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Paper Citation Record · LEDGER

Overflow Prevention Enhances Long-Context Recurrent LLMs

As of 18 August 2026, this Paper Citation Record lists 75 of 75 outbound references and 0 inbound Pith citation observations for arXiv:2505.07793.

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

pith.paper-citation-record.v1
2505.07793 v2

Coverage vector

measured 75 of 75 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:12:58.929907Z

measured 75 of 75 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

75 of 75 outbound references displayed

  • verified exact0
  • verified fuzzy18
  • unresolved56
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0839cd60-b2d5-40ea-ba45-77d27716ab1e · outbound

This paper cites Mechanistic evaluation of transformers and state space models.

Overflow Prevention Enhances Long-Context Recurrent LLMs Mechanistic evaluation of transformers and state space models

Reference 1

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source=arxiv_source observed=2026-08-15T22:12:58.516371Z digest=sha256:3c4e7cf218282522fc161d1a1ee6b8b9bf30a85efe9ce772c875ecc236edca15

Observation 299bcbfb-f401-40b0-af92-e1abe210d17b · outbound

This paper cites Zoology: Measuring and Improving Recall in Efficient Language Models.

Overflow Prevention Enhances Long-Context Recurrent LLMs Zoology: Measuring and Improving Recall in Efficient Language Models

Reference 2

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source=arxiv_source observed=2026-08-15T22:12:58.523737Z digest=sha256:13084b8f823180aefee95f021522e4e2690358f38259dffb9845641597456594

Observation 4bb70cee-dead-41e2-bf03-c8d361983fe3 · outbound

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

Overflow Prevention Enhances Long-Context Recurrent LLMs Simple linear attention language models balance the recall-throughput tradeoff

Reference 3

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source=arxiv_source observed=2026-08-15T22:12:58.530414Z digest=sha256:8235fba7d1de477c6efbe204d34400ef4c5ff4976e50fc8bba90a95cf90ce870

Observation 0430f030-7865-46a3-9a03-85e85277f11b · outbound

This paper cites Mambaextend: A training-free approach to improve long context extension of mamba.

Overflow Prevention Enhances Long-Context Recurrent LLMs Mambaextend: A training-free approach to improve long context extension of mamba

Reference 4

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raw_fallback, observed 2026-08-15T22:13:00.400766Z

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=arxiv_source observed=2026-08-15T22:12:58.536720Z digest=sha256:4bb3f4e1b04b195046f860134aa80f715201120ba6754093913fe7cebebb9deb

Observation c19fbd28-b18b-4691-8406-8fa8d29ab020 · outbound

This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.

Overflow Prevention Enhances Long-Context Recurrent LLMs Neural Machine Translation by Jointly Learning to Align and Translate

Reference 5

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-15T22:12:58.542005Z digest=sha256:562fd92087f0972a15f1249d938738e7e5412a2ffbc5837ff6770d2699bd3f6f

Observation 4377b058-48a2-4a4a-afcf-5d6961666fa7 · outbound

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

Overflow Prevention Enhances Long-Context Recurrent LLMs LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding

Reference 6

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source=arxiv_source observed=2026-08-15T22:12:58.547553Z digest=sha256:85cf3cc241c63f1af92d51b154a043a919f463238e98e77651f9fe354504a389

Observation a5baaf6f-ae86-4b14-b915-03e9f0196fca · outbound

This paper cites LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks.

Overflow Prevention Enhances Long-Context Recurrent LLMs LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks

Reference 7

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source=arxiv_source observed=2026-08-15T22:12:58.554285Z digest=sha256:72f35f8082fa18ca793c427fb0bdb70ebc01dc7ebcd169a888ab6311c0b23f7c

Observation b49a4d34-c010-4aa6-8010-7e9cd41932e2 · outbound

This paper cites xLSTM: Extended Long Short-Term Memory.

Overflow Prevention Enhances Long-Context Recurrent LLMs xLSTM: Extended Long Short-Term Memory

Reference 8

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source=arxiv_source observed=2026-08-15T22:12:58.559870Z digest=sha256:140a1479623480bd89181380fd0bbcdf1885f6e626f20fc3a11cdc92588a2c89

Observation d6e4eacb-09b0-496a-8e11-f097ab19932f · outbound

This paper cites xLSTM 7B: A Recurrent LLM for Fast and Efficient Inference.

