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Paper Citation Record · LEDGER

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models

As of 11 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2501.07815.

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

pith.paper-citation-record.v1
2501.07815 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:38:52.717542Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

28 of 28 outbound references displayed

  • verified exact4
  • verified fuzzy0
  • unresolved24
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6add0a04-565e-484e-93c3-8a45ccbba43a · outbound

This paper cites Graph of Thoughts: Solving Elaborate Problems with Large Language Models.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models Graph of Thoughts: Solving Elaborate Problems with Large Language Models

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-10T20:38:52.579600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:38:52.579600Z digest=sha256:bb2f368417fc5396e5dcb024d7f9aaec517bb097e024ca237a3f317d444e2341

Observation efa759ee-2609-4733-aa08-daf6ecc0ef3c · outbound

This paper cites Do large language models resemble humans in language use?.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models Do large language models resemble humans in language use?

Reference 2

Resolution
verified exact
local_arxiv, observed 2026-08-10T20:38:53.102984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T20:38:52.584737Z digest=sha256:3bda388cf08cf1e711b21aa0259a0be114bb83a8a3110eb7906cbd0190f19c5e

Observation 51103717-440d-46c8-b700-345050b93476 · outbound

This paper cites an unresolved cited work.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models Unresolved cited work

Reference 3

Resolution
unresolved
raw_fallback, observed 2026-08-10T20:38:53.268182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T20:38:52.589892Z digest=sha256:50de647bb3cf5544f9167b9c0c341aa3388cf59ba5a4c07d0d3c40cf937baa89

Observation 762c1b1c-f186-49ed-8b73-7ee35c98b5ec · outbound

This paper cites an unresolved cited work.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models Unresolved cited work

Reference 4

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unresolved
raw_fallback, observed 2026-08-10T20:38:53.251570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T20:38:52.594578Z digest=sha256:5caeb57f389b5597b39ded89f6e3d64920f781ba31c0e222de549a6e25d54b02

Observation 03e47ca4-d9ae-4c89-bb14-7f00c9e5c3fd · outbound

This paper cites Generative AI.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models Generative AI

Reference 5

Resolution
verified exact
local_arxiv, observed 2026-08-10T20:38:53.075900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T20:38:52.599529Z digest=sha256:f78d0c9bf2066fb73cbb06e92957c3299735c99ca43c686615d112b2d09eac73

Observation 352fb25a-9922-4701-a2ce-4bba764ce3df · outbound

This paper cites Stream of Search (SoS): Learning to Search in Language.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models Stream of Search (SoS): Learning to Search in Language

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-10T20:38:52.605152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:38:52.605152Z digest=sha256:6dc29ef6106055f02d7fab049d77883b93ee4a60fd3af3a8e67409e5c459963e

Observation 33298443-6420-4a14-9dd1-8f6386433dd9 · outbound

This paper cites In-Context Learning Creates Task Vectors.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models In-Context Learning Creates Task Vectors

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-10T20:38:52.611390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:38:52.611390Z digest=sha256:0ceacf135b0122a725c843586d7d5b8e74d012a2dca7999340ff40716fe4f609

Observation 13e98298-0258-44f4-8e57-b80da52d6099 · outbound

This paper cites MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-10T20:38:52.616621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:38:52.616621Z digest=sha256:9e354987143fe8899ef8a2dee39d4ea948d2605ca4a3ee3ef57f7a92b20261ef

Observation eb929e6c-54a6-48e3-9f07-39fec86f2aa1 · outbound

This paper cites Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction

Reference 9

Resolution
verified exact
local_arxiv, observed 2026-08-10T20:38:52.996626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T20:38:52.621976Z digest=sha256:edfe923b547d1a32adcc6d6a94250b390a8f3d52c345f630040f16d4854c859b

Observation a7a7b098-07fa-4932-93ae-8d49913ebe5f · outbound

This paper cites Large Language Models are Zero-Shot Reasoners.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models Large Language Models are Zero-Shot Reasoners

Reference 10

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no resolver link, observed 2026-08-10T20:38:52.627127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:38:52.627127Z digest=sha256:2346c318b423437109fa8b43c92901d6ae1f0a0829c9631a175644ba101414c8

Observation 45163d45-fac9-4715-964a-78340b51e047 · outbound

This paper cites Large Language Models Understand and Can be Enhanced by Emotional Stimuli.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models Large Language Models Understand and Can be Enhanced by Emotional Stimuli

Reference 11

Resolution
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no resolver link, observed 2026-08-10T20:38:52.632907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:38:52.632907Z digest=sha256:2c09139b510851e4215fd2986b1a22d19b9e9477d3cafb1c18fd00888cba612f

Observation fdfe4939-70ed-428b-bb84-43a16ef400ca · outbound

This paper cites an unresolved cited work.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models Unresolved cited work

Reference 12

Resolution
unresolved
raw_fallback, observed 2026-08-10T20:38:53.233192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T20:38:52.637684Z digest=sha256:9c046ac1474075c6e05e69bb1d7d4ff5bb62f32e8243c70a17fa670ce6041375

Observation 6512b1ca-58e3-4667-82b5-60f165edb258 · outbound

This paper cites Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-10T20:38:52.642415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:38:52.642415Z digest=sha256:c27adb7f25f7767c27c056b1313ec8aba53f08e9b1a11ca08027a00574d88112

Observation f4f47c10-edd7-40e8-8ba0-3ca0ac446637 · outbound

This paper cites Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine

Reference 14

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unresolved
no resolver link, observed 2026-08-10T20:38:52.647929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:38:52.647929Z digest=sha256:f442161574ff0661b76062e51da5f6c22b867699406c0d289c48429a44bc5976

