Paper Citation Record · LEDGER

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models

As of 9 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 1 inbound Pith citation observation for arXiv:2502.04405.

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

pith.paper-citation-record.v1
2502.04405 v2

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T00:24:58.402654Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-21T08:17:45.899981Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T08:19:52.521838Z

Reference resolution

53 of 53 outbound references displayed

verified exact2
verified fuzzy26
unresolved25
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 870dfbb3-ba2b-4802-9227-61d8ae08df33 · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-09T00:24:58.167739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b46ab9a-1fae-47ac-a917-4e39828b98dd · outbound

This paper cites Spikingbert: Distilling bert to train spiking language models using implicit differentiation.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Spikingbert: Distilling bert to train spiking language models using implicit differentiation

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:24:59.173631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 085c2485-b4fb-4881-8ca7-0dd2894b118e · outbound

This paper cites Spikingbert: Distilling bert to train spiking language models using implicit differentiation.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Spikingbert: Distilling bert to train spiking language models using implicit differentiation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:24:59.159089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2208d8b0-1e44-4f16-93b6-2b7e3821dc91 · outbound

This paper cites PaliGemma: A versatile 3B VLM for transfer.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models PaliGemma: A versatile 3B VLM for transfer

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-09T00:24:58.183374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6cfbd1a-08dd-47b7-993c-5c43bdffe296 · outbound

This paper cites Language models are few-shot learners.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Language models are few-shot learners

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-09T00:24:58.188410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 421629b9-48ab-494d-a0aa-589c41870c4a · outbound

This paper cites Optimized Potential Initialization for Low-latency Spiking Neural Networks.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Optimized Potential Initialization for Low-latency Spiking Neural Networks

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-09T00:24:58.626802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3c5cf868-bba9-4846-b49d-7270dfaf4eb8 · outbound

This paper cites Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T00:24:58.198760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f94b5579-ee22-4085-8f22-d1c0e4ba44f9 · outbound

This paper cites Spiking deep convolutional neural networks for energy-efficient object recognition.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Spiking deep convolutional neural networks for energy-efficient object recognition

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:24:59.133731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a5a86ae5-7b24-426e-9e84-89708cbf5603 · outbound

This paper cites Loihi: A neuromorphic manycore processor with on-chip learning.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Loihi: A neuromorphic manycore processor with on-chip learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-09T00:24:58.208209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9f15fe4-b046-4f69-8ab1-3098fabb4457 · outbound

This paper cites The growing energy footprint of artificial intelligence.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models The growing energy footprint of artificial intelligence

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:24:59.107930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3b62ef99-be2c-4091-b034-5ab16e100854 · outbound

This paper cites Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T00:24:58.216992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd0ca5af-0bb2-4bd9-b801-30d1488a8a29 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T00:24:58.222018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c7906ac-7a40-41f3-8b15-bc3aea361684 · outbound

This paper cites Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:24:59.082349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 46a96cfb-8f28-4602-a0d7-f46ec6594276 · outbound

This paper cites The Llama 3 Herd of Models.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models The Llama 3 Herd of Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T00:24:58.230738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b04947c-a5ad-4439-ab8f-59b897935a4e · outbound

This paper cites Rmp-snn: Residual membrane potential neuron for enabling deeper high-accuracy and low-latency spiking neural network.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Rmp-snn: Residual membrane potential neuron for enabling deeper high-accuracy and low-latency spiking neural network

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:24:59.066676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 796238d3-6dbf-41ac-9f3d-139bae15f038 · outbound

This paper cites Deep spiking neural network: Energy efficiency through time based coding.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Deep spiking neural network: Energy efficiency through time based coding

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:24:59.051348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 03de2584-a110-4e9a-93b6-899780862ee5 · outbound

This paper cites Reducing ann-snn conversion error through residual membrane potential.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Reducing ann-snn conversion error through residual membrane potential

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:24:59.036353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2269819e-c9a1-4df6-a1b5-5409d39d075c · outbound

This paper cites Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T00:24:58.248283Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08a66e9c-145f-4e0d-a752-d87bb5e359e0 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Distilling the Knowledge in a Neural Network

