DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning | Paper Citation Record

Observation 334858df-7240-43a7-b16d-83096baeda08 · outbound

This paper cites Moe-llava: Mixture of experts for large vision-language models.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Moe-llava: Mixture of experts for large vision-language models

Reference 1

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Observation ba29f426-5e72-44b1-bc25-5494617eeda7 · outbound

This paper cites The revolution of multimodal large language models: A survey.Findings of the association for computational linguistics: ACL 2024, pages 13590–13618, 2024.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning The revolution of multimodal large language models: A survey.Findings of the association for computational linguistics: ACL 2024, pages 13590–13618, 2024

Reference 2

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Observation 366ac47f-dead-4af2-8181-7612d2141d0d · outbound

This paper cites Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks

Reference 3

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This paper cites LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models

Reference 4

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Observation c72adca5-ade3-400e-9386-1962ca20f2de · outbound

This paper cites SVIT: Scaling up Visual Instruction Tuning.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning SVIT: Scaling up Visual Instruction Tuning

Reference 5

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Observation b5a79f69-402a-4a7b-83d2-f9545b2606e4 · outbound

This paper cites Sharegpt4v: Improving large multi-modal models with better captions.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Sharegpt4v: Improving large multi-modal models with better captions

Reference 6

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Observation 91739d52-682a-4d82-a064-4e427a967fcc · outbound

This paper cites Outrageously large neural networks: The sparsely-gated mixture-of-experts layer.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Outrageously large neural networks: The sparsely-gated mixture-of-experts layer

Reference 7

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Observation dbc19120-ca1a-4a08-a5a0-58e345d36f9d · outbound

This paper cites Scaling vision-language models with sparse mixture of experts.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Scaling vision-language models with sparse mixture of experts

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:28:04.550145Z

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Observation 9ec78643-ae4e-4410-81f5-5962a19bccc8 · outbound

This paper cites Mixtral of Experts.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Mixtral of Experts

Reference 9

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Observation 3df57005-5817-4f7d-9d5b-44f82f23eed8 · outbound

This paper cites Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM

Reference 10

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Observation 356b6255-c2ae-4714-a6ae-0b3dc5ecef43 · outbound

This paper cites Biomedical visual instruction tuning with clinician preference alignment.Advances in neural information processing systems, 37:96449–96467, 2024.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Biomedical visual instruction tuning with clinician preference alignment.Advances in neural information processing systems, 37:96449–96467, 2024

Reference 11

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Observation c6f6a0fb-f260-4526-8f71-63325011f901 · outbound

This paper cites FlexOlmo: Open Language Models for Flexible Data Use.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning FlexOlmo: Open Language Models for Flexible Data Use

Reference 12

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Observation 0e738a17-8ef7-4eea-ad26-e93bfaf8648f · outbound

This paper cites Train Separately, Merge Together: Modular Post-Training with Mixture-of-Experts.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Train Separately, Merge Together: Modular Post-Training with Mixture-of-Experts

Reference 13

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Observation c9bb56c3-53cc-4f0c-8270-321f09f60b18 · outbound

This paper cites Learning to instruct for visual instruction tuning.Advances in neural information processing systems, 2025.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Learning to instruct for visual instruction tuning.Advances in neural information processing systems, 2025

Reference 14

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Observation 9d474a29-4214-4402-918c-de2585c70acb · outbound

This paper cites LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics

Reference 15

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Observation 85546d3d-60d1-4803-a9eb-6eefab72f498 · outbound

This paper cites Visual instruction tuning.Advances in neural information processing systems, 36:34892–34916, 2023.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Visual instruction tuning.Advances in neural information processing systems, 36:34892–34916, 2023

Reference 16

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Observation 6a53dd71-0415-4882-a2b8-6edab3578e9f · outbound

This paper cites Learning transferable visual models from natural language supervision.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Learning transferable visual models from natural language supervision

Reference 17

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Observation 1c7c0de9-f5c9-4d80-8440-22babf1250cd · outbound

This paper cites Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality.See https://vicuna.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality.See https://vicuna