Observation 46cf9c62-b9a7-4fe6-ae8d-43d27b915d6f · outbound

This paper cites an unresolved cited work.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models Unresolved cited work

Reference 15

Resolution
unresolved
raw_fallback, observed 2026-08-10T20:38:53.214835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T20:38:52.652969Z digest=sha256:0043387fd3e5b40f64200226e76ffe121b968b36f2e9049a38387a211c994a46

Observation 7e7ead66-0636-4140-aa51-5fd6e3db65e8 · outbound

This paper cites an unresolved cited work.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models Unresolved cited work

Reference 16

Resolution
unresolved
raw_fallback, observed 2026-08-10T20:38:53.198192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T20:38:52.657624Z digest=sha256:a5999780612c0aac08cee07b1a9df10d0516859ec98ba7b6bc7aaea6b3f90dfb

Observation a59cbe9b-c6e5-4573-85a3-816b2942b074 · outbound

This paper cites Branch-Solve-Merge Improves Large Language Model Evaluation and Generation.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models Branch-Solve-Merge Improves Large Language Model Evaluation and Generation

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-10T20:38:52.662127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:38:52.662127Z digest=sha256:a876f6b8fbf86737f7ef20c4138daaff234a20c97b7ae4dfadddc9b0f60a84fc

Observation 00caaff1-5792-479b-89df-9369b03bcee0 · outbound

This paper cites Diagnostic Reasoning Prompts Reveal the Potential for Large Language Model Interpretability in Medicine.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models Diagnostic Reasoning Prompts Reveal the Potential for Large Language Model Interpretability in Medicine

Reference 18

Resolution
verified exact
local_arxiv, observed 2026-08-10T20:38:52.883156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T20:38:52.667157Z digest=sha256:9779907d36cb9175e4397ecfb38ed56d23b47a2031fec0121a73c82010c505c8

Observation 15ab0dd0-0d99-4441-afae-92eecf4c2626 · outbound

This paper cites SK-VQA: Synthetic Knowledge Generation at Scale for Training Context-Augmented Multimodal LLMs.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models SK-VQA: Synthetic Knowledge Generation at Scale for Training Context-Augmented Multimodal LLMs

Reference 19

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no resolver link, observed 2026-08-10T20:38:52.672173Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:38:52.672173Z digest=sha256:4a6f99176f2ae10cefe6919c5d522af2447727f5f31d4a8664e411358ab63705

Observation 4474e9c1-929e-43e8-b3fd-2a03340d5d31 · outbound

This paper cites an unresolved cited work.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models Unresolved cited work

Reference 20

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unresolved
raw_fallback, observed 2026-08-10T20:38:53.178814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T20:38:52.677343Z digest=sha256:554ede91c4ebaa015ea5c8b68508890ff3158458187f40cf38f533a71f0ace7e

Observation 5ff6bd93-cc21-4c01-a03b-f70e6b9672fd · outbound

This paper cites Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-10T20:38:52.682341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:38:52.682341Z digest=sha256:1cb465680e89cdef67e26c67d9e76ae482a5476efbad017981fd8ee6169a2d85

Observation c828e2b4-9b43-464e-bfe7-70c53322a3bb · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-10T20:38:52.687594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:38:52.687594Z digest=sha256:6f89b3962dd1b1db65527d9a3da76c141807628d07b8d6861e829c61450e028c

Observation b14b0107-8ec5-4790-8c4e-618dee3fd901 · outbound

This paper cites AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-10T20:38:52.692610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:38:52.692610Z digest=sha256:bdf4c574114c8148945c278542a8ae2ac93c33d1bd8597284965366865148817

Observation b79d0b3d-8f90-4bfb-9d3d-7f051fa3e9e6 · outbound

This paper cites The Rise and Potential of Large Language Model Based Agents: A Survey.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models The Rise and Potential of Large Language Model Based Agents: A Survey

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-10T20:38:52.697530Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:38:52.697530Z digest=sha256:af3a3b3c798bbb097e01d13960378095edc570d0cf0b3f6baf299031a8fa7066

Observation f36fde06-346e-4141-99dd-ac39ef0c11db · outbound

This paper cites an unresolved cited work.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models Unresolved cited work

Reference 25

Resolution
unresolved
raw_fallback, observed 2026-08-10T20:38:53.161809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T20:38:52.701864Z digest=sha256:44d82a0ab31832f5ae14faec8f24e5a10e3648c04fdd4c1db084b432b6bc85eb

Observation 59ae5a24-9f9c-4474-a7ff-b8ff9aecbadf · outbound

This paper cites Learning and Evaluating General Linguistic Intelligence.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models Learning and Evaluating General Linguistic Intelligence

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-10T20:38:52.706199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:38:52.706199Z digest=sha256:71c75e76f23978681dc46bca4438d15621f83b501d06055963c3d3f6d94c5b00

Observation 5f24a41f-6f12-427d-9d9d-e7fe4bde507a · outbound

This paper cites an unresolved cited work.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models Unresolved cited work

Reference 27

Resolution
unresolved
raw_fallback, observed 2026-08-10T20:38:53.141950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T20:38:52.712435Z digest=sha256:9272093bd4ab17cfc81df459d9dc63641f92a500a34d6229bf40e613119e1e9e

Observation 5c15bb68-e95a-42a8-af08-7213d403942d · outbound

This paper cites Universal and Transferable Adversarial Attacks on Aligned Language Models.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 28

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unresolved
no resolver link, observed 2026-08-10T20:38:52.717542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:38:52.717542Z digest=sha256:7411ab5bbb4c4511de0a1141c01f9cfe6ea6e567291a4f2896957de3f3d6e478

Pith citing papers

No inbound Pith citation observations are available.