Paper Citation Record · LEDGER

The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity?

As of 15 July 2026, this Paper Citation Record lists 12 of 12 outbound references and 1 inbound Pith citation observation for arXiv:2601.23045.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2601.23045 v2

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T11:50:26.030339Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-07-14T06:31:01.685423+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-09T20:29:20.805599Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-05-11T15:11:08.213986Z

Reference resolution

12 of 12 outbound references displayed

verified exact1
verified fuzzy5
unresolved0
parse uncertain0
malformed identifier1
metadata mismatch5

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 03ca46a3-ef95-4f33-9751-e0d5c028979b · outbound

This paper cites Oxford University Press, Oxford.

The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity? Oxford University Press, Oxford

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:40:49.382379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.

Observation a71130fc-396f-4e5b-994d-abd788ea5979 · outbound

This paper cites 1 Leo Breiman.

The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity? 1 Leo Breiman

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:40:49.384338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.

Observation 751e357b-2258-4bff-8f7f-2fed718fe972 · outbound

This paper cites Evaluating o1-Like LLMs: Unlocking Reasoning for Translation through Comprehensive Analysis.

The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity? Evaluating o1-Like LLMs: Unlocking Reasoning for Translation through Comprehensive Analysis

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-16T09:40:48.870504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.

Observation 1fb97668-efd1-463f-a9f8-2f12c60f37eb · outbound

This paper cites 1 Morris H.

The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity? 1 Morris H

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:40:49.380467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.

Observation c64bdb23-50dd-4214-b8de-40ce3dd56018 · outbound

This paper cites DeGroot and Stephen E.

The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity? DeGroot and Stephen E

Reference 5

Resolution: metadata mismatch; doi, observed 2026-05-16T09:40:48.381261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.

Observation 1fec7e69-7ee3-4939-8808-737dbd06948a · outbound

This paper cites The Platonic Representation Hypothesis.

The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity? The Platonic Representation Hypothesis

Reference 6

Resolution: metadata mismatch; local_arxiv, observed 2026-05-16T09:40:48.382769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.

Observation 4ddf0c5f-14d9-4900-950f-fec15ba9aff9 · outbound

This paper cites Scaling Laws for Neural Language Models.

The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity? Scaling Laws for Neural Language Models

Reference 7

Resolution: metadata mismatch; local_arxiv, observed 2026-05-16T09:40:48.379657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.

Observation 32d10fbb-f1e8-416b-9ce3-cb55a6938b40 · outbound

This paper cites S1: Simple test-time scaling.

The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity? S1: Simple test-time scaling

Reference 8

Resolution: metadata mismatch; doi, observed 2026-05-16T09:40:48.387522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.

Observation b5841dbf-0a3f-41e9-8b70-be941fc9937d · outbound

This paper cites Wait, we don’t need to “wait”! removing thinking tokens improves reasoning efficiency.

The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity? Wait, we don’t need to “wait”! removing thinking tokens improves reasoning efficiency

Reference 9

Resolution: metadata mismatch; doi, observed 2026-05-16T09:40:48.385081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.

Observation ca9c0c0b-6c9e-49c0-85cd-6280dd670722 · outbound

This paper cites <PROB>P(A), P(B), P(C), P(D)</PROB>.

The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity? <PROB>P(A), P(B), P(C), P(D)</PROB>

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:40:49.378636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.

Observation 5c4f62ed-58ea-44b2-beab-508bf34b1233 · outbound

This paper cites We sample 20’000 such trajectories, and use 10% as a holdout dataset for valuation loss.

The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity? We sample 20’000 such trajectories, and use 10% as a holdout dataset for valuation loss

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:40:49.376816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.

Observation 75844cc8-4911-4e46-8cdd-b41ecd603801 · outbound

This paper cites Wait”) im- proves efficiency, Lee et al. (2025) identify length-accuracy tradeoffs through “token complexity.

The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity? Wait”) im- proves efficiency, Lee et al. (2025) identify length-accuracy tradeoffs through “token complexity

Reference 12

Resolution: malformed identifier; raw_fallback, observed 2026-05-16T09:40:49.374070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.

Pith citing papers

Observation 0dd8cd81-77ef-4370-9b5e-874c2b1d88f6 · inbound

State Stream Transformer (SST) V2: Parallel Training of Nonlinear Recurrence for Latent Space Reasoning cites this paper.

State Stream Transformer (SST) V2: Parallel Training of Nonlinear Recurrence for Latent Space Reasoning The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity?

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-05-11T15:11:08.217067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.