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Why Your Agents Can’t Read Enterprise Documents — and How to Fix It

AI agents struggle to effectively process and extract information from complex enterprise documents due to limitations in context windows, reasoning capabilities, and handling of unstructured data for

tutorialsdatabricks

16 Apr 2026

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Tutorials

X-Diffusion: Training Diffusion Policies on Cross-Embodiment Human Demonstrations

DGX agent

arXiv:2511.04671v2 Announce Type: replace-cross Abstract: Human videos are a scalable source of training data for robot learning. However, humans and robots significantly differ in embodiment, making

tutorialsarxiv-cs-cv

16 Apr 2026

Accelerating decode-heavy LLM inference with speculative decoding on AWS Trainium and vLLM

Speculative decoding is a technique used to accelerate the slow, sequential token generation (decode stage) in LLM inference. This method significantly reduces latency and improves hardware utilizatio

tutorialsaws-ml-blog

Add Flexibility And Resiliency To Your Cloud With Reserved IPs!

Discover how Vultr's Reserved IPs enhance your cloud environment with robust control, simplified upgrades, and increased redundancy. Allocate specific IPs, streamline server migrations, and eliminate

tutorialsvultr

Announcing Vultr's New ISO Certifications

Discover Vultr’s commitment to IT service management and data security with ISO 20000 and ISO 27001 certifications. Learn how Vultr ensures compliance with industry best practices and protects your da

tutorialsvultr

Build a personal organization command center with GitHub Copilot CLI

Learn about the productivity tool one GitHub engineer built, and how AI supported the development process. The post Build a personal organization command center with GitHub Copilot CLI appeared first

tutorialsgithub-ai-blog

Characterizing higher-order representations through generative diffusion models explains human decoded neurofeedback performance

arXiv:2503.14333v4 Announce Type: replace-cross Abstract: Brains construct not only 'first-order' representations of the environment but also 'higher-order' representations about those representations

Data-driven Reachable Set Estimation with Tunable Adversarial and Wasserstein Distributional Guarantees

arXiv:2604.12654v1 Announce Type: cross Abstract: We study finite horizon reachable set estimation for unknown discrete-time dynamical systems using only sampled state trajectories. Rather than treati

Designing for Error Recovery in Human-Robot Interaction

arXiv:2604.12473v1 Announce Type: new Abstract: This position paper looks briefly at the way we attempt to program robotic AI systems. Many AI systems are based on the idea of trying to improve the pe

tutorialsarxiv-cs-ro

Direct Discrepancy Replay: Distribution-Discrepancy Condensation and Manifold-Consistent Replay for Continual Face Forgery Detection

arXiv:2604.12941v1 Announce Type: new Abstract: Continual face forgery detection (CFFD) requires detectors to learn emerging forgery paradigms without forgetting previously seen manipulations. Existin

Generating Effective CoT Traces for Mitigating Causal Hallucination

arXiv:2604.12748v1 Announce Type: new Abstract: Although large language models (LLMs) excel in complex reasoning tasks, they suffer from severe causal hallucination in event causality identification (

tutorialsarxiv-cs-cl

Heuristic Classification of Thoughts Prompting (HCoT): Integrating Expert System Heuristics for Structured Reasoning into Large Language Models

arXiv:2604.12390v1 Announce Type: new Abstract: This paper addresses two limitations of large language models (LLMs) in solving complex problems: (1) their reasoning processes exhibit Bayesian-like st

Instantiating Bayesian CVaR lower bounds in Interactive Decision Making Problems

arXiv:2604.12519v1 Announce Type: new Abstract: Recent work established a generalized-Fano framework for lower bounding prior-predictive (Bayesian) CVaR in interactive statistical decision making. In

It really took me a while to 'get it' when it comes to nbdev. But I gotta hand it to @jeremyphoward this way of working makes too much sense…

tutorialsjeremy-howard--x

It really took me a while to 'get it' when it comes to nbdev. But I gotta hand it to @jeremyphoward this way of working makes too much sense once you're used to it. As of today, I am working on tools

Iterative Compositional Data Generation for Robot Control

arXiv:2512.10891v4 Announce Type: replace-cross Abstract: Collecting robotic manipulation data is expensive, making it impractical to acquire demonstrations for the combinatorially large space of task

