Paper Citation Record · LEDGER

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

As of 23 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 25 inbound Pith citation observations for arXiv:2502.00674.

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

pith.paper-citation-record.v1
2502.00674 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T18:11:28.194047Z

measured 56 of 56 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 25 of 25 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T10:20:58.826813Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T02:35:53.862220Z

Reference resolution

31 of 31 outbound references displayed

verified exact0
verified fuzzy1
unresolved30
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f5c5f5fc-92e4-46de-9d41-bd4c15318cca · outbound

This paper cites GPT-4 Technical Report.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.084621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62e290fd-d6f3-4f53-aa3d-7b26aab95df3 · outbound

This paper cites Universal Self-Consistency for Large Language Model Generation.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? Universal Self-Consistency for Large Language Model Generation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.106422Z

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Observation 60a2ba35-8f01-458f-be95-7b590f0fca1d · outbound

This paper cites Improving Factuality and Reasoning in Language Models through Multiagent Debate.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? Improving Factuality and Reasoning in Language Models through Multiagent Debate

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.110501Z

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Observation 026daf01-b74e-4e52-9482-11b38839f09d · outbound

This paper cites Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.114379Z

Source-reported events for the cited work

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Observation 1cc04f74-a9be-40ee-b186-aca962c8c081 · outbound

This paper cites Are We Done with MMLU?.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? Are We Done with MMLU?

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.118550Z

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Observation 3b2335e3-504a-49f0-b015-26bb9832d7d9 · outbound

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

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.122049Z

Source-reported events for the cited work

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Observation 75f37d1c-a82e-4258-aaaa-c69bf3146d08 · outbound

This paper cites BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.125944Z

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Observation 95eda0fb-98f2-44d4-a858-6223ad2cfa2b · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? Measuring Massive Multitask Language Understanding

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.129112Z

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Unavailable: canonical work link unavailable.

Observation a6b5d1c0-2416-4aab-b984-82f1d07fcd96 · outbound

This paper cites Mixtral of Experts.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? Mixtral of Experts

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.135024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2049544f-d303-42bf-9664-f2c16521aa19 · outbound

This paper cites More Agents Is All You Need.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? More Agents Is All You Need

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.138099Z

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Unavailable: canonical work link unavailable.

Observation 7f479c68-b81b-4744-8438-a6a7a5cf0883 · outbound

This paper cites Mitigating the Alignment Tax of RLHF.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? Mitigating the Alignment Tax of RLHF

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.144815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 066b67fa-bd07-41a5-995a-5b4f6ab1a320 · outbound

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

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.148319Z

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Unavailable: canonical work link unavailable.

Observation 6904886a-658d-48c7-b0a1-f449ed55e4b3 · outbound

This paper cites SimPO: Simple Preference Optimization with a Reference-Free Reward.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? SimPO: Simple Preference Optimization with a Reference-Free Reward

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.152093Z

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Observation 86b9cb94-65cf-403b-af4a-d78036678a0b · outbound

This paper cites WARP: On the Benefits of Weight Averaged Rewarded Policies.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? WARP: On the Benefits of Weight Averaged Rewarded Policies

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.155863Z

Source-reported events for the cited work

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Observation 87f5683d-bf3e-4ef0-b70b-3152f5810ff5 · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.159372Z

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Unavailable: canonical work link unavailable.

Observation 7e8eafca-94da-4b57-ad1d-10ec0d3f8b58 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? Gemini: A Family of Highly Capable Multimodal Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.162824Z

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Unavailable: canonical work link unavailable.

Observation ae457d7c-c095-4333-ad0e-cfc1ebb184a3 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? Gemma 2: Improving Open Language Models at a Practical Size

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.166006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a740c73-6bab-4bfe-a0e7-46ed6fa6f139 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.170286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3cc974f2-a2c4-4171-8590-00b868f03bd0 · outbound

This paper cites Can Large Language Models Really Improve by Self-critiquing Their Own Plans?.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? Can Large Language Models Really Improve by Self-critiquing Their Own Plans?

