Paper Citation Record · LEDGER

Power and Limitations of Aggregation in Compound AI Systems

As of 19 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 1 inbound Pith citation observation for arXiv:2602.21556.

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

pith.paper-citation-record.v1
2602.21556 v2

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T21:09:53.841676Z

measured 59 of 59 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-09T20:20:26.414236Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T15:16:10.782925Z

Reference resolution

58 of 58 outbound references displayed

verified exact0
verified fuzzy0
unresolved58
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d50f016e-a0bb-4793-a944-34a623450a7a · outbound

This paper cites Multiagent evaluation mechanisms.

Power and Limitations of Aggregation in Compound AI Systems Multiagent evaluation mechanisms

Reference 1

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Observation 97295ece-5a69-406d-b116-f8852b8b5724 · outbound

This paper cites How we built our multi-agent research system.

Power and Limitations of Aggregation in Compound AI Systems How we built our multi-agent research system

Reference 2

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Observation b775d29f-7162-4589-b7eb-43e426c3fb62 · outbound

This paper cites Ask Me Anything: A simple strategy for prompting language models.

Power and Limitations of Aggregation in Compound AI Systems Ask Me Anything: A simple strategy for prompting language models

Reference 3

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Observation dbc429fe-930f-425d-b94b-9ad00c07ec25 · outbound

This paper cites Compound ai systems.

Power and Limitations of Aggregation in Compound AI Systems Compound ai systems

Reference 4

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Observation b8238315-ad47-4355-8780-d62216338614 · outbound

This paper cites Does the whole exceed its parts? the effect of ai explanations on complementary team performance.

Power and Limitations of Aggregation in Compound AI Systems Does the whole exceed its parts? the effect of ai explanations on complementary team performance

Reference 5

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Observation 7568e28b-578f-45a1-a08d-78b5a874cb92 · outbound

This paper cites Regression equilibrium.

Power and Limitations of Aggregation in Compound AI Systems Regression equilibrium

Reference 6

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Observation ad57e8c2-4ad6-4184-835d-e933084730be · outbound

This paper cites Multitask principal--agent problems: Optimal contracts, fragility, and effort misallocation.

Power and Limitations of Aggregation in Compound AI Systems Multitask principal--agent problems: Optimal contracts, fragility, and effort misallocation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T21:09:48.950710Z

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Observation 41e8f2b6-3b1b-439d-8363-223f75b647da · outbound

This paper cites Solutions Manual to Accompany Contract Theory, volume 1.

Power and Limitations of Aggregation in Compound AI Systems Solutions Manual to Accompany Contract Theory, volume 1

Reference 8

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Observation c5bc77a0-b054-4461-bee4-be19ce2cd4f0 · outbound

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

Power and Limitations of Aggregation in Compound AI Systems FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance

Reference 9

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Observation e2473506-036e-48c7-9c9c-e7be5bc58b8c · outbound

This paper cites Direct preference optimization with unobserved preference heterogeneity: The necessity of ternary preferences.

Power and Limitations of Aggregation in Compound AI Systems Direct preference optimization with unobserved preference heterogeneity: The necessity of ternary preferences

Reference 10

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Observation 3e5e7ecd-8d38-40e4-970e-4fa4cd7af835 · outbound

This paper cites Deep reinforcement learning from human preferences.

Power and Limitations of Aggregation in Compound AI Systems Deep reinforcement learning from human preferences

Reference 11

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Observation e755d06d-e9d0-4092-8f19-90794929fa07 · outbound

This paper cites Emergent alignment via competition.

Power and Limitations of Aggregation in Compound AI Systems Emergent alignment via competition

Reference 12

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Observation fc3f9a37-e4b3-4d1b-b7ea-df950e95512d · outbound

This paper cites Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback.

Power and Limitations of Aggregation in Compound AI Systems Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback

Reference 13

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Observation cf3358a2-13d2-4260-a988-dfd0c2063134 · outbound

This paper cites OR-Bench: An Over-Refusal Benchmark for Large Language Models.

Power and Limitations of Aggregation in Compound AI Systems OR-Bench: An Over-Refusal Benchmark for Large Language Models

Reference 14

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Observation 89d91a83-6b06-4717-b497-66f3d2b0a461 · outbound

This paper cites Mapping Social Choice Theory to RLHF.

