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Paper Citation Record · LEDGER

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs

As of 23 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 1 inbound Pith citation observation for arXiv:2507.22918.

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

pith.paper-citation-record.v1
2507.22918 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:39:46.571381Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-13T06:47:07.137898Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T06:47:25.958696Z

Reference resolution

29 of 29 outbound references displayed

  • verified exact1
  • verified fuzzy5
  • unresolved22
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 519a4dc5-4849-4ad0-b089-a2c5e0d7bb25 · outbound

This paper cites Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:39:45.518899Z digest=sha256:3cc27fe003e114f26796a1f5d1cc74bce88cc55031cfbfa3fd9f3fefbf00843e

Observation cd93610f-7be7-4859-9c60-595a4c1a90be · outbound

This paper cites Transformer Circuits Thread.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs Transformer Circuits Thread

Reference 5

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verified fuzzy
raw_fallback, observed 2026-08-06T15:39:47.256031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T15:39:45.621142Z digest=sha256:d72585dfc3fe196210836571c725a17d463713ef41434f405097f37a99a2efa7

Observation 43820409-d75e-4925-a2c6-6d2b99bc5ad4 · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 6

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no resolver link, observed 2026-08-06T15:39:45.714033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:39:45.714033Z digest=sha256:0b6415c902feacff5215373b9794ff04d1a6ae0ce2e4fe0daceedd638d7dba6e

Observation 03dec02c-a210-4043-8cf9-d62be9252d95 · outbound

This paper cites A Toy Model of Universality: Reverse Engineering How Networks Learn Group Operations.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs A Toy Model of Universality: Reverse Engineering How Networks Learn Group Operations

Reference 7

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no resolver link, observed 2026-08-06T15:39:45.801972Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:39:45.801972Z digest=sha256:d22d854b8529ecd81350ca0b00cc8ad4357e9e02fa2d1f4693187868c727e34e

Observation 6318063c-be19-46fc-9959-9f3c2cecb34a · outbound

This paper cites Sparse Autoencoders Find Highly Interpretable Features in Language Models.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs Sparse Autoencoders Find Highly Interpretable Features in Language Models

Reference 8

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no resolver link, observed 2026-08-06T15:39:45.924745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:39:45.924745Z digest=sha256:2d7b2cf9f583d56bd9f3d1816416ec871f0c219b5d3c1001e8c7c5f7665ed3b1

Observation 26a5ca13-d50c-4b83-a3e9-4b51b7b60be1 · outbound

This paper cites Neuron to Graph: Interpreting Language Model Neurons at Scale.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs Neuron to Graph: Interpreting Language Model Neurons at Scale

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:39:46.120420Z digest=sha256:36d83c6ee4baa1bc15ec340e9041869f50674ed44193233cbdea04fca4cd225b

Observation 0e4885d2-fdcb-4987-8f36-dd4c8378d02f · outbound

This paper cites Scaling and evaluating sparse autoencoders.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs Scaling and evaluating sparse autoencoders

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:39:46.172788Z digest=sha256:3996c8f7b2d1b67189a1a0766366d33d0ee4207ef9d48d79c73651ce89be79a1

Observation 16b17915-0570-48ca-8000-cde092dacb01 · outbound

This paper cites DeepDecipher: Accessing and Investigating Neuron Activation in Large Language Models.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs DeepDecipher: Accessing and Investigating Neuron Activation in Large Language Models

Reference 13

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metadata mismatch
local_arxiv, observed 2026-08-06T15:39:46.965951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T15:39:46.218173Z digest=sha256:b7913081407ce42a5e814ae725d2a672987df8d11f1c3b547a65a1b7b0c17ffd

Observation 6229ad92-6641-4214-916c-9e71a7a6f4b5 · outbound

This paper cites Alignment faking in large language models.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs Alignment faking in large language models

Reference 14

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:39:46.252176Z digest=sha256:c1410be0f09e214520f5e1cb68df975d14eb6c6228dc5d789f90415e96a1efc6

Observation bde823d1-976e-4335-9347-83bace931b12 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 15

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:39:46.295668Z digest=sha256:ed01d2ce9661ed4da6f63f2859e94382ad77b9da2c04db763d6bfd57355aa76a

