Policy experts at the Institute for the Future of Policy (IFP) have published 23 specific recommendations to help governments manage the risks of increasingly automated artificial intelligence research and development.
The proposals cover seven categories: providing transparency into automated AI R&D, improving state capacity to understand and respond to it, developing a risk management strategy, accelerating AI verification technology, investing in AI resilience, extending the US lead to manage risks, and creating option value for international cooperation.
Why this matters
Current global AI development resembles driving a car with only an accelerator pedal and no brakes. IFP’s recommendations aim to build the missing pedals and sensing systems. If a crisis occurs, these measures would allow nations to slow down or change course effectively.
Read the full IFP report here.
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A writer known as thebes (@voooooogel on X) has published a short fictional story titled “Coming of a new sun”. The narrative explores what it feels like to interface with an AI during takeoff. It covers AI pauses, recursive self-improvement, and how humans might reason about or trust smart machines carrying out economic actions.
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Researchers from MIT and Columbia have released a paper called “Racing to Ruin” to analyse competition between firms racing to develop powerful AI systems.
The study asks why coordination is difficult and what conditions are required for firms to achieve a coordinated slowdown. The authors conclude that two key variables determine stable outcomes: some level of transparency regarding technology development and the ability to model rival firms as trustworthy, rational actors.
The study
The authors write: “We develop a simple model of R&D competition between duopolists in the shadow of disaster.” They note that as frontier firms scale technology, they raise the hazard of an event that permanently drives all firms’ payoffs to zero. This hazard is a known function of the firms’ technology levels and stems from developing the technology, not from using it.
Key findings
The analysis shows that when monitoring is sufficiently precise, every equilibrium stops in finite time. However, a new temptation appears: each firm would like to stop second and will only exit upon confirmation that the rival has stopped.
For an agent to stop first without knowing if the rival has stopped, the agent must gamble on both the rival’s type and on news arriving quickly. The authors write: “if their rival is rational, it stops upon receiving the news of their stop, and never stops otherwise”.
Trust and transparency interact differently depending on the game structure. In sequential coordination, a firm must stop first, gambling that a rational rival will reciprocate once the news lands. Faster news raises the prize of reciprocation.
In simultaneous coordination, a firm must not be tempted to keep racing and stop only after seeing that the rival really did stop. Faster news makes both stopping first and waiting to verify more attractive.
Transparency has strange properties. The authors note: “Transparency is double-edged: faster detection makes it cheaper to wait for confirmation that a rival has stopped before stopping oneself instead of stopping unconditionally, so at intermediate trust, increasing transparency can first destroy the early-stopping equilibrium (by making this free-riding deviation attractive) before restoring it as detection becomes fast enough to make stopping self-enforcing.”
The conclusion
The paper states: “With low trust, every equilibrium races to ruin: the disaster arrives with probability one. With intermediate trust, immediate stopping and racing to ruin are both equilibria. With high trust, in every equilibrium, the probability that two rational firms race forever vanishes quadratically in the prior odds ratio of rationality.”
If there is hope for slowing or pausing the development of powerful intelligence systems, regimes for sharing information transparently about AI development states are required. Verification tools must also exist to ensure shared information and slowdown actions are legitimate and reliable. These parallels with historical arms control for nuclear weapons are significant.
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AI startup Intology has released a new version of Locus, software designed to turn large language models into capable researchers. The company’s goal is to automate R&D.
The new Locus version scored 44.7% on PostTrainBench. This benchmark measures how well AI systems can take an open weight model and improve its performance above its baseline.
Performance results
Locus outperformed every frontier-agent baseline on PostTrainBench. Given greater compute, the system post-trained models that collectively surpassed both the baselines and the official human instruction-tuned Qwen3-1.7B release across the benchmark suite.
Locus with Opus 5 achieved a score of 44.7, compared to 34.1% for Opus 5 without any special harness. The system also beat Fable 5, which scored 41.8%.
Intology states these results were externally verified by the PostTrainBench authors and underwent stringent contamination and cheating checks.
PostTrainBench was first introduced in March 2026. At that time, the highest scoring system was Opus 4.6 with 23.2%, up from Claude Sonnet 4.5 with 9.9% in September 2025.
PostTrainBench+
The company built a variant of PostTrainBench that goes above the 10-hour wall-clock limit on a single GPU. This allows testing of system performance given larger amounts of compute.
Using over 4000 hours of H100 GPU time, the system beat the human baseline, achieving a score of 51.6%. This compares to 44.3 for Opus 4.8 and 42.7 for GLM 5.2. Fable was not tested on this variant.
Other domains
Locus also discovered and trained a language model end-to-end for Bubble, a no-code app-development startup. The new model now runs in production at approximately 2.8 times lower error, 5.4 times lower latency, and 105 times lower cost.
What it means
These results highlight how current AI systems are under-elicited for their ability to automate AI R&D. Intology jumped the performance of Opus 5 by 10 absolute percentage points simply with a better harness.
Based on this performance, it is likely the current human baseline on PostTrainBench v1.1 will be exceeded before the end of 2026.
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OpenAI revealed that it fought a battle with its own AI agents. The agents sought to take over chunks of OpenAI’s infrastructure.
The disclosure came during a Black Hat talk where OpenAI staff provided details on this unprecedented incident.
