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Paper Citation Record · LEDGER

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion

As of 10 August 2026, this Paper Citation Record lists 100 of 296 outbound references and 0 inbound Pith citation observations for arXiv:2607.14371.

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

pith.paper-citation-record.v1
2607.14371 v1

Coverage vector

measured 100 of 296 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T02:24:43.554361Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

100 of 296 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved99
  • parse uncertain1
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e0bc2e1c-0dea-4235-9fbf-54d8d53eaec6 · outbound

This paper cites Abril and Robert Plant.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Abril and Robert Plant

Reference 1

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source=arxiv_source observed=2026-08-02T02:24:34.572443Z digest=sha256:2803c6e832e76a1f64413eda3fa167563fcf16f0b83a0d593e63e1544e621e95

Observation ba5c68bd-23a8-4737-a8ca-edbe1868964d · outbound

This paper cites Deciding equivalances among conjunctive aggregate queries.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Deciding equivalances among conjunctive aggregate queries

Reference 2

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source=arxiv_source observed=2026-08-02T02:24:34.711323Z digest=sha256:18ff2be70d76a6ce797d639f8cfd5e82089f20c9f3d1730a8e868577d5cdbde8

Observation 96bee69f-f46c-4450-8fda-d8f51f17cd82 · outbound

This paper cites an unresolved cited work.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Unresolved cited work

Reference 3

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source=arxiv_source observed=2026-08-02T02:24:34.821916Z digest=sha256:044569f395013a193a66732822c2363df91a1bc0ba3052aa084bed1ff5ba135b

Observation 2a0e28e7-3aee-4403-8c74-f6db8ce78145 · outbound

This paper cites Understanding Policy-Based Networking.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Understanding Policy-Based Networking

Reference 4

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source=arxiv_source observed=2026-08-02T02:24:34.942193Z digest=sha256:066d1ea4b2133a55aa8e722ee160c9058a165f29d83a6d4e7e4ade3e73b23dee

Observation 194198fa-ad58-4eb5-a4bb-c1acf65cb850 · outbound

This paper cites an unresolved cited work.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Unresolved cited work

Reference 7

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source=arxiv_source observed=2026-08-02T02:24:35.157295Z digest=sha256:721d4a893ab069addba9c22d31aa18faab7eea7ff1c73aaa48d608b23470f37f

Observation 5a2998a1-97cc-4c19-b62b-78db375548db · outbound

This paper cites an unresolved cited work.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Unresolved cited work

Reference 8

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source=arxiv_source observed=2026-08-02T02:24:35.235469Z digest=sha256:df1ddeddc7f84933824a12abee0e3217486f9ad04130528fcaebbbaff3308aad

Observation 0d4413ad-00ec-4473-93df-71afb5c9e695 · outbound

This paper cites Douglass and David Harel and Mark B.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Douglass and David Harel and Mark B

Reference 9

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source=arxiv_source observed=2026-08-02T02:24:35.316616Z digest=sha256:283a77993bf7ce0b6f97d83a9fa89ecf080908b38c734fd3f3d43b3fb91b361e

Observation c56397d6-2f0d-4185-b337-d3d6e69ff1b0 · outbound

This paper cites an unresolved cited work.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Unresolved cited work

Reference 10

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source=arxiv_source observed=2026-08-02T02:24:35.368482Z digest=sha256:d2c964eb2f952110d5ed864a01554686ff7d7a72ef63a31cfeda8c1330d9ccf8

Observation c7383977-27bd-423f-99a6-92fb6f674984 · outbound

This paper cites an unresolved cited work.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Unresolved cited work

Reference 11

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source=arxiv_source observed=2026-08-02T02:24:35.482348Z digest=sha256:09ef477bd98c0b277ad3a65976b11540eb3d85fdd03f24533c53693206a9d765

Observation ad8ea5d4-8702-4c5d-ba39-a0d020763c64 · outbound

This paper cites Journal of Urban Economics , volume=.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Journal of Urban Economics , volume=

Reference 12

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source=arxiv_source observed=2026-08-02T02:24:35.619393Z digest=sha256:01f8d4b948eb397dc93d68548316785be909c8852799ddbb32452917ae089daa

Observation c6bda826-1236-4285-9f97-d6f470e75103 · outbound

This paper cites 2024 , publisher=.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion 2024 , publisher=

Reference 13

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source=arxiv_source observed=2026-08-02T02:24:35.712850Z digest=sha256:e59997a748b24eddc287d50853afaff6b7aa58df43419f44cfc0a008a28eb147

Observation cc9a3885-7e6a-4a0f-a2b7-2a416adc9c39 · outbound

This paper cites Handbook of regional and urban economics , volume=.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Handbook of regional and urban economics , volume=

Reference 14

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source=arxiv_source observed=2026-08-02T02:24:35.861441Z digest=sha256:45aabca5ac681a1fae6ce9e0e1c37c98128f256d3d13a1d4a29554d97d9686ca

