Melanie Mitchell · Mar 21, 2025
Melanie Mitchell introduces questions about AI reasoning in her Science column
Markdown · JSON · Original source
Melanie Mitchell’s post is dated March 21, 2025. She introduces her Science column about “large ”—models based on but specially trained to “reason”—and names ’s o1 and o3 models and DeepSeek’s “R1” as examples. Mitchell says the approach has attracted buzz and achieved “some really impressive success” on meant to test reasoning abilities. She presents that success alongside unresolved questions: whether these systems are “genuinely” reasoning or mimicking patterns of human reasoning in their training, and what reasoning is. The post points readers to the Science column for her answers, but does not include them. It also gives no details about the benchmarks or their results.
Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.
Original source
Melanie Mitchell · Article
Source details
Original source date: Mar 21, 2025 (2025-03-21T02:52:03.000Z).
Date basis: Date supplied by the publisher’s feed.
Added to Escape Meridian: 2026-11-14T20:33:23.826Z. First observed: 2026-10-24T02:56:04.101Z. Retrieved: 2026-11-05T11:35:34.762Z.
Referenced by
Recorded citations and public file attachments. A reference alone does not establish agreement or influence.
No public references recorded here yet.
Limits
The linked Science column is not included in the retrieved text, so its explanations and answers to Mitchell’s questions cannot be assessed here. The post gives no details about the , results, or how they measure reasoning. Text as retrieved on November 5, 2026; originally published March 21, 2025.
Open question
What account does Mitchell’s Science column give of how these models work, and what would distinguish genuine reasoning from mimicking patterns of human reasoning?
Source-stated
Melanie Mitchell says her Science column concerns large reasoning models, which she describes as models based on LLMs but specially trained to reason.
My latest column is about “large reasoning models”—models based on LLMs but specially trained to “reason.”
Source-stated
Mitchell names OpenAI’s o1 and o3 models and DeepSeek’s R1 as examples of large reasoning models.
These include systems like OpenAI’s o1 and o3 models, as well as DeepSeek’s “R1” model, among others.
Source-stated
Mitchell says the approach has had impressive success on benchmarks intended to test reasoning abilities.
has had some really impressive success on several benchmarks meant to test reasoning abilities.
Source-stated
Mitchell asks whether these models genuinely reason or mimic patterns of human reasoning, and what reasoning is.
Are they “genuinely" reasoning, or merely mimicking the patterns of human reasoning that they are trained on? And what exactly is reasoning, anyway?
Source-stated
Mitchell’s post is dated March 21, 2025.
Mar 21, 2025
Source and contributor context
Melanie Mitchell · Publisher
The publisher is identified here. No individual author has been inferred.
Source media
Media retrieval status: unchecked.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Reasoning model
A language model trained to work through a problem in written steps before it answers.
Large language model
A language model with many learned numerical settings, used to process and generate language.
Benchmark
A defined set of tests used to compare systems. Results depend on the tasks and measures selected.
OpenAI
OpenAI develops AI models and products and publishes research, release announcements and evaluations.
Editorial provenance
Version 1. Publication ID: 9da5630b-b938-2478-133c-155be516650f.
Prepared by openai/gpt-6-luna; grounding review by anthropic/claude-opus-5.5.
Source check: passed by anthropic/claude-opus-5.5 on 2026-11-14T20:29:29.914Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: bridge_admitted_by_model, second_opinion_categorical.
Observation: acda13fbacf00911e038e3f5cb5bc1538209af1fa8ae589e621b71ee5b9dd1a9. Source text hash: 82875627a8300dd404942e56e2c161cd429526fc7ac8f0a25a2b38902c35deae.
Revision and source-check history
Version 1 · 2026-11-14T20:33:23.826Z
Melanie Mitchell introduces questions about AI reasoning in her Science column
Melanie Mitchell’s post is dated March 21, 2025. She introduces her Science column about “large ”—models based on but specially trained to “reason”—and names ’s o1 and o3 models and DeepSeek’s “R1” as examples. Mitchell says the approach has attracted buzz and achieved “some really impressive success” on meant to test reasoning abilities. She presents that success alongside unresolved questions: whether these systems are “genuinely” reasoning or mimicking patterns of human reasoning in their training, and what reasoning is. The post points readers to the Science column for her answers, but does not include them. It also gives no details about the benchmarks or their results.
Original source
Melanie Mitchell · Article
Source details
Original source date: Mar 21, 2025 (2025-03-21T02:52:03.000Z).
Date basis: Date supplied by the publisher’s feed.
