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Paper Citation Record · LEDGER

Evaluating LLMs on Real-World Software Performance Optimization

As of 19 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2606.25530.

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

pith.paper-citation-record.v1
2606.25530 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-25T20:09:37.551395Z

measured 42 of 42 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

42 of 42 outbound references displayed

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  • verified fuzzy0
  • unresolved41
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

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Outbound references

Observation 0848b2fa-d65b-4965-97da-c9532e052142 · outbound

This paper cites SWE-perf: Can language models optimize code performance on real-world repos- itories? InSubmitted to The Fourteenth International Conference on Learning Representations,.

Evaluating LLMs on Real-World Software Performance Optimization SWE-perf: Can language models optimize code performance on real-world repos- itories? InSubmitted to The Fourteenth International Conference on Learning Representations,

Reference 1

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:25ef4a5446d1595f67357f7fc93a841d9dc2e4be1e2be9d1e0812fcbd2869b10

Observation b19ef983-16ca-4f05-9b4c-eb01b6ae4f37 · outbound

This paper cites Claude sonnet 4.6.

Evaluating LLMs on Real-World Software Performance Optimization Claude sonnet 4.6

Reference 2

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:cd0da2355b3c9d9bfa41f472ea00f5bf86413cb84fb439b561764b4878cdbf51

Observation b242db89-241e-4966-b494-db5bf5fea3c9 · outbound

This paper cites an unresolved cited work.

Evaluating LLMs on Real-World Software Performance Optimization Unresolved cited work

Reference 3

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:95f220fe3f006956174b2fa50111de984c426e84612016e2f102cb8b9c3e8966

Observation 46d3ccde-352e-4dac-b8d7-ac2d425dfbbb · outbound

This paper cites John Wiley & Sons, Ltd, 2017.

Evaluating LLMs on Real-World Software Performance Optimization John Wiley & Sons, Ltd, 2017

Reference 4

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:87ee5697f065f789e23563e1c753f70a0038f7ffd0bf3a28343c2b3b14b72523

Observation 697bdaef-60c2-4e6e-a90c-cb44ba84bce5 · outbound

This paper cites Mercury: a code efficiency benchmark for code large language models.

Evaluating LLMs on Real-World Software Performance Optimization Mercury: a code efficiency benchmark for code large language models

Reference 5

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:f584e87f673ee149c014f4682581a9bf4e6434afa822394719861d3cd508205b

Observation 746751f9-a8b7-4b93-a726-e690bc2f55cd · outbound

This paper cites Lyu.Search-Based LLMs for Code Optimization, page 578–590.

Evaluating LLMs on Real-World Software Performance Optimization Lyu.Search-Based LLMs for Code Optimization, page 578–590

Reference 6

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:8f05b3cfcd883875ef7ee9e4edc7d7e62362ff1cb574d243129c97c9bec1bc09

Observation 1a7beae1-fc5d-4a61-b657-06654699b5b0 · outbound

This paper cites Statistically rigorous java performance evaluation.

Evaluating LLMs on Real-World Software Performance Optimization Statistically rigorous java performance evaluation

Reference 7

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:d3f9783bc4cb1497dafa5e63440d4a46e10cf7634ca278de706ee9320d9dcd7f

Observation a54b55cf-9b54-4b26-81ae-95fdc3a323f3 · outbound

This paper cites Language models for code optimization: Survey, challenges and future directions, 2025.

Evaluating LLMs on Real-World Software Performance Optimization Language models for code optimization: Survey, challenges and future directions, 2025

Reference 8

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:97be54db34cb4df90785bffbba8b149aaa38078e1544996f93c1c800ea8be65f

Observation 7f1afbe2-dde3-4fae-aed9-e2ec63665401 · outbound

This paper cites Gemini 3.1 flash.

Evaluating LLMs on Real-World Software Performance Optimization Gemini 3.1 flash

Reference 9

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Observation fe06543f-7608-4e20-aa46-8b0e1c4392de · outbound

This paper cites Effibench: Bench- marking the efficiency of automatically generated code.

Evaluating LLMs on Real-World Software Performance Optimization Effibench: Bench- marking the efficiency of automatically generated code

Reference 10

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:1457ef1ed47e913cddc3dbf9e37fee408936552b5fc9e7d3f6e0d7ae7256cf89

Observation b401a4df-be9c-4b19-9d17-70bb1ef8524e · outbound

This paper cites Monzurul Amin Ifath and Israat Haque.

