Import AI 468：RSI政策建议、PostTrainBench新SO

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Want to be able to deal with RSI? Here are 23 actionable policy ideas:

…IFP serves up some “low-regret” policy recommendations…

Policy experts with think tank IFP have published a set of ideas meant to help “policymakers begin addressing the risks of further automating AI R&D”. The recommendations involve 23 specific ideas falling across 7 specific categories. If adopted, these recommendations would also give countries, especially the United States, more moves they can make on the gameboard as powerful systems are developed, ideally giving them the ability to:

• Accelerate “the diffusion of AI capabilities, by allocating compute and talent towards inference and the development of new AI applications”.
• Accelerate “R&D to make further AI research automation safer, either by improving model safety directly or by boosting societal resilience”.

Seven categories of idea:

• “Provide transparency into automated AI R&D
• Improve state capacity to understand and respond to automated AI R&D
• Develop a risk management strategy for automated AI R&D that accelerates defensive and commercial AI uses
• Accelerate the development of AI verification technology
• Invest in AI resilience
• Extend the US AI lead to give the US more time to manage AI R&D automation risks
• Create option value for international cooperation on managing automated AI R&D risks”

Why this matters – the fewer options for dealing with RSI we have, the worse the outcomes will be: Right now, it’s as if the world is driving AI development in a car that only has an accelerator pedal and no brake pedal, let alone any kind of sophisticated telemetry for knowing things ranging from the speed of the car to the properties of the engine to the wear on the tires. Proposals like this from IFP will build out more of the proverbial pedals and sensing systems for the vehicle of the AI industry, which means if we need to change course or slow down we’ll be better able to during a moment of crisis.

Read more: How Should the US Prepare for Increasingly Automated AI R&D? (IFP).

A short story from thebes about smart machines and robot bodies:

…What might interfacing with an AI during takeoff feel like?…

Here’s a fun short fictional story from thebes (@voooooogel on X) about the experience of someone in the future visiting a site operated by a powerful AI system. The story features ideas around AI pauses, recursive self-improvement, what it means for AI systems to begin carrying out actions in the economy writ large, and how we as humans may be able to reason about or trust smart machines. Take a read of it!

Read the story here: Coming of a new sun (VGEL, website).

The two ingredients for a successful slowdown among rival AI firms: trust and transparency:

…Game theory analysis suggests slowdowns are possible…

Researchers with MIT and Columbia have analyzed the nature of competition between firms racing against one another to develop powerful AI systems and whether it’s possible for firms to achieve a coordinated slowdown. The paper, called Racing to Ruin, aims to answer “why exactly is coordination hard? And what would it take”?. The conclusion is that the two key variables in achieving stable outcomes are some level of transparency about technology development, as well as being able to model the other firms as trustworthy, rational actors.

What they study: “We develop a simple model of R&D competition between duopolists in the shadow of disaster,” they write. “As frontier firms scale the technology, they raise the hazard of an event that permanently drives all firms’ flow payoffs to zero. The hazard is a known function of the firms’ technology levels, and it comes from developing the technology, not from using it.”

What their analysis shows: “When monitoring is sufficiently precise, every equilibrium stops in finite time, but a new temptation appears: each firm would like to stop second, and exits only upon confirmation that the rival has stopped,” they write. “For an agent to stop first i.e., without knowing if their rival has stopped, she gambles on both their rival’s type and on news arriving quickly: if their rival is rational, it stops upon receiving the news of their stop, and never stops otherwise”.

Trust and transparency interact pretty differently depending on the type of game being played: “Sequential coordination asks a firm to stop first, gambling that a rational rival will reciprocate once the news lands. Hence, faster news raises the prize of reciprocation,” they write. “Conversely, simultaneous coordination requires that a firm 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: “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 key conclusion – avoiding death runs on the ability to trust other firms: “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,” they write.

Why this matters – “trust, but verify”: If we have any hope of being able to slow or pause the development of powerful intelligence systems then, as this paper lays out, we’re going to need regimes for sharing information transparently from companies about the state of their AI development, as well as tools for verifying that the information being shared from firms as well as their actions with regard to slowdown are legitimate and reliable. In this, there are many parallels with how arms control has historically worked in the context of nuclear weapons.

Read more: Racing to Ruin (arXiv).

A new SOTA on PostTrainBench hints at the automated AI R&D future:

…Intology also beats the human baseline (when given huge amounts of compute)…

AI startup Intology, whose goal “is to automate R&D”, has released a new version of Locus, software it has developed to turn LLMs into capable researchers. The new version of Locus is able to get a score of 44.7% on PostTrainBench, a benchmark which sees how well AI systems can take an open weight model and improve its performance above its baseline.

The results: Locus “outperforms every frontier-agent baseline on PostTrainBench, and given greater compute, post-trains models that collectively surpass both the baselines and the official human instruction-tuned Qwen3-1.7B release across the benchmark suite”.

Locus with Opus 5 gets a score of 44.7 (versus 34.1% for Opus 5 without any kind of special harness), and even beats Fable 5 (41.8%). “These results were externally verified by the PostTrainBench authors and underwent stringent contamination and cheating checks,” Intology writes.

PostTrainBench was first introduced in March 2026 (Import AI #449) and at the time the highest scoring system was Opus 4.6, getting 23.2%, up from Claude Sonnet 4.5 getting 9.9% in September 2025.