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GraphDecisionBench: Benchmarking System One Models on Graph Tasks

arXiv:2610.06354v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly explored for graph understanding and decision-making, while System One models such as Jev select direc

Local Ai

Content type

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graphsignal-top: a `top` for inference GPUs in the open-source Graphsignal profiler, kernels ranked by time, live, with readable names

DGX agent

A few weeks ago we posted the open-source Graphsignal profiler here: wrap vllm serve / sglang serve / whatever in graphsignal-run and it serves everything it measures as JSON, mainly so an agent can r

local-air-localllama

12 Oct 2026

Grounded-Dreamer: Robot Learning from World Model Synthetic Data with Physical Grounding

arXiv:2610.13164v1 Announce Type: new Abstract: Scaling robot manipulation policies requires large amounts of diverse, action-labeled data, yet collecting such data in the real world remains expensive

safetyarxiv-cs-ro

Ha gave this take to our team last week Will share some (very basic) stuff I vibe coded around this shortly Lots to be explored here!

Ha gave this take to our team last week Will share some (very basic) stuff I vibe coded around this shortly Lots to be explored here! One of my more controversial bets: the AI feed will be the form fa

agentsharrison-chase--x

Local Ai

HAFixAgent: History-Aware Agentic Program Repair

arXiv:2511.01047v4 Announce Type: replace-cross Abstract: Automated program repair (APR) has recently shifted toward large language models and agent-based systems, while prior work shows that reposito

local-aiarxiv-cs-ai

Harmonic analysis and circuit approximation of Sobolev functions on quantum input spaces

arXiv:2610.13477v1 Announce Type: cross Abstract: We derive approximation rates for Sobolev functions defined on the complex unit sphere and complex projective space, representing quantum states, and

HART: Hybrid Attention-RWKV Time series Foundation Model

arXiv:2610.12883v1 Announce Type: new Abstract: Non-autoregressive multi-step forecasting (NAF) enables time series foundation models to predict an entire future horizon in a single forward pass. Desp

Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learning

arXiv:2505.11304v4 Announce Type: replace-cross Abstract: Federated learning (FL) commonly involves clients with diverse communication and computational capabilities. Such heterogeneity can significan

researcharxiv-cs-ai

Hidden Data Challenges in Clinical Machine Learning

arXiv:2610.13350v1 Announce Type: new Abstract: Widely reused preprocessing pipelines for electronic health records can encode assumptions about when information became available, what clinical episod

safetyarxiv-cs-lg

Local Ai

Hide and Seek: HIDE Concepts to SEEK explanations for black box models

arXiv:2610.13935v1 Announce Type: cross Abstract: Interpreting machine learning model predictions is a central challenge, particularly for black-box vision models. Concept-based attribution methods in

local-aiarxiv-cs-ai

Hindsight Structured Advice Distillation: Steering Executor Agents with Advisors Trained on Their Failures and Task Characteristics

arXiv:2610.13000v1 Announce Type: new Abstract: Language-model agents are used for specialized work such as financial research, healthcare and law. Such agents are often capable enough for a task but

Hitting-Time Bounds for Asynchronous Q-Learning

arXiv:2610.13901v1 Announce Type: cross Abstract: We study the last iterate of standard asynchronous Q-learning from a single trajectory of a finite Markov decision process. The trajectory follows a f

HorizonNav: Geometry-Guided Video Diffusion for Anticipatory 3D Robot Navigation

arXiv:2610.13849v1 Announce Type: new Abstract: We present HorizonNav, a video diffusion-based framework for anticipatory robot navigation that transforms a single RGB image and a text prompt into a g

safetyarxiv-cs-ro

Tutorials

Host-Aware Web Crawling for LLM Pretraining Data

arXiv:2610.13321v1 Announce Type: new Abstract: Web crawling is a major source of large language model (LLM) pretraining data, yet data-selection pipelines discard a large share of crawled pages. We i

tutorialsarxiv-cs-cl

Hardware

How Much Computation Does Every Forecast Need? Structured Block Skipping in Two Time-Series Foundation Models

arXiv:2610.13208v1 Announce Type: new Abstract: Time-series foundation models use the same network depth for every input, although forecasting windows differ in structure and difficulty. We test wheth

hardwarearxiv-cs-lg

How Reliable are AMR Parser Outputs? Uncertainty Estimation for AMR Parsing

arXiv:2610.13236v1 Announce Type: cross Abstract: Recent advances have significantly improved the performance of Abstract Meaning Representation (AMR) parsers, yet determining when their outputs can b

researcharxiv-cs-ai

How to test agent tool descriptions with Harbor and Phoenix

Build a reproducible benchmark, compare tool use across agents, and test whether a description change reduces unnecessary work while preserving outcomes. The post How to test agent tool descriptions w

model-releasesarize-ai

How Well Do Prior-Data Fitted Networks Amortize Bayesian Inference?

