RCStat: A Statistical Framework for using Relative Contextualization in Transformers | Paper Citation Record

Observation 8012e4f4-718b-4f8d-9d90-23f187f973af · outbound

This paper cites Quantifying Attention Flow in Transformers.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Quantifying Attention Flow in Transformers

Reference 1

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Observation 1f2c6f46-961e-4a68-8afa-f1e5d2097c38 · outbound

This paper cites Cache-Craft: Managing Chunk-Caches for Efficient Retrieval-Augmented Generation.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Cache-Craft: Managing Chunk-Caches for Efficient Retrieval-Augmented Generation

Reference 2

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Observation daa129d7-3739-4faa-bc00-bcaee7f2294f · outbound

This paper cites Circuit tracing: Revealing computational graphs in language models.Transformer Circuits Thread, 2025.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Circuit tracing: Revealing computational graphs in language models.Transformer Circuits Thread, 2025

Reference 3

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Observation e9752e9e-b34f-49a5-aeab-4710489e18ab · outbound

This paper cites Why the 1-wasserstein distance is the area between the two marginal cdfs, 2021.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Why the 1-wasserstein distance is the area between the two marginal cdfs, 2021

Reference 4

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This paper cites Mechanistic interpretability meets vision language models: Insights and challenges.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Mechanistic interpretability meets vision language models: Insights and challenges

Reference 5

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Observation 9741ce28-a327-4d05-94ac-605cba6bbef5 · outbound

This paper cites PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information Funneling.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information Funneling

Reference 6

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Observation 11c2edea-ccd7-4683-aea8-76ea40ffa21c · outbound

This paper cites Palu: Compressing KV-Cache with Low-Rank Projection.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Palu: Compressing KV-Cache with Low-Rank Projection

Reference 7

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Observation 5328d520-3ebf-49c3-8bc7-32db8addeb22 · outbound

This paper cites Identifying Linear Relational Concepts in Large Language Models.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Identifying Linear Relational Concepts in Large Language Models

Reference 8

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Observation 27d4b52f-adfc-4b75-a52e-46c442caf4eb · outbound

This paper cites Transformer interpretability beyond attention visualization.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Transformer interpretability beyond attention visualization

Reference 9

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Observation d05fc0bd-2aaa-4676-b350-d8b46a267a92 · outbound

This paper cites Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T18:40:37.618938Z

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Observation d19af99b-580d-4b2b-9f10-a19e4aa77687 · outbound

This paper cites Learning to Attribute with Attention.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Learning to Attribute with Attention

Reference 11

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Observation 8e458e79-6ee5-43b4-a2eb-8b7260bb0e76 · outbound

This paper cites A Simple and Effective $L_2$ Norm-Based Strategy for KV Cache Compression.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers A Simple and Effective $L_2$ Norm-Based Strategy for KV Cache Compression

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T18:40:37.629546Z

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Observation 7fcf1d28-fe4a-4a9b-8672-6af3e98962f2 · outbound

This paper cites Get More with LESS: Synthesizing Recurrence with KV Cache Compression for Efficient LLM Inference.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Get More with LESS: Synthesizing Recurrence with KV Cache Compression for Efficient LLM Inference

Reference 13

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Observation 01533202-e92c-4774-9313-1cfca8857501 · outbound

This paper cites How GPT learns layer by layer.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers How GPT learns layer by layer

Reference 14

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Observation 3f9b1559-c872-4a36-82da-c6fec655bdd5 · outbound

This paper cites Position information in transformers: An overview.Computational Linguistics, 48(3):733–763, 2022.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Position information in transformers: An overview.Computational Linguistics, 48(3):733–763, 2022

Reference 15

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Observation 305bf029-f788-413c-a67e-86e59700806e · outbound

This paper cites Transcoders Find Interpretable LLM Feature Circuits.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Transcoders Find Interpretable LLM Feature Circuits

Reference 16

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Observation 1e339df4-d7cd-4b83-8e2a-49ae263bbb81 · outbound

This paper cites Copula theory: An introduction.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Copula theory: An introduction

Reference 17

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Observation aef9e929-03a3-4ec7-8dd0-5effa61bb1e4 · outbound

This paper cites On the biology of a large language model,.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers On the biology of a large language model,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:38.703890Z

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Observation cab9cd1a-069c-4f9d-b78a-55572332d3a3 · outbound

This paper cites Trapping LLM Hallucinations Using Tagged Context Prompts.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Trapping LLM Hallucinations Using Tagged Context Prompts

Reference 19

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Observation ac782359-d5a0-4a63-bc1e-bd32b18bedda · outbound

This paper cites Ada-KV: Optimizing KV Cache Eviction by Adaptive Budget Allocation for Efficient LLM Inference.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Ada-KV: Optimizing KV Cache Eviction by Adaptive Budget Allocation for Efficient LLM Inference

Reference 20

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Observation 7381500d-960b-4156-850e-6bbc21d4063f · outbound

This paper cites Model tells you what to discard: Adaptive kv cache compression for llms.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Model tells you what to discard: Adaptive kv cache compression for llms

