Paper Citation Record · LEDGER

Raven: High-Recall Sequence Modeling with Sparse Memory Routing

As of 19 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 1 inbound Pith citation observation for arXiv:2607.25357.

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

pith.paper-citation-record.v1
2607.25357 v1

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T02:44:01.819934Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:22:02.595935Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-16T00:22:02.788402Z

Reference resolution

49 of 49 outbound references displayed

verified exact0
verified fuzzy0
unresolved49
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e80b4544-3a6f-4d29-98c5-ac6e72e09e90 · outbound

This paper cites Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:00.686856Z

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Unavailable: canonical work link unavailable.

Observation aaf4977e-4344-4fc2-b8ce-7d5da3d9a12d · outbound

This paper cites Extending Context Window of Large Language Models via Positional Interpolation.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Extending Context Window of Large Language Models via Positional Interpolation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:00.905392Z

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Observation c4feb35b-3e90-470e-8c7b-c062a0d79aa2 · outbound

This paper cites Mom: Linear sequence modeling with mixture-of-memories.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Mom: Linear sequence modeling with mixture-of-memories

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.066325Z

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Observation 849ac18a-ee82-4d40-9a81-107c7bc56f75 · outbound

This paper cites The Llama 3 Herd of Models.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing The Llama 3 Herd of Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.103367Z

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Observation 653d6092-2031-4eaf-9b84-61e86a5c15f9 · outbound

This paper cites Repeat After Me: Transformers are Better than State Space Models at Copying.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.258380Z

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Observation 00cf7537-debd-4d4e-bf4c-fdaa7ab8e210 · outbound

This paper cites Peng Jin, Bo Zhu, Li Yuan, and Shuicheng Yan.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Peng Jin, Bo Zhu, Li Yuan, and Shuicheng Yan

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.292750Z

Source-reported events for the cited work

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Observation f8b427f3-4cd4-4da9-acd9-720214a8eb06 · outbound

This paper cites Forgetting Transformer: Softmax Attention with a Forget Gate.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Forgetting Transformer: Softmax Attention with a Forget Gate

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.453174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7c98af6-1196-4443-91a6-c02069b7e178 · outbound

This paper cites DeepSeek-V3 Technical Report.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing DeepSeek-V3 Technical Report

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.506001Z

Source-reported events for the cited work

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Observation 6eb66fed-bbc8-4f17-9df1-55159d5a73ac · outbound

This paper cites William Merrill, Jackson Petty, and Ashish Sabharwal.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing William Merrill, Jackson Petty, and Ashish Sabharwal

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.536563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a91589a0-0cb5-496e-a9a5-dab08c43a27a · outbound

This paper cites Landmark Attention: Random-Access Infinite Context Length for Transformers.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Landmark Attention: Random-Access Infinite Context Length for Transformers

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.559402Z

Source-reported events for the cited work

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Observation 9f38db78-4112-4975-b3fa-cf65f25edc25 · outbound

This paper cites Selective Rotary Position Embedding.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Selective Rotary Position Embedding

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.616412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af096754-c3b6-4507-be41-b5f893d81905 · outbound

This paper cites Thinking Slow, Fast: Scaling Inference Compute with Distilled Reasoners.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Thinking Slow, Fast: Scaling Inference Compute with Distilled Reasoners

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.671659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 942426ea-3dea-4c5e-a077-692e01c6c2eb · outbound

This paper cites Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.725636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16d6f1d8-30e6-4ae8-bb4c-af7a00612e5f · outbound

This paper cites ABC: Attention with Bounded-memory Control.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing ABC: Attention with Bounded-memory Control

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.745380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1860f79-3f7b-4f76-8888-93b9a2ee32e2 · outbound

This paper cites Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.748866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71aa1d7b-129d-41b4-bcc6-a5ea14786201 · outbound

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

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Know What You Don't Know: Unanswerable Questions for SQuAD

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.752057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18b80eac-9684-4ca0-9f12-32c618fa7063 · outbound

This paper cites Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.755790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cacaa487-f951-4a32-98ad-1e525f433c13 · outbound

This paper cites Understanding and improving length generalization in recurrent models.arXiv preprint arXiv:2507.02782,.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Understanding and improving length generalization in recurrent models.arXiv preprint arXiv:2507.02782,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.759166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 605cdf74-957a-4f14-ac62-1b1260b22e60 · outbound

This paper cites WinoGrande: An Adversarial Winograd Schema Challenge at Scale.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing WinoGrande: An Adversarial Winograd Schema Challenge at Scale

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.762231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09998d17-089b-4481-a1c0-3ed0235e0efe · outbound

This paper cites Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.768663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22bac524-52f5-44d2-9d11-4ec04458bb3e · outbound

This paper cites Learning to (Learn at Test Time): RNNs with Expressive Hidden States.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Learning to (Learn at Test Time): RNNs with Expressive Hidden States

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.775013Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 356e5538-2115-454f-b84b-fad34a047c83 · outbound

This paper cites Retentive Network: A Successor to Transformer for Large Language Models.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Retentive Network: A Successor to Transformer for Large Language Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.777943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ab79ce3-e90f-454b-96df-5f08e5183d8d · outbound

This paper cites Kimi Linear: An Expressive, Efficient Attention Architecture.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Kimi Linear: An Expressive, Efficient Attention Architecture

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.780935Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60dc94cc-e8dd-4649-9610-1df635ec155b · outbound

This paper cites An Empirical Study of Mamba-based Language Models.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing An Empirical Study of Mamba-based Language Models

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.787201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 039a1726-6e56-4852-8712-b95cf0acf84a · outbound