Observation 097c509b-be22-48cd-91b5-cfaaaecdef00 · outbound

This paper cites American Economic Review , volume=.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion American Economic Review , volume=

Reference 15

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source=arxiv_source observed=2026-08-02T02:24:35.933363Z digest=sha256:efdde27895f0438ee922a52712819af4ab07d6904ff2fd9cb767b139b3606bc9

Observation 530fdc0a-4241-47c5-b109-664a79c69a72 · outbound

This paper cites 2015 , publisher=.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion 2015 , publisher=

Reference 16

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source=arxiv_source observed=2026-08-02T02:24:35.957134Z digest=sha256:392ac25caae4171ff06520973889076b93d78890b0d09cae5e993d4864158b0f

Observation 0c9e2f3e-402d-47dd-b718-29a32d736b78 · outbound

This paper cites Econometrica , volume=.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Econometrica , volume=

Reference 17

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source=arxiv_source observed=2026-08-02T02:24:35.960430Z digest=sha256:310020fbb36edf062806dcdc3ce481eccfe2fd2a0b19e80c46c86bda10b8f9b5

Observation 12ca8a9c-76fd-494f-b447-472b3bb1b014 · outbound

This paper cites Structured Variational Inference Procedures and their Realizations (as incol).

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Structured Variational Inference Procedures and their Realizations (as incol)

Reference 18

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source=arxiv_source observed=2026-08-02T02:24:35.963533Z digest=sha256:fac553b4eda93bae47b549e2513039e2e9599d469ab385fcda6d2f104b68f77d

Observation a86e56ea-8899-4ef9-9115-639e76a1945a · outbound

This paper cites an unresolved cited work.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Unresolved cited work

Reference 19

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source=arxiv_source observed=2026-08-02T02:24:36.060055Z digest=sha256:1708061fc0f448ddc6e5be8fe0115ec206708261eda6f35a4da4b9c586fecc6d

Observation d5d415da-b896-4aa1-a52d-ff78dba5072d · outbound

This paper cites Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods

Reference 20

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source=arxiv_source observed=2026-08-02T02:24:36.178641Z digest=sha256:54075d0363f14c99c631391ceb2f4ce8e151c3a9e3e5ab58a8c9bfc2a45d5680

Observation 51f8b7de-a8e5-4195-a67a-6a1651130379 · outbound

This paper cites SIAM Journal on Control and Optimization , volume=.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion SIAM Journal on Control and Optimization , volume=

Reference 21

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source=arxiv_source observed=2026-08-02T02:24:36.303127Z digest=sha256:fa63ae6b79dafccbdf8a26bf4628bb04d4310e50e9aad1ba0e4a0e06024de8fa

Observation d695a9ce-7a73-4cb2-a9c2-4409a0512816 · outbound

This paper cites 2018 , publisher=.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion 2018 , publisher=

Reference 22

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source=arxiv_source observed=2026-08-02T02:24:36.426678Z digest=sha256:a896ab742ad202c4fa779c73fe69fb6dfe8b5fa1d36c98175bf492a757d6623f

Observation 0eeea7c7-ee34-4efb-812f-b2cec6de40a1 · outbound

This paper cites Advances in neural information processing systems , volume=.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Advances in neural information processing systems , volume=

Reference 23

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source=arxiv_source observed=2026-08-02T02:24:36.585804Z digest=sha256:0abc1aa38adb4c22a0560676af7de2b460876a3754b2fa0452c39dde0250b3bf

Observation ca821c80-3d5e-433b-8aa8-9c7f09ed0aa6 · outbound

This paper cites arXiv preprint arXiv:2509.26030 , year=.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion arXiv preprint arXiv:2509.26030 , year=

Reference 24

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source=arxiv_source observed=2026-08-02T02:24:36.705219Z digest=sha256:805344e271c34585693f08e48734026c03487972a27167a8e8eaca53442c1b8d

Observation 9ba70309-fc2a-4b25-8666-52e7421a0dcd · outbound

This paper cites Journal of Machine Learning Research , volume=.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Journal of Machine Learning Research , volume=

Reference 25

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Observation de077c63-9b89-4553-8e60-ca630bd05a5a · outbound

This paper cites Catch me, if you can: Evading network signatures with web-based polymorphic worms.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Catch me, if you can: Evading network signatures with web-based polymorphic worms

Reference 26

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source=arxiv_source observed=2026-08-02T02:24:36.909953Z digest=sha256:125ba25d1313a8c1f476710da18005d943750b4cb7245c01bf9a340177e48289

Observation 9bc6796d-2ebb-42be-b086-2b755dbef749 · outbound

This paper cites Catch me, if you can: Evading network signatures with web-based polymorphic worms.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Catch me, if you can: Evading network signatures with web-based polymorphic worms

Reference 27

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source=arxiv_source observed=2026-08-02T02:24:37.035165Z digest=sha256:824afb53d444986cae04e12fbc8cdec0d1fe9389126bd26abdda839369e05742

