Search: “models”

The ability of Codex (and Code) to solve problems on my various Windows machines has saved me so much effort. Just one of the most annoying …

applicationsethan-mollick--x

Ethan Mollick shares a positive experience about how GitHub Copilot's Codex model has significantly improved his productivity by helping solve problems across multiple Windows machines, describing it

24 Jun 2026

Human

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Local Ai

The vibe is shifting. Decentralized intelligence is making a comeback.

DGX agent

Clem Delangue discusses a resurgence of interest in decentralized intelligence systems and approaches, suggesting a shift away from centralized AI models toward more distributed architectures. The pos

local-aiclem-delangue--x

24 Jun 2026

TimeLAVA: Learning-Agnostic Data Valuation for Time Series

arXiv:2606.18729v2 Announce Type: replace-cross Abstract: Data valuation quantifies the intrinsic quality of individual samples to enable principled data curation, quality control, and robust learning

24 Jun 2026

VisChronos: Revolutionizing Image Captioning Through Real-Life Events

arXiv:2606.24058v1 Announce Type: new Abstract: This paper aims to bridge the semantic gap between visual content and natural language understanding by leveraging historical events in the real world a

applicationsarxiv-cs-cv

24 Jun 2026

A Gated Graph Neural Network Approach to Fast-Convergent Dynamic Average Estimation

arXiv:2606.20955v1 Announce Type: new Abstract: Dynamic average estimation is a critical problem in multi-agent systems, enabling agents to collaboratively estimate time-varying signals using only loc

A Geometric Task-Space Port-Hamiltonian Formulation for Redundant Manipulators

arXiv:2512.14349v2 Announce Type: replace-cross Abstract: We present a novel geometric port-Hamiltonian formulation of redundant manipulators performing a differential kinematic task eta=J(q)ot{q}, wh

researcharxiv-cs-ro

A Robust Framework for Secure Cardiovascular Risk Prediction: An Architectural Case Study of Differentially Private Federated Learning

arXiv:2603.13293v2 Announce Type: replace Abstract: Accurate cardiovascular risk prediction is crucial for preventive healthcare; however, the development of robust Artificial Intelligence (AI) models

applicationsarxiv-cs-lg

Agentic Time Machine as an Infrastructure for Future-Event Forecasting

arXiv:2606.21013v1 Announce Type: cross Abstract: Forecasting future events is a critical challenge for large language model (LLM) agents, spanning domains from elections and monetary policy to financ

Tools

[AINews] SpaceX is already a $28B/yr Neocloud

This article analyzes SpaceX's valuation and business model through the lens of AI infrastructure, arguing that SpaceX functions as a 'Neocloud' (a next-generation cloud computing platform) generating

toolslatent-space

Agents

AIR: Adaptive Interleaved Reasoning with Code in MLLMs

arXiv:2606.23678v1 Announce Type: new Abstract: Following the paradigm shift initiated by OpenAI o3, interleaved reasoning with code to enhance multimodal large language models (MLLMs) has become a pi

agentsarxiv-cs-cv

All Green, Still Broken: Real-Flow Verification Lessons from an LLM-Integrated, Multi-Market Web Application

arXiv:2606.22475v1 Announce Type: cross Abstract: Modern web applications increasingly combine three ingredients that are hard to test: output from large language models, multi-market internationaliza

applicationsarxiv-cs-lg

All Routes Lead to Collapse

arXiv:2606.22325v1 Announce Type: new Abstract: Attention sinks, representation collapse, and norm stratification are treated as transformer-specific pathologies. We show they are not specific to atte

Arbor: Explicit Geometric Conditioning for Controllable 3D Asset Generation

arXiv:2606.23514v1 Announce Type: new Abstract: Text and image conditioned 3D models now generate convincing assets, but they still offer little direct control over the space an object should occupy o

Associative Memory for Non-Stationary Environments: A Self-Sizing Generalization of Hopfield Networks

arXiv:2507.10443v3 Announce Type: replace-cross Abstract: The Hopfield network made associative memory (AM) the model system of neural computation, but it solves the problem only for a stationary worl

Attention-Spectrum Regularization for Replay-Free Continual Multimodal LLMs

arXiv:2606.23063v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly required to adapt to non-stationary streams of visual domains, question types, and user instru

AwakeForest: An Interactive Geospatial Platform for Large-Scale Forest Imagery

arXiv:2606.23542v1 Announce Type: new Abstract: Forest imagery analysis often involves multiple tightly coupled vision tasks, which must be performed under substantial variation in geographic regions,