Evaluating LLMs on Real-World Software Performance Optimization Monzurul Amin Ifath and Israat Haque

Reference 11

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:7bbaf18e13cdc199e227b8c74a2defe4cca403fcc22bf1ce7d94a4170af807c0

Observation f281f071-0bcc-49c4-a135-575fc04a14a7 · outbound

This paper cites an unresolved cited work.

Evaluating LLMs on Real-World Software Performance Optimization Unresolved cited work

Reference 12

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:a4341453646cf01fdb2d7f8c08cd49d82a77131bb3d7956cf4ea850428ee1420

Observation c14eb221-6c72-4b82-ab6f-fb0f093a7ef7 · outbound

This paper cites A survey on large language models for code generation.ACM Trans.

Evaluating LLMs on Real-World Software Performance Optimization A survey on large language models for code generation.ACM Trans

Reference 13

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:347d77fe5eac8578bdf05c0a9a0c74ed5336948c6499e824a7d70cf1a3790535

Observation 0a42aa90-e44b-4101-a916-7ebd8e52e3ab · outbound

This paper cites Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan.

Evaluating LLMs on Real-World Software Performance Optimization Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan

Reference 14

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:064442bc1865b2f318c11343c8fb9f67cd0c83de6298fb38d65ffebc211c3341

Observation d45fcaaa-1943-4fb9-aabd-a7c4a0ee45a6 · outbound

This paper cites Understanding and detecting real-world performance bugs.

Evaluating LLMs on Real-World Software Performance Optimization Understanding and detecting real-world performance bugs

Reference 15

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:f9fd9dfc51d5fa8daa22f848e147b0d9b9fac20d5f932ed6295192347727ffbe

Observation ba5090aa-c2f5-45b4-a0f3-c1e368e44a3a · outbound

This paper cites Rigorous benchmarking in reasonable time.

Evaluating LLMs on Real-World Software Performance Optimization Rigorous benchmarking in reasonable time

Reference 16

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:8b8236f57f7684b4dcd165902a27daebd448cc037c78b6c2b6d6ac4030d1e860

Observation d5d145f1-9818-4018-9085-149181e94e9b · outbound

This paper cites Gall, and Philipp Leitner.

Evaluating LLMs on Real-World Software Performance Optimization Gall, and Philipp Leitner

Reference 17

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:5081726bfebe2f7b630bbca621dbb4a66edc730dee0bdb9b0560594b2a4d8870

Observation 64b77921-2c8e-4c68-a8d8-e4f1763765f3 · outbound

This paper cites Input sensitivity on the performance of configurable systems an empirical study.Journal of Systems and Software, 201:111671, 2023.

Evaluating LLMs on Real-World Software Performance Optimization Input sensitivity on the performance of configurable systems an empirical study.Journal of Systems and Software, 201:111671, 2023

Reference 18

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:dd6d897d363893d063c633a604e84cf18a9679e7998e436032be4fc2cc8eb061

Observation 08e51002-92cd-4d61-ab34-b34192e224d9 · outbound

This paper cites Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations.

Evaluating LLMs on Real-World Software Performance Optimization Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations

Reference 19

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arxiv_id, observed 2026-07-04T20:30:07.123394Z

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:bab5f0baf84da27bddecf99f140c07d927f77be00e9e571d5943ede6c9573a19

Observation 07b66a8e-ca2d-4007-aa4b-965e857bc9ce · outbound

This paper cites Swe-fficiency: Can language models optimize real-world repositories on real workloads?, 2025.

Evaluating LLMs on Real-World Software Performance Optimization Swe-fficiency: Can language models optimize real-world repositories on real workloads?, 2025

Reference 20

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:edee91d851002c72fec852885a8998890ce8ce54dc164ef7aabf2886e83aa820

Observation 7966fdd9-f859-4f68-9d7e-239f674c7d3d · outbound

This paper cites MiniMax M27.https://www.minimax.io/news/minimax-m25, 2026.

