Paper Citation Record · LEDGER

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation

As of 22 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 4 inbound Pith citation observations for arXiv:2502.00330.

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

pith.paper-citation-record.v1
2502.00330 v1

Coverage vector

measured 69 of 69 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T19:31:49.817357Z

measured 73 of 73 standing notices

One-hop event checks from named stored sources.

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

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:20:38.727777Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T00:45:49.130430Z

Reference resolution

69 of 69 outbound references displayed

verified exact3
verified fuzzy11
unresolved55
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 22323952-9038-4a23-8ac2-b58d2d4badaa · outbound

This paper cites Many-Shot In-Context Learning.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Many-Shot In-Context Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.544383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77522cbb-eb53-455d-ad06-f5adf2ffe660 · outbound

This paper cites an unresolved cited work.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-09T19:31:50.832588Z

Source-reported events for the cited work

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

Observation 35a58ce4-02c4-4776-a78e-c815920df95c · outbound

This paper cites The claude 3 model family: Opus, sonnet, haiku.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation The claude 3 model family: Opus, sonnet, haiku

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.555278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6230d833-6c72-4613-99bc-c31cde6fcb2f · outbound

This paper cites Balandat, B.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Balandat, B

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:31:50.814914Z

Source-reported events for the cited work

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

Observation 3a007bfd-7256-4486-ab79-eb050dbce2d6 · outbound

This paper cites In-Context Learning with Long-Context Models: An In-Depth Exploration.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation In-Context Learning with Long-Context Models: An In-Depth Exploration

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.563990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6487a326-e40b-4ecd-aa72-8921317c0789 · outbound

This paper cites an unresolved cited work.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-09T19:31:50.803228Z

Source-reported events for the cited work

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

Observation a12de1fa-c685-4ca8-8c0e-8861ccd2b66f · outbound

This paper cites Brown, B.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Brown, B

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.574180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5b5ae10-c145-491a-898d-fff684ae40e7 · outbound

This paper cites Reliable Text-to-SQL with Adaptive Abstention.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Reliable Text-to-SQL with Adaptive Abstention

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.578435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8c9309a-5fe4-4556-b445-e7e57b3f0773 · outbound

This paper cites Chen, C.-K.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Chen, C.-K

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.582483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5230d40b-8b2a-48b8-be24-7312519482e4 · outbound

This paper cites an unresolved cited work.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-09T19:31:50.783589Z

Source-reported events for the cited work

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

Observation 92453c66-4d95-4b79-b71d-eb04dcb6fb54 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Training Verifiers to Solve Math Word Problems

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.590979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25a8979e-cbce-4df8-af59-7e5b88c77123 · outbound

This paper cites Case-based Reasoning for Natural Language Queries over Knowledge Bases.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Case-based Reasoning for Natural Language Queries over Knowledge Bases

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-09T19:31:50.149105Z

Source-reported events for the cited work

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

Observation 639d74ca-ee18-404b-acf1-9e2f19aae044 · outbound

This paper cites Daulton, X.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Daulton, X

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:31:50.772491Z

Source-reported events for the cited work

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

Observation 84eb21ed-d305-48c7-bbc3-a72986dbe812 · outbound

This paper cites A Tutorial on Bayesian Optimization.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation A Tutorial on Bayesian Optimization

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.603788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfad90fc-1cae-447d-9305-08a477ee8bf8 · outbound

This paper cites PAL: Program-aided Language Models.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation PAL: Program-aided Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.608226Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a4a4d7d-4ea8-4a9b-9489-23d16812b274 · outbound

This paper cites Gardner, G.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Gardner, G

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:31:50.760518Z

Source-reported events for the cited work

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

Observation dc9773dd-1b1b-4c4b-9956-376eadfe061f · outbound

This paper cites an unresolved cited work.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Unresolved cited work

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.616347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b771bfd-de87-4f1e-b074-1a2465964ed3 · outbound

This paper cites Memorization in In-Context Learning.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Memorization in In-Context Learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.620056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4dd8c063-3291-4902-975f-b8106714710f · outbound

This paper cites Guzm \'a n, P.-J.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Guzm \'a n, P.-J

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.624096Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e635ffc-ee73-4dd0-b87e-f4e8b058e873 · outbound

This paper cites Unsupervised Neural Machine Translation with Generative Language Models Only.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Unsupervised Neural Machine Translation with Generative Language Models Only

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-09T19:31:50.093287Z

Source-reported events for the cited work

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

Observation ba6e43f4-266f-4f76-8ccd-6d649533e3ff · outbound

This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.631967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d42a767e-55cb-4a96-bd8a-3c64ed5d88d9 · outbound

This paper cites Structured Prompting: Scaling In-Context Learning to 1,000 Examples.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Structured Prompting: Scaling In-Context Learning to 1,000 Examples

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.635788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 660b0a0b-8006-45de-97f9-d6aba02be194 · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Measuring Mathematical Problem Solving With the MATH Dataset