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:38.671550Z

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Observation b4d8b362-6db2-48ed-b96c-c6572bfe853c · outbound

This paper cites Q-Filters: Leveraging QK Geometry for Efficient KV Cache Compression.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Q-Filters: Leveraging QK Geometry for Efficient KV Cache Compression

Reference 22

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Observation e24723c4-db29-4af9-9f46-d041e08ed93e · outbound

This paper cites The Llama 3 Herd of Models.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers The Llama 3 Herd of Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T18:40:37.689409Z

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Observation 1aed923a-ce24-43cf-a0cf-9ee599b2ab65 · outbound

This paper cites When Attention Sink Emerges in Language Models: An Empirical View.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers When Attention Sink Emerges in Language Models: An Empirical View

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T18:40:37.693954Z

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Observation c6cf9256-9a67-49ec-87ed-8b344c4f9e32 · outbound

This paper cites Constructing a multi-hop QA dataset for comprehensive evaluation of reasoning steps.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Constructing a multi-hop QA dataset for comprehensive evaluation of reasoning steps

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:38.654875Z

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Observation dfe67b36-4ffb-4727-9f85-09d4f5178f48 · outbound

This paper cites Kvquant: Towards 10 million context length llm inference with kv cache quantization.Advances in Neural Information Processing Systems, 37:1270–1303, 2024.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Kvquant: Towards 10 million context length llm inference with kv cache quantization.Advances in Neural Information Processing Systems, 37:1270–1303, 2024

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T18:40:37.704381Z

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Observation b9deb93a-3755-4163-bd4e-9e5659880ff6 · outbound

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RCStat: A Statistical Framework for using Relative Contextualization in Transformers Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-15T18:40:38.627391Z

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Observation 9cb4cfdc-3873-4802-acbe-9cdfdcd47dc7 · outbound

This paper cites A Survey on Large Language Model Acceleration based on KV Cache Management.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers A Survey on Large Language Model Acceleration based on KV Cache Management

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T18:40:37.714828Z

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Observation 6a4790b3-9196-4544-8c64-350b0d678dcc · outbound

This paper cites Attributionbench: How hard is automatic attribution evaluation?, 2024.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Attributionbench: How hard is automatic attribution evaluation?, 2024

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:38.610575Z

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Observation c9814314-a471-4f53-8c83-39f247c467f6 · outbound

This paper cites Snapkv: Llm knows what you are looking for before generation.Advances in Neural Information Processing Systems, 37:22947–22970, 2024.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Snapkv: Llm knows what you are looking for before generation.Advances in Neural Information Processing Systems, 37:22947–22970, 2024

Reference 30

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Observation 4d668d59-d3ad-4d65-b31e-81391f3474b2 · outbound

This paper cites MatryoshkaKV: Adaptive KV Compression via Trainable Orthogonal Projection.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers MatryoshkaKV: Adaptive KV Compression via Trainable Orthogonal Projection

Reference 31

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Observation 223f8241-51f4-4cf4-963a-d0c00e2c7ad2 · outbound

This paper cites Rouge: A package for automatic evaluation of summaries.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Rouge: A package for automatic evaluation of summaries

Reference 32

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Observation f6d89aa7-468d-472c-b1ad-aef4f68b73f4 · outbound

This paper cites Minicache: Kv cache compression in depth dimension for large language models.Advances in Neural Information Processing Systems, 37:139997–140031, 2024.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Minicache: Kv cache compression in depth dimension for large language models.Advances in Neural Information Processing Systems, 37:139997–140031, 2024

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:38.571792Z

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Observation 83e54ccf-66d2-4858-9cb6-5b4c8e0c8f94 · outbound

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RCStat: A Statistical Framework for using Relative Contextualization in Transformers Unresolved cited work

Reference 34

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This paper cites Kivi: a tuning-free asymmetric 2bit quantization for kv cache.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Kivi: a tuning-free asymmetric 2bit quantization for kv cache

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:38.544805Z

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Observation 553580ca-d447-4a6a-93cb-07129b91fb41 · outbound

This paper cites A unified approach to interpreting model predictions.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers A unified approach to interpreting model predictions

Reference 36

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Observation ba88fbe6-d51e-4182-9a8e-9367bae4182c · outbound

This paper cites Copy Suppression: Comprehensively Understanding an Attention Head.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Copy Suppression: Comprehensively Understanding an Attention Head

Reference 37

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Observation 4a63ae2b-d52e-489a-999c-d968f2f359d0 · outbound

This paper cites Using Captum to Explain Generative Language Models.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Using Captum to Explain Generative Language Models

Reference 38

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Observation d5993c33-0524-4e41-b0ca-a3fe49eecb13 · outbound

This paper cites In-context Learning and Induction Heads.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers In-context Learning and Induction Heads

Reference 39

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Observation 9a364d7e-d5a4-4e9c-95c4-2246ae817c4b · outbound

This paper cites Transformers are Multi-State RNNs.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Transformers are Multi-State RNNs

Reference 40

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Observation ed704e74-903a-4ec1-9b61-fc279e3eb3e0 · outbound

