--- title: Introducing the Conceptual Reasoning Index | daily.dev description: A new benchmark suite called the Conceptual Reasoning Index (CRI) evaluates how well AI models perform on conceptual reasoning tasks that lack empirical... canonical: https://daily.dev/posts/introducing-the-conceptual-reasoning-index-q4wfdekg5 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Introducing the Conceptual Reasoning Index | daily.dev og:description: A new benchmark suite called the Conceptual Reasoning Index (CRI) evaluates how well AI models perform on conceptual reasoning tasks that lack empirical... og:url: https://daily.dev/posts/introducing-the-conceptual-reasoning-index-q4wfdekg5 og:image: https://api.daily.dev/og/posts/Q4wfDEKg5.png og:image:alt: Introducing the Conceptual Reasoning Index og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Introducing the Conceptual Reasoning Index **[Hacker News](https://daily.dev/sources/hn)** · 7 min read · 1 upvotes · 0 comments ## Summary A new benchmark suite called the Conceptual Reasoning Index (CRI) evaluates how well AI models perform on conceptual reasoning tasks that lack empirical verification, such as philosophical argumentation and decision theory. Built in collaboration with Anthropic, the CRI aggregates three benchmarks: LMCA (argument judgment), ACCoRD (logical consistency), and DTBench capabilities (decision-theoretic reasoning). Scores are published at conceptualreasoning.ai, with the top model, Opus 5, scoring 73.6 against an estimated ceiling of 91. Scores have risen roughly linearly since late 2024 with no sign of plateauing, and researchers estimate LMCA may start saturating in about a year while DTBench capabilities is already near its ceiling. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is the Conceptual Reasoning Index (CRI) and how is it scored? The Conceptual Reasoning Index is a weighted aggregate benchmark measuring AI models' conceptual reasoning ability, combining LMCA at 60%, ACCoRD at 20%, and DTBench capabilities at 20%. Scores range from 0, representing random guessing, to an estimated ceiling of roughly 91. As of August 10, 2026, Opus 5 scores highest at 73.6, with a 95% confidence interval of plus or minus 2.1. _daily.dev surfaces new AI benchmark releases for engineers tracking which models handle open-ended reasoning best._ ### What does the LMCA benchmark measure and how large is the dataset? LMCA, or Language Model Conceptual Argumentation, measures how well models judge the quality of arguments against position texts on topics like decision theory, philosophy, and AI risk, comparing model ratings against expert human ratings. The dataset contains 560 position texts with 1,461 arguments and 2,140 total ratings, mostly done by conceptual researcher Emery Cooper. _engineers evaluating LLM reasoning quality can follow benchmark methodology details like this via daily.dev._ ### How does the ACCoRD benchmark test logical consistency in AI models? ACCoRD checks whether a model's reported probability and preference judgments on conceptual questions are logically consistent, for example verifying that P(A) is greater than or equal to P(A and B) across separate model queries. The full dataset has close to 14,000 model-generated consistency constraints across 18 constraint types, with 567 manually approved constraints used in the CRI aggregate score. _teams assessing model reliability on open-ended questions can track consistency benchmarks like this on daily.dev._ ## Community take How the wider developer community reacted, aggregated from 1 discussion and 68 comments across hackernews (as of 2026-09-13). **TL;DR:** Commenters are largely dismissive, framing the benchmark as a self-serving, closed-source marketing exercise from a company that also topped its own leaderboard, while a smaller thread engages more seriously with the philosophical premise about AI monitoring AI. **Sentiment:** 5% positive · 15% mixed · 80% skeptical **The case for** - A few see genuine value in the ACCoRD-style consistency testing as a modest, useful direction for alignment work that's hard to RL against. - Some argue closed benchmarks are necessary to prevent labs from overfitting/gaming a public test set. **The pushback** - Many see an obvious conflict of interest in a company-funded benchmark on which that same company's model scores highest. - Several doubt Opus 5's real-world quality, citing it as inferior in chat, prose, and general use compared to competitors despite topping this benchmark. - Some call the opening premise about needing AI to understand AI risk incoherent, since it offers no assurance the AI's judgment is trustworthy or aligned. - A few dismiss the whole announcement as marketing slop or 'trust me bro' benchmarking with no real substance. **By community** - hackernews (skeptical): Strong skepticism dominates, mixing distrust of a vendor-funded benchmark that ranks its own model first with deeper objections to the underlying alignment premise. **Hottest debate:** Whether a benchmark funded by and favorable to the same company that built the top-scoring model can be trusted at all. **Open questions** - What would the plan be if AI turns out not to help humans understand the situation, as the framing assumes it will? - How many alternative benchmark designs were tried/rejected before landing on one where the sponsoring company's model scores highest? **Highlights** > Ah, yes -- A closed source benchmark that Anthropic paid for that Anthropic ranked highest. 