Paper Citation Record · LEDGER

Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 33 inbound Pith citation observations for arXiv:2212.14024.

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

pith.paper-citation-record.v1
2212.14024 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 33 of 33 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T14:21:29.523248Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T13:37:06.933383Z

Reference resolution

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 1ea6d07f-d20f-40ad-81e1-e3de92a82dc0 · inbound

REPLUG: Retrieval-Augmented Black-Box Language Models cites this paper.

REPLUG: Retrieval-Augmented Black-Box Language Models Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 71

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Observation 62496e19-289a-4958-a5dd-a495778e4024 · inbound

Generative Agents: Interactive Simulacra of Human Behavior cites this paper.

Generative Agents: Interactive Simulacra of Human Behavior Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:05:13.649153Z

Source-reported events for the cited work

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Observation 1cb37d4f-09d1-4d57-a953-947a58ccee5b · inbound

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

FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:56:45.504156Z

Source-reported events for the cited work

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Observation 9c82ac7a-e19c-4479-89bc-db3794c32e5b · inbound

Cognitive Architectures for Language Agents cites this paper.

Cognitive Architectures for Language Agents Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-16T19:33:44.384178Z

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Observation d7d29ee2-8ed1-47be-82fa-bc948e0eaff0 · inbound

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution cites this paper.

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 177

Resolution: verified exact; arxiv_id, observed 2026-05-16T08:12:31.674220Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b5ae6856-9135-4710-b02f-4de09cedd60e · inbound

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

DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:57:47.396120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 70c1e001-a35c-4827-9a97-772b690371bd · inbound

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

Retrieval-Augmented Generation for Large Language Models: A Survey Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 23

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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 Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 75

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Observation d848d174-1d0f-455a-855d-94a92598f344 · inbound

From Local to Global: A Graph RAG Approach to Query-Focused Summarization cites this paper.

From Local to Global: A Graph RAG Approach to Query-Focused Summarization Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T05:10:57.949874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f37124e3-613a-4c24-97ec-dfd0fb35eb9c · inbound

TextGrad: Automatic "Differentiation" via Text cites this paper.

TextGrad: Automatic "Differentiation" via Text Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 72

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T11:27:58.272076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5d900c7b-9313-4b93-9d58-fdeeeab756af · inbound

Towards an AI co-scientist cites this paper.

Towards an AI co-scientist Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 219

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Observation a6235a94-d23e-4e13-a5a9-72ac6907191e · inbound

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning cites this paper.

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:34:53.191442Z

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Observation a2d6c52e-70ca-41a9-a674-48b209b8e1c7 · inbound

From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems cites this paper.

From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 70

Resolution: verified exact; arxiv_id, observed 2026-05-19T11:52:16.413659Z

Source-reported events for the cited work

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Observation f3f30251-51d6-4668-95c3-1d1d1a1d87b5 · inbound

Can VLMs Unlock Semantic Anomaly Detection? A Framework for Structured Reasoning cites this paper.

Can VLMs Unlock Semantic Anomaly Detection? A Framework for Structured Reasoning Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-18T05:50:57.253751Z

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Observation 8637ee33-b3a0-4f82-8413-6550b6270ddd · inbound

Can VLMs Unlock Semantic Anomaly Detection? A Framework for Structured Reasoning cites this paper.

Can VLMs Unlock Semantic Anomaly Detection? A Framework for Structured Reasoning Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-21T20:50:36.572826Z

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Observation b01de7d2-48fa-4437-aae3-1f5dc499484e · inbound

Retrieval as a Decision: Training-Free Adaptive Gating for Efficient RAG cites this paper.

Retrieval as a Decision: Training-Free Adaptive Gating for Efficient RAG Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-17T21:55:20.175243Z

Source-reported events for the cited work

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Observation 78020edb-d5d1-4083-84dc-30682570676a · inbound

FEM-Bench: A Structured Scientific Reasoning Benchmark for Evaluating Code-Generating LLMs cites this paper.

FEM-Bench: A Structured Scientific Reasoning Benchmark for Evaluating Code-Generating LLMs Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 24

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A Probabilistic Framework for LLM-Based Model Discovery cites this paper.