arXiv:2610.13764v1 Announce Type: cross Abstract: Prior-data fitted networks (PFNs) are trained to approximate the Bayesian posterior predictive distribution (PPD) of a synthetic prior, yet results ab

Human and LLM Facilitation in Collaborative Design Meetings: A Computational Behavioral Comparison

arXiv:2610.13082v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly being explored as facilitators of group discussion and collaborative work, but the behavioral differences

Human2Dex: Dexterous Robot Learning from Wrist-Centric Human Demonstrations

arXiv:2610.13119v1 Announce Type: new Abstract: Collecting dexterous manipulation demonstrations for each robot hand requires repeated task-specific data acquisition. We ask whether one shared corpus

safetyarxiv-cs-ro

Humanize MLE Agents: Multi-Agent Session Alternation Beyond Single-Agent Limits

arXiv:2610.13494v1 Announce Type: new Abstract: Existing LLM-based agent approaches for long-horizon machine learning engineering (MLE) often use context engineering, implemented through custom harnes

agentsarxiv-cs-ai

Humanize Olympiad Agents: A Multi-Session Workflow for Olympiad Problem Solving with Solve, Review, Wiki, Verify

arXiv:2610.13632v1 Announce Type: new Abstract: Large language model (LLM) agents are rapidly improving on long-horizon scientific and olympiad tasks. Recent systems have produced a Lean-formalized re

Applications

HumanX: Toward Agile and Generalizable Humanoid Interaction Skills from Human Videos

arXiv:2602.02473v2 Announce Type: replace-cross Abstract: Enabling humanoid robots to perform agile and adaptive interactions has long been a core challenge in robotics. Current approaches are bottlen

applicationsarxiv-cs-lg

Hyper Algorithm Design Agent: Evolving Learnable Optimizer from Zero

arXiv:2609.35328v2 Announce Type: replace Abstract: Meta-Black-Box Optimization (MetaBBO) is one of the highlights in the recent AI for Optimization trend. This paradigm's bi-level workflow leverages

safetyarxiv-cs-ai

i don’t know whether this is a joke or serious but the whole challenge with generative AI is that it literally cannot be trusted to follow “…

i don’t know whether this is a joke or serious but the whole challenge with generative AI is that it literally cannot be trusted to follow “simple, generalizable instructions”. the inability of most p

safetygary-marcus--x

I made an open-source AI leaderboard that shows the best models your machine can run

model-releasesr-localllama

Hey everyone, I made whatmodel.sh To use it, Enter your RAM and GPU See what models you can run locally with its rank and the command to run it Compare them with API models on the same score Building

Image-Based Path-Planning for Navigation of Soft-Growing Robots in the Spinal Sub-Arachnoid Space

arXiv:2610.12631v1 Announce Type: new Abstract: Recently, soft robotic procedures are being developed aiming at achieving targeted minimally invasive operations or drug administration in specific loca

researcharxiv-cs-ro

In-Context Learning for System Identification: Probabilistic Formulation, Long-Sequence Handling, and Benchmark Validation

arXiv:2610.13411v1 Announce Type: cross Abstract: Traditional system identification typically involves two steps: initially, training data is processed to estimate a model of the system of interest; t

Incremental Retrieval-Augmented Generation for Human-in-the-Loop Decision Support

arXiv:2610.12666v1 Announce Type: cross Abstract: We introduce iRAG, a human-in-the-loop decision-support system combining Retrieval-Augmented Generation (RAG) with Hybrid Decision-Making. Unlike conv

agentsarxiv-cs-ai

Hardware

Inference-Time Steering Across Scaffolds and Epitopes for Sample-Efficient De Novo Nanobody Design

arXiv:2610.13844v1 Announce Type: new Abstract: Generative molecular design typically relies on over-generation and post-hoc filtering. Inference-time steering methods like Feynman-Kac (FK) steering i

hardwarearxiv-cs-lg

Tutorials

Infinite-Dimensional Closed-Loop Inverse Kinematics for Soft Robots via Neural Operators

arXiv:2602.18655v3 Announce Type: replace Abstract: For fully actuated rigid robots, kinematic inversion is a purely geometric problem, efficiently solved by closed-loop inverse kinematics (CLIK) sche

tutorialsarxiv-cs-ro

Infinitesimal Operator World Models: Finite-Sample Guarantees for Offline Reinforcement Learning

arXiv:2511.10383v2 Announce Type: replace-cross Abstract: Continuous-time stochastic processes underlie many natural and engineered systems. In healthcare, autonomous driving, and industrial control,

agentsarxiv-cs-lg

Inject a Turn, Not a Token: Learning Where to Redirect LLM Reasoning for Self-Correction

arXiv:2610.13672v1 Announce Type: cross Abstract: Large language models struggle to reliably identify and correct errors in their own reasoning. Unlike prior training-based approaches that fine-tune a

