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Paper Citation Record · LEDGER

The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models

As of 8 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2505.24874.

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

pith.paper-citation-record.v1
2505.24874 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:17:12.000740Z

measured 16 of 16 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

16 of 16 outbound references displayed

  • verified exact0
  • verified fuzzy7
  • unresolved9
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 66b1992f-8788-4edb-ad25-60a6840c2696 · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-07T12:17:11.148765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e0cdd6cd-63f6-433e-963a-4a5a77ce32c2 · outbound

This paper cites GPT-4o System Card.

The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models GPT-4o System Card

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-07T12:17:11.274176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:17:11.274176Z digest=sha256:aea4134c85a5df70ef75331daefa7f8f63d99d13c7f3618d40b3b174e6685a95

Observation d29145a0-daef-456b-b348-15183370ca92 · outbound

This paper cites MNIST handwritten digit database.

The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models MNIST handwritten digit database

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:17:13.217930Z

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=2026-08-07T12:17:11.416920Z digest=sha256:e57ad63ce4acc6a486f15d4dac92104c261df2d3f39f280e516675eceea40ed6

Observation 88b538fa-8a22-4b86-907e-273dab16413a · outbound

This paper cites Faithful Chain-of-Thought Reasoning.

The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models Faithful Chain-of-Thought Reasoning

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:17:13.044963Z

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=2026-08-07T12:17:11.457836Z digest=sha256:30591d96ec89f3ab815ff21cbfac7d3311602d5250889ac219aac8b533e929df

Observation 7d8f025b-9874-4e88-87d1-8888790c7dcc · outbound

This paper cites Dolphin: A Programmable Framework for Scalable Neurosymbolic Learn- ing.

The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models Dolphin: A Programmable Framework for Scalable Neurosymbolic Learn- ing

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-07T12:17:11.637443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:17:11.637443Z digest=sha256:d82acc8304cd5723b660104782b0be4514af1f35429b8e6df3242b7dea7c3178

Observation 27a1e408-1ea7-4434-8375-a671cc6bd76e · outbound

This paper cites by Houda Bouamor, Juan Pino, and Kalika Bali.

The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models by Houda Bouamor, Juan Pino, and Kalika Bali

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:17:12.642366Z

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.

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Observation ff6fdd38-b20b-4a89-a48e-229a12fb18a4 · outbound

This paper cites Drum: End-to-end differentiable rule mining on knowledge graphs.

The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models Drum: End-to-end differentiable rule mining on knowledge graphs

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-07T12:17:11.786462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:17:11.786462Z digest=sha256:a76e8af009bc4a72f742f31c6fd03c42cb9fb675bdb8120768e3f327e41cc193

Observation 7fd8f99c-d40b-4e37-9c3c-d7c1e5995a55 · outbound

This paper cites Right for the right concept: Revising neuro-symbolic concepts by interacting with their explanations.

The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models Right for the right concept: Revising neuro-symbolic concepts by interacting with their explanations

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:17:12.534716Z

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=2026-08-07T12:17:11.830336Z digest=sha256:15286ceba0e515e052f32023e15971fe8195c9ade39dd8d4292a5d09a289f5a5

Observation d0253741-2e5f-437c-b879-b843dfe1c829 · outbound

This paper cites Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi.

The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:17:12.387182Z

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=2026-08-07T12:17:12.000740Z digest=sha256:b7b12aea868cfc4ae6f5a965e25c7025c56e3bf248c118f01df80288a2305a66

Observation da5f7b63-bf69-456a-beef-a02d1ac7decb · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models Gemini: A Family of Highly Capable Multimodal Models

Reference 155

Resolution
unresolved
no resolver link, observed 2026-08-07T12:17:11.856808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:17:11.856808Z digest=sha256:ad3dbc0aa1f651784168917cf5c161fc77eb936ace38804b7034cd1c00d2bddb

Observation 770eaf43-e9f8-4729-a16d-c19671df1dcc · outbound

This paper cites Not all neuro-symbolic concepts are created equal: Analysis and mitigation of reasoning shortcuts.

The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models Not all neuro-symbolic concepts are created equal: Analysis and mitigation of reasoning shortcuts

Reference 202

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:17:12.753373Z

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.

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Observation 22eb4399-b9e0-4c8c-bcac-f6660bdcc979 · outbound

This paper cites LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench.

The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 2015

Resolution
unresolved
no resolver link, observed 2026-08-07T12:17:11.933468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:17:11.933468Z digest=sha256:a284b591c94a86080b9b82a4daa3bc53fcfd2c6f6d54c08515c2b989bbe7b711

Observation 52e237b8-2a25-49fa-b4c8-244c740dccaf · outbound

This paper cites Learn to Explain Efficiently via Neural Logic Inductive Learning.

The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models Learn to Explain Efficiently via Neural Logic Inductive Learning

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-07T12:17:11.956190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:17:11.956190Z digest=sha256:0446675f3699ecc6e36f0f334257e39d03c984a306cf4e31ac5c16393d28d4b7

Observation 233416bd-65b2-48a6-9ad5-63c7f663631c · outbound

This paper cites Deepproblog: Neural probabilistic logic programming.

The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models Deepproblog: Neural probabilistic logic programming

Reference 2023

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:17:12.908942Z

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=2026-08-07T12:17:11.544527Z digest=sha256:8d55adb8c01ae81f209728a4bf0864c29d9571350208d815e279bc79c2069916

Observation 482e33d8-dbb4-4d03-bb5e-35df3e88c7c4 · outbound

This paper cites The Llama 3 Herd of Models.

The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models The Llama 3 Herd of Models

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-07T12:17:11.206916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:17:11.206916Z digest=sha256:ffcac7da93a1515a3ab5fa6c18481cc4a49834f266698cf252588d962e64a4fe

Observation a9945a92-e04f-44d9-baf6-0664b2306f64 · outbound

This paper cites Scaling Laws for Neural Language Models.

The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models Scaling Laws for Neural Language Models

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-07T12:17:11.340236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:17:11.340236Z digest=sha256:083bd08da97ad6eded26ed50185867147a5e310496c2e2dec5b734d649164b7f

Pith citing papers

No inbound Pith citation observations are available.