Autonomous Agentic Data Engineering
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Exploring Autonomous Agentic Data Engineering for Model Specialization
arXiv:2605.30407v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated strong performance on general tasks, while often struggling to adapt to specialized domains without high-quality domain-specific data. Existing LLM-based data curation methods primarily rely on human-designed workflows, leaving it unexamined whether LLMs can autonomously execute an end-to-end data engineering pipeline for model specialization. We formalize \textbf{Autonomous Agentic Data...
Exploring Autonomous Agentic Data Engineering for Model Specialization
arXiv:2605.30407v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated strong performance on general tasks, while often struggling to adapt to specialized domains without high-quality domain-specific data. Existing LLM-based data curation methods primarily rely on human-designed workflows, leaving it unexamined whether LLMs can autonomously execute an end-to-end data engineering pipeline for model specialization. We formalize Autonomous Agentic Data Engineering, a...
WildRoadBench: A Wild Aerial Road-Damage Grounding Benchmark for Vision-Language Models and Autonomous Agents
arXiv:2605.20306v2 Announce Type: replace Abstract: We introduce WildRoadBench, a wild aerial road-damage grounding benchmark that couples direct visual grounding by vision-language models with autonomous research-and-engineering by LLM-driven agents on a single professionally annotated UAV corpus. The same image set and the same per-class AP_50 metric are evaluated under two protocols. The VLM Track measures whether a fixed VLM can localise domain-specific damage from one image and one...
EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning
arXiv:2606.03108v1 Announce Type: new Abstract: Autonomous LLM training is often framed as recipe search, which leaves the training harness largely static. This limitation sharpens in agentic RL, where shifting bottlenecks and scalar rewards mask diverse failure modes. We introduce EvoTrainer, an autonomous training framework that co-evolves LLM policies and training-side harnesses through empirical feedback: it diagnoses rollout-level evidence, revises diagnostics, backtests interventions,...
Post-Deterministic Distributed Systems: A New Foundation for Trustworthy Autonomous Infrastructure
Announce Type: new Abstract: For decades, distributed systems have typically assumed that correct participants execute protocol-specified behavior with stable, externally defined, and deterministic semantics. Classical theory has extensively parameterized network timing, communication topologies, and failure domains, but this participant model has remained comparatively fixed. The integration of autonomous reasoning engines, stochastic model-driven agents, and policy-driven actors into cloud...
Experiments in Agentic AI for Science
arXiv:2605.26305v2 Announce Type: replace Abstract: This paper details two novel frameworks for developing autonomous, agentic AI in scientific workflows. Both systems leverage a hybrid Local Body, Remote Brain architecture via Google Colab, utilizing Python-based local orchestrators to invoke large language model (LLM) cloud backends. The first agent, DeepTS/DeepCollector, automates the large-scale curation, extraction, and deduplication of time-series datasets.
Nvidia partners with LG robotics to build humanoid robots in South Korea
NVIDIA and LG Group are building an AI factory to accelerate LG Group’s next wave of AI-driven businesses, spanning robotics, autonomous driving, data center technologies and GPU cloud services. The AI factory will provide LG Group with accelerated computing infrastructure to train, simulate, validate and deploy AI-based applications across its key businesses. The collaboration brings together NVIDIA’s full-stack, end-to-end AI factory platform with LG Group’s global leadership in consumer...
$5 billion-plus company GitLab cuts hundreds of jobs, exits 22 countries; CEO blames it on AI
GitLab is laying off around 350 employees, roughly 14% of its workforce, and pulling out of 22 countries—a restructuring CEO Bill Staples is pinning on what he calls the "agentic era" of software development. The DevSecOps company announced the cuts on Tuesday alongside a first quarter that beat Wall Street, with revenue up 23% to $264.2 million and adjusted earnings two cents above estimates. Shares climbed 7% after hours to $34.05, pushing market cap past $5 billion.
When AI Builds Itself: Our progress toward recursive self-improvement
For most of AI’s history, humans drove every step in its development cycle. But at Anthropic, we are delegating a growing share of AI development to AI systems themselves, which is speeding up our work. Taken far enough, and given enough compute, that trend points to an AI system capable of fully autonomously designing and developing its own successor.
SVI-Bench: A Dynamic Microworld for Strategic Video Intelligence
arXiv:2605.31529v1 Announce Type: new Abstract: True video intelligence demands more than recognizing what is visible: it requires reasoning about why events unfold, predicting what would change under different conditions, and deciding what to do next. We refer to this progression, from perception through causal reasoning and simulation to strategic planning, as Strategic Video Intelligence (SVI).