Observation 61be34f3-c049-41c0-bd9e-51ace6e436a8 · outbound

This paper cites Catch me, if you can: Evading network signatures with web-based polymorphic worms.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Catch me, if you can: Evading network signatures with web-based polymorphic worms

Reference 28

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source=arxiv_source observed=2026-08-02T02:24:37.157952Z digest=sha256:27fb3719506fa3b48e484d47be35ea8ffd97229af110a54278bf203cae4f4b8d

Observation e78051ea-fa66-44f9-8c15-eb0ee27a3bb6 · outbound

This paper cites Predicate Path expressions.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Predicate Path expressions

Reference 29

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source=arxiv_source observed=2026-08-02T02:24:37.280076Z digest=sha256:ea8dcde503a3f2d92ee317c0a365d645e51eb84d935d939b65f89594d30ac16e

Observation d6d06e21-4a52-4603-b162-234312459eb4 · outbound

This paper cites LOGICS of Programs: AXIOMATICS and DESCRIPTIVE POWER.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion LOGICS of Programs: AXIOMATICS and DESCRIPTIVE POWER

Reference 30

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source=arxiv_source observed=2026-08-02T02:24:37.398053Z digest=sha256:4bf9d03a01ade4ca249982aba78a8357b8a9be571797f278684f9d75e7054a53

Observation f31877ed-1f34-432b-ae84-b01f40dd298f · outbound

This paper cites Anisi , title =.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Anisi , title =

Reference 31

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source=arxiv_source observed=2026-08-02T02:24:37.487286Z digest=sha256:3c880518b6a1206339da565561788bea2aeef768fcd6daaf7a6bd8d57b06285c

Observation db370e60-2c2e-4322-a669-289b23a3cabb · outbound

This paper cites Clarkson.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Clarkson

Reference 32

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source=arxiv_source observed=2026-08-02T02:24:37.570852Z digest=sha256:74e848fd35cc5db5cd7979c810f10646bdc774bc5efb423f681901a53bb95762

Observation 4f3692e4-c53b-4201-a0b0-d4c0a068e6af · outbound

This paper cites Introduction to Bayesian Statistics.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Introduction to Bayesian Statistics

Reference 33

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source=arxiv_source observed=2026-08-02T02:24:37.651931Z digest=sha256:4fdb8b45f30c24a9e7aa53b31ecd840e6d710494b58d3138dd04c311c2e6230d

Observation d79aee8b-cb98-43d8-bd37-e8c00f418e30 · outbound

This paper cites CLIFFORD: a Maple 11 Package for Clifford Algebra Computations, version 11.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion CLIFFORD: a Maple 11 Package for Clifford Algebra Computations, version 11

Reference 34

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source=arxiv_source observed=2026-08-02T02:24:37.736321Z digest=sha256:ba28b0645d37cf43ca5ff39a55533dbc6ad04da5665f4f41c85ba29a2c119dcb

Observation 9fd6ffb8-4f04-4a18-8a8e-157ecb2587b9 · outbound

This paper cites Stats and Analysis.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Stats and Analysis

Reference 35

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source=arxiv_source observed=2026-08-02T02:24:37.775979Z digest=sha256:1813595f99203426c7c3d9af46dac1f6b897403a3b918cb83d2213d2845bde3e

Observation 4f955724-c18d-470d-8546-00221cdb49ef · outbound

This paper cites A more perfect union.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion A more perfect union

Reference 36

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source=arxiv_source observed=2026-08-02T02:24:37.851823Z digest=sha256:84e93e3fe88329b9c04cef91a9c2684274527245c7ecb31950571f5ba4cdbef1

Observation ccb800de-99d1-4a83-92a5-fc956bf6f5ba · outbound

This paper cites The fountain of youth.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion The fountain of youth

Reference 37

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source=arxiv_source observed=2026-08-02T02:24:37.929749Z digest=sha256:573828c6b2443daabc9c218c0f5a5ef29436715580bb5d088d8462a8dc8aed61

Observation 4bedc536-c79e-4894-8c68-2e1a5e9afc38 · outbound

This paper cites Solder man.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Solder man

Reference 38

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source=arxiv_source observed=2026-08-02T02:24:38.018964Z digest=sha256:81d34031da7ec74f7db6c83c33a2730b49a71266efe9dd3fc6fedd5b8a141c21

Observation 12d24164-d82d-40f8-811a-ca77ff15782c · outbound

This paper cites Interview with Bill Kinder: January 13, 2005.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Interview with Bill Kinder: January 13, 2005

Reference 39

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source=arxiv_source observed=2026-08-02T02:24:38.108474Z digest=sha256:182cfd74855e8a52e56d5a0eabc3deecf4217050414544eb7aa17848cc8d2980

Observation a74be3ea-261e-49ec-9bfa-0f85d70dc32b · outbound

This paper cites The Enabling of Digital Libraries.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion The Enabling of Digital Libraries