Reference 18

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Observation f957e008-9515-4c7a-934f-39515723fb70 · outbound

This paper cites Coin: A benchmark of continual instruction tuning for multimodel large language models.Advances in neural information processing systems, 37:57817–57840, 2024.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Coin: A benchmark of continual instruction tuning for multimodel large language models.Advances in neural information processing systems, 37:57817–57840, 2024

Reference 19

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Observation 283204ce-6ce1-4d69-83f5-43c51c6837dc · outbound

This paper cites SEFE: Superficial and Essential Forgetting Eliminator for Multimodal Continual Instruction Tuning.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning SEFE: Superficial and Essential Forgetting Eliminator for Multimodal Continual Instruction Tuning

Reference 20

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Observation 8e983215-5422-4db2-bcfc-4ed1f80d0641 · outbound

This paper cites On token’s dilemma: Dynamic moe with drift-aware token assignment for continual learning of large vision language models.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning On token’s dilemma: Dynamic moe with drift-aware token assignment for continual learning of large vision language models

Reference 21

Resolution: verified exact; raw_fallback, observed 2026-08-15T14:28:03.967685Z

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Observation ef6921de-9f86-4de4-8985-bcf17cc5b6d8 · outbound

This paper cites Kss-moe: Knowledge space synergy framework in mixture of experts for continual visual instruction tuning.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Kss-moe: Knowledge space synergy framework in mixture of experts for continual visual instruction tuning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:28:04.428889Z

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Observation 2b312b7b-e6c0-4a33-938d-7795c2297616 · outbound

This paper cites Continual instruction tuning for large multimodal models.IEEE Transactions on Image Processing, 2026.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Continual instruction tuning for large multimodal models.IEEE Transactions on Image Processing, 2026

Reference 23

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Observation c660ca03-e2c8-43e0-89f1-3ad909bc1319 · outbound

This paper cites Model tailor: Mitigating catastrophic forgetting in multi-modal large language models.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Model tailor: Mitigating catastrophic forgetting in multi-modal large language models

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:28:04.391623Z

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Observation 4244ccb7-abc3-4f0a-aa46-e3d037045424 · outbound

This paper cites DiLoCo: Distributed Low-Communication Training of Language Models.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning DiLoCo: Distributed Low-Communication Training of Language Models

Reference 25

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Observation 48561d4b-d0da-4536-9d78-f7d1c1ef979d · outbound

This paper cites Task Formulation Matters When Learning Continually: A Case Study in Visual Question Answering.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Task Formulation Matters When Learning Continually: A Case Study in Visual Question Answering

Reference 26

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Observation b436b3ea-ad3e-45dc-b150-66d4d66c2e70 · outbound

This paper cites Model merging in llms, mllms, and beyond: Methods, theories, applications, and opportu- nities.ACM Computing Surveys, 58(8):1–41, 2026.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Model merging in llms, mllms, and beyond: Methods, theories, applications, and opportu- nities.ACM Computing Surveys, 58(8):1–41, 2026

Reference 27

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Observation b3765395-7332-4053-b3be-6b8725727466 · outbound

This paper cites Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:28:04.352771Z

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Observation 1802ec4e-ea08-40c8-95e5-34ed216ef2b9 · outbound

This paper cites Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models

Reference 29

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Observation 927032dd-58ed-4915-bd4f-835bfc739b9b · outbound

This paper cites Ties-merging: Resolving interference when merging models.Advances in neural information processing systems, 36:7093–7115, 2023.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Ties-merging: Resolving interference when merging models.Advances in neural information processing systems, 36:7093–7115, 2023

Reference 30

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Observation 4dbafb39-c0b6-4cd7-bf74-14c8b2dc5fc6 · outbound

This paper cites gpt-oss-120b & gpt-oss-20b Model Card.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning gpt-oss-120b & gpt-oss-20b Model Card

Reference 31

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Observation 6c2103a6-a1b7-4e66-937d-4834e5eda2e1 · outbound

This paper cites Scaling vision with sparse mixture of experts.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Scaling vision with sparse mixture of experts

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:28:04.321415Z

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Observation 9e1ecebd-4e17-45b6-a978-512d5f7c5644 · outbound

