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Paper Citation Record · LEDGER

Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2506.12822.

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

pith.paper-citation-record.v1
2506.12822 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T10:13:19.750837Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T09:59:44.448427Z

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 8674e6ba-bf86-4071-b0a2-26340077f810 · inbound

Occlusion-robust Stylization for Drawing-based 3D Animation cites this paper.

Occlusion-robust Stylization for Drawing-based 3D Animation Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-06T10:13:19.750837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T10:13:19.750837Z digest=sha256:e3ecd28a9d8007e81b18584dc264b69bba3003de72e0ce1ce0b8049f5ecc4f0f

Observation 5b704c8c-fc4a-4656-9aa9-60b8aa3f330d · inbound

Self-Rewarding Vision-Language Model via Reasoning Decomposition cites this paper.

Self-Rewarding Vision-Language Model via Reasoning Decomposition Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-18T21:06:50.849423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-18T21:03:31.606674Z digest=sha256:086342133c990b8166fd329553f0cd113ee4ff60ac19cde7b9f85f4cf31566d4

Observation 0e9d3458-f541-44cc-bb1f-2ae14dbf8d0c · inbound

SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot Reinforcement Learning cites this paper.

SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot Reinforcement Learning Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models

Reference 33

Resolution
unresolved
no resolver link, observed 2026-07-13T16:10:12.689957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T16:10:12.689957Z digest=sha256:8dca6987ff476486b0c0b3814c435c82496df16987118ebfb5a4164637dc419c

Observation 97bc0aea-a88f-49e0-9bdf-0e44fc281504 · inbound

Learning Process Rewards via Success Visitation Matching for Efficient RL cites this paper.

Learning Process Rewards via Success Visitation Matching for Efficient RL Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models

Reference 51

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T09:59:44.450686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-26T09:20:35.062060Z digest=sha256:91180bfe14ceae452306aba2c0cd441506b44a6700e261db84e871b25cb274c7

Observation 38e1e6d2-6dc9-4c28-bcfd-3ae2414ab0c7 · inbound

Towards General Language-Conditioned Latent Safety Filters cites this paper.

Towards General Language-Conditioned Latent Safety Filters Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models

Reference 213

Resolution
unresolved
no resolver link, observed 2026-08-04T00:49:36.677298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T00:49:36.677298Z digest=sha256:47f91d54f8670a47266eab06738f44fc3efa7033a314fc113dc5ed3b56762cf2