Paper Citation Record · LEDGER

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba

As of 28 July 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2510.04595.

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

pith.paper-citation-record.v1
2510.04595 v2

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-18T09:44:53.290259Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-07-28T06:31:03.373048+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

29 of 29 outbound references displayed

verified exact21
verified fuzzy3
unresolved0
parse uncertain0
malformed identifier1
metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 800aec82-b73b-4414-b839-fcd4fd96f25c · outbound

This paper cites Llamba: Scaling Distilled Recurrent Models for Efficient Language Processing.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba Llamba: Scaling Distilled Recurrent Models for Efficient Language Processing

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:46:12.467820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation 183659f9-af00-406b-ad67-4fea6c4eb65c · outbound

This paper cites Step-level Value Preference Optimization for Mathematical Reasoning.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba Step-level Value Preference Optimization for Mathematical Reasoning

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:46:12.439895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation 3953aa7a-b1cc-44f9-97f0-7bf7580b8dbd · outbound

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

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-18T09:46:12.395492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation aa578797-40b3-4bae-9c88-cda6935381f9 · outbound

This paper cites UltraFeedback: Boosting Language Models with Scaled AI Feedback.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba UltraFeedback: Boosting Language Models with Scaled AI Feedback

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-05-18T09:46:12.411859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation 68a8d43f-48d8-491b-a636-8ca46d18a0be · outbound

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

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-05-18T09:46:12.448956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation 8a2d2a27-ffd5-4448-9111-c83ea0879d6d · outbound

This paper cites KTO: Model Alignment as Prospect Theoretic Optimization.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba KTO: Model Alignment as Prospect Theoretic Optimization

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-18T09:46:12.408072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation f08f0572-1047-499d-8ff2-de6719b28a95 · outbound

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

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-05-18T09:46:12.368797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation cd3bac79-b0b7-4c0a-9cb0-45e60d677f15 · outbound

This paper cites Ariel Gera, Odellia Boni, Yotam Perlitz, Roy Bar-Haim, Lilach Eden, and Asaf Yehudai.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba Ariel Gera, Odellia Boni, Yotam Perlitz, Roy Bar-Haim, Lilach Eden, and Asaf Yehudai

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T09:46:12.377637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation ea6ba297-fae8-4e5b-9a3b-f67d90fe0193 · outbound

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

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-18T09:46:12.475980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation 484e2527-2485-4734-b792-761a3c65e4f0 · outbound

This paper cites BiLLM: Pushing the Limit of Post-Training Quantization for LLMs.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:46:12.386959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation 81a8fdbc-569d-4642-afff-4da4055c4bd1 · outbound

This paper cites Mistral 7B.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba Mistral 7B

Reference 11

Resolution: metadata mismatch; local_arxiv, observed 2026-05-18T09:46:12.420488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation 1634359b-11f0-46c9-868c-92ac777ae4dc · outbound

This paper cites MiniMax-01: Scaling Foundation Models with Lightning Attention.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba MiniMax-01: Scaling Foundation Models with Lightning Attention

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-18T09:46:12.416706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation 881585f2-2794-4fc5-8ae1-1d3046f8fbfb · outbound

This paper cites SpikeMba: Multi-Modal Spiking Saliency Mamba for Temporal Video Grounding.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba SpikeMba: Multi-Modal Spiking Saliency Mamba for Temporal Video Grounding

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:46:12.458835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation eb97572b-26e1-4c74-91c9-c784a1c99a72 · outbound

This paper cites DeepSeek-V3 Technical Report.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba DeepSeek-V3 Technical Report

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-18T09:46:12.429325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation 850d2bea-8beb-42f3-8a51-b90c3ee10d30 · outbound

This paper cites SpikeBERT: A Language Spikformer Learned from BERT with Knowledge Distillation.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba SpikeBERT: A Language Spikformer Learned from BERT with Knowledge Distillation

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:46:12.444279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation ce4196f0-1791-44da-899e-64520aaea039 · outbound

This paper cites Pointer Sentinel Mixture Models.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba Pointer Sentinel Mixture Models

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-05-18T09:46:12.399734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation 4dd17bdd-060f-4a9c-8776-90800a55f3cf · outbound

This paper cites Spike-temporal latent representation for energy-efficient event-to-video reconstruction.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba Spike-temporal latent representation for energy-efficient event-to-video reconstruction

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:46:12.403564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation a0949541-a4f8-426b-b0bf-1f8221704ab6 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba LLaMA: Open and Efficient Foundation Language Models

Reference 18

Resolution: metadata mismatch; local_arxiv, observed 2026-05-18T09:46:12.391272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation d4f3a5d1-95c3-47d0-81c0-650600dbfd64 · outbound

This paper cites Efficient Spiking Point Mamba for Point Cloud Analysis.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba Efficient Spiking Point Mamba for Point Cloud Analysis

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:46:12.472194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation d9f4bb16-75b8-4047-a75b-9ca85bfd065f · outbound

This paper cites SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based Spiking.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based Spiking

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:46:12.434277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation e73a38b8-909a-41d9-ab6f-06ffc7f20578 · outbound

This paper cites EfficientLLM: Scalable Pruning-Aware Pretraining for Architecture-Agnostic Edge Language Models.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba EfficientLLM: Scalable Pruning-Aware Pretraining for Architecture-Agnostic Edge Language Models

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:46:12.425193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation a2942cd0-9d4c-4d2e-b113-e806fd83cd5b · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-05-18T09:46:12.381901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation cb4d56b1-f4b5-46c7-b856-7465207216d5 · outbound

This paper cites LoLCATs: On Low-Rank Linearizing of Large Language Models.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba LoLCATs: On Low-Rank Linearizing of Large Language Models

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:46:12.373652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation 28df3c9b-c117-46fe-bca5-30df56047f98 · outbound

This paper cites SPikE-SSM: A Sparse, Precise, and Efficient Spiking State Space Model for Long Sequences Learning.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba SPikE-SSM: A Sparse, Precise, and Efficient Spiking State Space Model for Long Sequences Learning

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:46:12.454086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation 81e43a94-5f93-4484-8b0a-169e587bec58 · outbound

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

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T09:46:12.463285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation 526036a4-7b6d-4e57-bdbc-0dcce0bd14ef · outbound

This paper cites an unresolved cited work.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba Unresolved cited work

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:46:13.020388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation 60dfbe51-84c2-4f24-9527-fdad93d18799 · outbound

This paper cites an unresolved cited work.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba Unresolved cited work

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:46:13.023469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation c84f3d12-342d-45f2-b4b9-2838d396afaf · outbound

This paper cites C Experiments Setup Implement Details.In the distillation stage, we perform supervised fine-tuning on the GenQA Chen et al.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba C Experiments Setup Implement Details.In the distillation stage, we perform supervised fine-tuning on the GenQA Chen et al

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T09:46:13.026989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Observation e478f57c-faae-41c5-9c20-9908aa75d67c · outbound

This paper cites The sequence length is fixed at 2048 tokens, and the embedding layer remains frozen throughout training.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba The sequence length is fixed at 2048 tokens, and the embedding layer remains frozen throughout training

Reference 29

Resolution: malformed identifier; arxiv_id, observed 2026-05-18T09:46:12.364221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-28T06:31:03.373048+00:00.

Pith citing papers

No inbound Pith citation observations are available.