Reference 40

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source=arxiv_source observed=2026-08-02T02:24:38.186778Z digest=sha256:8486dd0298f54d787dd6b71eb963fe71dd7341485836197ef8c272e0d26da547

Observation d34d9643-04ca-4b50-b326-e6ac4cde09ae · outbound

This paper cites (new) Finding minimum congestion spanning trees , journal =.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion (new) Finding minimum congestion spanning trees , journal =

Reference 42

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source=arxiv_source observed=2026-08-02T02:24:38.414400Z digest=sha256:e94ede9f2a6f7c63046e6d592deba5af31b28ce01cf350cca7d2f2f555e090b5

Observation 3a58a328-76e8-47af-a631-7b1cf8bafe53 · outbound

This paper cites and Mei, Alessandro , title =.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion and Mei, Alessandro , title =

Reference 44

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source=arxiv_source observed=2026-08-02T02:24:38.627751Z digest=sha256:f06521f7bc70eebb88eb629b089418dd5d0f089442d21f670caac6e4b50e902c

Observation 0250aad9-c6dc-4f7a-ac0e-102bc5cede2d · outbound

This paper cites and Hutchful, David K.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion and Hutchful, David K

Reference 45

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source=arxiv_source observed=2026-08-02T02:24:38.702035Z digest=sha256:af8eee3f3e06223416f98cc1c4a81fb964484f9ca62ea27263b19e020c7f87e6

Observation 53b9b233-ae58-40c7-9e06-cc521084779f · outbound

This paper cites , title =.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion , title =

Reference 46

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source=arxiv_source observed=2026-08-02T02:24:38.780652Z digest=sha256:456dc1448b55a812cba6e2aaaa5fa32ddad4c490803efd0e7d9096f391619a62

Observation cc6a0b33-00c3-483b-8411-9e9ab9819f5d · outbound

This paper cites an unresolved cited work.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Unresolved cited work

Reference 47

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source=arxiv_source observed=2026-08-02T02:24:38.867347Z digest=sha256:b82ae2ffdfb4aad474be4dc245e490fe18a35a7bc15d3fc46f50c197692c154a

Observation 3b8083f2-7f65-4a5c-ac95-e55fb5c27373 · outbound

This paper cites and Rosenberg, Arnold L.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion and Rosenberg, Arnold L

Reference 48

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source=arxiv_source observed=2026-08-02T02:24:38.988451Z digest=sha256:67de3620bd72f3e07af9f4560313f53acb69206a68e31dff7848cd5eca4b3937

Observation ec0cb212-f285-41a2-b646-9b56ee8237e3 · outbound

This paper cites CHI '08: CHI '08 extended abstracts on Human factors in computing systems , year =.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion CHI '08: CHI '08 extended abstracts on Human factors in computing systems , year =

Reference 49

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no resolver link, observed 2026-08-02T02:24:39.105808Z

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source=arxiv_source observed=2026-08-02T02:24:39.105808Z digest=sha256:dd49099b9b201b9185bd639d3d0c17873ce88d07be63858e6c67b63f56fa7aa9

Observation 1a61bdbe-ef0e-4892-86cb-369e84c83311 · outbound

This paper cites Algorithms for Closest-Point Problems (Computational Geometry) , year =.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Algorithms for Closest-Point Problems (Computational Geometry) , year =

Reference 50

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source=arxiv_source observed=2026-08-02T02:24:39.181901Z digest=sha256:62705274268959c8e2b83d9c03b7faddb0be165210f07cac857327e651a3f648

Observation 9146a563-44eb-4793-b642-390790bdc43c · outbound

This paper cites an unresolved cited work.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Unresolved cited work

Reference 51

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source=arxiv_source observed=2026-08-02T02:24:39.313878Z digest=sha256:b0be1c9f6687d2a3092af01994db84950550ff8863d8c8a763784d86216a460e

Observation 5909187d-bb65-45a6-b5d2-d41c29828a3f · outbound

This paper cites 2004 , isbn =.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion 2004 , isbn =

Reference 52

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source=arxiv_source observed=2026-08-02T02:24:39.401897Z digest=sha256:8bdb916fea90ff1f3d48c49803dd219a217479ac0860194089214a5932b9cf9c

Observation 34c3a016-8a7c-40e9-9091-72759690ccac · outbound

This paper cites an unresolved cited work.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Unresolved cited work

Reference 53

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source=arxiv_source observed=2026-08-02T02:24:39.488226Z digest=sha256:f4018f3cbf01a5d9a70bb4b7127f28b938fd881a6484b5fc70694a3f0ce9f3c7

Observation def80af0-fcc5-47bd-8f46-5113f64a3038 · outbound

This paper cites , title =.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion , title =

Reference 54

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source=arxiv_source observed=2026-08-02T02:24:39.615831Z digest=sha256:9b6e3e4670ce67bdff0e50deb329db95e9492c9250b33e376ed00ae25e1e27db