Instance-Level Difficulty: A Missing Perspective in Machine Unlearning

arXiv:2410.03043v3 Announce Type: replace Abstract: Current research on deep machine unlearning primarily focuses on improving or evaluating the overall effectiveness of unlearning methods while overl

Introducing DigitalOcean Agent Data Services: The Data Foundation for Agents

Agents are only as capable as the context they can reach. An agent with reliable access to the right information makes decisions grounded in fact; one without it guesses. And the work agents produce i

agentsdigitalocean

Introducing DigitalOcean Insights: Built-in Observability for Your Agents, Applications, and Infrastructure

Coding agents have changed how fast teams ship, and more of the software they ship now includes agents. A production agent spans model calls, routing decisions, tools, and the infrastructure underneat

agentsdigitalocean

Introducing DigitalOcean Signals and Simulations: see how your agents and models really behave in production, then test the fix before it ships

model-releasesdigitalocean

You ship an agent. It mostly works. Then the feedback loop becomes a support ticket: a user got the wrong answer, a tool call looped four times, a refund went out that shouldn’t have. Standard observa

Introducing QwenGyre, bringing elastic RL to extreme-long-horizon (XLong) agent training. ✨ It launches with two core designs: 🥳 - Elastic …

Introducing QwenGyre, bringing elastic RL to extreme-long-horizon (XLong) agent training. ✨ It launches with two core designs: 🥳 - Elastic Scheduler: reallocates GPUs between rollout & training as dem

model-releasesqwen--x

Introducing RSI-Anything: Unlocking Recursive Self-Improvement with World Models 🚀 Can AI learn to improve itself by predicting the consequ…

Introducing RSI-Anything: Unlocking Recursive Self-Improvement with World Models 🚀 Can AI learn to improve itself by predicting the consequences of its actions? Built on JEPA-Anything, RSI-Anything us

safetyyann-lecun--x

Inverse Synthesis of Shock Time Series from Shock Response Spectra Using Ensembles of Conditional Variational Autoencoders

arXiv:2610.12820v1 Announce Type: new Abstract: The shock response spectrum (SRS) characterizes the peak responses of single-degree-of-freedom systems to transient acceleration. Because the mapping fr

JANUS: Enhancing Long-Horizon Autonomous Research with Co-Evolving World Models

arXiv:2610.13194v1 Announce Type: new Abstract: Long-horizon autonomous research has shown promise in accelerating scientific progress through sustained cycles of hypothesis generation, implementation

agentsarxiv-cs-ai

JEPA-kMMVAE: CSI Prediction for Time-Varying Wireless Channels

arXiv:2610.13315v1 Announce Type: cross Abstract: This paper considers channel state information (CSI) prediction using neural networks for time-varying multi-antenna channels from noisy historical ob

JitterVLA: A Replay Benchmark for Motion Quality in VLA-Driven Human-Robot Manipulation

arXiv:2610.13628v1 Announce Type: cross Abstract: Vision-language-action (VLA) policies issue sparse targets whose arrival at the robot controller is delayed and irregular, so a reasonable predicted a

JoyInside-VLM-Agent: Product-Conditioned Multimodal Policy Learning for Embodied Agents

arXiv:2610.12957v1 Announce Type: cross Abstract: Embodied agents must translate multimodal observations and user requests into executable actions while adapting to product constraints, changing goals

safetyarxiv-cs-ai

Kairos: Toward a Byte-Level Foundation Model for Every Modality

arXiv:2610.12495v1 Announce Type: cross Abstract: Small language models trained on a single local machine leave little room for architectural trial and error. Kairos combines bidirectional gated-Delta

researcharxiv-cs-ai

Applications

Kernel-based External Validity of Experimental Studies

arXiv:2610.13625v1 Announce Type: cross Abstract: The external validity of a study is the extent to which its findings generalize to unseen experimental conditions, such as datasets. We formalize the

applicationsarxiv-cs-lg

Kernelized Parameter Sensitivity Atlases for Amortized Conditional Interpretability

arXiv:2610.12737v1 Announce Type: new Abstract: Gradient-based attribution provides input-specific information about model parameters, but computing it via backpropagation for many samples is expensiv

KITE: A Knowledge-Injected Temporal Encoder for Time-Series Event Forecasting

arXiv:2610.12738v1 Announce Type: cross Abstract: Forecasting rare, high-cost events in multivariate time-series systems is hard because their precursors are seldom observed, so a purely data-driven m

safetyarxiv-cs-ai