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Paper Citation Record · LEDGER

Maximally-Informative Retrieval for State Space Model Generation

As of 8 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2506.12149.

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

pith.paper-citation-record.v1
2506.12149 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T01:03:47.725262Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

41 of 41 outbound references displayed

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  • verified fuzzy4
  • unresolved34
  • parse uncertain0
  • malformed identifier2
  • metadata mismatch0

External citation measurements

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Outbound references

Observation d436d826-185b-4b76-9794-79844b0b6ba5 · outbound

This paper cites please score these documents.

Maximally-Informative Retrieval for State Space Model Generation please score these documents

Reference 1

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Observation 4edf53fd-7aab-4e4a-89c8-f43ad3f1ea7d · outbound

This paper cites an unresolved cited work.

Maximally-Informative Retrieval for State Space Model Generation Unresolved cited work

Reference 4

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Observation 51c486a4-fe89-4fe7-bd01-8974ad7de86b · outbound

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

Maximally-Informative Retrieval for State Space Model Generation Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 7

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Observation a8f88f03-ca09-41e8-9e7d-6f602ce8d288 · outbound

This paper cites A Survey on In-context Learning.

Maximally-Informative Retrieval for State Space Model Generation A Survey on In-context Learning

Reference 8

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Observation 452edcf4-1187-45ff-855c-e0df48949e59 · outbound

This paper cites Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models.

Maximally-Informative Retrieval for State Space Model Generation Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models

Reference 9

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Observation 2fc2fef9-3e96-4494-b0b5-e1bbf0c42994 · outbound

This paper cites Precise Zero-Shot Dense Retrieval without Relevance Labels.

Maximally-Informative Retrieval for State Space Model Generation Precise Zero-Shot Dense Retrieval without Relevance Labels

Reference 10

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Observation a376758d-84f8-497b-97d4-69c0bc2e842c · outbound

This paper cites Retrieval-Augmented Generation for Large Language Models: A Survey.

Maximally-Informative Retrieval for State Space Model Generation Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 11

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Observation 8d88fdc2-6d33-43d0-9b35-74f09d5a7f72 · outbound

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

Maximally-Informative Retrieval for State Space Model Generation Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 12

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Observation c57f8f76-13b3-4a30-8ae8-34c19beb42a6 · outbound

This paper cites Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps.

Maximally-Informative Retrieval for State Space Model Generation Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps

Reference 13

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Observation 2a289bc2-1e89-4892-b14e-a911301816b8 · outbound

This paper cites Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG.

Maximally-Informative Retrieval for State Space Model Generation Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG

Reference 15

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Observation 3d345cbd-a49f-4ee7-9151-35efa35dd27d · outbound

This paper cites Backpropagated gradient representations for anomaly detection.

Maximally-Informative Retrieval for State Space Model Generation Backpropagated gradient representations for anomaly detection

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 1a60c1c9-16ec-4f2d-b9b3-f8a724d9926b · outbound

This paper cites NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models.

Maximally-Informative Retrieval for State Space Model Generation NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models

Reference 17

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Observation 8fc54169-f612-455b-bbcd-8a7f21a2cac2 · outbound

This paper cites Making Text Embedders Few-Shot Learners.

Maximally-Informative Retrieval for State Space Model Generation Making Text Embedders Few-Shot Learners

Reference 18

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Observation 44e5e7e5-209f-4799-a32c-15fde8f20cb3 · outbound

This paper cites SGPT: GPT Sentence Embeddings for Semantic Search.

Maximally-Informative Retrieval for State Space Model Generation SGPT: GPT Sentence Embeddings for Semantic Search

Reference 20

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Observation a188581d-fe40-43f5-907d-1982621dd0c7 · outbound

This paper cites MTEB: Massive Text Embedding Benchmark.

Maximally-Informative Retrieval for State Space Model Generation MTEB: Massive Text Embedding Benchmark

Reference 21

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Observation 647218f6-422f-44ab-a1ee-f0f9d6a8ecb7 · outbound

This paper cites Generative Representational Instruction Tuning.

