Paper Citation Record · LEDGER

Provence: efficient and robust context pruning for retrieval-augmented generation

As of 19 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 12 inbound Pith citation observations for arXiv:2501.16214.

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

pith.paper-citation-record.v1
2501.16214 v1

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T13:43:43.894144Z

measured 70 of 70 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:26:55.267840Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T01:36:44.287584Z

Reference resolution

58 of 58 outbound references displayed

verified exact4
verified fuzzy14
unresolved39
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9f323af1-3b13-4cea-8ea4-ef6451e97410 · outbound

This paper cites write newline.

Provence: efficient and robust context pruning for retrieval-augmented generation write newline

Reference 1

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Source-reported events for the cited work

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Observation 876d69a8-818f-436d-a066-f3a494add874 · outbound

This paper cites Llama 3 model card.

Provence: efficient and robust context pruning for retrieval-augmented generation Llama 3 model card

Reference 2

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Source-reported events for the cited work

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Observation 9ccb1e32-6ca0-4ac1-8990-c23d71881605 · outbound

This paper cites Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers.

Provence: efficient and robust context pruning for retrieval-augmented generation Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T13:43:43.659031Z

Source-reported events for the cited work

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Observation 9a73f5c3-fdf8-4195-a132-df8bffab480e · outbound

This paper cites Reliable, Adaptable, and Attributable Language Models with Retrieval.

Provence: efficient and robust context pruning for retrieval-augmented generation Reliable, Adaptable, and Attributable Language Models with Retrieval

Reference 4

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Source-reported events for the cited work

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Observation a9a4d413-c828-4262-bf09-e115b3608bfc · outbound

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

Provence: efficient and robust context pruning for retrieval-augmented generation M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation

Reference 5

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Source-reported events for the cited work

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Observation f36f2ebc-6cb7-47e9-872f-7051129ab909 · outbound

This paper cites Benchmarking large language models in retrieval-augmented generation.

Provence: efficient and robust context pruning for retrieval-augmented generation Benchmarking large language models in retrieval-augmented generation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T13:43:43.677349Z

Source-reported events for the cited work

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Observation d728e02c-26d6-4e2b-8221-b971e31f7ec6 · outbound

This paper cites an unresolved cited work.

Provence: efficient and robust context pruning for retrieval-augmented generation Unresolved cited work

Reference 7

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Source-reported events for the cited work

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Observation bc4771bf-9d0f-4fa7-bbc6-c248b969502e · outbound

This paper cites Adapting language models to compress contexts.

Provence: efficient and robust context pruning for retrieval-augmented generation Adapting language models to compress contexts

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T13:43:43.686890Z

Source-reported events for the cited work

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Observation 0c4d0884-609e-46b6-962e-2f772eb302ea · outbound

This paper cites Decontextualization: Making sentences stand-alone.

Provence: efficient and robust context pruning for retrieval-augmented generation Decontextualization: Making sentences stand-alone

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T13:43:43.690362Z

Source-reported events for the cited work

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Observation c3fc3029-da5b-40c5-8061-25faf0a97abe · outbound

This paper cites Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, and Jennimaria Palomaki.

Provence: efficient and robust context pruning for retrieval-augmented generation Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, and Jennimaria Palomaki

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T13:43:43.693663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d36d24ff-09bd-4a8c-9f9e-1232b403f63e · outbound

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

Provence: efficient and robust context pruning for retrieval-augmented generation Overview of the TREC 2019 deep learning track

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T13:43:43.697457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24bd1a38-60c6-46e6-96e8-7775c18a90e9 · outbound

This paper cites Ms marco: Benchmarking ranking models in the large-data regime.

Provence: efficient and robust context pruning for retrieval-augmented generation Ms marco: Benchmarking ranking models in the large-data regime

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T13:43:43.701595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2ca54ed-aac4-42d4-9a0d-45f94adab2ac · outbound

This paper cites Flashattention-2: Faster attention with better parallelism and work partitioning.

Provence: efficient and robust context pruning for retrieval-augmented generation Flashattention-2: Faster attention with better parallelism and work partitioning

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2edf021e-c0f6-45a6-8be8-8d03b3e9560c · outbound

This paper cites Multi-step retriever-reader interaction for scalable open-domain question answering.