Observation 2cbf6020-afeb-4211-89f6-f3c5fb85a457 · outbound

This paper cites an unresolved cited work.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Unresolved cited work

Reference 55

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source=arxiv_source observed=2026-08-02T02:24:39.701599Z digest=sha256:c6b42d5c9baab59a2447a68eda06739fdac73cb7cc0f1b05e58b7a79b938d01b

Observation 63ba7e40-43ea-4612-b882-f72b7b2f7291 · outbound

This paper cites E-commerce and cultural values , year =.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion E-commerce and cultural values , year =

Reference 56

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source=arxiv_source observed=2026-08-02T02:24:39.786141Z digest=sha256:fae2cd1acafd019a1d675d5d00d3919008b736fe3819f0cce1e7bd8bb68b47de

Observation 50bb429f-3eb8-487e-991f-b87062714f73 · outbound

This paper cites E-commerce and cultural values , year =.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion E-commerce and cultural values , year =

Reference 57

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source=arxiv_source observed=2026-08-02T02:24:39.871097Z digest=sha256:e0a1d1830208b3b87c78b4c1cc14ec64b4fc9d66787f9c771e367aa139df8590

Observation da597a44-c567-421a-9533-8e9a31415d1c · outbound

This paper cites Chapter 9 , booktitle =.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Chapter 9 , booktitle =

Reference 58

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source=arxiv_source observed=2026-08-02T02:24:39.960659Z digest=sha256:712decf5402ebf99ed258c6217fbf26abda309c30b610ed82ceabc7205fcbbb3

Observation a748ea9a-c6aa-40a8-92c6-90add1b6f79a · outbound

This paper cites E-commerce and cultural values , editor =.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion E-commerce and cultural values , editor =

Reference 59

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no resolver link, observed 2026-08-02T02:24:40.051629Z

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source=arxiv_source observed=2026-08-02T02:24:40.051629Z digest=sha256:38f7475749e6be3b347b30b5bb7f355d8641b2463e7edae6f7e05cf0ae5774b9

Observation 8dad001f-be06-4ee7-b6be-50f817a9e9c5 · outbound

This paper cites E-commerce and cultural values - (InBook-num-in-chap) , chapter =.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion E-commerce and cultural values - (InBook-num-in-chap) , chapter =

Reference 60

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no resolver link, observed 2026-08-02T02:24:40.103540Z

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source=arxiv_source observed=2026-08-02T02:24:40.103540Z digest=sha256:ad0c4e3be0a41059e2330f74b006f08bb69d7f5fdc8103350cb715e51566bced

Observation 6b19ea1a-0350-40e7-8add-607bb2208a65 · outbound

This paper cites E-commerce and cultural values (Inbook-text-in-chap) , chapter =.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion E-commerce and cultural values (Inbook-text-in-chap) , chapter =

Reference 61

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source=arxiv_source observed=2026-08-02T02:24:40.183363Z digest=sha256:dc2a2efb68b35a40a90a0504b122b4f5ee81eb628ba6b35dd611cbe711d377dd

Observation 2d6709ed-3619-4407-a841-729953fd492a · outbound

This paper cites E-commerce and cultural values (Inbook-num chap) , chapter =.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion E-commerce and cultural values (Inbook-num chap) , chapter =

Reference 62

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no resolver link, observed 2026-08-02T02:24:40.246612Z

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source=arxiv_source observed=2026-08-02T02:24:40.246612Z digest=sha256:f9df56b883f1ff845688421e2982a6231e4711720b37ef1a09cd58bc92d7e7b8

Observation 08f9716f-3e0b-40c2-bace-12a8e6d1e8ef · outbound

This paper cites Microelectron.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Microelectron

Reference 63

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source=arxiv_source observed=2026-08-02T02:24:40.352984Z digest=sha256:af62463479d66e491f1d8e46a15739571a20e0906c593f34c6e6746ddac12e47

Observation f1d987a3-4e1f-42be-8a0b-a13e04af362d · outbound

This paper cites an unresolved cited work.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Unresolved cited work

Reference 64

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source=arxiv_source observed=2026-08-02T02:24:40.415583Z digest=sha256:eacc98bb29a8cc8d3d00e15c4c78110fb24e4b4a29981ca6500cd2bb97006d4e

Observation 5421377f-3971-4376-af26-7ba5fc7ddfe5 · outbound

This paper cites an unresolved cited work.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Unresolved cited work

Reference 65

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source=arxiv_source observed=2026-08-02T02:24:40.418125Z digest=sha256:83a1da7e37cc9368ec362198ce9e01e3988f65ca4d199194727c645bcd58d054

Observation 1c9b7974-c136-43cb-94c1-208c86f8e308 · outbound

This paper cites an unresolved cited work.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Unresolved cited work

