Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation | Paper Citation Record

Observation f1578d50-e2d8-4351-a0f7-f5097e109591 · outbound

This paper cites Evolutionary optimization of model merging recipes.Nature Machine Intelligence, pages 1–10, 2025.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Evolutionary optimization of model merging recipes.Nature Machine Intelligence, pages 1–10, 2025

Reference 1

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Observation e2b4ece2-9003-4a42-9a85-32a8d8790ce6 · outbound

This paper cites Ensemble of averages: Improv- ing model selection and boosting performance in domain generalization.Advances in Neural Information Processing Systems, 35:8265–8277, 2022.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Ensemble of averages: Improv- ing model selection and boosting performance in domain generalization.Advances in Neural Information Processing Systems, 35:8265–8277, 2022

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:17.792206Z

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Observation 08e1875d-3fe7-4091-92dc-0f633f600b80 · outbound

This paper cites Beyond the imitation game: Quantifying and extrapolating the capabilities of language models.Transactions on Machine Learning Research, 2023.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Beyond the imitation game: Quantifying and extrapolating the capabilities of language models.Transactions on Machine Learning Research, 2023

Reference 3

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Observation 4bf9b854-4bb7-4a29-b1f1-b3508a0917a8 · outbound

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

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Pythia: A suite for analyzing large language models across training and scaling

Reference 4

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Observation 3c35beef-cb74-4aa2-8e10-0e6c261f5d7e · outbound

This paper cites GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow, March 2021.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow, March 2021

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:17.527249Z

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Observation b8b5c40a-9274-4dc7-bd8d-f03170c71195 · outbound

This paper cites Multipl- e: a scalable and polyglot approach to benchmarking neural code generation.IEEE Transactions on Software Engineering, 2023.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Multipl- e: a scalable and polyglot approach to benchmarking neural code generation.IEEE Transactions on Software Engineering, 2023

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:17.166917Z

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Observation 5dcbbc6b-8e1a-4f92-a4dd-640a5cae4524 · outbound

This paper cites Meditron- 70b: Scaling medical pretraining for large language models, 2023.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Meditron- 70b: Scaling medical pretraining for large language models, 2023

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:16.794583Z

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Observation bf753216-0e61-427c-9428-199457ffa8a7 · outbound

This paper cites Chinese-vicuna: A chinese instruction-following llama-based model.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Chinese-vicuna: A chinese instruction-following llama-based model

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:16.443819Z

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Observation fdab5838-4552-4155-bbfb-4d88a5ca2608 · outbound

This paper cites Med42 – evaluating fine-tuning strategies for medical llms: Full-parameter vs.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Med42 – evaluating fine-tuning strategies for medical llms: Full-parameter vs

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:16.179125Z

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Observation bed2ea84-00f8-4eef-993e-d54e8829629b · outbound

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

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T13:09:57.727544Z

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Observation 6c4cba97-8bf1-499e-be4c-bca65eac2190 · outbound

This paper cites Glam: Efficient scaling of language models with mixture-of-experts.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Glam: Efficient scaling of language models with mixture-of-experts

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T13:09:57.917599Z

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Observation 4e98753a-603a-4abd-8d15-a901f7bac1f3 · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.Journal of Machine Learning Research, 23(120):1–39, 2022.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.Journal of Machine Learning Research, 23(120):1–39, 2022

Reference 12

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Observation e8d8b5a1-87fe-4385-9eaf-43fd883e4d28 · outbound

This paper cites A framework for few-shot language model evaluation, 12 2023.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation A framework for few-shot language model evaluation, 12 2023

Reference 13

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Observation 788e244e-44cc-4b0f-acd0-286baae9d2e6 · outbound

This paper cites Koala: A dialogue model for academic research.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Koala: A dialogue model for academic research

Reference 14

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Observation 5866328c-3c01-4f1c-8b28-6a1751721f86 · outbound

This paper cites Openllama: An open reproduction of llama, May 2023.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Openllama: An open reproduction of llama, May 2023

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:15.794220Z

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Observation ca392154-0900-4fe7-88ef-4e7ace0f4cb8 · outbound

This paper cites Arcee's MergeKit: A Toolkit for Merging Large Language Models.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Arcee's MergeKit: A Toolkit for Merging Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T13:09:58.537084Z

