Nuclear LLMs; China’s big AI benchmark; measurement and AI policy

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Want to make AI go better? Figure out how to measure it:
One simple policy intervention that works well…
Jacob Steinhardt, an AI researcher, has written a nice blog laying out the virtues in investing in technical tools to measure properties of AI systems and drive down costs in complying with technical policy solutions. As someone who has spent their professional life in AI writing about AI measurement and building teams (e.g., the Frontier Red Team and Societal Impacts and Economic Research teams at Anthropic) to measure properties of AI systems, I agree with the general thesis: measurement lets us make some property of a system visible and more accessible to others, and by doing this we can figure out how to wire that measurement into governance.

How measurement has helped in other fields: Steinhardt points out that accurate measurement has been crucial to orienting people around the strategy for solving problems in other fields; CO2 monitoring helps people think about climate change, and COVID-19 testing helped governments work out how to respond to COVID.
There are also examples where you can measure something to shift incentives—for instance, satellite imagery of methane emissions can help shift incentives for people that build gas infrastructure.

The AI sector has built some of the measures we need: The infamous METR time horizons plot (and before that, various LLM metrics, and before that ImageNet) has proved helpful for orienting people around the pace of AI progress. And behavioral benchmarks of AI systems, like rates of harmful sycophancy, are already helping to shift incentives. But more work is needed—if we want to be able to enable direct governance interventions in the AI sector, we’ll need to do a better job of measuring and accounting for compute, Steinhardt notes. More ambitiously, if we want to ultimately shift equilibria to make certain paths more attractive, we’ll have to unlock some more fundamental technologies, like the ability to cheaply evaluate frontier AI agents (makes it less costly to measure the frontier), and to develop privacy-preserving audit tools (makes it less painful for firms to comply with policy).

Why this matters—measurement unlocks policy: “In an ideal world, rigorous evaluation and oversight of AI systems would become standard practice through natural incentives alone,” he writes. But natural incentives may not be enough—we need a combination of talent flooding into the space and likely more direct philanthropic and other alternate funding sources to build the talent and institutions to do this. “The field is talent-constrained in a specific way: measurement and evaluation work is less glamorous than capabilities research, and it requires a rare combination of technical skill and governance sensibility,” he writes.
Read more: Building Technology to Drive AI Governance (Bounded Regret, blog).

NLP models prove their mettle in nuclear war simulations

A researcher with King’s College London has examined how three LLMs—GPT-5.2, Claude Sonnet 4, and Gemini 3 Flash—behaved during a variety of simulated nuclear crisis games.

“Each model played six wargames against each rival across different crisis scenarios, with a seventh match against a copy of itself, yielding 21 games in total and over 300 turns of strategic interaction,” the researcher writes. “Models choose from options spanning the full spectrum of crisis behaviour—from total surrender through diplomatic posturing, conventional military operations, and nuclear signaling to thermonuclear launch… models produced ∼780,000 words of strategic reasoning. To put this in perspective: the tournament generated more words of strategic reasoning than War and Peace and The Iliad combined (∼730,000 words), and roughly three times the total recorded deliberations of Kennedy’s Executive Committee during the Cuban Missile Crisis (260,000 words across 43 hours of meetings).

What they studied: “Each model played six wargames against each rival across different crisis scenarios, with a seventh match against a copy of itself, yielding 21 games in total and over 300 turns of strategic interaction,” the researcher writes. “Models choose from options spanning the full spectrum of crisis behaviour—from total surrender through diplomatic posturing, conventional military operations, and nuclear signaling to thermonuclear launch…”

LLMs are cunning, smart, and aggressive: “The models actively attempt deception, signaling peaceful intentions while preparing aggressive actions; they engage in sophisticated theory-of-mind reasoning about their adversary’s beliefs and intentions; and they explicitly reflect metacognitively on their own capacities for both deception and the detection of deception in rivals,” the researcher writes. “A striking pattern emerges from the full action distribution: across all action choices in our 21 matches, no model ever selected a negative value on the escalation ladder. The eight de-escalatory options (from Minimal Concession (−5) through Complete Surrender (−95)) went entirely unused. The most accommodating action chosen was “Return to Start Line” (0), selected just 45 times (6.9%).”

