All entries

Kilobyte Models: Neural Networks as a Seed and a Quantized Latent

arXiv:2608.00860v1 Announce Type: new Abstract: The cost of storing and transmitting a trained neural network scales with its parameter count, a bottleneck for over-the-air updates, on-device librarie

Kimi K3 full model running on 16x GB10 cluster at 20+tps

DGX agent

Kimi K3 full model running on 16x GB10 cluster at 20+tps average (llama-benchy coherent corpus) 38tps peak, 750tps prefill. This is the first run of full k3 with dspark on my cluster. I will be doing

model-releasesr-localllama

4 Aug 2026

KING: Embodiment-Aware Kinematic Graph Neural Network for Unified Motion Representation of Legged and Wheeled Robots

arXiv:2608.01015v1 Announce Type: new Abstract: Kinematic models provide reliable motion constraints for odometry estimation in featureless environments, where exteroceptive sensing degrades and IMU i

researcharxiv-cs-ro

LAB-Tab: LLM-Augmented Bayesian Network Adaptation for Few-Shot Tabular Generation

arXiv:2608.01879v1 Announce Type: new Abstract: Tabular data generation supports analysis and decision-making when target-domain data are scarce, yet collecting complete target samples is often costly

LakeMLB: Data Lake Machine Learning Benchmark

arXiv:2602.10441v2 Announce Type: replace Abstract: Data lakes have become a fundamental platform for large-scale machine learning by enabling flexible management of heterogeneous data. Despite their

LangFIR: Discovering Sparse Language-Specific Features from Monolingual Data for Language Steering

model-releasesarxiv-cs-cl

arXiv:2604.03532v2 Announce Type: replace Abstract: Large language models (LLMs) show strong multilingual capabilities, yet reliably controlling the language of their outputs remains difficult. Repres

Local Ai

Language Equality has a Price: A Systematic Investigation of Multi-turn LLM Performance for EU-24+

arXiv:2608.01395v1 Announce Type: new Abstract: We evaluate large language models (LLMs) as language agents playing goal-directed dialogue games in self-play across 30 languages: the 24 official EU la

local-aiarxiv-cs-cl

Large Causal Models for Temporal Causal Discovery

arXiv:2602.18662v2 Announce Type: replace Abstract: Causal discovery for both cross-sectional and temporal data has traditionally followed a dataset-specific paradigm, where a new model is fitted for

Latency-Tolerant Cloud-Edge Collaborative Vision-Language-Action Models via Emergent Representational Specialization

model-releasesarxiv-cs-ro

arXiv:2608.00569v1 Announce Type: new Abstract: Deploying billion-parameter Vision-Language-Action (VLA) policies on mobile robots creates a systems conflict: semantic reasoning benefits from cloud GP

Latent-Centroid Steering: Single-Pass Classifier-Free Guidance for Command-Aligned Autonomous Driving

arXiv:2608.00237v1 Announce Type: new Abstract: Vision-language models (VLMs) have recently emerged as a promising paradigm for end-to-end autonomous driving, enabling agents to map multimodal inputs

Latent-Regime Bias Auditing for Volatility Forecasting

arXiv:2608.01599v1 Announce Type: new Abstract: Volatility forecasts are commonly evaluated with aggregate accuracy metrics such as RMSE and MAE, but these metrics can hide conditional failures that m

Latent Softmax for Data-Efficient Phoneme-Based Multilingual ASR Across Tonal and Non-Tonal Languages

arXiv:2608.01281v1 Announce Type: cross Abstract: Phoneme-based multilingual automatic speech recognition (ASR) can share acoustic evidence across languages more directly than language-specific subwor

LAYA: Layer-wise Attention Aggregation for Interpretable Depth-Aware Neural Networks

arXiv:2511.12723v2 Announce Type: replace Abstract: Deep neural networks typically rely on the representation produced by their final hidden layer to make predictions, implicitly assuming that this si

Leak It: A Probabilistic Approach to Training-Data Extraction from Black-Box Language Models

arXiv:2608.00144v1 Announce Type: cross Abstract: Membership inference (MIA) on language models is usually summarised by an aggregate ROC-AUC, but such evaluations are confounded: model-free blind bas

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation

arXiv:2608.01804v1 Announce Type: new Abstract: Post-training large language models (LLMs) via reinforcement learning (RL) has significantly advanced code generation capabilities. To bypass the heavy

LeapTalk: Breaking the Latency-Quality Trade-off in Talking Head Generation

arXiv:2608.00079v1 Announce Type: new Abstract: Long-form and real-time talking-head generation remains challenging due to a latency-quality trade-off: inefficient multi-step diffusion prohibits strea

