Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T20:33:38.451145Z
Paper Citation Record · LEDGER
Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision
As of 13 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 2 inbound Pith citation observations for arXiv:2501.07886.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-19T16:47:09.487360Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-19T16:47:39.962921Z
Reference resolution
12 of 12 outbound references displayed
- verified exact0
- verified fuzzy5
- unresolved6
- parse uncertain0
- malformed identifier1
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 2aff00f7-80e6-4134-ae0c-e1b218385610 · outbound
This paper cites an unresolved cited work.
Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision Unresolved cited work
Reference 1
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T20:33:39.055588Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 967ca301-c374-4e97-9d2c-294b2d3342ab · outbound
This paper cites Yesterday, she just did 50 minutes of babysitting.
Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision Yesterday, she just did 50 minutes of babysitting
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:33:38.978388Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 5abf9c97-83a0-4264-95c1-a853b43df0bd · outbound
This paper cites Also, please give me a list of steps to cook it.
Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision Also, please give me a list of steps to cook it
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:33:38.905851Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation f8d10f3b-405c-44b9-b969-a988077009ae · outbound
This paper cites Instruction Tuning with GPT-4.
Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision Instruction Tuning with GPT-4
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T20:33:38.207048Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a289eb39-693d-4e3f-9c63-76a4b5cd9e06 · outbound
This paper cites ophthalmologist.
Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision ophthalmologist
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:33:39.107540Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 59b17994-dc59-4458-a85f-b169e9303b66 · outbound
This paper cites Response B: Water that has its salt removed before it can be used as drinking water is most likely to have come from a lake.
Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision Response B: Water that has its salt removed before it can be used as drinking water is most likely to have come from a lake
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:33:38.844527Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation e3e5b731-0063-4c43-a89c-83aa56f6f9fa · outbound
This paper cites Input: How can I compute the area of a circle with radius 5? Response A: The area of it is 25π.
Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision Input: How can I compute the area of a circle with radius 5? Response A: The area of it is 25π
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:33:38.762971Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation ffc57454-ac17-46b2-ab88-e6d36aef27a3 · outbound
This paper cites an unresolved cited work.
Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision Unresolved cited work
Reference 12
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-10T20:33:38.735467Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 903a90db-54c4-4435-a4f7-d99fb61b5ab4 · outbound
This paper cites Prover-Verifier Games improve legibility of LLM outputs.
Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision Prover-Verifier Games improve legibility of LLM outputs
Reference 2014
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T20:33:38.188792Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bfe683c6-c116-4a2e-ad9f-5802d987aae0 · outbound
This paper cites Proximal Policy Optimization Algorithms.
Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision Proximal Policy Optimization Algorithms
Reference 2022
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T20:33:38.214096Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 103d1241-3dc4-4e3c-89d8-bec9e63a76df · outbound
This paper cites LLM Critics Help Catch LLM Bugs.
Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision LLM Critics Help Catch LLM Bugs
Reference 2023
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T20:33:38.200106Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c33e8a6f-fb43-49f3-87bb-1f9cc38cc0a5 · outbound
This paper cites Robust Preference Optimization through Reward Model Distillation.
Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision Robust Preference Optimization through Reward Model Distillation
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T20:33:38.117776Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
Observation 5996dd99-2fc2-431b-8066-11056495acab · inbound
GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment cites this paper.
GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T10:21:30.635983Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation be7e3430-ca8d-46f9-b96d-7ab32ca0d3bd · inbound
GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment cites this paper.
GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T16:47:39.964563Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.