All entries

Bloomberg: Sources: Sea's Shopee is cutting hundreds of developer jobs globally; the reductions started this week and amount to about 8% of Shopee's developer workforce — Sea Ltd.'s Shopee is cutting

industrytechmeme

Industry

Sources: the ECB temporarily barred Revolut from releasing new products in the European Economic Area last summer until the company had rectified 'deficiencies' (Financial Times)

DGX agent

Financial Times: Sources: the ECB temporarily barred Revolut from releasing new products in the European Economic Area last summer until the company had rectified “deficiencies” — Europe's most valuab

industrytechmeme

10 Jun 2026

Sources: Trump administration officials have told CAISI to halt publication of its model assessments while an EO President Trump signed last week is implemented (Amrith Ramkumar/Wall Street Journal)

Amrith Ramkumar / Wall Street Journal: Sources: Trump administration officials have told CAISI to halt publication of its model assessments while an EO President Trump signed last week is implemented

safetytechmeme

Industry

South Korean regulators fined Coupang $409M for a data leak in 2025 that exposed ~33.7M accounts and escalated into a diplomatic conflict with the US (Shinhye Kang/Bloomberg)

Shinhye Kang / Bloomberg: South Korean regulators fined Coupang $409M for a data leak in 2025 that exposed ~33.7M accounts and escalated into a diplomatic conflict with the US — A South Korean regulat

industrytechmeme

SPACE: Source-free Proxy Anchor Concept Erasure for MLLMs

arXiv:2606.09868v1 Announce Type: cross Abstract: As Multimodal Large Language Models (MLLMs) face growing privacy risks and regulatory constraints, machine unlearning (MU) has emerged as a crucial so

SPACR: Single-Pass Adaptive Training of Uncertainty-Aware Conformal Regressors

arXiv:2606.10734v1 Announce Type: new Abstract: Conformal Prediction (CP) provides robust uncertainty guarantees for predictive models, but is typically applied post hoc, which misaligns model trainin

SPARX: Secure and Privacy-Aware Approximate CNN Acceleration with Edge RISC-V SoC

arXiv:2606.09946v1 Announce Type: cross Abstract: Edge-AI systems increasingly require real-time CNN inference under strict energy, performance, security, and privacy constraints. Approximate computin

local-aiarxiv-cs-cv

Spatial-Omni: Spatial Audio Understanding Integration in Multimodal LLMs via FOA Encoding

arXiv:2606.10738v1 Announce Type: cross Abstract: Recent multimodal large language models mainly process audio as monaural signals, thereby discarding the spatial cues contained in spatial audio for s

local-aiarxiv-cs-ai

Tutorials

Spatially Selective Self-Training for Unsupervised Building Change Detection

arXiv:2606.10775v1 Announce Type: new Abstract: Unsupervised building change detection aims to learn building-change masks from unlabeled bi-temporal remote sensing images. Existing label-free methods

tutorialsarxiv-cs-cv

Spatio-Temporal Attention Graph Neural Network: Explaining Causalities With Attention

arXiv:2603.10676v2 Announce Type: replace Abstract: Industrial Control Systems (ICS) underpin critical infrastructure and face growing cyber-physical threats due to the convergence of operational tech

Tutorials

Spatiotemporal Graph Transformer for 3D Neighborhood Interaction and Quality Prediction in Metal Additive Manufacturing

arXiv:2606.10227v1 Announce Type: new Abstract: Metal additive manufacturing enables the fabrication of complex parts, but achieving consistent build quality remains challenging due to interactions in

tutorialsarxiv-cs-lg

Spatiotemporal Seismic Hazard Assessment Using VQ-VAE and Seismic Statistical Features

arXiv:2606.10069v1 Announce Type: new Abstract: In this paper we build upon a previous study in which we demonstrated, using XGBoost and earthquake catalogue data from Japan and Chile, that a set of 6

local-aiarxiv-cs-lg

SPDM: Geometry-Modulated State Space Modeling with Manifold Constraints for Time Series Forecasting

model-releasesarxiv-cs-lg

arXiv:2606.09917v1 Announce Type: new Abstract: Multivariate time series forecasting requires capturing the continuously evolving correlation structure among interacting variables. Existing state-spac