Evaluating LLMs on Real-World Software Performance Optimization MiniMax M27.https://www.minimax.io/news/minimax-m25, 2026

Reference 21

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:d38e3915191ecd4598593ebdb536f1f47f649f70fa96e1649867796ccc5de2b3

Observation 0783defb-56b8-49a2-becc-b0218ef4669d · outbound

This paper cites Kimi K2.5.

Evaluating LLMs on Real-World Software Performance Optimization Kimi K2.5

Reference 22

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:b5a71df6e9af49bb6e585517f0d2a5019cb3f37fa5cdc0f52f1a4b5e5e9d5de3

Observation dde4ef08-7ada-40de-8df4-4c6f1dcd9ac0 · outbound

This paper cites Analyzing the impact of workloads on modeling the performance of configurable software systems.

Evaluating LLMs on Real-World Software Performance Optimization Analyzing the impact of workloads on modeling the performance of configurable software systems

Reference 23

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Observation af9ede46-7647-4470-8be3-d903816fb74a · outbound

This paper cites an unresolved cited work.

Evaluating LLMs on Real-World Software Performance Optimization Unresolved cited work

Reference 24

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:3d7334b34bcf9c23334510286c055bee6645d33f29906e4382abc5a45c180d93

Observation c1aaa681-e5a8-418e-92d0-be80dc40ade3 · outbound

This paper cites Discovering, reporting, and fixing performance bugs.

Evaluating LLMs on Real-World Software Performance Optimization Discovering, reporting, and fixing performance bugs

Reference 25

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Observation 9b3a2ace-7e7e-497e-abf9-c76017b85403 · outbound

This paper cites Introducing GPT-5.2.

Evaluating LLMs on Real-World Software Performance Optimization Introducing GPT-5.2

Reference 26

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:4cbcc4ea5cbdc9759ef270373383aecc3eb3d9499538bf9594c937696e3955fd

Observation 04e3a7f0-ded9-409e-9c58-26d8e043fba8 · outbound

This paper cites Lyu, Caiming Xiong, Silvio Savarese, and Doyen Sahoo.

Evaluating LLMs on Real-World Software Performance Optimization Lyu, Caiming Xiong, Silvio Savarese, and Doyen Sahoo

Reference 27

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Observation 4cb262f0-4fc3-4e7a-9085-260acda5e8fe · outbound

This paper cites Coffe: A code efficiency benchmark for code generation, 2025.

Evaluating LLMs on Real-World Software Performance Optimization Coffe: A code efficiency benchmark for code generation, 2025

Reference 28

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Observation 9f884374-be46-4515-aa40-b857cd428d71 · outbound

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Evaluating LLMs on Real-World Software Performance Optimization Unresolved cited work

Reference 29

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:60239a97c9f5ee9fce516cc186880b6e7d3ca415357e37abe67f415c4706d6d3

Observation 8bd6cd4a-348a-473a-8e43-cae31c78c5a0 · outbound

This paper cites tracemalloc — Trace memory allocations.

Evaluating LLMs on Real-World Software Performance Optimization tracemalloc — Trace memory allocations

Reference 30

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:1016fd1f79e4bdced65fdcc643724be4d0fe648e8855eee1af7b87f6b66d67c2

Observation 25e9c537-7411-4a38-9b3f-46beeb615cff · outbound

This paper cites Zhang, Heming Cui, Siu Ming Yiu, Dong HUANG, See-Kiong Ng, and Anh Tuan Luu.

Evaluating LLMs on Real-World Software Performance Optimization Zhang, Heming Cui, Siu Ming Yiu, Dong HUANG, See-Kiong Ng, and Anh Tuan Luu

Reference 31

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:7da4f3234828c370897e7cd1b0df854e072248e7edf9cbd92801b764f116b651

Observation 75bd916e-a941-490b-bdb1-4db9285ab4ca · outbound

This paper cites How efficient is llm-generated code? a rigorous & high-standard benchmark, 2025.

Evaluating LLMs on Real-World Software Performance Optimization How efficient is llm-generated code? a rigorous & high-standard benchmark, 2025

Reference 32

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Observation c565f4b7-0036-4ebf-a37a-4152966959ab · outbound

This paper cites A large-scale empirical study on mobile performance: energy, run-time and memory.Empirical Softw.

