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Paper Citation Record · LEDGER

Lorentz Group Equivariant Neural Network for Particle Physics

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2006.04780.

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

pith.paper-citation-record.v1
2006.04780 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:31:43.260065Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T11:39:46.497110Z

Reference resolution

0 of 0 outbound references displayed

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  • verified fuzzy0
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  • malformed identifier0
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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 1744c83a-bf64-492e-ad31-bf383309cfb0 · inbound

Graph theory inspired anomaly detection at the LHC cites this paper.

Graph theory inspired anomaly detection at the LHC Lorentz Group Equivariant Neural Network for Particle Physics

Reference 125

Resolution
unresolved
no resolver link, observed 2026-08-15T18:31:43.260065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T18:31:43.260065Z digest=sha256:fda5c7dea5a381d5f59acf28aaecdce8aa2fa91af9be42464b58781580229986

Observation 9ab77b6a-0990-46a4-a79b-6e5feeea064f · inbound

Explicit or Implicit? Encoding Physics at the Precision Frontier cites this paper.

Explicit or Implicit? Encoding Physics at the Precision Frontier Lorentz Group Equivariant Neural Network for Particle Physics

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-03T02:35:29.479808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T02:35:29.479808Z digest=sha256:e137d7b705e89175e742d96b41e11a6ae5d5135bfd49356a33b450f99d692ae7

Observation 916e124a-1ba7-499b-992e-63f127b339e6 · inbound

Geometric algebra as the input language of collider foundation models cites this paper.

Geometric algebra as the input language of collider foundation models Lorentz Group Equivariant Neural Network for Particle Physics

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-20T17:03:37.483165Z

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.

source=arxiv_source observed=2026-05-20T17:00:59.473456Z digest=sha256:1f2205a02b878590c8b09ee2d6916e2a3e7cb7bc384eeb5b940486065fff52a6

Observation 23227249-73cc-4888-8609-11e1c3864786 · inbound

One Generator, Any Process: LLM-Conditioning for the LHC cites this paper.

One Generator, Any Process: LLM-Conditioning for the LHC Lorentz Group Equivariant Neural Network for Particle Physics

Reference 16

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T11:39:46.498558Z

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.

source=arxiv_source observed=2026-06-26T07:53:57.250401Z digest=sha256:176f00b9318e6a95ea165e3996af83ae869997e38ab91aa0e43ed876b5e98d2e

Observation ccfe2171-4bf9-4036-aaa1-2c7597b3410a · inbound

One Generator, Any Process: LLM-Conditioning for the LHC cites this paper.

One Generator, Any Process: LLM-Conditioning for the LHC Lorentz Group Equivariant Neural Network for Particle Physics

Reference 21

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T10:14:36.126072Z

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.

source=arxiv_source observed=2026-06-30T10:13:09.503522Z digest=sha256:a71bcd8c01c00109a67e8043da7c652de3dfba87c13d299a00773289b6e77c68

Observation 3605a95e-99b2-4f53-80ef-6bd41682f156 · inbound

Predict before you train: Scaling Laws for particle physics foundation models cites this paper.

Predict before you train: Scaling Laws for particle physics foundation models Lorentz Group Equivariant Neural Network for Particle Physics

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-07-30T23:56:38.643131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T23:56:38.643131Z digest=sha256:d1d98d754fadfca9155cbf4893fb6e6d1d54f38c7bc94da18df35ea7bcce5530