Search: “framework”

Casual: Token maxxing Sweaty: Token minning Meta: Token min-maxing

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Francois Chollet presents a framework for understanding different approaches to token usage in AI models: casual users maximize tokens for flexibility, competitive users minimize tokens for efficiency

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Research

Category-Adaptive Cross-Modal Semantic Refinement and Transfer for Open-Vocabulary Multi-Label Recognition

DGX agent

arXiv:2412.06190v2 Announce Type: replace Abstract: Benefiting from the generalization capability of CLIP, recent vision language pre-training (VLP) models have demonstrated the ability to capture a w

researcharxiv-cs-cv

23 Jun 2026

Cohort-Anchored Foundation Models for Electronic Health Records: From Risk Scores to Auditable Peer Cohorts

arXiv:2606.21885v1 Announce Type: new Abstract: Foundation models have achieved remarkable performance across medical question answering, imaging, and electronic health record (EHR) tasks, yet reliabl

Convergence of Gradient Descent for General Neural Network Architectures Beyond the NTK Regime

arXiv:2606.23364v1 Announce Type: new Abstract: Training dynamics is central to understanding neural networks, yet its theoretical analysis remains difficult even for simple architectures and becomes

Coupled Routing and Configuration Optimization for Multi-Viewpoint Robotic Inspection

arXiv:2606.20739v1 Announce Type: new Abstract: We present a unified framework that turns a set of 6-DoF inspection viewpoints into a time-optimal, collision-free route for a 9-DoF robotic system. Unl

researcharxiv-cs-ro

DASIP: Dynamic Test-Time Compute Scaling for Robot Control with Stochastic Interpolant Policies

arXiv:2511.20906v2 Announce Type: replace Abstract: Diffusion- and flow-based policies deliver state-of-the-art performance on long-horizon robotic manipulation and imitation learning tasks. However,

safetyarxiv-cs-ro

Deep Generative Transformers for Probabilistic Time Series and Spatiotemporal Forecasting

arXiv:2603.11909v2 Announce Type: replace Abstract: Reliable uncertainty quantification is paramount for forecasting multivariate time series and spatiotemporal data. While Transformer architectures e

tutorialsarxiv-cs-lg

Deep material network for homogenization of piezoelectric composites

arXiv:2606.22566v1 Announce Type: cross Abstract: Piezoelectric composites are widely used in sensors, actuators, transducers, and energy-harvesting devices because their effective electromechanical p

Deep RL for Fast Long-Horizon Operations Scheduling on NASA's Carruthers Geocorona Observatory Mission

arXiv:2606.22159v1 Announce Type: cross Abstract: Spacecraft operations scheduling is a highly constrained, long-horizon combinatorial optimization problem that traditionally relies on heuristics, con

Direct Raw Audio Signal Processing via Reservoir Computing: An Investigation into 'Feature-Free' Architectures

arXiv:2606.21335v1 Announce Type: cross Abstract: This paper evaluates Reservoir Computing (RC) as an autonomous, 'feature-free' framework for audio processing, designed to eliminate traditional, hand

agentsarxiv-cs-lg

DPLAN: Minimal Connectivity to Floorplan Generation

arXiv:2606.21159v1 Announce Type: cross Abstract: Automated floor plan generation is an important problem in computational architectural design. The goal is to construct a floor plan from user-defined

researcharxiv-cs-ro

DUET: Decentralized Bilevel Optimization without Lower-Level Strong Convexity

arXiv:2606.21153v1 Announce Type: cross Abstract: Decentralized bilevel optimization (DBO) provides a powerful framework for multi-agent systems to solve local bilevel tasks in a decentralized fashion

agentsarxiv-cs-lg

Dynamic estimation of slowly varying sequences

arXiv:2606.23655v1 Announce Type: new Abstract: We consider the problem of sequentially approximating functions of each element in a slowly-varying sequence, i.e. one where the magnitude alpha_i of th

Evolving Spatial Weights for Cartographic Synthesis

arXiv:2606.22252v1 Announce Type: new Abstract: The integration of multiple thematic data layers into a single composite map, known as the cartographic synthesis problem, is typically addressed throug

FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data

arXiv:2606.23515v1 Announce Type: cross Abstract: Frameworks for ensuring fairness in machine learning typically focus on learning fair models from existing data. But this endeavor is often undermined

Fairness under Graph Uncertainty: Achieving Interventional Fairness with Partially Known Causal Graphs over Clusters of Variables

arXiv:2602.23611v2 Announce Type: replace-cross Abstract: Algorithmic decisions about individuals require predictions that are not only accurate but also fair with respect to sensitive attributes such