This paper cites Peering into the mind of language models: An approach for attribution in contextual question answering.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Peering into the mind of language models: An approach for attribution in contextual question answering

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:38.519400Z

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Observation 2fbde6c1-98f3-48f5-9272-cb6cf9637235 · outbound

This paper cites Explanations of deep language models explain language representations in the brain.arXiv e-prints, pages arXiv–2502, 2025.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Explanations of deep language models explain language representations in the brain.arXiv e-prints, pages arXiv–2502, 2025

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:38.503969Z

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Observation 8d4d2c5d-b597-47d9-a64a-29089508eeea · outbound

This paper cites Know What You Don't Know: Unanswerable Questions for SQuAD.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Know What You Don't Know: Unanswerable Questions for SQuAD

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T18:40:37.787806Z

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Observation 4ef857cf-fcbb-495c-abd8-ce1dcd52cc5d · outbound

This paper cites On the efficacy of eviction policy for key-value constrained generative language model inference.CoRR, 2024.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers On the efficacy of eviction policy for key-value constrained generative language model inference.CoRR, 2024

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:38.486911Z

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Observation 2e1a404d-6b50-4246-b0e6-58bec4bfb7a4 · outbound

This paper cites ” why should i trust you?” explaining the predictions of any classifier.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers ” why should i trust you?” explaining the predictions of any classifier

Reference 45

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Observation 90e9ab55-09e7-4bc6-8b36-f0139649037d · outbound

This paper cites A primer in bertology: What we know about how bert works.Transactions of the association for computational linguistics, 8:842–866, 2021.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers A primer in bertology: What we know about how bert works.Transactions of the association for computational linguistics, 8:842–866, 2021

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:38.460289Z

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Observation f5a3ccee-d3c3-4f44-b991-af7eb942878f · outbound

This paper cites Occam’s laser: Occlusion-based attribution maps for 3d object detectors on lidar data.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Occam’s laser: Occlusion-based attribution maps for 3d object detectors on lidar data

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:38.444618Z

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Observation f2c4f655-5d18-4e25-8e33-0504c43c1d50 · outbound

This paper cites SEMQA: Semi-Extractive Multi-Source Question Answering.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers SEMQA: Semi-Extractive Multi-Source Question Answering

Reference 48

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Observation 1c62f442-745c-491d-ba79-2870f64873ab · outbound

This paper cites Axiomatic attribution for deep networks.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Axiomatic attribution for deep networks

Reference 49

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Observation 14bfbd0b-037f-4519-8957-b2b86a0bc537 · outbound

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RCStat: A Statistical Framework for using Relative Contextualization in Transformers Unresolved cited work

Reference 50

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Observation 72709996-23f1-405b-8e73-ab53102e910d · outbound

This paper cites Gomez, Łukasz Kaiser, and Illia Polosukhin.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Gomez, Łukasz Kaiser, and Illia Polosukhin

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:38.402354Z

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Observation f82e7637-1a9c-4116-aec2-a4531ca909ee · outbound

This paper cites Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned

Reference 52

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Observation bb9fd9ce-bc2f-485b-8b75-5a69f0c2c50c · outbound

This paper cites Model Tells You Where to Merge: Adaptive KV Cache Merging for LLMs on Long-Context Tasks.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Model Tells You Where to Merge: Adaptive KV Cache Merging for LLMs on Long-Context Tasks

Reference 53

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Observation 0f1c76c7-be1f-447d-97b1-3e5dd466d175 · outbound

This paper cites Efficient Streaming Language Models with Attention Sinks.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Efficient Streaming Language Models with Attention Sinks

Reference 54

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Observation eb90ba01-2e48-4c1a-8361-16419156258a · outbound

This paper cites Hallucination is Inevitable: An Innate Limitation of Large Language Models.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Hallucination is Inevitable: An Innate Limitation of Large Language Models

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-15T18:40:37.843440Z

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This paper cites Automatic evaluation of attribution by large language models.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Automatic evaluation of attribution by large language models

Reference 56

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Observation 873d4d6c-8252-4ca8-877c-455e27325931 · outbound

This paper cites H2o: Heavy-hitter oracle for efficient generative inference of large language models.Advances in Neural Information Processing Systems, 36:34661–34710, 2023.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers H2o: Heavy-hitter oracle for efficient generative inference of large language models.Advances in Neural Information Processing Systems, 36:34661–34710, 2023

Reference 57

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This paper cites Explainability for large language models: A survey.ACM Transactions on Intelligent Systems and Technology, 15(2):1–38, 2024.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Explainability for large language models: A survey.ACM Transactions on Intelligent Systems and Technology, 15(2):1–38, 2024

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-15T18:40:37.857511Z

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Observation fa8b4651-870d-467b-8c88-ff62a261af59 · outbound

This paper cites anchorpersonalstoriestotheland.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers anchorpersonalstoriestotheland

Reference 59

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Observation 4c4d43da-573c-4e6e-beab-b349e67ead4d · outbound

This paper cites an unresolved cited work.

RCStat: A Statistical Framework for using Relative Contextualization in Transformers Unresolved cited work

Reference 2025

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