Paper Citation Record · LEDGER

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts

As of 20 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 7 inbound Pith citation observations for arXiv:2508.07785.

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

pith.paper-citation-record.v1
2508.07785 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T21:55:13.064504Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T18:22:45.702572Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T16:17:08.639901Z

Reference resolution

51 of 51 outbound references displayed

verified exact1
verified fuzzy16
unresolved34
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7676596d-c99a-49b2-aa59-78e27fae7dad · outbound

This paper cites write newline.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:06.816646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c68d5b98-d0ea-47dd-b79a-9a441f6a0837 · outbound

This paper cites AIME Problems and Solutions , 2025.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts AIME Problems and Solutions , 2025

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:55:16.682909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d02bad1b-39c1-4d8e-ad3b-78ac8b9c324e · outbound

This paper cites Alternating Updates for Efficient Transformers.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Alternating Updates for Efficient Transformers

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:55:16.503512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fefeb148-5bf1-4bc4-a7eb-c97d06c60276 · outbound

This paper cites MultiPL-E: A Scalable and Polyglot Approach to Benchmarking Neural Code Generation.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts MultiPL-E: A Scalable and Polyglot Approach to Benchmarking Neural Code Generation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:55:16.306383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 86c53e88-b9b7-4736-9498-c8f3ad700b54 · outbound

This paper cites Parallel Scaling Law for Language Models.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Parallel Scaling Law for Language Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:07.259301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d95b45b3-cd18-4a39-8896-f107b38db248 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Training Verifiers to Solve Math Word Problems

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:07.431498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd6e4cf4-29f3-4ed7-ae53-ba6c37202771 · outbound

This paper cites SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:07.597205Z

Source-reported events for the cited work

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Observation 4140f997-a7a4-40f2-aab9-82356fc314af · outbound

This paper cites DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:07.713368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46efc729-4bcc-469c-9444-c125fa14dbf2 · outbound

This paper cites Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:07.854608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37ff43e3-a5ab-4578-99be-a70024fff0e8 · outbound

This paper cites Gemma 3 Technical Report.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Gemma 3 Technical Report

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:07.978354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78042d42-7a17-4dbe-a7d3-5e12b995001c · outbound

This paper cites Gemini2.5 Pro.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Gemini2.5 Pro

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:55:16.098969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4bf8e837-e990-457e-be4f-de4e7b9b50d1 · outbound

This paper cites The Llama 3 Herd of Models.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts The Llama 3 Herd of Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:08.272501Z

Source-reported events for the cited work

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Observation 6cb2f381-324d-4b12-b91f-3c0b76008cf6 · outbound

This paper cites big.LITTLE Processing with ARL Cortex-A15 & Cortex-A7.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts big.LITTLE Processing with ARL Cortex-A15 & Cortex-A7

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:55:15.902931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b13f4784-d55b-4f6d-a596-d08a170f6b85 · outbound

This paper cites CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:08.615607Z

Source-reported events for the cited work

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Observation 9c45b934-2f91-40d8-bfcb-6552f3c52bc9 · outbound

This paper cites Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:08.825228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6dcf1869-0e67-41e1-ad5c-e49f477b41ca · outbound

This paper cites OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:08.994598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 763b8b34-4a12-49be-b432-b80d84a108db · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Measuring Massive Multitask Language Understanding

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:09.138629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation adead57f-9f1d-4a51-8870-45e2ff57f614 · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Measuring Mathematical Problem Solving With the MATH Dataset

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:09.306553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01b10de9-14d1-4812-9f22-cce7dfc0fc34 · outbound

This paper cites Harder Tasks Need More Experts: Dynamic Routing in MoE Models.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Harder Tasks Need More Experts: Dynamic Routing in MoE Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:09.449759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d2cc0f5-0bb2-4567-8714-ac502c1b5e85 · outbound

This paper cites C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:55:15.766528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7a2fae88-c8b7-441e-a300-6c77883d71e3 · outbound

This paper cites dots.llm1 Technical Report.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts dots.llm1 Technical Report

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:09.763377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f51f57a4-f0db-4722-a417-97110b83b206 · outbound

This paper cites GPT-4o System Card.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts GPT-4o System Card

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:09.900691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87d074a1-00cd-4702-aa34-5b9acf2439d7 · outbound

This paper cites LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:10.077000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe5eb39e-ed16-42fa-b6bb-5cf5b6bb1b45 · outbound

This paper cites Mixtral of Experts.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Mixtral of Experts

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:10.279732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60b066d4-cd5a-4f24-bb97-1ac7c5b0d370 · outbound

This paper cites MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:10.476732Z

Source-reported events for the cited work

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Observation 84a7267f-9501-4e0a-acf0-b17ec6c8a55e · outbound

This paper cites Sparse Upcycling: Training mixture-of-experts from dense checkpoints.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Sparse Upcycling: Training mixture-of-experts from dense checkpoints

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:55:15.591887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1f2df3e9-76d1-4a86-8b11-bbd865b874f8 · outbound

This paper cites CMMLU: Measuring massive multitask language understanding in Chinese.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts CMMLU: Measuring massive multitask language understanding in Chinese

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:10.829310Z

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Unavailable: canonical work link unavailable.

