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Paper Citation Record · LEDGER

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation

As of 20 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 0 inbound Pith citation observations for arXiv:2509.05605.

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

pith.paper-citation-record.v1
2509.05605 v1

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:26:28.333393Z

measured 62 of 62 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

62 of 62 outbound references displayed

  • verified exact0
  • verified fuzzy1
  • unresolved61
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 66cf6ac5-e364-4eaf-bedd-224fbf9a02b9 · outbound

This paper cites GPT-4 Technical Report.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation GPT-4 Technical Report

Reference 1

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source=arxiv_source observed=2026-08-15T16:26:28.077854Z digest=sha256:90bcca1f4abf3eaa48e18393a1cfec78a14f0524daa4394cdea0f7949de10f0a

Observation cdf17676-2052-4615-a015-06fcc0696441 · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 2

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source=arxiv_source observed=2026-08-15T16:26:28.082915Z digest=sha256:1055b2378b271c6883316f58b81c55ef96021b42d070f74e9f4d9f2d17395ccb

Observation 2e670908-0928-4ef4-acfc-9d51d7257c25 · outbound

This paper cites an unresolved cited work.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Unresolved cited work

Reference 3

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source=arxiv_source observed=2026-08-15T16:26:28.087997Z digest=sha256:7bb390b7c3829857ad658a68208376d1326e433b09d36910890c6ec1b2a532c1

Observation 9184842c-6ea2-4e13-baf6-0a9d6477f4c8 · outbound

This paper cites an unresolved cited work.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Unresolved cited work

Reference 4

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source=arxiv_source observed=2026-08-15T16:26:28.092293Z digest=sha256:7b2707ddd3c7e6b64efdeabdac5ed55c7f77908904e3db45ca6c1fe48a699a17

Observation 9330844c-8772-43f5-9c0c-a05bf4dabdbd · outbound

This paper cites Language Models are Few-Shot Learners.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Language Models are Few-Shot Learners

Reference 5

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source=arxiv_source observed=2026-08-15T16:26:28.096652Z digest=sha256:facd20ba83b52a13f8d270dace76d14ee9e5918d4477c415f551cdf0701042cd

Observation e5850e99-6e4a-4aef-a3eb-123cdc7cbed8 · outbound

This paper cites an unresolved cited work.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Unresolved cited work

Reference 6

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source=arxiv_source observed=2026-08-15T16:26:28.100887Z digest=sha256:af6aab04f56493848a6d79d0aab634d6cdcc3dfc7767e9d0cf88ebb95dfab406

Observation 1dcc3079-23e6-4eed-bbdc-6585e841c9cc · outbound

This paper cites Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

Reference 7

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source=arxiv_source observed=2026-08-15T16:26:28.105415Z digest=sha256:9cdf21b02eb2b295d3fbe7a8fdecea2905b492e777be052669b1b6e044864dd2

Observation ad62c2ad-16cb-4875-87c4-a525c8d70a95 · outbound

This paper cites SPaR: Self-Play with Tree-Search Refinement to Improve Instruction-Following in Large Language Models.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation SPaR: Self-Play with Tree-Search Refinement to Improve Instruction-Following in Large Language Models

Reference 8

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source=arxiv_source observed=2026-08-15T16:26:28.109702Z digest=sha256:609c6b1a9398b99713ff6cf70bcf95084f0c4e1b9d36f611b28c0417260e44b5

Observation 529fc7e9-1908-40e0-b5e4-88a4a1c9c449 · outbound

This paper cites Glass, and Pengcheng He.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Glass, and Pengcheng He

Reference 9

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source=arxiv_source observed=2026-08-15T16:26:28.113882Z digest=sha256:08b5e1037e13f2ebbf21ab0b7d1a68855c65fc95fafecd168cbbdafb5212e60c

Observation 28129f54-96d9-464c-bd28-57364f2a0450 · outbound

This paper cites UltraFeedback: Boosting Language Models with Scaled AI Feedback.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation UltraFeedback: Boosting Language Models with Scaled AI Feedback

Reference 10

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source=arxiv_source observed=2026-08-15T16:26:28.117962Z digest=sha256:a64cf5703c7b6ae0bce73b7a5a0afbcb08bfcc9ee8287cc2011de7f65d1bb49a

Observation 0dc989de-5001-4318-afe4-57189e6dcc74 · outbound

This paper cites Enhancing Chat Language Models by Scaling High-quality Instructional Conversations.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Enhancing Chat Language Models by Scaling High-quality Instructional Conversations

Reference 11

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source=arxiv_source observed=2026-08-15T16:26:28.122407Z digest=sha256:7523cde247737ab4082ae3510f26631ce27ad9efca86a32e8f666d4df4cd4932

