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Paper Citation Record · LEDGER

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory

As of 9 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 3 inbound Pith citation observations for arXiv:2506.12350.

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

pith.paper-citation-record.v1
2506.12350 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T01:04:33.188878Z

measured 34 of 34 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T22:50:33.214346Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T06:49:37.712152Z

Reference resolution

31 of 31 outbound references displayed

  • verified exact1
  • verified fuzzy7
  • unresolved22
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

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Outbound references

Observation 5b11d9d1-86ef-42c9-8247-4415029ad08c · outbound

This paper cites Therefore, a preference matching distribution exists.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Therefore, a preference matching distribution exists

Reference 1

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

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Observation d6efb15f-2b56-4a38-b874-2a29f44bad6b · outbound

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

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback

Reference 4

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source=pdf_text observed=2026-08-07T01:04:30.630057Z digest=sha256:73473029d198e79e120a2af18e5d3a260f7677bf10a44820f9373afb9de8679d

Observation a701d99e-19f1-4c20-b602-d115cabc1f85 · outbound

This paper cites Mapping Social Choice Theory to RLHF.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Mapping Social Choice Theory to RLHF

Reference 5

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source=pdf_text observed=2026-08-07T01:04:30.717742Z digest=sha256:a29ffc566bc35059771f18a0081f4f2925029f9f1ad858d944d8951f7b86ac48

Observation 37cfa10b-53f2-4d10-807e-fffd6a485e01 · outbound

This paper cites Policy Optimization in RLHF: The Impact of Out-of-preference Data.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Policy Optimization in RLHF: The Impact of Out-of-preference Data

Reference 8

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source=pdf_text observed=2026-08-07T01:04:31.048119Z digest=sha256:824fe2977bb04059120bcae3f07ca11385ded2bcdc3bc40fd3832ff22510168b

Observation e1530569-83dd-4e7b-9891-ac9968612789 · outbound

This paper cites Jackpot! Alignment as a Maximal Lottery.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Jackpot! Alignment as a Maximal Lottery

Reference 10

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source=pdf_text observed=2026-08-07T01:04:31.269934Z digest=sha256:752e51578b523ddcb53abcb42a164e670fa9aaa98e719b1a016a79a867ad1e89

Observation f056459f-c02d-4a49-91fb-13dadd438e7c · outbound

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

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory AI Alignment and Social Choice: Fundamental Limitations and Policy Implications

Reference 11

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source=pdf_text observed=2026-08-07T01:04:31.412637Z digest=sha256:e4fad2f048fed866c8618eb5d626bbe8d55f91c4f398498ab38db7b313d4187d

Observation af2407b8-2d5e-41c8-a244-5ddb9e26359a · outbound

This paper cites Nash Learning from Human Feedback.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Nash Learning from Human Feedback

Reference 12

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source=pdf_text observed=2026-08-07T01:04:31.531656Z digest=sha256:f6038b32c516fb4d5cb6ced73c49065c66a7cadcff67aae4bbb367c45ae41fbd

Observation 88259934-527d-46dc-b40b-0a6c150812d2 · outbound

This paper cites RLHF from Heterogeneous Feedback via Personalization and Preference Aggregation.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory RLHF from Heterogeneous Feedback via Personalization and Preference Aggregation

Reference 14

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source=pdf_text observed=2026-08-07T01:04:31.735488Z digest=sha256:f90db0449b4c87bf2831fad15a7ace56672dac54ecd5657258b421b090d18128

Observation d60b0dca-4b57-46e9-82f0-746fb3ec9b80 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Proximal Policy Optimization Algorithms

Reference 15

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source=pdf_text observed=2026-08-07T01:04:31.830315Z digest=sha256:b57ac70ccb686fb882d0c9cb4543d29f7c16541f8ed10c5c2e7a9f46fe9bd30f

Observation 699e2fc7-0744-4fe0-9bf4-189127a6bc72 · outbound

This paper cites Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF

Reference 17

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source=pdf_text observed=2026-08-07T01:04:32.028901Z digest=sha256:1af433eacdaa6ccb63025124607d4341e9307044f73cdaa34f52eda421cbf04a

Observation 92b86db4-ffbe-4594-be5f-5f1bfa313a7d · outbound

This paper cites Understanding the performance gap between online and offline alignment algorithms.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Understanding the performance gap between online and offline alignment algorithms

Reference 18

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source=pdf_text observed=2026-08-07T01:04:32.093625Z digest=sha256:5b83ca6b06bb033e1856d43ec142f8a01bb953db4b04e7b46e9b85bac10ea49c

Observation f2767bef-1860-470f-b3ee-a9578e512d76 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Gemini: A Family of Highly Capable Multimodal Models