0/10 > — [lanyard-textile on hackernews · 4 comments](https://news.ycombinator.com/item?id=49286687) > Opus 5 scored highest, too, which is just lol Opus 5 is okay at coding. It is unbelievably awful to chat with, though. Very snippy, snarky, and it loves to "push back" even when it's inappropriate to do so. Besides, where "conceptual" things like science and math are concerned, it's starkly inferior to 5.6-Sol. Where writing prose is concerned, Kimi-K3 runs circles around it. I refuse to believe Opus 5 is at the top of any non-cherrypicked benchmark, unless it has to do with very narrow coding tasks. > — [A\_D\_E\_P\_T on hackernews](https://news.ycombinator.com/item?id=49287842) > This is a modest start on an important direction for AI Alignment work; which is, as the authors observe, commonly comprised of tasks which are not readily empirically verifiable and not easily mathematically modeled - so it's hard to get at with normal RL techniques. I find the ACCoRD benchmark the most interesting, because you could theoretically scale it up from the baseline mode of testing two instances of the same model for their `P(A) ≥ P(A&B)` respectively, you could do `P(A)≥ P(A&B) && P(A) ≥ P(A&C) && P(A&B) ≥ P(A&B&C) && P(A&C) ≥ P(A&B&C) ...` etc i.e. a swarm of model instances could be collectively measured for consistency for even more confidence, right? At any rate, even the basic idea of measuring a model for consistency in beliefs improves our ability to bound the amount of trust we can put on it with introspection methods. > — [eutropia on hackernews](https://news.ycombinator.com/item?id=49287300) > I enjoy using the models. I also get that there are shenanigans and that marketing is happening, but as long as the models are this effective I can't much bring myself to care. I suspect most of their users are the same. > — [velcrovan on hackernews · 1 comments](https://news.ycombinator.com/item?id=49286692) > I love how many ways we can interpret that line. "Hey Claude, our stuff needs to make more money. We are at risk for losing more." "Rest assured, the 'situation' will only worsen if you resist our benevolent offer." "We're aren't even at AGI yet, but I for one welcome our new agentic overlords." > — [NBJack on hackernews](https://news.ycombinator.com/item?id=49287099) **Source threads** - [hackernews](https://news.ycombinator.com/item?id=49285909) · 43 points · 68 comments ## Similar posts on daily.dev - [Evaluating AI Agents Live at the Grounded Reasoning Cup](https://daily.dev/posts/evaluating-ai-agents-live-at-the-grounded-reasoning-cup-b5dtabzfo) · databricks · 0 upvotes · 0 comments - [Lessons From the Leaderboard: What 5,000\+ Kagglers Taught Us About Improving AI Reasoning](https://daily.dev/posts/lessons-from-the-leaderboard-what-5-000-kagglers-taught-us-about-improving-ai-reasoning-ac1af6pnt) · NVIDIA Developer · 2 upvotes · 0 comments - [Is AI Reasoning Right for the Wrong Reasons?](https://daily.dev/posts/is-ai-reasoning-right-for-the-wrong-reasons--fx8ivx3mm) · Hacker News · 0 upvotes · 0 comments --- Tags: [#llm](https://daily.dev/tags/llm), [#anthropic](https://daily.dev/tags/anthropic), [#ai-safety](https://daily.dev/tags/ai-safety), [#ai-governance](https://daily.dev/tags/ai-governance) [View this post on daily.dev](https://daily.dev/posts/introducing-the-conceptual-reasoning-index-q4wfdekg5) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Introducing the Conceptual Reasoning Index","url":"https://daily.dev/posts/introducing-the-conceptual-reasoning-index-q4wfdekg5","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/introducing-the-conceptual-reasoning-index-q4wfdekg5"},"datePublished":"2026-08-13T14:53:47.689Z","dateModified":"2026-09-13T19:44:19.239Z","description":"A new benchmark suite called the Conceptual Reasoning Index (CRI) evaluates how well AI models perform on conceptual reasoning tasks that lack empirical...","image":"https://media.daily.dev/image/upload/s--58gMhC4P--/f_auto/v1722860399/public/Placeholder%2012","thumbnailUrl":"https://media.daily.dev/image/upload/s--58gMhC4P--/f_auto/v1722860399/public/Placeholder%2012","isAccessibleForFree":true,"articleSection":"Hacker News","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Hacker News","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/hn","url":"https://daily.dev/sources/hn"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/introducing-the-conceptual-reasoning-index-q4wfdekg5","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":1},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"llm,anthropic,ai-safety,ai-governance","timeRequired":"PT7M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Hacker News","item":"https://daily.dev/sources/hn"},{"@type":"ListItem","position":3,"name":"Introducing the Conceptual Reasoning Index"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/introducing-the-conceptual-reasoning-index-q4wfdekg5#faq","mainEntity":[{"@type":"Question","name":"What is the Conceptual Reasoning Index (CRI) and how is it scored?","acceptedAnswer":{"@type":"Answer","text":"The Conceptual Reasoning Index is a weighted aggregate benchmark measuring AI models' conceptual reasoning ability, combining LMCA at 60%, ACCoRD at 20%, and DTBench capabilities at 20%. 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