Search: “concepts”

The Standard Interpretable Model: A general theory of interpretable machine learning to deductively design interpretable methods using Lagrangian mechanics

arXiv:2606.12289v1 Announce Type: cross Abstract: As Artificial Intelligence models grow in complexity, interpretability has become an indispensable tool for understanding, debugging, and controlling

11 Jun 2026

Research

Towards Deep Learning Surrogate for the Forward Problem in Electrocardiology: A Scalable Alternative to Physics-Based Models

DGX agent

arXiv:2512.13765v2 Announce Type: replace-cross Abstract: The forward problem in electrocardiology, computing body surface potentials from cardiac electrical activity, is traditionally solved using ph

researcharxiv-cs-ai

11 Jun 2026

Agents

Business World Model

arXiv:2606.10044v1 Announce Type: new Abstract: Businesses are increasingly adopting AI-enabled tools to improve productivity, reduce costs, and enhance products and services. However, the transformat

agentsarxiv-cs-ai

Can we trust our models? Epistemic calibration in second-order classification

arXiv:2606.10777v1 Announce Type: new Abstract: Uncertainty estimation is critical for deploying machine learning models in high-stakes settings. However, classical calibration only assesses the relia

Detecting Knowledge Gaps from Conversational AI Interactions Using Curriculum Prerequisite Graphs

arXiv:2606.10736v1 Announce Type: cross Abstract: Large online courses generate thousands of student questions directed at conversational AI teaching assistants, yet these interaction logs remain larg

Evaluating Research-Level Math Proofs via Strict Step-Level Verification

arXiv:2606.10799v1 Announce Type: new Abstract: Large Language Models (LLMs) struggle to rigorously verify complex mathematical proofs. Standard global evaluation approaches suffer from 'context poiso

Everyone Operating At The Frontier Satya Nadella, Chairman & CEO, Microsoft, interviewed by @saranormous & @eladgil (No Priors) and @swyx (L…

Everyone Operating At The Frontier Satya Nadella, Chairman & CEO, Microsoft, interviewed by @saranormous & @eladgil (No Priors) and @swyx (Latent Space) Crossover special at Microsoft Build 2026. Summ

model-releasesswyx--x

Few-step Generative Models as Lossy Compression

arXiv:2606.10450v1 Announce Type: new Abstract: DiffC provides a principled way to reuse pre-trained diffusion models for lossy compression, but its encoding and decoding procedures remain slow becaus

researcharxiv-cs-cv

From Perception to Action: Can UI Interventions Foster Sustainable LLM Chatbot

arXiv:2606.10861v1 Announce Type: cross Abstract: LLM-powered chatbots are increasingly embedded in everyday workflows, raising sustainability concerns due to their energy use. Most mitigation strateg

GAGI: A Gini-Adjusted GDP-per-Capita Index for Distribution-Aware Macroeconomic Welfare Monitoring

arXiv:2606.09944v1 Announce Type: cross Abstract: GDP per capita is the default lens through which governibng bodies track the economic prosperity and consequences of economic events , yet it is blind

Industry

Here is a thought that might sounds odd/wrong the first time you hear it: recorded media is a phase. A long one, but a phase. For most of hu…

industrycristobal-valenzuela--x

Here is a thought that might sounds odd/wrong the first time you hear it: recorded media is a phase. A long one, but a phase. For most of human history, media was live. Stories were told around fires

KG-SoftMAP: Soft Knowledge-Graph Priors for Bayesian Network Structure Learning from Sparse Discrete Data

arXiv:2606.10358v1 Announce Type: cross Abstract: Learning Bayesian network (BN) structure from sparse discrete data is hard: when each instance records only a few variables, most variable pairs lack

Learning Doubly Sparse Explicitly Conditioned Transforms

arXiv:2606.10975v1 Announce Type: new Abstract: Finding convenient spaces in which certain hypotheses regarding an assumed sparse structure of natural signals hold true has become a desirable result i

Minimum Distortion Quantization with Specified Output Distribution

arXiv:2606.10458v1 Announce Type: cross Abstract: We derive the optimal quantizer of a real-valued random variable W with distribution P_W such that 1) the distribution of the quantization output X th

Moonshine: An Autonomous Mathematical Research Agent Centered on Conjecture Generation

arXiv:2606.10806v1 Announce Type: new Abstract: Moonshine is an autonomous agent whose central objective is to generate mathematical conjectures. Its core capability is to extract structure from class