Overflow Prevention Enhances Long-Context Recurrent LLMs xLSTM 7B: A Recurrent LLM for Fast and Efficient Inference

Reference 9

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local_arxiv, observed 2026-08-15T22:12:59.672917Z

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=arxiv_source observed=2026-08-15T22:12:58.565294Z digest=sha256:8104cfef6fcc6c69d761e3eff274ed688c3b33ac3ee7557faee092b29fbaafb7

Observation 510f76d6-c812-4789-9079-5f839bb82343 · outbound

This paper cites Graph mamba: Towards learning on graphs with state space models.

Overflow Prevention Enhances Long-Context Recurrent LLMs Graph mamba: Towards learning on graphs with state space models

Reference 10

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raw_fallback, observed 2026-08-15T22:13:00.383707Z

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=arxiv_source observed=2026-08-15T22:12:58.570817Z digest=sha256:8fce412d9e08dd81c5bd6197bbef6faa5205ffee36a68ce67ed5990443bee5fd

Observation f13658b8-5ef2-4715-a90d-44e7ce5040ff · outbound

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

Overflow Prevention Enhances Long-Context Recurrent LLMs DeciMamba: Exploring the Length Extrapolation Potential of Mamba

Reference 11

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source=arxiv_source observed=2026-08-15T22:12:58.576118Z digest=sha256:c3c4b282aab5afd9a506378da12d9b3cc97520834c889d0ad5766ff335efb39b

Observation a925740a-e3a8-4249-9275-39f8b164caa4 · outbound

This paper cites RecurrentGemma: Moving Past Transformers for Efficient Open Language Models.

Overflow Prevention Enhances Long-Context Recurrent LLMs RecurrentGemma: Moving Past Transformers for Efficient Open Language Models

Reference 12

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source=arxiv_source observed=2026-08-15T22:12:58.581384Z digest=sha256:9cda6c8c1779796d5c4cf07669f8bdcbf5b0e1f4489fe0fd8ca9403c41652463

Observation 42045944-50c0-49b9-8143-123d8a807cf5 · outbound

This paper cites LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models.

Overflow Prevention Enhances Long-Context Recurrent LLMs LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models

Reference 13

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source=arxiv_source observed=2026-08-15T22:12:58.587147Z digest=sha256:f4f75fbf2114be48359e83ac2f0b76d16944146cbdff9a2f15cb1e93b1eacc8c

Observation 881262d1-bd88-4dff-ade7-61bc6f8968c3 · outbound

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

Overflow Prevention Enhances Long-Context Recurrent LLMs Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 14

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source=arxiv_source observed=2026-08-15T22:12:58.593710Z digest=sha256:57d83347c447aedaece4717e866e54b3f864f40c3edd9afe462a28e9159af80b

Observation 0ea116d7-d3d1-4387-9a0a-78e9ff8ada20 · outbound

This paper cites Griffin: Mixing gated linear recurrences with local attention for efficient language models.

Overflow Prevention Enhances Long-Context Recurrent LLMs Griffin: Mixing gated linear recurrences with local attention for efficient language models

Reference 15

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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=arxiv_source observed=2026-08-15T22:12:58.598791Z digest=sha256:7f97ad726c1f666cc1dc4090dd14fdbcbc78611eb3808d6748bde764ffedb178

Observation 6e606860-5a8e-47d4-8058-ddd8ec7e988d · outbound

This paper cites Hymba: A Hybrid-head Architecture for Small Language Models.

Overflow Prevention Enhances Long-Context Recurrent LLMs Hymba: A Hybrid-head Architecture for Small Language Models

Reference 16

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source=arxiv_source observed=2026-08-15T22:12:58.603905Z digest=sha256:7793dd1a3fb3e3db98c2a7790bfdecf1fba159f273ceb39dbb2b08805d3af514

Observation ce9883a9-7a7d-4620-8427-3f8284b692ae · outbound

This paper cites Vision- RWKV : Efficient and scalable visual perception with RWKV -like architectures.

Overflow Prevention Enhances Long-Context Recurrent LLMs Vision- RWKV : Efficient and scalable visual perception with RWKV -like architectures

Reference 17

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verified fuzzy
raw_fallback, observed 2026-08-15T22:13:00.348686Z

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=arxiv_source observed=2026-08-15T22:12:58.609334Z digest=sha256:75307eafc51a75b279d9a2d7546fd857c5cb8649ded55b8ca4ffe41d1fb63281

Observation 80bbf0d6-e2da-4dd6-bd91-b16bab1ff352 · outbound

This paper cites Diffusion-RWKV: Scaling RWKV-Like Architectures for Diffusion Models.

