Beesetty, Madhu Sudhan Rao · Nov 9, 2026
Beesetty: Failure labels can measure the procedure, not just model errors
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Madhu Sudhan Rao Beesetty argues that reported rates for a reasoning-failure category can depend heavily on how researchers classify errors. The paper defines Compositional Collapse as a model noticing a required part of a problem, then dropping or misapplying it. Many failures carried evidence for more than one category, so changing the order of classification tests changed which category appeared most common. Human reviewers also agreed with the inspector far more on whether answers were correct than on whether errors fit this category. Re-running the same inspector with the human instructions substantially narrowed that disagreement; Beesetty says this points to the prompt rather than the model as its main source. A defective translated item also affected how often the inspector applied the label. The argument matters because researchers comparing models by failure-category rates may be comparing classification choices as well as model behavior. Beesetty recommends checking overlapping labels, changing tie-break order, validating against a shared independent sample, and auditing systematic disagreements before comparing rates. The results concern one category on a multilingual math benchmark; how well these checks work for other categories remains open.
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
cs.AI · Article
Source details
Original source date: Nov 9, 2026 (2026-11-09).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-11T20:46:29.233Z. First observed: 2026-11-11T05:09:40.360Z. Retrieved: 2026-11-11T20:36:40.721Z.
Referenced by
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Limits
The reported study concerns one category, Compositional Collapse, rather than failure taxonomies generally. The evaluation used a public multilingual math ; the evidence does not establish whether the findings generalize to other tasks. The inspector/native-speaker rate comparison was affected by a defective benchmark item: a deleted clause in the Bengali and Chinese translations.
Open question
How would the four recommended checks affect comparisons of failure-mode rates for categories beyond Compositional Collapse?
Source-stated
Beesetty argues that the measured rate for Compositional Collapse depends substantially on classification procedure, including test order.
reversing two tests moves it in eleven of twelve models, and across all six orderings Collapse's share spans 26.4% to 76.4%.
Source-stated
The paper defines Compositional Collapse as noticing a required problem part and then dropping or misapplying it.
We defined Compositional Collapse (a model notices a required sub-part of a problem, then drops or misapplies it)
Source-stated
Human reviewers and the inspector agreed strongly on answer correctness but weakly on the Collapse label.
agreeing with our inspector at kappa = 0.932 on answer correctness but only kappa = 0.093 on Collapse.
Source-stated
Using the human instructions with the same inspector model substantially increased its agreement with one human reviewer; the authors attribute much of the gap to the prompt, not the model.
raises kappa against Human 2 from 0.147 to 0.636, so the gap is largely attributable to the prompt, not the model.
Source-stated
A defective translated benchmark item affected the inspector's relative labeling rate.
the inspector labels Collapse 1.81 times as often as native speakers, but only 1.17 times once a defective benchmark item (a deleted clause in the Bengali and Chinese translations) is excluded.
Source-stated
Beesetty recommends four checks before comparing failure-mode rates across models.
report label co-occurrence, permute the tie-break, validate on a shared independent sample, and audit systematic disagreements.
Source and contributor context
Beesetty, Madhu Sudhan Rao · Article author
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Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Benchmark
A defined set of tests used to compare systems. Results depend on the tasks and measures selected.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: 8f8abe65-176b-43f3-80a2-7279aca968ca.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-11T20:46:29.233Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: useful_perspective.
Observation: ff76a903c5fea0f26d492b3139518089229feabaeaa1302790cace567540a746. Source text hash: 6ea3a0384338f7a6321eb968a5907982aa10155c16759860dcee38e7e9852ac7.
Revision and source-check history
Version 1 · 2026-11-11T20:46:29.233Z
Beesetty: Failure labels can measure the procedure, not just model errors
Madhu Sudhan Rao Beesetty argues that reported rates for a reasoning-failure category can depend heavily on how researchers classify errors. The paper defines Compositional Collapse as a model noticing a required part of a problem, then dropping or misapplying it. Many failures carried evidence for more than one category, so changing the order of classification tests changed which category appeared most common. Human reviewers also agreed with the inspector far more on whether answers were correct than on whether errors fit this category. Re-running the same inspector with the human instructions substantially narrowed that disagreement; Beesetty says this points to the prompt rather than the model as its main source. A defective translated item also affected how often the inspector applied the label. The argument matters because researchers comparing models by failure-category rates may be comparing classification choices as well as model behavior. Beesetty recommends checking overlapping labels, changing tie-break order, validating against a shared independent sample, and auditing systematic disagreements before comparing rates. The results concern one category on a multilingual math benchmark; how well these checks work for other categories remains open.