BASIL: Bayesian Application for Scientific Iteration and Learning

arXiv:2606.21092v1 Announce Type: new Abstract: We introduce BASIL, a user-friendly desktop application for process optimization. BASIL employs a Bayesian approach, incorporating special acquisition f

tutorialsarxiv-cs-lg

Beyond Flat Labels: Level-Restricted Contrastive Learning for Hierarchical Fine-Grained Vision Classification

arXiv:2606.21838v1 Announce Type: new Abstract: Multimodal contrastive learning has enabled zero-shot visual classification by aligning images with textual categories. However, in hierarchically struc

Bridging Semantics and Kinematics: A Modular Framework for Zero-Shot Robotic Manipulation

arXiv:2606.23157v1 Announce Type: new Abstract: This paper presents a modular training-free framework for zero-shot, language-guided robotic manipulation in semi-structured environments. The architect

researcharxiv-cs-ro

Hardware

.@cartesia runs one of the hardest inference workloads: real-time voice. Their stack has to keep long-lived streams moving, serve millions o…

.@cartesia runs one of the hardest inference workloads: real-time voice. Their stack has to keep long-lived streams moving, serve millions of audio minutes a day, and hold model latency around 90ms. T

hardwaretogether-ai--x

Causal Gaussian Processes for Robust Treatment Effect Evaluation with Unobserved Confounding

arXiv:2606.21809v1 Announce Type: new Abstract: The presence of confounding bias poses a key challenge in policy evaluation, as the target causal effects of actions are not identifiable (i.e., underde

Causal Variational Deep Embedding: A Family of Interventional Generators for Confounded Images

arXiv:2606.21806v1 Announce Type: new Abstract: Deep generative models reproduce the observational distribution of their training data, inheriting any spurious associations it contains. A common sourc

Collapsed Effective Operators for Higher-order Structures

arXiv:2606.23517v1 Announce Type: new Abstract: Higher-order structures are powerful relational modeling tools, yet existing spectral operators decompose the topology into separate ranks, leaving prac

Confidence-Uncertainty Boundary Calibration for Bayesian Deep Learning in Medical Image Analysis

arXiv:2602.11973v2 Announce Type: replace Abstract: In critical decision support systems based on medical imaging, the reliability of AI-assisted decision-making is as relevant as predictive accuracy.

safetyarxiv-cs-cv

CuDi: Curve Distillation for Efficient and Controllable Exposure Adjustment

arXiv:2207.14273v2 Announce Type: replace Abstract: We present Curve Distillation, CuDi, for efficient and controllable exposure adjustment without the requirement of paired or unpaired data during tr

local-aiarxiv-cs-cv

CuratorKIT : Data Curation and Synthetic Data Generation for LLM Post-Training

arXiv:2606.21631v1 Announce Type: cross Abstract: Data curation is a critical part of post-training pipelines for large language models, yet existing tools often treat ingestion, deduplication, synthe

Tools

Cursor now shows you a leaderboard of the most popular plugins, skills, and MCPs across your team. Add any to your setup with one click from…

Cursor has introduced a leaderboard feature that displays the most popular plugins, skills, and Model Context Protocols (MCPs) being used across a team, allowing users to easily add any of these tools

toolscursor--x

CurvSegFlow: Time-Conditioned Flow Matching for Robust Segmentation of Curvilinear Structures in Noisy Biomedical Images

arXiv:2606.21608v1 Announce Type: new Abstract: Accurate segmentation of curvilinear structures remains challenging in biomedical imaging due to their thin geometry, complex topology, and sensitivity

tutorialsarxiv-cs-cv

Deep learning-based detection of cessation of breathing in pre-term infants

arXiv:2606.23213v1 Announce Type: new Abstract: Apnoea of prematurity is characterised by recurrent episodes of cessation of breathing and remains difficult to detect reliably using routinely monitore

Deep Unrolled Networks in Representation Space Applied to MRI Reconstruction

arXiv:2606.21602v1 Announce Type: cross Abstract: Deep unrolled networks (DUNs) integrate physical forward models with learned regularization in cascaded network architectures, achieving exceptional p

tutorialsarxiv-cs-cv

Diffusion Integrated Gradients: Controllable Path Generation for Flexible Feature Attribution

arXiv:2606.22314v1 Announce Type: cross Abstract: Path-based attribution methods such as Integrated Gradients (IG) are widely adopted for their strong axiomatic properties and effectiveness in attribu

tutorialsarxiv-cs-cv

Do Sparse Autoencoders Learn Meaningful Concept Hierarchies?

