Knowledge catalogue
arXiv:2604.09418v1 Announce Type: new Abstract: This paper studies Automated Instruction Revision (AIR), a rule-induction-based method for adapting large language models (LLMs) to downstream tasks usi
Introducing DDTree: accelerates speculative decoding by drafting a tree with one block diffusion pass, then verifying multiple likely continuations together. Paper: https://liranringel.github.io/ddtre
This r/MachineLearning discussion thread addresses questions and concerns from researchers navigating the IJCAI 2026 author rebuttal phase, during which approximately 70% of submitted papers remained
Really interesting ideas are going to be increasingly at a premium as the cost of executing those ideas drops. (Our research and others shows AI is quite good at generating interesting ideas, but not
I am a Web3 Ambassador at World Liberty Financial. There are 12 of us on the team page. 4 are named Trump. 3 are named Witkoff. The page calls us 'the passionate minds shaping the future of finance.'
What if a model became the computer itself? NEW paper from Meta. (bookmark this one) What if the model wasn't just using the computer, but became the computer? New research from Meta AI and KAUST make
I just built my own wiki generator plugin for my agents. My agents can now generate wikis for anything I ask. One of my favorite wikis is called PaperWiki. This is a great example of what @karpathy de
If you’ve encountered garbled output like this while using GLM-5 or GLM-5.1 on our official service, the issue is now resolved. We've patched the underlying inference-side bugs and will be releasing a
I was unable to directly access the YouTube video at the provided URL, and my search did not return results specifically identifying that video's content. YouTube videos cannot be fetched or read a...
arXiv:2610.20871v1 Announce Type: new Abstract: This paper presents a comprehensive methodology, backed by simulation and experimental results, for an adaptive mission planning strategy that is mathem
arXiv:2610.21674v1 Announce Type: new Abstract: In this paper, we present a lightweight algorithm for modelling tabular data containing both continuous and discrete variables. The method uses empirica
arXiv:2610.21654v1 Announce Type: new Abstract: This paper presents the Digital Typhoon Dataset V3, a new version of the longest typhoon satellite image dataset spanning nearly 50 years, designed to b
arXiv:2610.21862v1 Announce Type: new Abstract: This paper introduces TagInsert, a Transformer-based model that challenges the common left-to-right (L2R) generation paradigm in Transformers, for seque
arXiv:2504.00070v2 Announce Type: replace Abstract: This paper introduces Fuzzy Attention Network-Based Transformer (FANTF), an approach for time series forecasting, classification, and anomaly detect
arXiv:2606.14977v2 Announce Type: replace-cross Abstract: This paper provides identification results to characterize a fairness-accuracy (FA) frontier, and statistical inference tools to test hypothes
arXiv:2610.20936v1 Announce Type: new Abstract: Generating dynamically feasible motions for legged and manipulating robots requires solving a large, nonlinear, non-convex optimal control problem (OCP)
arXiv:2610.09895v1 Announce Type: cross Abstract: We study a behavioral mechanism for artificial partiality in conversational agents. The paper reports no human-subjects data and makes no claim about
arXiv:2610.21400v1 Announce Type: new Abstract: Rigid closed chains can admit indeterminate internal reactions even when their motion is fully specified. This paper develops a modular inverse-dynamics
arXiv:2603.14645v2 Announce Type: replace Abstract: In this paper, we study the diffusability (learnability) of variational autoencoders (VAEs) in latent diffusion. We first derive distribution-free b
arXiv:2411.10957v2 Announce Type: replace-cross Abstract: This paper addresses domain adaptation in graph-structured data under chronological splits. We consider transductive semi-supervised node clas
arXiv:2610.20894v1 Announce Type: cross Abstract: This paper investigates the statistical behavior of Stochastic Gradient Descent (SGD) with constant step size under the framework of iterated random f
arXiv:2411.16164v2 Announce Type: replace Abstract: Text-to-image synthesis (T2I) focuses on generating high-quality images from textual descriptions In this survey, we review 490 papers on T2I spanni
arXiv:2507.03411v2 Announce Type: replace Abstract: In the era of Industry 5.0, data-driven decision-making has become indispensable for planning in service systems such as tourism. This paper propose
arXiv:2610.20764v1 Announce Type: new Abstract: This paper challenges the understanding capabilities of existing AI paradigms and emerging world models by asking a fundamental question: How quickly ca
arXiv:2610.15380v1 Announce Type: new Abstract: Bridging the structural gap between biological brains and artificial neural networks presents a pathway to altered computational dynamics. This paper in
arXiv:2610.19358v1 Announce Type: new Abstract: In this paper we study the behavior of transformer networks generating text over long periods of time. We show that even a three-token, single-layer att
arXiv:2610.15995v1 Announce Type: cross Abstract: This paper presents AutoSec-RAN, a specification-grounded large language model (LLM) framework for automated security triage of Open Radio Access Netw
arXiv:2610.18047v1 Announce Type: new Abstract: In this paper, our goal is to represent text embeddings as sketches for three main reasons. First, to visualize text representation and to explore their
arXiv:2610.19429v1 Announce Type: new Abstract: This paper presents a comprehensive comparison of four representative reinforcement learning (RL) methods in autonomous vehicle (AV) decision-making, in
arXiv:2610.20426v1 Announce Type: new Abstract: This paper considers reliable in-sequence molecular communication, where bio-nanomachines exchange molecule-encoded information in a specified order, an