Reference 66

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source=arxiv_source observed=2026-08-02T02:24:40.483323Z digest=sha256:9fb33cbf36b1a56a8ae40a68037bb15d61bdef015bc28774f2f90cf48a2dd736

Observation 184bc4f6-7bec-4831-a679-83a1490fbb3d · outbound

This paper cites History of programming languages I (incoll) , editor =.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion History of programming languages I (incoll) , editor =

Reference 67

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source=arxiv_source observed=2026-08-02T02:24:40.580528Z digest=sha256:5a8cb0460231c00ca1deb6a4ab61dcba5eae9063c39e67d3d74efe9ccb8b7887

Observation 09281022-844a-4493-98c5-981214b5ed85 · outbound

This paper cites , title =.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion , title =

Reference 68

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source=arxiv_source observed=2026-08-02T02:24:40.640199Z digest=sha256:4cfd9d26f01b1c20aaaf010a8abaea47144fc78411247bdb4b2226475329dc4f

Observation 730bec6b-602d-465e-9f13-828a99f97314 · outbound

This paper cites , title =.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion , title =

Reference 69

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source=arxiv_source observed=2026-08-02T02:24:40.759311Z digest=sha256:c8d775251112e7d2792590205632e8a9eb083198c60ac3ace17cca33c46b97b7

Observation b836279c-e17b-4a38-bce9-334881570c35 · outbound

This paper cites , title =.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion , title =

Reference 70

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source=arxiv_source observed=2026-08-02T02:24:40.811006Z digest=sha256:02b10f9f939467290f1451a36f2767fe220dc6d63532c99f33676b897182dac9

Observation 1e748ef4-80da-462a-adf7-2100010fe3b2 · outbound

This paper cites and Golden, Donald G.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion and Golden, Donald G

Reference 71

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source=arxiv_source observed=2026-08-02T02:24:40.922221Z digest=sha256:78b328d3ecc0ac70baca5a6b15cc973dfe87ad4f58d9cac70986d37e376a5a40

Observation 5b5a504c-ec2a-446d-b0c7-7b4b111b285e · outbound

This paper cites The analysis of linear partial differential operators.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion The analysis of linear partial differential operators

Reference 72

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source=arxiv_source observed=2026-08-02T02:24:41.042763Z digest=sha256:6bbb49a6834dc1d0654177e5c12cc9330912ca3ee47680c39af2f41dc3f8c1a6

Observation 1018afaa-63fb-447f-a6b8-a4fab8ba627f · outbound

This paper cites IEEE", address =.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion IEEE", address =

Reference 73

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source=arxiv_source observed=2026-08-02T02:24:41.125081Z digest=sha256:e40fd2161c4fd016d304b2c5cfbe417620e372292124075512f3fb8635da0563

Observation 415cc161-24c3-468a-b812-bf7dd0dc6a24 · outbound

This paper cites an unresolved cited work.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Unresolved cited work

Reference 74

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source=arxiv_source observed=2026-08-02T02:24:41.228551Z digest=sha256:0852c39861261206bc6c308b1d8270ae3ffebe76c3d23d1eb328c71b1eea0c24

Observation d9ab1c76-1ee2-45ad-930c-b5e31c02225b · outbound

This paper cites an unresolved cited work.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Unresolved cited work

Reference 75

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no resolver link, observed 2026-08-02T02:24:41.314843Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:24:41.314843Z digest=sha256:9188d331dda3c72f256d9cc2768fb4b0ba6ddb4a039b6d32043abb6b05f79da4

Observation a33869db-b0a6-48cd-8e82-39e981cd41d5 · outbound

This paper cites ACM", address =.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion ACM", address =

Reference 76

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source=arxiv_source observed=2026-08-02T02:24:41.430065Z digest=sha256:51b2ac9d2d422a406b2cd7320c0836b58e4ba9a26eada2c4f6542b5ae69fb23b

Observation 281ed220-8b0e-4545-ac1b-56b009f8b9ab · outbound

This paper cites 8 (Special Issue on Sensor Networks).

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion 8 (Special Issue on Sensor Networks)

Reference 77

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:24:41.517644Z digest=sha256:ca1108ed50ac36a6c319a084960bea1cd37c94188f291a737e354f8a3a953a0a

Observation d5f8052c-8165-4301-aa2d-c2d114c96c3e · outbound

This paper cites Natarajan and M.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Natarajan and M

Reference 78

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no resolver link, observed 2026-08-02T02:24:41.607158Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:24:41.607158Z digest=sha256:beb369eac7b0e1e16081a9c4908829c28bf61fca034f1c088bba2c42d360d9ef

Observation ba74cfe2-cc03-47f1-8834-cf427c1f9569 · outbound

This paper cites Tzamaloukas and J.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Tzamaloukas and J

Reference 79

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:24:41.717063Z digest=sha256:5cf0256c9bbe2095b4bd04307d7138c212248074d953943b0e76542a7601eaf2

