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Paper Citation Record · LEDGER

Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 23 inbound Pith citation observations for arXiv:2310.06117.

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

pith.paper-citation-record.v1
2310.06117 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 23 of 23 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 23 of 23 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T13:39:04.494165Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

23
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation cfbcbca7-8b06-4e0c-af4a-b42d0a5fb475 · inbound

Retrieval-Augmented Generation for Large Language Models: A Survey cites this paper.

Retrieval-Augmented Generation for Large Language Models: A Survey Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-24T05:13:57.253072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-24T05:10:25.171044Z digest=sha256:01b32b180c9755fee105295f52b1f3b6aa9d1f9cbc7bed6c302903d46af5b117

Observation e6fe12d0-1c1f-4f56-9ec5-21e917093270 · inbound

A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications cites this paper.

A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-12T21:52:10.531385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-12T21:52:09.938550Z digest=sha256:2e4f21f44070049ef6edf1842578eda4b1f45a224cf3b30162ac25708d9cbd19

Observation 4dc73ae3-bcf3-471d-b530-b19577ff57f3 · inbound

Retrieval-Augmented Generation for AI-Generated Content: A Survey cites this paper.

Retrieval-Augmented Generation for AI-Generated Content: A Survey Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

Reference 167

Resolution
verified exact
arxiv_id, observed 2026-05-15T13:32:17.439874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-15T13:32:17.177021Z digest=sha256:890f8d2f55a37c07850c5661e781b1a0a379380559e4a0b98f2346cbc2d8ce61

Observation 156be518-07b4-479c-bef4-228c180b9436 · inbound

A Survey on Retrieval-Augmented Text Generation for Large Language Models cites this paper.

A Survey on Retrieval-Augmented Text Generation for Large Language Models Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

Reference 171

Resolution
metadata mismatch
arxiv_id, observed 2026-05-24T02:15:55.466661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-24T02:15:05.379583Z digest=sha256:f94b0b869b3a824e88e29761805bc2fe675a303fb89f595eaadb15cf792dda72

Observation ed36927d-7a76-4306-9040-3a6f680a9518 · inbound

Automated Design of Agentic Systems cites this paper.

Automated Design of Agentic Systems Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

Reference 237

Resolution
verified exact
arxiv_id, observed 2026-05-15T08:07:54.944244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-15T08:07:54.611771Z digest=sha256:47665024d96b85dee2eaee1b9d68af592fc2700dd30033e46313e3de45d85e1c

Observation c4413f70-d3ab-4320-859d-f7c35454dcfd · inbound

Trustworthiness in Retrieval-Augmented Generation Systems: A Survey cites this paper.

Trustworthiness in Retrieval-Augmented Generation Systems: A Survey Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-05-23T21:08:25.899442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-23T21:08:11.787013Z digest=sha256:79939c14ad64315f36091da4087548ecddf2c9f2c29d1590ed9fd89c3246666c

Observation 1cc21dba-20e9-4191-ab69-50361d04441c · inbound

Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification cites this paper.

Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-08T13:39:04.494165Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T13:39:04.494165Z digest=sha256:113dd2cdc2b0cc7b704b0d28543692e4666abc4b2b201780cf770e786c994f9d

Observation c2a9b18a-12fc-485c-bec6-e54b6479fda0 · inbound

Exp4Fuse: A Rank Fusion Framework for Enhanced Sparse Retrieval using Large Language Model-based Query Expansion cites this paper.

Exp4Fuse: A Rank Fusion Framework for Enhanced Sparse Retrieval using Large Language Model-based Query Expansion Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-07T10:38:19.369050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:38:19.369050Z digest=sha256:9a4a86b25898d2bad4a0edceb4c2938bd308d4c9931fc97449e2763c9212358a

Observation e167c696-0bfd-40cc-97ec-8f869892c140 · inbound

Which Prompting Technique Should I Use? An Empirical Investigation of Prompting Techniques for Software Engineering Tasks cites this paper.

Which Prompting Technique Should I Use? An Empirical Investigation of Prompting Techniques for Software Engineering Tasks Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-07T10:19:40.500713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:40.500713Z digest=sha256:6b8211cc1814be6f320d69e7dbc5b9c6b71c3abf52ac25d2231883fbfbeb8c78

Observation 36c13163-c5cd-4311-9966-32ef2a0b6fa1 · inbound

Mixture of Reasonings: Teach Large Language Models to Reason with Adaptive Strategies cites this paper.

Mixture of Reasonings: Teach Large Language Models to Reason with Adaptive Strategies Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-06T21:16:51.141936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T21:16:51.141936Z digest=sha256:5fa50929eab24dccab444b5f92ac18b2eb2d1c9fe5ca8ae80aa911470285ff79

Observation fe944529-0b51-4ea8-bb1c-d475a6063c62 · inbound

Prompt Engineering for Requirements Engineering: A Literature Review and Roadmap cites this paper.

Prompt Engineering for Requirements Engineering: A Literature Review and Roadmap Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-06T18:40:06.251852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:40:06.251852Z digest=sha256:ef4713cf4b3a7572c15a538506e3aeabb0efe7a4ff0ec33b99a70d985fd98c72

Observation 25caec08-9223-4691-b21c-239564640108 · inbound

Orchestration for Domain-specific Edge-Cloud Language Models cites this paper.

Orchestration for Domain-specific Edge-Cloud Language Models Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-06T18:10:15.645433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:10:15.645433Z digest=sha256:4789032773d0bcd98fbfbcfb022ad1145cb7379a0b03e62f5b93433aa31e5dad

Observation 26853f87-053a-495c-a25f-4f087ae136a3 · inbound

Vis-CoT: A Human-in-the-Loop Framework for Interactive Visualization and Intervention in LLM Chain-of-Thought Reasoning cites this paper.

