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Paper Citation Record · LEDGER

Beyond Attention: Signed Integrated Gradients Attribution in a BiomeGPT-Style Microbiome Transformer

As of 18 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2608.06486.

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

pith.paper-citation-record.v1
2608.06486 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:36:51.361018Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

18 of 18 outbound references displayed

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  • verified fuzzy3
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f4a72d73-b7ea-4211-a8de-9be82bb71b6b · outbound

This paper cites Towards better understanding of gradient-based attribution methods for Deep Neural Networks.

Beyond Attention: Signed Integrated Gradients Attribution in a BiomeGPT-Style Microbiome Transformer Towards better understanding of gradient-based attribution methods for Deep Neural Networks

Reference 1

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Observation 24ed0be4-03d4-4b56-ac64-00b8f87f22a6 · outbound

This paper cites McIver, Kelsey N.

Beyond Attention: Signed Integrated Gradients Attribution in a BiomeGPT-Style Microbiome Transformer McIver, Kelsey N

Reference 2

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verified fuzzy
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Source-reported events for the cited work

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Observation b38737b4-b9e9-4540-a78d-c731b9b4c308 · outbound

This paper cites scGPT: toward building a foundation model for single-cell multi-omics using generative AI.Nature Meth- ods, 21:1470–1480, 2024.

Beyond Attention: Signed Integrated Gradients Attribution in a BiomeGPT-Style Microbiome Transformer scGPT: toward building a foundation model for single-cell multi-omics using generative AI.Nature Meth- ods, 21:1470–1480, 2024

Reference 3

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Observation 9400bef7-c90f-44da-a54c-cd91928db76d · outbound

This paper cites Bert: Pre-training of deep bidi- rectional transformers for language understanding.

Beyond Attention: Signed Integrated Gradients Attribution in a BiomeGPT-Style Microbiome Transformer Bert: Pre-training of deep bidi- rectional transformers for language understanding

Reference 4

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Source-reported events for the cited work

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Observation 0154ec32-b479-4901-b847-fc453db83924 · outbound

This paper cites Revisiting deep learning models for tabular data.

Beyond Attention: Signed Integrated Gradients Attribution in a BiomeGPT-Style Microbiome Transformer Revisiting deep learning models for tabular data

Reference 5

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Source-reported events for the cited work

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Observation 1e7738d1-e4ca-4869-b50f-3d588aba7299 · outbound

This paper cites Attention is not Explanation.

Beyond Attention: Signed Integrated Gradients Attribution in a BiomeGPT-Style Microbiome Transformer Attention is not Explanation

Reference 6

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Unavailable: canonical work link unavailable.

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Observation 6c34ec0e-9c08-4f5e-bb42-50a8fe6da2ff · outbound

This paper cites Explaining Explanations: Axiomatic Feature Interactions for Deep Networks.

Beyond Attention: Signed Integrated Gradients Attribution in a BiomeGPT-Style Microbiome Transformer Explaining Explanations: Axiomatic Feature Interactions for Deep Networks

Reference 7

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local_arxiv, observed 2026-08-15T14:36:51.484947Z

Source-reported events for the cited work

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Observation c93ad33a-3fa3-43e3-a227-a980a04551b3 · outbound

This paper cites Captum: A unified and generic model interpretability library for PyTorch.

Beyond Attention: Signed Integrated Gradients Attribution in a BiomeGPT-Style Microbiome Transformer Captum: A unified and generic model interpretability library for PyTorch

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 1a0fda12-e052-4029-925f-99c7abf94dd9 · outbound

This paper cites Rethinking attention- model explainability through faithfulness violation test.

Beyond Attention: Signed Integrated Gradients Attribution in a BiomeGPT-Style Microbiome Transformer Rethinking attention- model explainability through faithfulness violation test

Reference 9

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation d4f7a0c3-28b8-48ce-90ce-5a04790ae4b5 · outbound

This paper cites Medearis, Siyao Zhu, and Ali R.

Beyond Attention: Signed Integrated Gradients Attribution in a BiomeGPT-Style Microbiome Transformer Medearis, Siyao Zhu, and Ali R

Reference 10

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verified exact
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Source-reported events for the cited work

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Observation 5b77edb1-b6a1-490e-909a-7e88b2b9b932 · outbound

This paper cites Dowd, Curtis Huttenhower, Martin Morgan, Nicola Segata, and Levi Waldron.

Beyond Attention: Signed Integrated Gradients Attribution in a BiomeGPT-Style Microbiome Transformer Dowd, Curtis Huttenhower, Martin Morgan, Nicola Segata, and Levi Waldron

Reference 11

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Observation 15043c56-7ab2-4cbd-93f3-6e3acfe73065 · outbound

This paper cites David, and Xiaoli Fern.

Beyond Attention: Signed Integrated Gradients Attribution in a BiomeGPT-Style Microbiome Transformer David, and Xiaoli Fern

Reference 12

Resolution
verified exact
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Source-reported events for the cited work

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Observation 5c5f32a5-e48c-4330-a2ce-cd754afd0652 · outbound

This paper cites Uniform Discretized Integrated Gradients: An effective attribution based method for explaining large language models.

Beyond Attention: Signed Integrated Gradients Attribution in a BiomeGPT-Style Microbiome Transformer Uniform Discretized Integrated Gradients: An effective attribution based method for explaining large language models

Reference 13

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verified exact
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Source-reported events for the cited work

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Observation 6f984c30-20e5-48ae-af8b-cd44d3a2d41c · outbound

This paper cites Axiomatic Attribution for Deep Networks.

Beyond Attention: Signed Integrated Gradients Attribution in a BiomeGPT-Style Microbiome Transformer Axiomatic Attribution for Deep Networks

Reference 14

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Observation 02841b8f-442a-4f97-a2cd-c390d572c3c3 · outbound

This paper cites Theodoris, Ling Xiao, Anant Chopra, Mark D.

Beyond Attention: Signed Integrated Gradients Attribution in a BiomeGPT-Style Microbiome Transformer Theodoris, Ling Xiao, Anant Chopra, Mark D

Reference 15

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Observation 61122468-fe3f-4b56-9edb-ff32c734851d · outbound

This paper cites Attention is not not Explanation.

Beyond Attention: Signed Integrated Gradients Attribution in a BiomeGPT-Style Microbiome Transformer Attention is not not Explanation

Reference 16

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Observation ce201d59-ff1a-4f80-9fb7-062351f640b5 · outbound

This paper cites scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data.Nature Machine Intelligence, 4:852–866, 2022.

Beyond Attention: Signed Integrated Gradients Attribution in a BiomeGPT-Style Microbiome Transformer scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data.Nature Machine Intelligence, 4:852–866, 2022

Reference 17

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Observation e3e1d634-ea63-42f2-a9cc-d0e7a1404a3c · outbound

This paper cites URLhttps://doi.org/10.1038/s41587-023-01688-w.

Beyond Attention: Signed Integrated Gradients Attribution in a BiomeGPT-Style Microbiome Transformer URLhttps://doi.org/10.1038/s41587-023-01688-w

Reference 2023

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Pith citing papers

No inbound Pith citation observations are available.