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Paper Citation Record · LEDGER

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders

As of 19 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2507.18918.

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

pith.paper-citation-record.v1
2507.18918 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:12:07.565182Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

33 of 33 outbound references displayed

  • verified exact1
  • verified fuzzy2
  • unresolved30
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 338e9752-2541-4f5e-9eff-8556e9c50f03 · outbound

This paper cites an unresolved cited work.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Unresolved cited work

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 30dae47e-ea84-4631-bb72-8cac32d9f511 · outbound

This paper cites an unresolved cited work.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Unresolved cited work

Reference 2

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source=arxiv_source observed=2026-08-15T18:12:07.445295Z digest=sha256:cffe7c163ffdc734eb02394558d50bdba186c6a23413bb75cbe2b981827b034f

Observation 2f8fcf33-469f-439c-ba78-92fcc59d02c0 · outbound

This paper cites an unresolved cited work.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Unresolved cited work

Reference 3

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Observation 95582e47-0852-48c3-89db-470327b74b9d · outbound

This paper cites Systematic Inequalities in Language Technology Performance across the World's Languages.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Systematic Inequalities in Language Technology Performance across the World's Languages

Reference 4

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-15T18:12:07.452951Z digest=sha256:9394c81c5e9dbf0d7e73ddca4e0d86b3bac7eb0b9eee468363ea8ca27d7c0307

Observation 84951191-8638-47f1-9c88-b9a332d33934 · outbound

This paper cites an unresolved cited work.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Unresolved cited work

Reference 5

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source=arxiv_source observed=2026-08-15T18:12:07.456879Z digest=sha256:552335c089541e982356e6d2339f816f6ea21f3362afc358ad30c0492f15af1e

Observation d24892a7-df8f-44cf-a001-43ec47384f1c · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 6

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source=arxiv_source observed=2026-08-15T18:12:07.460681Z digest=sha256:5757fce357726ba470ac56733520b8a7e4e5bb81e552ad824cc48de7720f5a39

Observation 0cd44d18-a358-4baa-b2e9-508c23dda289 · outbound

This paper cites Unsupervised Cross-lingual Representation Learning at Scale.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Unsupervised Cross-lingual Representation Learning at Scale

Reference 7

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source=arxiv_source observed=2026-08-15T18:12:07.464739Z digest=sha256:cbdb0751f157d7e8ff1a956363a1d0f82eb6c4d6054799968240f24ed052e9de

Observation c8a9de56-83c1-4a5d-9f4f-54012f242757 · outbound

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

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Sparse Autoencoders Find Highly Interpretable Features in Language Models

Reference 8

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source=arxiv_source observed=2026-08-15T18:12:07.468581Z digest=sha256:2b60b53d8611a7c7fd98ebdeca32252063b13ae91f29a72b35e5ddfbd6c987cb

Observation f9f247ba-77bc-42a9-a6ec-8edfb29ef416 · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Gemma: Open Models Based on Gemini Research and Technology

Reference 9

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source=arxiv_source observed=2026-08-15T18:12:07.472358Z digest=sha256:99d6bf3177d1f977815f6a2041f80efb6b935399e60fbb3d46d16124093081c9

Observation 2d5cdec2-966e-4907-bb4a-70d84ddbc6a0 · outbound

This paper cites The Secret is in the Spectra: Predicting Cross-lingual Task Performance with Spectral Similarity Measures.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders The Secret is in the Spectra: Predicting Cross-lingual Task Performance with Spectral Similarity Measures

Reference 10

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:12:07.476011Z digest=sha256:9f4b1c4eec5f3e3b7d71a906dfc90f083c67ffd7038e76e480b3d6c8eee22ba5

Observation 43a8ccbc-ed78-492c-9528-4713f8c44c46 · outbound

This paper cites Identifying Necessary Elements for BERT's Multilinguality.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Identifying Necessary Elements for BERT's Multilinguality

Reference 11

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local_arxiv, observed 2026-08-15T18:12:07.763933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T18:12:07.479660Z digest=sha256:5e386765a555695cf3d74cfb29ecab0bc28a3a85e6ded1e981c5cee6a77015b8

Observation aa21e598-c817-4c42-80a1-e37880f20baa · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Measuring Massive Multitask Language Understanding

Reference 12

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source=arxiv_source observed=2026-08-15T18:12:07.483681Z digest=sha256:5f1c69554b6386ad17a465728648672e2ef45f87148bdb6bebd51b19afe69b17