Vis-CoT: A Human-in-the-Loop Framework for Interactive Visualization and Intervention in LLM Chain-of-Thought Reasoning Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-05T12:38:29.147743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T12:38:29.147743Z digest=sha256:f508767dba1e75053d006ae91eb7d3b0c2b2d96d2cd9a4752d7c36d669a614d3

Observation 783d4722-f542-4da3-8f32-9cdc05625efe · inbound

PromptGuard: An Orchestrated Prompting Framework for Principled Synthetic Text Generation for Vulnerable Populations using LLMs with Enhanced Safety, Fairness, and Controllability cites this paper.

PromptGuard: An Orchestrated Prompting Framework for Principled Synthetic Text Generation for Vulnerable Populations using LLMs with Enhanced Safety, Fairness, and Controllability Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-04T20:04:29.311700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T20:04:29.311700Z digest=sha256:1337ca3ea75a770f28ed2a0982235da473fa61044e1d126c5313864a7bbb05c6

Observation 84742c1c-108b-4c31-a49f-3433fa917cf5 · inbound

Library Hallucinations in LLM-Generated Code: A Risk Analysis Grounded in Developer Queries cites this paper.

Library Hallucinations in LLM-Generated Code: A Risk Analysis Grounded in Developer Queries Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

Reference 77

Resolution
verified exact
arxiv_id, observed 2026-05-21T22:34:23.740552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-21T22:31:20.358758Z digest=sha256:984c50a56866a702219d7ed6189d4d7d505a3ae39fc3043f3c9d5d0def4f60a5

Observation 098c7efe-9193-4f68-9e94-2c311ad448d7 · inbound

Living Databases: A Unified Model for Continuous Schema Evolution, Versioning, and Transformations cites this paper.

Living Databases: A Unified Model for Continuous Schema Evolution, Versioning, and Transformations Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

Reference 81

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T16:01:15.226457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-09T18:55:47.627625Z digest=sha256:2caf52a3e6a73791b811a169b77256fce50aa66c4032a114f94a307e63c242a6

Observation 1381e7a9-f482-4e4d-a62e-b49f3679519e · inbound

UnAC: Adaptive Visual Prompting with Abstraction and Stepwise Checking for Complex Multimodal Reasoning cites this paper.

UnAC: Adaptive Visual Prompting with Abstraction and Stepwise Checking for Complex Multimodal Reasoning Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

Reference 19

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T23:16:37.364379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-07T17:35:28.050906Z digest=sha256:e8f93607d37912831b7a4b16d145d7c9ffa25b6251ac172b4eb24b185f76eed0

Observation bf1878ce-f032-4fd6-877d-875da77a1774 · inbound

Making Abstraction Concrete: A Design Space and Interaction Model of Abstraction in Interactive Systems cites this paper.

Making Abstraction Concrete: A Design Space and Interaction Model of Abstraction in Interactive Systems Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

Reference 202

Resolution
verified exact
arxiv_id, observed 2026-05-13T01:27:02.733322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-13T01:22:00.906626Z digest=sha256:50029c1eaa9732e758f75cb2ffd768add64c96ad008d6f69e63d32cbc958c6e1

Observation 488369e0-efca-4e18-9dd5-b0de2f298e32 · inbound

PluRule: A Benchmark for Moderating Pluralistic Communities on Social Media cites this paper.

PluRule: A Benchmark for Moderating Pluralistic Communities on Social Media Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

Reference 99

Resolution
verified exact
arxiv_id, observed 2026-05-20T14:08:20.537094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-20T14:05:14.737146Z digest=sha256:aa5c2f973eac98ef997e9d9970a61a9c14cd0de191d37d97bb412b44e8756b0e

Observation 191bd4e4-8c78-4396-8d64-b66f09083cf6 · inbound

Efficient RAG with Intent-Aware Retrieval and Semantics-Preserving Chunking cites this paper.

Efficient RAG with Intent-Aware Retrieval and Semantics-Preserving Chunking Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

Reference 17

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T20:56:13.782755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-28T17:38:21.242007Z digest=sha256:ed017741830f460c943f4b62173800d56ff3aee70b5b4c8f49a8176c42e656da

Observation 660a15dc-e192-499f-bfbc-0e07d097b17c · inbound

Dive into Ambiguity: A*-Inspired Multi-Agents Commonsense Obfuscation Attack on LLM Prompts cites this paper.

Dive into Ambiguity: A*-Inspired Multi-Agents Commonsense Obfuscation Attack on LLM Prompts Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-07-01T21:26:16.384008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T16:54:12.354178Z digest=sha256:dd727caa55f743645ad7ff069c203bf5dda272c3e5679f82b036242887b4226f

Observation 53701612-88d6-481a-a419-81a2224bf354 · inbound

MetaPS: Adaptive Programmatic Strategy Selection for Market Agents cites this paper.

MetaPS: Adaptive Programmatic Strategy Selection for Market Agents Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

Reference 24

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T08:39:42.653310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-26T11:06:28.690956Z digest=sha256:f170b90fbce0f1e6a3513df9fd95b478d53754b33a493adb6cd49ede22a1743c

Observation 4debd1c6-de6d-4732-8f2b-35f435005107 · inbound

Training Language Models to Cooperate with Inference-Time Controllers cites this paper.

Training Language Models to Cooperate with Inference-Time Controllers Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

Reference 9

Resolution
unresolved
no resolver link, observed 2026-07-30T13:09:01.372470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T13:09:01.372470Z digest=sha256:d6fdf154b0b5a5aef529ec8955a17a3bcbb026a0f390f651082e523d5b0fd651