Pith. sign in

Paper Citation Record · LEDGER

A Quantitative Experimental Repeated Measures Study of Training Dynamics in a Small Llama Style Language Model Under a Compute-Aware Token Budget

As of 9 August 2026, this Paper Citation Record lists 7 of 7 outbound references and 0 inbound Pith citation observations for arXiv:2606.13370.

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

pith.paper-citation-record.v1
2606.13370 v1

Coverage vector

measured 7 of 7 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-27T06:32:34.505731Z

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 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

7 of 7 outbound references displayed

  • verified exact2
  • verified fuzzy0
  • unresolved2
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5c93b90b-becd-49f2-b54a-ff07e961a5cf · outbound

This paper cites URL https://www.pnas.org/doi/abs/10.1073/pnas.1903070116.

A Quantitative Experimental Repeated Measures Study of Training Dynamics in a Small Llama Style Language Model Under a Compute-Aware Token Budget URL https://www.pnas.org/doi/abs/10.1073/pnas.1903070116

Reference 1

Resolution
metadata mismatch
doi, observed 2026-06-27T07:10:41.858571Z

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=2026-06-27T06:32:34.505731Z digest=sha256:55c165112a5beab915ee8f7adfdb57850c6f2859a5a996e857e13bcaa8deab00

Observation 8de65850-d52d-4255-b044-af5814f5f063 · outbound

This paper cites Proceedings of the 4th International Conference on Computer Communication and Artificial Intelligence (CCAI) , year =.

A Quantitative Experimental Repeated Measures Study of Training Dynamics in a Small Llama Style Language Model Under a Compute-Aware Token Budget Proceedings of the 4th International Conference on Computer Communication and Artificial Intelligence (CCAI) , year =

Reference 2

Resolution
unresolved
no resolver link, observed 2026-06-27T06:32:34.505731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-27T06:32:34.505731Z digest=sha256:0515b39d019bb2e8e3f50697e9044ad7402128b70f7320a209bc1dddb9972ce1

Observation d3a5c45d-fc6c-4b6b-91fc-7faed1a04116 · outbound

This paper cites , title =.

A Quantitative Experimental Repeated Measures Study of Training Dynamics in a Small Llama Style Language Model Under a Compute-Aware Token Budget , title =

Reference 3

Resolution
unresolved
no resolver link, observed 2026-06-27T06:32:34.505731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-27T06:32:34.505731Z digest=sha256:8e8ec4c04df20298734e607159d98c0acfde832c192666be9a396f3d9dc1d6bc

Observation 1f3e73d1-8bcc-4e21-89c9-42703e026813 · outbound

This paper cites Training Compute-Optimal Large Language Models.

A Quantitative Experimental Repeated Measures Study of Training Dynamics in a Small Llama Style Language Model Under a Compute-Aware Token Budget Training Compute-Optimal Large Language Models

Reference 4

Resolution
metadata mismatch
local_arxiv, observed 2026-07-03T15:18:33.984278Z

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=2026-06-27T06:32:34.505731Z digest=sha256:6ba319399497e57d2853bd66edbb11ae66e8e3175a8820edf23df49c38678521

Observation aed5bf34-b2dd-4c7f-a48d-9ca821346e0c · outbound

This paper cites Scaling Laws for Neural Language Models.

A Quantitative Experimental Repeated Measures Study of Training Dynamics in a Small Llama Style Language Model Under a Compute-Aware Token Budget Scaling Laws for Neural Language Models

Reference 5

Resolution
metadata mismatch
local_arxiv, observed 2026-07-03T15:18:33.986864Z

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=2026-06-27T06:32:34.505731Z digest=sha256:22072588ea77e0c8aec3402cfd37affe84f2614f46ba79ae4722e30573e886b1

Observation b289148b-60bd-4035-8959-3c5fbf6fd51a · outbound

This paper cites Deep Double Descent: Where Bigger Models and More Data Hurt.

A Quantitative Experimental Repeated Measures Study of Training Dynamics in a Small Llama Style Language Model Under a Compute-Aware Token Budget Deep Double Descent: Where Bigger Models and More Data Hurt

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-07-03T15:18:33.989672Z

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=2026-06-27T06:32:34.505731Z digest=sha256:29f8635e152176b5ac30a35222011e6ea2b3e086ae85d8322c45fc5e7e22a579

Observation 50074ec7-f641-4545-83ee-1923d278472a · outbound

This paper cites 2026 , publisher =.

A Quantitative Experimental Repeated Measures Study of Training Dynamics in a Small Llama Style Language Model Under a Compute-Aware Token Budget 2026 , publisher =

Reference 7

Resolution
verified exact
doi, observed 2026-06-27T07:10:41.856932Z

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=2026-06-27T06:32:34.505731Z digest=sha256:4534b225146684ba23c1b509dc70e77bb85f179bfdfabbee128fdb7f9d4540b1

Pith citing papers

No inbound Pith citation observations are available.