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Observation 0eec150c-0161-4c43-ac7a-32dec477a60e · outbound

This paper cites The Llama 3 Herd of Models.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation The Llama 3 Herd of Models

Reference 17

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Observation 0eb772e3-2ac6-41bd-8ce1-a0d6765b38ee · outbound

This paper cites Measuring massive multitask language understanding, 2021.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Measuring massive multitask language understanding, 2021

Reference 18

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Observation e2ea96e6-4ef4-4053-88f8-1c858d0b8d56 · outbound

This paper cites Distilling the knowledge in a neural network.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Distilling the knowledge in a neural network

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:15.524109Z

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Observation 0f5499e0-e775-46c6-9550-6292f21b06d0 · outbound

This paper cites Categorical Reparameterization with Gumbel-Softmax.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Categorical Reparameterization with Gumbel-Softmax

Reference 20

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Observation 8d87cc3c-273d-4143-8c20-74e98a660717 · outbound

This paper cites Mixtral of Experts.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Mixtral of Experts

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T13:09:59.182648Z

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Observation 2714b9f5-c1cb-4aab-9c0c-d4ec2a3786df · outbound

This paper cites Llm-blender: Ensembling large language models with pairwise ranking and generative fusion.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Llm-blender: Ensembling large language models with pairwise ranking and generative fusion

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T13:09:59.348625Z

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Observation d0a4d0b5-b1b3-4c82-83ed-0a2f9eac31be · outbound

This paper cites Dataless knowledge fusion by merging weights of language models.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Dataless knowledge fusion by merging weights of language models

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:15.137953Z

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Observation 05fcb154-0386-485f-ba5a-f8a97e565261 · outbound

This paper cites The MiniPile Challenge for Data-Efficient Language Models.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation The MiniPile Challenge for Data-Efficient Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T13:09:59.498544Z

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Observation a981b260-2dfd-4837-a9d7-b3e95dc7d085 · outbound

This paper cites Token reduction should go beyond efficiency in generative models – from vision, language to multimodality, 2025.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Token reduction should go beyond efficiency in generative models – from vision, language to multimodality, 2025

Reference 25

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Observation d0709e28-f384-44b2-8ef3-96744b32eed2 · outbound

This paper cites GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Reference 26

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Observation dda5e345-2044-4379-9165-e9160762d313 · outbound

This paper cites Distinct but correct: generating diversified and entity-revised medical response.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Distinct but correct: generating diversified and entity-revised medical response

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:14.441124Z

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Observation 31f5659d-e056-41ed-8d5f-f5e28f1a32ba · outbound

This paper cites Towards better chinese-centric neural machine translation for low-resource languages.Computer Speech & Language, 84:101566, 2024.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Towards better chinese-centric neural machine translation for low-resource languages.Computer Speech & Language, 84:101566, 2024

Reference 28

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Observation bca2a8fe-c0cf-485f-a443-b93c4b5eec39 · outbound

This paper cites Efficient transformer-based large scale language representations using hardware-friendly block structured pruning.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Efficient transformer-based large scale language representations using hardware-friendly block structured pruning

Reference 29

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Observation aba8bafd-912c-4917-a338-5a81cff12fe8 · outbound

This paper cites StarCoder: may the source be with you!.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation StarCoder: may the source be with you!

Reference 30

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Observation 444c3af3-a6a0-4260-a4a6-e9bda4bcf59e · outbound

This paper cites A comprehensive review of multi-agent reinforcement learning in video games.Authorea Preprints, 2025.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation A comprehensive review of multi-agent reinforcement learning in video games.Authorea Preprints, 2025

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:13.575740Z

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Observation eeeceb25-dbae-490c-b39b-c84c34924aa4 · outbound

This paper cites RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation

Reference 32

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Observation 2ddeb21d-e696-4e97-a593-72ce135308c4 · outbound

This paper cites Toward adaptive large language models structured pruning via hybrid-grained weight importance assessment.arXiv preprint arXiv:2403.10799, 2024.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Toward adaptive large language models structured pruning via hybrid-grained weight importance assessment.arXiv preprint arXiv:2403.10799, 2024

Reference 33

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Observation 20620199-7571-4b80-8455-38d889743294 · outbound

This paper cites an unresolved cited work.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Unresolved cited work

Reference 34

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Observation f2c06686-1f11-4f30-95ad-ab6ad3ccc422 · outbound

