Paper Citation Record · LEDGER

GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings

As of 17 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 1 inbound Pith citation observation for arXiv:2509.10844.

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

pith.paper-citation-record.v1
2509.10844 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:57:35.532165Z

measured 24 of 24 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T00:25:47.970957Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:39:57.391121Z

Reference resolution

23 of 23 outbound references displayed

verified exact1
verified fuzzy10
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c832fb8e-9fe6-4090-893d-b72e92258f8c · outbound

This paper cites Greenback bears and fiscal hawks: Finance is a jungle and text embeddings must adapt.

GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings Greenback bears and fiscal hawks: Finance is a jungle and text embeddings must adapt

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:57:36.055892Z

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.

Observation b920e36e-bc57-4aff-9d71-05615d529d24 · outbound

This paper cites CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval.

GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T15:57:35.452377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation badd1e79-fb5a-4084-852c-18e8b8c99590 · outbound

This paper cites Mteb: Massive text em- bedding benchmark.

GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings Mteb: Massive text em- bedding benchmark

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:57:36.040284Z

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.

Observation 4b719bed-4547-4497-a4e9-54c40a2b46bb · outbound

This paper cites Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang.

GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:57:36.024349Z

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.

Observation e3b89ad8-c9af-47b2-87c6-30b00118cb8c · outbound

This paper cites 11 Nils Reimers and Iryna Gurevych.

GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings 11 Nils Reimers and Iryna Gurevych

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:57:36.008034Z

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.

Observation c36da233-e671-4eb5-b79d-e33e0ec6bad8 · outbound

This paper cites Pooling And Attention: What Are Effective Designs For LLM-Based Embedding Models?.

GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings Pooling And Attention: What Are Effective Designs For LLM-Based Embedding Models?

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T15:57:35.478661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d597e0b-cc8a-43ad-8a9c-fefe372bd98b · outbound

This paper cites FinMTEB: Finance massive text embedding benchmark.

GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings FinMTEB: Finance massive text embedding benchmark

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:57:35.991870Z

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.

Observation 7ea93fd7-6362-4be6-bb47-7c941072155b · outbound

This paper cites Faster gaze prediction with dense networks and Fisher pruning.

GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings Faster gaze prediction with dense networks and Fisher pruning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T15:57:35.489166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34c6276e-20b8-408c-b08f-15dc1af159ed · outbound

This paper cites The information bottleneck method.

GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings The information bottleneck method

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T15:57:35.498528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6fec956-0125-48b2-aaf6-8cebfa795c9a · outbound

This paper cites Adapting general-purpose embedding models to private datasets using keyword-based retrieval.

GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings Adapting general-purpose embedding models to private datasets using keyword-based retrieval

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:57:35.958758Z

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.

Observation 6aed5ddd-0372-4f45-bdcb-f466fd9f8235 · outbound

This paper cites Miles Williams, George Chrysostomou, Vitor Jeronymo, and Nikolaos Aletras.

GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings Miles Williams, George Chrysostomou, Vitor Jeronymo, and Nikolaos Aletras

Reference 18

Resolution: verified exact; raw_fallback, observed 2026-08-15T15:57:35.683469Z

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.

Observation 5b07af61-dec9-4805-9128-cb2be2f7b699 · outbound

This paper cites Lawyer gpt: A legal large language model with enhanced domain knowledge and reasoning capabilities.

GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings Lawyer gpt: A legal large language model with enhanced domain knowledge and reasoning capabilities

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:57:35.944342Z

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.

Observation 86ca92fa-532d-4ade-8a38-1bf5f9cd4128 · outbound

This paper cites Pruning as a domain-specific llm extractor.

GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings Pruning as a domain-specific llm extractor

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:57:35.929718Z

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.

Observation c48cc42b-9237-44b6-a29d-cb195285a5b0 · outbound

This paper cites Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models.

GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T15:57:35.522216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15b8d1c6-3066-48ad-880c-23c41ea320f1 · outbound

This paper cites an unresolved cited work.

GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:57:35.915113Z

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.

Observation 79559c1c-0582-4fe1-8a3e-e695ba225ad3 · outbound

This paper cites We then compute Pearson correlation coefficients between the normalized rank scores of different methods across all common parameters.

GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings We then compute Pearson correlation coefficients between the normalized rank scores of different methods across all common parameters

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:57:35.900901Z

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.

Observation 11814fad-f47e-46c4-b3ec-17f0637bfdee · outbound

This paper cites Scaling Laws for Neural Language Models.

GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings Scaling Laws for Neural Language Models

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-15T15:57:35.436590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e7ea578-801a-4e36-873e-0e02164b19f6 · outbound

This paper cites Finescope: Precision pruning for domain-specialized large language models using sae-guided self-data cultivation.arXiv preprint arXiv:2505.00624,.

GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings Finescope: Precision pruning for domain-specialized large language models using sae-guided self-data cultivation.arXiv preprint arXiv:2505.00624,

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-15T15:57:35.430744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50caf9b9-0363-4a63-b6bf-f1b2198fbaf5 · outbound

This paper cites Large language models are overparameterized text encoders.

GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings Large language models are overparameterized text encoders

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:57:35.975740Z

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.

Observation f069ec5c-4154-4887-9e2c-a5698861b6c6 · outbound

This paper cites ChEmbed: Enhancing Chemical Literature Search Through Domain-Specific Text Embeddings.

GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings ChEmbed: Enhancing Chemical Literature Search Through Domain-Specific Text Embeddings

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-15T15:57:35.441936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c3a4749-998f-46c3-a781-fa28f05baab3 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings Representation Learning with Contrastive Predictive Coding

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-15T15:57:35.462962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80c5be37-df5a-4de0-8271-f14a99c01db5 · outbound

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

GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T15:57:35.425777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8e41a1f-9a10-46ce-9f9d-6faa41c67812 · outbound

This paper cites Towards Domain Specification of Embedding Models in Medicine.

GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings Towards Domain Specification of Embedding Models in Medicine

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-15T15:57:35.447104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 37987e58-2983-432c-b889-40778e4bafa6 · inbound

Revealing Training Data Exposure in Vision Language Large Models via Parameter Gradients cites this paper.

Revealing Training Data Exposure in Vision Language Large Models via Parameter Gradients GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-07-04T16:39:57.392966Z

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.