Local Ai

Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learning

arXiv:2606.10250v1 Announce Type: cross Abstract: Class imbalance is a common problem in deep learning that severely degrades performance. In federated learning (FL), it is a critical factor contribut

local-aiarxiv-cs-ai

NoiseSDF2NoiseSDF: Learning Clean Neural Fields from Noisy Supervision

arXiv:2507.13595v3 Announce Type: replace Abstract: Reconstructing accurate implicit surface representations from point clouds remains a challenging task, particularly when data is captured using low-

researcharxiv-cs-cv

On the Controllability-Fidelity Frontier in Diffusion Editing

arXiv:2606.09901v1 Announce Type: cross Abstract: Diffusion-based generative models enable powerful image editing capabilities, but achieving precise control while maintaining fidelity and safety rema

safetyarxiv-cs-cv

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

What makes a harness a harness: necessary and sufficient conditions for an agent harness

arXiv:2606.10106v1 Announce Type: cross Abstract: The term agent harness now circulates widely in software engineering with generative artificial intelligence. It names the layer that wraps a language

Always back to the basics: LatentMoE was probably inspired by MLA, which was inspired by LoRA, which was inspired by SVD, which was inspired…

researchsebastian-raschka--x

This post traces the historical lineage of machine learning techniques, suggesting that LatentMoE builds upon a chain of progressively developed methods including MLA, LoRA, and SVD, emphasizing how m

Agents

An Information-Theoretic Definition for Open-Ended Learning

arXiv:2606.08369v1 Announce Type: cross Abstract: A growing body of work points to the great promise of AI systems that can continually expand their capabilities as they operate in an open-ended envir

agentsarxiv-cs-ai

Backward Coherence and Hidden-State Stability in Recurrent Neural Networks: A Quasi-Reverse-Martingale Theory

arXiv:2606.08934v1 Announce Type: new Abstract: Recurrent neural networks maintain a hidden state h_t, but its probabilistic meaning is often unclear. We study hidden-state stability through backward

Data-driven discovery of governing differential equations across physical systems

arXiv:2606.09638v1 Announce Type: new Abstract: Differential equations play a critical role in scientific discovery because they provide a mathematical framework to describe the behaviour of physical

DICE: Entropy-Regularized Equilibrium Selection for Stable Multi-Agent LLM Coordination

model-releasesarxiv-cs-lg

arXiv:2606.08068v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems often fail to reliably outperform a single strong model equipped with best-of-N sampling. We argue that a

Evaluating Advanced Prompting on Gemini Flash for Multi-Hop Biomedical QA

arXiv:2606.07548v1 Announce Type: cross Abstract: The MedHopQA challenge presents a critical test for Large Language Models (LLMs): complex, multi-hop reasoning in the high-stakes biomedical domain. T

Frankenstein in the Pipeline: Computational Epistemicide in Facial Recognition

arXiv:2606.07628v1 Announce Type: cross Abstract: While the eugenic roots of computer vision are well-documented in critical technology studies, less attention has been paid to the operational mechani

safetyarxiv-cs-cv

Agents

Governance Controls for AI-Generated Test Artifacts in Autonomous Software Testing

arXiv:2606.08806v1 Announce Type: cross Abstract: Artificial Intelligence (AI) and Large Language Models (LLMs) are increasingly used in autonomous software testing; however, AI-generated test artifac

agentsarxiv-cs-ai

Tutorials

Graph Neural Networks for Predicting Solvability of Finite Groups

arXiv:2606.07619v1 Announce Type: new Abstract: We present a Graph Neural Network (GNN) framework for the classification of finite groups according to their solvability. Using graph representations as

tutorialsarxiv-cs-lg

Applications

In-Context Learning of Stochastic Differential Equations with Foundation Inference Models

arXiv:2502.19049v3 Announce Type: replace Abstract: Stochastic differential equations (SDEs) describe dynamical systems where deterministic flows, governed by a drift function, are superimposed with r

applicationsarxiv-cs-lg

Industrial policy for the Intelligence Age

This OpenAI publication likely discusses how governments and policymakers should approach industrial policy in an era dominated by artificial intelligence and computational intelligence capabilities.

safetyopenai

Learning a Semantic Calibration Network for Open-Vocabulary Semantic Segmentation

arXiv:2606.08001v1 Announce Type: new Abstract: Semantic image segmentation assigns a predefined category label to each pixel, has achieved significant progress lately. Open-Vocabulary Segmentation (O

researcharxiv-cs-cv

MC-CPO: Mastery-Conditioned Constrained Policy Optimization for Pedagogically Safe Intelligent Tutoring Systems

arXiv:2604.04251v2 Announce Type: replace Abstract: Intelligent tutoring systems increasingly rely on reinforcement learning to personalise instruction, yet optimising for observable engagement signal