Reference 19

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source=pdf_text observed=2026-08-07T01:04:32.208926Z digest=sha256:adcd45810f8a7a0a749fa30ec2f40931ca876e96832c1ac1bffd12d4e262015d

Observation e894c17b-8779-4d88-b5c3-7ae66709f369 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory LLaMA: Open and Efficient Foundation Language Models

Reference 20

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Observation c8b38568-b696-4339-b24e-92ad0dda7007 · outbound

This paper cites Zhilin Wang, Yi Dong, Jiaqi Zeng, Virginia Adams, Makesh Narsimhan Sreedhar, Daniel Egert, Olivier Delalleau, Jane Scowcroft, Neel Kant, Aidan Swope, et al.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Zhilin Wang, Yi Dong, Jiaqi Zeng, Virginia Adams, Makesh Narsimhan Sreedhar, Daniel Egert, Olivier Delalleau, Jane Scowcroft, Neel Kant, Aidan Swope, et al

Reference 21

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source=pdf_text observed=2026-08-07T01:04:32.350165Z digest=sha256:defb7b4368ff763008c2c5b974e55a887760397560d1aad18bacc23b4d06df82

Observation 5f3c4032-9f7c-458e-964b-d3b0f333e6ba · outbound

This paper cites On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization

Reference 22

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source=pdf_text observed=2026-08-07T01:04:32.454213Z digest=sha256:cb9beb0d7a47dd60122f3df2ddb827c5186c77bdcbb3affe47d3187f83303a5c

Observation 7a2cbe65-6812-4d08-8b31-c3678f0730a1 · outbound

This paper cites Restoring calibration for aligned large language models: A calibration-aware fine-tuning approach.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Restoring calibration for aligned large language models: A calibration-aware fine-tuning approach

Reference 23

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source=pdf_text observed=2026-08-07T01:04:32.522528Z digest=sha256:df4739e3516b262be380113876f835eef27901039af23bb3bd17fccccf1a5e23

Observation 7defe16f-f701-4713-bf0f-45a0706a7933 · outbound

This paper cites Iterative preference learning from human feedback: Bridging theory and practice for rlhf under kl- constraint.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Iterative preference learning from human feedback: Bridging theory and practice for rlhf under kl- constraint

Reference 24

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

source=pdf_text observed=2026-08-07T01:04:32.636118Z digest=sha256:8aa4ad25494b04427b278f931f72b99b5116c78f0e09dcf47595a4cd99b7ff76

Observation b33ced5a-b656-45c1-8310-f2c9ba8bd1ee · outbound

This paper cites Asymptotics of language model alignment.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Asymptotics of language model alignment

Reference 25

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

source=pdf_text observed=2026-08-07T01:04:32.741501Z digest=sha256:a71bad5e8c3863f9eab3764a46fcd2cc2255d91ba03ed47a824b7acd5c9ba874

Observation 3fcb66fe-0fc2-42c9-a6cf-fd8b5072d6a7 · outbound

This paper cites Provable Multi-Party Reinforcement Learning with Diverse Human Feedback.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Provable Multi-Party Reinforcement Learning with Diverse Human Feedback

Reference 26

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source=pdf_text observed=2026-08-07T01:04:32.828481Z digest=sha256:d02597d8259aa5b3ab38bf6d54ce556f299a71e65a93958a93501e1db28f3eed

Observation c87aacea-1141-4d61-b508-6f6cba18e980 · outbound

This paper cites an unresolved cited work.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Unresolved cited work

Reference 27

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source=pdf_text observed=2026-08-07T01:04:32.907603Z digest=sha256:0463c0237b3cae3009b2f39347fade6a33eed5fc161d3d5a4194ea3506b68cd4

Observation 6ff25476-08d1-443b-8c00-679588b028d9 · outbound

This paper cites The work by Tang et al.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory The work by Tang et al

Reference 29

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source=pdf_text observed=2026-08-07T01:04:33.030370Z digest=sha256:32bf51bcb0a29770d0604ec165ca7f5398625bd474e89d3e76e21508c3f04299

Observation 861998dd-ec03-4cda-bb5b-b6c69066a382 · outbound

This paper cites Outside of fine-tuning, model editing has emerged as a complementary strategy to modify LLM behavior across tasks [Jin et al., 2025].

