All entries

A meshfree exterior calculus for generalizable and data-efficient learning of physics from point clouds

model-releasesarxiv-cs-ai

arXiv:2605.08436v1 Announce Type: cross Abstract: We introduce a meshfree exterior calculus (MEEC) for learning structure-preserving descriptions of physics on point clouds, and use it to build MEEC-N

Content type

All Blog X Post Paper YouTube Reddit GitHub

Research

A Minimum Variance Path Principle for Accurate and Stable Score-Based Density Ratio Estimation

DGX agent

arXiv:2602.00834v4 Announce Type: replace-cross Abstract: Score-based methods are powerful across machine learning, but they face a paradox: theoretically path-independent, yet practically path-depend

researcharxiv-cs-ai

12 May 2026

A new initialisation to Control Gradients in Sinusoidal Neural network

arXiv:2512.06427v2 Announce Type: replace Abstract: Proper initialisation strategy is of primary importance to mitigate gradient explosion or vanishing when training neural networks. Yet, the impact o

A Paired Point-of-Care Ultrasound Dataset for Image Quality Enhancement and Benchmarking via a cGAN Baseline

arXiv:2605.08282v1 Announce Type: cross Abstract: Purpose: We aim to enhance the image quality of point-of-care ultrasound (POCUS) devices using deep learning and a novel paired dataset of POCUS and h

A Physical Theory of Backpropagation: Exact Gradients from the Least-Action Principle

arXiv:2602.02281v2 Announce Type: replace-cross Abstract: Backpropagation is typically presented as a symbolic procedure: a backward pass topologically distinct from inference, with non-local error si

A probabilistic framework for crystal structure denoising, phase classification, and order parameters

arXiv:2512.11077v3 Announce Type: replace-cross Abstract: Atomistic simulations generate large volumes of noisy structural data, yet extracting phase labels and continuous order parameters (OPs) in a

A Prompt-Aware Structuring Framework for Reliable Reuse of AI-Generated Content in the Agentic Web

arXiv:2605.09283v1 Announce Type: new Abstract: The evolution of Large Language Models (LLMs) and the software agents built on them (AI agents) marks a turning point in the transition from a human-cen

A PyTorch Library of Turing-Complete Neural Networks

arXiv:2605.08150v1 Announce Type: new Abstract: We present a PyTorch package that compiles neural networks and their weights from Turing machine descriptions, producing models that exactly simulate th

A Qualitative Test-Risk Mechanism for Scaling Behavior in Normalized Residual Networks

arXiv:2605.08297v1 Announce Type: cross Abstract: The scaling behavior, in which test performance often improves as model size and data increase, is a central empirical phenomenon in modern deep learn

A Quantum Inspired Variational Kernel and Explainable AI Framework for Cross Region Solar and Wind Energy Forecasting

model-releasesarxiv-cs-ai

arXiv:2605.09032v1 Announce Type: cross Abstract: Reliable short horizon forecasting of solar and wind generation is a structural prerequisite of any modern power system yet most published forecasters

A Radius of Robust Feasibility Approach to Directional Sensors in Uncertain Terrain

arXiv:2510.19407v2 Announce Type: replace-cross Abstract: A sensor has the ability to probe its surroundings. However, uncertainties in its exact location can significantly compromise its sensing perf

applicationsarxiv-cs-ro

A Random-Matrix Criterion for Initializing Gated Recurrent Neural Networks

arXiv:2605.10650v1 Announce Type: new Abstract: Proper weight initialization prior to training has historically been one of the key factors that helped kick off the deep learning revolution. Initializ

A Real-Calibrated Synthetic-First Data Engine

arXiv:2605.09699v1 Announce Type: cross Abstract: Modern computer vision systems increasingly encounter performance limitations in data-scarce domains, where collecting large-scale, high-quality label

applicationsarxiv-cs-cv

A Reconfigurable Multiplier Architecture for Error-Resilient Applications in RISC-V Core

arXiv:2605.08785v1 Announce Type: cross Abstract: Neural Networks (NNs) have been widely adopted due to their outstanding efficacy and adaptability across computer vision and deep learning application

A Recursive Decomposition Framework for Causal Structure Learning in the Presence of Latent Variables

arXiv:2605.10651v1 Announce Type: cross Abstract: Constraint-based causal discovery is widely used for learning causal structures, but heavy reliance on conditional independence (CI) testing makes it

applicationsarxiv-cs-ai

A Reflective Storytelling Agent for Older Adults: Integrating Argumentation Schemes and Argument Mining in LLM-Based Personalised Narratives

arXiv:2605.10531v1 Announce Type: new Abstract: This work investigates whether knowledge-driven large language model (LLM)-based storytelling can support purposeful narrative interaction with a digita

