applications

The Mechanistic Emergence of Symbol Grounding in Language Models

arXiv:2510.13796v3 Announce Type: replace Abstract: Symbol grounding (Harnad, 1990) describes how symbols such as words acquire their meanings by connecting to real-world sensorimotor experiences. Rec

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There are lots of issues with GenAI implementations but surveys seem to suggest broad positive ROI. Data & stories about AI failures are rea…

DGX agent

There are lots of issues with GenAI implementations but surveys seem to suggest broad positive ROI. Data & stories about AI failures are real and useful but need to be clearer about what they are meas

applicationsethan-mollick--x

4 Jun 2026

Towards Evaluating the Robustness of Visual State Space Models

arXiv:2406.09407v3 Announce Type: replace Abstract: Vision State Space Models (VSSMs), a novel architecture that combines the strengths of recurrent neural networks and latent variable models, have de

Trusted healthcare AI hinges on data foundations, not models alone

Healthcare AI is moving out of the pilot phase and into production environments, where the gap between a compelling demo and a clinically trustworthy output has never been more consequential. As AI ag

TSMC struggles to keep up with AI demand: ‘We can only support so much’

Taiwan Semiconductor Manufacturing Co. - the world's biggest semiconductor-maker - is struggling to meet demands from American customers even with its factory buildout in the US, according to reports

applicationsthe-verge-ai

Ultra-Fast Neural Video Compression

arXiv:2606.04410v1 Announce Type: new Abstract: While neural video codecs (NVCs) have demonstrated superior compression ratio, their prohibitive computational complexity remains a critical barrier to

UltraEP: Unleash MoE Training and Inference on Rack-Scale Nodes with Near-Optimal Load Balancing

arXiv:2606.04101v1 Announce Type: cross Abstract: Large-scale expert parallelism (EP) is becoming pivotal for training and serving frontier MoE models, but it also amplifies device-level expert load i

Uncertainty-Aware End-to-End Co-Design of Neural Network Processors: From Training and Mapping to Fabrication

arXiv:2606.04850v1 Announce Type: cross Abstract: Designing a neural network processor is an end-to-end co-design problem: network architecture and training budget determine the inference workload; ha

Updates to Legal Terms

Vercel announced updates to its legal terms and conditions in June 2026, likely covering changes to their service agreements, privacy policies, or terms of service. This update would apply to users of

applicationsvercel-blog

Using Text-Based Causal Inference to Disentangle Factors Influencing Online Review Ratings

arXiv:2606.04286v1 Announce Type: new Abstract: Online reviews provide valuable insights into the perceived quality of facets of a product or service. While aspect-based sentiment analysis has focused

Variance Reduction for Heavy-Tailed Monetization Metrics in Ranking Experiments via Post-Stratification

arXiv:2606.04110v1 Announce Type: new Abstract: Online evaluation of ranking and retrieval systems often relies on downstream monetization metrics such as app revenue or creator earnings. These metric

Vibe coding moves from weekend hobby to enterprise change management mandate

Artificial intelligence is rewriting how software gets built inside large enterprises — not just at the tooling level, but at the level of culture, governance and organizational change. The rise of vi

What to expect during Broadcom’s ‘Modern Private Cloud’ event: Join theCUBE June 9

Enterprise AI is forcing companies to rethink where their most sensitive workloads should live, and private cloud is moving deeper into the center of that decision. For many organizations, the cloud c

Z-FLoc: Zero-Shot Floorplan Localization via Geometric Primitives

arXiv:2606.04788v1 Announce Type: new Abstract: Visual localization -- estimating a camera pose within a pre-existing map -- is a fundamental problem in computer vision. Floorplans are an attractive m

A Nonmonotone Gradient-Based Algorithm for Symmetric Nonnegative Matrix Factorization and Graph Clustering

arXiv:2606.02887v1 Announce Type: new Abstract: Symmetric nonnegative matrix factorization (Symmetric NMF) approximates a matrix as WW^T with nonnegative rectangular factor W. It has broad application

A Robust Optimization Approach to Sparse Principal Component Analysis

arXiv:2606.03553v1 Announce Type: cross Abstract: While principal component analysis (PCA) is a fundamental tool for dimensionality reduction, its dense representations make it ill-suited for high-dim

AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE

arXiv:2606.03631v1 Announce Type: cross Abstract: Multivariate time series classification (MTSC) is pivotal in high-stakes domains, such as clinical diagnosis and industrial fault detection, where saf