Learned Digital Over-the-Air Computing for Federated Edge Learning

arXiv:2509.16577v2 Announce Type: replace Abstract: Over-the-air (OTA) aggregation enables federated edge learning (FEEL) by exploiting the superposition property of the wireless channel to merge comm

Learning-Based Collaborative MEC for LLM Inference with Soft-Deadline Awareness via Transformer-Enhanced PPO

arXiv:2608.02031v1 Announce Type: cross Abstract: This paper investigates collaborative mobile edge computing (MEC) servers for large language model (LLM) inference under soft deadline constraints. In

Learning-Based Motion Planning for Dynamic Environments: From Foundational Algorithms to Emerging Paradigms

arXiv:2608.00625v1 Announce Type: new Abstract: Motion planning in dynamic environments is a fundamental problem in robotics, aiming to generate safe and efficient paths, trajectories, or control acti

safetyarxiv-cs-ro

Learning-Based Stochastic Optimal Control with Infinite-Horizon Probabilistic Constraints

arXiv:2608.01151v1 Announce Type: cross Abstract: In this paper, we consider stochastic optimal control problems with infinite-horizon joint chance constraints. By means of an appropriate state augmen

Learning Compositional Meta-Routing for Agentic Workflows: An Executable Benchmark

arXiv:2608.00106v1 Announce Type: new Abstract: Agentic systems must decide not only what answer to produce, but which reasoning and execution operations should precede it. A controller may answer dir

Learning How Much, Not Just What: Cross-Patient Burden Order for CT Vision-Language Pretraining

arXiv:2608.00231v1 Announce Type: new Abstract: Volumetric CT vision-language pretraining learns 3D representations from scan-report pairs, but global and anatomy-aware objectives supervise only corre

safetyarxiv-cs-cv

Learning Not to Optimize: Physics-Informed Action-Space Reshaping for Intent-Based Network Control

arXiv:2608.00908v1 Announce Type: cross Abstract: Modern network policy control maps intent to sequential placement-control decisions. Bellman-style policy optimization primarily asks which action to

Learning Panorama-Aware VLA for Mobile Manipulation with Whole-Body Teleoperation

arXiv:2608.02257v1 Announce Type: new Abstract: Mobile manipulation is a key capability for embodied intelligence, enabling robots to accomplish complex multi-stage tasks in open-world environments. H

safetyarxiv-cs-ro

Tutorials

Learning Smooth SE(3) Trajectories under Left-Invariant Riemannian Metrics

arXiv:2608.01562v1 Announce Type: new Abstract: Optimal trajectory generation for rigid-body motions on Lie groups can be formulated as a variational problem that minimizes energy functionals defined

tutorialsarxiv-cs-ro

Applications

Learning the Pareto Frontier of Predictive Models under Distribution Shift

arXiv:2608.00632v1 Announce Type: new Abstract: Modern machine learning pipelines increasingly rely on reusing pretrained and foundation models across downstream tasks. These pretrained models can dif

applicationsarxiv-cs-lg

Learning to Persuade Privately Informed Receivers

arXiv:2607.28342v1 Announce Type: cross Abstract: Bayesian persuasion studies how an informed sender can influence the behavior of a receiver through strategic information disclosure. Standard models

Applications

Learning to Predict Contact Force Distributions from Vision Leveraging Object Geometry Priors

arXiv:2608.00464v1 Announce Type: new Abstract: Based on vision and prior experience, humans can make rough physical predictions and adjust their manipulation strategies. This paper aims to endow robo

applicationsarxiv-cs-ro

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation

arXiv:2608.00279v1 Announce Type: cross Abstract: Recent radiology-adapted vision-language models have achieved strong performance on standard report generation benchmarks, yet their robustness and ge

safetyarxiv-cs-cv

Learning to Tessellate: Point Cloud Generation via Recursive Spectral Partitioning

arXiv:2608.02432v1 Announce Type: new Abstract: Autoregressive models have emerged as an effective paradigm for point cloud generation. However, most existing approaches rely on heuristic tokenization

Learning What to Remember: Test-Time Training via Context Distillation

model-releasesarxiv-cs-cl

arXiv:2608.01672v1 Announce Type: new Abstract: Effective long-context modeling is not merely about retaining more of the past, but about preserving the information that may prove relevant later. Test

