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Paper Citation Record · LEDGER

BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2406.00832.

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

pith.paper-citation-record.v1
2406.00832 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:11:14.681741Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T14:51:42.008449Z

Reference resolution

0 of 0 outbound references displayed

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

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation f23a1978-c153-4140-af44-9127a7b0e72f · inbound

Self-Improvement in Language Models: The Sharpening Mechanism cites this paper.

Self-Improvement in Language Models: The Sharpening Mechanism BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling

Reference 2004

Resolution
unresolved
no resolver link, observed 2026-08-12T00:11:14.681741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T00:11:14.681741Z digest=sha256:ec91d1fa0562c1bcdf9adbb15c383af6973293c67caedca32477aba00ecd7848

Observation 616ea392-bb98-41d2-8d18-81d086f24233 · inbound

InfAlign: Inference-aware language model alignment cites this paper.

InfAlign: Inference-aware language model alignment BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-10T23:57:54.278193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:57:54.278193Z digest=sha256:2705c5d1615454ce81160dd30d63e97003b6e2774af151e003600ebfd3fb17ef

Observation 6c82f396-8222-435c-935f-81f828343a2b · inbound

BoKDiff: Best-of-K Diffusion Alignment for Target-Specific 3D Molecule Generation cites this paper.

BoKDiff: Best-of-K Diffusion Alignment for Target-Specific 3D Molecule Generation BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-10T14:09:57.636495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:09:57.636495Z digest=sha256:51c0b237adf9e4e0e21802cfeac9b8fafa446fcb5122ee3790e94376fa6c53d6

Observation 75f37d1c-a82e-4258-aaaa-c69bf3146d08 · inbound

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? cites this paper.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-09T18:11:28.125944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T18:11:28.125944Z digest=sha256:99e37435175e4044abf8b44161be4b85c3b32133d7ca3415bb73dd5274a154e8

Observation 398f34eb-ae02-4e0d-9034-56db8e4d97f6 · inbound

CoDe: Blockwise Control for Denoising Diffusion Models cites this paper.

CoDe: Blockwise Control for Denoising Diffusion Models BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-09T17:10:17.042098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T17:10:17.042098Z digest=sha256:052420073db3a69efce99566252f11fe61b4f8320d8baaa65a327b3079aa0c7b

Observation eb7c576b-ffe9-4c0b-842d-b2cfb32a03f7 · inbound

Reviving The Classics: Active Reward Modeling in Large Language Model Alignment cites this paper.

Reviving The Classics: Active Reward Modeling in Large Language Model Alignment BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-09T11:47:17.545110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T11:47:17.545110Z digest=sha256:5034ce1949e6df0fe5ac2d736cef1ba3ef55cfaecdb48d2f9798739dbafb2e7a

Observation 9d9880a1-80ff-40c3-bc4f-2f9990694c83 · inbound

Exploring the Limit of Outcome Reward for Learning Mathematical Reasoning cites this paper.

Exploring the Limit of Outcome Reward for Learning Mathematical Reasoning BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-08T14:27:51.054553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T14:27:51.054553Z digest=sha256:7d3d913647ae05d902ca6d21934c49dc97823ee36b1601ca0d1e95aac5f5bc41

Observation bfea97b9-bc02-4c6d-95dd-0ad90cae42ec · inbound

Language Model Networks: Supervision-Efficient Learning through Dense Communication cites this paper.

Language Model Networks: Supervision-Efficient Learning through Dense Communication BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-05-22T14:51:42.011625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-22T14:50:45.735917Z digest=sha256:90024d46f37c1bbaf37801617210701506688a514319c25ad25653613f391304

Observation 759f7deb-c904-489c-9723-b0b1552b1806 · inbound

BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute cites this paper.

BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-06T22:05:27.168369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T22:05:27.168369Z digest=sha256:bab93ee62972285f0f9f660bfcf1bd7fbb46e72e5b5c62adb8e6574a96994710

Observation ab799296-c1a3-44d0-b8c4-81e837b32367 · inbound

Best-of-N through the Smoothing Lens: KL Divergence and Regret Analysis cites this paper.

Best-of-N through the Smoothing Lens: KL Divergence and Regret Analysis BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-06T19:28:54.801422Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T19:28:54.801422Z digest=sha256:f59291ee85346f52af595d3c6b53cf3338432dfe92fc8fbbbe33827b55aebf09

Observation 93ddae34-6421-4d2e-a4a4-6e5dfa256901 · inbound

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities cites this paper.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-06T16:34:25.013301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:34:25.013301Z digest=sha256:935e1066952af917f7b07830a93afe4c1cb8cd6b9461edc1bd6740bac4caeb7d

Observation 029cafa6-61b0-474c-94af-e58103c25f84 · inbound

A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement cites this paper.

A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-21T23:30:46.064326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-05-21T23:26:38.457193Z digest=sha256:a828d6c87c2a1f72b0173db5d23fd70312195ee8ee03f5b03728c27d81004592

Observation 33c0561a-5738-4944-9f00-cccbbf46acb6 · inbound

MARS-SQL: A multi-agent reinforcement learning framework for Text-to-SQL cites this paper.

MARS-SQL: A multi-agent reinforcement learning framework for Text-to-SQL BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling

Reference 11

Resolution
metadata mismatch
arxiv_id, observed 2026-05-18T01:32:17.408062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-18T01:31:40.920567Z digest=sha256:130c3fdc1bf0e0c7327896cef79533683ee89d8863a24f1bd56dde0b1394b52f