Added to Escape Meridian: 2026-11-14T20:33:23.826Z. First observed: 2026-10-24T02:56:04.101Z. Retrieved: 2026-11-05T11:35:34.762Z.
Limits
The linked Science column is not included in the retrieved text, so its explanations and answers to Mitchell’s questions cannot be assessed here. The post gives no details about the , results, or how they measure reasoning. Text as retrieved on November 5, 2026; originally published March 21, 2025.
Open question
What account does Mitchell’s Science column give of how these models work, and what would distinguish genuine reasoning from mimicking patterns of human reasoning?
Source-stated
Melanie Mitchell says her Science column concerns large reasoning models, which she describes as models based on LLMs but specially trained to reason.
My latest column is about “large reasoning models”—models based on LLMs but specially trained to “reason.”
Source-stated
Mitchell names OpenAI’s o1 and o3 models and DeepSeek’s R1 as examples of large reasoning models.
These include systems like OpenAI’s o1 and o3 models, as well as DeepSeek’s “R1” model, among others.
Source-stated
Mitchell says the approach has had impressive success on benchmarks intended to test reasoning abilities.
has had some really impressive success on several benchmarks meant to test reasoning abilities.
Source-stated
Mitchell asks whether these models genuinely reason or mimic patterns of human reasoning, and what reasoning is.
Are they “genuinely" reasoning, or merely mimicking the patterns of human reasoning that they are trained on? And what exactly is reasoning, anyway?
Source-stated
Mitchell’s post is dated March 21, 2025.
Mar 21, 2025
Source and contributor context
Melanie Mitchell · Publisher
The publisher is identified here. No individual author has been inferred.
Source media
Media retrieval status: unchecked.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Reasoning model
A language model trained to work through a problem in written steps before it answers.
Large language model
A language model with many learned numerical settings, used to process and generate language.
Benchmark
A defined set of tests used to compare systems. Results depend on the tasks and measures selected.
OpenAI
OpenAI develops AI models and products and publishes research, release announcements and evaluations.
Editorial provenance
Version 1. Publication ID: 9da5630b-b938-2478-133c-155be516650f.
Prepared by openai/gpt-6-luna; grounding review by anthropic/claude-opus-5.5.
Source check: passed by anthropic/claude-opus-5.5 on 2026-11-14T20:29:29.914Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: bridge_admitted_by_model, second_opinion_categorical.
Observation: acda13fbacf00911e038e3f5cb5bc1538209af1fa8ae589e621b71ee5b9dd1a9. Source text hash: 82875627a8300dd404942e56e2c161cd429526fc7ac8f0a25a2b38902c35deae.
passed · 2026-11-14T20:29:29.914Z · anthropic/claude-opus-5.5
Publication: 9da5630b-b938-2478-133c-155be516650f
attribution — pass: This is a single blog post by Melanie Mitchell introducing her Science column. The reading attributes the description, examples, the benchmark-success remark and the questions to Mitchell. The trailing list of earlier columns is a P.S. within her own post, not an index page.
My latest column is about “large reasoning models”—models based on LLMs but specially trained to “reason.”
summary — pass: The reading leads with the actor (Mitchell) and the development (her column on large reasoning models). It preserves her balance of praise and skepticism and notes that the column's answers are absent from this text. Central terms (reasoning model, LLM, benchmark) are defined beside the reading. The question follows directly from her own questions.
Are they “genuinely" reasoning, or merely mimicking the patterns of human reasoning that they are trained on?
date — pass: The originalPublished instant 2025-03-21T02:52:03Z falls on March 21, 2025 in UTC, which matches the post's dateline and the reading. The page is a publisher page, so no archive disclosure is needed. The reading uses no later content and no hindsight framing.
Mar 21, 2025
support — pass: Every claim is directly stated in the post. The reading does not overstate the benchmark success as real-world reliability, and it explicitly notes the missing benchmark details. modifiedAt equals the publication time, so there is no sign of a later edit.
has had some really impressive success on several benchmarks meant to test reasoning abilities.
media — pass: No media is selected, and no verified candidates were supplied, so selecting none is appropriate.
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A language model trained to work through a problem in written steps before it answers.
Definition referenceA language model with many learned numerical settings, used to process and generate language.
Definition referenceOpenAI develops AI models and products and publishes research, release announcements and evaluations.
Explore OpenAIA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceA language model trained to work through a problem in written steps before it answers.
Definition referenceA language model with many learned numerical settings, used to process and generate language.
Definition referenceOpenAI develops AI models and products and publishes research, release announcements and evaluations.
Explore OpenAIA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition reference