Search: “models”

Luke Alonso has uploaded an NVFP4 of GLM 5.2 467GB, would fit on 4x DGX Sparks (~$20k)

Luke Alonso has released a quantized version (NVFP4 format) of GLM 5.2 totaling 467GB in size, which can be deployed across four DGX Spark systems with an estimated hardware cost of approximately $20,

local-aiclem-delangue--x

20 Jun 2026

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Industry

Many people think any given ML project is 99% training. In reality, it’s 50% evaluation, 40% data cleaning, 8% integration, and 2% training.…

DGX agent

Many people think any given ML project is 99% training. In reality, it’s 50% evaluation, 40% data cleaning, 8% integration, and 2% training. The first two set the noise floor for learning. No ML magic

industryclem-delangue--x

20 Jun 2026

Industry

So true

So true Many people think any given ML project is 99% training. In reality, it’s 50% evaluation, 40% data cleaning, 8% integration, and 2% training. The first two set the noise floor for learning. No

industryelon-musk--x

20 Jun 2026

Yeah I agree. The big winner is going to be Ollama: I've offloaded all my supervisory, code review, and ontology learning agents to my $20 a…

Yeah I agree. The big winner is going to be Ollama: I've offloaded all my supervisory, code review, and ontology learning agents to my $20 a month Ollama subscription, because I keep getting weight li

local-aiollama--x

20 Jun 2026

Applications

I suspect that companies underestimate the value of using higher intelligence for tasks where weaker AIs seem to be good enough to hit KPIs …

applicationsethan-mollick--x

I suspect that companies underestimate the value of using higher intelligence for tasks where weaker AIs seem to be good enough to hit KPIs at a lower price. At least build architectures where you can

19 Jun 2026

A Controlled Study of Decoding-Time Truthfulness Methods on Instruction-Tuned LLMs

arXiv:2606.12160v1 Announce Type: new Abstract: In this work, we introduce CHAIR (Classifier of Hallucination As ImproveR), a supervised framework for detecting hallucinations by analyzing internal lo

A Geometric Profile of Semantic Information in Text: Frame-Conditional Uniqueness and a Trade-Off Triangle for Scalar Summaries

arXiv:2606.11222v1 Announce Type: new Abstract: How much meaning does a text carry? Shannon's theory measures uncertainty over symbols and is intentionally indifferent to meaning, while pairwise metri

A Riemannian Approach to Low-Rank Optimal Transport

arXiv:2606.12120v1 Announce Type: new Abstract: Low-rank optimal transport (OT) mitigates the quadratic scaling of classical solvers, yet existing approaches rely heavily on first-order mirror-descent

A Turbo-Inference Strategy for Object Detection and Instance Segmentation

arXiv:2606.12371v1 Announce Type: new Abstract: Object detection and instance segmentation tasks are closely related. Existing top-down instance segmentation methods usually follow a detect-then-segme

local-aiarxiv-cs-cv

A2SG:Adaptive and Asymmetric Surrogate Gradients for Training Deep Spiking Neural Networks

arXiv:2606.11236v1 Announce Type: cross Abstract: Training deep spiking neural networks (SNNs) remains challenging due to sharp loss landscapes and temporal inconsistency caused by surrogate gradients

Adv-TGD: Adversarial Text-Guided Diffusion for Face Recognition Impersonation Attacks

arXiv:2606.11615v1 Announce Type: new Abstract: The widespread adoption of face recognition (FR) technologies raises serious privacy concerns, as facial data can be exploited without consent. To addre

AerialClaw: An Open-Source Framework for LLM-Driven Autonomous Aerial Agents

arXiv:2606.12142v1 Announce Type: cross Abstract: Unmanned aerial vehicles (UAVs) are increasingly used in inspection, search and rescue, environmental monitoring, and emergency response. However, mos

Agents

Agents All the Way Down; A Methodology for Building Custom AI Agents from Substrate to Production

arXiv:2606.11869v1 Announce Type: cross Abstract: Custom AI agents areagents that live inside their own application, talk to their own data and tools, enforce their own security boundaries, and carry

agentsarxiv-cs-ai

Agreement in Representation Space for Open-Ended Self-Consistency

arXiv:2606.12003v1 Announce Type: new Abstract: Self-consistency improves LLM reasoning by sampling multiple outputs and selecting the most consistent answer, but existing formulations largely rely on

AI Researchers Must Help Lead Arms Control to Mitigate Military AI Risks

arXiv:2606.11533v1 Announce Type: cross Abstract: The advancement of AI capabilities compels researchers and the public to be more aware of its potential worldwide impact. A pressing near-term concern

safetyarxiv-cs-ai

Ambient Diffusion Policy: Imitation Learning from Suboptimal Data in Robotics

arXiv:2606.12365v1 Announce Type: cross Abstract: We propose Ambient Diffusion Policy, a simple and principled method for imitation learning from suboptimal data in robotics. High-quality, task-specif

local-aiarxiv-cs-ai

Are LLMs Bad at Moral Reasoning?