Speaker Group Encoding in Self-supervised Speech Recognition Models

arXiv:2606.10654v1 Announce Type: new Abstract: We investigate what self-supervised speech recognition models (S3Ms) learn about speaker groups (SGs). We examine several states of S3Ms: pretrained, fi

safetyarxiv-cs-cl

Speech Meets ELF: Audio Conditional Continuous-Target Diffusion for Speech Recognition and Translation

arXiv:2606.10368v1 Announce Type: cross Abstract: Speech-to-text (S2T) systems for recognition (ASR) and translation (S2TT) typically generate discrete text tokens. In contrast, continuous-target lang

researcharxiv-cs-ai

SpeechJBB: Probing Safety Alignment and Comprehension in Large Audio Language Models under Code-Switched Speech

arXiv:2606.06037v2 Announce Type: cross Abstract: Large audio language models (LALMs) are increasingly deployed in real-world applications, yet their safety alignment is still primarily evaluated on m

safetyarxiv-cs-cl

Hardware

SpenseGPT: Practical One-shot Pruning Enabling Sparse and Dense GEMMs for LLM Inference

arXiv:2606.10445v1 Announce Type: cross Abstract: Semi-structured 2:4 sparsity is widely supported by modern accelerators, providing up to a 2x theoretical speedup. However, its strict 50% sparsity co

hardwarearxiv-cs-cl

Spiking Neural Network inference on FPGAs with hls4ml

arXiv:2606.10008v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) provide a naturally temporal machine-learning framework. Their neurons maintain an internal state and propagate informa

SpineReport: Automated 3D Quantification and Reporting of Lumbar Spine Degeneration on MRI

model-releasesarxiv-cs-cv

arXiv:2606.10021v1 Announce Type: new Abstract: Lumbar spine conditions are a leading cause of disability worldwide, yet reliable quantification of degeneration from MRI remains challenging. In clinic

SSR-Merge: Subspace Signal Routing for Training-Free LoRA Merging in Diffusion Models

model-releasesarxiv-cs-cv

arXiv:2606.10617v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) merging can efficiently combine diverse generative capabilities from multiple trained LoRAs for a diffusion model. However, e

STAGE-Claw: Automated State-based Agent Benchmarking for Realistic Scenarios

arXiv:2606.10394v1 Announce Type: new Abstract: Large language models are increasingly used to power personal agents for everyday applications, but evaluating these agents remains a challenge. Existin

Standard Language Ideology in AI-Generated Language

arXiv:2406.08726v3 Announce Type: replace Abstract: Large language models (LLMs) generate text that reinforces standard language ideology: a bias towards certain language varieties that are granted mo

safetyarxiv-cs-cl

STEDiff: Strengthening Text Embedding for Text-to-Image Alignment in Diffusion Model

arXiv:2606.10653v1 Announce Type: new Abstract: Although pretrained text-to-image (T2I) generation models can produce high-quality images, they often fail to faithfully reflect the semantic intent of

safetyarxiv-cs-cv

Stochastic weather generators for high-frequency wind vector time series

arXiv:2606.09941v1 Announce Type: cross Abstract: Surface winds can vary substantially from one minute to the next, so there is scope for studying its variation on this fine time scale. Restricting to

Stop Early, Spend Less: Hidden-State Probes as a Practical Recipe for Streaming Moderation of LLM Outputs

arXiv:2606.10487v1 Announce Type: cross Abstract: Deploying large language models in user-facing systems requires efficient output safety filtering. Existing approaches typically rely on a separate mo

Agents

Stop hand-tuning kernels: How Neuron Agentic Development accelerates AWS Trainium optimizations

Today, we’re announcing the Neuron Agentic Development capabilities: a collection of AI agents and skills that make this possible for developers building on AWS Trainium and AWS Inferentia. In this po

agentsaws-ml-blog

STORM: Stepwise Token Optimization with Reward-Guided Beam Search

arXiv:2606.10621v1 Announce Type: cross Abstract: Modern retrieval increasingly relies on dense and learned-sparse neural models that are effective but require encoding the entire corpus into a specia

researcharxiv-cs-ai

Streaming Knowledge Compilation: Proactive Materiality-Scored Pinning for Time-Evolving LLM Wikis

model-releasesarxiv-cs-cl

arXiv:2606.09877v1 Announce Type: cross Abstract: LLM wiki systems compile knowledge into pre-filled KV caches for efficient inference, but assume a static corpus -- an assumption that fails whenever

Structure from Reasoning, Numbers from Search: On-Premise Open LLMs as Structural Priors for Coupled MIMO Controller Tuning

arXiv:2606.11015v1 Announce Type: new Abstract: Tuning controllers for strongly coupled multi-input multi-output (MIMO) industrial processes is hard: decentralized classical auto-tuning ignores loop i

Structure-Preserving Learning Improves Geometry Generalization in Neural PDEs

arXiv:2602.02788v2 Announce Type: replace-cross Abstract: We aim to develop physics foundation models for science and engineering that provide real-time solutions to Partial Differential Equations (PD