Power and Limitations of Aggregation in Compound AI Systems Mapping Social Choice Theory to RLHF

Reference 15

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Observation 804cf1d0-eda4-462d-8655-a5e98a52f41c · outbound

This paper cites Incentive Design with Spillovers.

Power and Limitations of Aggregation in Compound AI Systems Incentive Design with Spillovers

Reference 16

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Observation af239772-4bc4-4bcd-a614-8c3dda7e50d4 · outbound

This paper cites Ensemble methods in machine learning.

Power and Limitations of Aggregation in Compound AI Systems Ensemble methods in machine learning

Reference 17

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Observation 08c3fa7a-d1b1-47e8-90da-563d32f239df · outbound

This paper cites Impacts of aggregation on model diversity and consumer utility.

Power and Limitations of Aggregation in Compound AI Systems Impacts of aggregation on model diversity and consumer utility

Reference 18

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Observation 500b38bd-ae8b-4b0f-9617-d585275b73b9 · outbound

This paper cites Human-algorithm collaboration: Achieving complementarity and avoiding unfairness.

Power and Limitations of Aggregation in Compound AI Systems Human-algorithm collaboration: Achieving complementarity and avoiding unfairness

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T21:09:49.973020Z

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Observation 6ec484f6-5faf-41ad-b2f6-a73d0da325d2 · outbound

This paper cites Improving factuality and reasoning in language models through multiagent debate.

Power and Limitations of Aggregation in Compound AI Systems Improving factuality and reasoning in language models through multiagent debate

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T21:09:50.051137Z

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Observation abdc40ce-d23d-441e-bde2-c5c052ec4f2c · outbound

This paper cites A market for accuracy: Classification under competition.

Power and Limitations of Aggregation in Compound AI Systems A market for accuracy: Classification under competition

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-02T21:09:50.128531Z

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Observation edec1dfa-3996-46d4-aa50-c8b9c32fd205 · outbound

This paper cites Bayesian persuasion with multiple senders and rich signal spaces.

Power and Limitations of Aggregation in Compound AI Systems Bayesian persuasion with multiple senders and rich signal spaces

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-02T21:09:50.203845Z

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Observation 551547b7-8d69-4119-a622-2bb0160cf755 · outbound

This paper cites Distortion of AI Alignment: Does Preference Optimization Optimize for Preferences?.

Power and Limitations of Aggregation in Compound AI Systems Distortion of AI Alignment: Does Preference Optimization Optimize for Preferences?

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-02T21:09:50.286275Z

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Observation ec88ce40-6e5e-48bc-a141-e62b32b9a075 · outbound

This paper cites An analysis of the principal-agent problem.

Power and Limitations of Aggregation in Compound AI Systems An analysis of the principal-agent problem

Reference 24

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Observation 19a10269-01ef-402c-b7f6-e9ca35116f4d · outbound

This paper cites Moral hazard and observability.

Power and Limitations of Aggregation in Compound AI Systems Moral hazard and observability

Reference 25

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Observation e48d46e3-9882-47af-82fd-507b9f879d07 · outbound

This paper cites Moral hazard in teams.

Power and Limitations of Aggregation in Compound AI Systems Moral hazard in teams

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-02T21:09:50.518036Z

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Observation 2612ef70-e79a-48da-acaf-7b85fe4ebe7a · outbound

This paper cites Multitask principal--agent analyses: Incentive contracts, asset ownership, and job design.

Power and Limitations of Aggregation in Compound AI Systems Multitask principal--agent analyses: Incentive contracts, asset ownership, and job design

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-02T21:09:50.595290Z

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Observation df4977b1-02c4-4a08-99ff-1fbfd9c39a30 · outbound

This paper cites Strategic Classification With Externalities.

Power and Limitations of Aggregation in Compound AI Systems Strategic Classification With Externalities

Reference 28

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Observation c57a163a-458e-4056-bf74-d67ec3e89580 · outbound

This paper cites Is best-of-n the best of them? coverage, scaling, and optimality in inference-time alignment.

Power and Limitations of Aggregation in Compound AI Systems Is best-of-n the best of them? coverage, scaling, and optimality in inference-time alignment

Reference 29

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Observation 66a61a4a-7768-4f2d-aa5a-19875a9786ed · outbound

This paper cites The Consensus Game: Language Model Generation via Equilibrium Search.