This paper cites The Mamba in the Llama: Distilling and Accelerating Hybrid Models.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing The Mamba in the Llama: Distilling and Accelerating Hybrid Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.790175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce02fde9-9344-406a-81d9-9fd55414709f · outbound

This paper cites RNNs are not Transformers (Yet): The Key Bottleneck on In-context Retrieval.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing RNNs are not Transformers (Yet): The Key Bottleneck on In-context Retrieval

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.793280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74ace254-099d-42ae-be6d-e2461ee23e5a · outbound

This paper cites Gated Linear Attention Transformers with Hardware-Efficient Training.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Gated Linear Attention Transformers with Hardware-Efficient Training

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.796404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1eabf48-e436-4c8a-95b0-9608324d89f6 · outbound

This paper cites Gated Delta Networks: Improving Mamba2 with Delta Rule.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Gated Delta Networks: Improving Mamba2 with Delta Rule

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.799536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fda620e3-d1d9-4c23-835d-636d034b7e18 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.802620Z

Source-reported events for the cited work

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Observation 3108fa4a-0c09-4c80-9a01-83e56e3d107c · outbound

This paper cites Understanding Transformer from the Perspective of Associative Memory.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Understanding Transformer from the Perspective of Associative Memory

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.805678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce706b0d-9076-4126-8622-9cb44b754d95 · outbound

This paper cites Then, S′ t[i]−S t[i] =D t[ik] ∆At ̸=0.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Then, S′ t[i]−S t[i] =D t[ik] ∆At ̸=0

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.808436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee30a630-ad70-4f74-af07-9bd1c94c20fd · outbound

This paper cites One can look at Raven update as applying sparse TTTand only updating selected slots.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing One can look at Raven update as applying sparse TTTand only updating selected slots

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.816918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4801ea70-a22f-45ab-a1ed-e3bfbab65656 · outbound

This paper cites (2025) for more details on the TTT framework.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing (2025) for more details on the TTT framework

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.819934Z

Source-reported events for the cited work

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Observation d28c09fc-6c65-4cb9-87c0-d364abbe8d83 · outbound

This paper cites ot = (Sv t )⊤ softmax Sk t qt.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing ot = (Sv t )⊤ softmax Sk t qt

Reference 256

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.813895Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9b26766-8b97-4bf6-85f2-4ad45d9c6397 · outbound

This paper cites Categorical Reparameterization with Gumbel-Softmax.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Categorical Reparameterization with Gumbel-Softmax

Reference 1991

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.225363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b65c926-6cc7-47c8-bd5a-53e7910d19e9 · outbound

This paper cites Jamba: A Hybrid Transformer-Mamba Language Model.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Jamba: A Hybrid Transformer-Mamba Language Model

Reference 1992

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.352753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a8c50b6-c8cf-41b5-9cc9-2139ead39228 · outbound

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

Raven: High-Recall Sequence Modeling with Sparse Memory Routing RoFormer: Enhanced Transformer with Rotary Position Embedding

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.771686Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f238a3ee-72f4-42a9-893d-9997efbaaa27 · outbound

This paper cites The learning rate follows a cosine scheduler with 1B tokens of warmup.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing The learning rate follows a cosine scheduler with 1B tokens of warmup

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.811139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation adb6bd9a-75e2-4853-af72-5e6c81296650 · outbound

This paper cites Query-Key Normalization for Transformers.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Query-Key Normalization for Transformers

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.196752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd8029ce-2182-4b5b-85e8-8723633e43bb · outbound

This paper cites MesaNet: Sequence Modeling by Locally Optimal Test-Time Training.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing MesaNet: Sequence Modeling by Locally Optimal Test-Time Training

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.784214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 540d22d0-cc26-46d9-b931-ce68e36d6a4d · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.022592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8397736a-771d-4c27-9dd5-058910f07946 · outbound

This paper cites PIQA: Reasoning about Physical Commonsense in Natural Language.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing PIQA: Reasoning about Physical Commonsense in Natural Language

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:00.838113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6133297-6749-42d7-bb0d-11fb83897860 · outbound

This paper cites Llamba: Scaling Distilled Recurrent Models for Efficient Language Processing.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Llamba: Scaling Distilled Recurrent Models for Efficient Language Processing

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:00.629498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8a7e29e-1584-4e8a-a705-6b2dde1c4911 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.765490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6a26fc0-e7a3-436a-89db-e9bcd1a11cc8 · outbound

This paper cites Mamba: Linear-time sequence modeling with selective state spaces.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Mamba: Linear-time sequence modeling with selective state spaces

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.159652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ee26bfa-42ca-4364-b248-5e0c1c389792 · outbound

This paper cites Simple linear attention language models balance the recall-throughput tradeoff.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Simple linear attention language models balance the recall-throughput tradeoff

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:00.520073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68c66a9b-2880-4726-b5dc-345d555fed32 · outbound

This paper cites Longformer: The Long-Document Transformer.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Longformer: The Long-Document Transformer

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:00.582780Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3ea2d40-7641-4342-9fa0-a7b5f45f92a6 · outbound

This paper cites Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:00.967244Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7bffd605-2ec9-4b2c-be23-9e8a8d272c04 · outbound

This paper cites github.io/blog/2026/pe/.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing github.io/blog/2026/pe/

Reference 2026

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:00.426067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation b9fb66c5-1395-4f65-9c33-ce51a60c8852 · inbound

MARCH: Scaling Recurrent Memory with Content-Routed State Anchors cites this paper.

MARCH: Scaling Recurrent Memory with Content-Routed State Anchors Raven: High-Recall Sequence Modeling with Sparse Memory Routing

Reference 70

Resolution: metadata mismatch; local_arxiv, observed 2026-08-16T00:22:02.793982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.