Provence: efficient and robust context pruning for retrieval-augmented generation Multi-step retriever-reader interaction for scalable open-domain question answering

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:43:44.642749Z

Source-reported events for the cited work

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Observation e078e2b7-7ae8-49e0-ba2a-58853f8e9a9d · outbound

This paper cites S yllabus QA : A course logistics question answering dataset.

Provence: efficient and robust context pruning for retrieval-augmented generation S yllabus QA : A course logistics question answering dataset

Reference 15

Resolution: verified exact; doi, observed 2026-08-10T13:43:44.024491Z

Source-reported events for the cited work

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Observation fa69091d-33d5-403d-98c3-36acd68f9d29 · outbound

This paper cites In-context autoencoder for context compression in a large language model.

Provence: efficient and robust context pruning for retrieval-augmented generation In-context autoencoder for context compression in a large language model

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:43:44.628189Z

Source-reported events for the cited work

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Observation 1d8efd84-cabb-4f80-a4d6-d29e43a45b0f · outbound

This paper cites Debertav3: Improving deberta using electra-style pre-training with gradient-disentangled embedding sharing, 2021 a.

Provence: efficient and robust context pruning for retrieval-augmented generation Debertav3: Improving deberta using electra-style pre-training with gradient-disentangled embedding sharing, 2021 a

Reference 17

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Source-reported events for the cited work

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Observation b22ebd61-bd80-421c-bbcf-3744556a92d3 · outbound

This paper cites \ DEBERTA \ : \ DECODING \ - \ enhanced \ \ bert \ \ with \ \ disentangled \ \ attention \.

Provence: efficient and robust context pruning for retrieval-augmented generation \ DEBERTA \ : \ DECODING \ - \ enhanced \ \ bert \ \ with \ \ disentangled \ \ attention \

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:43:44.604202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d3340bd8-c50f-4fe2-b6c4-4222f8ea5d1f · outbound

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

Provence: efficient and robust context pruning for retrieval-augmented generation Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation

Reference 19

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Observation 11b844dd-1902-4c7a-b06c-bc4c687962e5 · outbound

This paper cites RAGGED: Towards Informed Design of Scalable and Stable RAG Systems.

Provence: efficient and robust context pruning for retrieval-augmented generation RAGGED: Towards Informed Design of Scalable and Stable RAG Systems

Reference 20

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Source-reported events for the cited work

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Observation 919528fb-8509-47e8-b126-73bc02b36fc0 · outbound

This paper cites DSLR : Document refinement with sentence-level re-ranking and reconstruction to enhance retrieval-augmented generation.

Provence: efficient and robust context pruning for retrieval-augmented generation DSLR : Document refinement with sentence-level re-ranking and reconstruction to enhance retrieval-augmented generation

Reference 21

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3fabb209-202f-4b25-a0de-04a9e3793dfb · outbound

This paper cites Atlas: Few-shot Learning with Retrieval Augmented Language Models.

Provence: efficient and robust context pruning for retrieval-augmented generation Atlas: Few-shot Learning with Retrieval Augmented Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T13:43:43.740364Z

Source-reported events for the cited work

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Observation 81928140-c9d2-4823-b8b4-a5b21e884b81 · outbound

This paper cites LLML ingua: Compressing prompts for accelerated inference of large language models.

Provence: efficient and robust context pruning for retrieval-augmented generation LLML ingua: Compressing prompts for accelerated inference of large language models

Reference 23

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Source-reported events for the cited work

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Observation 90d1f8be-42c6-41a9-9df9-4b71b73e8113 · outbound

This paper cites L ong LLML ingua: Accelerating and enhancing LLM s in long context scenarios via prompt compression.

Provence: efficient and robust context pruning for retrieval-augmented generation L ong LLML ingua: Accelerating and enhancing LLM s in long context scenarios via prompt compression

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:43:44.585492Z

Source-reported events for the cited work

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Observation d08e8726-92e5-474a-aeca-20b0100c4763 · outbound

This paper cites Solar 10.7b: Scaling large language models with simple yet effective depth up-scaling, 2023.

Provence: efficient and robust context pruning for retrieval-augmented generation Solar 10.7b: Scaling large language models with simple yet effective depth up-scaling, 2023

Reference 25

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Source-reported events for the cited work

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Observation ef941511-7690-4aff-9858-541c3429426a · outbound

This paper cites Natural questions: a benchmark for question answering research.

