Claim LD01-SL-C37 (v1.0)

Testing OpenAI’s o1, a model trained to reason step by step, the authors of “Embers of Autoregression” found that it substantially outperformed earlier models “in many cases” but “still displays the same qualitative trends”: its accuracy at decoding a shift cipher ranged from 47% in the lowest-probability case to 92% in the highest, and it used more reasoning tokens on low-probability examples.

Research opportunities this claim motivates

What this claim leaves open for research. Each is a hypothesis about a gap, not a finding.

Do the failure patterns of 2023–2024 language models persist in reasoning models?OpenAI's o1 shows the same pattern at higher accuracy (47% to 92%).

Provenance and status

Claim type: ESTIMATE (as printed: “ESTIMATE”)
Publication status: ELIGIBLE (PREPRINT) (as printed: “ELIGIBLE WITH PREPRINT QUALIFIER”)
Verification state: CONTENT_VERIFIED_PRIMARY (PREPRINT) (as printed: “CONTENT VERIFIED PREPRINT”; verified by: UNKNOWN)
Does not license: A claim about later reasoning models, or that o1 cannot do these tasks
Source (field 6, as printed): McCoy, Yao, Friedman, Hardy & Griffiths, “When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1,” arXiv:2410.01792, 2024
Source type (field 7): PREPRINT (as printed: “Preprint”)
Institutional position: Mixed: independent academic (Yale University, Princeton University), Roundtable, and AI developer (one author at OpenAI, whose model is tested; he joined after the original project was completed)
Maturity (claim evidence / publication maturity): EMERGING (as printed: “EMERGING”)
Recorded date: 2026-10-16
Currentness / correction: This is the current version. No correction or concern relation is recorded on its Sources.

Version

Current version v1.0, resolved through the explicit current pointer CLAIM--LD01-SL-C37.

Versions:v1.0 · current

Pinned Sources (field 6, version-specific)

“McCoy, Yao, Friedman, Hardy & Griffiths, “When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1,” arXiv:2410.01792, 2024” → SRC-SL-MCCOY-ET-AL-O1-ARXIV-2410-01792--v1.0 — McCoy, Yao, Friedman, Hardy & Griffiths, “When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1,” arXiv:2410.01792, 2024; class PREPRINT (explicit public provenance pin)

All 25 canonical Claim fields (raw values and normalized values)

#	Canonical field	Canonical value (verbatim)	Normalized (Claim Value Vocabulary v1.0.2)
1	CLAIM ID	LD01-SL-C37	—
2	PUBLIC-FACING CLAIM	Testing OpenAI’s o1, a model trained to reason step by step, the authors of “Embers of Autoregression” found that it substantially outperformed earlier models “in many cases” but “still displays the same qualitative trends”: its accuracy at decoding a shift cipher ranged from 47% in the lowest-probability case to 92% in the highest, and it used more reasoning tokens on low-probability examples.	—
3	CLAIM TYPE	ESTIMATE	base_types: ESTIMATE
4	RESEARCH STRAND	B — Evidence that could tell the accounts apart	—
5	MATURITY	EMERGING	states: EMERGING
6	SOURCE	McCoy, Yao, Friedman, Hardy & Griffiths, “When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1,” arXiv:2410.01792, 2024	source_ids: SRC-SL-MCCOY-ET-AL-O1-ARXIV-2410-01792
7	SOURCE TYPE	Preprint	classes: PREPRINT
8	INSTITUTIONAL POSITION	Mixed: independent academic (Yale University, Princeton University), Roundtable, and AI developer (one author at OpenAI, whose model is tested; he joined after the original project was completed)	—
9	SCOPE	Deterministic text tasks in English; OpenAI o1 compared with earlier models, 2024	—
10	DESIGN	Re-test of earlier tasks varying output probability and task frequency, with accuracy and the number of reasoning tokens as measures	—
11	SAMPLE / DATASET	Tasks from the authors’ 2023 study, including shift ciphers, Pig Latin, article swapping and list reversal	—
12	MODEL / TOOL / VERSION	OpenAI o1 (preview, 2024) and earlier models	—
13	COMPARATOR	High- and low-probability versions of the same task; earlier models	—
14	MAIN RESULT	Shift-cipher accuracy 47% (lowest probability) to 92% (highest); large gains on rare task variants, but sensitivity to probability persists; more tokens used where probability is low	—
15	INFERENTIAL TARGET	Whether a model optimized for reasoning still shows traces of next-word prediction	—
16	DOES NOT LICENSE	A claim about later reasoning models, or that o1 cannot do these tasks	—
17	VERIFICATION STATE	CONTENT VERIFIED PREPRINT	state: CONTENT_VERIFIED_PRIMARY · qualifiers: PREPRINT · verified_by: UNKNOWN
18	LIMITATIONS	Read in arXiv version 2 (4 October 2024); o1’s workings are not public; ceiling effects limit some comparisons, as the authors note; one author works for the developer of the model tested; Anthropic’s Claude 3, made by the developer of the model that drafted this debate, is among the models compared	—
19	CONTRADICTORY / SUPPORTING SOURCES	The original study: LD01-SL-C18	—
20	GEOGRAPHY / LANGUAGE	English task set	—
21	DISTRIBUTIONAL LEDGER	Not analysed in this claim	—
22	R01 / FIELD-MAP LINKS	LD-01, T4, D3	—
23	WHAT WOULD CHANGE THIS CLAIM?	Reasoning models whose accuracy no longer depends on output probability	—
24	PUBLICATION STATUS	ELIGIBLE WITH PREPRINT QUALIFIER	state: ELIGIBLE · qualifiers: PREPRINT
25	RECORDED DATE	2026-10-16	—

