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Paper Citation Record · LEDGER

COSMOS: Predictable and Cost-Effective Adaptation of LLMs

As of 19 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2505.01449.

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

pith.paper-citation-record.v1
2505.01449 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:17:37.922358Z

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

  • verified exact0
  • verified fuzzy9
  • unresolved32
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a385e7cb-d4b9-4510-8355-8dc0aa96e912 · outbound

This paper cites LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 1

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source=pdf_text observed=2026-08-16T05:17:37.757884Z digest=sha256:c6a685799e63bbd28985603bac49d6f399cddd7617c98734bc8cd976dfcaa0f8

Observation e88622bb-574f-4644-be17-1726236e2ebb · outbound

This paper cites D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

Reference 2

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source=pdf_text observed=2026-08-16T05:17:37.763497Z digest=sha256:6ddbb6adf6e457e65c666746a36c170ec945b6aa248c89395b05ab3b9656918c

Observation f2c84246-4652-4eec-9841-9245ed1cb267 · outbound

This paper cites Data shunt: Collaboration of small and large models for lower costs and better performance.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Data shunt: Collaboration of small and large models for lower costs and better performance

Reference 3

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verified fuzzy
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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=pdf_text observed=2026-08-16T05:17:37.767993Z digest=sha256:f9278ac3831fdeff91e850c8738d6336d8938176101941377d2397a4ef0b79f9

Observation c0cb4af4-4dd5-4315-9032-1698df4ffbf0 · outbound

This paper cites FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance

Reference 4

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source=pdf_text observed=2026-08-16T05:17:37.772377Z digest=sha256:eb4a3afef881511b1ad5561464de9c2d34b390edab22d3c006cff9bd355b8e8a

Observation 40f5eb59-1ff3-48f4-8657-8751caa0125c · outbound

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

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 5

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source=pdf_text observed=2026-08-16T05:17:37.777071Z digest=sha256:cb00e0ca6ae82afb072a88e7ddb9ee31c0cfcb51f965446652b17abe66836eb2

Observation af3f61b0-6d9c-4196-a97f-c0aea987ef57 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Training Verifiers to Solve Math Word Problems

Reference 6

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source=pdf_text observed=2026-08-16T05:17:37.781727Z digest=sha256:c4795e400cceab53845f50a0e1ae455dea7e33268f65bfe1b81d07e69d387781

Observation 50e178ac-8538-43da-bfa0-e06b413eae77 · outbound

This paper cites Qlora: Efficient finetuning of quantized llms.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Qlora: Efficient finetuning of quantized llms

Reference 7

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source=pdf_text observed=2026-08-16T05:17:37.786544Z digest=sha256:ac693eea902e3201bcda8c3693598758894e68797f9f30b8cd09c86aac31469b

Observation b48b4569-b540-4870-98d4-5db4bb55d2fd · outbound

This paper cites The Llama 3 Herd of Models.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs The Llama 3 Herd of Models

Reference 8

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source=pdf_text observed=2026-08-16T05:17:37.790502Z digest=sha256:63ea8654c0cb8b993a28359b90601a93a465c6d203789c48ea25d5123c75f9ae

Observation 5b3048f0-41ac-45e5-baa7-7ad496a900d7 · outbound

This paper cites Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training

Reference 9

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source=pdf_text observed=2026-08-16T05:17:37.794174Z digest=sha256:edf6c59318c695ee682fba4f3cf3694fca466390beb3c776a36b01fe684eee2b

Observation 559253ee-d7fd-46e6-933d-81ac07baae7a · outbound

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

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Gemma: Open Models Based on Gemini Research and Technology

Reference 10

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source=pdf_text observed=2026-08-16T05:17:37.798240Z digest=sha256:ed9f62d53f824a89b9910ae0f3681ff8c56f3cf2d7fa3de775f43f205e46ab80

Observation 1978347e-9f75-4832-8dd5-e4aedf68fe43 · outbound

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

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Gemma 2: Improving Open Language Models at a Practical Size

Reference 11

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source=pdf_text observed=2026-08-16T05:17:37.802245Z digest=sha256:e2af01a1120f27e1e517171b70808438454f1f7c243ce7e23029b946fb00ea8d