A Resilient Solution for Sewer Overflow Monitoring across Cloud and Edge

arXiv:2605.10592v1 Announce Type: new Abstract: Aging combined sewer systems in many historical cities are increasingly stressed by extreme rainfall events, which can trigger combined sewer overflows

A Robust Out-of-Distribution Detection Framework via Synergistic Smoothing

arXiv:2605.08191v1 Announce Type: cross Abstract: Reliable out-of-distribution (OOD) detection is a critical requirement for the safe deployment of machine learning systems. Despite recent progress, s

A Scalable Entity-Based Framework for Auditing Bias in LLMs

arXiv:2601.12374v2 Announce Type: replace-cross Abstract: Existing approaches to bias evaluation in large language models (LLMs) trade ecological validity for statistical control, relying either on ar

safetyarxiv-cs-ai

Tutorials

A Semantic-Sampling Framework for Evaluating Calibration in Open-Ended Question Answering

arXiv:2605.08432v1 Announce Type: cross Abstract: Calibration measures whether a model's predicted confidence aligns with its empirical accuracy, and is central to the reliable deployment of large lan

tutorialsarxiv-cs-ai

A Simulated Federated Analysis of MS-Induced Brain Lesions

arXiv:2605.08223v1 Announce Type: new Abstract: Federated techniques such as federated learning and federated analysis have emerged as a powerful paradigm for enabling multi-center research on sensiti

applicationsarxiv-cs-lg

A Single Deep Preference-Conditioned Policy for Learning Pareto Coverage Sets

arXiv:2605.08946v1 Announce Type: new Abstract: Preference-conditioned multi-objective reinforcement learning aims to learn a single policy that captures trade-offs across preferences, but under nonli

safetyarxiv-cs-lg

A Single-Layer Model Can Do Language Modeling

arXiv:2605.10643v1 Announce Type: new Abstract: Modern language models scale depth by stacking layers, each holding its own state - a per-layer KV cache in transformers, a per-layer matrix in Mamba, G

researcharxiv-cs-cl

A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Models

arXiv:2605.08504v1 Announce Type: new Abstract: We investigate the origins of massive activations in large language models (LLMs) and identify a specific layer named the extbf{Massive Emergence Layer

researcharxiv-cs-cl

A Single Neuron Is Sufficient to Bypass Safety Alignment in Large Language Models

arXiv:2605.08513v1 Announce Type: cross Abstract: Safety alignment in language models operates through two mechanistically distinct systems: refusal neurons that gate whether harmful knowledge is expr

safetyarxiv-cs-ai

A Spectral Framework for Closed-Form Relative Density Estimation

arXiv:2605.10668v1 Announce Type: new Abstract: We propose a closed-form spectral framework for relative log-density estimation in linearly parameterized probabilistic models, including unnormalized a

A Stability Benchmark of Generative Regularizers for Inverse Problems

arXiv:2605.10076v1 Announce Type: cross Abstract: Generative (diffusion) priors demonstrate remarkable performance in addressing inverse problems in imaging. Yet, for scientific and medical imaging, i

A startup wants to pull magnesium from seawater without torching the environment. Another wants to take small language models to the podium …

model-releasesai21-labs--x

A startup wants to pull magnesium from seawater without torching the environment. Another wants to take small language models to the podium for enterprise customers. Today @jason and @alex sat down wi

A Tale of Two Problems: Multi-Task Bilevel Learning Meets Equality Constrained Multi-Objective Optimization

arXiv:2605.09094v1 Announce Type: new Abstract: In recent years, bilevel optimization (BLO) has attracted significant attention for its broad applications in machine learning. However, most existing w

A true exponential!

A true exponential! Oy. According to a new paper in The Lancet, the rate of made-up citations in biomedical papers has increased by more than 12x since 2023. https://www.thelancet.com/journals/lancet/

safetygary-marcus--x

A Two-Stage Motion-Aware Framework for mmWave-based Human Mesh Recovery

arXiv:2605.08530v1 Announce Type: new Abstract: Millimeter-wave (mmWave) radar has emerged as a promising sensing modality for human perception due to its robustness under challenging environmental co

researcharxiv-cs-cv

A Unified Lyapunov-IQC Framework for Uniform Stability of Smooth Quadratic First-Order Accelerated Optimizers

arXiv:2605.08488v1 Announce Type: cross Abstract: We develop a unified Lyapunov-integral quadratic constraint (IQC) framework for establishing uniform stability of first-order accelerated optimization

