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Paper Citation Record · LEDGER

LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement

As of 18 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 5 inbound Pith citation observations for arXiv:2504.16053.

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

pith.paper-citation-record.v1
2504.16053 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:16:19.822359Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T13:07:40.860889Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-17T06:41:16.473267Z

Reference resolution

25 of 25 outbound references displayed

  • verified exact0
  • verified fuzzy4
  • unresolved19
  • parse uncertain0
  • malformed identifier2
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 526bb6aa-cde5-49fc-abc0-6e846de1db75 · outbound

This paper cites GPT-4 Technical Report.

LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement GPT-4 Technical Report

Reference 1

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source=pdf_text observed=2026-08-16T11:16:19.694826Z digest=sha256:f700cc456072830e7990bfcee86ad5c5d1b75d90fc4af7603b72da7419cd72f6

Observation 288e23f2-7ef9-40f5-94fb-100e89821106 · outbound

This paper cites We observe that the last two rows of channels, although they have a receptive field covering all 2,000 tokens in Fig.

LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement We observe that the last two rows of channels, although they have a receptive field covering all 2,000 tokens in Fig

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:16:19.807933Z digest=sha256:65d1255eb2dc832be4d7c929868f4ae1b2c9e6dde9d66f84a43166fba4d4a322

Observation 021c7a22-b247-4b44-84f0-21ce7882fa0b · outbound

This paper cites The Zamba2 Suite: Technical Report.

LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement The Zamba2 Suite: Technical Report

Reference 7

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source=pdf_text observed=2026-08-16T11:16:19.730935Z digest=sha256:64b98e3f7eda63a6f71e2592eca855a146d2852a7d89e0e167375b3ff58edbbd

Observation b4146327-3be2-4f98-8eaf-2590d127ce31 · outbound

This paper cites A comprehensive survey on process-oriented automatic text summarization with exploration of llm-based methods.

LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement A comprehensive survey on process-oriented automatic text summarization with exploration of llm-based methods

Reference 9

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source=pdf_text observed=2026-08-16T11:16:19.742190Z digest=sha256:66315588c4876ae5396bf018bb627b6027e1602f3f99ed05ed020434a2f92ccb

Observation 03035e36-5904-421e-9f33-76549f898f5a · outbound

This paper cites Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and Franc ¸ois Fleuret.

LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and Franc ¸ois Fleuret

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:16:19.746928Z digest=sha256:190ce1898347a08539ec4f61e3800e496a6ed0aa51ff2baec67eaf9197106c97

Observation fa88ec44-6377-4e00-8e02-208ca80d3ada · outbound

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

LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement Jamba: A Hybrid Transformer-Mamba Language Model

Reference 11

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:16:19.752110Z digest=sha256:bf4c201b9f78818f7f91fa79d13d64db58536027604264d9bee5feb4ebec9706

Observation 70365ecb-3888-4f3f-8f6d-22d2f60a6581 · outbound

This paper cites VMamba: Visual State Space Model.

LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement VMamba: Visual State Space Model

Reference 12

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source=pdf_text observed=2026-08-16T11:16:19.757303Z digest=sha256:e59266c23b980db879109adb7d8c673901f82d6a9d6c3f6c78bafe4578f4ab5f

Observation 71177d4b-1e2d-4675-a3c3-c1d345639586 · outbound

This paper cites Erik Nijkamp, Tian Xie, Hiroaki Hayashi, Bo Pang, Congying Xia, Chen Xing, Jesse Vig, Semih Yavuz, Philippe Laban, Ben Krause, et al.

LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement Erik Nijkamp, Tian Xie, Hiroaki Hayashi, Bo Pang, Congying Xia, Chen Xing, Jesse Vig, Semih Yavuz, Philippe Laban, Ben Krause, et al

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:16:19.762265Z digest=sha256:49409970a93a62839855b29a35f62ffc1c5bc7030ce88bf725d64efa4f72caae

Observation 7afcf09d-4d24-4abf-9ed5-e0472eed165f · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 15

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:16:19.772478Z digest=sha256:d5b5c6413846bc694550e93f40e24f53ac06ad39a8357755027ef6523f964752

Observation e61c12ea-8e26-4231-8e17-28310d661e10 · outbound

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

LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement An Empirical Study of Mamba-based Language Models

Reference 16

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source=pdf_text observed=2026-08-16T11:16:19.776923Z digest=sha256:2a9bc7f717202ce9ad5cb8ecb21548670768ce6679b6ab0e61d33a2eb79f803d

Observation 9a21da8d-e79a-4c96-888c-a18f82d1c9a3 · outbound

This paper cites Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces.

LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 17

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source=pdf_text observed=2026-08-16T11:16:19.782228Z digest=sha256:1eb13f6ec5ebc808bf6a636280b6f99803517a00007024116dcce7b2fff80898

Observation 95d82fc9-b042-4fd8-8e2d-30c4588cc09e · outbound

This paper cites Falcon Mamba: The First Competitive Attention-free 7B Language Model.

LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement Falcon Mamba: The First Competitive Attention-free 7B Language Model

Reference 18

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source=pdf_text observed=2026-08-16T11:16:19.788618Z digest=sha256:1925ebef0b7b84f6576ef08a677686e6895f28a0a1593f069b01d41ae32e8965

Observation 4fdf16e4-116a-49c4-a8e4-f77c7fa3900d · outbound

This paper cites an unresolved cited work.

LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement Unresolved cited work

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:16:19.793436Z digest=sha256:c6a444e9f7e330854ccd20f864e57f947843a4624fc492c89acc9c6e884f81a4

Observation 55452d46-dcf5-43cc-b32b-a195afe05521 · outbound

This paper cites an unresolved cited work.

LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement Unresolved cited work

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:16:19.798522Z digest=sha256:b1f6de0a1b187c8f167009a0aaf12f68e4feb347b27f401350dc81d7eb4179c2

Observation b847a6cb-5f81-4c16-b0b0-0870e741530d · outbound

This paper cites The channels are sorted by their cumulative decay on the sampled sequence.

LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement The channels are sorted by their cumulative decay on the sampled sequence

Reference 21

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:16:19.803210Z digest=sha256:d9545ebaf3cd57c9f3ba4837b0e3fefa4bfb79cbd48460d080dee0c7fcd0192e

Observation cf021766-c354-4f76-b632-5d900356930e · outbound

This paper cites Model Method 4k 8k 16k 24k 32k 40k A vg.

LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement Model Method 4k 8k 16k 24k 32k 40k A vg

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:16:19.812699Z digest=sha256:5447836c8a045e8dacb74b7314c9c5578669ca538329e58eec69dce37aa43842

Observation 12ca5645-70eb-43bb-8bde-5f913f46823b · outbound

This paper cites an unresolved cited work.

LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement Unresolved cited work

Reference 24

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:16:19.817568Z digest=sha256:27840ab7ae0a94790f14a40cc8e32119d344434ce0e77f60ca9b67bc8d913428

Observation 7a36ee1d-f805-4305-a81e-c60f3991d45a · outbound

This paper cites Model Method 4k 8k 16k 24k 32k 40k 48k 64k 80k 96k A vg.

LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement Model Method 4k 8k 16k 24k 32k 40k 48k 64k 80k 96k A vg

Reference 25

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:16:19.822359Z digest=sha256:cc007b95e9171d0d301312b8464b38e01937c0fa1d545853c7434f9d015f389e

Observation eaae4b28-9c72-443c-a2a8-ba8ecf97c31f · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 2012

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source=pdf_text observed=2026-08-16T11:16:19.724460Z digest=sha256:d5cc2ecd699b02563277931e2acbd1d5fc419a1b6a805f4bc87307cd70aa6d3b

Observation acd6faff-2466-42e6-8bcb-ef53dc3162cd · outbound

This paper cites Compressive Transformers for Long-Range Sequence Modelling.

LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement Compressive Transformers for Long-Range Sequence Modelling

Reference 2019

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source=pdf_text observed=2026-08-16T11:16:19.767433Z digest=sha256:cb008d55eaba8efd99f38c7e1c02cf2047ed812d21c21fa9cfbaacd6c3e8edd1

Observation 9a8824e7-f577-4f62-89c5-822cc4b57b4c · outbound

This paper cites DeciMamba: Exploring the Length Extrapolation Potential of Mamba.

LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement DeciMamba: Exploring the Length Extrapolation Potential of Mamba

Reference 2020

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source=pdf_text observed=2026-08-16T11:16:19.712744Z digest=sha256:981835e0dfb861bae2a6c66364f48206d31c9da8664a51b8156413ff0ae8f047

Observation 9a4381d4-c535-49b8-aa6b-16ba1ee22d51 · outbound

This paper cites RULER: What's the Real Context Size of Your Long-Context Language Models?.

LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement RULER: What's the Real Context Size of Your Long-Context Language Models?

Reference 2021

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source=pdf_text observed=2026-08-16T11:16:19.737215Z digest=sha256:13e3579ffccbeec530e5e87a31b170fb814b3e956924cad8ee36f54310f41502

Observation 1614b5da-c376-4b49-b8bd-8c429387d99d · outbound

This paper cites Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality.

LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 2022

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source=pdf_text observed=2026-08-16T11:16:19.718852Z digest=sha256:97ed3462671b864fad70a4d34e19adca112dcdf7f396091d8c7d620a32c1e74b

Observation a9ab1208-465f-4941-80cd-ce3b06deaed0 · outbound

This paper cites Longformer: The Long-Document Transformer.

LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement Longformer: The Long-Document Transformer

Reference 2023

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source=pdf_text observed=2026-08-16T11:16:19.707045Z digest=sha256:e23f973eebeac1ee26669b4547e00152561a36da86607076927600de6e034e24

Observation 7f9e12f9-4d83-4b40-85a6-44573a310f50 · outbound

This paper cites LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding.

LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding

Reference 2024

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source=pdf_text observed=2026-08-16T11:16:19.701120Z digest=sha256:eeeb707cd7ed560e259d4d9e067226fdb47afb305dda4e0845fd87a1147613f0

Pith citing papers

Observation b68bbc81-3d7a-486b-aa51-72c095e77cc8 · inbound

SCOUT: Toward Sub-Quadratic Attention via Segment Compression for Optimized Utility in Transformers cites this paper.

SCOUT: Toward Sub-Quadratic Attention via Segment Compression for Optimized Utility in Transformers LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement

Reference 14

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source=pdf_text observed=2026-08-05T13:07:40.860889Z digest=sha256:3d9016f8ce4788a0452afa2ad7d9776d404440cbf945ad5d5f4329532476c44e

Observation 2651c743-3332-4b07-a97c-79635fc44a12 · inbound

TTT3R: 3D Reconstruction as Test-Time Training cites this paper.

TTT3R: 3D Reconstruction as Test-Time Training LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement

Reference 99

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arxiv_id, observed 2026-05-17T06:41:16.476187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-17T06:41:16.306593Z digest=sha256:bf9f9b46c41cf4db2890fdcc2cd30c55209578608d07a9f92087a55846c0d51a

Observation c94bee6a-1de7-4680-905d-720f5aa6a596 · inbound

Echoes Over Time: Unlocking Length Generalization in Video-to-Audio Generation Models cites this paper.

Echoes Over Time: Unlocking Length Generalization in Video-to-Audio Generation Models LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement

Reference 47

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arxiv_id, observed 2026-05-15T19:56:33.456230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T19:53:18.200223Z digest=sha256:d2fecafa0a8bc3d36c6641d55a14dcaff7a8e06fea2f0f4080d3cc24f68902db

Observation e96f7c04-2722-41a0-844d-48efaf7154bc · inbound

Optimal Decay Spectra for Linear Recurrences cites this paper.

Optimal Decay Spectra for Linear Recurrences LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement

Reference 12

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arxiv_id, observed 2026-05-11T07:01:00.295254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-10T17:21:15.178258Z digest=sha256:b08417bdaf45a585ddd0d90945c0c7a4c97ff7a5bd873c2191de9c8d92e7922a

Observation 127c31a5-746b-4876-add1-27c2d2e1c0a8 · inbound

Consistent and Editable: A Balanced Framework for Text-Guided Video Editing cites this paper.

Consistent and Editable: A Balanced Framework for Text-Guided Video Editing LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement

Reference 47

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source=pdf_text observed=2026-07-11T09:28:19.511968Z digest=sha256:4479f0627b61a077d158cda0b6f8ab856b8467489f7d2f228f52c4ae8ef1881a