Paper Citation Record · LEDGER

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2

As of 12 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 1 inbound Pith citation observation for arXiv:2503.18002.

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

pith.paper-citation-record.v1
2503.18002 v2

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T10:59:50.677991Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T22:12:13.114405Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T19:36:09.343466Z

Reference resolution

41 of 41 outbound references displayed

verified exact2
verified fuzzy8
unresolved30
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 70d1dad7-910d-4107-85ab-471266a7225b · outbound

This paper cites Q-S5: Towards Quantized State Space Models.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Q-S5: Towards Quantized State Space Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.551524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d07cd02a-8606-4a9d-9354-7c941292f787 · outbound

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

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 PIQA : Reasoning about Physical Commonsense in Natural Language

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.555691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30d4d342-5b02-42f9-959f-a6538c64a5d3 · outbound

This paper cites Quamba: A Post-Training Quantization Recipe for Selective State Space Models.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Quamba: A Post-Training Quantization Recipe for Selective State Space Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.559237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4e8b5f6-62ec-4773-875d-ae996d713beb · outbound

This paper cites Learning Phrase Representations using RNN Encoder – Decoder for Statistical Machine Translation.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Learning Phrase Representations using RNN Encoder – Decoder for Statistical Machine Translation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.563014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c90f1cd1-4425-4f4e-8813-7934513b4d4a · outbound

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

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.566014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88be67c0-727b-4c66-b3be-8f9c9ff9a9cc · outbound

This paper cites Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.569372Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf734201-3916-4444-8ac3-93ff8d6790a7 · outbound

This paper cites Dauphin, Angela Fan, Michael Auli, and David Grangier.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Dauphin, Angela Fan, Michael Auli, and David Grangier

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:59:51.291258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a459b556-c69b-4bb4-8c63-48f2591bcbf5 · outbound

This paper cites Fonseca Guerra, Prasad Joshi, Philipp Plank, and Sumedh R.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Fonseca Guerra, Prasad Joshi, Philipp Plank, and Sumedh R

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.575346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a7d0f5b-784e-4286-987d-9561f0d94c55 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.578079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a0a94d6-6e74-4b90-a4cf-e2911ee7df2f · outbound

This paper cites LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.582074Z

Source-reported events for the cited work

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Observation 97f00a6b-8e00-42b9-8d8f-7acfdfee14eb · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.585780Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 902176ef-7064-4452-bb62-be8d205bb209 · outbound

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

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.589328Z

Source-reported events for the cited work

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Observation 3de5c54d-84c1-481b-9122-698eeb084c47 · outbound

This paper cites Diagonal state spaces are as effective as structured state spaces.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Diagonal state spaces are as effective as structured state spaces

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:59:51.281248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6ef88ac7-d1f0-4b2a-a404-1854027640f3 · outbound

This paper cites Multiplication-Free Transformer Training via Piecewise Affine Operations.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Multiplication-Free Transformer Training via Piecewise Affine Operations

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-08T10:59:51.070102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 124fdf37-b321-4136-8a77-7f46bb17a56f · outbound

This paper cites The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.599189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88fecf4c-f01a-4aa0-a1d4-ccff7ff6ef8f · outbound

This paper cites Can a Suit of Armor Conduct Electricity ? A New Dataset for Open Book Question Answering.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Can a Suit of Armor Conduct Electricity ? A New Dataset for Open Book Question Answering

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:59:51.270853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bd343422-6a0d-4537-976c-062c23848da9 · outbound

This paper cites Alireo-400m: A lightweight italian language model, 2024.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Alireo-400m: A lightweight italian language model, 2024

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:59:51.261830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fd57e755-0d6e-4d5b-92fa-697941e69d01 · outbound

This paper cites Dnnfusion: accelerating deep neural networks execution with advanced operator fusion.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Dnnfusion: accelerating deep neural networks execution with advanced operator fusion

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.608293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c155204-3352-4994-8841-0a53b96ddb00 · outbound

This paper cites Efficient neuromorphic signal processing with loihi 2.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Efficient neuromorphic signal processing with loihi 2

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:59:51.252269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fe26da76-f111-4ba7-a970-59d488498caf · outbound

This paper cites Paxon Frady, Daniel Ben Dayan Rubin, Sophia Sanborn, Sumit Bam Shrestha, Friedrich T.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Paxon Frady, Daniel Ben Dayan Rubin, Sophia Sanborn, Sumit Bam Shrestha, Friedrich T

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.614327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c23e7732-2338-49e1-8e73-83d9b00d7af2 · outbound

