LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models | Paper Citation Record

Observation 886e512a-e6d9-4a72-975f-7d8aef3b21a5 · outbound

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

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Spikingbert: Distilling bert to train spiking language models using implicit differentiation

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.360238Z

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Observation 6535676c-57b7-4716-aac3-a6828d00002a · outbound

This paper cites Pythia: A suite for analyzing large language models across training and scaling.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Pythia: A suite for analyzing large language models across training and scaling

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T21:43:08.519435Z

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Observation 62a490c6-75e6-4446-be7e-f8d1e451b3b1 · outbound

This paper cites GPT-NeoX-20B: An Open-Source Autoregressive Language Model.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models GPT-NeoX-20B: An Open-Source Autoregressive Language Model

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T21:43:08.523808Z

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Observation 22780db4-1356-4f04-87e8-b0fd640981d6 · outbound

This paper cites Spikeprop: backpropagation for networks of spiking neurons.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Spikeprop: backpropagation for networks of spiking neurons

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.337498Z

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Observation c0109412-902a-49cc-a75c-c2b7b7c69a78 · outbound

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

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T21:43:08.533198Z

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Observation 62816357-126a-44a1-a0a7-bf7fb097d7a4 · outbound

This paper cites Spiking deep convolutional neural networks for energy-efficient object recognition.International Journal of Computer Vision, 113:54–66, 2015.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Spiking deep convolutional neural networks for energy-efficient object recognition.International Journal of Computer Vision, 113:54–66, 2015

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.323924Z

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Observation 4b210b3f-5c90-4992-9435-6b24a7bd8c0c · outbound

This paper cites FAS: Fast ANN-SNN Conversion for Spiking Large Language Models.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models FAS: Fast ANN-SNN Conversion for Spiking Large Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T21:43:08.542657Z

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Observation 6e732d4d-d81c-451a-9aa6-1af1b8af90d6 · outbound

This paper cites Loihi: A neuromorphic manycore processor with on-chip learning.Ieee Micro, 38(1):82–99, 2018.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Loihi: A neuromorphic manycore processor with on-chip learning.Ieee Micro, 38(1):82–99, 2018

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.310063Z

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Observation 4d1503a7-3ac3-4bf7-a272-886353e230a0 · outbound

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

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T21:43:08.551245Z

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Observation 028bd473-ce2b-431d-b0b4-09e9fa0728e6 · outbound

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

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T21:43:08.555875Z

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Observation 91c2ce1b-2e26-4548-a227-86de7a4e7c19 · outbound

This paper cites Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing.2015 International Joint Conference on Neural Networks (IJCNN), pages 1–8, 2015.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing.2015 International Joint Conference on Neural Networks (IJCNN), pages 1–8, 2015

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.288107Z

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Observation ca686685-e976-417f-9a66-bb8205b3b952 · outbound

This paper cites Memristor-based neuromorphic chips.Advanced Materials, 36(14):2310704, 2024.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Memristor-based neuromorphic chips.Advanced Materials, 36(14):2310704, 2024

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.274384Z

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Observation 2dbd2051-4d67-4410-a2f4-1716810082e3 · outbound

This paper cites Cambridge University Press, 2014.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Cambridge University Press, 2014

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T21:43:08.569182Z

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Observation b9d354d8-9671-4eb7-bc39-7aa0e1f02cf0 · outbound

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

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Reducing ann-snn conversion error through residual membrane potential

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.250179Z

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Observation 54b463c5-d8bf-446b-82cb-9293184667f7 · outbound

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

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T21:43:08.578043Z

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Observation 9672458f-61d1-41f4-8321-a35ea054cf57 · outbound

This paper cites LM-HT SNN: Enhancing the Performance of SNN to ANN Counterpart through Learnable Multi-hierarchical Threshold Model.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models LM-HT SNN: Enhancing the Performance of SNN to ANN Counterpart through Learnable Multi-hierarchical Threshold Model

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T21:43:08.582521Z

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Observation f6f94385-26f4-4e6d-b30e-33c185a6c53c · outbound

This paper cites Towards high-performance spiking transformers from ann to snn conversion.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Towards high-performance spiking transformers from ann to snn conversion

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.235792Z

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Observation 8bda04ee-34e6-4d64-87fc-dc1a354dbcae · outbound

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

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models The information pathways hypothesis: Transformers are dynamic self- ensembles

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.222124Z

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Observation 9964460f-cfbb-4713-8330-440923b447b8 · outbound

This paper cites an unresolved cited work.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-15T21:43:09.207495Z

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Observation 7071773f-8e8b-446a-93c9-4af8fc2fdbe0 · outbound

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

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Spatio-temporal approximation: A training-free snn conversion for transformers

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.192723Z

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Observation 35068dbc-731d-4958-aae9-eb1c76fc0e9b · outbound

This paper cites Bloom: A 176b-parameter open-access multilingual language model.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Bloom: A 176b-parameter open-access multilingual language model

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T21:43:08.601501Z

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Observation d02c0d7f-f76d-4821-8a9d-fbf20e603e7c · outbound

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

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Efficient and accurate conversion of spiking neural network with burst spikes

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.167751Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4f22a1e7-456b-4b1d-be93-5eaec827f516 · outbound

