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Paper Citation Record · LEDGER

Polynomial Composition Activations: Unleashing the Dynamics of Large Language Models

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2411.03884.

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

pith.paper-citation-record.v1
2411.03884 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T10:23:36.108763Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T10:29:45.056958Z

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 dace6bb5-6d43-4815-9962-b00131593473 · inbound

More Expressive Feedforward Layers: Part I. Token-Adaptive Mixing of Activations cites this paper.

More Expressive Feedforward Layers: Part I. Token-Adaptive Mixing of Activations Polynomial Composition Activations: Unleashing the Dynamics of Large Language Models

Reference 48

Resolution
verified exact
arxiv_id, observed 2026-06-29T19:43:54.832658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-29T19:37:51.563121Z digest=sha256:904e0bb8bcbd4fcb50c94100887585472a8a0af367d1f5d2943fcd872e3b9781

Observation 7468e6db-faf6-406e-92cb-73fef9649443 · inbound

It's Much Easier for Neural Networks to learn Game of Life Dynamics with the Right Activation Function: Polynomial Kolmogorov-Arnold Networks cites this paper.

It's Much Easier for Neural Networks to learn Game of Life Dynamics with the Right Activation Function: Polynomial Kolmogorov-Arnold Networks Polynomial Composition Activations: Unleashing the Dynamics of Large Language Models

Reference 42

Resolution
verified exact
arxiv_id, observed 2026-07-04T10:29:45.058482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-06-26T08:48:49.171949Z digest=sha256:cfc7f67d475ded70d233fcf56a60938042268aa0c614285c1e75055567aca722

Observation 438ef524-aa38-4754-8938-d742ed559470 · inbound

Is SwiGLU's Open Positive Tail Necessary? Evidence from Closed-Tail Gating with MemGLU cites this paper.

Is SwiGLU's Open Positive Tail Necessary? Evidence from Closed-Tail Gating with MemGLU Polynomial Composition Activations: Unleashing the Dynamics of Large Language Models

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-10T10:23:36.108763Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T10:23:36.108763Z digest=sha256:a992337ebf13c7108f16474c9adb5652fec368c9bade28903247bc9357304cd6