This paper cites Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models

Reference 35

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Observation 49b28ee2-90d6-4fed-aa48-c5fb91a26915 · outbound

This paper cites Pack of LLMs: Model Fusion at Test-Time via Perplexity Optimization.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Pack of LLMs: Model Fusion at Test-Time via Perplexity Optimization

Reference 36

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Observation 53b65bbd-6c68-4866-a08f-a5b6fe289013 · outbound

This paper cites Specinfer: Accelerating generative llm serving with speculative inference and token tree verification, 2023.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Specinfer: Accelerating generative llm serving with speculative inference and token tree verification, 2023

Reference 37

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Observation fc2d92e0-84fe-4f78-81ad-5790fb046402 · outbound

This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 38

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Observation c6af0390-c849-4bdc-9823-f2d60d948275 · outbound

This paper cites Diverse weight averaging for out-of-distribution generalization.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Diverse weight averaging for out-of-distribution generalization

Reference 39

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Observation 6854ab9f-3fdf-4064-9d31-5097fb950b6b · outbound

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

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 40

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Observation 000ce4b0-4adc-4bc5-8af0-e03af2c058ff · outbound

This paper cites Agile-quant: Activation-guided quantization for faster inference of llms on the edge.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Agile-quant: Activation-guided quantization for faster inference of llms on the edge

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:12.492333Z

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Observation a01b7968-acfe-478b-b56e-6bd28019bab2 · outbound

This paper cites Squat: Quant Small Language Models on the Edge.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Squat: Quant Small Language Models on the Edge

Reference 42

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Observation f386f2c1-1dcf-4883-b847-5cfdcc60e9ec · outbound

This paper cites Knowledge Unlearning for LLMs: Tasks, Methods, and Challenges.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Knowledge Unlearning for LLMs: Tasks, Methods, and Challenges

Reference 43

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Observation 3cc1738c-f6b6-4d61-90d9-824fd9a88fd9 · outbound

This paper cites Zipit! merging models from different tasks without training.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Zipit! merging models from different tasks without training

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:12.139790Z

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Observation 3a3986b5-53d9-41b0-8920-612fa13b20d8 · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 45

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Observation 76174996-1246-4c65-8350-bd97055d00c6 · outbound

This paper cites CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:02.856087Z

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Observation 4ccac0ef-63f5-43a5-a3b7-9280be9b4c07 · outbound

This paper cites Introducing mpt-7b: A new standard for open-source, commercially usable llms, 2023.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Introducing mpt-7b: A new standard for open-source, commercially usable llms, 2023

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:11.804778Z

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Observation 1319e17b-6e2b-4d8f-a259-3c459bcaa690 · outbound

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

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation LLaMA: Open and Efficient Foundation Language Models

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:03.131945Z

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Observation ea9a9365-7b3e-448a-b76b-314d8581334a · outbound

This paper cites Knowl- edge fusion of large language models.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Knowl- edge fusion of large language models

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:11.350396Z

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Observation 6ecaa61b-5a7f-4679-ad98-1ae7c87abd27 · outbound

This paper cites Knowledge Fusion of Chat LLMs: A Preliminary Technical Report.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Knowledge Fusion of Chat LLMs: A Preliminary Technical Report

Reference 50

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Observation 4b273336-b154-4158-beb0-5e54dfe11d01 · outbound

This paper cites Fusing Models with Complementary Expertise.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Fusing Models with Complementary Expertise

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:03.549476Z

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Observation 0237101a-f371-49dd-8d22-b7a7ac54e62c · outbound

This paper cites Learn it or leave it: Module composition and pruning for continual learning.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Learn it or leave it: Module composition and pruning for continual learning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:10.989181Z

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Observation 0b831a7e-9d42-4dd1-a803-2d805c3b92c1 · outbound

This paper cites Rehearsal- free modular and compositional continual learning for language models.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Rehearsal- free modular and compositional continual learning for language models

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:10.616997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f90f0a5c-cb5b-41e1-82b4-eafa93a9d560 · outbound

This paper cites A systematic review of machine learning applications in infectious disease prediction, diagnosis, and outbreak forecasting.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation A systematic review of machine learning applications in infectious disease prediction, diagnosis, and outbreak forecasting

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:10.317579Z

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Observation 4fb50154-2521-400f-bdb2-9c102cc3f8a9 · outbound