Provence: efficient and robust context pruning for retrieval-augmented generation Natural questions: a benchmark for question answering research

Reference 26

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Source-reported events for the cited work

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Observation 49a47dd4-81dc-4bad-b275-a6c7b2946b4e · outbound

This paper cites Gonzalez, Hao Zhang, and Ion Stoica.

Provence: efficient and robust context pruning for retrieval-augmented generation Gonzalez, Hao Zhang, and Ion Stoica

Reference 27

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Source-reported events for the cited work

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Observation 28d7a8e0-21db-46fb-b9ec-7a75d192f929 · outbound

This paper cites LangChain Documentation.

Provence: efficient and robust context pruning for retrieval-augmented generation LangChain Documentation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:43:44.548828Z

Source-reported events for the cited work

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Observation 7f4527cc-7f8a-4124-b4d3-dcbd19a8a72f · outbound

This paper cites Naver Labs Europe (SPLADE) @ TREC Deep Learning 2022.

Provence: efficient and robust context pruning for retrieval-augmented generation Naver Labs Europe (SPLADE) @ TREC Deep Learning 2022

Reference 29

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Source-reported events for the cited work

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Observation 24dae181-a43f-4d6f-8577-9ac3b1807a9a · outbound

This paper cites Splade-v3: New baselines for splade, 2024.

Provence: efficient and robust context pruning for retrieval-augmented generation Splade-v3: New baselines for splade, 2024

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:43:44.538299Z

Source-reported events for the cited work

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Observation f193502c-e30d-4fef-84df-1fea6a6bd496 · outbound

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

Provence: efficient and robust context pruning for retrieval-augmented generation Retrieval- Augmented Generation for Knowledge - Intensive NLP Tasks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:43:44.526717Z

Source-reported events for the cited work

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Observation 1d6abf8c-dfac-4128-aad2-01e742e150b0 · outbound

This paper cites Compressing context to enhance inference efficiency of large language models.

Provence: efficient and robust context pruning for retrieval-augmented generation Compressing context to enhance inference efficiency of large language models

Reference 32

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Source-reported events for the cited work

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Observation 83015a25-b2b4-4fdd-a77d-681a0d40158d · outbound

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

Provence: efficient and robust context pruning for retrieval-augmented generation Pyserini: A python toolkit for reproducible information retrieval research with sparse and dense representations

Reference 33

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Source-reported events for the cited work

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Observation f20e400d-4762-4a54-bc67-d1af94093e48 · outbound

This paper cites RA - DIT : Retrieval-augmented dual instruction tuning.

Provence: efficient and robust context pruning for retrieval-augmented generation RA - DIT : Retrieval-augmented dual instruction tuning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:43:44.515674Z

Source-reported events for the cited work

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Observation d34049d2-560d-4a55-add0-38e922bc6164 · outbound

This paper cites Pisco: Pretty simple compression for retrieval-augmented generation.

Provence: efficient and robust context pruning for retrieval-augmented generation Pisco: Pretty simple compression for retrieval-augmented generation

Reference 35

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Source-reported events for the cited work

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Observation 0a727486-1d1e-4c12-bffe-1d7e897ac772 · outbound

This paper cites When not to trust language models: Investigating effectiveness of parametric and non-parametric memories.

Provence: efficient and robust context pruning for retrieval-augmented generation When not to trust language models: Investigating effectiveness of parametric and non-parametric memories

Reference 37

Resolution: verified exact; doi, observed 2026-08-10T13:43:43.998869Z

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Observation f69b1452-feb0-4910-b4e3-6dc32ed2a7bb · outbound

This paper cites Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference.

Provence: efficient and robust context pruning for retrieval-augmented generation Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference

Reference 39

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This paper cites Overview of BioASQ 2023: The Eleventh BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering, pp.\ 227--250.

Provence: efficient and robust context pruning for retrieval-augmented generation Overview of BioASQ 2023: The Eleventh BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering, pp.\ 227--250

Reference 40

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Source-reported events for the cited work

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Observation fade1b07-7464-49d2-975a-f8cc4e24ab56 · outbound

This paper cites Ms marco: A human generated machine reading comprehension dataset.