Observation 5600ccf9-079b-4800-9695-e1ef08accd50 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Measuring Massive Multitask Language Understanding

Reference 12

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source=pdf_text observed=2026-08-16T05:17:37.805904Z digest=sha256:b826ef4d6ae089cfa01197f1a2bc68b0c0c7d22cbb77de357742da47b27b5b0f

Observation 64c55efa-9b7c-41f0-b34a-6f83ad2dfe98 · outbound

This paper cites A., Welbl, J., Clark, A., et al.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs A., Welbl, J., Clark, A., et al

Reference 13

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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=pdf_text observed=2026-08-16T05:17:37.809409Z digest=sha256:c618fde4a4a769c5eacae7179e27b49cf57b141cbde49e6ccc099d3a81cba3b1

Observation 5de80a18-fc33-477e-9c96-a4dd8725b6d5 · outbound

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

COSMOS: Predictable and Cost-Effective Adaptation of LLMs LoRA: Low-Rank Adaptation of Large Language Models

Reference 14

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source=pdf_text observed=2026-08-16T05:17:37.812632Z digest=sha256:19705595f35cee4fae67ae10da9b89c12ec157ce8fd7c722bd35683418dd3956

Observation 86a46550-d776-4f6c-a424-04b5ca98f1c8 · outbound

This paper cites RouterBench: A Benchmark for Multi-LLM Routing System.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs RouterBench: A Benchmark for Multi-LLM Routing System

Reference 15

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source=pdf_text observed=2026-08-16T05:17:37.815957Z digest=sha256:39cb038fa80a6681cf48647b6c70a0b2d991fea785cf36e3bcced612e723292a

Observation 36d51cec-536e-4d00-aa7f-78dd62081756 · outbound

This paper cites Mistral 7B.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Mistral 7B

Reference 16

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source=pdf_text observed=2026-08-16T05:17:37.819798Z digest=sha256:23128f144f1acee9068878b91b23e1eb35a26d2782e49f388e26efc5f795e586

Observation 4be3fc77-cf12-42ea-b099-94dbbf050d25 · outbound

This paper cites Scaling Laws for Neural Language Models.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Scaling Laws for Neural Language Models

Reference 17

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source=pdf_text observed=2026-08-16T05:17:37.823113Z digest=sha256:60dbbce7103e3221d39c417610208feabc7078ecdb01356b870113d5b37daf8d

Observation 7d8e7153-be8b-4fd0-a777-74b0ce6c0253 · outbound

This paper cites The power of scale for parameter-efficient prompt tuning.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs The power of scale for parameter-efficient prompt tuning

Reference 18

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source=pdf_text observed=2026-08-16T05:17:37.826917Z digest=sha256:45dc555c48711d420d3e22c6a490352894d298dc7760a3a8bb6cc2658591f306

Observation 172f1c0d-1f03-49b3-92c3-6b44fb397a58 · outbound

This paper cites Let's Verify Step by Step.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Let's Verify Step by Step

Reference 19

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source=pdf_text observed=2026-08-16T05:17:37.830382Z digest=sha256:36eccd5f30f8d3ae86b502c8691eda25866f4faaff705c031e7ab158e4ab90eb

Observation b48b2aec-c63b-4f1a-9bc0-cd2853c4d175 · outbound

This paper cites Grammar-aligned decoding, 2024.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Grammar-aligned decoding, 2024

Reference 20

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source=pdf_text observed=2026-08-16T05:17:37.833786Z digest=sha256:c486d3cf0ab50283e70a9f1111500e625d163c372b6cb49b765bfaa97af43e2d

Observation 612d5170-2809-4e1d-b958-5faa28ce1d64 · outbound

This paper cites Qwen2 technical report.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Qwen2 technical report

Reference 21

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

source=pdf_text observed=2026-08-16T05:17:37.837049Z digest=sha256:41ff4662b059a2076f73db3c61a621fad2b302b399eb86c672f713c30111ac6d

Observation 4e38f7b1-7f04-43da-abae-f4e2350c4d9c · outbound

This paper cites an unresolved cited work.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Unresolved cited work