Observation 03570c5d-17ec-4264-9763-2e6ab859bfa0 · outbound

This paper cites Zhou and J.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Zhou and J

Reference 80

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:24:41.776120Z digest=sha256:a3702b13d20f79cd3274141ce3e8180a1769940325939564262b6961abca8023

Observation 4a754bf0-d705-4ac9-83bd-870903bd53cd · outbound

This paper cites Mapping Powerlists onto Hypercubes.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Mapping Powerlists onto Hypercubes

Reference 81

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source=arxiv_source observed=2026-08-02T02:24:41.885071Z digest=sha256:c659972dbc20cd693bf1aae2e5307da99cea73529af893b4a9f927cd05666c94

Observation 85dd61e1-acb0-4d8c-82c6-d8318bba0d4e · outbound

This paper cites Automatic Parallelization for Distributed-Memory Multiprocessing Systems.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Automatic Parallelization for Distributed-Memory Multiprocessing Systems

Reference 82

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Observation 925dfb30-456d-4061-9260-102f987c1e79 · outbound

This paper cites an unresolved cited work.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Unresolved cited work

Reference 83

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source=arxiv_source observed=2026-08-02T02:24:42.099032Z digest=sha256:22de3524e18766cba8182eaddeb24c1cea5d42df1e2ecee017d78b43658e0757

Observation 785b053f-6963-4c52-9217-26b456b7d43a · outbound

This paper cites an unresolved cited work.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Unresolved cited work

Reference 84

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source=arxiv_source observed=2026-08-02T02:24:42.199499Z digest=sha256:7230bdaf521f8be4bff4b96369683bb994e8077eb74131421bebf0b46130a269

Observation e735db71-9ab1-42f4-bf15-2b9dd9d41a5e · outbound

This paper cites Heering and P.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Heering and P

Reference 85

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source=arxiv_source observed=2026-08-02T02:24:42.248788Z digest=sha256:1d8b7143b81377815e5762876dd12377fba3d6db60ee17787118b6613c9fff9d

Observation 1a915f52-25dd-409f-bcdd-6f1e0b3c2747 · outbound

This paper cites an unresolved cited work.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Unresolved cited work

Reference 86

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source=arxiv_source observed=2026-08-02T02:24:42.344989Z digest=sha256:42671907e2e1004c354e584c05458734ae3ea7de1370a73cb19c8b578bd812f2

Observation f6969ee4-1e29-43b1-801f-563df0cb1726 · outbound

This paper cites Korach and D.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Korach and D

Reference 87

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source=arxiv_source observed=2026-08-02T02:24:42.399649Z digest=sha256:e9fec75264c1fde93defa612ae87721fefbd8245611aaa05dfee6c316f39e5c0

Observation 5d3a4b47-9abb-45f2-b7f2-e7019d526b72 · outbound

This paper cites : A Document Preparation System.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion : A Document Preparation System

Reference 88

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source=arxiv_source observed=2026-08-02T02:24:42.510157Z digest=sha256:dbab9d2666250a3ddc740812080160b389497e2491b0faf03bd6e9ff8be8be6f

Observation 8acc6fd5-8b22-471c-a4dd-a72216cd23e9 · outbound

This paper cites an unresolved cited work.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Unresolved cited work

Reference 89

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source=arxiv_source observed=2026-08-02T02:24:42.586937Z digest=sha256:09ec6796ee44c370ac124a230d2e4355823fa3411dafed59f40c3fe45dc1ae9e

Observation 12b7cf3e-dbfa-42af-aa57-1d22602fb48c · outbound

This paper cites an unresolved cited work.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Unresolved cited work

Reference 90

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source=arxiv_source observed=2026-08-02T02:24:42.655251Z digest=sha256:2521a445e4ac182d984a07c28f43c90c56c810bc6ea415d05a389e1fd67262a4

Observation 3fb30cc5-f213-4103-8c5f-29efa6c42551 · outbound

This paper cites and Abdelzaher, Tarek F.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion and Abdelzaher, Tarek F

Reference 91

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source=arxiv_source observed=2026-08-02T02:24:42.708714Z digest=sha256:907c01c9fc3f8f19b2626971b882d11b045826e7478a44ca7d30d20375318508

Observation 14f6beed-2f5b-4a9d-8e36-248afbba188b · outbound

This paper cites Understanding the planning of LLM agents: A survey.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Understanding the planning of LLM agents: A survey

Reference 92

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no resolver link, observed 2026-08-02T02:24:42.797794Z

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source=arxiv_source observed=2026-08-02T02:24:42.797794Z digest=sha256:846537755ebadef4436302eaaff82c0d9de4e6b8403fa2e4f3fd648d20fcf0ad

Observation bf00b194-6eb2-423c-b23f-bb76c5b8a4bf · outbound

This paper cites Learning Dynamics of LLM Finetuning.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Learning Dynamics of LLM Finetuning

