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Paper Citation Record · LEDGER

Bi-Factorial Preference Optimization: Balancing Safety-Helpfulness in Language Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2408.15313.

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

pith.paper-citation-record.v1
2408.15313 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:21:58.799911Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T05:21:58.879423Z

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 f8037334-eadc-4a1f-8d46-ec3f0acf2601 · inbound

Reinforcement Learning from Human Feedback with High-Confidence Safety Constraints cites this paper.

Reinforcement Learning from Human Feedback with High-Confidence Safety Constraints Bi-Factorial Preference Optimization: Balancing Safety-Helpfulness in Language Models

Reference 54

Resolution
verified exact
local_arxiv, observed 2026-08-07T05:21:58.886951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T05:21:58.799911Z digest=sha256:8e741834b9936f9a14b54912590a03c53e8838aa3c95935d658ea249d2128975

Observation 9b5adc77-6d28-43f2-9e6d-1e8fccb9b27a · inbound

Safe Inference-Time Alignment via Lagrangian Reward Augmentation cites this paper.

Safe Inference-Time Alignment via Lagrangian Reward Augmentation Bi-Factorial Preference Optimization: Balancing Safety-Helpfulness in Language Models

Reference 108

Resolution
unresolved
no resolver link, observed 2026-07-12T07:05:47.150308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T07:05:47.150308Z digest=sha256:a39d1741f0167e447316f62c38358a87083e0421eccb689be00046c2c8d32035

Observation 54debcdb-9c32-4bc0-b0c1-269c2620e896 · inbound

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints cites this paper.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Bi-Factorial Preference Optimization: Balancing Safety-Helpfulness in Language Models

Reference 84

Resolution
unresolved
no resolver link, observed 2026-07-13T05:26:06.829382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T05:26:06.829382Z digest=sha256:62745eb6095a1b15e8485677cf35dad1b7e9e5c41f2ce624cea71a792426f6c5