Measuring a hate speech spectrum with faceted Rasch item response theory and perspective-aware, explainable-by-design deep learning

arXiv:2009.10277v2 Announce Type: replace-cross Abstract: We propose a system for measuring hate speech on a continuous, interval-valued spectrum ranging from genocidal to supportive speech by combini

Memetic Capture: A Pluralistic Policy Framework for Governing AI-Driven Cultural Disempowerment

arXiv:2606.07802v1 Announce Type: cross Abstract: Culture is the most insidious vector of gradual human disempowerment by AI: unlike economic or political displacement, cultural displacement attacks t

Optical Reasoning: Rethinking Images as an Expressive Reasoning Medium Beyond Text

arXiv:2606.09585v1 Announce Type: new Abstract: Chain-of-Thought (CoT) improves the performance of Large Language Models (LLMs) and has been extended to Multimodal Large Language Models (MLLMs). More

Tutorials

Orange Lab: Lowering Barriers to Data Mining through Embedded Interactive Workflows

arXiv:2606.09239v1 Announce Type: new Abstract: While visual programming of data analysis workflows has become an important vehicle for the democratization of data science, such systems remain largely

tutorialsarxiv-cs-lg

Parameter Tuning with Generalization Guarantees for GPU-Accelerated Linear Programming

model-releasesarxiv-cs-lg

arXiv:2606.08638v1 Announce Type: cross Abstract: Recent research has developed practical, parallelizable first-order methods for large scale linear programming, but performance is highly dependent on

Applications

Personalized and Robust Proactive Robot Assistance with Uncertainty-Guided LLM Reasoning

arXiv:2606.08458v1 Announce Type: new Abstract: Proactive robot assistance in household environments requires accurate prediction of human activities and object usage under dynamic and noisy condition

applicationsarxiv-cs-ro

phepy: Visual benchmarks and improvements for out-of-distribution detectors

model-releasesarxiv-cs-lg

arXiv:2503.05169v2 Announce Type: replace Abstract: Applying machine learning to increasingly high-dimensional problems with sparse or biased training data increases the risk that a model is used on i

Position: Anthropomorphic Misalignment Research Needs Stronger Evidence

arXiv:2606.07612v1 Announce Type: cross Abstract: We argue that many Anthropomorphic Misalignment Research (AMR) studies need stronger evidence to ensure that they can provide a robust foundation for

Proxy Reward Internalization and Mechanistic Exploitation: A Learned Precursor to Reward Hacking and Its Generalization

arXiv:2606.09711v1 Announce Type: new Abstract: Reward hacking is usually studied after it becomes visible, once a model earns high proxy reward while failing the intended task. We instead study what

Local Ai

PTL-Diffusion: Manifold-Aware Diffusion with Periodic Terminal Laws

arXiv:2606.09816v1 Announce Type: cross Abstract: Standard diffusion models typically use a single time-homogeneous Gaussian terminal distribution as the reference law for generation. While this choic

local-aiarxiv-cs-ai

RAPID: Layer-Wise Redundancy-Aware Pruning and Importance-Driven Token Merging for Efficient ViT

arXiv:2606.08156v1 Announce Type: cross Abstract: Vision Transformers (ViTs) achieve strong performance but suffer from high computational costs due to quadratic self-attention complexity. Although to

Shared Latent Structures Enable Unified Backdoor Detection and Mitigation in LLMs

arXiv:2606.07963v1 Announce Type: new Abstract: Backdoor attacks in large language models (LLMs) are often treated as isolated trigger-response failures, motivating defenses tailored to specific trigg

Applications

Simultaneous hyperkinetic movement disorders phenotyping: a cross-cohort pediatric transfer study using routine videos, markerless pose estimation and a tabular foundation model

arXiv:2606.07674v1 Announce Type: new Abstract: Objective: To develop and externally test a video-based framework for simultaneous detection of hyperkinetic MDs phenomenologies: dystonia, tremor, myoc

applicationsarxiv-cs-cv

Local Ai

Sound and Complete Neurosymbolic Reasoning with LLM-Grounded Interpretations

arXiv:2507.09751v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but exhibit problems with l

local-aiarxiv-cs-ai

Sparse Autoencoders Reveal Interpretable and Steerable Features in VLA Models

model-releasesarxiv-cs-ro

arXiv:2603.19183v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have emerged as a promising approach for general-purpose robot manipulation. However, little research has mechan