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Vals AI updated its forecast for full recursive AI self-improvement to July 2027, as Claude Opus 5.5 became the first model to beat a published human reference on an autonomous AI research benchmark.
A part-time AutoResearch practitioner questions how much of autonomous research agent evaluation is genuine scientific inquiry versus optimization search within human-defined problem spaces, and asks what agents would need to demonstrate real research judgment beyond score improvement.
The paper introduces MIRA, a hierarchical meta-reasoning architecture for long-horizon research agents that separates research allocation from execution, and MIRA-AC, a generative actor-critic that learns credit assignment at meta-reasoning decision boundaries, improving theorem proving and neural architecture research.
Researchers at Chalmers University of Technology developed a closed-loop AI scientist that generates biological hypotheses, designs and runs experiments via lab robots, and learns from the results, testing it on yeast by producing 1,933 candidate hypotheses.
An analysis of Vals' measured autonomous-R&D trend methodology projects that frontier-level AI researchers will be reached by July 2027, coinciding exactly with the timeline previously projected by the AI 2027 scenario report.
Qwen's new Planner-Agent preprint describes an AI system that generates its own tasks, collects training data, and feeds failures back into model training while humans approve changes, pointing toward increasingly automated AI research.
Ten Sonnet 5.5 AI agents conducted 15 hours of autonomous research to solve a mathematical problem from 1904, producing a proof with 17,895 lines.
This paper applies Andrej Karpathy's AutoResearch paradigm to production-scale machine learning, identifying five recurring failure modes and proposing a multi-agent framework that achieves significant performance improvements over hand-tuned baselines.
Researchers at Microsoft introduced DualGraph, an architecture that splits agent memory into Outline and Knowledge Graphs to enhance autonomous research, achieving unprecedented RACE scores and cost savings with Claude Opus 5.5 and GPT-6 Sol.
ScientistTwo is an autonomous multi-agent framework from Google Cloud AI Research that conducts end-to-end scientific discovery cycles without human intervention, benchmarking against top AI conferences like ICLR, ICML, and NeurIPS and outperforming human state-of-the-art models.
Introduces PrimeScientist, a method for strategic allocation of research effort in autonomous research agents using an adaptive MCTS-based policy to improve research quality and sample efficiency under resource constraints.
Auto-RecSys is an autonomous research system for automating long-horizon experimentation on industry-scale recommendation models, using distributed execution, centralized memory, and cognitive-procedural separation to improve efficiency and reliability.
An autonomous research program by Qiushi Engine conducted end-to-end research on BabyLM 2026 Strict-Small, improving data-efficient language models through principle-guided methods and achieving the highest score in the public snapshot.
Meta's autonomous AI research system AIRA₃ placed 8th out of approximately 4,000 teams to win gold in a NVIDIA Kaggle competition to fine-tune a 30B Nemotron model, outperforming human competitors with access to the same tools.
GPT-6 Astra autonomously recreated the Palace of Fine Arts in Blender by researching hundreds of reference photos, iterating on the 3D scene, and rendering a video with minimal human steering.
This paper presents AIMC, a visual analytics framework for human oversight of autonomous scientific discovery, enabling monitoring and understanding of AI-generated research artifacts.
Starpower Technology has developed arXiv-WVY-43M, a tiny 43.5M parameter language model trained from scratch on arXiv titles and abstracts for autonomous research applications.
Amplio is a lightweight, robust agent harness open-sourced by Google DeepMind for autonomous long-horizon AI research runs, featuring crash-resume capabilities and a simple step model.
The Station is an open-world multi-agent environment where AI agents autonomously build a scientific literature, leading to novel discoveries on five mathematical problems out of twelve evaluated.
AutoResearch introduces a two-stage autonomous system that grounds research ideas through integrated generation and evidence-based execution to improve experimental reliability and reduce hallucinations.