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Paper Citation Record · LEDGER

Compile Scene Graphs with Reinforcement Learning

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2504.13617.

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

pith.paper-citation-record.v1
2504.13617 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 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 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:31:16.244853Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:10:08.028362Z

Reference resolution

0 of 0 outbound references displayed

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  • verified fuzzy0
  • unresolved0
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  • 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 dcfc2b59-51ec-41ed-b2ca-a510e7d13a85 · inbound

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models cites this paper.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Compile Scene Graphs with Reinforcement Learning

Reference 89

Resolution
unresolved
no resolver link, observed 2026-08-07T14:31:16.244853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:31:16.244853Z digest=sha256:853bf50b0d3ede84f669922e1a6504a5ad650d5a758a493cbed7fef30f40db0d

Observation aa3c6233-1240-410d-b735-770dffa707f5 · inbound

WeThink: Toward General-purpose Vision-Language Reasoning via Reinforcement Learning cites this paper.

WeThink: Toward General-purpose Vision-Language Reasoning via Reinforcement Learning Compile Scene Graphs with Reinforcement Learning

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-07T05:27:05.106433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:27:05.106433Z digest=sha256:76554c82de361b230dd3276dc007d3139f69048bc1fea1a91aa38158a89ec2e1

Observation d77b65c8-098f-4c77-90f0-53a9c4dbb24a · inbound

3D-R1: Enhancing Reasoning in 3D VLMs for Unified Scene Understanding cites this paper.

3D-R1: Enhancing Reasoning in 3D VLMs for Unified Scene Understanding Compile Scene Graphs with Reinforcement Learning

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-06T10:49:46.852987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T10:49:46.852987Z digest=sha256:2811b26369d410fa5777e6fcfc8238617489c99cc65902be384c5c9fa1f018eb

Observation 204d6904-3f0c-4a47-b150-6d88ee4dda7f · inbound

Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle cites this paper.

Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle Compile Scene Graphs with Reinforcement Learning

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-04T16:07:28.527593Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T16:07:28.527593Z digest=sha256:2bbf022e0724afd78c8643755cd0cb07fc1bf38fb359e8fc22ec83b78d423450

Observation 5e1f95ee-15d3-4f39-ab9a-a06880eb7999 · inbound

SpatialThinker: Reinforcing Scene Graph-Grounded Spatial Reasoning via Dense Rewards cites this paper.

SpatialThinker: Reinforcing Scene Graph-Grounded Spatial Reasoning via Dense Rewards Compile Scene Graphs with Reinforcement Learning

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-03T23:08:49.130584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T23:08:49.130584Z digest=sha256:e6282915f66ed576011289b7eafea5efe9d7e3e08be93edf9a8bb5c8f85fa0b1

Observation 4a85d152-fe43-48fa-ab4b-ade445dc0f52 · inbound

SenBen: Sensitive Scene Graphs for Explainable Content Moderation cites this paper.

SenBen: Sensitive Scene Graphs for Explainable Content Moderation Compile Scene Graphs with Reinforcement Learning

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-11T08:01:01.823622Z

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=pdf_text observed=2026-05-10T16:51:01.201885Z digest=sha256:597d99e190e438abe03b0d8cfc848bab5022c2c2a2ad6bb02b6c4c5bce0789cb

Observation 3d27042d-c771-4e3e-841d-ca07e0e60bb1 · inbound

SenBen: Sensitive Scene Graphs for Explainable Content Moderation cites this paper.

SenBen: Sensitive Scene Graphs for Explainable Content Moderation Compile Scene Graphs with Reinforcement Learning

Reference 5

Resolution
unresolved
no resolver link, observed 2026-07-12T23:42:32.347933Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T23:42:32.347933Z digest=sha256:564797090d2c9453a12943f61921c90b90bd0e12589e4b8fd3e4817ccae86963

Observation 4d6ec6b9-ea07-4623-b0e2-32f5633290a9 · inbound

SceneGraphVLM: Dynamic Scene Graph Generation from Video with Vision-Language Models cites this paper.

SceneGraphVLM: Dynamic Scene Graph Generation from Video with Vision-Language Models Compile Scene Graphs with Reinforcement Learning

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-14T20:42:58.271106Z

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=pdf_text observed=2026-05-14T20:39:56.448121Z digest=sha256:5c6b4957a7944f8f25c3c96da0afdb7812d3453d189574b6aca18f5a5f0f07f1

Observation c9f1ed04-3efb-4c0d-a9c8-210155f569b5 · inbound

OracleAnalyser: Analysing Implicit Semantics of Oracle Bone Scripts through MLLMs with Post-training cites this paper.

OracleAnalyser: Analysing Implicit Semantics of Oracle Bone Scripts through MLLMs with Post-training Compile Scene Graphs with Reinforcement Learning

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-07-04T20:10:08.030470Z

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=pdf_text observed=2026-06-25T20:36:16.927195Z digest=sha256:28c32948773d947a83975657568ca81eb977e381d34ee15dc1fe7a69f43de9ba