Paper Citation Record · LEDGER

MoPEQ: Mixture of Mixed Precision Quantized Experts

As of 21 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2509.02512.

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

pith.paper-citation-record.v1
2509.02512 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:41:32.175324Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

46 of 46 outbound references displayed

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External citation measurements

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Outbound references

Observation 24040821-4b9b-4b4e-a1c3-c5be5cbc81ad · outbound

This paper cites Molmoe-1b-0924.

MoPEQ: Mixture of Mixed Precision Quantized Experts Molmoe-1b-0924

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:41:32.616064Z

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Observation 19badd8f-86a3-4d32-bd63-a1ee945bda78 · outbound

This paper cites Randomized algorithms for estimating the trace of an implicit symmetric positive semi- definite matrix.

MoPEQ: Mixture of Mixed Precision Quantized Experts Randomized algorithms for estimating the trace of an implicit symmetric positive semi- definite matrix

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:41:32.606298Z

Source-reported events for the cited work

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Observation ea2cb83c-2cb9-4947-8f0e-c07deef69175 · outbound

This paper cites Lan- guage models are few-shot learners.

MoPEQ: Mixture of Mixed Precision Quantized Experts Lan- guage models are few-shot learners

Reference 3

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Observation b1a63551-bbe7-41cc-b42b-365f3254a05c · outbound

This paper cites A Survey on Mixture of Experts in Large Language Models.

MoPEQ: Mixture of Mixed Precision Quantized Experts A Survey on Mixture of Experts in Large Language Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T16:41:32.009599Z

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Observation f1641370-7172-43bf-94e4-474c384660ae · outbound

This paper cites Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs.

MoPEQ: Mixture of Mixed Precision Quantized Experts Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T16:41:32.013229Z

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Observation eca98d07-cd4e-4b3c-924d-af7a21002fd8 · outbound

This paper cites Palm: Scaling language modeling with pathways.

MoPEQ: Mixture of Mixed Precision Quantized Experts Palm: Scaling language modeling with pathways

Reference 6

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Observation 8a104721-1b4b-49b6-8953-6e7569ec6ebc · outbound

This paper cites DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models.

MoPEQ: Mixture of Mixed Precision Quantized Experts DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T16:41:32.025478Z

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Observation 2ea871a8-0576-4dc9-8add-e0688f366342 · outbound

This paper cites Deepseek-vl2.

MoPEQ: Mixture of Mixed Precision Quantized Experts Deepseek-vl2

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:41:32.583930Z

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Observation 9e15e2b3-5cdf-4096-ba21-e72f67acdbcd · outbound

This paper cites Deepseek-vl2 small.

MoPEQ: Mixture of Mixed Precision Quantized Experts Deepseek-vl2 small

Reference 9

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Observation 6d47aade-fb48-427d-a9a5-cf742f7f9dea · outbound

This paper cites Deepseek-vl2 tiny.

MoPEQ: Mixture of Mixed Precision Quantized Experts Deepseek-vl2 tiny

Reference 10

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Source-reported events for the cited work

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Observation 15e050cf-96a1-4d30-8149-15acf0baf607 · outbound

This paper cites Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models.

MoPEQ: Mixture of Mixed Precision Quantized Experts Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models

Reference 11

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Observation 32458c72-180d-44f6-aa5e-9a2c9404acd5 · outbound

This paper cites Hawq: Hessian aware quantization of neural networks with mixed-precision.

MoPEQ: Mixture of Mixed Precision Quantized Experts Hawq: Hessian aware quantization of neural networks with mixed-precision

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:41:32.555727Z

Source-reported events for the cited work

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Observation eedb48ca-184c-4a46-bde9-8443aa95c8eb · outbound

This paper cites Hawq-v2: Hessian aware trace-weighted quantization of neural networks.

MoPEQ: Mixture of Mixed Precision Quantized Experts Hawq-v2: Hessian aware trace-weighted quantization of neural networks

Reference 13

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Observation 3a67ce05-e79b-4bfb-a55e-c39a92bda0ae · outbound

This paper cites Vlmevalkit: An open-source toolkit for evaluating large multi-modality models.