Field 22 references resolved against Field Map v0.2: LD-01 (LIVING_DEBATE); T4 (TENSION); D3 (DOMAIN)

Cited by Living Debate versions

LD-01 v0.1

Research-of-record provenance

Materialized by sociolens-materializer 0.6.0 at 2026-09-28T20:00:00.000Z; normalization contract SocioLens — Claim Value Vocabulary v1.0.2 — Pre-Spike Materialization Contract (v1.0.2). Content seal SHA-256 dc86dfb7d23915cdf6452778d3e6a3963abb0f1a8a13ad075f5a46900749499d.

Repository-authored inputs (not Drive exports)

SocioLens claim — LD01-SL-C37 v1.0 (v1.0) — written in the SocioLens repository by Project Lead (Claude, AI) on 2026-10-16; no Drive identity exists. Authority: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and the Owner's choice in the SocioLens project chat on 16 October 2026 to build the LD-01 debate; a new Claim, first version, written from the project's LD-01 source notes, checked against full texts, official web pages or official abstracts. Block “ENTRY 37 — LD01-SL-C37” SHA-256 64bf3527da5d3b35ce70be5c531fb4288ff9d41d4835f787706ff0a0099d59fe; fixture fixtures/authored/claims/LD01-SL-C37--v1.0.

Builder decisions (not Drive data)

VERSION = v1.0 — basis: Claim version taken from the repository-authored claim fixture version (SocioLens claim — LD01-SL-C37 v1.0); the first version of a new Claim of the LD-01 debate
CURRENT_POINTER = true — basis: repository-authored claim "SocioLens claim — LD01-SL-C37 v1.0" (Project Lead (Claude, AI), 2026-10-16); basis: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and the Owner's choice in the SocioLens project chat on 16 October 2026 to build the LD-01 debate; a new Claim, first version, written from the project's LD-01 source notes, checked against full texts, official web pages or official abstracts
FIELD_6_SOURCE_REFERENCE = SRC-SL-MCCOY-ET-AL-O1-ARXIV-2410-01792--v1.0 — basis: field 6 of ENTRY 37 — LD01-SL-C37 cites "McCoy, Yao, Friedman, Hardy & Griffiths, “When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1,” arXiv:2410.01792, 2024"; the only field-6 segment of this ClaimVersion's repository-authored ledger block.
SOURCE_PROVENANCE_PIN = SRC-SL-MCCOY-ET-AL-O1-ARXIV-2410-01792--v1.0 — basis: explicit version-pinned public provenance of this ClaimVersion (Amendment v1.2 §6): the pinned field-6 Source record "McCoy, Yao, Friedman, Hardy & Griffiths, “When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1,” arXiv:2410.01792, 2024".