Provence: efficient and robust context pruning for retrieval-augmented generation Ms marco: A human generated machine reading comprehension dataset

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:43:44.492956Z

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Observation ac244822-55f6-43b6-bdfb-25a2b5ce77a3 · outbound

This paper cites Passage re-ranking with bert, 2020.

Provence: efficient and robust context pruning for retrieval-augmented generation Passage re-ranking with bert, 2020

Reference 42

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This paper cites Vicky Zhao, Lili Qiu, and Dongmei Zhang.

Provence: efficient and robust context pruning for retrieval-augmented generation Vicky Zhao, Lili Qiu, and Dongmei Zhang

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:43:44.477109Z

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Observation 83614b84-4de1-46bd-9098-0e839d6b9896 · outbound

This paper cites PyTorch: an imperative style, high-performance deep learning library.

Provence: efficient and robust context pruning for retrieval-augmented generation PyTorch: an imperative style, high-performance deep learning library

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:43:44.466559Z

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Observation 28d8a173-472f-40c7-9b88-d29159568c33 · outbound

This paper cites BERGEN : A benchmarking library for retrieval-augmented generation.

Provence: efficient and robust context pruning for retrieval-augmented generation BERGEN : A benchmarking library for retrieval-augmented generation

Reference 45

Resolution: verified exact; doi, observed 2026-08-10T13:43:43.978462Z

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Observation e4dd6d9d-c3a6-421a-a4dd-222a7cebfd27 · outbound

This paper cites Context Embeddings for Efficient Answer Generation in RAG.

Provence: efficient and robust context pruning for retrieval-augmented generation Context Embeddings for Efficient Answer Generation in RAG

Reference 46

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This paper cites Real-time open-domain question answering with dense-sparse phrase index.

Provence: efficient and robust context pruning for retrieval-augmented generation Real-time open-domain question answering with dense-sparse phrase index

Reference 47

Resolution: verified exact; doi, observed 2026-08-10T13:43:43.965454Z

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Observation 52fa9d98-75da-47a9-b018-3aa203ff4094 · outbound

This paper cites RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder.

Provence: efficient and robust context pruning for retrieval-augmented generation RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder

Reference 48

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This paper cites BEIR : A heterogeneous benchmark for zero-shot evaluation of information retrieval models.

Provence: efficient and robust context pruning for retrieval-augmented generation BEIR : A heterogeneous benchmark for zero-shot evaluation of information retrieval models

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-10T13:43:43.853130Z

Source-reported events for the cited work

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Observation c3f58978-4fa5-40f5-be0a-2bf3bfe60b7e · outbound

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Provence: efficient and robust context pruning for retrieval-augmented generation Llama 2: Open foundation and fine-tuned chat models, 2023

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-10T13:43:43.856373Z

Source-reported events for the cited work

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Observation 47756eb7-6981-4968-9279-a6f4e5aed3bd · outbound

This paper cites Learning to Filter Context for Retrieval-Augmented Generation.

Provence: efficient and robust context pruning for retrieval-augmented generation Learning to Filter Context for Retrieval-Augmented Generation

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-10T13:43:43.860281Z

Source-reported events for the cited work

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Observation 9f5f1073-bc63-40f3-a72d-ab146236aaa8 · outbound

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Provence: efficient and robust context pruning for retrieval-augmented generation Transformers: State-of-the-art natural language processing

Reference 52

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Source-reported events for the cited work

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This paper cites RECOMP : Improving retrieval-augmented LM s with context compression and selective augmentation.

Provence: efficient and robust context pruning for retrieval-augmented generation RECOMP : Improving retrieval-augmented LM s with context compression and selective augmentation

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:43:44.437898Z

Source-reported events for the cited work

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Observation e29e5fd9-5c97-4f7b-893c-7c2eb6d6d345 · outbound

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Provence: efficient and robust context pruning for retrieval-augmented generation Unresolved cited work

Reference 54

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Source-reported events for the cited work

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Observation 23172091-3e84-4033-bc60-768a694c016d · outbound

This paper cites CompAct: Compressing Retrieved Documents Actively for Question Answering.