Observation d9b9f68f-ef4b-431f-abc0-b633ce3fd7a8 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders LoRA: Low-Rank Adaptation of Large Language Models

Reference 13

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no resolver link, observed 2026-08-15T18:12:07.488131Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:12:07.488131Z digest=sha256:27f918d2c19a3d9036a79e09d133ce84b7c71bdb84b7209c509db807f960393a

Observation 9610adb6-cb65-4d53-ac34-7525a812593b · outbound

This paper cites an unresolved cited work.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Unresolved cited work

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T18:12:07.491710Z digest=sha256:e8743126b1d9464b0728f0c124156c4942d5942c1aee98f93d5cd2388e42bce5

Observation 18b34b9e-5ad8-43c2-b84c-b983a4f96d36 · outbound

This paper cites Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback

Reference 16

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:12:07.499680Z digest=sha256:23ec403e62d7d8c43dc4ff90bc4c14dda396c528647f20cb71d9b72d297d6aa3

Observation ac351c88-0f6b-467e-b448-a4f9f350efd8 · outbound

This paper cites Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2

Reference 17

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source=arxiv_source observed=2026-08-15T18:12:07.503183Z digest=sha256:537e7e77461beb5f10587da30e4a535d6a4c76b79cbc0389fbf58d28cb1e0782

Observation 65ced990-2faf-40ae-9479-e8107dfd4336 · outbound

This paper cites Unraveling Babel: Exploring Multilingual Activation Patterns of LLMs and Their Applications.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Unraveling Babel: Exploring Multilingual Activation Patterns of LLMs and Their Applications

Reference 18

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source=arxiv_source observed=2026-08-15T18:12:07.506862Z digest=sha256:9d0ab94a2135aaf6fc19b5519e9142fe5d6cd6eff71ad17bc80c834d3706df9f

Observation cd027f01-ae11-43ac-9eea-d2075c23a12a · outbound

This paper cites Interpreting Attention Layer Outputs with Sparse Autoencoders.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Interpreting Attention Layer Outputs with Sparse Autoencoders

Reference 19

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source=arxiv_source observed=2026-08-15T18:12:07.510290Z digest=sha256:b4bd1261565866f4a3b3f4a9c676ea2c75f94c94585ad089860ee851dbbc8336

Observation 3cf4eab1-5070-485f-beae-68169c7a2eca · outbound

This paper cites GPT-4 Technical Report.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders GPT-4 Technical Report

Reference 20

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source=arxiv_source observed=2026-08-15T18:12:07.514015Z digest=sha256:8669221390389f4a20a9fbcdd72fb1ba5b9a7346d78d35e43c121d0e5e274995

Observation 595945d9-5b3a-4bbe-a9e5-72bf3a91ff0b · outbound

This paper cites IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators

Reference 21

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source=arxiv_source observed=2026-08-15T18:12:07.517155Z digest=sha256:489d73e707806cbfff987139220f0a5b4bf39d82a6389b77ea342cc86d2f0cc7

Observation c39cf441-6218-4598-97e6-1175032c620a · outbound

This paper cites How multilingual is Multilingual BERT?.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders How multilingual is Multilingual BERT?

Reference 22

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-15T18:12:07.521529Z digest=sha256:d30f79fd2d697c9f873193b99d00dac0d988df2f1b7a5bed9e3c27ea75960fd5

Observation 693ea4bc-6062-434b-bbc4-edb4fb6fb2c9 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Gemma 2: Improving Open Language Models at a Practical Size

Reference 23

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source=arxiv_source observed=2026-08-15T18:12:07.525198Z digest=sha256:623e6ded666a9fd5efd087e3a5b21914d1442cf2c1fe88ac0a8b21761ec052f9

Observation a4f78c07-9115-4035-9b3e-258232668620 · outbound

This paper cites ChatGPT MT: Competitive for High- (but not Low-) Resource Languages.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders ChatGPT MT: Competitive for High- (but not Low-) Resource Languages

Reference 24

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source=arxiv_source observed=2026-08-15T18:12:07.528680Z digest=sha256:2ba053373b50057bc46d82322e0244ea157a84616696df217c59fe0d8f1eb5b7

Observation 56e94ebc-a771-43bf-ab5b-7e8125e9545e · outbound

This paper cites Fine-tuning Whisper on Low-Resource Languages for Real-World Applications.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Fine-tuning Whisper on Low-Resource Languages for Real-World Applications