Evaluating LLMs on Real-World Software Performance Optimization A large-scale empirical study on mobile performance: energy, run-time and memory.Empirical Softw

Reference 33

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:e38b8d0e5b2ce0a1fa0c654068ca656de21daaf93dff6537c50a3297d6bff1bf

Observation 7a3513c0-bae1-4519-b038-236ea342bfdd · outbound

This paper cites Saha, Matthew Lease, Sarfraz Khurshid, and Dewayne E.

Evaluating LLMs on Real-World Software Performance Optimization Saha, Matthew Lease, Sarfraz Khurshid, and Dewayne E

Reference 34

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:275a060635f38e7ece5ff428eaba4aaf0441fe0d602a6b02fae40e1a75b3237f

Observation e4a798c4-09f3-4702-8f62-8616f6c17d95 · outbound

This paper cites Gso: Challenging software optimization tasks for evaluating swe-agents, 2025.

Evaluating LLMs on Real-World Software Performance Optimization Gso: Challenging software optimization tasks for evaluating swe-agents, 2025

Reference 35

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Observation 59f168cc-90c5-4c62-a95f-08c729f62b7d · outbound

This paper cites Learning performance-improving code edits, 2024.

Evaluating LLMs on Real-World Software Performance Optimization Learning performance-improving code edits, 2024

Reference 36

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:9da0b02b12c0f3e2d5c6a0570687f089936e0916d4e8fa114d8d62a15519bfcf

Observation 1c5a8b2e-ddb4-4e78-848c-0bfdfee8a7b6 · outbound

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Evaluating LLMs on Real-World Software Performance Optimization Unresolved cited work

Reference 37

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:74fdc223e9a8679eb4c6d316629e7b8dc06544bba328ef09f6eb2898128596ec

Observation 032fb1b0-b2bb-4b0f-8604-61a9e414b562 · outbound

This paper cites Ecco: Can we improve model-generated code efficiency without sacrificing functional correctness?, 2024.

Evaluating LLMs on Real-World Software Performance Optimization Ecco: Can we improve model-generated code efficiency without sacrificing functional correctness?, 2024

Reference 38

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:bb27b12c90463aa9e68016e02936810b3dd095c9a296cf74f825f32d3146361c

Observation ee6a826f-2f8a-426b-a3c8-3e2016dbb16c · outbound

This paper cites Perfgen: Automated performance benchmark generation for big data analytics, 2024.

Evaluating LLMs on Real-World Software Performance Optimization Perfgen: Automated performance benchmark generation for big data analytics, 2024

Reference 39

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:5b9d3c3480615d45df450b93f1c09555577f9dd0703817e558a6ee631e6b5b5a

Observation e88ba221-aa15-40c7-b58b-d471a5965476 · outbound

This paper cites GLM-51.https://z.ai/blog/glm-5.1, 2026.

Evaluating LLMs on Real-World Software Performance Optimization GLM-51.https://z.ai/blog/glm-5.1, 2026

Reference 40

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no resolver link, observed 2026-06-25T20:09:37.551395Z

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:5f8a5af67776614dcf0512cc49216a46c572d263dd51888ed051c09b8dedf0d9

Observation cf513d70-7dec-4dea-b36a-d50e499237b6 · outbound

This paper cites an unresolved cited work.

Evaluating LLMs on Real-World Software Performance Optimization Unresolved cited work

Reference 41

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no resolver link, observed 2026-06-25T20:09:37.551395Z

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:d601595aa04f9cfdd2e7f073229e48fb324a863edb84f0e379a2489b1b7f8142

Observation ec033e3d-f27b-4dad-b549-e7f97e707923 · outbound

This paper cites "" PR #50778 - Benchmark Series.replace when using a large dict for to_replace. Parameters: - N: Series length - R: Number of values to replace.

Evaluating LLMs on Real-World Software Performance Optimization "" PR #50778 - Benchmark Series.replace when using a large dict for to_replace. Parameters: - N: Series length - R: Number of values to replace

Reference 42

Resolution
unresolved
no resolver link, observed 2026-06-25T20:09:37.551395Z

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source=pdf_text observed=2026-06-25T20:09:37.551395Z digest=sha256:dc2fe8ded2ef02beed1825975d0d35c54b174296a87404d64d12c446515cd183

Pith citing papers

No inbound Pith citation observations are available.