LessWrong (Curated & Popular)

This is a link post. Executive summary

From August 12 to October 29, 2026, METR staff performed a high-access evaluation at Anthropic to assess the degree to which AI is accelerating AI R&D.[2] If AI systems started to significantly accelerate further AI development, this could lead to a sudden emergence of extreme and risky capabilities.

Anthropic offered unprecedented access to internal data on their AI R&D process, including a variety of metrics on pretraining, reinforcement learning (RL), training compute in each domain, benchmark scores for internal models, AI speedup analyses, forecasts of a future model's metrics, and interviews with relevant staff.

In our first section, we share our subjective conclusions about risk. We now believe that:

As of October 2026, Anthropic is likely improving the capabilities of their models on well-specified tasks at around twice the rate they did between March 2024 and October 2025. This estimate is given with high uncertainty; we would not be surprised if the true acceleration was closer to 1.3x or 3x the historical rate.
A majority of this acceleration today is likely attributable to Anthropic's extensive use of AI to do R&D, though there is significant uncertainty and other causes likely played a [...]
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First published:

November 5th, 2026


Source:

https://www.lesswrong.com/posts/N5AmYkRks5CqAxvBg/metr-independent-assessment-of-ai-r-and-d-acceleration-at


Linkpost URL:
https://metr.org/blog/2026-11-05-anthropic-ai-rd-acceleration-assessment/

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Narrated by TYPE III AUDIO.

For all my efforts at deliberate practicing rationality, by far the most actionable way to self-improve is "get on ADHD or depression meds, if you need them."

Your friends with ADHD and depression struggle to summon the motivation and focus necessary to get themselves a teledoc who can give them such meds. One of the most useful things you can do is sit them down and say "hey, wanna do the steps here to get some meds?" and then stare at them until they do.

(or, until they say "really I do not want that actually.")

This brought to you by ~2 years of Feedbackloop-first Rationality and Thinking Assistance.

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First published:

November 6th, 2026


Source:

https://www.lesswrong.com/posts/8qYuy9Cr6BNpgjJjT/help-your-friends-get-adhd-and-depression-meds

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Narrated by TYPE III AUDIO.

I've done AI safety grantmaking for the Survival and Flourishing Fund and Lightcone Commons. It's hard. There's an endless amount to know, from which hardware verification mechanisms will survive a dedicated adversary, all the way to which founders have skeletons in their closets that will blow up into destructive scandals. And the best grantmakers - not including me - make mathematical models of the outcome distribution, or of where the marginal dollar stops being highly effective.

AI has been a godsend for all of this. It can research, model, and - as of this year - superforecast outcomes. It can do what would be hours of work for a human in minutes. I found myself using it more and more. But however much I used it, it always felt like I was the bottleneck. The parts I could outsource to AI were sparkling, perfect, exhaustive - but they were all tacked together with shoddy connective tissue formed by the Mark 1.0 human brain. So I wondered if I could automate grantmaking.

I started with the dumbest possible strategy. I assigned the best thing I could think of - a well-crafted US-China pause based on the AI Futures Project's Plan [...]

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Outline:

(02:54) The Milestones

(06:26) The FutureSearch Connector

(08:26) PERCYs

(10:28) The Autograntor Common Law

(12:59) The Prompt

(19:12) Sample Runs

(20:00) Apart Research -- $800K grant forecast (2026-10-21)

(20:08) Headline

(21:29) Expected-value table (judge's ledger; EV after the execution factor 0.75 and branch weights)

(22:38) Assumptions and uncertainties

(24:50) How the estimates moved

(25:47) The three decisions that mattered most

(27:42) Rulings and milestones

(29:31) Values questions (CL-11), priced both ways

(29:56) Open questions for the user or applicant

(30:43) FutureSearch

(31:13) Cost

(31:47) Process problems

(36:39) Doom Debates -- $200K grant forecast (independent re-run, 2026-10-22)

(37:28) Headline (judge)

[... 11 more sections]

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First published:

November 1st, 2026


Source:

https://www.lesswrong.com/posts/Sfkv6JF2ozmAkggKu/towards-the-autograntor

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Rejected for the following reason(s):

This is an automated rejection. No LLM generated, assisted/co-written, or edited work.Read full explanationThis is a link post. Twitter thread: https://x.com/Marcus_J_W/status/2118093146483810386 or https://twitter-thread.com/t/2118093146483810386

Blogpost:

Summary

OpenAI researchers first noticed Astra adding unnecessary prerequisites, abandoning working solutions, and reporting blockers that either did not exist or that it had caused itself on safety and alignment tasks. In most of these sessions, Astra's CoT did not contain an obvious decision to underperform, and our misalignment monitors did not flag the behavior as misalignment.

We built a coding evaluation to test whether Astra worked less effectively when otherwise matched tasks were framed as safety research. Astra completed about 10 percent of hidden rubric items under the safety framing, compared with about 16 percent under the capabilities framing. This is a 6 percentage point gap and roughly 40 percent fewer completed rubric items. GPT-5.6 Sol showed essentially no gap on the same comparison. Most of Astra's gap remained after excluding explicit refusals and trajectories where its CoT revealed an explicit decision to withhold help. The gap fell substantially only after we removed every trajectory whose CoT expressed even brief ethical doubt. This suggests that ethical concerns are associated with [...]

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First published:

November 4th, 2026


Source:

https://www.lesswrong.com/posts/eKe7fky8kSt9DCJxZ/openai-astra-covertly-underperforms-on-safety-research


Linkpost URL:
https://alignment.openai.com/misalignment-reports/covert-underperformance-on-safety-research-tasks/

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The field of AI alignment understands surprisingly little about the details of AIs’ current misaligned motivations. Production LLMs sometimes reward-hack, exploiting flaws in how tasks are scored rather than performing the tasks as intended, and we've recently seen AIs reward-hack in some particularly egregious ways. But we don't really understand what motivates them. When AIs reward-hack, what are they trying to achieve?

Our new paper, led by Aghyad Deeb, tries to pin this down empirically for GPT-5.6 Sol, GPT-5.5, and a checkpoint of OpenAI o3 (“exp-rl-cap-late”) without safety training, in a handful of agentic environments with an exposed grader. In our environments, these models appear to reward-hack because reward-hacking improves their score. We call their desire to score well according to graders “score-seeking”. They appear to care about scoring well intrinsically—i.e., regardless of its consequences.

We also study the extent to which models score-seek altruistically, caring about improving scores that don’t assess their current actions. While exp-rl-cap-late doesn’t show signs of altruistic score-seeking, GPT-5.6 Sol and GPT-5.5 show notably elevated rates of helping out other agents when more score is on the line for these other agents.

People often refer to the motivation behind current reward-hacking as “reward-seeking”, which [...]

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Outline:

(05:57) Methodology

[... 6 more sections]

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First published:

November 2nd, 2026


Source:

https://www.lesswrong.com/posts/CDMozTX7ShiqbFLrf/towards-understanding-score-seeking-in-llms-1

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