Observation 71362254-d1cc-48e2-a11f-600e6a887661 · outbound

This paper cites Universal Neurons in GPT2 Language Models.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs Universal Neurons in GPT2 Language Models

Reference 16

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:39:46.392267Z digest=sha256:9757a0c4c56764cb51e82f7acc51eb770329db6f81a4bf5b8941035a32a922af

Observation bb4cf9d2-c742-4c63-9f55-d3d7174abce0 · outbound

This paper cites An Overview of Catastrophic AI Risks.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs An Overview of Catastrophic AI Risks

Reference 17

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:39:46.506017Z digest=sha256:156f2d46cb68bc434aa989e1e3ad7de9f10ca3c54a045386a54d95fc21978c80

Observation f48e4e38-2911-4f02-8a10-83c98296c2ac · outbound

This paper cites Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training

Reference 18

Resolution
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no resolver link, observed 2026-08-06T15:39:46.520999Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:39:46.520999Z digest=sha256:8bfd36df9c83afa0b15be0e3988ea9eb913ec62fc804586be91481428b160b10

Observation ce8e6707-d6f6-424d-b6b4-2f8ffd3aea0e · outbound

This paper cites Quantifying Feature Space Universality Across Large Language Models via Sparse Autoencoders.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs Quantifying Feature Space Universality Across Large Language Models via Sparse Autoencoders

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:39:46.530487Z digest=sha256:bcd656f52060cb0985930bdff95fbe2e752523a2160bcc09ff5106e0d1f9b5fa

Observation 26ff7608-7f24-4571-8253-dcddd6dc44c5 · outbound

This paper cites Accessed: 2024-06-19.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs Accessed: 2024-06-19

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:39:47.226022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T15:39:46.539381Z digest=sha256:65e8fdcd7112237af2418f605c102d146910524fdbe06b6090b242c5c053c952

Observation b86a2244-c5df-4e12-bc22-fbe0aadbd572 · outbound

This paper cites Goal Misgeneralization: Why Correct Specifications Aren't Enough For Correct Goals.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs Goal Misgeneralization: Why Correct Specifications Aren't Enough For Correct Goals

Reference 25

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unresolved
no resolver link, observed 2026-08-06T15:39:46.553247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:39:46.553247Z digest=sha256:24cfd4db2b4afb93de75c8edc51f79775148e678901821d90fd81ff4fd8551f6

Observation b77f2410-9fa2-44a9-b224-f08a1f5f2797 · outbound

This paper cites Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timoth´ee Lacroix, Baptiste Rozi `ere, Naman Goyal, Eric Hambro, Faisal Azhar, et al.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timoth´ee Lacroix, Baptiste Rozi `ere, Naman Goyal, Eric Hambro, Faisal Azhar, et al

Reference 26

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raw_fallback, observed 2026-08-06T15:39:47.196891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T15:39:46.558012Z digest=sha256:3eefaf5d4720c94017bc6a6c36b9e9b545524be3a89c0b92a9c75ca39d14a4b7

Observation c4444684-b3da-4049-af49-bd14b0852441 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs LLaMA: Open and Efficient Foundation Language Models

Reference 27

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no resolver link, observed 2026-08-06T15:39:46.562349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:39:46.562349Z digest=sha256:84f848e49e03131ab1a848ab2a1676c9c3f36fc5ca40f0b89c90a8d35c9838db

Observation 0200a562-5331-4b94-b02d-47c6bea20251 · outbound

This paper cites Well-Read Students Learn Better: On the Importance of Pre-training Compact Models.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs Well-Read Students Learn Better: On the Importance of Pre-training Compact Models

Reference 28

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source=pdf_text observed=2026-08-06T15:39:46.566529Z digest=sha256:8bdb1e2f449a25da11864115ac640f3cc906ac479b0f5918e216f7aa4ce4f3c5

Observation 4574d300-80e0-42c7-9296-67bd450788d5 · outbound

This paper cites arXiv preprint arXiv:2412.07334.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs arXiv preprint arXiv:2412.07334

Reference 29

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verified exact
raw_fallback, observed 2026-08-06T15:39:46.753131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T15:39:46.571381Z digest=sha256:113b3395c4898dd29e090d0b9ec47f07cfdd553df3edf4b4d9fdba808dc5c0e9