arXiv:2606.11635v1 Announce Type: cross Abstract: For highly capable AI systems to operate safely in dynamic, open-ended environments, they must be able to identify, understand, and respond to moral r

Attention by Synchronization in Coupled Oscillator Networks

arXiv:2606.12059v1 Announce Type: new Abstract: We address transformer attention on energy-constrained physical substrates. Softmax attention requires exponentiation and global reduction, operations w

Bridging Day and Night: Unsupervised Cross-Domain Re-Identification with Synergistic Prompt and Prototype Learning

arXiv:2606.12258v1 Announce Type: new Abstract: Cross-domain day-night re-identification (ReID) is fundamentally challenged by the substantial visual appearance discrepancies between daytime and night

Applications

Contactless 3D Human Body Measurement Using Depth Cameras for Smart Health Monitoring

arXiv:2606.11578v1 Announce Type: new Abstract: Contactless body measurement technologies are becoming increasingly significant for smart health monitoring, digital health applications, and remote pat

applicationsarxiv-cs-cv

Context-Driven Incremental Compression for Multi-Turn Dialogue Generation

arXiv:2606.12411v1 Announce Type: new Abstract: Modern conversational agents condition on an ever-growing dialogue history at each turn, incurring redundant attention and encoding costs that grow with

Cross-Domain Multi-Person Human Activity Recognition via Near-Field Wi-Fi Sensing

arXiv:2510.17816v2 Announce Type: replace-cross Abstract: Wi-Fi-based human activity recognition (HAR) provides substantial convenience and has emerged as a thriving research field, yet the coarse spa

DarkVGGT: Seeing Through Darkness Using Thermal Geometry without Daylight Tax

arXiv:2606.11326v1 Announce Type: new Abstract: Recent feed-forward 3D reconstruction methods have demonstrated strong performance and flexibility in efficient end-to-end scene geometry estimation fro

Tutorials

DepthMaster: Unified Monocular Depth Estimation for Perspective and Panoramic Images

arXiv:2606.12368v1 Announce Type: new Abstract: While monocular depth estimation has achieved significant progress, achieving generalized metric depth estimation for both narrow field-of-view (FoV) pe

tutorialsarxiv-cs-cv

Discovery and inference beyond linearity for epidemiological data by integrating Bayesian regression, tree ensembles and Shapley values

arXiv:2505.00571v3 Announce Type: replace-cross Abstract: Machine Learning (ML) is gaining popularity in epidemiology and healthcare studies for hypothesis-free discovery of risk and protective factor

local-aiarxiv-cs-lg

Tools

Don't miss the exact text though: 'We’re changing Fable 5’s safeguards for frontier LLM development to make them visible' - make them visibl…

Don't miss the exact text though: 'We’re changing Fable 5’s safeguards for frontier LLM development to make them visible' - make them visible means they're undoing the truly egregious (dare I say 'una

toolssimon-willison--x

DynaTok: Token-Based 4D Reconstruction from Partial Point Clouds

arXiv:2606.12189v1 Announce Type: new Abstract: We address 4D reconstruction from partial point cloud sequences, where depth-sensor observations are incomplete, unordered, and lack explicit temporal c

Hardware

Echoes of the Prior: A Computational Phenomenology of Forgetting

arXiv:2606.12340v1 Announce Type: new Abstract: Memory is not merely the storage of data; it is the scaffolding of reality. When biological memory fades, the world does not simply turn black; it regre

hardwarearxiv-cs-cv

Energy-Conserved Neural Pipelines: Attenuating Error Propagation in Modular Neural Networks via Physical Conservation Constraints

arXiv:2606.11341v1 Announce Type: new Abstract: Modular neural network pipelines suffer from error compounding: noise at any module boundary propagates and potentially amplifies through subsequent mod

safetyarxiv-cs-lg

ERN-Net : Evolving Reason Node-Net for Document Binarization

arXiv:2606.11710v1 Announce Type: new Abstract: This paper presents ERN-Net, an Evolving Reason Node-Net for efficient document image binarization. ERN-Net enhances degradation-sensitive regions, such

Applications

From Uniform to Learned Graph Priors: Diffusion for Structure Discovery

arXiv:2606.11831v1 Announce Type: cross Abstract: Neural relational inference (NRI) methods discover interaction graphs from trajectories through variational reasoning on discrete potential edges. How

applicationsarxiv-cs-ai

Geometric Metrics and LLMs: What They Measure and When They Work

arXiv:2509.25359v2 Announce Type: replace-cross Abstract: We present a systematic stress-test of geometric metrics for LLM evaluation. Rank-based geometric properties of internal representations have