Observation c0129569-0134-4805-94b0-874a94cc9bc1 · outbound

This paper cites Unleashing LLM Reasoning Capability via Scalable Question Synthesis from Scratch.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Unleashing LLM Reasoning Capability via Scalable Question Synthesis from Scratch

Reference 12

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source=arxiv_source observed=2026-08-15T16:26:28.126677Z digest=sha256:9fac009e58d9e59ad9fc80a7888fccf1db781f9e4642bea6c4176875996ab5be

Observation 827526c0-ccb4-4d65-8c1c-fd3bc4a72070 · outbound

This paper cites Self-Boosting Large Language Models with Synthetic Preference Data.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Self-Boosting Large Language Models with Synthetic Preference Data

Reference 13

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source=arxiv_source observed=2026-08-15T16:26:28.130817Z digest=sha256:bf6abbb2f5e49ddce9d1116aac7926c1189a50a5a3a7afb9b98dfb2868a50f98

Observation b9e25239-6e6b-4202-b0ba-8344602acddb · outbound

This paper cites Legend: Leveraging Representation Engineering to Annotate Safety Margin for Preference Datasets.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Legend: Leveraging Representation Engineering to Annotate Safety Margin for Preference Datasets

Reference 14

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source=arxiv_source observed=2026-08-15T16:26:28.135081Z digest=sha256:6707254794339385975c8604ef45f5fa2bc85d198d977b83eed2d84a25e37b7b

Observation 05120fad-7583-4620-a656-2e67b7148c5a · outbound

This paper cites Textbooks Are All You Need.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Textbooks Are All You Need

Reference 15

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source=arxiv_source observed=2026-08-15T16:26:28.139214Z digest=sha256:7f6a2f0424a4b59f311dfda62008bfa72722222c592df6032e10c44977fde68c

Observation 025ab5cd-d755-454f-9bdf-20fa5f48a748 · outbound

This paper cites an unresolved cited work.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Unresolved cited work

Reference 16

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-15T16:26:28.143419Z digest=sha256:465212e57ae9e18e87b1a11145346e5bf56bfd9e41ec1aa673d264741e31f4f2

Observation c5f22c5b-81ff-457a-b9e9-8f127504e2d2 · outbound

This paper cites Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal

Reference 17

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source=arxiv_source observed=2026-08-15T16:26:28.147335Z digest=sha256:e2bfe9c5aa4495a31745e1f4c84267ba856fc9a6935326aecdd0a51acaccf9ee

Observation 9c7c6a5c-d27d-4e3a-af12-1118a9374e9c · outbound

This paper cites Editing Models with Task Arithmetic.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Editing Models with Task Arithmetic

Reference 18

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source=arxiv_source observed=2026-08-15T16:26:28.151613Z digest=sha256:4d2049aa2fe8359eb77e82145606a0e860a0acb0f1b77fc20c02417a4932fab6

Observation b39543e8-506b-4790-9fcd-20abcb4732c3 · outbound

This paper cites Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2

Reference 19

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source=arxiv_source observed=2026-08-15T16:26:28.155957Z digest=sha256:a66bbcf735ebf770d5ba6f78f7396949b4d4dfa71474c074d627f0da6a53bfc6

Observation ed2b16a6-e50d-4d97-8481-959b12427a30 · outbound

This paper cites Aligner: Efficient Alignment by Learning to Correct.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Aligner: Efficient Alignment by Learning to Correct

Reference 20

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source=arxiv_source observed=2026-08-15T16:26:28.160021Z digest=sha256:6d7a20413972e2ed9adacb3b3056885d0122a04d5c7c2ca111519e2d2b028f2a

Observation 1e2647a7-ae0f-4d91-ab5a-4fddeb433b5a · outbound

This paper cites Spread Preference Annotation: Direct Preference Judgment for Efficient LLM Alignment.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Spread Preference Annotation: Direct Preference Judgment for Efficient LLM Alignment

Reference 21

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source=arxiv_source observed=2026-08-15T16:26:28.164132Z digest=sha256:4df91897fc94a003432f050326f4b2d9cae79d7f0f87180ef1035ffee32cafcb

Observation 38c95821-9801-48c7-af24-261acf5f0d59 · outbound

This paper cites Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models

Reference 22

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source=arxiv_source observed=2026-08-15T16:26:28.168263Z digest=sha256:8d04c856f8811b4dfcf93b0efa206cdb5cb656ce4660bb68af3fd727285eea8e

Observation 01f9cf6c-beaf-49f1-893e-f12a54b3f944 · outbound

This paper cites an unresolved cited work.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Unresolved cited work