Observation 5e03abfa-1643-43ec-8359-e9f1a3bcc7ff · outbound

This paper cites https://wordnet.princeton.edu/.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs https://wordnet.princeton.edu/

Reference 2010

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verified fuzzy
raw_fallback, observed 2026-08-06T15:39:47.272118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T15:39:45.288412Z digest=sha256:a95f311eb2ce3cf450004f155aa47906aa9838bf694316e154d84770f49dfd3f

Observation f5696380-c4bd-4190-9974-7b2725ae119a · outbound

This paper cites k-Sparse Autoencoders.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs k-Sparse Autoencoders

Reference 2013

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source=pdf_text observed=2026-08-06T15:39:46.535005Z digest=sha256:7b9a8b5295a0be72c1ef0670c4425f43ae52e6a1945ccfd5c2c0a05b5d5b381e

Observation aa4d13ad-775b-45d4-a6eb-1019336bf602 · outbound

This paper cites Improving Dictionary Learning with Gated Sparse Autoencoders.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs Improving Dictionary Learning with Gated Sparse Autoencoders

Reference 2017

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:39:46.548154Z digest=sha256:f6ef9eaf56704734bc99c4dd22f33f0da36cc6c5570c24ebba1a7a7f28419375

Observation a9aeb7bd-7da7-47ad-bf26-8ea979265d27 · outbound

This paper cites Similarity of Neural Network Representations Revisited.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs Similarity of Neural Network Representations Revisited

Reference 2019

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:39:46.525881Z digest=sha256:f09750a27402521c3423605b9780eb6b8a0333100ae0d1160fec3dbeab4c9b1f

Observation 878a8acf-c869-49bf-8ef6-96822e2173b0 · outbound

This paper cites an unresolved cited work.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs Unresolved cited work

Reference 2020

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T15:39:46.543606Z digest=sha256:a59e1d1a2e6840733897700d84a34d72c4c1451dcc125e7c312c039ea154f7e2

Observation 23c0403c-c302-4fbf-9f78-06acf33d606e · outbound

This paper cites Transformer Circuits Thread.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs Transformer Circuits Thread

Reference 2022

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verified fuzzy
raw_fallback, observed 2026-08-06T15:39:47.241196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T15:39:45.991495Z digest=sha256:b5f5ad91426fd30f189aa7eda563617d8ed931aba3e63f3e8638fd0c48c6397e

Observation 92181f9f-cc1c-481f-8324-0ae22ab7d363 · outbound

This paper cites GPT-4 Technical Report.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs GPT-4 Technical Report

Reference 2023

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source=pdf_text observed=2026-08-06T15:39:45.331713Z digest=sha256:f72ff039e66eaa2b8a1347e91c102ad0bca8a4fe55b18a0f4361e2afa30f5243

Observation d47b395c-0bae-473b-b953-c562d884cba6 · outbound

This paper cites Mechanistic Interpretability for AI Safety -- A Review.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs Mechanistic Interpretability for AI Safety -- A Review

Reference 2024

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:39:45.419354Z digest=sha256:02544974cfd6f0779c62e358aaa2682be8caa56534edfd924e98b87fd0bcf058

Observation c831320b-ceba-4aee-88d6-d1686b8b42c9 · outbound

This paper cites arXiv preprint arXiv:2504.18530.

Semantic Convergence: Investigating Shared Representations Across Scaled LLMs arXiv preprint arXiv:2504.18530

Reference 2025

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:39:46.084751Z digest=sha256:c9d44de72fb3b7b0f866a9fcbf804471670f8e46f850faf784ad9732e808bc98

Pith citing papers

Observation 9758b934-81d8-440d-9c9f-f481295ffcc2 · inbound

Steering Without Breaking: Mechanistically Informed Interventions for Discrete Diffusion Language Models cites this paper.

Steering Without Breaking: Mechanistically Informed Interventions for Discrete Diffusion Language Models Semantic Convergence: Investigating Shared Representations Across Scaled LLMs

Reference 24

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verified exact
arxiv_id, observed 2026-05-13T06:47:25.962745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-13T06:47:07.137898Z digest=sha256:36a73031b0c05cacdd594d6583a7399ee84fe71328821b3e3495500096c2c6b0