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Paper Citation Record · LEDGER

Addressing the Abstraction and Reasoning Corpus via Procedural Example Generation

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2404.07353.

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

pith.paper-citation-record.v1
2404.07353 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:50:53.144820Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T13:09:50.031494Z

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 c79aa9ea-5071-4214-a99b-faa88fc614d4 · inbound

From Reasoning to Generalization: Knowledge-Augmented LLMs for ARC Benchmark cites this paper.

From Reasoning to Generalization: Knowledge-Augmented LLMs for ARC Benchmark Addressing the Abstraction and Reasoning Corpus via Procedural Example Generation

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-07T14:50:53.144820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:50:53.144820Z digest=sha256:999d3d7e51cce1e893c13cec2f81191bba7513b11a87bcc8bc04bee24ec7779e

Observation a6dc8f4f-2e9a-4306-a004-505d356cc263 · inbound

GIFARC: Synthetic Dataset for Leveraging Human-Intuitive Analogies to Elevate AI Reasoning cites this paper.

GIFARC: Synthetic Dataset for Leveraging Human-Intuitive Analogies to Elevate AI Reasoning Addressing the Abstraction and Reasoning Corpus via Procedural Example Generation

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-07T13:53:53.534519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:53:53.534519Z digest=sha256:47a63d0d1ba08822bf7e37126a277eeb1b14a022c0d19d9e656cb5731b50e29e

Observation 108773d4-fbb1-4ee2-889b-4865f2505a0d · inbound

Channel-Wise MLPs Improve the Generalization of Recurrent Convolutional Networks cites this paper.

Channel-Wise MLPs Improve the Generalization of Recurrent Convolutional Networks Addressing the Abstraction and Reasoning Corpus via Procedural Example Generation

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-06T00:01:42.106854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T00:01:42.106854Z digest=sha256:87d671a8e6e530a360e92170998d6624e62127084e7d6252d746e644c246927d

Observation fcbb218d-860d-41db-b9a5-96a9d3896751 · inbound

One Step Forward and K Steps Back: Better Reasoning with Denoising Recursion Models cites this paper.

One Step Forward and K Steps Back: Better Reasoning with Denoising Recursion Models Addressing the Abstraction and Reasoning Corpus via Procedural Example Generation

Reference 164

Resolution
verified exact
arxiv_id, observed 2026-05-10T11:05:09.093993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-10T04:56:35.796962Z digest=sha256:40ea4663530baeec0031b575ff1cb84da4d28d13a597ac98264966e81cddcedb

Observation d1b58157-b5f7-4b4a-980c-e1c579511916 · inbound

Slots, Transitions, Loops: Learning Composable World Models for ARC cites this paper.

Slots, Transitions, Loops: Learning Composable World Models for ARC Addressing the Abstraction and Reasoning Corpus via Procedural Example Generation

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-07-03T10:37:56.821794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-27T09:55:40.978838Z digest=sha256:973773aa21e7930dde8d76177ef32e7966205fd59751e69d41d9f6ef58050b62

Observation 34753d3b-497c-4259-be34-2ff0be16c5f3 · inbound

Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale cites this paper.

Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale Addressing the Abstraction and Reasoning Corpus via Procedural Example Generation

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-07-02T22:17:25.458601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-07-02T22:10:59.568675Z digest=sha256:69c578af9745b2a0ec1707b520f70c2d0456bec0e96de892fe8aca89c44f98b6

Observation 1f2d5690-f596-4361-a89d-f1233672292e · inbound

DiARC: Distinguishing Positive and Negative Samples Helps Improving ARC-like Reasoning Ability of Large Language Models cites this paper.

DiARC: Distinguishing Positive and Negative Samples Helps Improving ARC-like Reasoning Ability of Large Language Models Addressing the Abstraction and Reasoning Corpus via Procedural Example Generation

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-07-04T13:09:50.033638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-26T05:32:59.640335Z digest=sha256:24b3998675de54fceb5acb9f0efe76fbb82787a42f9911ce32a6c4d8454cd51c

Observation 9d14954c-6451-4daf-855b-a8b4ce06d5fc · inbound

DiARC: Distinguishing Positive and Negative Samples Helps Improving ARC-like Reasoning Ability of Large Language Models cites this paper.

DiARC: Distinguishing Positive and Negative Samples Helps Improving ARC-like Reasoning Ability of Large Language Models Addressing the Abstraction and Reasoning Corpus via Procedural Example Generation

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-06-29T17:23:45.523947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-29T05:19:07.877346Z digest=sha256:c67d62cfdcf5ea6970609d1093975a7165f11087a68f0a40f5945d6eec244ebc

Observation aaf29b1e-ac5a-4427-8f27-ff5670152d15 · inbound

Modality-Driven Search with Holistic Trace Judging for ARC-AGI-2 cites this paper.

Modality-Driven Search with Holistic Trace Judging for ARC-AGI-2 Addressing the Abstraction and Reasoning Corpus via Procedural Example Generation

Reference 13

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T10:25:41.645971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-07-01T05:33:22.524746Z digest=sha256:cb94d96ff22310dec158a0d11225cbcc3e614a18eb5135b137c6e605e1167cce

Observation 3e7421be-3102-4dd4-94be-54240d829534 · inbound

From Global to Factor-Wise Expert Composition in Discrete Diffusion Models cites this paper.

From Global to Factor-Wise Expert Composition in Discrete Diffusion Models Addressing the Abstraction and Reasoning Corpus via Procedural Example Generation

Reference 16

Resolution
unresolved
no resolver link, observed 2026-07-14T03:24:09.456287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T03:24:09.456287Z digest=sha256:ce0b22a454dad8dab7e279031c560a1d00d5c60105bfb27578ea5630975f696d

Observation bc71134f-ae03-41d1-898d-8a8b2cf267ae · inbound

TraceViT: Grounded Trace Supervision for Visual Abstract Reasoning cites this paper.

TraceViT: Grounded Trace Supervision for Visual Abstract Reasoning Addressing the Abstraction and Reasoning Corpus via Procedural Example Generation

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-03T04:06:55.828772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T04:06:55.828772Z digest=sha256:ed8f46a65a4457e07f3e067a9f70689b5c109a855babbf14e6d630c093378a83

Observation 165b4248-4d4d-41e8-ae31-4d301dd7c12c · inbound

Recursive Vision Language Models for General Symbolic Reasoning cites this paper.

Recursive Vision Language Models for General Symbolic Reasoning Addressing the Abstraction and Reasoning Corpus via Procedural Example Generation

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-06T00:11:25.481855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:11:25.481855Z digest=sha256:0433e074c013c09f2661b219e556bc63ee9721132f566751309f9c1ea450b698