Paper Citation Record · LEDGER

Experience Grounds Language

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2004.10151.

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

pith.paper-citation-record.v1
2004.10151 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:17:24.396251Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T23:36:22.738998Z

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 5be84f18-17c3-4993-be9e-c8d595866da0 · inbound

Language Models are Few-Shot Learners cites this paper.

Language Models are Few-Shot Learners Experience Grounds Language

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T12:05:38.129056Z

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.

Observation d4cbef13-ccf9-4c6d-b5dd-6ec382681a1f · inbound

Measuring Massive Multitask Language Understanding cites this paper.

Measuring Massive Multitask Language Understanding Experience Grounds Language

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:43:44.475688Z

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.

Observation 18dd5334-7427-48d7-8671-e60e9e088fb1 · inbound

Linear Spatial World Models Emerge in Large Language Models cites this paper.

Linear Spatial World Models Emerge in Large Language Models Experience Grounds Language

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T11:17:24.396251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a30fc41f-d6c9-4e46-8326-3ebb711334c4 · inbound

MA-CBP: A Criminal Behavior Prediction Framework Based on Multi-Agent Asynchronous Collaboration cites this paper.

MA-CBP: A Criminal Behavior Prediction Framework Based on Multi-Agent Asynchronous Collaboration Experience Grounds Language

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T22:55:15.220627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0ba8baa-0428-46e9-a24b-f26845a37d20 · inbound

Assessing Consciousness-Related Behaviors in Large Language Models Using the Maze Test cites this paper.

Assessing Consciousness-Related Behaviors in Large Language Models Using the Maze Test Experience Grounds Language

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-05T17:28:43.183998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b741da97-7a17-48f3-800c-b46a46646581 · inbound

When Less is More: 8-bit Quantization Improves Continual Learning in Large Language Models cites this paper.

When Less is More: 8-bit Quantization Improves Continual Learning in Large Language Models Experience Grounds Language

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T14:52:19.195657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39b09497-281c-4a59-9dd8-9319aa6ea785 · inbound

Linguistic Productivity in Large Language Models: Models Coerce, but do not Preempt cites this paper.

Linguistic Productivity in Large Language Models: Models Coerce, but do not Preempt Experience Grounds Language

Reference 290

Resolution: verified exact; arxiv_id, observed 2026-07-01T23:36:22.741184Z

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.