It's Tax Day, and no one knows how to file for prediction market winnings

As of Tax Day 2026, the IRS has issued no formal guidance on how to classify and report prediction market winnings, leaving traders and tax professionals in a grey area where gains could plausibly be

tutorialsars-technica

Joint Flashback Adaptation for Forgetting-Resistant Instruction Tuning

arXiv:2505.15467v2 Announce Type: replace-cross Abstract: Large language models have achieved remarkable success in various tasks. However, it is challenging for them to learn new tasks incrementally

KoCo: Conditioning Language Model Pre-training on Knowledge Coordinates

arXiv:2604.12397v1 Announce Type: new Abstract: Standard Large Language Model (LLM) pre-training typically treats corpora as flattened token sequences, often overlooking the real-world context that hu

tutorialsarxiv-cs-cl

Last year ChatGPT were perfect as a traveling guide. Now it kinda screw me over.

A Reddit user on r/ChatGPT shares a personal account of relying on ChatGPT as a travel planning guide with good results in the past, but experiencing problems more recently — likely due to outdated in

tutorialsr-chatgpt

Learn more 🔗 https://www.pinecone.io/blog/dedicated-read-nodes-ga/

Pinecone has announced the general availability (GA) of Dedicated Read Nodes, a feature designed to improve query performance and scalability for vector database workloads. Dedicated Read Nodes allow

tutorialspinecone--x

Limit Bandwidth Overage!

Vultr provides guidance on managing and limiting bandwidth overage charges on their cloud infrastructure platform. The article likely covers strategies such as monitoring bandwidth usage through the V

tutorialsvultr

Listening Deepfake Detection: A New Perspective Beyond Speaking-Centric Forgery Analysis

arXiv:2604.12650v1 Announce Type: new Abstract: Existing deepfake detection research has primarily focused on scenarios where the manipulated subject is actively speaking, i.e., generating fabricated

LLM-Guided Semantic Bootstrapping for Interpretable Text Classification with Tsetlin Machines

arXiv:2604.12223v1 Announce Type: cross Abstract: Pretrained language models (PLMs) like BERT provide strong semantic representations but are costly and opaque, while symbolic models such as the Tsetl

LLM-Guided Task- and Affordance-Level Exploration in Reinforcement Learning

arXiv:2509.16615v2 Announce Type: replace Abstract: Reinforcement learning (RL) is a promising approach for robotic manipulation, but it can suffer from low sample efficiency and requires extensive ex

tutorialsarxiv-cs-ro

NoisePrints: Distortion-Free Watermarks for Authorship in Private Diffusion Models

arXiv:2510.13793v2 Announce Type: replace Abstract: With the rapid adoption of diffusion models for visual content generation, proving authorship and protecting copyright have become critical. This ch

RLGT: A reinforcement learning framework for extremal graph theory

arXiv:2602.17276v2 Announce Type: replace Abstract: Reinforcement learning (RL) is a subfield of machine learning that focuses on developing models that can autonomously learn optimal decision-making

RPRA: Predicting an LLM-Judge for Efficient but Performant Inference

arXiv:2604.12634v1 Announce Type: new Abstract: Large language models (LLMs) face a fundamental trade-off between computational efficiency (e.g., number of parameters) and output quality, especially w

Sample Complexity of Autoregressive Reasoning: Chain-of-Thought vs. End-to-End

arXiv:2604.12013v1 Announce Type: new Abstract: Modern large language models generate text autoregressively, producing tokens one at a time. To study the learnability of such systems, Joshi et al. (CO

SDFed: Bridging Local Global Discrepancy via Subspace Refinement and Divergence Control in Federated Prompt Learning

arXiv:2602.08590v4 Announce Type: replace Abstract: Vision-language pretrained models offer strong transferable representations, yet adapting them in privacy-sensitive multi-party settings is challeng

Siamese Foundation Models for Crystal Structure Prediction

arXiv:2503.10471v2 Announce Type: replace-cross Abstract: Predicting crystal structures from chemical compositions is a fundamental challenge in materials discovery, complicated by complex 3D geometri

Social Learning Strategies for Evolved Virtual Soft Robots

arXiv:2604.12482v1 Announce Type: cross Abstract: Optimizing the body and brain of a robot is a coupled challenge: the morphology determines what control strategies are effective, while the control pa

StructDiff: A Structure-Preserving and Spatially Controllable Diffusion Model for Single-Image Generation

arXiv:2604.12575v1 Announce Type: new Abstract: This paper introduces StructDiff, a generative framework based on a single-scale diffusion model for single-image generation. Single-image generation ai