Apoha, which is developing a 'Liquid State Intelligence' data layer to measure molecules in the real world, emerges from stealth with $36M led by Singular (Mike Butcher/Pathfounders)

Mike Butcher / Pathfounders: Apoha, which is developing a “Liquid State Intelligence” data layer to measure molecules in the real world, emerges from stealth with 36M led by Singular — Apoha has emerg

applicationstechmeme

Assessing Pause Thresholds for empirical Translation Process Research

arXiv:2604.01410v2 Announce Type: replace Abstract: Text production (and translations) proceeds in the form of stretches of typing, interrupted by keystroke pauses. It is often assumed that fast typin

Attend to Anything: Foundation Model for Unified Human Attention Modeling

arXiv:2606.03540v1 Announce Type: new Abstract: Existing human attention (saliency) modeling methods persist as highly fragmented across modalities, scenes, and task formulations. Consequently, even w

AugMask: Training Diffusion Models on Incomplete Tabular Data via Stochastic Augmentation and Masking

arXiv:2606.03347v1 Announce Type: cross Abstract: Score-based diffusion models have emerged as prominent deep generative models; however, their application to tabular data remains challenging because

Benchmarking Speech-to-Speech Translation Models

arXiv:2606.03241v1 Announce Type: new Abstract: Speech-to-speech translation (S2ST) has advanced rapidly, but offline evaluation lacks a unified protocol: studies report non-overlapping metric subsets

Binary Road Surface Classification Using Machine Learning on Production Vehicle Signals During Cruising

arXiv:2606.02762v1 Announce Type: new Abstract: Knowledge of real-time road slipperiness, or even better, a refined estimate of peak grip potential, is a critical input for vehicle warning and interve

Building Trust in Black-box Optimization: A Comprehensive Framework for Explainability

arXiv:2410.14573v2 Announce Type: replace-cross Abstract: Optimizing costly black-box functions within a constrained evaluation budget presents significant challenges in many real-world applications.

Calibrating Urban Traffic Simulation from Sparse Road Observations via Genetic Optimization

arXiv:2606.03823v1 Announce Type: new Abstract: Urban traffic simulation is a critical tool for infrastructure planning, including the placement of electric vehicle charging stations. However, realist

Conditional Hypothesis Generation for LLM-Based Text Analysis with Researcher-Specified Covariates

arXiv:2606.03029v1 Announce Type: cross Abstract: A core goal of computational social science is to discover interpretable differences in how language varies across outcomes of interest, such as polit

Core-based Hierarchies for Efficient GraphRAG

arXiv:2603.05207v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge. However, existing vector-based method

Day 2 at #MSBuild is about what it takes to move beyond generic foundation models. Think customization, inference performance, and getting p…

applicationsfireworks-ai--x

Day 2 at #MSBuild is about what it takes to move beyond generic foundation models. Think customization, inference performance, and getting production-ready AI deployed at scale. @chahvivi will lead a

Discovery to Execution: Scaling Agents with Toolboxes and Routines in Microsoft Foundry

applicationsmicrosoft-foundry

Tooling doesn’t break at a small scale—it breaks when teams move to production. AI adoption accelerates, so does the number of tools available to them. Discovering, managing and securing the right too

Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selection

arXiv:2606.03251v1 Announce Type: new Abstract: In nature, events that affect some individuals or groups but not others constitute an implicit intervention and are known as natural experiments. For ex

Does Language Shift Break Medical Vision-Language Models? Indonesian Radiology Visual Question Answering Case Study

arXiv:2606.03693v1 Announce Type: new Abstract: Medical Vision-Language Models (VLMs) are typically evaluated on English radiology visual question answering benchmarks, leaving their robustness under

E2LLM: Towards Efficient LLM Serving in Heterogeneous Edge/Fog Environments

arXiv:2606.03770v1 Announce Type: cross Abstract: Large Language Models (LLMs) have become integral to modern applications, yet their deployment remains challenging. Beyond executing the models themse

Exposure management evolves from vulnerability scanning to full-stack AI defense

AI has fundamentally broken the economics of cybersecurity and exposure management, compressing exploit windows from days to minutes and forcing organizations to rethink how they inventory, prioritize