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.174002Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b4ea834-0598-4868-99c1-8c5573ebcc13 · outbound

This paper cites Mixture-of-Agents Enhances Large Language Model Capabilities.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? Mixture-of-Agents Enhances Large Language Model Capabilities

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.177534Z

Source-reported events for the cited work

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Observation 39881245-e23f-4840-b4cf-a3b20b9a554e · outbound

This paper cites Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.181242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cab92b4d-03b8-4cf1-b4ee-69ae968902e3 · outbound

This paper cites Qwen2 Technical Report.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? Qwen2 Technical Report

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.184221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6a2c73c-5fbd-4f8c-916b-3af1b95021ce · outbound

This paper cites WPO: Enhancing RLHF with Weighted Preference Optimization.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? WPO: Enhancing RLHF with Weighted Preference Optimization

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.187014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3f19c79-7190-454a-8bb5-2e15464bb263 · outbound

This paper cites DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.190289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05b6ed2b-2cc4-40dd-a844-607a0af43435 · outbound

This paper cites This metric provides a quantitative measure of diversity based on the distribution of similarity scores among the samples.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? This metric provides a quantitative measure of diversity based on the distribution of similarity scores among the samples

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:11:28.496564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 841ace05-388c-48ac-846d-b0398f6135a5 · outbound

This paper cites Qwen Technical Report.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? Qwen Technical Report

Reference 1990

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.089207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fff154a-5fb1-4115-b2e1-ff34a3180661 · outbound

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

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? Measuring Mathematical Problem Solving With the MATH Dataset

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.132201Z

Source-reported events for the cited work

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Observation c3860c71-e172-46a8-b888-aafa58327361 · outbound

This paper cites MoA is All You Need: Building LLM Research Team using Mixture of Agents.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? MoA is All You Need: Building LLM Research Team using Mixture of Agents

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.101723Z

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Observation cbdcf36f-8c23-4679-a5c6-6c45cfa1f22a · outbound

This paper cites Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.141367Z

Source-reported events for the cited work

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Observation 877e828b-90fb-4ff9-9a91-1fe3392820fd · outbound

This paper cites Large Language Monkeys: Scaling Inference Compute with Repeated Sampling.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.093469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f94775e2-417c-4ae4-bce8-fbb811c4a495 · outbound

This paper cites ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs.

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-09T18:11:28.097949Z

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Unavailable: canonical work link unavailable.

Pith citing papers

Observation dd312a38-b50a-40bb-8035-fc42a18c168a · inbound

The Avengers: A Simple Recipe for Uniting Smaller Language Models to Challenge Proprietary Giants cites this paper.

The Avengers: A Simple Recipe for Uniting Smaller Language Models to Challenge Proprietary Giants Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T14:12:35.899732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8d63913-ab70-455d-94e4-b3d9ef9f2aba · inbound

A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement cites this paper.

A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:30:46.127698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c6e1b00e-c859-4a1a-b896-c15f9330d962 · inbound

MAC: Masked Agent Collaboration Boosts Large Language Model Medical Decision-Making cites this paper.

MAC: Masked Agent Collaboration Boosts Large Language Model Medical Decision-Making Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-19T03:12:56.597860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d26b3cd4-b532-4037-adf7-807fa791a3a4 · inbound

Towards Generalized Routing: Model and Agent Orchestration for Adaptive and Efficient Inference cites this paper.

Towards Generalized Routing: Model and Agent Orchestration for Adaptive and Efficient Inference Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-04T22:03:02.011254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2e1f9b0-0753-4a1e-80e9-aa77ddfde37c · inbound

SANet: A Semantic-aware Agentic AI Networking Framework for Cross-layer Optimization in 6G cites this paper.

SANet: A Semantic-aware Agentic AI Networking Framework for Cross-layer Optimization in 6G Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-16T19:23:19.704354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bb3387f5-6e8d-4d6a-954d-753b09efa882 · inbound

Pyramid MoA: A Probabilistic Framework for Cost-Optimized Anytime Inference cites this paper.

Pyramid MoA: A Probabilistic Framework for Cost-Optimized Anytime Inference Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-15T21:01:39.079307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation efc449e2-c2b8-4f27-8fe2-a80dc9809ae1 · inbound

When Agents Disagree: The Selection Bottleneck in Multi-Agent LLM Pipelines cites this paper.