HAMNO: A Hierarchical Adaptive Multi-scale Neural Operator with Physics-Informed Learning for Dynamical Systems

arXiv:2606.11963v1 Announce Type: new Abstract: Neural operators provide a powerful framework for learning solution mappings of partial differential equations directly in function space. However, many

local-aiarxiv-cs-lg

Hierarchical Probabilistic Conformal Prediction for Distributed Energy Resources Adoption

arXiv:2411.12193v4 Announce Type: replace-cross Abstract: The rapid growth of distributed energy resources (DERs) presents both opportunities and operational challenges for electric grid management. A

Industry

How frontier teams are reinventing AI-native development

Organizations are increasingly focused on AI-native development architectures where frontier agents handle complex tasks including identity, state, policies, evals, and long-running workflows through

industryaws-ml-blog

Tutorials

Information-Theoretic Decomposition for Multimodal Interaction Learning

arXiv:2606.11614v1 Announce Type: cross Abstract: Multimodal learning hinges on capturing redundant, unique, and synergistic information across modalities, which collectively constitute multimodal int

tutorialsarxiv-cs-ai

ISAP-3D: Identity-Slot Aligned Part-Aware 3D Generation

arXiv:2606.12099v1 Announce Type: new Abstract: Part-aware 3D generation aims to synthesize structured objects with semantically meaningful components, yet often suffers from structural ambiguity due

Agents

ISE: An Execution-Grounded Recipe for Multi-Turn OS-Agent Trajectories

arXiv:2606.11520v1 Announce Type: cross Abstract: Training capable OS agents requires data that simultaneously captures structured user intents, multi-turn task delegation, and grounded tool execution

agentsarxiv-cs-ai

Knowledge Manifold: A Riemannian Geometric Framework for Semantic Mapping and Geodesic Analysis of Scientific Literature

arXiv:2606.05907v2 Announce Type: replace-cross Abstract: We present the knowledge manifold: a Riemannian geometric space in which a corpus of documents is arranged according to semantic positional re

LAST: Bridging Vision-Language and Action Manifolds via Gromov-Wasserstein Alignment

arXiv:2606.11221v1 Announce Type: new Abstract: We take a Gromov-Wasserstein perspective on Vision-Language-Action (VLA) learning, where the goal is to make the relational geometry of action represent

Learning from almost nothing: How neural networks survive heavy input corruption

arXiv:2606.11319v1 Announce Type: new Abstract: Learning from imperfect data is a central theme in machine learning, connecting practical questions of robustness to fundamental questions of learnabili

Learning Instance-Adaptive Low-Rank Orthogonal Subspaces for Clothes-Changing Person Re-Identification

arXiv:2606.11661v1 Announce Type: new Abstract: Clothes-changing person re-identification (CC-ReID) aims to recognize individuals despite drastic appearance changes caused by clothing variation. While

LSTM based IoT Device Identification

arXiv:2304.13905v2 Announce Type: replace-cross Abstract: While the use of the Internet of Things is becoming more and more popular, many security vulnerabilities are emerging with the large number of

MA-DLE: Speech-based Automatic Depression Level Estimation via Memory Augmentation

arXiv:2606.11197v1 Announce Type: cross Abstract: Speech-based automatic estimation of depression levels is essential for enabling early detection and timely intervention, particularly in resource-con

Machine-learning-based multipoint optimization of fluidic injection parameters for improving nozzle performance

arXiv:2409.12707v2 Announce Type: replace-cross Abstract: Fluidic injection offers a promising solution to improve the performance of the overexpanded single expansion ramp nozzles (SERNs) during vehi

Mahalanobis-Guided Latent OOD Detection for Hybrid ES-DRL Control in Time-Varying Systems

arXiv:2606.11474v1 Announce Type: new Abstract: In this paper, we study Mahalanobis-guided latent out-of-distribution (OOD) detection for test-time RL controller switching in nonlinear time-varying sy

safetyarxiv-cs-lg

Massive Open-Vocabulary Keyword Spotting

arXiv:2606.11279v1 Announce Type: cross Abstract: Automatic speech recognition systems have been shown to under-perform when it comes to transcribing words rarely seen in the training data, namely spe

Mirror Descent Beyond Euclidean Stability: An Exponential Separation in Initialization Sensitivity

arXiv:2606.11431v1 Announce Type: new Abstract: Mirror Descent (MD) extends Gradient Descent (GD) beyond Euclidean geometry and has recently reappeared as a lens for KL-regularized policy optimization

safetyarxiv-cs-lg