Reference 22

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source=pdf_text observed=2026-08-16T05:17:37.841142Z digest=sha256:35e0a8222f221c6c8cb767e68ae99c4fca4859891c8fa9b7ec39f1c56c2e7bfe

Observation 11932654-76ed-4a38-9ef7-889c64d850eb · outbound

This paper cites Rethinking neural operations for diverse tasks.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Rethinking neural operations for diverse tasks

Reference 23

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source=pdf_text observed=2026-08-16T05:17:37.844989Z digest=sha256:a425b38fc1096a763b9824ec6e1974a1f57cea05760449e03d47525c822ead3b

Observation 9e17a3da-30cc-491c-b621-b6aa385e7489 · outbound

This paper cites an unresolved cited work.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Unresolved cited work

Reference 24

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source=pdf_text observed=2026-08-16T05:17:37.849223Z digest=sha256:9941dcd9724d889919360f2ab9d9e494bd42f216c065212f51ebbd526bbd5e7f

Observation 197fc689-0a2c-4572-bea1-598284974168 · outbound

This paper cites The probabilistic relevance framework: Bm25 and beyond.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs The probabilistic relevance framework: Bm25 and beyond

Reference 25

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source=pdf_text observed=2026-08-16T05:17:37.853999Z digest=sha256:1ae0c5464b3d305e76e75af442b3f3265c9a9df448fcc96f0ad7a11182710d7a

Observation f1019de0-c494-4423-a481-935e171695da · outbound

This paper cites Observational Scaling Laws and the Predictability of Language Model Performance.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Observational Scaling Laws and the Predictability of Language Model Performance

Reference 26

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source=pdf_text observed=2026-08-16T05:17:37.858238Z digest=sha256:e9cf894eeba5e98effc669388c4bdb2909ab4c95e55550bd65adcfc433124247

Observation 0d3de6b5-2bcf-4169-a928-c7c2aafe95b1 · outbound

This paper cites Archon: An Architecture Search Framework for Inference-Time Techniques.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Archon: An Architecture Search Framework for Inference-Time Techniques

Reference 27

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source=pdf_text observed=2026-08-16T05:17:37.862428Z digest=sha256:b431fd76161f03fa30ac0fc2ae9bac68dcfc1e5ef87fc7c0dcfad8d55165b872

Observation bffae0c5-125f-4957-b64b-0b89d7876a43 · outbound

This paper cites L., Bhagavatula, C., and Choi, Y.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs L., Bhagavatula, C., and Choi, Y

Reference 28

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

source=pdf_text observed=2026-08-16T05:17:37.866541Z digest=sha256:6d43a50bd47517bf89df13cc061a82d5696739f85cf8ea50188b17089944f8a8

Observation 21f2a752-e6aa-44f0-8390-f0bbc851c28f · outbound

This paper cites Fly-swat or cannon? cost-effective language model choice via meta- modeling.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Fly-swat or cannon? cost-effective language model choice via meta- modeling

Reference 29

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

source=pdf_text observed=2026-08-16T05:17:37.870423Z digest=sha256:a3f2e578d2ab2288f948d2d78809e0acaae4e6cb7e18017dca03e4277c00a0fd

Observation baef6bec-0199-4e96-8e23-a3a8148a085d · outbound

This paper cites M., Staten, C., Khodak, M., Neubig, G., and Talwalkar, A.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs M., Staten, C., Khodak, M., Neubig, G., and Talwalkar, A

Reference 30

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

source=pdf_text observed=2026-08-16T05:17:37.874205Z digest=sha256:72e1616344087ca3b91237949cbb30bb3b523b940f60b9cd4b0f0409e136522b

Observation 1c8cc3be-1655-41f4-b1a2-7e0348838187 · outbound

This paper cites Large Language Model Routing with Benchmark Datasets.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Large Language Model Routing with Benchmark Datasets

Reference 31

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source=pdf_text observed=2026-08-16T05:17:37.877990Z digest=sha256:320f87e00677752b2afb06678729dfcdff65e7c4f43fa40ab0bc6483df9a89a0