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.639628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 975d8d87-5589-4263-9422-133a7f44ef03 · outbound

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

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation LoRA: Low-Rank Adaptation of Large Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.643390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 511d629a-c45c-46c9-b22f-3bb2703c94cb · outbound

This paper cites Amortizing intractable inference in large language models.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Amortizing intractable inference in large language models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.647107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83c0b738-d009-48d4-87bd-3ad0b198b3f7 · outbound

This paper cites Many-Shot In-Context Learning in Multimodal Foundation Models.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Many-Shot In-Context Learning in Multimodal Foundation Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.650798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a582c19-cd76-47fa-a927-383caa098606 · outbound

This paper cites DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.655210Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66627f5e-b22b-441d-b6f4-bdbf3713add2 · outbound

This paper cites an unresolved cited work.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-09T19:31:50.735030Z

Source-reported events for the cited work

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

Observation 4354c004-6fc0-477a-882b-61549d37fd5b · outbound

This paper cites an unresolved cited work.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-09T19:31:50.723911Z

Source-reported events for the cited work

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

Observation a21a9a79-447f-4c7c-9431-dcd66a2b580e · outbound

This paper cites Gecko: Versatile Text Embeddings Distilled from Large Language Models.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Gecko: Versatile Text Embeddings Distilled from Large Language Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.667709Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb0bfda4-214c-4c3f-b311-1ef3f4ce5702 · outbound

This paper cites an unresolved cited work.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Unresolved cited work

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.671441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3d62391-e2e8-4e88-88c6-52955d67aaf3 · outbound

This paper cites an unresolved cited work.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-08-09T19:31:50.713089Z

Source-reported events for the cited work

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

Observation fb661719-a9cb-4651-b13c-f16a0a3648c9 · outbound

This paper cites In-Context Learning with Many Demonstration Examples.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation In-Context Learning with Many Demonstration Examples

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.678670Z

Source-reported events for the cited work

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Observation b9e4393a-a825-4167-8602-22ae438c1dc0 · outbound

This paper cites Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.682406Z

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Unavailable: canonical work link unavailable.

Observation 28c8bd9a-894d-41cc-9479-5b96c297bdf6 · outbound

This paper cites Lin and K.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Lin and K

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:31:50.700957Z

Source-reported events for the cited work

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

Observation c696bb43-8109-41a5-a8a8-db19019b4e2a · outbound

This paper cites an unresolved cited work.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Unresolved cited work

Reference 36

Resolution: unresolved; raw_fallback, observed 2026-08-09T19:31:50.688048Z

Source-reported events for the cited work

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

Observation 17f093f2-b324-47c3-86c9-5a3ab4ba9c2b · outbound

This paper cites an unresolved cited work.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Unresolved cited work

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.693221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc25a264-3f1c-4d21-b0ac-b1506e6ed1bb · outbound

This paper cites When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.697043Z

Source-reported events for the cited work

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Observation 00dd4f4f-c7da-45ec-b445-6fd9a169abbf · outbound

This paper cites Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.701468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3e96d6b-0423-4c09-bc6d-fe30b6dccb89 · outbound

This paper cites Paria, K.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Paria, K

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:31:50.649845Z

Source-reported events for the cited work

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Observation 9f03562b-a9fc-4640-9221-fb8f536391df · outbound

This paper cites Bidirectional Language Models Are Also Few-shot Learners.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Bidirectional Language Models Are Also Few-shot Learners

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-08-09T19:31:49.945525Z

Source-reported events for the cited work

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

Observation dc8a4d5a-9b83-4cd9-9d94-a3745c6e4c77 · outbound

This paper cites Pourreza, H.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Pourreza, H

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:31:50.604038Z

Source-reported events for the cited work

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

Observation 4e15ee53-4655-4450-b398-0c7e95223a29 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.716195Z

Source-reported events for the cited work

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Observation 50e138e8-a87c-4f50-9c5d-77cc07ffb5d0 · outbound

This paper cites an unresolved cited work.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Unresolved cited work

Reference 44

Resolution: unresolved; raw_fallback, observed 2026-08-09T19:31:50.541309Z

Source-reported events for the cited work

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

Observation 8f35967a-938e-4430-91d9-009b1cabdb8c · outbound

This paper cites Rubin, J.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Rubin, J

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.723469Z

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Observation 36f22b71-fdaa-46fb-b896-51256915539c · outbound

This paper cites Samek, G.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Samek, G

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:31:50.529944Z

Source-reported events for the cited work

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Observation 024f8843-cf65-4c26-8035-257181917da4 · outbound

This paper cites an unresolved cited work.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Unresolved cited work

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.730853Z

Source-reported events for the cited work

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Observation 45d8ef58-545d-42ee-8eb2-745f99310d89 · outbound

This paper cites Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.734032Z

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Observation a7bfcc57-035a-4f00-b208-04f99499a761 · outbound

This paper cites an unresolved cited work.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-08-09T19:31:50.512001Z

