Paper Citation Record · LEDGER

Relational reasoning and inductive bias in transformers and large language models

As of 18 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 2 inbound Pith citation observations for arXiv:2506.04289.

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

pith.paper-citation-record.v1
2506.04289 v3

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-19T11:31:37.942517Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-25T05:55:10.325836Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-25T05:55:24.048782Z

Reference resolution

24 of 24 outbound references displayed

verified exact13
verified fuzzy1
unresolved0
parse uncertain0
malformed identifier2
metadata mismatch8

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9c8d0f6d-8481-4f9a-80ee-f4db954aa033 · outbound

This paper cites Relational inductive biases, deep learning, and graph networks.

Relational reasoning and inductive bias in transformers and large language models Relational inductive biases, deep learning, and graph networks

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-05-19T11:32:17.043089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 36c59af0-bf1c-4c8b-8762-8ff41a486722 · outbound

This paper cites Language Models are Few-Shot Learners.

Relational reasoning and inductive bias in transformers and large language models Language Models are Few-Shot Learners

Reference 2

Resolution: metadata mismatch; local_arxiv, observed 2026-05-19T11:32:17.085214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f5168972-ccb1-4915-93ad-292d95d9719c · outbound

This paper cites doi: 10.1016/ j.neuron.2004.08.028.

Relational reasoning and inductive bias in transformers and large language models doi: 10.1016/ j.neuron.2004.08.028

Reference 3

Resolution: malformed identifier; raw_fallback, observed 2026-05-19T11:32:17.686408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4990f11e-79bd-4ee3-8f3b-054361c89b28 · outbound

This paper cites Bayes in the age of intelligent machines.

Relational reasoning and inductive bias in transformers and large language models Bayes in the age of intelligent machines

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-19T11:32:17.097350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 714b7c92-b921-4ae1-bb17-8deabcce06fc · outbound

This paper cites Chari and L.

Relational reasoning and inductive bias in transformers and large language models Chari and L

Reference 5

Resolution: malformed identifier; doi_truncated, observed 2026-05-19T11:32:16.577194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8e35194c-940f-4415-8e5c-35608d4b4961 · outbound

This paper cites doi: 10.1371/journal.pcbi.1011954.

Relational reasoning and inductive bias in transformers and large language models doi: 10.1371/journal.pcbi.1011954

Reference 6

Resolution: verified exact; doi, observed 2026-05-19T11:32:16.554451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 192c7291-b456-40f6-beab-2e1ea47c8884 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Relational reasoning and inductive bias in transformers and large language models Adam: A Method for Stochastic Optimization

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-05-19T11:32:17.103534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0d9a6ceb-041d-4082-a39a-4dbcf736d38c · outbound

This paper cites Lake and Marco Baroni , title =.

Relational reasoning and inductive bias in transformers and large language models Lake and Marco Baroni , title =

Reference 8

Resolution: metadata mismatch; doi, observed 2026-05-19T11:32:16.548938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f25a7f41-a1b5-4ede-9895-a6bd18260cdc · outbound

This paper cites doi: 10.1126/science.aab3050.

Relational reasoning and inductive bias in transformers and large language models doi: 10.1126/science.aab3050

Reference 9

Resolution: verified exact; doi, observed 2026-05-19T11:32:16.536283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation eabbf519-060a-46c6-9b5d-0c85ed215682 · outbound

This paper cites ISBN 979-8-89176-195-7.

Relational reasoning and inductive bias in transformers and large language models ISBN 979-8-89176-195-7

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T11:32:17.065382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a6a080be-8530-4baa-b0cd-d1510c030573 · outbound

This paper cites LLMs for Relational Reasoning: How Far are We?.

Relational reasoning and inductive bias in transformers and large language models LLMs for Relational Reasoning: How Far are We?

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-19T11:32:17.078277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ea0de533-3d88-4418-9d76-988ea9569f05 · outbound

This paper cites URL https://www.

Relational reasoning and inductive bias in transformers and large language models URL https://www

Reference 12

Resolution: verified exact; doi, observed 2026-05-19T11:32:16.542952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 12eb77e6-85cb-492d-88ef-c74469e98d08 · outbound

This paper cites Thomas Miconi and Kenneth Kay.

