Paper Citation Record · LEDGER

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering

As of 4 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 1 inbound Pith citation observation for arXiv:2604.24334.

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

pith.paper-citation-record.v1
2604.24334 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-08T03:56:49.260151Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-03T06:30:56.289259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T18:01:58.830209Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

33 of 33 outbound references displayed

verified exact9
verified fuzzy10
unresolved2
parse uncertain0
malformed identifier2
metadata mismatch10

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cc4fcaea-35e3-4889-b594-0d9c8f1af851 · outbound

This paper cites https : / / www.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering https : / / www

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:28:22.864723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation ce0bebd5-d644-49c2-b63c-e1e20c800688 · outbound

This paper cites Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:28:22.893578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 4cd5a2b4-3581-40b1-88e8-31291e0ac286 · outbound

This paper cites Latent dirichlet allocation.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering Latent dirichlet allocation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:28:22.887290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 7d25e897-9d36-4f74-9943-908df51fb0c0 · outbound

This paper cites Improving language models by retrieving from trillions of tokens.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering Improving language models by retrieving from trillions of tokens

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-17T12:55:41.994998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation b20a6e91-1996-417d-8565-65ae950b9319 · outbound

This paper cites On the resemblance and containment of documents.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering On the resemblance and containment of documents

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:28:22.889999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 6be7762d-ccb1-4605-8209-7287d75558fe · outbound

This paper cites Identifying and filtering near-duplicate documents.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering Identifying and filtering near-duplicate documents

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:28:22.881745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 5ba8a442-3f5f-43be-8662-df87d84bf28f · outbound

This paper cites M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T22:39:03.722424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 10e574d9-3ce3-4e00-a19a-445892a20c60 · outbound

This paper cites ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T00:14:28.150782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 40145df3-59a1-4c7a-a37f-021fc81ae235 · outbound

This paper cites original-date: 2022-10-05T17:58:44Z.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering original-date: 2022-10-05T17:58:44Z

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:28:22.878683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 76a5cc8b-b925-4b06-a6d6-586e06a9c24c · outbound

This paper cites Enhancing Chat Language Models by Scaling High-quality Instructional Conversations.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering Enhancing Chat Language Models by Scaling High-quality Instructional Conversations

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T17:25:08.436185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 4db77d1c-8d2c-4a0d-aac6-758f34577928 · outbound

This paper cites WebFAQ: A Multilingual Collection of Natural Q&A Datasets for Dense Retrieval.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering WebFAQ: A Multilingual Collection of Natural Q&A Datasets for Dense Retrieval

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-09T00:14:28.063092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation d7d2643d-5ae1-4d91-8668-905a3b79c5c0 · outbound

This paper cites Precise Zero-Shot Dense Retrieval without Relevance Labels.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering Precise Zero-Shot Dense Retrieval without Relevance Labels

Reference 12

Resolution: verified exact; doi, observed 2026-05-09T00:14:28.167176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 272458cc-b7e2-4f5f-8455-263e294be037 · outbound

This paper cites Retrieval-Augmented Generation for Large Language Models: A Survey.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T00:14:28.145332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 5d7d6a41-21ef-4fe7-a258-b3adf716a04d · outbound

This paper cites BERTopic: Neural topic modeling with a class-based TF-IDF procedure.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering BERTopic: Neural topic modeling with a class-based TF-IDF procedure

Reference 14

Resolution: metadata mismatch; local_arxiv, observed 2026-05-11T21:51:33.648345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 730af53a-1eee-484b-9d96-151eada96ef2 · outbound

This paper cites REALM: Retrieval-Augmented Language Model Pre-Training.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering REALM: Retrieval-Augmented Language Model Pre-Training

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-15T09:59:16.231557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 793c7ae9-0a6e-4fd5-8981-0a72b4b757f5 · outbound

This paper cites an unresolved cited work.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-05-26T21:28:22.871515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation c1551767-024f-4310-8e36-69cffa56c4ba · outbound

This paper cites Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:51:33.688605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 28ea42ca-97d6-4b8e-88d4-5bf01a324eea · outbound

This paper cites author Montani, I.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering author Montani, I

Reference 18

Resolution: metadata mismatch; doi, observed 2026-05-09T00:14:28.171140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T15:08:47.996614+00:00.