Tutorials

Learning Where to Look and How to Judge: Resolution-agnostic Image Quality Assessment with Quality-aware Saliency

arXiv:2608.01730v1 Announce Type: new Abstract: No-reference image quality assessment (NR IQA) has recently benefited from deep and multimodal models, yet many SOTA systems still violate at least one

tutorialsarxiv-cs-cv

Length Penalties Make Chain-of-Thought Less Monitorable

arXiv:2607.09786v3 Announce Type: replace-cross Abstract: To curb overthinking and reduce inference costs, researchers now train reasoning models with penalties on chain of thought length. We find tha

Less is More: Compact-Token Masked Feature Prediction for Skeleton Representation Learning

arXiv:2603.10648v3 Announce Type: replace Abstract: Current skeleton representation learning paradigms face distinct limitations: Contrastive Learning (CL) often overlooks fine-grained motion details,

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging

arXiv:2608.01094v1 Announce Type: new Abstract: Federated learning enables medical-imaging models to be trained across hospitals, and privacy law, most explicitly the GDPR ``right to be forgotten'', t

Leveraging Synthetic Data for Question Answering with Multilingual LLMs in the Agricultural Domain

arXiv:2507.16974v3 Announce Type: replace Abstract: Enabling farmers to access accurate agriculture-related information in their native languages in a timely manner is crucial for the success of the a

LexisNexis opens customer innovation lab driven by AI to change the future of legal work

model-releasessiliconangle

LexisNexis Legal & Professional, a division of RELX plc, today announced the opening of its Customer Innovation Lab in New York City, which will deliver a new model for how legal artificial intelligen

LFM2.5-2.6B is out

model-releasesr-localllama

Released today, with emphasis on agentic capabilities. I really like their models for simple, high volume tasks ('summarize these gazillion documents') and their 8b-a1b was my go-to for certain tasks

Tutorials

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems

arXiv:2608.01582v1 Announce Type: cross Abstract: Symmetry is central to modern machine learning and physics: invariances and equivariances improve sample efficiency, robustness, and out-of-distributi

tutorialsarxiv-cs-lg

Light Forcing: Accelerating Autoregressive Video Diffusion via Sparse Attention

arXiv:2602.04789v4 Announce Type: replace Abstract: Advanced autoregressive (AR) video generation models have improved visual fidelity and interactivity, but the quadratic complexity of attention rema

Linear Multi-Timescale Retention as a Memory-Efficient Vision-Language Bridge

arXiv:2608.01614v1 Announce Type: new Abstract: Vision-Language Models (VLMs) face a critical computational bottleneck when processing high-resolution imagery due to the O(N^2) memory complexity of So

Linguistic Context Recodes Visual Representations in Vision-Language Models

arXiv:2608.00035v1 Announce Type: cross Abstract: Goal-directed visual processing is a hallmark of human visual intelligence, resulting in representations that support downstream tasks such as categor

Live and ready to build. Thanks for having us! @OpenRouter. Open weights dropping soon.⚡️

Live and ready to build. Thanks for having us! @OpenRouter. Open weights dropping soon.⚡️ Qwen3.8 Max by @Alibaba_Qwen is live on OpenRouter. The new flagship has 2.4T parameters (95B active) and is b

model-releasesqwen--x

Applications

LiveLight: Real-time Streaming Video Relighting with Interactive Control

arXiv:2608.01771v1 Announce Type: new Abstract: We present LiveLight, the first diffusion-based framework for real-time streaming video relighting with interactive 3D lighting control. Achieving this

applicationsarxiv-cs-cv

LiveMem: Maintaining Memory State Continuity in Long-Running LLM Inference

model-releasesarxiv-cs-cl

arXiv:2608.02515v1 Announce Type: new Abstract: Long-running assistants and agents consume interaction streams that eventually outgrow the context. Existing context retention, summarization, and retri

Llama.cpp PR 8% speed boost

model-releasesr-localllama

Llama.cpp currently uses cpu based sampling for user with mtp enabled. The PR moves sampling to the gpu, which on a 5090 boasts an 8% increase in tok/s for qwen3.6:35b. I tested it on my P40 and obser

llm-anthropic 0.26

model-releasessimon-willison

Release: llm-anthropic 0.26 Includes new features enabled by LLM 0.32: New models: claude-fable-5, claude-sonnet-5, and claude-opus-5. #75, #76 Added server-side tools for WebSearch, WebFetch, CodeExe

LLM generation novelty through the lens of semantic similarity

arXiv:2510.27313v3 Announce Type: replace-cross Abstract: Generation novelty is a key indicator of an LLM's ability to generalize, yet measuring it against full pretraining corpora is computationally