Reference 23

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-15T16:26:28.172421Z digest=sha256:8b692a26ceed7c3fc3a10eb3d1ac7f65c0a9ac2969d36c5dd2d2551a98a6b6e3

Observation 1a10f64c-d628-43d3-87a3-ac3292aef1bc · outbound

This paper cites Hashimoto.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Hashimoto

Reference 24

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source=arxiv_source observed=2026-08-15T16:26:28.176615Z digest=sha256:66950df0e339ca19253621cadd671460b71aebeb07554e3d9b8e123f461dc09a

Observation 27ea4841-40c7-48ee-9621-fced0599f39e · outbound

This paper cites Holistic Evaluation of Language Models.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Holistic Evaluation of Language Models

Reference 25

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source=arxiv_source observed=2026-08-15T16:26:28.180554Z digest=sha256:00af9ef67a7fc0c264579e50cf4c58889250f50c531d05dd8b211baaf54ef879

Observation f92d8037-876b-4bcd-8982-03d9791a4c34 · outbound

This paper cites CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Control.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Control

Reference 26

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source=arxiv_source observed=2026-08-15T16:26:28.184749Z digest=sha256:a1c0abf185126225848bd7fe573ce9cecf76c56c3109291d6cc7e1490684b779

Observation f4b40501-cf7f-4db8-979f-6c6a494664bc · outbound

This paper cites Aligning Large Language Models with Human Preferences through Representation Engineering.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Aligning Large Language Models with Human Preferences through Representation Engineering

Reference 27

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source=arxiv_source observed=2026-08-15T16:26:28.189157Z digest=sha256:41bd065f5fefeab212d01e2338830845619a5cc1b0103d2e50d845128a6e1810

Observation 5a2be0e1-423f-48a9-8a67-8811483321bd · outbound

This paper cites an unresolved cited work.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Unresolved cited work

Reference 28

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-15T16:26:28.193270Z digest=sha256:dcc434fdff2f73b5adb8b31219629b1920548ca5dfc0b64d1c41a50a4fb46da4

Observation 844276d1-41a8-416a-9280-a9e2de82f034 · outbound

This paper cites SimPO: Simple Preference Optimization with a Reference-Free Reward.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation SimPO: Simple Preference Optimization with a Reference-Free Reward

Reference 29

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Observation 9e386091-3e8b-4de5-b2e5-dfc5a0ea95d6 · outbound

This paper cites WebGPT: Browser-assisted question-answering with human feedback.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation WebGPT: Browser-assisted question-answering with human feedback

Reference 30

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source=arxiv_source observed=2026-08-15T16:26:28.201235Z digest=sha256:84b77b2345713c796582d62e44222b59324d21d70eb917e10ecbd06eb949df02

Observation 53f79934-eba7-4cef-abcb-20f16e597f32 · outbound

This paper cites an unresolved cited work.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Unresolved cited work

Reference 31

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Observation 0acd3a7c-3d69-4ce9-97af-0d078c6a4ce0 · outbound

This paper cites an unresolved cited work.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Unresolved cited work

Reference 32

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source=arxiv_source observed=2026-08-15T16:26:28.209141Z digest=sha256:7236dbe8ecac89659c14b99d4523a12be9553ac1478b52480f537e8d90a9e687

Observation dbbe9980-6246-467f-a867-cbbd0eec6d06 · outbound

This paper cites an unresolved cited work.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Unresolved cited work

Reference 33

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source=arxiv_source observed=2026-08-15T16:26:28.213160Z digest=sha256:3d6656bda6a7d7de3057035d9e8202bd1d832f28224de6ca3fb0fb64d1f149d9

Observation 91ff14f0-7105-44d0-8e0e-58382c039e89 · outbound

This paper cites an unresolved cited work.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Unresolved cited work

Reference 34

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source=arxiv_source observed=2026-08-15T16:26:28.217067Z digest=sha256:2ba20bb3faa80f3f8cb627bf890ba030e736a1d93faa01f038e03c96cd83098e

Observation c26ac2cc-cf58-4694-ad3b-9b683f6bac3b · outbound

This paper cites an unresolved cited work.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Unresolved cited work

Reference 35

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-15T16:26:28.222201Z digest=sha256:e1d69806269ab574fa2edfa67d4fff103ea0b0ba3ca473ddf54e9c5f49865da1

Observation 1c2c299d-2685-4036-902d-be10934c3d51 · outbound

This paper cites an unresolved cited work.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Unresolved cited work

Reference 36

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source=arxiv_source observed=2026-08-15T16:26:28.226231Z digest=sha256:359e282d18d78e947e069cfaad659a3bb9987c66ea15c155b97c3807ebcb8fd5