Relational reasoning and inductive bias in transformers and large language models Thomas Miconi and Kenneth Kay

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T11:32:17.690396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4c162f29-7cd5-4bc6-99f9-a9be792ef909 · outbound

This paper cites Miconi and K.

Relational reasoning and inductive bias in transformers and large language models Miconi and K

Reference 14

Resolution: metadata mismatch; doi, observed 2026-05-19T11:32:16.567092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dc2a171e-863e-4536-a5c5-e182db5027d0 · outbound

This paper cites doi: 10.1016/0010-0285(76)90025-6.

Relational reasoning and inductive bias in transformers and large language models doi: 10.1016/0010-0285(76)90025-6

Reference 15

Resolution: verified exact; doi, observed 2026-05-19T11:32:16.571764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3d6066d2-b942-4d4c-8a01-ab56157b4f0c · outbound

This paper cites doi: 10.1016/j.neuron.2023.02.014.

Relational reasoning and inductive bias in transformers and large language models doi: 10.1016/j.neuron.2023.02.014

Reference 16

Resolution: verified exact; doi, observed 2026-05-19T11:32:16.560567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fd97d6c3-e88d-41fb-8122-263cd356424c · outbound

This paper cites The mechanistic basis of data dependence and abrupt learning in an in-context classification task.

Relational reasoning and inductive bias in transformers and large language models The mechanistic basis of data dependence and abrupt learning in an in-context classification task

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T11:32:17.071864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 40fd2e3a-7623-47cf-bc56-a9e2ef42d7db · outbound

This paper cites Do pretrained Transformers Learn In-Context by Gradient Descent?.

Relational reasoning and inductive bias in transformers and large language models Do pretrained Transformers Learn In-Context by Gradient Descent?

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-19T11:32:17.057473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4698b82d-42dd-407a-b85f-ad99283f887c · outbound

This paper cites The Transient Nature of Emergent In-Context Learning in Transformers.

Relational reasoning and inductive bias in transformers and large language models The Transient Nature of Emergent In-Context Learning in Transformers

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-19T11:32:17.091261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6a60ea88-84aa-4e0c-a9dc-f49d81c173ac · outbound

This paper cites What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation.

Relational reasoning and inductive bias in transformers and large language models What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-19T11:32:17.050874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 03ee11e8-62d2-4be3-a57a-533221fc89b5 · outbound

This paper cites Schema-learning and rebinding as mechanisms of in-context learning and emergence.

Relational reasoning and inductive bias in transformers and large language models Schema-learning and rebinding as mechanisms of in-context learning and emergence

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-19T11:32:17.036233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2fc68891-a848-4849-a594-b8a72ae4cb1d · outbound

This paper cites doi: 10.1016/j.beproc.2008.02.017.

Relational reasoning and inductive bias in transformers and large language models doi: 10.1016/j.beproc.2008.02.017

Reference 22

Resolution: verified exact; doi, observed 2026-05-19T11:32:16.514905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2e30047d-4a9c-444e-87b7-29c4fee4d301 · outbound

This paper cites Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned , shorttitle =.

Relational reasoning and inductive bias in transformers and large language models Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned , shorttitle =

Reference 23

Resolution: metadata mismatch; doi, observed 2026-05-19T11:32:16.529472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c528e740-4087-4c9d-a4a3-ba90d01358c5 · outbound

This paper cites Do Large Language Models Latently Perform Multi-Hop Reasoning?.

Relational reasoning and inductive bias in transformers and large language models Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T11:32:17.030178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

Observation 1c3e3c65-db5b-4604-87c7-d9011e6247c1 · inbound

Deep sequence models tend to memorize geometrically; it is unclear why cites this paper.

Deep sequence models tend to memorize geometrically; it is unclear why Relational reasoning and inductive bias in transformers and large language models

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-05-21T20:40:36.355877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7e18a1d7-e343-4056-9d56-447abd53efc1 · inbound

A mathematical theory of balancing relational generalization and memorization cites this paper.

A mathematical theory of balancing relational generalization and memorization Relational reasoning and inductive bias in transformers and large language models

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-05-25T05:55:24.051456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.