Federated Learning for Global Carbon Emission Forecasting: A Hybrid Time-Series Approach with Statistical and Neural Models

arXiv:2606.22618v1 Announce Type: new Abstract: Climate change, primarily driven by carbon dioxide (CO2) emissions, requires accurate forecasting tools to support effective mitigation policies and sus

FPAS: Frontier-Based Path Planning with Adaptive Sampling for Large-Scale Unknown Environments

arXiv:2606.22838v1 Announce Type: new Abstract: In this work, we propose Frontier-based Path Planning with Adaptive Sampling (FPAS), a novel framework designed for efficient goal-reaching in large-sca

tutorialsarxiv-cs-ro

FPGA-Accelerated Neuromorphic Vision System for Real-Time Orbital Object Detection

arXiv:2606.20727v1 Announce Type: cross Abstract: The escalating congestion in orbital space demands advanced monitoring solutions. This work presents a comprehensive open-source framework for neuromo

Fusing Backdoors, Machine Learning, and Optimization for Large-Scale Parametric Mixed-Integer Programs

arXiv:2606.21440v1 Announce Type: new Abstract: Large-scale optimization problems are often solved repeatedly under similar structural conditions, leading to substantial computational overhead. This o

Hardware

Genesis Workbench: A blueprint for industry AI in life sciences, powered by Databricks and NVIDIA

Genesis Workbench is a collaborative framework developed by Databricks and NVIDIA designed to accelerate AI adoption in the life sciences industry by providing integrated tools and best practices. The

hardwaredatabricks

Gradient Flow Through Diagram Expansions: Learning Regimes and Explicit Solutions

arXiv:2602.04548v2 Announce Type: replace Abstract: We develop a general mathematical framework to analyze scaling regimes and derive explicit analytic solutions for gradient flow (GF) in large learni

GraphPFN: A Prior-Data Fitted Graph Foundation Model

arXiv:2509.21489v3 Announce Type: replace Abstract: Graph foundation models face several fundamental challenges including transferability across diverse domains and data scarcity, which calls into que

Local Ai

Homographic Navigation: Geometry-Driven Camera Guidance for Deterministic Planar Capture

arXiv:2606.22834v1 Announce Type: new Abstract: We present homographic navigation, a geometry-centric framework for guiding camera acquisition toward precise capture of planar regions. Rather than tre

local-aiarxiv-cs-cv

Improved Algorithms for Nash Welfare in Linear Bandits

arXiv:2601.22969v2 Announce Type: replace Abstract: Nash regret has recently emerged as a principled fairness-aware performance metric for stochastic multi-armed bandits, motivated by the Nash Social

Interpretable Machine Learning for Predicting Startup Funding, Patenting, and Exits

arXiv:2510.09465v2 Announce Type: replace Abstract: This study develops an interpretable machine learning framework to forecast startup outcomes, including funding, patenting, and exit. A firm-quarter

Kolmogorov-Arnold Reservoir Computing

arXiv:2606.19984v2 Announce Type: replace Abstract: Reservoir computing offers a lightweight framework for forecasting dynamical systems but may struggle to capture long-range dependencies due to limi

Morphology-Aware Multimodal Representation Learning for Insect Phylogenetic Reconstruction

model-releasesarxiv-cs-cv

arXiv:2606.22077v1 Announce Type: new Abstract: Morphological traits provide important evidence for phylogenetic reconstruction and evolutionary relationship analysis. Recent image-based approaches ha

NewPINNs: Physics-Informing Neural Networks Using Conventional Solvers for Partial Differential Equations

arXiv:2601.17207v2 Announce Type: replace Abstract: We introduce NewPINNs, a physics-informing learning framework that couples neural networks with conventional numerical solvers for solving different

tutorialsarxiv-cs-lg

NullFlow: One-Step Generative Reconstruction

arXiv:2606.22696v1 Announce Type: new Abstract: We propose NullFlow, a principled framework for one-step generative image reconstruction. Our key idea is to confine the generative flow to a measuremen

tutorialsarxiv-cs-cv

Okta expands Cross App Access ecosystem to secure AI agent connections

Identity and access management company Okta Inc. today said more than 25 software makers have signed on to its Cross App Access framework, which routes the connections artificial intelligence agents m

agentssiliconangle

Orthogonal Discrepancy Kernels for Learning with Partial Physics

arXiv:2606.21199v1 Announce Type: cross Abstract: We introduce a semi-parametric framework for nonlinear system identification, which decouples discrepancy functions from physics-based components. Ort