Fine-tuning to production inference is the gap where teams get stuck. At #MSBuild today, our own Rob Ferguson, @danielhanchen (@UnslothAI) a…

applicationsfireworks-ai--x

Fine-tuning to production inference is the gap where teams get stuck. At #MSBuild today, our own Rob Ferguson, @danielhanchen (@UnslothAI) and @marksaroufim (@coreautoai) discuss: model customization

Harvey just published a study showing a hybrid setup, open source GLM 5.1 as primary worker, routing to Opus 4.7 only when needed beats pure…

applicationsclem-delangue--x

Harvey just published a study showing a hybrid setup, open source GLM 5.1 as primary worker, routing to Opus 4.7 only when needed beats pure Opus 4.7 on quality and costs less. This is the multi-model

Hey, its our paper!

applicationsethan-mollick--x

Hey, its our paper! One of the most-viewed PNAS articles in the last week is “Persuading large language models to comply with objectionable requests.” Explore the article here: https://ow.ly/wOxl50Z6f

Hierarchical Federated Learning with Dynamic Clustering and Adaptive Regularization for Robust Infrastructure Inspection

arXiv:2606.03084v1 Announce Type: new Abstract: The deployment of data-driven computer vision models for structural health monitoring (SHM) is heavily constrained by the data silo dilemma due to strin

High-Dimensional Latents Should Be Diagnosed Through Phase Structure

arXiv:2606.02600v1 Announce Type: cross Abstract: We study autoencoder and variational-autoencoder latent spaces through the lens of spin-glass theory. The paper has two components. First, we formaliz

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification

arXiv:2606.02767v1 Announce Type: cross Abstract: Kalman filtering performance is highly sensitive to model mismatch and noise covariance tuning. Learning-based approaches address these limitations bu

Hybrid Autoregressive-Diffusion Model for Real-Time Sign Language Production

arXiv:2507.09105v4 Announce Type: replace Abstract: Earlier Sign Language Production (SLP) models typically relied on autoregressive decoding, which naturally preserves temporal causality but suffers

If you want to download or modify it: https://github.com/emollick/snake-awakening

applicationsethan-mollick--x

This appears to be a GitHub repository shared by Ethan Mollick containing code or a project called 'snake-awakening' that is available for public download and modification. Based on the repository str

KnapSpec: Self-Speculative Decoding via Adaptive Layer Selection as a Knapsack Problem

arXiv:2602.20217v2 Announce Type: replace-cross Abstract: Self-speculative decoding (SSD) accelerates LLM inference by skipping layers to create an efficient draft model, yet existing methods often re

Layup Parts, which aims to digitize the composite manufacturing supply chain for aerospace, defense, and motorsports, raised a $42M Series A led by Marlinspike (Sean O'Kane/TechCrunch)

Sean O'Kane / TechCrunch: Layup Parts, which aims to digitize the composite manufacturing supply chain for aerospace, defense, and motorsports, raised a $42M Series A led by Marlinspike — Before Zack

applicationstechmeme

Making Embodied AI Reliable: A Community Agenda from Testing to Formal Verification

arXiv:2606.03593v1 Announce Type: cross Abstract: Embodied AI systems are increasingly deployed in open-world environments, yet ensuring their reliability remains a fundamental challenge. Drawing on d

applicationsarxiv-cs-ro

Mastercard says it plans to offer on-chain settlement using several regulated USD stablecoins, initially supporting USDC, PYUSD, USDG, USDP, RLUSD, and SoFiUSD (Helene Braun/CoinDesk)

Helene Braun / CoinDesk: Mastercard says it plans to offer on-chain settlement using several regulated USD stablecoins, initially supporting USDC, PYUSD, USDG, USDP, RLUSD, and SoFiUSD — The company p

applicationstechmeme

Merit or networks? What decides where research is published

arXiv:2606.03763v1 Announce Type: cross Abstract: Does scientific publishing reward the quality of ideas or the advantage of connections? The question is universal to prestige-driven science, yet it h

Mitigating Spurious Correlations with Memorization-Guided Dataset De-Biasing

arXiv:2606.02830v1 Announce Type: new Abstract: Real-world datasets often contain spurious correlations that are not causally related to the target label. When such correlations dominate the majority

ModuLoop : Low-Level Code Generation using Modular Synthesizer and Closed-Loop Debugger for Robotic Control

arXiv:2606.03047v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated impressive performance across various domains, including code generation and problem solving. However, th

applicationsarxiv-cs-ro