Power and Limitations of Aggregation in Compound AI Systems The Consensus Game: Language Model Generation via Equilibrium Search

Reference 30

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Observation 265bc117-f345-4d59-819a-e1db7d12e6b0 · outbound

This paper cites Improved bayes risk can yield reduced social welfare under competition.

Power and Limitations of Aggregation in Compound AI Systems Improved bayes risk can yield reduced social welfare under competition

Reference 31

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Observation 58ed631d-e57e-48a2-b9c2-086b6ceb355a · outbound

This paper cites Adversaries can misuse combinations of safe models.

Power and Limitations of Aggregation in Compound AI Systems Adversaries can misuse combinations of safe models

Reference 32

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Observation 8494f277-f18c-46fd-aeee-52f65d684c6b · outbound

This paper cites Uncovering gaps in how humans and llms interpret subjective language.

Power and Limitations of Aggregation in Compound AI Systems Uncovering gaps in how humans and llms interpret subjective language

Reference 33

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Observation df655c6e-75a6-44d3-85f8-87b75e2b70b5 · outbound

This paper cites Debating with more persuasive llms leads to more truthful answers.

Power and Limitations of Aggregation in Compound AI Systems Debating with more persuasive llms leads to more truthful answers

Reference 34

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Observation f1ff9683-0cab-4d62-a025-c6ac019066a5 · outbound

This paper cites How do classifiers induce agents to invest effort strategically? ACM Transactions on Economics and Computation (TEAC), 8 0 (4): 0 1--23, 2020.

Power and Limitations of Aggregation in Compound AI Systems How do classifiers induce agents to invest effort strategically? ACM Transactions on Economics and Computation (TEAC), 8 0 (4): 0 1--23, 2020

Reference 35

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Observation c47f5f43-eb4a-444f-928a-ee502163e86f · outbound

This paper cites The condorcet jury theorem, free speech, and correlated votes.

Power and Limitations of Aggregation in Compound AI Systems The condorcet jury theorem, free speech, and correlated votes

Reference 36

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Observation f3b18ed7-b5ba-475f-acda-a038cad91340 · outbound

This paper cites The Theory of Incentives: The Principal-Agent Model.

Power and Limitations of Aggregation in Compound AI Systems The Theory of Incentives: The Principal-Agent Model

Reference 37

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Observation 8bb7fc85-4d88-4839-af0d-d75954ab98fd · outbound

This paper cites Rank-order tournaments as optimum labor contracts.

Power and Limitations of Aggregation in Compound AI Systems Rank-order tournaments as optimum labor contracts

Reference 38

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Observation 67cd8815-4750-4810-be36-bf444c00fe7a · outbound

This paper cites Alpacaeval: An automatic evaluator of instruction-following models, 2023.

Power and Limitations of Aggregation in Compound AI Systems Alpacaeval: An automatic evaluator of instruction-following models, 2023

Reference 39

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Observation 18d39a60-88ae-4196-98ce-ca6f73a46281 · outbound

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Power and Limitations of Aggregation in Compound AI Systems Strategic ranking

Reference 40

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Observation c01e027e-f63d-4b5a-883c-c563b0b0010b · outbound

This paper cites Recast: Strengthening llms' complex instruction following with constraint-verifiable data.

Power and Limitations of Aggregation in Compound AI Systems Recast: Strengthening llms' complex instruction following with constraint-verifiable data

Reference 41

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Observation b5b3e28d-af30-43d2-98ad-40393f0d1c84 · outbound

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Power and Limitations of Aggregation in Compound AI Systems Distributed algorithms

Reference 42

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Observation ec057275-683f-4603-af67-69543d0ef6c1 · outbound

This paper cites AI Alignment and Social Choice: Fundamental Limitations and Policy Implications.

Power and Limitations of Aggregation in Compound AI Systems AI Alignment and Social Choice: Fundamental Limitations and Policy Implications

Reference 43

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Observation 6b87ff97-c7c3-4674-87ff-49ea38692f30 · outbound

This paper cites Training language models to follow instructions with human feedback.

Power and Limitations of Aggregation in Compound AI Systems Training language models to follow instructions with human feedback

Reference 44

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Observation 4104c5c6-ef1b-4a7e-b8e1-4bf73a607627 · outbound

This paper cites Citeme: Can language models accurately cite scientific claims? Advances in Neural Information Processing Systems, 37: 0 7847--7877, 2024.