Provence: efficient and robust context pruning for retrieval-augmented generation CompAct: Compressing Retrieved Documents Actively for Question Answering

Reference 55

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Provence: efficient and robust context pruning for retrieval-augmented generation Making retrieval-augmented language models robust to irrelevant context

Reference 56

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Provence: efficient and robust context pruning for retrieval-augmented generation Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selection

Reference 57

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This paper cites @esa (Ref.

Provence: efficient and robust context pruning for retrieval-augmented generation @esa (Ref

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-10T13:43:43.885712Z

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Observation b628030b-56ba-4652-9707-ed6e62bed083 · outbound

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Provence: efficient and robust context pruning for retrieval-augmented generation Unresolved cited work

Reference 59

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Provence: efficient and robust context pruning for retrieval-augmented generation FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation

Reference 60

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Pith citing papers

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SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression cites this paper.

SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression Provence: efficient and robust context pruning for retrieval-augmented generation

Reference 9

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Observation 17bca2c9-7134-4c0f-a7fd-39e5efea9a6b · inbound

Shifting from Ranking to Set Selection for Retrieval Augmented Generation cites this paper.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation Provence: efficient and robust context pruning for retrieval-augmented generation

Reference 4

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MemTool: Optimizing Short-Term Memory Management for Dynamic Tool Calling in LLM Agent Multi-Turn Conversations cites this paper.

MemTool: Optimizing Short-Term Memory Management for Dynamic Tool Calling in LLM Agent Multi-Turn Conversations Provence: efficient and robust context pruning for retrieval-augmented generation

Reference 7

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Squeez: Task-Conditioned Tool-Output Pruning for Coding Agents cites this paper.

Squeez: Task-Conditioned Tool-Output Pruning for Coding Agents Provence: efficient and robust context pruning for retrieval-augmented generation

Reference 1

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Grounded Cache Routing for Retrieval-Augmented Generation: When Is It Safe to Reuse an Answer? cites this paper.

Grounded Cache Routing for Retrieval-Augmented Generation: When Is It Safe to Reuse an Answer? Provence: efficient and robust context pruning for retrieval-augmented generation

Reference 17

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LongAttnComp: Cross-Family Context Compression for Long-Context Reasoning cites this paper.

LongAttnComp: Cross-Family Context Compression for Long-Context Reasoning Provence: efficient and robust context pruning for retrieval-augmented generation

Reference 16

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EASE-TTT: Evidence-Aligned Selective Test-Time Training for Long-Context Question Answering cites this paper.

EASE-TTT: Evidence-Aligned Selective Test-Time Training for Long-Context Question Answering Provence: efficient and robust context pruning for retrieval-augmented generation

Reference 66

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T17:17:15.092994Z

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Observation c5d0c930-6cb7-45bd-b2fc-4dd788eea1ac · inbound

CoACT: Action-Preserving Observation Compression for Coding Agents cites this paper.

CoACT: Action-Preserving Observation Compression for Coding Agents Provence: efficient and robust context pruning for retrieval-augmented generation

Reference 37

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What to Keep, What to Forget: A Rate--Distortion View of Memory Compaction in LLMs and Agents cites this paper.

What to Keep, What to Forget: A Rate--Distortion View of Memory Compaction in LLMs and Agents Provence: efficient and robust context pruning for retrieval-augmented generation

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-07-10T01:36:44.288699Z

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Observation 4cb5a356-c2f5-49bc-a8f3-8dddd8d48967 · inbound

Shapley Context Pruning: A Cooperative Game Perspective for Context Reranking and Pruning cites this paper.

Shapley Context Pruning: A Cooperative Game Perspective for Context Reranking and Pruning Provence: efficient and robust context pruning for retrieval-augmented generation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T14:32:11.918203Z

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RAGOCR: Optical Compression of Retrieval-Augmented Text via Visual Representation cites this paper.

RAGOCR: Optical Compression of Retrieval-Augmented Text via Visual Representation Provence: efficient and robust context pruning for retrieval-augmented generation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T00:23:52.085900Z

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Observation a666322a-f9b9-4548-aa9f-9c44e31583fe · inbound

Lightweight Chunk Selection for Mobile Retrieval-Augmented Generation cites this paper.

Lightweight Chunk Selection for Mobile Retrieval-Augmented Generation Provence: efficient and robust context pruning for retrieval-augmented generation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T00:45:24.205509Z

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