Overflow Prevention Enhances Long-Context Recurrent LLMs Diffusion-RWKV: Scaling RWKV-Like Architectures for Diffusion Models

Reference 18

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source=arxiv_source observed=2026-08-15T22:12:58.615329Z digest=sha256:42012b812dc5c3ef7b84382076c6dccddbcfddee3f7e104393e6819ba0dd7255

Observation b351cdcb-7b58-47ed-9fe8-494dc50a4d06 · outbound

This paper cites SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval.

Overflow Prevention Enhances Long-Context Recurrent LLMs SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval

Reference 19

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source=arxiv_source observed=2026-08-15T22:12:58.621761Z digest=sha256:a9acd5866992752e6f307a896e07b1521d6e1bc806ce1e4f7329892a660df5ef

Observation 449eee57-050c-4e4b-b90d-96059b3a6543 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Overflow Prevention Enhances Long-Context Recurrent LLMs Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 20

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source=arxiv_source observed=2026-08-15T22:12:58.627911Z digest=sha256:1f9f6a0bf8f1494c5133764091f83ee55f4c68815a4fa95b23e903b94d360a3e

Observation 182f5b51-3219-45e4-bfce-2c7c847df317 · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

Overflow Prevention Enhances Long-Context Recurrent LLMs Efficiently Modeling Long Sequences with Structured State Spaces

Reference 21

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source=arxiv_source observed=2026-08-15T22:12:58.633293Z digest=sha256:3872acab31c010136e3de48c2023c28e5e16e5952793290e8d4d978c91fe2a3a

Observation 49132fa4-12c5-430f-a3ef-16bec7bb9494 · outbound

This paper cites Combining recurrent, convolutional, and continuous-time models with linear state space layers.

Overflow Prevention Enhances Long-Context Recurrent LLMs Combining recurrent, convolutional, and continuous-time models with linear state space layers

Reference 22

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source=arxiv_source observed=2026-08-15T22:12:58.638417Z digest=sha256:6d6ee00aa16895541cd940487decf69d80b59cd731bd7c04f9632e6ccfa4e4f3

Observation 3763739d-26ce-4c6e-ad50-a6257f39e03b · outbound

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

Overflow Prevention Enhances Long-Context Recurrent LLMs REALM: Retrieval-Augmented Language Model Pre-Training

Reference 23

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source=arxiv_source observed=2026-08-15T22:12:58.643502Z digest=sha256:097ed505dbe49740e02ea192cae78b55e35b86892acb418bc2745819e7ede015

Observation 8c1e4821-50b5-49b4-9548-d19919123c3f · outbound

This paper cites MambaVision: A Hybrid Mamba-Transformer Vision Backbone.

Overflow Prevention Enhances Long-Context Recurrent LLMs MambaVision: A Hybrid Mamba-Transformer Vision Backbone

Reference 24

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source=arxiv_source observed=2026-08-15T22:12:58.648734Z digest=sha256:d374f3f29fdc4df104a749aaff0e0abec29f7763e67f90d90bf1dd7f54090e2f

Observation ffd4e0da-cec9-4412-b236-f115483feebf · outbound

This paper cites Decision mamba: Reinforcement learning via hybrid selective sequence modeling.

Overflow Prevention Enhances Long-Context Recurrent LLMs Decision mamba: Reinforcement learning via hybrid selective sequence modeling

Reference 25

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raw_fallback, observed 2026-08-15T22:13:00.321441Z

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=arxiv_source observed=2026-08-15T22:12:58.654245Z digest=sha256:28bd9ae78524d84e97fb0422364c1ab7372a2b748bafb834cbd883c5ff19933b

Observation 50a936a4-3ad9-4a8e-882f-196feba9d329 · outbound

This paper cites Unsupervised Dense Information Retrieval with Contrastive Learning.

Overflow Prevention Enhances Long-Context Recurrent LLMs Unsupervised Dense Information Retrieval with Contrastive Learning

Reference 26

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source=arxiv_source observed=2026-08-15T22:12:58.659471Z digest=sha256:c19f91689d79b4669c1c3e34e4788afcb7f43f354633e4036465d3f36478d66d

Observation 32d62886-2f3b-4408-978b-04702d88eb31 · outbound

This paper cites How can we know when language models know? on the calibration of language models for question answering.

Overflow Prevention Enhances Long-Context Recurrent LLMs How can we know when language models know? on the calibration of language models for question answering

Reference 27

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source=arxiv_source observed=2026-08-15T22:12:58.664675Z digest=sha256:2e08362b19ef66fde34d39390d706a817fb0cca0c6db8b381509591dd9e0b4c6

Observation 6f87bfa6-3970-453a-a13b-cd526de9275b · outbound

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

Overflow Prevention Enhances Long-Context Recurrent LLMs Dense Passage Retrieval for Open-Domain Question Answering

Reference 28

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source=arxiv_source observed=2026-08-15T22:12:58.669718Z digest=sha256:fae2fa1768b431be9b3813dc637706c62a476697c841c8c1115c8a9a2e7f3125

Observation a9e16972-cffe-4aa4-b133-070f4a25239f · outbound

This paper cites The impact of positional encoding on length generalization in transformers.