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Outside of fine-tuning, model editing has emerged as a complementary strategy to modify LLM behavior across tasks [Jin et al., 2025]

Reference 30

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Observation 7aa67290-0996-43c2-8634-c003f6d7a217 · outbound

This paper cites [2024], which constrain its robustness relative to reinforcement learning techniques like PPO.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory [2024], which constrain its robustness relative to reinforcement learning techniques like PPO

Reference 31

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Observation cad5854e-c103-4d4f-8d73-ebc7127bc9aa · outbound

This paper cites Learn Your Reference Model for Real Good Alignment.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Learn Your Reference Model for Real Good Alignment

Reference 2006

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source=pdf_text observed=2026-08-07T01:04:30.926721Z digest=sha256:968da187cfac83e6f82958248dc90fa71f5bd9f02a2b55aff7918ebfbe96cc14

Observation 1b4f020e-eabd-446e-9266-f8a868b6dc13 · outbound

This paper cites Learning Linear Utility Functions From Pairwise Comparison Queries.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Learning Linear Utility Functions From Pairwise Comparison Queries

Reference 2009

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local_arxiv, observed 2026-08-07T01:04:33.883031Z

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Observation 80dbad57-95b5-4a4b-a3b3-b85a5bc8976e · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 2016

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source=pdf_text observed=2026-08-07T01:04:30.242850Z digest=sha256:ed8c7a6e470396e13e2ae9bbecf19a7668d2e4ad7ed0d58e4dda4687419de3ab

Observation 5f332d57-5d8f-4179-ba1e-cbc868b75180 · outbound

This paper cites Fundamental Limits of Game-Theoretic LLM Alignment: Smith Consistency and Preference Matching.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Fundamental Limits of Game-Theoretic LLM Alignment: Smith Consistency and Preference Matching

Reference 2017

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local_arxiv, observed 2026-08-07T01:04:33.572321Z

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source=pdf_text observed=2026-08-07T01:04:31.958892Z digest=sha256:0021193739d82dae39a31f57f2330364bde722ed677761f7f81d7d71ac496a5c

Observation 0093612e-68fb-4bcc-999e-531b37b21540 · outbound

This paper cites GPT-4 Technical Report.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory GPT-4 Technical Report

Reference 2020

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source=pdf_text observed=2026-08-07T01:04:31.655495Z digest=sha256:9699d9a9ae8c18d8b7214b4998cee5960b0ef1538e3a8c61bda93ff6d560d085

Observation 074dd878-1664-4c9d-88dc-fa80b3d653da · outbound

This paper cites MaxMin-RLHF: Alignment with Diverse Human Preferences.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory MaxMin-RLHF: Alignment with Diverse Human Preferences

Reference 2023

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source=pdf_text observed=2026-08-07T01:04:30.349050Z digest=sha256:8fd85d39b52af2200012ebc7342c6e8b464635f07eefe8768a07e6893487308d

Observation 19322493-ba98-49d0-bc1a-954f3af1adf5 · outbound

This paper cites Dataset Reset Policy Optimization for RLHF.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Dataset Reset Policy Optimization for RLHF

Reference 2024

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source=pdf_text observed=2026-08-07T01:04:30.492218Z digest=sha256:d0db90d62ae276c44fdf6cea18e880342ff855a9d5e7b249c12df5e09eb57c70

Observation 8a24506e-5f96-4b25-8720-621acdde4d0f · outbound

This paper cites Statistical Impossibility and Possibility of Aligning LLMs with Human Preferences: From Condorcet Paradox to Nash Equilibrium.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Statistical Impossibility and Possibility of Aligning LLMs with Human Preferences: From Condorcet Paradox to Nash Equilibrium

Reference 2025

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Pith citing papers

Observation 5deb5269-7ec9-4442-b42c-f9b9117dcbea · inbound

Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers cites this paper.

Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory

Reference 151

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source=pdf_text observed=2026-08-07T22:50:33.214346Z digest=sha256:596e8e0ce1d2a2b18b7c07f163ef2eac0ea427c8dff3cc6a735f97a88c6143ed

Observation 381d7141-7902-4833-9a51-9c632f59e13b · inbound

Transitivity in Inhomogeneous Random Tournaments cites this paper.

Transitivity in Inhomogeneous Random Tournaments Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory

Reference 51

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arxiv_id, observed 2026-07-02T01:06:24.252873Z

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source=pdf_text observed=2026-06-28T12:45:56.736538Z digest=sha256:e05fe8771e00a256408b8b5ffbb574a67dd75636fe963d88449e7beeb91451b3

Observation c78663c3-2f78-44a3-b449-3e9eaf0d3262 · inbound

AI Alignment From Social Choice Perspectives cites this paper.

AI Alignment From Social Choice Perspectives Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory

Reference 81

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arxiv_id, observed 2026-07-04T06:49:37.714149Z

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

source=pdf_text observed=2026-06-26T14:12:36.892697Z digest=sha256:605ecaca746ea34fe2f3a83d7bcc896b87bc2ea6fd7c92b1122b0256fb495d50