Structured Adaptive Tensor Prediction for Streaming Data

arXiv:2606.10085v1 Announce Type: new Abstract: Matrix-valued time series arise in a wide range of applications, such as spatio-temporal data from medical imaging and geophysics. Existing methods are

subtle shift: some folks seemed to have shifted from expecting truly exponential progress to being happy they can find measurable progress a…

subtle shift: some folks seemed to have shifted from expecting truly exponential progress to being happy they can find measurable progress at all. and another, even larger group has grown concerned ab

safetygary-marcus--x

Superficial Beliefs in LLM Decision-Making

arXiv:2606.11016v1 Announce Type: new Abstract: We ask whether large language models (LLMs) merely imitate rationales when choosing between two options, or whether their choices reflect a systematic u

researcharxiv-cs-ai

Supervised Fine-tuning with Synthetic Rationale Data Hurts Real-World Disease Prediction

arXiv:2606.10279v1 Announce Type: new Abstract: Supervised fine-tuning with synthetic rationale data is widely assumed to improve language model performance on clinical prediction tasks by teaching mo

applicationsarxiv-cs-ai

Support sufficiency as action-sufficient compression: a single-cycle rate-regret formulation

arXiv:2606.09858v1 Announce Type: cross Abstract: Robust decision-making requires compression. A system that forms a rich support state cannot usually preserve its full structure at the point of actio

Industry

Survey: 53% of Americans fear AI could put someone in their household out of work; Democrats are more likely than Republicans to worry about AI's impact on jobs (Reuters)

Reuters: Survey: 53% of Americans fear AI could put someone in their household out of work; Democrats are more likely than Republicans to worry about AI's impact on jobs — Half of Americans fear that

industrytechmeme

'Switch to a cheaper model to save money' is a problem because cheaper models are worse (maybe they are good enough for a particular purpose…

applicationsethan-mollick--x

'Switch to a cheaper model to save money' is a problem because cheaper models are worse (maybe they are good enough for a particular purpose, but still worse). More often a better approach is hierarch

Swivuriso: The South African Next Voices Multilingual Speech Dataset

arXiv:2512.02201v3 Announce Type: replace Abstract: This paper introduces Swivuriso, a 3000-hour multilingual speech dataset developed as part of the African Next Voices project, to support the develo

applicationsarxiv-cs-cl

SynIB: Informational Bottleneck for Maximizing Synergy in Multimodal Learning

arXiv:2606.09853v1 Announce Type: new Abstract: A central objective in multimodal learning is to capture synergy: task-relevant information that arises only from the joint use of multiple modalities,

applicationsarxiv-cs-lg

Synthesizable Molecular Generation via Soft-constrained GFlowNets with Rich Chemical Priors

arXiv:2602.04119v2 Announce Type: replace Abstract: The application of generative models for experimental drug discovery campaigns is severely limited by the difficulty of designing molecules de novo

safetyarxiv-cs-lg

T1-Bench: Benchmarking Multi-Scenario Agents in Real-World Domains

arXiv:2606.11070v1 Announce Type: cross Abstract: Recent advances in reasoning and tool-calling capabilities of large language models (LLMs) have enabled increasingly capable agentic systems. However,

Agents

TabClaw: An Interactive and Self-Evolving Agent for Spreadsheet Manipulation and Table Reasoning

arXiv:2606.10316v1 Announce Type: new Abstract: Spreadsheets and tables are widely used representations for structured data analysis, but effective analysis still requires substantial manual effort an

agentsarxiv-cs-cl

Agents

TaCarla: A comprehensive benchmarking dataset for end-to-end autonomous driving

arXiv:2602.23499v4 Announce Type: replace-cross Abstract: Collecting a high-quality dataset is a critical task that demands meticulous attention to detail, as overlooking certain aspects can render th

agentsarxiv-cs-ai

TacForeSight: Force-Guided Tactile World Model for Contact-Rich Manipulation

arXiv:2606.11184v1 Announce Type: new Abstract: Contact-rich manipulation requires robots to continuously perceive and regulate evolving physical interactions under dynamic contact transitions or comp

local-aiarxiv-cs-ro

Taking 3D Characters From Comfy To Blender https://x.com/i/broadcasts/1vKpPPVrwMmKE

This Twitter broadcast demonstrates a workflow for exporting and importing 3D character models from ComfyUI, a node-based generative AI tool, into Blender for further refinement and animation. The ses

local-aicomfyui--x

Task Robustness via Re-Labelling Vision-Action Robot Data

arXiv:2606.10918v1 Announce Type: cross Abstract: The recent trend in scaling models for robot learning has resulted in impressive policies that can perform various manipulation tasks and generalize t

safetyarxiv-cs-lg

TD-Grokking: Learning from Zero-Reward Problems by Training-Time Decomposition

arXiv:2606.09883v1 Announce Type: cross Abstract: Large language models (LLMs) have made remarkable progress in reasoning tasks, largely driven by post-training paradigms, especially reinforcement lea

Temporal Context Conditioning for Seasonality-Aware Precipitation Nowcasting of High-Intensity Rainfall

arXiv:2606.09959v1 Announce Type: cross Abstract: Precipitation nowcasting is increasingly being approached with deep learning models that learn directly from recent radar observations. Although such