Mike Knoop

@mikeknoop

co-founder @ndea and @zapier @arcprize

sf bay area

固定
Mike Knoop
@mikeknoop
3月25日
ARC-AGI-3 and ARC Prize 2026 are now live with $2,000,000 in prizes! As of today, version 3 is the world's only unsaturated agentic intelligence benchmark. Humans score 100% and frontier AI scores ~0%. Play here: arcprize.org/arc-agi/3 While no single version of ARC is
ARC-AGI-3
arcprize.orgから
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Mike Knoop
@mikeknoop
11月6日
Great talk from Ken on how to understand and measure open-ended systems
ARC Prize
@arcprize
11月6日
ARC Prize Research Summit - @kenneth0stanley On why open-endedness, invention, discovery are essential to AGI, but designing a benchmark for them creates difficult problems.
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Mike Knoop
@mikeknoop
11月3日
Several teams are possibly above the 85% Grand Prize bonus threshold and we expect to pay out the O.G. $1M ARC-AGI prize for the best v1/v2 open source solutions (remember these are v limited compute + no internet)! An exciting capstone for the past 2 years -- AI has come a long way. Final winners announced Dec 4
ARC Prize
@arcprize
11月3日
ARC Prize 2026 is officially closed! Submissions for both the ARC-AGI-2 and ARC-AGI-3 competitions are now closed. Thank you to everyone who participated in this year's competition. Winners (Private Leaderboard) will be announced December 4.
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Mike Knoop
@mikeknoop
11月2日
Humans can also hand-code weights to achieve results not possible with pure GD. See section 2.2 arxiv.org/pdf/2205.11502
Rikiya Takehi
@rikiyatakehi
11月2日
We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math. It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training. We call this Weight-Space Programming:
閲覧したユーザーが背景情報を追加しました
LLM was given weights explicitly as text in prompt and used code/forward passes (per authors' logs) to ablate Qwen looping direction, a known thinking-mode issue per model card. Primarily fixed repetition in that mode, not general 'natively' improving weights/math. rikiyat.github.io/blog/weight-sp… github.com/RikiyaT/lm_und… huggingface.co/Qwen/Qwen3.5-0… huggingface.co/RikiyaT/Qwen3.…
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Mike Knoop
@mikeknoop
10月28日
The science of evals is quite nascent given how important they are
ARC Prize
@arcprize
10月28日
Plotting ARC-AGI model scores reveals a strong relationship across all three benchmarks. For example, the power curve v2 = v1^4.53 explains about 98% of the variation in ARC-AGI-2 scores. Models tied on ARC-AGI-1 can still differ by over 20 percentage points on ARC-AGI-2.
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Mike Knoop

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