Maximally-Informative Retrieval for State Space Model Generation Generative Representational Instruction Tuning

Reference 22

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Observation 29ffc58b-7840-47fe-88eb-34cc7a3acd1e · outbound

This paper cites Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs.

Maximally-Informative Retrieval for State Space Model Generation Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs

Reference 23

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Observation 2b9aafad-f054-4ae3-96bd-d83281a2ade0 · outbound

This paper cites Learning To Retrieve Prompts for In-Context Learning.

Maximally-Informative Retrieval for State Space Model Generation Learning To Retrieve Prompts for In-Context Learning

Reference 25

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Observation 65617e23-9756-471f-9212-a1b2904468cb · outbound

This paper cites REPLUG: Retrieval-Augmented Black-Box Language Models.

Maximally-Informative Retrieval for State Space Model Generation REPLUG: Retrieval-Augmented Black-Box Language Models

Reference 26

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Observation 11726fce-96ee-470e-b29d-4cf9ce4c43f0 · outbound

This paper cites DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Models.

Maximally-Informative Retrieval for State Space Model Generation DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Models

Reference 27

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Observation 2b834920-2978-4e49-95ab-4adc32723030 · outbound

This paper cites Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions.

Maximally-Informative Retrieval for State Space Model Generation Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions

Reference 28

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Observation 353b9b99-d5c4-4d5c-a71b-0a13aa93f396 · outbound

This paper cites LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders.

Maximally-Informative Retrieval for State Space Model Generation LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 30

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Observation cc3b28d5-6ef8-4682-a9ed-27341af16e6d · outbound

This paper cites Improving Text Embeddings with Large Language Models.

Maximally-Informative Retrieval for State Space Model Generation Improving Text Embeddings with Large Language Models

Reference 31

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Observation c102901f-5b9f-4dff-b944-e282343dfe4d · outbound

This paper cites RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation.

Maximally-Informative Retrieval for State Space Model Generation RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation

Reference 32

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Observation 67b3deca-150a-4095-a79f-e02b4da90314 · outbound

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

Maximally-Informative Retrieval for State Space Model Generation Gated Linear Attention Transformers with Hardware-Efficient Training

Reference 33

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Observation 1c9b01fd-0bab-41a8-a2f4-a418aca57d95 · outbound

This paper cites HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering.

Maximally-Informative Retrieval for State Space Model Generation HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

Reference 34

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Observation 88ea77f6-d9d0-47d4-994f-eb7501a612dc · outbound

This paper cites Improving Language Models via Plug-and-Play Retrieval Feedback.

Maximally-Informative Retrieval for State Space Model Generation Improving Language Models via Plug-and-Play Retrieval Feedback

Reference 35

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source=pdf_text observed=2026-08-07T01:03:47.177878Z digest=sha256:b8494bfcb0f9ae056bdc4bf40f8bdcc754b8c816f93a3dcecd7ffcf605160f44

Observation c63a52f5-da8a-487e-a74e-2c40a13bfbdd · outbound

This paper cites B'MOJO: Hybrid State Space Realizations of Foundation Models with Eidetic and Fading Memory.

Maximally-Informative Retrieval for State Space Model Generation B'MOJO: Hybrid State Space Realizations of Foundation Models with Eidetic and Fading Memory

Reference 36

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T01:03:47.277389Z digest=sha256:937108bc2abc25665f19e8f1878701a6c71b19d4db52c5b83da206796ba8e4cf

Observation 96c6e191-6f50-4877-9589-cf024216896c · outbound

This paper cites As explored in previous works, the Mamba2 architecture can be interpreted as linear attention with alearnedcausal mask (Yang et al., 2023; Dao & Gu, 2024; Bick et al., 2024).

Maximally-Informative Retrieval for State Space Model Generation As explored in previous works, the Mamba2 architecture can be interpreted as linear attention with alearnedcausal mask (Yang et al., 2023; Dao & Gu, 2024; Bick et al., 2024)

Reference 37

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T01:03:47.378475Z digest=sha256:66f5b8ac48e216ab6d52904a78bb04be6c1cc107d9eb4ab8b90d24e7e46251de

Observation 0d0d3704-487e-4147-8966-52b9293da29f · outbound

This paper cites the input state (forward+backward pass through the model) across benchmarks for Mamba2 models.