This paper cites Mamba-PTQ: Outlier Channels in Recurrent Large Language Models.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Mamba-PTQ: Outlier Channels in Recurrent Large Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.617449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 109f18de-f77a-474c-9784-22de02c0ddc2 · outbound

This paper cites Hierarchically Gated Recurrent Neural Network for Sequence Modeling.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Hierarchically Gated Recurrent Neural Network for Sequence Modeling

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:59:51.244312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 53e3cf3f-bd85-4455-9a3e-65a41694f548 · outbound

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

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 HGRN2: Gated Linear RNNs with State Expansion

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.623523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6826b23a-fbbf-4c52-9cd3-bae42a357acd · outbound

This paper cites Qwen2.5: A party of foundation models, September 2024.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Qwen2.5: A party of foundation models, September 2024

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.626782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 393b237a-7dd7-4ee2-8eee-425924066756 · outbound

This paper cites WinoGrande : an adversarial winograd schema challenge at scale.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 WinoGrande : an adversarial winograd schema challenge at scale

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.629862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation adb65129-ff86-47a0-a718-1c0a661a67ad · outbound

This paper cites Efficient Video and Audio Processing with Loihi 2.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Efficient Video and Audio Processing with Loihi 2

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.633054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14d779be-6e6e-4839-a640-ec546ab88118 · outbound

This paper cites Attention Is All You Need.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Attention Is All You Need

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.635876Z

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Unavailable: canonical work link unavailable.

Observation 2fa8e278-46cf-4602-acda-3b573b3cd830 · outbound

This paper cites Convfusion: A model for layer fusion in convolutional neural networks.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Convfusion: A model for layer fusion in convolutional neural networks

Reference 28

Resolution: metadata mismatch; raw_fallback, observed 2026-08-08T10:59:50.856036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6246f918-a824-47f6-8f94-f354fd21136f · outbound

This paper cites SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.642283Z

Source-reported events for the cited work

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Observation 16861dc3-4291-4ec0-9a94-d4f4e0e4b93e · outbound

This paper cites Qwen2 Technical Report.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Qwen2 Technical Report

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.645776Z

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Unavailable: canonical work link unavailable.

Observation a682584d-9e1a-478b-9da2-5037505ffd6e · outbound

This paper cites ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.648954Z

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Unavailable: canonical work link unavailable.

Observation ddee30eb-2fd6-40a8-bf52-8074ae220fdc · outbound

This paper cites Metaformer is actually what you need for vision.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Metaformer is actually what you need for vision

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:59:51.230765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 96a99644-e0a4-4ce8-afd6-f632e794aa6f · outbound

This paper cites an unresolved cited work.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Unresolved cited work

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.653929Z

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Observation 03d8da19-4df3-43b4-b2cb-bfda618affd0 · outbound

This paper cites Root Mean Square Layer Normalization.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Root Mean Square Layer Normalization

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:59:51.222634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 02c94ed9-570e-49a3-90f9-23946585a80d · outbound

This paper cites Binarized Neural Machine Translation.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Binarized Neural Machine Translation

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-08-08T10:59:50.750132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ded996f7-07c9-43d5-ac14-f9251b64c4b4 · outbound

This paper cites SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.661408Z

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Observation e4542c1a-6d73-401a-907c-8ed73181bfef · outbound

This paper cites Scalable MatMul-free Language Modeling.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Scalable MatMul-free Language Modeling

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.663875Z

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Observation 06d6c2b9-c044-4b27-8960-4cc748f6d179 · outbound

This paper cites write newline.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 write newline

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.667053Z

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Observation b30a4a11-a9c3-43ad-a5a7-094045aba897 · outbound

This paper cites @esa (Ref.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 @esa (Ref

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.670933Z

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Observation a2a8c48d-6a15-457c-8a9c-3003690d44e4 · outbound

This paper cites an unresolved cited work.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Unresolved cited work

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.674611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4199790d-3cbb-4ed4-81a2-4fed1c045869 · outbound

This paper cites an unresolved cited work.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 Unresolved cited work

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.677991Z

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Unavailable: canonical work link unavailable.

Pith citing papers

Observation 273b8b39-1496-4912-ad48-4a9bf090009e · inbound

Model-Native Computing Architecture: Envisioning Future System Architecture Through the Lens of Computer Architecture cites this paper.

Model-Native Computing Architecture: Envisioning Future System Architecture Through the Lens of Computer Architecture Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-01T19:36:09.344934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.