Reference 25

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source=arxiv_source observed=2026-08-15T18:12:07.532281Z digest=sha256:d68e1cfbf79f14735a6d83366b99c89f9b71dc1df34707bede067323d6f7c892

Observation f6a1ed88-a265-4b69-9b33-88e91f636db2 · outbound

This paper cites Turner, Callum McDougall, Monte MacDiarmid, Alex Tamkin, Esin Durmus, Tristan Hume, Francesco Mosconi, C.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Turner, Callum McDougall, Monte MacDiarmid, Alex Tamkin, Esin Durmus, Tristan Hume, Francesco Mosconi, C

Reference 26

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T18:12:07.535894Z digest=sha256:e46dd71939dbc8a641c137ec1ea85180e9abb54a19145816efb422c194937702

Observation 43c6d25f-51a1-47db-a601-b8a65e6d4c87 · outbound

This paper cites o rg Tiedemann, Mikko Aulamo, Daria Bakshandaeva, Michele Boggia, Stig-Arne Gr \.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders o rg Tiedemann, Mikko Aulamo, Daria Bakshandaeva, Michele Boggia, Stig-Arne Gr \

Reference 27

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no resolver link, observed 2026-08-15T18:12:07.539077Z

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source=arxiv_source observed=2026-08-15T18:12:07.539077Z digest=sha256:ee18c9ddeb27f56524a460783bdf335a234492931bea692aa7eae64accf8552d

Observation c079181b-6477-4c7c-a75a-ad2a598e78a8 · outbound

This paper cites an unresolved cited work.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Unresolved cited work

Reference 28

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raw_fallback, observed 2026-08-15T18:12:07.874412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T18:12:07.544343Z digest=sha256:a29cc39e0882405d69f6bfd31c06f75425c1ff396455ecf78be931c529f70e2e

Observation fc43fd43-4f86-4daf-b10a-cd8b862e6b69 · outbound

This paper cites true features.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders true features

Reference 29

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verified fuzzy
raw_fallback, observed 2026-08-15T18:12:07.862506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T18:12:07.547717Z digest=sha256:93df0d21a3975f5f4d79dda94d507f53eca46fa51771f3cf88b16a6c6988f2ca

Observation 18aae1e9-5d63-4c40-b02a-283974f426ae · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 30

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source=arxiv_source observed=2026-08-15T18:12:07.550986Z digest=sha256:7fc45f301bb66894e629beca2e848a26a5427ab5d443364d2fbe0294016ea8ea

Observation dd8fad50-0d86-4d6e-b3ee-260b2164c0b2 · outbound

This paper cites Converging to a Lingua Franca: Evolution of Linguistic Regions and Semantics Alignment in Multilingual Large Language Models.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Converging to a Lingua Franca: Evolution of Linguistic Regions and Semantics Alignment in Multilingual Large Language Models

Reference 31

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source=arxiv_source observed=2026-08-15T18:12:07.554627Z digest=sha256:1a2934789a167ad6eea855d9bd3c321a4bc43fcd5715a7234cef94e5824bbb86

Observation e6ffd819-0129-4a67-b9f3-6e1a4a93c22e · outbound

This paper cites Investigating Layer Importance in Large Language Models.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders Investigating Layer Importance in Large Language Models

Reference 32

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source=arxiv_source observed=2026-08-15T18:12:07.558217Z digest=sha256:70d76b0158365002fb7d84fb9e1aa8b4dacf79a7ea2b701f1a3590a6a6fa4609

Observation b16c7baa-af1b-46f6-bae1-019d67671d3d · outbound

This paper cites online" 'onlinestring :=.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders online" 'onlinestring :=

Reference 33

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source=arxiv_source observed=2026-08-15T18:12:07.561681Z digest=sha256:b121ff25540b5c3847a850396b53e316994b91373cfb0aba62633dd92c5869b8

Observation cd4e9386-d0e7-46b2-b79f-1e9d61eea54c · outbound

This paper cites write newline.

Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders write newline

Reference 34

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no resolver link, observed 2026-08-15T18:12:07.565182Z

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source=arxiv_source observed=2026-08-15T18:12:07.565182Z digest=sha256:73d9a3667e0109b6c503ab7b68f8c726e6ab4b0a1b192c156e9a342e9128fc0e

Pith citing papers

No inbound Pith citation observations are available.