Knowledge catalogue
arXiv:2607.13812v1 Announce Type: cross Abstract: We introduce TCAM-Diff, a novel 3D medical image generation model that reduces the memory requirements to encode and generate high-resolution 3D data.
arXiv:2607.13077v1 Announce Type: cross Abstract: Large language models (LLMs) often produce homogeneous outputs, raising concerns that AI coding assistants may lead to convergence in the software art
arXiv:2607.13660v1 Announce Type: new Abstract: Contrastive Language-Image Pretraining (CLIP) representations form a semantic embedding space governed by cosine similarity, reflecting an intrinsic hyp
arXiv:2607.13479v1 Announce Type: cross Abstract: Estimating the full shape of a deformable object is especially challenging when vision is unavailable: in the dark, inside an opaque bag, behind the m
arXiv:2607.13456v1 Announce Type: new Abstract: We introduce a novel mathematical framework for analyzing and generating complex tree-shaped 3D objects, such as botanical trees and plants, which defor
arXiv:2606.14999v2 Announce Type: replace Abstract: Scientific user facilities generate X-ray scattering data faster than traditional workflows can process them. We address this challenge across two s
arXiv:2604.25834v2 Announce Type: replace Abstract: With the rapid development of the Internet, users have increasingly higher expectations for the recommendation accuracy of online content consumptio
arXiv:2607.11987v1 Announce Type: new Abstract: Perineural invasion (PNI) is associated with poor postoperative outcomes in intrahepatic cholangiocarcinoma, but it is confirmed by surgical pathology.
arXiv:2607.11020v2 Announce Type: replace Abstract: Continual learning promises a language model that keeps acquiring knowledge after training, with each new fact written into its weights. Whether wei
arXiv:2011.02565v2 Announce Type: replace-cross Abstract: Temporal abstraction allows reinforcement learning agents to represent knowledge and develop strategies over different temporal scales. The op
arXiv:2607.12319v1 Announce Type: new Abstract: As vision-language models (VLMs) are increasingly deployed in geospatial question answering and visual scene understanding, improving their spatial cogn
arXiv:2607.12145v1 Announce Type: cross Abstract: True causal relationships are rarely known, and inferring causal graphs from data is hard. A fundamental challenge is how to assess whether a given ca
arXiv:2607.12438v1 Announce Type: new Abstract: Deep networks trained with label noise often learn clean structure before memorizing corrupted labels. We show that this transition leaves a spectral si
arXiv:2607.11941v1 Announce Type: new Abstract: Computed tomography (CT) is a critical imaging modality for clinical diagnosis, but reducing radiation dose inevitably introduces severe noise and struc
arXiv:2607.11875v2 Announce Type: replace-cross Abstract: We present a theoretical framework to explain the emergence of inductive reasoning abilities in Transformer language models. While previous wo
arXiv:2607.12443v1 Announce Type: new Abstract: Motivated by the power of large language models, there has been renewed interest in the Gold-Angluin model of language identification in the limit, with
arXiv:2607.11974v1 Announce Type: cross Abstract: Most neural partial differential equation (PDE) surrogates learn how fields evolve after a grid has already been chosen. However, before any operator
arXiv:2607.12711v1 Announce Type: new Abstract: Vision--Language Models (VLMs) have recently demonstrated promising performance on structured visual reasoning tasks, including grid-based puzzles. Howe
arXiv:2607.12782v1 Announce Type: new Abstract: Hyperspectral foundation models learn transferable spectral-spatial representations from large-scale unlabeled data. They provide an effective paradigm
arXiv:2607.11921v1 Announce Type: cross Abstract: A key question in operator learning is how to design surrogate operators with provable approximation guarantees in reasonable computational time. Wher
arXiv:2602.04029v2 Announce Type: replace-cross Abstract: Relational Foundation Models (RFMs) facilitate data-driven decision-making by learning from complex multi-table databases. However, the divers
arXiv:2607.11916v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) with evolutionary computation has emerged as a powerful paradigm for automated heuristic design in com
arXiv:2603.27270v2 Announce Type: replace Abstract: Credal sets, i.e., closed convex sets of probability measures, provide a natural framework to represent aleatoric and epistemic uncertainty in machi
arXiv:2603.24787v2 Announce Type: replace Abstract: Routing has emerged as a promising strategy for balancing performance and cost in large language model (LLM) systems that combine lightweight models
arXiv:2602.09907v2 Announce Type: replace-cross Abstract: College students increasingly use AI chatbots to support academic reading, yet we lack granular understanding of how these interactions shape
arXiv:2607.11975v1 Announce Type: cross Abstract: Current machine unlearning methods predominantly rely on global, coarse-grained intervention strategies. They lack precise pilot signals to guide the
arXiv:2607.12094v1 Announce Type: cross Abstract: Reliable detection of out-of-distribution (OOD) samples is crucial for the safe deployment of machine learning models. Neural networks often produce o
arXiv:2607.12372v1 Announce Type: new Abstract: Multimodal semantic segmentation (MSS) is essential for robust perception in complex environments, yet its potential remains largely untapped because of
arXiv:2510.11917v2 Announce Type: replace Abstract: Dementia disorders such as Alzheimer's disease (AD) and frontotemporal dementia (FTD) exhibit overlapping electrophysiological signatures in EEG tha
Highly-recommended overview of metacognition in LLMs. (bookmark it) Interesting behaviors in LLMs like confidence calibration, self-verification, knowing when to stop, and knowing what you do not know
