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Paper Citation Record · LEDGER

Enhancing Numerical Prediction in LLMs via Smooth MMD Alignment

As of 22 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2606.27731.

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

pith.paper-citation-record.v1
2606.27731 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T11:50:26.030339Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

12 of 12 outbound references displayed

  • verified exact2
  • verified fuzzy0
  • unresolved6
  • parse uncertain0
  • malformed identifier2
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f3a34833-b7ac-42db-9297-12279c8c5287 · outbound

This paper cites naacl-main.191.

Enhancing Numerical Prediction in LLMs via Smooth MMD Alignment naacl-main.191

Reference 1

Resolution
metadata mismatch
doi, observed 2026-06-29T04:53:06.048891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T04:49:38.723360Z digest=sha256:c2542030af0567cbcddff1f9a178ce4858f4877e384d3427327d340bfa4ea62f

Observation 62561783-33df-4842-af46-853ec9bb220d · outbound

This paper cites Self-alignment pretraining for biomedical entity representations.

Enhancing Numerical Prediction in LLMs via Smooth MMD Alignment Self-alignment pretraining for biomedical entity representations

Reference 2

Resolution
verified exact
doi, observed 2026-06-29T04:53:06.051312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T04:49:38.723360Z digest=sha256:f376198396a707791ade4aaa2da8c993c6a1af50829814ca85c130c4f22e6e31

Observation 83f29b5e-1fba-4c66-a453-cbbbdd80a74e · outbound

This paper cites naacl-main.168/.

Enhancing Numerical Prediction in LLMs via Smooth MMD Alignment naacl-main.168/

Reference 3

Resolution
verified exact
doi, observed 2026-06-29T04:53:06.053339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T04:49:38.723360Z digest=sha256:24b5f593c660c5b092746a56d7153c74c7a4bbe508925e047498098d1bdf234b

Observation 9c2eb21d-69b0-418f-a9c1-60a15b1489c5 · outbound

This paper cites Analysing Mathematical Reasoning Abilities of Neural Models.

Enhancing Numerical Prediction in LLMs via Smooth MMD Alignment Analysing Mathematical Reasoning Abilities of Neural Models

Reference 4

Resolution
malformed identifier
local_arxiv, observed 2026-06-29T04:53:06.057553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-07-11T11:50:26.030339Z digest=sha256:8e596bf64e96b665e4d08885d3d68bc9e74ff7d4c7c8d6086092ecb61fa32b6b

Observation ec932359-7026-4ee1-b449-94b8129f9552 · outbound

This paper cites Transformers: State-of-the-Art Natural Language Processing.

Enhancing Numerical Prediction in LLMs via Smooth MMD Alignment Transformers: State-of-the-Art Natural Language Processing

Reference 5

Resolution
metadata mismatch
doi, observed 2026-06-29T04:53:06.044558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T04:49:38.723360Z digest=sha256:f0aea55fd9e04b1221b61e518b159bd5bad6179bfb5509a801839b5b67cb9235

Observation f9c066a9-0154-4f07-9464-a4bdde403a8a · outbound

This paper cites 12 Enhancing Numerical Prediction in LLMs via Smooth MMD Alignment A.

Enhancing Numerical Prediction in LLMs via Smooth MMD Alignment 12 Enhancing Numerical Prediction in LLMs via Smooth MMD Alignment A

Reference 6

Resolution
unresolved
no resolver link, observed 2026-06-29T04:49:38.723360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T04:49:38.723360Z digest=sha256:71bffc5fcab03cbdef428d2c9b13c3e45d1a07f5cb9d5f62be24ac5c512a240c

Observation 9c372fd6-b5fa-4c56-9ed3-7c305e24d503 · outbound

This paper cites an unresolved cited work.

Enhancing Numerical Prediction in LLMs via Smooth MMD Alignment Unresolved cited work

Reference 7

Resolution
unresolved
no resolver link, observed 2026-06-29T04:49:38.723360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T04:49:38.723360Z digest=sha256:6b1afd42a44d4b91619966d29c195953d37e73a258af40030cc954339ed76b05

Observation 0c63f36e-b44f-487f-8637-5613a0623bae · outbound

This paper cites The distributions are discrete vectorsp,q∈∆ N.

Enhancing Numerical Prediction in LLMs via Smooth MMD Alignment The distributions are discrete vectorsp,q∈∆ N

Reference 8

Resolution
unresolved
no resolver link, observed 2026-06-29T04:49:38.723360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T04:49:38.723360Z digest=sha256:661ef3c38df5ece21eeeb344b7a66f417aa1c1db7991b3b567635f5e511da47d

Observation b38fc4b5-8798-4c96-a703-d00f3198523c · outbound

This paper cites an unresolved cited work.

Enhancing Numerical Prediction in LLMs via Smooth MMD Alignment Unresolved cited work

Reference 9

Resolution
unresolved
no resolver link, observed 2026-06-29T04:49:38.723360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T04:49:38.723360Z digest=sha256:6db88cf8ab2f86505e155d6451812ade190ed31c48d8c48a50be6af0b7cbfbdd

Observation 63d1ce1b-59ac-4e93-ae85-b5d23e1ce5dd · outbound

This paper cites an unresolved cited work.

Enhancing Numerical Prediction in LLMs via Smooth MMD Alignment Unresolved cited work

Reference 10

Resolution
unresolved
no resolver link, observed 2026-06-29T04:49:38.723360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T04:49:38.723360Z digest=sha256:070652993d0c199c23c82f800d603546554b3069fe518c91aff04a8294337321

Observation a72a4e6e-2f05-46ae-92ef-ffb39a1b7c6b · outbound

This paper cites Thus: NX i=1 NX j=1 Kijr2 i = NX i=1 r2 i   NX j=1 Kij   = NX i=1 degir2 i =r ⊤Dr,(20) whereD=diag(deg 1,.

Enhancing Numerical Prediction in LLMs via Smooth MMD Alignment Thus: NX i=1 NX j=1 Kijr2 i = NX i=1 r2 i   NX j=1 Kij   = NX i=1 degir2 i =r ⊤Dr,(20) whereD=diag(deg 1,

Reference 11

Resolution
unresolved
no resolver link, observed 2026-06-29T04:49:38.723360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T04:49:38.723360Z digest=sha256:695ae679e7914af9650faa09c67084b7eb933cff6b6855d29dc0c7d91997afd2

Observation 9e7647ef-cb77-4225-a9d4-af1267b97170 · outbound

This paper cites 14 Enhancing Numerical Prediction in LLMs via Smooth MMD Alignment C.

Enhancing Numerical Prediction in LLMs via Smooth MMD Alignment 14 Enhancing Numerical Prediction in LLMs via Smooth MMD Alignment C

Reference 12

Resolution
malformed identifier
arxiv_id, observed 2026-06-29T19:13:53.342300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T04:49:38.723360Z digest=sha256:1738ed27900f2f02ebd3fb3ab4ce8b42ed602b2c004b20760870a638275e5189

Pith citing papers

No inbound Pith citation observations are available.