Power and Limitations of Aggregation in Compound AI Systems Citeme: Can language models accurately cite scientific claims? Advances in Neural Information Processing Systems, 37: 0 7847--7877, 2024

Reference 45

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Observation 1960e949-5356-45bf-b602-d17cfe94ae35 · outbound

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Power and Limitations of Aggregation in Compound AI Systems Competition and Diversity in Generative AI

Reference 46

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Observation 26a35d66-5010-4a60-84cf-e9b242532877 · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

Power and Limitations of Aggregation in Compound AI Systems Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 47

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Observation 02e28d33-848f-49b7-85f4-a804264969da · outbound

This paper cites Direct Alignment with Heterogeneous Preferences.

Power and Limitations of Aggregation in Compound AI Systems Direct Alignment with Heterogeneous Preferences

Reference 48

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Observation df02ea31-9c72-4cc9-aa88-f4c37f7eec81 · outbound

This paper cites A Mathematical Abstraction for Balancing the Trade-off Between Creativity and Reality in Large Language Models.

Power and Limitations of Aggregation in Compound AI Systems A Mathematical Abstraction for Balancing the Trade-off Between Creativity and Reality in Large Language Models

Reference 49

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Observation 225c2efc-0a5b-4da2-9b36-15e995167063 · outbound

This paper cites Multitask agency and contract choice: An empirical exploration.

Power and Limitations of Aggregation in Compound AI Systems Multitask agency and contract choice: An empirical exploration

Reference 50

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Observation 56eb09af-4ce6-45d8-9c53-b8dd38d8087c · outbound

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Power and Limitations of Aggregation in Compound AI Systems Multi-agent reinforcement learning: Independent vs

Reference 51

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Observation 7c5b7a25-fd14-4905-9cc4-c1f3b8fbcc4a · outbound

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Power and Limitations of Aggregation in Compound AI Systems SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models

Reference 52

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Observation b2abef6c-e5dc-4d64-8599-89f7ee421f2c · outbound

This paper cites Self-consistency improves chain of thought reasoning in language models.

Power and Limitations of Aggregation in Compound AI Systems Self-consistency improves chain of thought reasoning in language models

Reference 53

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Observation c2d0732e-488f-4631-9300-a6ec474658de · outbound

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Power and Limitations of Aggregation in Compound AI Systems Benchmarking complex instruction-following with multiple constraints composition

Reference 54

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Observation b0b28776-18b7-4624-a61e-1a53f048d2ec · outbound

This paper cites Heterogeneous Data Game: Characterizing the Model Competition Across Multiple Data Sources.

Power and Limitations of Aggregation in Compound AI Systems Heterogeneous Data Game: Characterizing the Model Competition Across Multiple Data Sources

Reference 55

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Observation 8b442a11-c518-45c6-a73f-2e8ebfb48ed9 · outbound

This paper cites What Prompts Don't Say: Understanding and Managing Underspecification in LLM Prompts.

Power and Limitations of Aggregation in Compound AI Systems What Prompts Don't Say: Understanding and Managing Underspecification in LLM Prompts

Reference 56

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Observation 7c43beee-49cc-49bd-bc0e-d96b2c7d46cb · outbound

This paper cites Cot-based synthesizer: Enhancing llm performance through answer synthesis, 2025.

Power and Limitations of Aggregation in Compound AI Systems Cot-based synthesizer: Enhancing llm performance through answer synthesis, 2025

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-02T21:09:53.777602Z

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Observation cb084f28-d2d3-43ca-9080-f141ee79517a · outbound

This paper cites Consequences of misaligned ai.

Power and Limitations of Aggregation in Compound AI Systems Consequences of misaligned ai

Reference 58

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Pith citing papers

Observation 75310491-50ba-4ac7-bc57-4ea0a4de647c · inbound

The Cost of Consensus: Isolated Self-Correction Prevails Over Unguided Homogeneous Multi-Agent Debate cites this paper.

The Cost of Consensus: Isolated Self-Correction Prevails Over Unguided Homogeneous Multi-Agent Debate Power and Limitations of Aggregation in Compound AI Systems

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-07-09T02:19:51.091671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.