PIAvatar: Physically Interactive Avatars via Deformation Gradient Decoupling

arXiv:2606.21162v1 Announce Type: cross Abstract: 3D human avatars have shown impressive visual fidelity driven by pose-conditioned models, yet they still lack the physical ability required for intera

researcharxiv-cs-cv

Predictive Repair Management Using a Multi-Head Attention Transformer and Online Learning

arXiv:2606.21364v1 Announce Type: new Abstract: Accurate prediction of repair duration is an important challenge in product maintenance due to its implications for resource allocation, customer satisf

Published by @OReillyMedia here: https://www.oreilly.com/radar/drivetrain-approach-data-products/

tutorialsjeremy-howard--x

The Drivetrain Approach is a framework for building effective data products, developed by Jeremy Howard and published by O'Reilly Media, that focuses on defining business objectives, identifying lever

Robust Tightly-Coupled Filter-Based Monocular Visual-Inertial State Estimation and Graph-Based Evaluation for Autonomous Drone Racing

arXiv:2603.02742v2 Announce Type: replace Abstract: Autonomous drone racing (ADR) demands state estimation that is simultaneously computationally efficient and resilient to the perceptual degradation

agentsarxiv-cs-ro

RouteJudge: An Open Platform for Reproducible and Preference-Aware LLM Routing

arXiv:2606.18774v2 Announce Type: replace Abstract: We present RouteJudge, an online pairwise preference evaluation framework for LLM routing systems, with a public platform available at https://route

Local Ai

Simplex-Constrained Sparse Bagging: Transitioning from Uniform Priors to Sparse Posteriors in Ensemble Learning

arXiv:2606.13589v2 Announce Type: replace Abstract: We present Simplex-Constrained Sparse Bagging (SCSB), a mathematically rigorous framework for post-training compression and probability calibration

local-aiarxiv-cs-lg

Towards CSI-Native Foundation Models: A Channel-Adaptive Roadmap for 6G

arXiv:2606.20670v1 Announce Type: new Abstract: Wireless foundation models offer a path toward reusable channel state information (CSI) intelligence for sixth-generation (6G) systems. However, existin

Uncertainty-Aware Domain Adaptation for Vitiligo Segmentation in Clinical Photographs

arXiv:2512.11791v2 Announce Type: replace Abstract: Accurately quantifying vitiligo extent in routine clinical photographs is crucial for longitudinal monitoring of treatment response. We propose a tr

researcharxiv-cs-cv

Industry

You can map the collapse of the West by its denial of the True, then the Good, and finally the Beautiful. All that’s left to replace them ar…

This post suggests a philosophical framework for analyzing Western cultural decline, proposing a sequential loss of commitment to truth, goodness, and beauty as core values. The statement implies that

industryelon-musk--x

ZeroDex: Zero-Shot Long-Horizon Dexterous Manipulation via Multi-View 3D-Grounded VLM Reasoning

arXiv:2606.19340v2 Announce Type: replace Abstract: We present ZeroDex, a zero-shot framework for long-horizon dexterous manipulation that grounds language instructions into executable 3D task plans f

safetyarxiv-cs-ro

Nvidia introduces Halos for Robotics to bridge the physical AI safety gap

Nivida Corp. today announced Halos for Robotics, the industry’s first full framework for robotic safety systems that encompasses building, testing and managing complete artificial intelligence robotic

safetysiliconangle

22 Jun 2026

A slightly improved DVD-JEPA demo [P]

researchr-machinelearning

This post likely presents an enhanced demonstration of DVD-JEPA, a video variant of the Joint Embedding Predictive Architecture model. The JEPA framework has been extended to video tasks (V-JEPA) , an

21 Jun 2026

Hermes Agent v0.17.0 - The Reach Release Changelog below:

Hermes Agent v0.17.0, released by Nous Research, is a major update referred to as 'The Reach Release' that introduces new capabilities and improvements to the Hermes Agent framework. The changelog det

agentsnous-research--x

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

researcharxiv-cs-cl

11 Jun 2026

A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data

arXiv:2601.17717v3 Announce Type: replace Abstract: Large Language Models (LLMs) have emerged as powerful tools for generating data across various modalities. By transforming data from a scarce resour

applicationsarxiv-cs-ai

11 Jun 2026

AGE-MIL: Anchor-Guided Evidence Learning for Patient-Level Prediction