Observation f29b6e90-467a-4a53-94cd-d3e8faa8a9f9 · outbound

This paper cites Transformer Layers as Painters.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Transformer Layers as Painters

Reference 37

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Observation f94abd80-195a-4736-9a48-d3e717cd2a93 · outbound

This paper cites an unresolved cited work.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Unresolved cited work

Reference 38

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source=arxiv_source observed=2026-08-15T16:26:28.234349Z digest=sha256:591efd3d013f0a664e57f0c91bd33b3c9a2f67b7201bb6890cc5c69f2333f47e

Observation cc5a5e99-64cb-419f-8ae3-1f2d316e3d4d · outbound

This paper cites Hashimoto.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Hashimoto

Reference 39

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source=arxiv_source observed=2026-08-15T16:26:28.238364Z digest=sha256:2479ec42a9f0db764a40453f459e19fd16318cdbab60daaf95a5c5285971605f

Observation f699c2c4-ce71-466b-bd0f-67b962e34a02 · outbound

This paper cites an unresolved cited work.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Unresolved cited work

Reference 40

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source=arxiv_source observed=2026-08-15T16:26:28.242221Z digest=sha256:7caa4373c9579d46a4e845390f7fe4d01d628edabcbab5597664f045063f2f79

Observation 52d026ba-6fd6-4e0e-85b7-70e56c2621a5 · outbound

This paper cites Daniel Freeman, Theodore R.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Daniel Freeman, Theodore R

Reference 41

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raw_fallback, observed 2026-08-15T16:26:28.986769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-15T16:26:28.246014Z digest=sha256:f381bd22e18a1862b7726ed062974ef724fae63c0feadff1bd5b3b10a4d88067

Observation 31044c9b-c5f4-4420-a51a-5e7b59f8bb1e · outbound

This paper cites Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing

Reference 42

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source=arxiv_source observed=2026-08-15T16:26:28.250013Z digest=sha256:d13693e254ac49b8a4398f4b4617b9b534c2fa051596818d8f8abbe2f8c88e96

Observation 71a8e492-c989-48f9-bf03-c6ee3ae064ab · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 43

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source=arxiv_source observed=2026-08-15T16:26:28.254256Z digest=sha256:bd3150e39a8ace4bd7d32f1d94ac1711955bbb5313d3a2d1604b9ec7de66f33a

Observation 3abe4705-e3ba-4589-bf49-2191e774d1ae · outbound

This paper cites Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts

Reference 44

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-15T16:26:28.258543Z digest=sha256:2302c4d08fd3313a0694101b40e7e31de48469f5f69a206a6efa105392afa797

Observation f29bbc14-9b95-48e3-94d1-d20bc83f40fe · outbound

This paper cites Improving Text Embeddings with Large Language Models.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Improving Text Embeddings with Large Language Models

Reference 45

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source=arxiv_source observed=2026-08-15T16:26:28.262513Z digest=sha256:32af94c61012489933613548f89ca54aa814f42deac64a7c8ece2f036557345b

Observation 86556b56-1481-4737-939c-a1ab376e9b8a · outbound

This paper cites Self-Taught Evaluators.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Self-Taught Evaluators

Reference 46

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source=arxiv_source observed=2026-08-15T16:26:28.266680Z digest=sha256:f1453112e39879a2af5bd819566d14728be8212d426610aa443b2b91297a7fa3

Observation 5ac8f2ec-9ef7-4302-8c30-8edbec086fba · outbound

This paper cites an unresolved cited work.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Unresolved cited work

Reference 47

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-15T16:26:28.270685Z digest=sha256:4ea3b0cfec3d76c1b59058dda39567da10f297cb54f5b72171ed9a90ae6b5d9d

Observation 2a9ad9c1-9123-4fe3-a4c9-e469c242c1f5 · outbound

This paper cites Ethical and social risks of harm from Language Models.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Ethical and social risks of harm from Language Models

Reference 48

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source=arxiv_source observed=2026-08-15T16:26:28.274599Z digest=sha256:eb50186d70fff5eec20355a14d52f75d92822be4170685a703dc40e9b48a26bf

Observation f4b0f4fa-b604-42a8-98eb-15ad11b18e6b · outbound

This paper cites Language Models Learn to Mislead Humans via RLHF.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Language Models Learn to Mislead Humans via RLHF

Reference 49

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source=arxiv_source observed=2026-08-15T16:26:28.278753Z digest=sha256:29382028767e927de47dd6540f09c69e889a86b95afd701813e48959fc7eb506