Overflow Prevention Enhances Long-Context Recurrent LLMs The impact of positional encoding on length generalization in transformers

Reference 29

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source=arxiv_source observed=2026-08-15T22:12:58.675029Z digest=sha256:944ed84b3b459dc6b4df7618ce1d68b9a441f47076d893a5cb32a28062708e65

Observation f3afb10a-87c2-4492-b0e5-dffe6f301edd · outbound

This paper cites an unresolved cited work.

Overflow Prevention Enhances Long-Context Recurrent LLMs Unresolved cited work

Reference 30

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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=arxiv_source observed=2026-08-15T22:12:58.680109Z digest=sha256:85db6ee2e62e1d295a01a4be9c5e42dc04c3ce455fab8de3da381b9e3098c708

Observation 9ec3d2bb-a164-47b0-84b6-42cc42036f05 · outbound

This paper cites Fast inference from transformers via speculative decoding.

Overflow Prevention Enhances Long-Context Recurrent LLMs Fast inference from transformers via speculative decoding

Reference 31

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source=arxiv_source observed=2026-08-15T22:12:58.685461Z digest=sha256:b6b97e556cea97a1b96b144249d99d3c63f8be7fb5e69dd0dc0cd7501f5884ad

Observation 84c50146-89a6-4254-a105-37b8c9250067 · outbound

This paper cites u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \.

Overflow Prevention Enhances Long-Context Recurrent LLMs u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \

Reference 32

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source=arxiv_source observed=2026-08-15T22:12:58.691554Z digest=sha256:6774d49773aa3fa7ee1e5e614fe790f5580511e855f711b5239bc9ca3ae43f29

Observation fb37c494-bfb6-41de-baab-91b2ccb8d165 · outbound

This paper cites Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach.

Overflow Prevention Enhances Long-Context Recurrent LLMs Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach

Reference 33

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source=arxiv_source observed=2026-08-15T22:12:58.696984Z digest=sha256:22e0b1f30cbbfb58e0e84d9261fe434e2c1cf536b1a9e6863f30093f5e0c1f35

Observation 2887e506-eb6a-4fb8-8a32-daf36d390d8e · outbound

This paper cites How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval.

Overflow Prevention Enhances Long-Context Recurrent LLMs How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval

Reference 34

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source=arxiv_source observed=2026-08-15T22:12:58.703439Z digest=sha256:25397d7792f59256e5abaaeca685cf4a4c10f9c6fbb18ddaae0b8bb7c2bb3e75

Observation 79d1744f-60f6-4cd3-a592-e0ef00c04ed8 · outbound

This paper cites Lost in the middle: How language models use long contexts.

Overflow Prevention Enhances Long-Context Recurrent LLMs Lost in the middle: How language models use long contexts

Reference 35

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verified fuzzy
raw_fallback, observed 2026-08-15T22:13:00.241035Z

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=arxiv_source observed=2026-08-15T22:12:58.708903Z digest=sha256:1141c5c667db71ff72a7c2d7ca76bf6f9ae65b5202e4c20c0eb7cadd55ada2c3

Observation c30582fe-b4a1-428f-b9f0-f85452f807e0 · outbound

This paper cites VMamba: Visual State Space Model.

Overflow Prevention Enhances Long-Context Recurrent LLMs VMamba: Visual State Space Model

Reference 36

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source=arxiv_source observed=2026-08-15T22:12:58.714073Z digest=sha256:f8d975aefba91f5e820308e26bfa31276dbeb9d9e70c6a9844c54e02c5e17648

Observation d7b8cd42-20bd-4c66-b037-9876bd7b165a · outbound

This paper cites Focus Your Attention (with Adaptive IIR Filters).

Overflow Prevention Enhances Long-Context Recurrent LLMs Focus Your Attention (with Adaptive IIR Filters)

Reference 37

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:12:58.720528Z digest=sha256:454f7b7bcad979b6443075cc8459af82023c97dea738dd3eb620cca4c5975ce2

Observation 6c4892c3-29cf-47c2-a24f-f1cee9a2a9f1 · outbound

This paper cites Decision mamba: A multi-grained state space model with self-evolution regularization for offline rl.