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T00:24:58.252648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bf360d9-1455-48a5-84c6-6f361bf1757a · outbound

This paper cites The information pathways hypothesis: Transformers are dynamic self- ensembles.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models The information pathways hypothesis: Transformers are dynamic self- ensembles

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:24:59.019800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cd22969e-d51b-4d28-96cb-a85009054397 · outbound

This paper cites Spatio-temporal approximation: A training-free snn conversion for transformers.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Spatio-temporal approximation: A training-free snn conversion for transformers

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:24:59.002339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 675024da-dec7-4a51-8fcd-b98e6a0835f9 · outbound

This paper cites Training deep spiking neural networks using backpropagation.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Training deep spiking neural networks using backpropagation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:24:58.986596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 03a85090-cfda-4234-92e6-4ba3a68284a4 · outbound

This paper cites Efficient and accurate conversion of spiking neural network with burst spikes.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Efficient and accurate conversion of spiking neural network with burst spikes

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:24:58.971409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fa52cab0-9215-4332-ba17-dab1aade9c11 · outbound

This paper cites A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T00:24:58.274648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b56cc26a-35ce-4f19-ae9b-ef14e5333312 · outbound

This paper cites Error-aware conversion from ann to snn via post-training parameter calibration.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Error-aware conversion from ann to snn via post-training parameter calibration

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:24:58.956320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b43ff8d5-24a1-4daa-97be-fb2335ef64e4 · outbound

This paper cites Learnable surrogate gradient for direct training spiking neural networks.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Learnable surrogate gradient for direct training spiking neural networks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:24:58.940834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 87bccfec-2875-493b-8b85-f38b3a30a8d5 · outbound

This paper cites Visual instruction tuning.Advances in neural information processing systems, 36, 2024.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Visual instruction tuning.Advances in neural information processing systems, 36, 2024

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-09T00:24:58.287570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 435e0b26-3bd7-4fbe-96c1-4a4bcedaaf15 · outbound

This paper cites Fineweb-edu, May 2024.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Fineweb-edu, May 2024

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:24:58.915716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5c4f211c-b4b8-4341-a01e-867dd8ac3f1a · outbound

This paper cites Spikebert: A language spikformer learned from bert with knowledge distillation, 2024.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Spikebert: A language spikformer learned from bert with knowledge distillation, 2024

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:24:58.901074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7e23c143-9dbd-4e91-b0d5-9de187c85aea · outbound

This paper cites Spiking convolutional neural networks for text classification.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Spiking convolutional neural networks for text classification

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:24:58.886177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ab1aa6f0-5adc-4748-97fa-84fd1931f0e4 · outbound

This paper cites A million spiking-neuron integrated circuit with a scalable communication network and interface.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models A million spiking-neuron integrated circuit with a scalable communication network and interface

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-09T00:24:58.305228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6bb92f8-038c-4123-ba89-aca8bb4eb2f6 · outbound

This paper cites Neftci, Hesham Mostafa, and Friedemann Zenke.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Neftci, Hesham Mostafa, and Friedemann Zenke

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:24:58.861888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6dddb0ef-ff1e-4d84-befc-60281dbd61a1 · outbound

This paper cites Language models are unsupervised multitask learners.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Language models are unsupervised multitask learners

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-09T00:24:58.314069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea12d302-bd19-4d56-851e-0f4634850c77 · outbound

This paper cites Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-09T00:24:58.318291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b90697f-5539-4097-a366-d9539418ba21 · outbound

This paper cites Conversion of continuous-valued deep networks to efficient event-driven networks for image classification.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Conversion of continuous-valued deep networks to efficient event-driven networks for image classification

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:24:58.837885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2394fcd0-9af4-4fdc-a2fa-61a034130c83 · outbound

This paper cites Wang, Chiao Liu, and Kaushik Roy.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Wang, Chiao Liu, and Kaushik Roy

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-09T00:24:58.327333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27d45094-bdae-48cf-ac16-05f671c236ca · outbound