SubFlow: Sub-mode Conditioned Flow Matching for Diverse One-Step Generation

arXiv:2604.12273v1 Announce Type: cross Abstract: Flow matching has emerged as a powerful generative framework, with recent few-step methods achieving remarkable inference acceleration. However, we id

TimeSAF: Towards LLM-Guided Semantic Asynchronous Fusion for Time Series Forecasting

arXiv:2604.12648v1 Announce Type: cross Abstract: Despite the recent success of large language models (LLMs) in time-series forecasting, most existing methods still adopt a Deep Synchronous Fusion str

Ultra-low-light computer vision using trained photon correlations

arXiv:2604.11993v1 Announce Type: new Abstract: Illumination using correlated photon sources has been established as an approach to allowing high-fidelity images to be reconstructed from noisy camera

Using Learning Progressions to Guide AI Feedback for Science Learning

arXiv:2603.03249v2 Announce Type: replace Abstract: Generative artificial intelligence (AI) offers scalable support for formative feedback, yet most AI-generated feedback relies on task-specific rubri

tutorialsarxiv-cs-cl

We @TeraflopAI have worked together with @johngfriedman and @daftengine to open-sourced all major filings from SEC EDGAR completely for free…

tutorialsjeremy-howard--x

We @TeraflopAI have worked together with @johngfriedman and @daftengine to open-sourced all major filings from SEC EDGAR completely for free on @huggingface. It is now more important than ever to push

Why Did Apple Fall: Evaluating Curiosity in Large Language Models

arXiv:2510.20635v2 Announce Type: replace-cross Abstract: Curiosity serves as a pivotal conduit for human beings to discover and learn new knowledge. Recent advancements of large language models (LLMs

A Data-driven Loss Weighting Scheme across Heterogeneous Tasks for Image Denoising

arXiv:2301.06081v4 Announce Type: replace-cross Abstract: In a variational denoising model, weight in the data fidelity term plays the role of enhancing the noise-removal capability. It is profoundly

A Diffusion-Contrastive Graph Neural Network with Virtual Nodes for Wind Nowcasting in Unobserved Regions

arXiv:2604.10328v1 Announce Type: cross Abstract: Accurate weather nowcasting remains one of the central challenges in atmospheric science, with critical implications for climate resilience, energy se

A Mechanistic Analysis of Looped Reasoning Language Models

arXiv:2604.11791v1 Announce Type: cross Abstract: Reasoning has become a central capability in large language models. Recent research has shown that reasoning performance can be improved by looping an

A robust and adaptive MPC formulation for Gaussian process models

arXiv:2507.02098v2 Announce Type: replace-cross Abstract: In this paper, we present a robust and adaptive model predictive control (MPC) framework for uncertain nonlinear systems affected by bounded d

A Tale of Two Temperatures: Simple, Efficient, and Diverse Sampling from Diffusion Language Models

arXiv:2604.09921v1 Announce Type: new Abstract: Much work has been done on designing fast and accurate sampling for diffusion language models (dLLMs). However, these efforts have largely focused on th

A Unified Theory of Sparse Dictionary Learning in Mechanistic Interpretability: Piecewise Biconvexity and Spurious Minima

arXiv:2512.05534v4 Announce Type: replace-cross Abstract: As AI models achieve remarkable capabilities across diverse domains, understanding what representations they learn and how they encode concept

AbLWR:A Context-Aware Listwise Ranking Framework for Antibody-Antigen Binding Affinity Prediction via Positive-Unlabeled Learning

arXiv:2604.11272v1 Announce Type: cross Abstract: Accurate prediction of antibody-antigen binding affinity is fundamental to therapeutic design, yet remains constrained by severe label sparsity and th

ActDistill: General Action-Guided Self-Derived Distillation for Efficient Vision-Language-Action Models

arXiv:2511.18082v3 Announce Type: replace Abstract: Recent Vision-Language-Action (VLA) models have shown impressive flexibility and generalization, yet their deployment in robotic manipulation remain

Adoption and Effectiveness of AI-Based Anomaly Detection for Cross Provider Health Data Exchange

arXiv:2604.09630v1 Announce Type: cross Abstract: This study investigates the adoption and effectiveness of AI-based anomaly detection in cross-provider electronic health record (EHR) environments. It

Advancing Reasoning in Diffusion Language Models with Denoising Process Rewards

arXiv:2510.01544v2 Announce Type: replace Abstract: Diffusion-based large language models offer a non-autoregressive alternative for text generation, but enabling them to perform complex reasoning rem