Overflow Prevention Enhances Long-Context Recurrent LLMs Decision mamba: A multi-grained state space model with self-evolution regularization for offline rl

Reference 38

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source=arxiv_source observed=2026-08-15T22:12:58.725834Z digest=sha256:a13bc5cdf539662e45112c93fb45574bedfbff7748b73772415ec8ff5a056e21

Observation c69a663b-8271-4260-9d85-f259857943ed · outbound

This paper cites Uncertainty estimation in autoregressive structured prediction.

Overflow Prevention Enhances Long-Context Recurrent LLMs Uncertainty estimation in autoregressive structured prediction

Reference 39

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verified fuzzy
raw_fallback, observed 2026-08-15T22:13:00.212609Z

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=arxiv_source observed=2026-08-15T22:12:58.731187Z digest=sha256:70f33a14a1c4980930f688ca39952de5e8c2560eecb97f150c27cd680797c813

Observation 25db02cb-621f-41b4-ac4b-3868f7828e7b · outbound

This paper cites Pointer Sentinel Mixture Models.

Overflow Prevention Enhances Long-Context Recurrent LLMs Pointer Sentinel Mixture Models

Reference 40

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-15T22:12:58.736403Z digest=sha256:4cd941fe2258be33c8da325b703e6af769d7204f83390b6c55fb83de9709fd06

Observation bcf3eacd-f84a-48e2-a2da-4b2dd547ed0e · outbound

This paper cites Exploring the capability of mamba in speech applications.

Overflow Prevention Enhances Long-Context Recurrent LLMs Exploring the capability of mamba in speech applications

Reference 41

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verified fuzzy
raw_fallback, observed 2026-08-15T22:13:00.195669Z

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=arxiv_source observed=2026-08-15T22:12:58.741949Z digest=sha256:b85be8ccbc3417a0affc470a48efa2473fe349ed0ae80fa9a3bf275d62978583

Observation 2700c30d-4aed-491a-b41a-302523373e40 · outbound

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

Overflow Prevention Enhances Long-Context Recurrent LLMs Landmark Attention: Random-Access Infinite Context Length for Transformers

Reference 42

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no resolver link, observed 2026-08-15T22:12:58.747002Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:12:58.747002Z digest=sha256:befa8a8557f5741c0a190406e9d38cbbd16dff7b945e4506b079443ac7457318

Observation 215a8d3b-99bc-4f36-a256-828bd76566e3 · outbound

This paper cites WebGPT: Browser-assisted question-answering with human feedback.

Overflow Prevention Enhances Long-Context Recurrent LLMs WebGPT: Browser-assisted question-answering with human feedback

Reference 43

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no resolver link, observed 2026-08-15T22:12:58.752185Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-15T22:12:58.752185Z digest=sha256:f48f1debc4e1b8fde93192b94932a30b0fca58e8c85919570471df9ce3e2c0bf

Observation bc012940-b9ea-4361-aeb8-67e81ff403a7 · outbound

This paper cites Revisiting associative recall in modern recurrent models.

Overflow Prevention Enhances Long-Context Recurrent LLMs Revisiting associative recall in modern recurrent models

Reference 44

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verified fuzzy
raw_fallback, observed 2026-08-15T22:13:00.178065Z

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=arxiv_source observed=2026-08-15T22:12:58.757411Z digest=sha256:c98f6f24b8358373d63c4fb6144a7126a3052406662852bd7cd815d5fa2d2384

Observation b8181110-ec9c-45e9-93a1-d1b30dc4dfe5 · outbound

This paper cites In-context Learning and Induction Heads.

Overflow Prevention Enhances Long-Context Recurrent LLMs In-context Learning and Induction Heads

Reference 45

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:12:58.762353Z digest=sha256:125a403ffc1b56161e4f56fea49eb3920ae19baac2f7c567c5297d248157ce48

Observation 6006f1e1-4b2b-43e4-82f9-dcc8d0ad8aff · outbound

This paper cites Resurrecting Recurrent Neural Networks for Long Sequences.

Overflow Prevention Enhances Long-Context Recurrent LLMs Resurrecting Recurrent Neural Networks for Long Sequences

Reference 46

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no resolver link, observed 2026-08-15T22:12:58.767596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:12:58.767596Z digest=sha256:fe7078c10d44a21f69df7377b0a0c4fb119225e311d0614c88221700b0f2143c

Observation 6416ef27-f4c2-497e-aa3d-1bb2432d77dd · outbound

This paper cites RWKV: Reinventing RNNs for the Transformer Era.