Observation 0a9c9413-3d3a-4448-958a-096170e80aeb · outbound

This paper cites Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge

Reference 50

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no resolver link, observed 2026-08-15T16:26:28.282926Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-15T16:26:28.282926Z digest=sha256:ce628530eef7b6c40fc255c2a08f8b1f1b73eabe6a2e3ddea1b5d70b2dcd4abf

Observation 72ade002-bab1-40f0-92c5-d8d95c574586 · outbound

This paper cites WizardLM: Empowering large pre-trained language models to follow complex instructions.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation WizardLM: Empowering large pre-trained language models to follow complex instructions

Reference 51

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source=arxiv_source observed=2026-08-15T16:26:28.287446Z digest=sha256:4896ce1e3dda68131e1cac5ea17bc814436a90b4b2f059936a973a003774cc7c

Observation b8efebee-8cf5-444f-82b4-ec0e0de00563 · outbound

This paper cites Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing

Reference 52

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source=arxiv_source observed=2026-08-15T16:26:28.291535Z digest=sha256:393f31aabe0f55fa06264318e6eef15b9e5c907c09624d51291992f017ccde92

Observation 081e16bc-34c9-4643-bd6c-44f61643b2e9 · outbound

This paper cites Rethinking Benchmark and Contamination for Language Models with Rephrased Samples.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Rethinking Benchmark and Contamination for Language Models with Rephrased Samples

Reference 53

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source=arxiv_source observed=2026-08-15T16:26:28.295748Z digest=sha256:2a2a9d31c283c6552becdb2d5043c1cf1b5523ea66c9a1d3240a506e313a0c62

Observation cff26305-7447-474e-91a4-c5fda0a5799b · outbound

This paper cites Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning

Reference 54

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-15T16:26:28.299762Z digest=sha256:dca37d850d747fa9c4fcafed91392e5740672042f51263c2184537a49134517f

Observation 9e05a5bf-779d-4ae6-b276-58af7a81de00 · outbound

This paper cites an unresolved cited work.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Unresolved cited work

Reference 55

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T16:26:28.304020Z digest=sha256:3abcc5db301695ee76b2afa80e4184eeffe96b1300362b73d270c164cba0c271

Observation d7bd3656-d3c4-4d7e-bdca-adc3cee26df6 · outbound

This paper cites ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search

Reference 56

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no resolver link, observed 2026-08-15T16:26:28.307933Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T16:26:28.307933Z digest=sha256:05e4369230697cf6d0fbd4fa33761d769e675c91315d275b5295d6fba7bfe96f

Observation 84b31d12-c8d8-4a3f-807a-4bff65f89a7a · outbound

This paper cites TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space

Reference 57

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T16:26:28.312016Z digest=sha256:cd5fa8a7438fac750b3b356d0ec4ff4e65223efb7c65f6499a741947404b5d8f

Observation 3f29823c-e246-4938-abf1-fe746c40fe39 · outbound

This paper cites an unresolved cited work.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Unresolved cited work

Reference 58

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T16:26:28.316269Z digest=sha256:d3fa22c3d3600e3051f6b9854d893757311fd1c5252913fb3b9097f6ea1ae983

Observation 7045ec7b-c87c-4e50-972b-5c28e6e7fa43 · outbound

This paper cites LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Reference 59

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no resolver link, observed 2026-08-15T16:26:28.320793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T16:26:28.320793Z digest=sha256:2f904b331557bd84a557f0877e360d56fb2b9375bc2fce7c23e151775459b871

Observation 9c183357-56c7-4558-9331-d03f8f8dd81f · outbound

This paper cites Representation Engineering: A Top-Down Approach to AI Transparency.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Representation Engineering: A Top-Down Approach to AI Transparency

Reference 60

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source=arxiv_source observed=2026-08-15T16:26:28.324931Z digest=sha256:9077aaaf5ba638126e601db80de34ae48be05eb7c78270a46d6aa9bc78889737

Observation 47874377-6f59-4226-82bb-ed1cf87b9a1c · outbound

This paper cites online" 'onlinestring :=.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation online" 'onlinestring :=

Reference 61

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-15T16:26:28.328967Z digest=sha256:941589464838c8fce009bcad299a497ac9fcd6eecdf1a496e2cc89677e4e4ed0

Observation b724e026-3dfe-4368-9071-c7d4dabe1eab · outbound

This paper cites write newline.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation write newline

Reference 62

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source=arxiv_source observed=2026-08-15T16:26:28.333393Z digest=sha256:d8a5629b75858eab8df71b892eb79e2025aef7556bb6acb70b642479388bd352

Pith citing papers

No inbound Pith citation observations are available.