MoPEQ: Mixture of Mixed Precision Quantized Experts Vlmevalkit: An open-source toolkit for evaluating large multi-modality models

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:41:32.539821Z

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Observation 173deb43-2a42-4cd2-896b-27970609d3f5 · outbound

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

MoPEQ: Mixture of Mixed Precision Quantized Experts GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 15

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Observation 271600e9-e5e9-4938-9c29-085b248a0c61 · outbound

This paper cites MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models.

MoPEQ: Mixture of Mixed Precision Quantized Experts MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 16

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Observation fcf34064-5c2e-473b-a4cf-360a5d0ba75f · outbound

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

MoPEQ: Mixture of Mixed Precision Quantized Experts DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 17

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Observation b1f8362e-98ac-4b02-af34-6a6ad571a912 · outbound

This paper cites Mixture Compressor for Mixture-of-Experts LLMs Gains More.

MoPEQ: Mixture of Mixed Precision Quantized Experts Mixture Compressor for Mixture-of-Experts LLMs Gains More

Reference 18

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Observation 1fccf869-d0eb-47ba-bc1e-7a97a0f3f063 · outbound

This paper cites A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines.

MoPEQ: Mixture of Mixed Precision Quantized Experts A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:41:32.529113Z

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Observation 2a0e092a-f86c-4d92-b9b6-cdeb6412df20 · outbound

This paper cites Intel autoround.

MoPEQ: Mixture of Mixed Precision Quantized Experts Intel autoround

Reference 20

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Source-reported events for the cited work

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Observation fb1e6bb7-d0ae-4a57-9e54-29450d93ffe9 · outbound

This paper cites Mixtral of Experts.

MoPEQ: Mixture of Mixed Precision Quantized Experts Mixtral of Experts

Reference 21

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Observation 5023d3d8-abe8-4059-ab90-30d8cc498f23 · outbound

This paper cites A diagram is worth a dozen images.

MoPEQ: Mixture of Mixed Precision Quantized Experts A diagram is worth a dozen images

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T16:41:32.079898Z

Source-reported events for the cited work

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Observation 6b9fa6e9-8ede-4b9c-96da-b25c551c53cd · outbound

This paper cites Efficient memory management for large language model serving with pagedattention.

MoPEQ: Mixture of Mixed Precision Quantized Experts Efficient memory management for large language model serving with pagedattention

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:41:32.502776Z

Source-reported events for the cited work

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Observation 760e1b72-90d1-4fc3-9f7d-0a099ebef168 · outbound

This paper cites QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Experts.

MoPEQ: Mixture of Mixed Precision Quantized Experts QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Experts

Reference 24

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Observation 7efd16f1-9324-413f-a7af-23826f4ea0a0 · outbound

This paper cites MoE-LLaVA: Mixture of Experts for Large Vision-Language Models.

MoPEQ: Mixture of Mixed Precision Quantized Experts MoE-LLaVA: Mixture of Experts for Large Vision-Language Models

Reference 25

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Observation 446b3b4c-6dc1-43dc-b003-f9d583bfb87f · outbound

This paper cites Awq: Activation-aware weight quantization for on-device llm compression and acceleration.

MoPEQ: Mixture of Mixed Precision Quantized Experts Awq: Activation-aware weight quantization for on-device llm compression and acceleration

Reference 26

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Observation e3fa15f0-d15f-47c9-94c0-ce2c06779c92 · outbound

This paper cites DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model.

MoPEQ: Mixture of Mixed Precision Quantized Experts DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 27

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Source-reported events for the cited work

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Observation 76e382de-9d50-47da-8221-2e306ebb3cde · outbound

This paper cites DeepSeek-V3 Technical Report.

MoPEQ: Mixture of Mixed Precision Quantized Experts DeepSeek-V3 Technical Report

Reference 28

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Source-reported events for the cited work

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Observation 7a15768f-cd61-47c5-aa42-83d95bffcd15 · outbound

This paper cites A Survey on Inference Optimization Techniques for Mixture of Experts Models.

MoPEQ: Mixture of Mixed Precision Quantized Experts A Survey on Inference Optimization Techniques for Mixture of Experts Models

Reference 29

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Observation c6607214-15dd-42ce-b7f2-d63bba02e765 · outbound

This paper cites LLM-QAT: Data-Free Quantization Aware Training for Large Language Models.