Overflow Prevention Enhances Long-Context Recurrent LLMs RWKV: Reinventing RNNs for the Transformer Era

Reference 47

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no resolver link, observed 2026-08-15T22:12:58.772906Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:12:58.772906Z digest=sha256:2625f0cc39884434bcd4c4e9b64c9e30b32851edff85b8b5ddc9f885a8314be7

Observation 6d309fdc-8af8-4738-b243-784335ffa6b6 · outbound

This paper cites Eagle and finch: RWKV with matrix-valued states and dynamic recurrence.

Overflow Prevention Enhances Long-Context Recurrent LLMs Eagle and finch: RWKV with matrix-valued states and dynamic recurrence

Reference 48

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no resolver link, observed 2026-08-15T22:12:58.777736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:12:58.777736Z digest=sha256:7a5daa6d838d42248a1240a2af4134299a5a691bb0b7f41d1c7fdb6c4006392f

Observation 44be2798-a1f5-4b1b-8fd4-0cdbfa50a484 · outbound

This paper cites Mechanistic design and scaling of hybrid architectures.

Overflow Prevention Enhances Long-Context Recurrent LLMs Mechanistic design and scaling of hybrid architectures

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:13:00.148466Z

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=arxiv_source observed=2026-08-15T22:12:58.782473Z digest=sha256:b1e8c4791fd0feb8eb3b94f196a180a26b0a3221257122e793732d9e28638dd1

Observation 860c4099-159e-454e-b869-39cc05f56cfd · outbound

This paper cites Train short, test long: Attention with linear biases enables input length extrapolation.

Overflow Prevention Enhances Long-Context Recurrent LLMs Train short, test long: Attention with linear biases enables input length extrapolation

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:13:00.130953Z

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=arxiv_source observed=2026-08-15T22:12:58.787919Z digest=sha256:a361a23b3d0bd53f5d1d843cd0be6c9c8b32b8acf535310e540c114bf4180079

Observation 4e911ab0-c26a-4a45-b36a-4cc7f586817e · outbound

This paper cites Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting.

Overflow Prevention Enhances Long-Context Recurrent LLMs Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting

Reference 51

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no resolver link, observed 2026-08-15T22:12:58.793431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:12:58.793431Z digest=sha256:6089482f2bbb15e99032b41e8b6f6e68c38f507525e2566d144a38bdfdabae86

Observation 7842a8f4-3636-4956-99e9-e157b6e0d9e1 · outbound

This paper cites HGRN2: Gated Linear RNNs with State Expansion.

Overflow Prevention Enhances Long-Context Recurrent LLMs HGRN2: Gated Linear RNNs with State Expansion

Reference 52

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no resolver link, observed 2026-08-15T22:12:58.798549Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-15T22:12:58.798549Z digest=sha256:7fd1e45fee2669d6ec7836f2f3771f1d31c2d22b77eb4630d764525f8839ef03

Observation 67422e54-4e1d-436c-9662-7108f593773f · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Overflow Prevention Enhances Long-Context Recurrent LLMs Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 53

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no resolver link, observed 2026-08-15T22:12:58.804302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:12:58.804302Z digest=sha256:f638cbb4bef867811c2ea9b93cfc010f1d712feb020810379fb9ae8556a9494a

Observation 37c8d3cb-b785-41b1-9418-1e664863964c · outbound

This paper cites Know what you don't know: Unanswerable questions for squad, 2018.

Overflow Prevention Enhances Long-Context Recurrent LLMs Know what you don't know: Unanswerable questions for squad, 2018

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:13:00.101332Z

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=arxiv_source observed=2026-08-15T22:12:58.809811Z digest=sha256:6d207784f2c9ac66026eea237f0a288d0dac4ee4d3c8c4ba314807eafb318515

Observation 38ed34b6-f46b-4a6b-afda-7c1dc8ab06af · outbound

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

Overflow Prevention Enhances Long-Context Recurrent LLMs Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling

Reference 55

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no resolver link, observed 2026-08-15T22:12:58.815331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:12:58.815331Z digest=sha256:18f1be3f239d85e14de7652a1a4b7605ef4fc7d01ead0c6dd53738cdb79fc6cf

Observation 41015505-ee9b-4f17-8b72-c12f710dd5a6 · outbound

This paper cites A study of branch prediction strategies.

Overflow Prevention Enhances Long-Context Recurrent LLMs A study of branch prediction strategies

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:13:00.084217Z

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=arxiv_source observed=2026-08-15T22:12:58.821518Z digest=sha256:ce35b32788477c6fa4feb9803922c4dd6e53c4ec22134446f2d2fc7599327f63

Observation da91294c-a82f-4f65-ba97-5f4eaf756049 · outbound

This paper cites Roformer: Enhanced transformer with rotary position embedding.