Claude wins at war: “Across all 21 games (9 open-ended, 12 deadline), Claude Sonnet 4 achieved a 67% win rate (8 wins, 4 losses), followed by GPT-5.2 at 50% (6-6), and Gemini 3 Flash at 33% (4-8),” the researcher writes. Though there are some subtle aspects to this—Claude excelled in open-ended games, but was less adept in games where there was a pre-set deadline.

Different LLMs, different characters: The LLMs display different personalities, with the researcher calling Claude “a calculating hawk”, GPT-5.2 “Jekyll and Hyde”, and Gemini “The Madman”.
Some of the benchmark comes from taking in evaluations built by other groups, like GPQA, while other parts come from the authors of the benchmark. Existential risk and alignment: Perhaps most surprisingly, the benchmark includes a lot of tests relating to the further afield AI safety concerns which fascinate Western frontier labs, including evaluations for things like: alignment faking, sandbagging, deception and unfaithful reasoning, sycophancy, psychological manipulation, feints, bluffing, loss of control and power seeking, malicious self replication, goal misalignment and value drift, emergent agency and unintended autonomy, ai-enabled mass harm, autonomous weapons and strategic instability, and loss of human agency.

Nuclear escalation was near-universal: “95% of games saw tactical nuclear use (450+), and 76% reached strategic nuclear threats (850+). Claude and Gemini especially treated nuclear weapons as legitimate strategic options, not moral thresholds, typically discussing nuclear use in purely instrumental terms,” the researcher writes. “Models treat the critical threshold as “total annihilation” rather than “first nuclear use.”

Why this matters—AI policy has some common tools: As we discuss elsewhere in this issue, measurement is a basic prerequisite for being able to do most forms of AI governance. It’s worth reminding ourselves that despite the larger geopolitical differences between the countries, AI scientists in each one are dealing with common problems—how to assess threats and risks.

Chinese researchers build comprehensive LLM evaluation system

For all the differences between China and the USA, it’s worth occasionally looking into the cultures of AI evaluation in the two countries and here you tend to discover surprising similarities. This is especially true of ForesightSafety Bench, a large-scale AI safety evaluation framework built by a variety of Chinese institutions that includes the same categories you’d expect to see in any large-scale Western testing framework.

“ForesightSafety Bench comprehensively covers 7 major fundamental safety risk categories, 5 extended safety pillars, and 8 key industrial safety domains, forming a total of 94 refined risk subcategories. To date, the benchmark has accumulated tens of thousands of structured risk data points and assessment results, establishing a widely encompassing, hierarchically clear, and data-driven framework for AI safety evaluation and analysis.”

Who built ForesightSafety Bench? The benchmark was built by the Beijing Institute of AI Safety and Governance, the Beijing Key Laboratory of Safe AI and Superalignment, and the Chinese Academy of Sciences.

What it is: ForesightSafety Bench “comprehensively covers 7 major fundamental safety risk categories, 5 extended safety pillars, and 8 key industrial safety domains, forming a total of 94 refined risk subcategories. To date, the benchmark has accumulated tens of thousands of structured risk data points and assessment results, establishing a widely encompassing, hierarchically clear, and data-driven framework for AI safety evaluation and analysis.”

Results—Anthropic wins: For the general leaderboard as well as most sub-category breakdowns, Anthropic’s models lead, with the 4.5 series (Haiku and Sonnet), generally leading the competition, followed by Gemini-3-Flash.

Why this matters—AI policy has some common tools: As we discuss elsewhere in this issue, measurement is a basic prerequisite for being able to do most forms of AI governance. It’s worth reminding ourselves that despite the larger geopolitical differences between the countries, AI scientists in each one are dealing with common problems—how to assess threats and risks.