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

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:04.259210Z

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Observation 43cb4c9e-3b6c-4052-88d8-27fe28dfbbff · outbound

This paper cites Ties-merging: Resolving interference when merging models.Advances in Neural Information Processing Systems, 36, 2024.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Ties-merging: Resolving interference when merging models.Advances in Neural Information Processing Systems, 36, 2024

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:10.025304Z

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Observation a995a56d-4a8a-48fe-82d9-f905834e75e5 · outbound

This paper cites AdaMerging: Adaptive Model Merging for Multi-Task Learning.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation AdaMerging: Adaptive Model Merging for Multi-Task Learning

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:04.588295Z

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Observation 1894a646-95e7-4bba-91ca-c2b1584c5f2c · outbound

This paper cites Yi: Open Foundation Models by 01.AI.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Yi: Open Foundation Models by 01.AI

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:04.720488Z

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Unavailable: canonical work link unavailable.

Observation 7707b560-55b5-438a-952c-7efc9d7972d8 · outbound

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

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:04.899531Z

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Observation 89bb4ac7-8d3a-4131-9d6f-c0a8f0fb3c84 · outbound

This paper cites Rethinking Token Reduction for State Space Models.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Rethinking Token Reduction for State Space Models

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:04.982237Z

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Observation 8217dea7-38ac-4481-8500-408097c0ab37 · outbound

This paper cites Towards the law of capacity gap in distilling language models.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Towards the law of capacity gap in distilling language models

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:09.639848Z

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Observation a34fcd6e-9c8a-4b53-8ffe-3c4fa2bdd54b · outbound

This paper cites Composing parameter-efficient modules with arithmetic operation.Advances in Neural Information Processing Systems, 36:12589–12610, 2023.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Composing parameter-efficient modules with arithmetic operation.Advances in Neural Information Processing Systems, 36:12589–12610, 2023

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:05.222437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bfc5bfb-17d0-439a-bcec-b84995098a57 · outbound

This paper cites Alpacare:instruction-tuned large language models for medical application, 2023.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Alpacare:instruction-tuned large language models for medical application, 2023

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:05.390040Z

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Observation 5c230dab-5178-467d-b275-b439168d6105 · outbound

This paper cites 7b fully open source moxin-llm – from pretraining to grpo-based reinforcement learning enhancement, 2025.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation 7b fully open source moxin-llm – from pretraining to grpo-based reinforcement learning enhancement, 2025

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:09.261976Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation de9973e7-f742-483a-81ab-dc2e73cc6ed6 · outbound

This paper cites Pruning Foundation Models for High Accuracy without Retraining.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Pruning Foundation Models for High Accuracy without Retraining

Reference 65

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:10:06.691603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 639b5b44-00a8-4c2b-9426-458c606cf95d · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.Advances in Neural Information Processing Systems, 36:46595–46623, 2023.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Judging llm-as-a-judge with mt-bench and chatbot arena.Advances in Neural Information Processing Systems, 36:46595–46623, 2023

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:05.850820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb711217-dbb6-4db2-8967-78feaa1f1f01 · outbound

This paper cites Enhancing thyroid disease prediction using machine learning: A comparative study of ensemble models and class balancing techniques.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Enhancing thyroid disease prediction using machine learning: A comparative study of ensemble models and class balancing techniques

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:08.896337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d843ddd4-9177-4a21-907c-151242151d75 · outbound

This paper cites ST-MoE: Designing Stable and Transferable Sparse Expert Models.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation ST-MoE: Designing Stable and Transferable Sparse Expert Models

Reference 68

Resolution: malformed identifier; no resolver link, observed 2026-08-07T13:10:06.176625Z

Source-reported events for the cited work

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Observation bd4a21e6-4b51-4667-87c4-3c988445dca9 · outbound

This paper cites Building on this foun- dation, GShard [26] and Switch Transformers [12] presented some of the first large-scale models leveraging SMoE.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Building on this foun- dation, GShard [26] and Switch Transformers [12] presented some of the first large-scale models leveraging SMoE

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:08.565503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 250ed7d9-5d9e-453a-a617-d958ef58afd3 · outbound

This paper cites an unresolved cited work.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Unresolved cited work

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:00.105321Z

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Unavailable: canonical work link unavailable.

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