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Paper Citation Record · LEDGER

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT

As of 22 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2506.18297.

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

pith.paper-citation-record.v1
2506.18297 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:27:11.349146Z

measured 34 of 34 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

34 of 34 outbound references displayed

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  • unresolved26
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External citation measurements

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Outbound references

Observation 233ee537-d65a-4813-9192-8651eac38711 · outbound

This paper cites Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension

Reference 1

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Observation 7875c9f5-ae1e-4f56-b1bc-7ae30ef537fc · outbound

This paper cites The probabilistic relevance framework: Bm25 and beyond,.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT The probabilistic relevance framework: Bm25 and beyond,

Reference 2

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Observation 6c261510-af11-4f9b-b3f8-9d29d7d325e2 · outbound

This paper cites Dense passage retrieval for open-domain question an- swering,.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT Dense passage retrieval for open-domain question an- swering,

Reference 3

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Observation fe9528ea-4198-47a7-9ada-05f6aaa79ad7 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 4

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 1bb266c7-0258-47fa-9389-48db0eea2ef5 · outbound

This paper cites Passage Re-ranking with BERT.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT Passage Re-ranking with BERT

Reference 5

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Observation 29bb34a1-d5a4-4596-9c07-aa2bc3b9982d · outbound

This paper cites Document ranking with a pretrained sequence-to-sequence model,.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT Document ranking with a pretrained sequence-to-sequence model,

Reference 6

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Source-reported events for the cited work

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Observation c3e8f393-b157-4656-ab28-57016b3c36cb · outbound

This paper cites Towards General Text Embeddings with Multi-stage Contrastive Learning.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT Towards General Text Embeddings with Multi-stage Contrastive Learning

Reference 7

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Observation bc81df79-b7ed-4fbe-adb4-79a2606b4de1 · outbound

This paper cites Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference

Reference 8

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Observation c1699780-bf8e-4fc7-b51a-7a186e0a7e3b · outbound

This paper cites MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers

Reference 9

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Observation 2b51fcb3-c2d4-41ab-8b84-8524fabb24da · outbound

This paper cites Decoupled weight decay regularization,.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT Decoupled weight decay regularization,

Reference 10

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Observation a7918ac2-3f3b-4dc0-ac4f-00962e7aedd4 · outbound

This paper cites Symbolic Discovery of Optimization Algorithms.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT Symbolic Discovery of Optimization Algorithms

Reference 11

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Observation 9ddc1246-65c6-4c87-b0f8-a3252f403dbd · outbound

This paper cites MS MARCO: A Human Generated MAchine Reading COmprehension Dataset.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

Reference 12

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Observation b69500f1-3ce7-47e0-8a2b-8c6371dc5091 · outbound

This paper cites Overview of the TREC 2019 deep learning track.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT Overview of the TREC 2019 deep learning track

Reference 13

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Observation fc4a16ff-afc3-4049-a875-ffd28b017b61 · outbound

This paper cites Modal: Serverless compute for ai and data workflows,.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT Modal: Serverless compute for ai and data workflows,

Reference 14

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Source-reported events for the cited work

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Observation acef205b-c166-4239-8434-fdc91dbd8213 · outbound

This paper cites Pretrained Transformers for Text Ranking: BERT and Beyond.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT Pretrained Transformers for Text Ranking: BERT and Beyond

Reference 15

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Observation e01dc00f-3770-4994-8f61-d429cadf84d8 · outbound

This paper cites Complementing lexical retrieval with semantic residual embedding,.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT Complementing lexical retrieval with semantic residual embedding,

Reference 16

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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.

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Observation 695c01fd-6f05-4155-868b-99613c8ea533 · outbound

This paper cites Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation

Reference 17

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Observation 9cf0096e-a2ce-441d-b3db-c567dc62fd37 · outbound

This paper cites RoFormer: Enhanced Transformer with Rotary Position Embedding.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT RoFormer: Enhanced Transformer with Rotary Position Embedding

Reference 18

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Observation aee5d88b-4cff-43f5-99d0-dda36fbe0630 · outbound

This paper cites FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness

Reference 19

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Observation b6c95c10-604c-400f-8096-5f2c3a8d79f9 · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 20

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Observation 8cc6ccca-8a56-434e-9060-b00dc847744d · outbound

This paper cites Adam: A method for stochastic optimization,.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT Adam: A method for stochastic optimization,

Reference 21

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Observation 6271e9e3-887a-4b4e-8b95-1fa13c015d10 · outbound

This paper cites Overview of the TREC 2020 deep learning track.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT Overview of the TREC 2020 deep learning track

Reference 22

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Observation 4850a694-bdd4-4858-982b-62b65b716f40 · outbound

This paper cites trec eval information retrieval evaluation software,.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT trec eval information retrieval evaluation software,

Reference 23

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Observation f27bcb54-fae8-449e-acba-7e4facc025bd · outbound

This paper cites Transformers: State-of- the-art natural language processing,.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT Transformers: State-of- the-art natural language processing,

Reference 24

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Observation 91534e39-fc50-4f18-92ad-6316bf38a847 · outbound

This paper cites GLU Variants Improve Transformer.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT GLU Variants Improve Transformer

Reference 25

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Observation b657d56e-2722-4d76-95cd-ba4470c30b5d · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 26

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Observation d738fa1e-afb2-42ff-91a3-06fe1877bf8b · outbound

This paper cites Experiment tracking with weights and biases,.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT Experiment tracking with weights and biases,

Reference 27

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Observation ddce5a89-84d7-4485-a300-ec4506dd0b09 · outbound

This paper cites Pyserini: A Python toolkit for reproducible information retrieval research with sparse and dense representations,.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT Pyserini: A Python toolkit for reproducible information retrieval research with sparse and dense representations,

Reference 28

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Observation c740d6d7-d8e8-4e13-b71a-39cef92a126c · outbound

This paper cites Distilling Dense Representations for Ranking using Tightly-Coupled Teachers.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT Distilling Dense Representations for Ranking using Tightly-Coupled Teachers

Reference 29

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Observation 97263fab-2260-4fd6-a1f7-46e9b260e3d3 · outbound

This paper cites Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval

Reference 30

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Observation 81c1de74-b7cf-4faa-9825-87664f6739a7 · outbound

This paper cites Nboost: Neural boosting search results,.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT Nboost: Neural boosting search results,

Reference 31

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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.

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Observation c86fa2a3-25ae-4c72-9b05-8f1d2ed650ce · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT Adam: A Method for Stochastic Optimization

Reference 2017

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Observation 1474fe5d-d8f5-4a3d-928c-3abcb56e319d · outbound

This paper cites Decoupled Weight Decay Regularization.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT Decoupled Weight Decay Regularization

Reference 2019

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Observation f4c7ad5a-95ff-4127-9df4-965685508bac · outbound

This paper cites Complementing Lexical Retrieval with Semantic Residual Embedding.

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT Complementing Lexical Retrieval with Semantic Residual Embedding

Reference 2021

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local_arxiv, observed 2026-08-06T23:27:11.779120Z

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Pith citing papers

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