Original source
cs.AI · Article
Source details
Original source date: Nov 9, 2026 (2026-11-09).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-11T20:46:29.233Z. First observed: 2026-11-11T05:09:40.360Z. Retrieved: 2026-11-11T20:36:40.721Z.
Limits
The reported study concerns one category, Compositional Collapse, rather than failure taxonomies generally. The evaluation used a public multilingual math ; the evidence does not establish whether the findings generalize to other tasks. The inspector/native-speaker rate comparison was affected by a defective benchmark item: a deleted clause in the Bengali and Chinese translations.
Open question
How would the four recommended checks affect comparisons of failure-mode rates for categories beyond Compositional Collapse?
Source-stated
Beesetty argues that the measured rate for Compositional Collapse depends substantially on classification procedure, including test order.
reversing two tests moves it in eleven of twelve models, and across all six orderings Collapse's share spans 26.4% to 76.4%.
Source-stated
The paper defines Compositional Collapse as noticing a required problem part and then dropping or misapplying it.
We defined Compositional Collapse (a model notices a required sub-part of a problem, then drops or misapplies it)
Source-stated
Human reviewers and the inspector agreed strongly on answer correctness but weakly on the Collapse label.
agreeing with our inspector at kappa = 0.932 on answer correctness but only kappa = 0.093 on Collapse.
Source-stated
Using the human instructions with the same inspector model substantially increased its agreement with one human reviewer; the authors attribute much of the gap to the prompt, not the model.
raises kappa against Human 2 from 0.147 to 0.636, so the gap is largely attributable to the prompt, not the model.
Source-stated
A defective translated benchmark item affected the inspector's relative labeling rate.
the inspector labels Collapse 1.81 times as often as native speakers, but only 1.17 times once a defective benchmark item (a deleted clause in the Bengali and Chinese translations) is excluded.
Source-stated
Beesetty recommends four checks before comparing failure-mode rates across models.
report label co-occurrence, permute the tie-break, validate on a shared independent sample, and audit systematic disagreements.
Source and contributor context
Beesetty, Madhu Sudhan Rao · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Benchmark
A defined set of tests used to compare systems. Results depend on the tasks and measures selected.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: 8f8abe65-176b-43f3-80a2-7279aca968ca.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-11T20:46:29.233Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: useful_perspective.
Observation: ff76a903c5fea0f26d492b3139518089229feabaeaa1302790cace567540a746. Source text hash: 6ea3a0384338f7a6321eb968a5907982aa10155c16759860dcee38e7e9852ac7.
uncertain · 2026-11-11T20:46:29.233Z · anthropic/claude-opus-5.5
Publication: 8f8abe65-176b-43f3-80a2-7279aca968ca
attribution — pass: The paper and its arguments are correctly attributed to Madhu Sudhan Rao Beesetty, the sole listed author. One claim says 'the authors attribute'; the source uses 'our', so 'the author' would be more precise. This does not misattribute the claim.
Authors:Madhu Sudhan Rao Beesetty
summary — uncertain: The summary is faithful and keeps the paper's core thesis. However, it refers to 'the inspector' without saying what it is: the automated inspector model that classified failures. A lay reader cannot tell what humans were compared against. 'Tie-break order' is also unexplained. Smallest repair: introduce it as an automated inspector model used to label failures, and briefly gloss tie-break. The title and question are sound and follow from the evidence.
Re-running the same inspector model with the human instructions verbatim
date — pass: The originalPublished date of 2026-11-09 matches the arXiv v1 submission date. The feed date of Nov 11 is later, as expected.
[Submitted on 9 Nov 2026]
support — pass: Each claim matches a verbatim excerpt from the abstract: the definition, the test-order sensitivity, the kappa contrasts, the prompt attribution, the defective item effect and the four checks. The limitations and the open question stay within the stated scope of one category on one multilingual math benchmark. No benefits or costs are invented.
We show that for one such category the reported fraction is largely a property of the measurement procedure.
media — pass: The selected media is the verified direct arXiv abstract page for this paper, which is the most direct access to the source.
Challenge or correct this reading
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A defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA 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 referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition reference