arXiv:2610.15976v1 Announce Type: cross Abstract: This paper investigates a new role of randomness in stochastic policies--as a medium for embedding and communicating information. Specifically, we con
arXiv:2610.19524v1 Announce Type: new Abstract: Accurate path loss (PL) estimation is essential for wireless network planning and optimization. This paper proposes HankelKANet, a lightweight physics-i
arXiv:2610.18435v1 Announce Type: cross Abstract: Referees increasingly use large language models (LLMs), while finance and accounting journals disagree about whether to allow it. We ask which part of
arXiv:2610.20203v1 Announce Type: cross Abstract: This paper investigates the use of Behavior Cloning (BC) for autonomous driving from a bird's-eye view (BEV) perspective in simulated urban environmen
arXiv:2601.20776v2 Announce Type: replace Abstract: This paper rethinks steady-hand robotic manipulation by using a weakly supervised framework that fuses calibration-aware perception with admittance
arXiv:2610.19331v1 Announce Type: new Abstract: This paper presents LLM-jp-4, a family of Japanese sovereign large language models (LLMs) developed with an emphasis on transparency, reproducibility, a
arXiv:2610.19042v1 Announce Type: new Abstract: Understanding the mechanism behind why diffusion models memorize their training data remains an open question in generative modeling. In this paper, we
arXiv:2610.19734v1 Announce Type: new Abstract: In this paper we present a method for optimally estimating the rotation and translation between a camera and a 3-D object from point and/or line corresp
arXiv:2610.02022v2 Announce Type: replace Abstract: AI-based ideation systems are often evaluated on the novelty of their ideas, and that judgment is increasingly delegated to large language models. S
arXiv:2610.19431v1 Announce Type: new Abstract: This paper presents S^3-Nav, a structured spatial-semantic decision-making framework for language-guided robot navigation under partial observability. R
arXiv:2610.19505v1 Announce Type: new Abstract: This paper proposes the SKG (Symplectic-Kernel Gradient) reasoning engine for law-induced inference and induction. Laws are a priori; data provide insta
arXiv:2610.19135v1 Announce Type: cross Abstract: This paper extends the applicability of probabilistic graphical models to non Gaussian data. It establishes a characterization of the pairwise Markov
arXiv:2507.21137v2 Announce Type: replace Abstract: In this paper we try to answer the question 'What constitutes Sudoku difficulty rating across different Sudoku websites?' Using two distinct methods
arXiv:2511.00198v2 Announce Type: replace-cross Abstract: Autoregressive language models are usually trained with next-token prediction under a fixed left-to-right target order. This paper studies tar
arXiv:2610.18704v1 Announce Type: new Abstract: In this paper, we propose an extremely efficient, training-free method to extract token-level reward signals directly from an existing deep reward model
arXiv:2508.02773v4 Announce Type: replace-cross Abstract: The convergence of Web3 technologies and AI agents is a rapidly evolving frontier that is reshaping decentralized ecosystems. This paper prese
arXiv:2610.20429v1 Announce Type: new Abstract: Though modern AI models like LLMs predict discrete tokens, today's robotic foundation models represent actions as continuous vectors. In this paper, we
arXiv:2610.18939v1 Announce Type: cross Abstract: This paper studies Wong--Zakai convergence and Fourier neural operator learning for stochastic Volterra integral equations (SVIEs) describing random s
arXiv:2610.17312v1 Announce Type: new Abstract: This paper proposes a method for constructing a neural network that achieves perfect classification of all samples in a given training set, without taki
arXiv:2508.06313v2 Announce Type: replace Abstract: This paper presents a unified system-level modeling and control framework for an all-electric heavy-duty robotic manipulator (HDRM) driven by electr
At the moment only the flux1-krea-dev code has been release. Qwen-Image, Ideogram 4, and Flux2 'coming soon'. Project: https://rajabi2001.github.io/area/ Paper: https://arxiv.org/abs/2610.17227 Demo:
arXiv:2610.18594v1 Announce Type: cross Abstract: This paper proposes an Autonomous Cyber Operations (ACO) gym, a unified framework integrating high-fidelity network emulation, realistic user behaviou
arXiv:2610.14856v1 Announce Type: new Abstract: This paper studies the problem of recovering motion from monocular video, even when the target skeleton topology differs from the subject observed in th
arXiv:2606.17192v2 Announce Type: replace Abstract: This paper develops constrained diffusion models with primal-dual inference (PDI) to sample from optimal distributions of entropy-regularized optimi
arXiv:2601.18313v2 Announce Type: replace-cross Abstract: This paper proposes a strictly convex chance-constrained stochastic control framework that accounts for uncertainty in control specifications
arXiv:2610.15126v1 Announce Type: cross Abstract: Decentralized learning must balance collaboration with personalization across heterogeneous agents. This paper studies when cooperation can support st
arXiv:2610.14296v1 Announce Type: cross Abstract: High benchmark scores in learning-based vulnerability detection mask severe degradation under distribution shifts. This paper demonstrates that calibr
arXiv:2610.15996v1 Announce Type: new Abstract: In this paper, we propose the Dissipative Lagrangian Neural Network (dLNN), a continuous-time system identification technique that separately learns the
arXiv:2610.16142v1 Announce Type: cross Abstract: This paper develops a unified framework for distributional regression with arbitrary response variables. Rather than relying on scalar thresholds or E
arXiv:2610.15580v1 Announce Type: new Abstract: This paper presents an algorithm for detecting and fitting multiple ellipsoids in 2D, 3D and higher-dimensional data. The representation is based on tan