arXiv:2606.22994v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) have become an important tool for unsupervised concept discovery in large models. To make the resulting feature spaces more i

tutorialsarxiv-cs-lg

DPO Unchained: Your Training Algorithm is Secretly Disentangled in Human Choice Theory (and its Loss' Convexity is Dispensable)

arXiv:2507.07855v4 Announce Type: replace Abstract: Normative theories allow one to elicit key parts of a ML algorithm from first principles, which is crucial at a time of championed scrutiny for ML w

Dual-Stream EEG Decoding for 3D Visual Perception

arXiv:2606.22182v1 Announce Type: new Abstract: This paper explores a novel brain decoding model for 3D shape perception through a dual pathway architecture mirroring biological vision. Our bio-inspir

Durability-Aware Multi-Objective Optimization of the Jansen Linkage: Trading Gait Quality Against Joint Wear

arXiv:2606.22129v1 Announce Type: new Abstract: The Jansen linkage is a single-degree-of-freedom planar leg mechanism whose eleven 'holy numbers' were evolved by Theo Jansen to optimize the foot-path

applicationsarxiv-cs-ro

Efficient Training of Boltzmann Generators Using Off-Policy Log-Dispersion Regularization

arXiv:2602.03729v2 Announce Type: replace Abstract: Sampling from unnormalized probability densities is a central challenge in computational science. Boltzmann generators are generative models that en

Enhancing Cognitive Workload Classification Using Integrated LSTM Layers and CNNs for fNIRS Data Analysis

arXiv:2407.15901v2 Announce Type: replace Abstract: Functional near-infrared spectroscopy (fNIRS) is employed as a non-invasive method to monitor functional brain activation by capturing changes in th

EvoRubrics: Dynamic Rubrics as Rewards via Adversarial Co-Evolution for LLM Reinforcement Learning

arXiv:2606.23038v1 Announce Type: new Abstract: Rubric-based rewards offer interpretable and fine-grained optimization signals for reinforcement learning in open-ended tasks where verifiable answers a

Explanations for Automatic Speech Recognition

arXiv:2302.14062v2 Announce Type: replace-cross Abstract: We address quality assessment for neural network based ASR by providing explanations that help increase our understanding of the system and ul

Exposing the Illusion of Erasure in Knowledge Editing for LLMs

arXiv:2606.23276v1 Announce Type: new Abstract: Knowledge Editing (KE) has emerged as a frontier for updating specific facts in LLMs without costly retraining, but its reliability and underlying mecha

applicationsarxiv-cs-lg

FairSAM: Fair Classification on Corrupted Image Data Through Sharpness-Aware Minimization

arXiv:2503.22934v2 Announce Type: replace Abstract: Image classification models trained on clean data often degrade sharply when exposed to corrupted test or deployment data, such as images with impul

Hardware

Fast-TurboQuant: A Multiplier-Free Online Vector Quantization Approach

arXiv:2606.21448v1 Announce Type: new Abstract: As large language models scale, memory bandwidth for key-value caches and retrieval-augmented generation systems becomes a critical bottleneck. While 1-

hardwarearxiv-cs-lg

Fed-CausalDiff: Decoupled Synchronization for Federated Do-Simulation and Policy Evaluation

arXiv:2606.22510v1 Announce Type: new Abstract: While federated learning enables collaborative modelling on decentralised data, standard methods merely fit historical observations. This purely observa

Federated learning with heavy-tailed gradient noise and communication noise: a variance-reduction based algorithm

arXiv:2606.22466v1 Announce Type: new Abstract: Federated learning (FL) is an emerging distributed machine learning paradigm that enables local devices to jointly train a global model while keeping da

Few-Shot Hyperspectral Aphid Detection via FastGAN Synthetic Data Generation, Transformer-Based Classification and Explainable AI

arXiv:2606.21267v1 Announce Type: new Abstract: Early detection of aphid infestation in crops is essential for preventing yield loss and reducing unnecessary pesticide use. Hyperspectral imaging combi

FLM-Occ: Feed-forward Likelihood Maximization for Efficient Indoor Occupancy Prediction

arXiv:2606.21373v1 Announce Type: new Abstract: Recent indoor occupancy prediction methods adopt Gaussian primitives as a sparse 3D representation for computational efficiency. However, their training

local-aiarxiv-cs-cv

Flow Matching for Efficient and Scalable Data Assimilation

arXiv:2508.13313v5 Announce Type: replace-cross Abstract: Data assimilation (DA) estimates a dynamical system's state from noisy observations. Recent generative models like the ensemble score filter (

From Gradient Clipping to Structural Refinement: Improving DPSGD for Medical Image Segmentation

arXiv:2606.21763v1 Announce Type: new Abstract: Medical image segmentation is widely used for disease detection but relies on sensitive data, raising privacy concerns as trained models can leak inform

safetyarxiv-cs-cv