Source-reported events for the cited work

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Observation 554c026c-57d5-4b8e-987e-bf39cca34fee · outbound

This paper cites Sundararajan, A.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Sundararajan, A

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.742104Z

Source-reported events for the cited work

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Observation d776de99-8421-4079-a66f-82c719245336 · outbound

This paper cites an unresolved cited work.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-09T19:31:50.493451Z

Source-reported events for the cited work

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Observation 90490939-35c1-4fda-9ff0-2490c59cab90 · outbound

This paper cites an unresolved cited work.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Unresolved cited work

Reference 52

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Source-reported events for the cited work

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Observation 432aaf5c-aa83-450c-bafc-293e030b904a · outbound

This paper cites an unresolved cited work.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Unresolved cited work

Reference 53

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Observation 83b6ad74-f4f8-4b08-a3b1-8b6e6feeebb9 · outbound

This paper cites an unresolved cited work.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-09T19:31:50.482074Z

Source-reported events for the cited work

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

Observation 29dc469b-9d85-4563-813e-50128349fa13 · outbound

This paper cites an unresolved cited work.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Unresolved cited work

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.763900Z

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Observation be8b986c-59d1-49fc-bae9-9c936b9a189e · outbound

This paper cites Williams and C.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Williams and C

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:31:50.470665Z

Source-reported events for the cited work

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

Observation 816a1479-9103-4af5-b92b-82b966cbd19e · outbound

This paper cites an unresolved cited work.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Unresolved cited work

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.771469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3f9c2a1-6077-4851-b0fd-4db5a440add3 · outbound

This paper cites an unresolved cited work.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-09T19:31:50.452263Z

Source-reported events for the cited work

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

Observation c868cd13-4574-4895-b02a-db05854016f2 · outbound

This paper cites In-context Learning with Retrieved Demonstrations for Language Models: A Survey.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation In-context Learning with Retrieved Demonstrations for Language Models: A Survey

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.778725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0fa9a75-081f-493b-8071-0db245f4ea03 · outbound

This paper cites Zelikman, Y.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Zelikman, Y

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:31:50.440009Z

Source-reported events for the cited work

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

Observation 1ad22d23-c1ab-40d6-8b80-0c15a828a50b · outbound

This paper cites Zhan and H.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Zhan and H

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:31:50.429198Z

Source-reported events for the cited work

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

Observation 3f30cae5-f0f3-4656-8b8e-b6c69b06d315 · outbound

This paper cites Zhang, A.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Zhang, A

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:31:50.417980Z

Source-reported events for the cited work

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

Observation 6e0df3ca-0af6-4c2e-922a-e8c7116a024e · outbound

This paper cites an unresolved cited work.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-09T19:31:50.394715Z

Source-reported events for the cited work

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

Observation a4ebf66a-a781-4d7f-8dd3-e1f9aa67382d · outbound

This paper cites an unresolved cited work.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Unresolved cited work

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.797261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61f3389f-9411-45ee-87c8-00af82540241 · outbound

This paper cites an unresolved cited work.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-09T19:31:50.310009Z

Source-reported events for the cited work

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

Observation b08be786-efb1-4ce9-9f99-abd39698ed9f · outbound

This paper cites write newline.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation write newline

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.804666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b08ddabf-0a3b-4be7-8b52-00d472641ba9 · outbound

This paper cites @esa (Ref.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation @esa (Ref

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.809346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc7c8c53-9101-4b7f-a5cc-ae5836aae7b6 · outbound

This paper cites an unresolved cited work.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Unresolved cited work

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.813383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a3c49b5-7c37-40be-9c29-72b2449e9009 · outbound

This paper cites an unresolved cited work.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation Unresolved cited work

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.817357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 0779aaff-c599-4381-9905-2f654356be60 · inbound

A Survey of Scaling in Large Language Model Reasoning cites this paper.

A Survey of Scaling in Large Language Model Reasoning From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation

Reference 201

Resolution: verified exact; arxiv_id, observed 2026-05-22T21:22:09.038951Z

Source-reported events for the cited work

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

Observation c993669c-f7b4-4e3f-b7ec-005ed0ee6eb1 · inbound

Towards Compute-Optimal Many-Shot In-Context Learning cites this paper.

Towards Compute-Optimal Many-Shot In-Context Learning From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T15:20:38.727777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 129635e6-fa89-48c8-9997-8fa4768e56a6 · inbound

From Implicit Exploration to Structured Reasoning: Leveraging Guideline and Refinement for LLMs cites this paper.

From Implicit Exploration to Structured Reasoning: Leveraging Guideline and Refinement for LLMs From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T23:56:34.895604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d78df414-50fb-4e86-b77f-511f7c9fa75c · inbound

MILES: Modular Instruction Memory with Learnable Selection for Self-Improving LLM Reasoning cites this paper.

MILES: Modular Instruction Memory with Learnable Selection for Self-Improving LLM Reasoning From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-07-09T00:45:49.131892Z

Source-reported events for the cited work

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