Observation de813f25-8b4f-48be-909c-aaee25482f34 · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 32

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source=pdf_text observed=2026-08-16T05:17:37.882058Z digest=sha256:924dd5ed2ab066ef03fb5e2f501f48bb5cba61af339717aceb7f7cfe832d0241

Observation 69d8d470-5558-44dd-8cab-4694183abc19 · outbound

This paper cites Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together

Reference 33

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source=pdf_text observed=2026-08-16T05:17:37.885957Z digest=sha256:2afe0bee29fc8b4b6ca542000b18757b302c9df483ae5dc2121dda81318133de

Observation cafeccec-5e79-48db-8b0b-010a079f09f7 · outbound

This paper cites Chain-of-Thought Reasoning Without Prompting.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Chain-of-Thought Reasoning Without Prompting

Reference 34

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source=pdf_text observed=2026-08-16T05:17:37.890249Z digest=sha256:5cdbe8e243488f971055c208f074bf9908f3c970ce227838d6641301283d4cfa

Observation bc5a8f8a-96e0-4414-8d82-7aba5ad95225 · outbound

This paper cites Finetuned Language Models Are Zero-Shot Learners.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Finetuned Language Models Are Zero-Shot Learners

Reference 35

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source=pdf_text observed=2026-08-16T05:17:37.894403Z digest=sha256:73d310727f68c705542fc673f1fac118c1333c9ef52109ec1bd2ebaf491eaef8

Observation 6c532b69-f397-4571-a496-a5b23b49873c · outbound

This paper cites V ., Zhou, D., et al.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs V ., Zhou, D., et al

Reference 36

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raw_fallback, observed 2026-08-16T05:17:38.382643Z

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

source=pdf_text observed=2026-08-16T05:17:37.898697Z digest=sha256:1a2f3d00e82cab39d59da9c38f6b3a4871016b8e19d45c6186909d2929ecb70a

Observation cbb915af-e343-4c3a-a9c7-0b98dac65a6a · outbound

This paper cites From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models

Reference 37

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unresolved
no resolver link, observed 2026-08-16T05:17:37.903172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:17:37.903172Z digest=sha256:c309417f186afa12840268ac3a666f94e575fad127c40c0f035d8394ca16384e

Observation ce00ad48-d923-4f11-97ea-9ee4e8ea4d92 · outbound

This paper cites PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-16T05:17:37.907493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:17:37.907493Z digest=sha256:a6dd9bd87eb860f81d6cb4af257235a2f9b60fc79a82b78a6abe28b4a922075f

Observation f473890c-8ac1-406e-9ad1-48edb6e233ec · outbound

This paper cites Tree of Thoughts: Deliberate Problem Solving with Large Language Models.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-16T05:17:37.911693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:17:37.911693Z digest=sha256:53da5430915c3468bd29b9d1baed0e755780eb1f1c543d085905ecdfa8bd1b0d

Observation 73c8174f-1a55-42eb-94b2-cddd25d5d5a7 · outbound

This paper cites Hyper-Parameter Optimization: A Review of Algorithms and Applications.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Hyper-Parameter Optimization: A Review of Algorithms and Applications

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-16T05:17:37.915157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:17:37.915157Z digest=sha256:f9de95ee0821f4cd853b71e982eb3ae687b92d7afe43204057822e4ba8b69464

Observation 348fadd8-14d8-43b7-8563-4927146f2b48 · outbound

This paper cites Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient Reasoning.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient Reasoning

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-16T05:17:37.918676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:17:37.918676Z digest=sha256:f1873deaff878628c5cb96ff53518cbe820c19f78b493ee98ea87485eb83b578

Observation 65e71986-e44f-4b33-813b-461c209e08db · outbound

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

COSMOS: Predictable and Cost-Effective Adaptation of LLMs HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 42

Resolution
malformed identifier
no resolver link, observed 2026-08-16T05:17:37.922358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:17:37.922358Z digest=sha256:282b2d7c914eb4dc36799caf8261b9c81c6aaf45777cd0a61be1b0a56400a01e

Pith citing papers

No inbound Pith citation observations are available.