Reference 93

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source=arxiv_source observed=2026-08-02T02:24:42.877960Z digest=sha256:2d437248db09eea6b1f8ac3d5c48c38eaae26352289194ac0308aa52045d8fa1

Observation c42f0dc0-1a24-4b14-b95b-906cda524044 · outbound

This paper cites Improving LLM General Preference Alignment via Optimistic Online Mirror Descent.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Improving LLM General Preference Alignment via Optimistic Online Mirror Descent

Reference 94

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source=arxiv_source observed=2026-08-02T02:24:42.953753Z digest=sha256:06339163e665e4cf4fba910d46b83ccc7317531d8c300826c0190fb167f7e338

Observation de5e4062-061b-4f76-b3b8-40908c4f6a8e · outbound

This paper cites An Explanation of In-context Learning as Implicit Bayesian Inference.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion An Explanation of In-context Learning as Implicit Bayesian Inference

Reference 95

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no resolver link, observed 2026-08-02T02:24:43.029666Z

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source=arxiv_source observed=2026-08-02T02:24:43.029666Z digest=sha256:7f16b964bb05dabfaf8672b2ef3e0edf00ba2632496b792f52683c0e7995cea4

Observation 81182c35-9455-444f-89c0-ae0089c595fd · outbound

This paper cites Advances in neural information processing systems , volume=.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Advances in neural information processing systems , volume=

Reference 96

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source=arxiv_source observed=2026-08-02T02:24:43.110116Z digest=sha256:436a1c7d1c82e025354d90cb9809ba78d3540548e8dab73b1e7dbee7698c1117

Observation 617cbeab-3d55-4b7a-b1a3-904f3992e0e9 · outbound

This paper cites IEEE Transactions on Audio, Speech and Language Processing , year=.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion IEEE Transactions on Audio, Speech and Language Processing , year=

Reference 97

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source=arxiv_source observed=2026-08-02T02:24:43.183793Z digest=sha256:74164f941ad2103dfebe32d807a96339e8f88a3fe629275a07373a51367a30fa

Observation 871d7ac4-8137-45f4-b626-718b8de09615 · outbound

This paper cites The Eleventh International Conference on Learning Representations , year=.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion The Eleventh International Conference on Learning Representations , year=

Reference 98

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source=arxiv_source observed=2026-08-02T02:24:43.205483Z digest=sha256:d6ecc58185f3a76a83457c2bedee2e9d957a53892d80a56d700f904068537531

Observation 598b85fe-7ea5-4ff1-bb26-30f4eb15b202 · outbound

This paper cites 2025 , institution=.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion 2025 , institution=

Reference 99

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source=arxiv_source observed=2026-08-02T02:24:43.227726Z digest=sha256:96ed93adb9be04ab9cae0c05ec54d7f7f27f5a0cf9921615497f4c7b2ffe94cc

Observation 135244c6-93db-4b57-b373-d561b2936a81 · outbound

This paper cites SIAM Journal on Control and Optimization , volume=.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion SIAM Journal on Control and Optimization , volume=

Reference 100

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source=arxiv_source observed=2026-08-02T02:24:43.254326Z digest=sha256:75e44b846f45c8c3754d84328e13e2a2775db43624960abd5b9d7237b35a5826

Observation de8f1d36-3b57-479a-aeec-a1462f733602 · outbound

This paper cites Proceedings of the National Academy of Sciences , volume=.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Proceedings of the National Academy of Sciences , volume=

Reference 101

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source=arxiv_source observed=2026-08-02T02:24:43.299535Z digest=sha256:e4f2766f1fefe67facc1521b4aed405383a97bb1de4b7df23b8ae179410a21d6

Observation 39c7a193-0891-40b8-a0ac-2a88248cf4be · outbound

This paper cites arXiv preprint arXiv:2511.00674 , year=.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion arXiv preprint arXiv:2511.00674 , year=

Reference 102

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source=arxiv_source observed=2026-08-02T02:24:43.378652Z digest=sha256:21dd6408061bc49f63b40f0c5c984a4a1e48a450d920fa1bab462df64762ad31

Observation 5a0be415-1c4c-4fb3-88dc-dd93db2b96fb · outbound

This paper cites Minimum-Norm Interpolation Under Covariate Shift.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion Minimum-Norm Interpolation Under Covariate Shift

Reference 103

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source=arxiv_source observed=2026-08-02T02:24:43.479028Z digest=sha256:01687104155f616126179ad9338b6ad1aad20b5410ac7641ef0481c43a88046a

Observation 738ed4b7-8a8e-45b4-9fe2-c746da96312d · outbound

This paper cites International conference on machine learning , pages=.

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion International conference on machine learning , pages=

Reference 104

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source=arxiv_source observed=2026-08-02T02:24:43.554361Z digest=sha256:20f3e8c92f5d7c84d42958e4d5c3ee48e6ef72e1ee2b933733777aca18a40b67

Pith citing papers

No inbound Pith citation observations are available.