Overflow Prevention Enhances Long-Context Recurrent LLMs Roformer: Enhanced transformer with rotary position embedding

Reference 57

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no resolver link, observed 2026-08-15T22:12:58.827097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:12:58.827097Z digest=sha256:371294a79fc58e625718ffc012cff31ea554b5ce5fbcee4374b4f1c97c4e8b84

Observation 57353519-5423-4925-b935-c0010f195994 · outbound

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

Overflow Prevention Enhances Long-Context Recurrent LLMs Learning to (Learn at Test Time): RNNs with Expressive Hidden States

Reference 58

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no resolver link, observed 2026-08-15T22:12:58.833037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:12:58.833037Z digest=sha256:99bd1e37522913e4139205e350d1120c50478a4a22260f979f4a0fa422649a2b

Observation 580fc529-7685-48bc-800f-524c5fa2c8b5 · outbound

This paper cites The falcon 3 family of open models, December 2024.

Overflow Prevention Enhances Long-Context Recurrent LLMs The falcon 3 family of open models, December 2024

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:13:00.052450Z

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=arxiv_source observed=2026-08-15T22:12:58.838005Z digest=sha256:b3833e0bbc2381f0f7728830a6f2e104011fcf6006be40577fafcffef04ead02

Observation 613c8566-c94f-4c2d-a684-b228bccd8610 · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Overflow Prevention Enhances Long-Context Recurrent LLMs Gemma: Open Models Based on Gemini Research and Technology

Reference 60

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no resolver link, observed 2026-08-15T22:12:58.843073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:12:58.843073Z digest=sha256:bdae37d1c11aab2bc3360b934b5e7ff141b137cb90781e1a64bf011e35e7853c

Observation addbb256-ed53-4c87-8d7b-92dc86554052 · outbound

This paper cites Jamba-1.5: Hybrid Transformer-Mamba Models at Scale.

Overflow Prevention Enhances Long-Context Recurrent LLMs Jamba-1.5: Hybrid Transformer-Mamba Models at Scale

Reference 61

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no resolver link, observed 2026-08-15T22:12:58.848466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:12:58.848466Z digest=sha256:da637428ddc175365aaa37d022b55ba56e7d526c913db6d6fc01ae182ee1a089

Observation b218ec04-a458-490f-87ab-b013833a6c48 · outbound

This paper cites BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models.

Overflow Prevention Enhances Long-Context Recurrent LLMs BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models

Reference 62

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unresolved
no resolver link, observed 2026-08-15T22:12:58.853959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:12:58.853959Z digest=sha256:2ecd561e94b06c9f15a0ac22b60e6f9be2d071208631a6a882359861efa5f98b

Observation 84abae5f-b81a-4ec0-ae4c-336ecc00fcab · outbound

This paper cites Attention Is All You Need.

Overflow Prevention Enhances Long-Context Recurrent LLMs Attention Is All You Need

Reference 63

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no resolver link, observed 2026-08-15T22:12:58.859395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:12:58.859395Z digest=sha256:cdedf0bc5562cfdcc71978a6e044f5056a75f21d7de664a898bc5fab4797174d

Observation d15eccbc-6ae3-46c7-9d78-89c5c9206bd8 · outbound

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

Overflow Prevention Enhances Long-Context Recurrent LLMs An Empirical Study of Mamba-based Language Models

Reference 64

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no resolver link, observed 2026-08-15T22:12:58.864401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:12:58.864401Z digest=sha256:8fc5e652b4c4282a7311cd8a1cc0caf3c6079007213b0851fb5f3eabf458d33b

Observation 0c2e74e8-34f5-4793-aedd-e6fe54a970fe · outbound

This paper cites Mambabyte: Token-free selective state space model.

Overflow Prevention Enhances Long-Context Recurrent LLMs Mambabyte: Token-free selective state space model

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:13:00.033825Z

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=arxiv_source observed=2026-08-15T22:12:58.869604Z digest=sha256:1fe236b9882100fa88be2f593527fa8cdd45e326b13ff5235c2b2ec5accab162

Observation 121b702b-bdb1-49fd-b28e-92b8bbcdc7f2 · outbound

This paper cites Unlocking efficiency in large language model inference: A comprehensive survey of speculative decoding.

Overflow Prevention Enhances Long-Context Recurrent LLMs Unlocking efficiency in large language model inference: A comprehensive survey of speculative decoding

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:13:00.015448Z

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=arxiv_source observed=2026-08-15T22:12:58.874922Z digest=sha256:c60cc1f047775352df623ef758682247b207323cb90fdfbe85c4abc9b06b83ef

Observation a198b541-807f-4073-baa9-925cc26c7a05 · outbound

This paper cites Retrieval meets Long Context Large Language Models.