This paper cites DeepSeek-V3 Technical Report.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models DeepSeek-V3 Technical Report

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T21:43:08.608825Z

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Observation 1adba8e6-1373-4743-bc34-1c06359d4fec · outbound

This paper cites Power efficient division and square root unit.IEEE Transactions on Computers, 61(8):1059–1070, 2012.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Power efficient division and square root unit.IEEE Transactions on Computers, 61(8):1059–1070, 2012

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.152836Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4716667b-3bd9-433a-adc0-4b65fd81f1d5 · outbound

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

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Spikebert: A language spikformer learned from bert with knowledge distillation, 2024

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T21:43:08.617269Z

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Observation ac371c32-3019-4cb7-b009-680d25b3126d · outbound

This paper cites Spiking convolutional neural networks for text classification.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Spiking convolutional neural networks for text classification

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.128559Z

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Observation 2b873698-bfcd-4002-849a-98820d96511d · outbound

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

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Neftci, Hesham Mostafa, and Friedemann Zenke

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T21:43:08.626014Z

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Observation adb64981-5630-4403-9eff-ee270cb22727 · outbound

This paper cites Hardware implementation of the exponential function using taylor series.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Hardware implementation of the exponential function using taylor series

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.103810Z

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Observation 2131aa5d-71a3-45d2-b8a2-8cb53acdef44 · outbound

This paper cites Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T21:43:08.634823Z

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Observation 5e4cd774-751a-4231-9bc6-03787d9a8196 · outbound

This paper cites DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networks.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networks

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T21:43:08.639008Z

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Observation bc8c4e06-1f43-47e0-9a12-344311043e32 · outbound

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

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T21:43:08.644436Z

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Observation a37979f8-5cb2-4e31-84ad-c3716007423d · outbound

This paper cites Conversion of continuous-valued deep networks to efficient event-driven networks for image classification.Frontiers in neuroscience, 11:294078, 2017.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Conversion of continuous-valued deep networks to efficient event-driven networks for image classification.Frontiers in neuroscience, 11:294078, 2017

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.078614Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0ce99825-1a54-4329-98fc-517388d877f4 · outbound

This paper cites Multitask prompted training enables zero-shot task generalization.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Multitask prompted training enables zero-shot task generalization

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.064303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a041fa91-d6ba-4e11-8c27-ee2e35b9fbed · outbound

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

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Astrocyte-Enabled Advancements in Spiking Neural Networks for Large Language Modeling

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T21:43:08.657182Z

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Observation 1f273228-0995-440d-95f6-3359511ce099 · outbound

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

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models One-step spiking transformer with a linear complexity

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.050035Z

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Observation 027f0ed1-3901-4a83-8ae3-6480519a1fdd · outbound

This paper cites Optimized spiking neurons can classify images with high accuracy through temporal coding with two spikes.Nature Machine Intelligence, 3(3):230–238, 2021.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Optimized spiking neurons can classify images with high accuracy through temporal coding with two spikes.Nature Machine Intelligence, 3(3):230–238, 2021

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.036493Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a9dc1115-3ad5-4162-aca3-b23aff5f0a87 · outbound

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

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Learning general purpose distributed sentence representations via large scale multi-task learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.022167Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f5aa10dc-d404-4ed1-b18c-2a12ff0037eb · outbound

This paper cites Generalized leaky integrate-and-fire models classify multiple neuron types.Nature communications, 9(1):709, 2018.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Generalized leaky integrate-and-fire models classify multiple neuron types.Nature communications, 9(1):709, 2018

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T21:43:08.675369Z

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Observation 275560d2-1a38-41db-a81c-84fba0bbd9a8 · outbound

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LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Unresolved cited work

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T21:43:08.679533Z

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Observation 71f8741a-868d-4b0f-8195-baeb8683790c · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T21:43:08.683567Z

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Observation 8f7a9e18-5a6c-45a9-8b23-560261be4658 · outbound

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

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models SpikeLM: Towards General Spike-Driven Language Modeling via Elastic Bi-Spiking Mechanisms

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-15T21:43:08.687904Z

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Observation 56b07f8f-aa85-4d51-a705-33e77f40bab8 · outbound

This paper cites Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip.Nature Communications, 15(1):4464, 2024.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip.Nature Communications, 15(1):4464, 2024

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:08.989822Z

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Observation 757e4372-a2eb-445f-885a-b54552407b0f · outbound

This paper cites Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-15T21:43:08.797324Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 16160aab-b9f0-4466-8015-4885d162ad6a · outbound

This paper cites SpikeZIP-TF: Conversion is All You Need for Transformer-based SNN.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models SpikeZIP-TF: Conversion is All You Need for Transformer-based SNN

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T21:43:08.701130Z

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Observation 9eb69945-cd47-4df2-8bfb-e4803e8ee0c0 · outbound

This paper cites The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks.Neural computation, 33(4):899–925, 2021.

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks.Neural computation, 33(4):899–925, 2021

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T21:43:08.705468Z

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Observation b2770edf-ec6e-4ef0-913b-97418bd0cd88 · outbound

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

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models OPT: Open Pre-trained Transformer Language Models

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-15T21:43:08.709480Z

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Observation d1f2b0ed-19c2-4956-9905-a04137307e8a · outbound

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

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-15T21:43:08.713730Z

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