AI productivity is best measured by connecting AI usage to validated downstream outcomes. Tokens, prompts, and generated lines show activity, but they do not prove value. A better measurement model tr
New research from Google DeepMind on effective model routing. LLM routers get judged on accuracy and cost. Both can look great while the router is meaningless. If every model in your society responds
Using generative AI enables parallel execution of comprehensive user flow testing at scale. This solution demonstrates how to build a cloud-deployed UX testing platform that automatically generates te
Everyone keeps asking me how to build a second brain or an LLM wiki. Here is the easiest setup I have found. I took my Wiki Builder skill, installed it into HyperAgent (@hyperagentapp) as a reusable s
arXiv:2204.04883v2 Announce Type: replace-cross Abstract: With the advent of the cloud computing era, the cost of creating, capturing, and managing information has gradually decreased. The amount of d
arXiv:2607.08465v1 Announce Type: new Abstract: I-JEPA and V-JEPA learn by matching latent predictions to target encoder outputs rather than regenerating the original input, and this has worked well f
arXiv:2607.07743v1 Announce Type: cross Abstract: Self-organization is an emergent property of life, driven by the collective behavior of individual components acting on local information. Biological
In this post, you will learn four deployment patterns for taking models that have already been quantized with Unsloth and deploying them on AWS infrastructure. The patterns use Amazon Elastic Compute
arXiv:2607.08765v1 Announce Type: new Abstract: In this work, we present Canvas360, a two-stage framework for in-context panoramic generation that combines geometry-aware pretraining with downstream t
arXiv:2607.08475v1 Announce Type: new Abstract: Multi-modality transportation refers to urban systems composed of multiple transportation modes, such as traffic flow and public transit, whose dynamics
I fell for a lot of progressive nonsense, and hard. I was literally trans. But two things I can honestly say I never fell for were 1. alternative ways of knowing. Science obviously works, and it’s har
'I think it's important for people to understand how code works.' Geoffrey Litt's Design Eng track keynote is live now: https://www.youtube.com/watch?v=WkBPX-oDMnA Thank you @NotionHQ for supporting h
arXiv:2607.08004v1 Announce Type: new Abstract: Object detection in geospatial scenes, such as satellite and aerial imagery, poses significant challenges due to the varying orientations and densities
arXiv:2510.01788v2 Announce Type: replace Abstract: This work focuses on learning non-canonical Hamiltonian dynamics from data, where long-term predictions require the preservation of structure both i
arXiv:2607.07884v1 Announce Type: new Abstract: In this short note we consider the gradient descent dynamics of deep scalar linear networks, f(x) = prod_{l=1}^L w_l x, which enjoy exact time-course so
arXiv:2603.02204v2 Announce Type: replace Abstract: Selective conformal prediction can yield substantially tighter uncertainty sets when we can identify calibration examples that are exchangeable with
Sam Altman should be in prison. But if we can’t have that, I’ll settle for him being a broke pariah. OpenAI hardware chief Tang Tan allegedly helped coach recruits on how to evade Apple’s data securit
arXiv:2607.08641v1 Announce Type: new Abstract: Over the last few years, there has been an increased interest in making machine learning models more interpretable. Although a great deal of effort goes
arXiv:2607.08572v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) often follow a fixed Think-then-Answer paradigm, which is inefficient in heterogeneous multitask settings becau
arXiv:2511.07624v3 Announce Type: replace Abstract: Markerless motion tracking has advanced rapidly in the past 10 years and currently offers powerful opportunities for behavioural, clinical, and biom
arXiv:2607.07846v1 Announce Type: new Abstract: VectorizationLLM is a specialized Large Language Model based on Google open-weight LLMs. The model is designed to assist students to learn smart vectori
Volkswagen CEO Oliver Blume presented a comprehensive restructuring plan to the supervisory board aimed at making the group more robust and competitive in a challenging environment. The plan reportedl
arXiv:2607.08312v1 Announce Type: new Abstract: How should language interface with a world model's discrete symbol system? The dominant paradigm -- end-to-end injection of LLM/VLM features into robot
arXiv:2607.06740v1 Announce Type: cross Abstract: Soft robots have attracted significant attention in applications such as medical intervention, rehabilitation, and robotic manipulation due to their i
arXiv:2607.07470v1 Announce Type: new Abstract: Why does contrastive learning with simple images and augmentations yield useful representations for downstream tasks? We address this question by analyt
A Visual Introduction to Information Theory (bookmark it) Information Theory is such an beautiful and powerful subject. In the era of AI, it's worth spending time learning about it. Here is a highly-r
Adam Thierer had written the landmark book on 'Permissionless Innovation.' We get the best ideas when we don't have to ask the government in advance for permission to invent. Protecting open source AI
arXiv:2211.14966v2 Announce Type: replace Abstract: Deep neural networks (DNNs) are highly vulnerable to adversarial attacks. Ideally, a robust model should perform well on both perturbed training dat
Also this graph that shows what an 'AI-pilled' startup looks like, and how uncommon it is even among startups: the answer when founders are asked 'Without GenAI, how many additional employees would yo
arXiv:2602.06838v3 Announce Type: replace Abstract: Federated learning enables collaborative model training across distributed clients while preserving data privacy. However, in practical deployments,