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Paper Citation Record · LEDGER

Lorentz Group Equivariant Neural Network for Particle Physics

As of 22 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 2026-08-22T06:32:14.747728+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=sha256:7aed0046cdce4ce2bce9553543992c9908c2c696571cc9b40008a5ebd2e49d9b

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:bbc2f6137c18acd1e9c4420808c43ec5eba714c8435b54e33f3b54146e3802f8

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 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-20T17:00:59.473456Z digest=sha256:f3715d4aa577e94fb82658b933f93cba53aad52b4294ca0a33f5b8b1d9ff6069

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 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-06-26T07:53:57.250401Z digest=sha256:ea896b5742af98bb1f6c68d16e186e0fe342098f89b4efff6c6ac791fa9d1864

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 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-06-30T10:13:09.503522Z digest=sha256:e9876797da9fc8b163c71daa244e8af787dec1f6233a1cf25c8d015cff418418

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:c3729b4820f54535c9d1f256a897268f721bb60d2caf8a864afe6ea8c2caead3