A Probabilistic Framework for LLM-Based Model Discovery Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 1927

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Distributional Open-Ended Evaluation of LLM Cultural Value Alignment Based on Value Codebook cites this paper.

Distributional Open-Ended Evaluation of LLM Cultural Value Alignment Based on Value Codebook Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 26

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Observation 4a585c86-2bd8-4400-9f98-299ffddeb897 · inbound

Illocutionary Explanation Planning for Source-Faithful Explanations in Retrieval-Augmented Language Models cites this paper.

Illocutionary Explanation Planning for Source-Faithful Explanations in Retrieval-Augmented Language Models Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 26

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Observation ca0d6b47-535b-49bb-b636-29537ba998bf · inbound

Adaptive ToR: Complexity-Aware Tree-Based Retrieval for Pareto-Optimal Multi-Intent NLU cites this paper.

Adaptive ToR: Complexity-Aware Tree-Based Retrieval for Pareto-Optimal Multi-Intent NLU Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 21

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Observation 9d08fa80-d540-4f6b-b3c4-09d8d455c0a0 · inbound

RAQG-QPP: Query Performance Prediction with Retrieved Query Variants and Retrieval Augmented Query Generation cites this paper.

RAQG-QPP: Query Performance Prediction with Retrieved Query Variants and Retrieval Augmented Query Generation Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:41:27.392324Z

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Observation 953794ef-e93c-4d9b-bf35-fc3c7e77e996 · inbound

Retrieval is Cheap, Show Me the Code: Executable Multi-Hop Reasoning for Retrieval-Augmented Generation cites this paper.

Retrieval is Cheap, Show Me the Code: Executable Multi-Hop Reasoning for Retrieval-Augmented Generation Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:07:54.323763Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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Can We Trust AI-Inferred User States. A Psychometric Framework for Validating the Reliability of Users States Classification by LLMs in Operational Environments cites this paper.

Can We Trust AI-Inferred User States. A Psychometric Framework for Validating the Reliability of Users States Classification by LLMs in Operational Environments Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 6

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Observation 2e332014-fe3d-40a6-b16a-a49ac0b702a8 · inbound

Mechanistically Interpretable Neural Encoding Reveals Fine-Grained Functional Selectivity in Human Visual Cortex cites this paper.

Mechanistically Interpretable Neural Encoding Reveals Fine-Grained Functional Selectivity in Human Visual Cortex Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 70

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:38:56.253578Z

Source-reported events for the cited work

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Observation 379b8033-c05b-4fd3-b018-03dccf0ff17a · inbound

Anything2Skill: Compiling External Knowledge into Reusable Skills for Agents cites this paper.

Anything2Skill: Compiling External Knowledge into Reusable Skills for Agents Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-03T01:07:29.751042Z

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Carolina Guide: A Multi-Agent RAG System with Institutional Guardrails for Academic Policy Assistance cites this paper.

Carolina Guide: A Multi-Agent RAG System with Institutional Guardrails for Academic Policy Assistance Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 10

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AB-RAG: Adaptive Budgeted Retrieval-Augmented Generation for Reliable Question Answering cites this paper.

AB-RAG: Adaptive Budgeted Retrieval-Augmented Generation for Reliable Question Answering Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-06-30T09:24:32.911653Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 917a9439-39e5-4ec0-ac08-44e37bc02adc · inbound

From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents cites this paper.

From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 44

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ffa94f7f-111b-4c40-ad14-aabd0a3c7876 · inbound

MamaBench: Benchmarking LLM Robustness in Maternal and Child Health Diagnosis through Counterfactual Clinical Perturbation cites this paper.

MamaBench: Benchmarking LLM Robustness in Maternal and Child Health Diagnosis through Counterfactual Clinical Perturbation Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 10

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DeLIVeR: Decomposed Learning for Information-grounded Veracity Recognition via Reinforced Knowledge Graph Exploration cites this paper.

DeLIVeR: Decomposed Learning for Information-grounded Veracity Recognition via Reinforced Knowledge Graph Exploration Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 7

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Visual prompt engineering for video models cites this paper.

Visual prompt engineering for video models Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 1

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What makes prompts a graph: necessary and sufficient conditions for prompt graph engineering cites this paper.

What makes prompts a graph: necessary and sufficient conditions for prompt graph engineering Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 22

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