MoPEQ: Mixture of Mixed Precision Quantized Experts LLM-QAT: Data-Free Quantization Aware Training for Large Language Models

Reference 30

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Source-reported events for the cited work

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Observation 1f601a82-cb7b-4750-b5c5-342e8d979044 · outbound

This paper cites Learn to explain: Multimodal reasoning via thought chains for science question answering.

MoPEQ: Mixture of Mixed Precision Quantized Experts Learn to explain: Multimodal reasoning via thought chains for science question answering

Reference 31

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Source-reported events for the cited work

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Observation bf9efe5f-5155-405b-8edd-38fb5a17c00a · outbound

This paper cites Docvqa: A dataset for vqa on document images.

MoPEQ: Mixture of Mixed Precision Quantized Experts Docvqa: A dataset for vqa on document images

Reference 32

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Source-reported events for the cited work

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Observation ee25f440-f4ea-40a6-aa42-b50e7f80460f · outbound

This paper cites Infographicvqa.

MoPEQ: Mixture of Mixed Precision Quantized Experts Infographicvqa

Reference 33

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Source-reported events for the cited work

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Observation 53385fcc-1bc1-4689-9546-92c4219b206c · outbound

This paper cites OLMoE: Open Mixture-of-Experts Language Models.

MoPEQ: Mixture of Mixed Precision Quantized Experts OLMoE: Open Mixture-of-Experts Language Models

Reference 34

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Observation ff741dfd-f31f-42bc-a1df-c5678a7522e3 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

MoPEQ: Mixture of Mixed Precision Quantized Experts Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 35

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Observation 675d8403-faa9-45fc-a1e6-575fb63afbc3 · outbound

This paper cites Towards vqa models that can read.

MoPEQ: Mixture of Mixed Precision Quantized Experts Towards vqa models that can read

Reference 36

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Observation 8fe81e67-d0d6-4d61-9f77-4d52e0d07fff · outbound

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

MoPEQ: Mixture of Mixed Precision Quantized Experts LLaMA: Open and Efficient Foundation Language Models

Reference 37

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Observation 8b0e460f-5dae-4ac0-8dbf-aa6239e85eea · outbound

This paper cites Efficient Large Language Models: A Survey.

MoPEQ: Mixture of Mixed Precision Quantized Experts Efficient Large Language Models: A Survey

Reference 38

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This paper cites DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding.

MoPEQ: Mixture of Mixed Precision Quantized Experts DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding

Reference 39

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Observation 4ed95c99-da27-4235-94f3-855fd23f9480 · outbound

This paper cites Smoothquant: Accurate and effi- cient post-training quantization for large language models.

MoPEQ: Mixture of Mixed Precision Quantized Experts Smoothquant: Accurate and effi- cient post-training quantization for large language models

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T16:41:32.145492Z

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Observation 3f5668c7-57f4-4b93-81a4-ca1608fecbc4 · outbound

This paper cites Zeroquant: Ef- ficient and affordable post-training quantization for large- scale transformers.

MoPEQ: Mixture of Mixed Precision Quantized Experts Zeroquant: Ef- ficient and affordable post-training quantization for large- scale transformers

Reference 41

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MoPEQ: Mixture of Mixed Precision Quantized Experts A Survey on Multimodal Large Language Models

Reference 42

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Observation 282f8ba5-6beb-476a-af8b-cad8150c9514 · outbound

This paper cites Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for ex- pert agi.

MoPEQ: Mixture of Mixed Precision Quantized Experts Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for ex- pert agi

Reference 43

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Observation 57521fa1-9814-42f8-b5e9-774d7c3ac7dc · outbound

This paper cites Vision-language models for vision tasks: A survey.

MoPEQ: Mixture of Mixed Precision Quantized Experts Vision-language models for vision tasks: A survey

Reference 44

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Observation d93d5894-c246-404c-91a2-a11af72e3b90 · outbound

This paper cites MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?.

MoPEQ: Mixture of Mixed Precision Quantized Experts MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?

Reference 45

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Observation 49f4e81a-8e4c-4175-8948-e0ce0c88e946 · outbound

This paper cites A survey on model compression for large language models.

MoPEQ: Mixture of Mixed Precision Quantized Experts A survey on model compression for large language models

Reference 46

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