This paper cites Multi- modal contrastive learning with limoe: the language-image mixture of experts.Advances in Neural Information Processing Systems, 35:9564–9576, 2022.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Multi- modal contrastive learning with limoe: the language-image mixture of experts.Advances in Neural Information Processing Systems, 35:9564–9576, 2022

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This paper cites Microsoft coco: Common objects in context.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Microsoft coco: Common objects in context

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Observation 5c10a25a-2d1c-47f3-9f57-37c96c5da5af · outbound

This paper cites Gqa: A new dataset for real-world visual reasoning and compositional question answering.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Gqa: A new dataset for real-world visual reasoning and compositional question answering

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Observation 1502c9a6-9f85-42af-b24e-8700222cd88a · outbound

This paper cites Ocr-vqa: Visual question answering by reading text in images.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Ocr-vqa: Visual question answering by reading text in images

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Observation 03a8c1c8-75aa-4965-9449-602721776b37 · outbound

This paper cites Towards vqa models that can read.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Towards vqa models that can read

Reference 37

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Observation 0e0bf72d-2b72-4ef5-9749-bafbe7994992 · outbound

This paper cites Visual genome: Connecting language and vision using crowdsourced dense image annotations.International journal of computer vision, 123(1):32–73, 2017.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Visual genome: Connecting language and vision using crowdsourced dense image annotations.International journal of computer vision, 123(1):32–73, 2017

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Observation 9123a8bf-8b55-4a70-974a-507c59e8fddc · outbound

This paper cites Qwen Technical Report.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Qwen Technical Report

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Observation 17a2eb29-623b-4d65-9604-27ee01e025df · outbound

This paper cites Phi-2: The surprising power of small language models.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Phi-2: The surprising power of small language models

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:28:04.224648Z

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Observation f58eccf1-afb2-4b72-b1f0-fa07de793470 · outbound

This paper cites Stable LM 2 1.6B Technical Report.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Stable LM 2 1.6B Technical Report

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Observation d85702ac-6cc8-4a2c-b355-753fb2351244 · outbound

This paper cites Making the v in vqa matter: Elevating the role of image understanding in visual question answering.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Making the v in vqa matter: Elevating the role of image understanding in visual question answering

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Observation 4d799211-7be4-4559-b49b-1c24a029d440 · outbound

This paper cites Learn to explain: Multimodal reasoning via thought chains for science question answering.Advances in neural information processing systems, 35:2507–2521, 2022.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Learn to explain: Multimodal reasoning via thought chains for science question answering.Advances in neural information processing systems, 35:2507–2521, 2022

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Observation 6c674b43-3624-4908-a16d-8ee5946e1ace · outbound

This paper cites Evaluating object hallucination in large vision-language models.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Evaluating object hallucination in large vision-language models

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

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

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This paper cites SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

Reference 46

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Observation a0a5bfdd-ca85-4524-a12a-cdff0f782880 · outbound

This paper cites MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities

Reference 47

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Observation 7e61a100-a497-4616-9b73-f557a769f6a2 · outbound

This paper cites Open technical problems in open-weight ai model risk management.Transactions on Machine Learning Research, 2025.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Open technical problems in open-weight ai model risk management.Transactions on Machine Learning Research, 2025

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:28:04.145289Z

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Observation 39244b89-6a3e-4823-8faa-1fa97e178d37 · outbound

This paper cites To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning

Reference 49

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Observation c6cc7074-b024-47ca-876d-a851c4191c9a · outbound

This paper cites Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-15T14:28:03.487166Z

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Observation a5396789-b7bf-476a-87ae-b47731077739 · outbound

This paper cites MIMIC-IT: Multi-Modal In-Context Instruction Tuning.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning MIMIC-IT: Multi-Modal In-Context Instruction Tuning

Reference 51

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Observation a254c35e-5805-4286-8ac8-7d48549f39c1 · outbound

This paper cites Improved baselines with visual instruction tuning.

DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning Improved baselines with visual instruction tuning

Reference 52

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T14:28:03.659876Z

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