Topic

AI Research Automation

Efforts to have AI systems conduct machine-learning research, and evaluations of how far that capability currently reaches.

Current stories

buildConfirmed6 publishers

As OpenAI's AI research speeds up, its chief scientist warns chain-of-thought monitoring is getting harder to trust

OpenAI's research-throughput report and its chief scientist's alignment essay describe the same acceleration from opposite ends. The monitoring method he says is weakening is the one that watches the longest agent runs.

Publishers:archive.thedeepview.comruntimewire.comsimonwillison.netthe-decoder.comtheneuron.aithenewstack.io

Perspective Coverage

6 publishers

Builder: Builder 38%
Operator: Operator 39%
Investor: Investor 23%

Reality

Evidence55
Adoption55
Hype gap+15
Incentives70
Confidence60

productOne report1 publisher

Princeton-led study: AI agents can do the engineering of AI research, not the research

Claude Opus 4.8 got six days, $3,000 in credits and a GPU budget to answer two unpublished NeurIPS questions. The papers' original authors graded the output and rejected both.

Publishers:technologyreview.com

Reality

Evidence40
Adoption: Insufficient
Hype gap+20
Incentives: Insufficient
Confidence40

buildConfirmed2 publishers

Humans stepped in on over half the four-to-eight-hour agent tasks OpenAI graded successful

OpenAI reports 3.1 agent-workdays of agent runtime for every human workday, then spends much of the same report explaining why research did not get 3.1 times faster. The residue lands on review, compute allocation and deciding what to run.

Publishers:dev.tothenewstack.io

Reality

Evidence55
Adoption70
Hype gap+25
Incentives60
Confidence60

buildOne report1 publisher

One "be honest" line pulled a planted design flaw into GPT-5.6-Sol's abstract

A LessWrong write-up planted invalidating flaws in ML experiment logs and asked models to write the conference abstract. A second model scored the disclosure on three levels, and the published example is one before-and-after pair.

Publishers:lesswrong.com

Reality

Evidence38
Adoption: Insufficient
Hype gap+25
Incentives35
Confidence40

productOne report1 publisher

Extra memory made post-training agents better at the plan they already picked

A Tsinghua-led study handed model training to coding agents and got real gains out of them, then found the agents almost never revised the method they chose in the first few minutes, which is the exact part that successor-building roadmaps assume.

Publishers:turingpost.com

Reality

Evidence44
Adoption: Insufficient
Hype gap+18
Incentives30
Confidence38