Maximally-Informative Retrieval for State Space Model Generation the input state (forward+backward pass through the model) across benchmarks for Mamba2 models

Reference 40

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raw_fallback, observed 2026-08-07T01:03:48.567778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T01:03:47.649882Z digest=sha256:4fbddcc90987d745d2bff2b2f1ec8c3e4621f651d46f7188996f619e19182b7f

Observation 94c5acf8-63ad-4e13-8180-a66a5a6180d8 · outbound

This paper cites The red dots represent a gradient optimization trajectory over the question loss landscape starting from the mean context state (green).

Maximally-Informative Retrieval for State Space Model Generation The red dots represent a gradient optimization trajectory over the question loss landscape starting from the mean context state (green)

Reference 41

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raw_fallback, observed 2026-08-07T01:03:48.348464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T01:03:47.725262Z digest=sha256:78744321117dbff118dfec4a890d2a4b75470198634c7206a7256b8a3f017572

Observation f8a4254e-7d9e-4e91-91ee-a6c8b8797dc2 · outbound

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

Maximally-Informative Retrieval for State Space Model Generation M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation

Reference 2005

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source=pdf_text observed=2026-08-07T01:03:44.639300Z digest=sha256:442f2c6cd0e1378cac6790d92bc8548785dacced76104d7467bbf8b7cf282693

Observation 2edd0261-b578-43f6-91d2-c77c2db1d45c · outbound

This paper cites RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation.

Maximally-Informative Retrieval for State Space Model Generation RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation

Reference 2009

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source=pdf_text observed=2026-08-07T01:03:46.292777Z digest=sha256:4cb21c6a8955562684281cb52789be074a5d22febc2bed2d7fdcb7fa6f344e72

Observation 621ddee3-13b9-4f86-89ca-9359f729ed7a · outbound

This paper cites Text Embeddings by Weakly-Supervised Contrastive Pre-training.

Maximally-Informative Retrieval for State Space Model Generation Text Embeddings by Weakly-Supervised Contrastive Pre-training

Reference 2017

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source=pdf_text observed=2026-08-07T01:03:46.701386Z digest=sha256:cab7fcee11ef9a801efd1815643ae0d3723d7878f7069e868331113ba6e0a442

Observation 1d6da748-4da5-43cf-b9d0-c09d6537bfd5 · outbound

This paper cites SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generation.

Maximally-Informative Retrieval for State Space Model Generation SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generation

Reference 2018

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source=pdf_text observed=2026-08-07T01:03:47.103532Z digest=sha256:9e3f5758e67adc8404acd05fed68170e6dae4993e80a0220f8081d73db4962a4

Observation 33f10e3c-cfd2-4aca-bd1c-c9c71f84e935 · outbound

This paper cites Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection.

Maximally-Informative Retrieval for State Space Model Generation Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

Reference 2019

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Observation 796e0ac9-9489-4632-9175-4cf972c6ccab · outbound

This paper cites Active Retrieval Augmented Generation.

Maximally-Informative Retrieval for State Space Model Generation Active Retrieval Augmented Generation

Reference 2020

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Observation 56947658-e0ed-4554-8a66-0e2c4e56c285 · outbound

This paper cites an unresolved cited work.

Maximally-Informative Retrieval for State Space Model Generation Unresolved cited work

Reference 2022

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Observation 9c1d01d6-68f4-440a-8796-21d6d4fe050a · outbound

This paper cites MS MARCO: A Human Generated MAchine Reading COmprehension Dataset.

Maximally-Informative Retrieval for State Space Model Generation MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

Reference 2023

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This paper cites Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models.

Maximally-Informative Retrieval for State Space Model Generation Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models

Reference 2024

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Observation 52b19520-aa0b-48ef-a54b-7bf1f4ceb81b · outbound

This paper cites Gradients as Features for Deep Representation Learning.

Maximally-Informative Retrieval for State Space Model Generation Gradients as Features for Deep Representation Learning

Reference 2025

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