A Unified Pair-GRPO Family: From Implicit to Explicit Preference Constraints for Stable and General RL Alignment

arXiv:2605.06375v1 Announce Type: cross Abstract: Large language model (LLM) alignment via reinforcement learning from human preferences (RLHF) suffers from unstable policy updates, ambiguous gradient

A Unified Representation of Neural Networks Architectures

arXiv:2512.17593v3 Announce Type: replace Abstract: In this paper we consider the limiting case of neural networks (NNs) architectures when the number of neurons in each hidden layer and the number of

A Versatile AI Agent for Rare Disease Diagnosis and Risk Gene Prioritization

arXiv:2605.06226v2 Announce Type: replace Abstract: Accurate and timely diagnosis is essential for effective treatment, particularly in the context of rare diseases. However, current diagnostic workfl

A Visuo-Tactile Data Collection System with Haptic Feedback for Coarse-to-Fine Imitation Learning

arXiv:2605.08757v1 Announce Type: new Abstract: We present a visuo-tactile data-collection system that generates temporally structured, contact-rich demonstrations for imitation learning. Conventional

researcharxiv-cs-ro

Hardware

AAAC: Activation-Aware Adaptive Codebooks for 4-bit LLM Weight Quantization

arXiv:2605.08692v1 Announce Type: cross Abstract: Post-training weight-only quantization to 4 bits is widely used to reduce the memory and compute costs of large language model inference. Existing PTQ

hardwarearxiv-cs-cl

Above and Below: Heterogeneous Multi-robot SLAM Across Surface and Underwater Domains

arXiv:2605.09811v1 Announce Type: new Abstract: Multi-robot simultaneous localization and mapping (SLAM) is a fundamental task in multi-robot operations. Robots must have a common understanding of the

agentsarxiv-cs-ro

Absurd World: A Simple Yet Powerful Method to Absurdify the Real-world for Probing LLM Reasoning Capabilities

arXiv:2605.09678v1 Announce Type: new Abstract: While extremely powerful and versatile at various tasks, the thinking capabilities of large language models (LLMs) are often put under scrutiny as they

applicationsarxiv-cs-ai

Accelerating Power Method with Fast Sketching for Stronger Low-Rank Approximation

arXiv:2605.09755v1 Announce Type: cross Abstract: The power method is one of the most fundamental tools for extracting top principal components from data through low-rank matrix approximation. Yet, wh

Accelerating Zeroth-Order Spectral Optimization with Partial Orthogonalization from Power Iteration

arXiv:2605.09034v1 Announce Type: new Abstract: Zeroth-order (ZO) optimization has become increasingly popular and important in fine-tuning large language models (LLMs), especially on edge devices due

local-aiarxiv-cs-lg

Acceptance Cards:A Four-Diagnostic Standard for Safe Fine-Tuning Defense Claims

model-releasesarxiv-cs-ai

arXiv:2605.10575v1 Announce Type: cross Abstract: Safe fine-tuning defenses are often endorsed on the basis of a held-out gap reduction, but the same reduction can come from sampling noise, subject ar

Accurate Trajectory Tracking with MPCC for Flapping-Wing MAVs

arXiv:2605.06042v2 Announce Type: replace Abstract: Flapping-wing micro aerial vehicles offer quieter and safer operation than rotary-wing drones, yet achieving precise autonomous control of bird-scal

agentsarxiv-cs-ro

Ace-Skill: Bootstrapping Multimodal Agents with Prioritized and Clustered Evolution

arXiv:2605.08887v1 Announce Type: new Abstract: Self-evolving agents present a promising path toward continual adaptation by distilling task interactions into reusable knowledge artifacts. In practice

Achieving Better Local Regret Bound for Online Non-Convex Bilevel Optimization

arXiv:2602.06457v2 Announce Type: replace Abstract: Online bilevel optimization (OBO) has emerged as a powerful framework for many machine learning problems. Prior works have developed several algorit

Action-Guided Attention for Video Action Anticipation

model-releasesarxiv-cs-cv

arXiv:2603.01743v2 Announce Type: replace Abstract: Anticipating future actions in videos is challenging, as the observed frames provide only evidence of past activities, requiring the inference of la

ActivationReasoning: Logical Reasoning in Latent Activation Spaces

arXiv:2510.18184v3 Announce Type: replace-cross Abstract: Large language models (LLMs) excel at generating fluent text, but their internal reasoning remains opaque and difficult to control. Sparse aut

safetyarxiv-cs-ai

Tutorials

Active Learning for Gaussian Process Regression Under Self-Induced Boltzmann Weights

arXiv:2605.10654v1 Announce Type: cross Abstract: We consider the active learning problem where the goal is to learn an unknown function with low prediction error under an unknown Boltzmann distributi

tutorialsarxiv-cs-ai