization of LLM Graph Reasoners

arXiv:2511.10234v3 Announce Type: replace-cross Abstract: While promising, graph reasoners based on Large Language Models (LLMs) lack built-in invariance to symmetries in graph representations. Operat

Machine Learning Methods for Studying Latent Neural Activity Dynamics

arXiv:2606.10530v1 Announce Type: cross Abstract: Recent developments in brain recording are driving a demand for machine learning tools capable of decoding the latent structure of large populations o

MAD: Manifold Attracted Diffusion

arXiv:2509.24710v2 Announce Type: replace-cross Abstract: Score-based diffusion models are a highly effective method for generating samples from a distribution of images. We consider scenarios where t

Magnetic HIP-NN for spin dynamics in disordered itinerant magnets

model-releasesarxiv-cs-lg

arXiv:2606.10349v1 Announce Type: cross Abstract: We present a magnetic extension of the Hierarchically Interacting Particle Neural Network (HIP-NN) that enables large-scale simulations of electron-me

Making Time Editable in Video Diffusion Transformers

arXiv:2606.10183v1 Announce Type: cross Abstract: Modern Diffusion Transformers for video generation provide limited control over the progression of time and the editing of temporal dynamics. We propo

ManiSplat: Manipulation Trajectory Synthesis from Monocular Video via Decoupled 3D Gaussian Splatting

arXiv:2606.10645v1 Announce Type: new Abstract: Reconstructing dynamic and interactive 3D scenes from real-world observations remains a fundamental challenge in computer vision and robotics. While rec

MARCH: Model-Assisted Reinforcement Learning for the Perceptive Control of Humanoids over Sparse Footholds

arXiv:2606.10288v1 Announce Type: new Abstract: Perceptive bipedal locomotion over sparse terrain remains a difficult challenge: model-based methods are precise but brittle to uncertainty, while model

safetyarxiv-cs-ro

Maximum Matching Accuracy: An Instance Segmentation Evaluation Metric Utilizing Globally Optimal Matching

arXiv:2606.10107v1 Announce Type: new Abstract: Reliable evaluation of instance segmentation models requires metrics that accurately and consistently reflect segmentation quality. However, the metrics

researcharxiv-cs-cv

Mean Flow Distillation: Robust and Stable Distillation for Flow Matching Models

arXiv:2606.11155v1 Announce Type: new Abstract: Flow Matching models have demonstrated strong performance across a wide range of generative tasks. However, their reliance on ODE-based iterative sampli