Observation 3fb31cec-1f4f-4329-9cfb-9c0669e38bb7 · outbound

This paper cites From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:10.966584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ad87fe3-b066-46b6-8953-1a9cb4981def · outbound

This paper cites Let's Verify Step by Step.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Let's Verify Step by Step

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:55:15.410461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2d888468-3aab-40a5-985e-f451e40346d6 · outbound

This paper cites DeepSeek-V3 Technical Report.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts DeepSeek-V3 Technical Report

Reference 30

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54ae30d0-78ff-41ea-b565-f86c823771c8 · outbound

This paper cites Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:55:15.253228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b96b046b-199a-494b-b1ca-ca55d87d36e4 · outbound

This paper cites Decoupled Weight Decay Regularization.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Decoupled Weight Decay Regularization

Reference 32

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Observation 3e22ebaa-1bc4-486b-8595-765cab14ceec · outbound

This paper cites The Llama 4 herd: The beginning of a new era of natively multimodal AI innovation , 2025.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts The Llama 4 herd: The beginning of a new era of natively multimodal AI innovation , 2025

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:55:15.136131Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c41a22d5-685c-4201-8fd1-d5395c8cf01e · outbound

This paper cites Mistral-Small-3.1.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Mistral-Small-3.1

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:55:14.940663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c7dd9abf-8bae-4531-9603-310de87de6fc · outbound

This paper cites Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:11.541674Z

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Unavailable: canonical work link unavailable.

Observation 2a6ac5a3-c867-4bab-8509-570232ed60ab · outbound

This paper cites GPQA: A Graduate-Level Google-Proof Q&Q Benchmark.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts GPQA: A Graduate-Level Google-Proof Q&Q Benchmark

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:55:14.776307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 44db8fda-58fc-4b25-9cae-de6a99f4b59c · outbound

This paper cites an unresolved cited work.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Unresolved cited work

Reference 37

Resolution: unresolved; raw_fallback, observed 2026-08-05T21:55:14.608392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 88a5e5cc-9338-4a73-bf29-022355545a49 · outbound

This paper cites MoE Travels 3 , 2025.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts MoE Travels 3 , 2025

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:55:14.354341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1ba115c0-06c2-41ad-b018-3de5f41d8e2c · outbound

This paper cites Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:11.905074Z

Source-reported events for the cited work

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Observation d881d742-b658-4c7b-9103-eb7fd2a6fc44 · outbound

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

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:11.985304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cda85eb4-3161-48d4-9fb6-15107936552e · outbound

This paper cites MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:55:14.166527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 878d5138-3799-4e4c-8c82-ff0eabd13369 · outbound

This paper cites ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:12.177334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b363cbf-77cf-4047-985d-32cec6091d2d · outbound

This paper cites Magicoder: Empowering Code Generation with OSS-Instruct.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Magicoder: Empowering Code Generation with OSS-Instruct

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:12.283379Z

Source-reported events for the cited work

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Observation 9bebb342-0395-4d26-9fe9-46fa482775ec · outbound

This paper cites Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:55:13.927086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d74ccebb-3105-4eed-a728-3562a9e3ddaf · outbound

This paper cites Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:12.444636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 554358da-f55a-4dfe-b8c3-e29382ae0344 · outbound

This paper cites Patil, Ion Stoica, and Joseph E.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Patil, Ion Stoica, and Joseph E

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:55:13.743015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a8e25cb4-66e2-4655-9723-5cda2318714c · outbound

This paper cites Qwen2.5 Technical Report.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Qwen2.5 Technical Report

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:12.617089Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28a74e1a-399e-4588-b7bd-c20d935ce484 · outbound

This paper cites Qwen3 Technical Report.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Qwen3 Technical Report

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:12.751578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20139d0c-fe49-4c00-873a-d7cd5f43f916 · outbound

This paper cites AdaMoE: Token-Adaptive Routing with Null Experts for Mixture-of-Experts Language Models.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts AdaMoE: Token-Adaptive Routing with Null Experts for Mixture-of-Experts Language Models

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-08-05T21:55:13.290078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ec54b7b3-8a57-4a66-9565-59282b8f0699 · outbound

This paper cites AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:12.954012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83a73289-26f4-4bc6-8d53-1478e10102a8 · outbound

This paper cites Instruction-Following Evaluation for Large Language Models.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Instruction-Following Evaluation for Large Language Models

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:13.064504Z

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Pith citing papers

Observation 88da6abf-22c9-40c5-a791-ceb4895750e6 · inbound

Beyond Sunk Costs: Boosting LLM Pre-training Efficiency via Orthogonal Growth of Mixture-of-Experts cites this paper.

Beyond Sunk Costs: Boosting LLM Pre-training Efficiency via Orthogonal Growth of Mixture-of-Experts Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-21T20:40:36.521036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1cf4391e-e513-48dc-b90d-0ede8c0cb014 · inbound

Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts cites this paper.

Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-10T03:29:21.508447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b61a53bc-fb10-4999-8bab-62856c1f888f · inbound

Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts cites this paper.

Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:06:15.396431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 701af203-5b6b-41f6-8ce2-89c8d2d939ce · inbound

SMoES: Soft Modality-Guided Expert Specialization in MoE-VLMs cites this paper.

SMoES: Soft Modality-Guided Expert Specialization in MoE-VLMs Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:36:18.687780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 23095635-0530-4661-aab8-4ce0832d69b3 · inbound

Post-Trained MoE Can Skip Half Experts via Self-Distillation cites this paper.

Post-Trained MoE Can Skip Half Experts via Self-Distillation Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:03:15.157753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5797a3b5-4da2-435d-81a7-c52fc706c5dd · inbound

Post-Trained MoE Can Skip Half Experts via Self-Distillation cites this paper.

Post-Trained MoE Can Skip Half Experts via Self-Distillation Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-30T18:25:00.095976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 898f4dbc-f439-49a9-8f51-e5172c780184 · inbound

Reversible Foundations: Training a 120B Sparse MoE through State-Preserving Scaling cites this paper.

Reversible Foundations: Training a 120B Sparse MoE through State-Preserving Scaling Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts

Reference 67

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:17:08.641657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.