Observation bae61d07-2286-4ae3-9ab2-8b939eb81a2a · outbound

This paper cites Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering

Reference 19

Resolution: verified exact; doi, observed 2026-05-09T00:14:28.136380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 2f6cdeaa-b1aa-42cc-ae09-7b4fb6d06969 · outbound

This paper cites Dense Passage Retrieval for Open-Domain Question Answering.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering Dense Passage Retrieval for Open-Domain Question Answering

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T21:24:42.733368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 3f2e66ff-f9d3-4558-8622-ae98dc496755 · outbound

This paper cites Deduplicating Training Data Makes Language Models Better.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering Deduplicating Training Data Makes Language Models Better

Reference 21

Resolution: verified exact; doi, observed 2026-05-09T00:14:28.090922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 3994e180-d176-4b78-a5cf-24838028c059 · outbound

This paper cites Ullman.Mining of Massive Datasets.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering Ullman.Mining of Massive Datasets

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:28:22.897070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 47188752-5c92-45b9-bea2-d69bc39d64d8 · outbound

This paper cites Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Reference 23

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T20:41:58.301631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation c21c33ae-9956-45b9-93e1-33b9ccccbdc7 · outbound

This paper cites Pointer Sentinel Mixture Models.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering Pointer Sentinel Mixture Models

Reference 24

Resolution: malformed identifier; local_arxiv, observed 2026-05-11T21:51:33.679466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 92b3a032-107f-4028-a845-66d0504c2fe4 · outbound

This paper cites Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-15T09:51:47.316034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation f7b58db5-6f3b-48ca-8af1-997509c250ba · outbound

This paper cites an unresolved cited work.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-05-26T21:28:22.900616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 3b7b0a0b-1ac8-4e9b-b59c-c4690a7b66f6 · outbound

This paper cites June 2021.URL: https : / / huggingface.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering June 2021.URL: https : / / huggingface

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:28:22.868612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 84e6e3cb-cfa3-4355-a40b-24763ad83690 · outbound

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

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 28

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T14:54:09.090339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 49f505c4-764e-4c9b-95d9-357e99fbcc63 · outbound

This paper cites Lost in the middle: An emergent property from information retrieval demands in LLMs.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering Lost in the middle: An emergent property from information retrieval demands in LLMs

Reference 29

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T00:14:28.128955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 30cac83d-b86c-4697-a24b-0cc0189587f9 · outbound

This paper cites RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-15T13:07:16.607810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 625311e8-c691-476e-a5f7-32a7d1917661 · outbound

This paper cites en-US.URL: https://bidenwhitehouse.archives.gov/state-of-the-union- 2024/(visited on 12/17/2025).

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering en-US.URL: https://bidenwhitehouse.archives.gov/state-of-the-union- 2024/(visited on 12/17/2025)

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:28:22.875069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation bf412ea7-e9d5-498d-9890-4915a14f13f9 · outbound

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

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T21:28:22.884716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 7fd9338a-9da1-4db5-8038-b4699f576411 · outbound

This paper cites RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs.

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs

Reference 33

Resolution: malformed identifier; arxiv_id, observed 2026-05-11T21:51:33.676238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Pith citing papers

Observation 93496f4a-4c83-4546-bc98-236f84f9ee3d · inbound

Cross-Attention Calibrated Deduplication for Retrieval-Augmented Generation System cites this paper.

Cross-Attention Calibrated Deduplication for Retrieval-Augmented Generation System Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-31T18:01:58.830209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.