This paper cites Astrocyte-Enabled Advancements in Spiking Neural Networks for Large Language Modeling.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Astrocyte-Enabled Advancements in Spiking Neural Networks for Large Language Modeling

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-08-09T00:24:58.504324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 52bf8d65-17d1-4393-af0a-9924513d6bef · outbound

This paper cites One-step spiking transformer with a linear complexity.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models One-step spiking transformer with a linear complexity

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:24:58.813877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 392191dd-9a3d-4a97-9ea3-173f59612711 · outbound

This paper cites diagnosis please.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models diagnosis please

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:24:58.798158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 25549767-096c-44f5-9cbc-8f7089f20006 · outbound

This paper cites Learning general purpose distributed sentence representations via large scale multi-task learning.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Learning general purpose distributed sentence representations via large scale multi-task learning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:24:58.783999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e42d4345-233e-4ad5-a848-42a9278154f6 · outbound

This paper cites an unresolved cited work.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Unresolved cited work

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-09T00:24:58.348857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 576cd0c3-6540-4898-aafa-bad7ae80899e · outbound

This paper cites Toward high-accuracy and low-latency spiking neural networks with two-stage optimization.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Toward high-accuracy and low-latency spiking neural networks with two-stage optimization

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:24:58.760559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b9fdb72c-60f7-4cb3-9efd-a018187feeb6 · outbound

This paper cites Spatio-temporal backpropagation for training high-performance spiking neural networks.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Spatio-temporal backpropagation for training high-performance spiking neural networks

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:24:58.745429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 99cf7293-7a28-4a6b-8430-15df90e82ce6 · outbound

This paper cites SpikeLM: Towards General Spike-Driven Language Modeling via Elastic Bi-Spiking Mechanisms.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models SpikeLM: Towards General Spike-Driven Language Modeling via Elastic Bi-Spiking Mechanisms

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-09T00:24:58.362065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5e5c6da-f81a-4719-8da0-fbbf63de77da · outbound

This paper cites Training spiking neural networks with local tandem learning.Advances in Neural Information Processing Systems, 35:12662–12676, 2022.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Training spiking neural networks with local tandem learning.Advances in Neural Information Processing Systems, 35:12662–12676, 2022

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-09T00:24:58.366450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa7a53b9-7770-4863-a10a-159fe7cc9343 · outbound

This paper cites Spike- driven transformer.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Spike- driven transformer

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-09T00:24:58.370588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30ac90d7-f941-4370-93d1-5c221dcf13c9 · outbound

This paper cites Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-09T00:24:58.374943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1058f5d-daff-4fa8-918e-4e570119d20a · outbound

This paper cites The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:24:58.702673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 45725c55-be36-4c6a-ac2a-20215f881c16 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models OPT: Open Pre-trained Transformer Language Models

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-09T00:24:58.383117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a2a2c85-5c32-43f7-8ae2-3dfb9838f9f9 · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-09T00:24:58.387680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f68d463-ace0-4752-81e4-1117f8d7bee7 · outbound

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

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-09T00:24:58.392035Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56c10f83-16a6-4ab4-8a44-96b84993ce5f · outbound

This paper cites an unresolved cited work.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-08-09T00:24:58.687847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 581e2ada-c4e9-4030-a7ae-595d423c6170 · outbound

This paper cites For example, on GPT-2 models, the energy consumption of FAS and QCFC is 7.04% and 8.99% under 4 and 8 time steps, respectively.

FAS: Fast ANN-SNN Conversion for Spiking Large Language Models For example, on GPT-2 models, the energy consumption of FAS and QCFC is 7.04% and 8.99% under 4 and 8 time steps, respectively

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:24:58.673021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

Observation 9e2044ca-2d7f-48ec-b28a-77d82ed98aca · inbound

Plug-and-Play Spiking Operators: Breaking the Nonlinearity Bottleneck in Spiking Transformers cites this paper.

Plug-and-Play Spiking Operators: Breaking the Nonlinearity Bottleneck in Spiking Transformers FAS: Fast ANN-SNN Conversion for Spiking Large Language Models

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-21T08:19:52.523778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.