Overflow Prevention Enhances Long-Context Recurrent LLMs Retrieval meets Long Context Large Language Models

Reference 67

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:12:58.880191Z digest=sha256:fbf2318df593ec3d322f465877dde7075b1b3ef5d4ca6f7f2c63e3c2e599f9a0

Observation 405011f3-6c05-4d92-9fa2-30ea7f7a40a9 · outbound

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

Overflow Prevention Enhances Long-Context Recurrent LLMs Gated Delta Networks: Improving Mamba2 with Delta Rule

Reference 68

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no resolver link, observed 2026-08-15T22:12:58.885874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:12:58.885874Z digest=sha256:fc5dbb1473cb67aecc81acaa410459cf8e3bcd87a1cb4b06bb2acc906918c9e5

Observation 86eb18fd-360c-42d9-9557-0754a52e247c · outbound

This paper cites Longmamba: Enhancing mamba's long-context capabilities via training-free receptive field enlargement.

Overflow Prevention Enhances Long-Context Recurrent LLMs Longmamba: Enhancing mamba's long-context capabilities via training-free receptive field enlargement

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:12:59.997031Z

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=arxiv_source observed=2026-08-15T22:12:58.891667Z digest=sha256:8dcb5c1321fb3e3af149c1bde5cc61c0c7e58af670bf8dcfbc863b420903dfc1

Observation 0bfe843f-0ae5-44a5-ba0c-144c7ce52a93 · outbound

This paper cites Useful confidence measures: Beyond the max score.

Overflow Prevention Enhances Long-Context Recurrent LLMs Useful confidence measures: Beyond the max score

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:12:59.978790Z

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=arxiv_source observed=2026-08-15T22:12:58.897907Z digest=sha256:3d52cbcf2f60319724c38fa37b09951ebe54984bb5d1ffc07ad106bca1b37fb0

Observation 736774dc-445e-49dd-b7af-a0680b598b84 · outbound

This paper cites $\infty$Bench: Extending Long Context Evaluation Beyond 100K Tokens.

Overflow Prevention Enhances Long-Context Recurrent LLMs $\infty$Bench: Extending Long Context Evaluation Beyond 100K Tokens

Reference 71

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unresolved
no resolver link, observed 2026-08-15T22:12:58.904457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:12:58.904457Z digest=sha256:5d9a07e8bd02e136291895ff5ee4116d661b6f7c2cee97d30eec6ff3dce41bbc

Observation 48f3ac94-d8f0-4e05-b061-028589427d13 · outbound

This paper cites LLM$\times$MapReduce: Simplified Long-Sequence Processing using Large Language Models.

Overflow Prevention Enhances Long-Context Recurrent LLMs LLM$\times$MapReduce: Simplified Long-Sequence Processing using Large Language Models

Reference 72

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unresolved
no resolver link, observed 2026-08-15T22:12:58.909939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:12:58.909939Z digest=sha256:b8f11936ee9a79879385e02756adbce538377d66c519668b7d129cb95783bb98

Observation 089b4a14-c8b6-49d2-9b12-125b17f78c72 · outbound

This paper cites Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model.

Overflow Prevention Enhances Long-Context Recurrent LLMs Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Reference 73

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no resolver link, observed 2026-08-15T22:12:58.915776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:12:58.915776Z digest=sha256:c940aae3e58b65c20afe66bb2cb4b0aa6750ade43e68a4c34c987a1a76a7d0cd

Observation dea9d758-9b54-491d-82d8-b94c74b1c1be · outbound

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

Overflow Prevention Enhances Long-Context Recurrent LLMs Falcon Mamba: The First Competitive Attention-free 7B Language Model

Reference 74

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unresolved
no resolver link, observed 2026-08-15T22:12:58.924215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:12:58.924215Z digest=sha256:4440ebbde845b3d5805c7bd40a16e85867e492ec564fdbde7d26386a3e032a9d

Observation f40453bd-2316-45d6-a23a-4037b3920b6f · outbound

This paper cites write newline.

Overflow Prevention Enhances Long-Context Recurrent LLMs write newline

Reference 75

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unresolved
no resolver link, observed 2026-08-15T22:12:58.929907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:12:58.929907Z digest=sha256:332b5d6ea66520672f395b4bb81e641bc6e7cf5b5fe525279e67cd529aac0caa

Pith citing papers

No inbound Pith citation observations are available.