When Agents Disagree: The Selection Bottleneck in Multi-Agent LLM Pipelines Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T17:52:54.777529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7989332e-9ff9-4fc1-89ab-3e340bfed179 · inbound

Refute-or-Promote: An Adversarial Stage-Gated Multi-Agent Review Methodology for High-Precision LLM-Assisted Defect Discovery cites this paper.

Refute-or-Promote: An Adversarial Stage-Gated Multi-Agent Review Methodology for High-Precision LLM-Assisted Defect Discovery Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-10T03:14:08.036447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3bb498d0-8929-4cc2-ad25-11f7561b7aac · inbound

Feedback Over Form: Why Execution Feedback Matters More Than Pipeline Topology in 1-3B Code Generation cites this paper.

Feedback Over Form: Why Execution Feedback Matters More Than Pipeline Topology in 1-3B Code Generation Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:31:07.900933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e05a2ec6-f018-4197-87d9-d7d0a668fe63 · inbound

A Communication-Theoretic Framework for LLM Agents: Cost-Aware Adaptive Reliability cites this paper.

A Communication-Theoretic Framework for LLM Agents: Cost-Aware Adaptive Reliability Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T03:06:19.449040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4fdda3c0-d80a-43de-963e-bc55451799a7 · inbound

Conflict-Resilient Multi-Agent Reasoning via Signed Graph Modeling cites this paper.

Conflict-Resilient Multi-Agent Reasoning via Signed Graph Modeling Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-20T05:48:04.402984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 38f7be95-357d-4826-ae99-f6394daaaf1a · inbound

From Talking Words to Sharing Thoughts: Scalable Multi-LLM Aggregation via Structured Message Passing cites this paper.

From Talking Words to Sharing Thoughts: Scalable Multi-LLM Aggregation via Structured Message Passing Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-06-28T19:32:34.793486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 53f3176b-e6c3-4c89-b2a7-03817e77fe8c · inbound

The Ringelmann Effect in Multi-Agent LLM Systems: A Scaling Law for Effective Team Size cites this paper.

The Ringelmann Effect in Multi-Agent LLM Systems: A Scaling Law for Effective Team Size Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T21:56:15.575218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 77330c8b-66df-44bc-bfdf-0b28e04cb089 · inbound

MOSAIC: Efficient Mixture-of-Agent Scheduling via Adaptive Aggregation and Inference Concurrency cites this paper.

MOSAIC: Efficient Mixture-of-Agent Scheduling via Adaptive Aggregation and Inference Concurrency Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Reference 169

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T01:46:26.660933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation dc7545c2-920c-4b65-b142-a213aa6189d1 · inbound

SAGE: A Quantitative Evaluation of Socialized Evolution in Agent Ecosystems cites this paper.

SAGE: A Quantitative Evaluation of Socialized Evolution in Agent Ecosystems Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-02T03:26:29.544533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 95d301f8-fc3f-4db9-bcc3-28649118f90b · inbound

Quantum-Inspired Trace-Augmented Evidence Selection for Reasoning over Structured Hypothesis Spaces cites this paper.

Quantum-Inspired Trace-Augmented Evidence Selection for Reasoning over Structured Hypothesis Spaces Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-02T17:37:14.606270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d7bf5828-aaec-4f26-b4f5-393b48678ad4 · inbound

The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes cites this paper.

The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Reference 142

Resolution: verified exact; arxiv_id, observed 2026-07-03T05:57:41.557729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2abc00b9-cb0e-4b08-8102-c79f4bda660b · inbound

ReM-MoA: Reasoning Memory Sustains Mixture-of-Agents Scaling cites this paper.

ReM-MoA: Reasoning Memory Sustains Mixture-of-Agents Scaling Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T17:30:00.780744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5b5ad1f4-9ad7-4094-9b23-956c4ad5419d · inbound

When Does Combining Language Models Help? A Co-Failure Ceiling on Routing, Voting, and Mixture-of-Agents Across 67 Frontier Models cites this paper.

When Does Combining Language Models Help? A Co-Failure Ceiling on Routing, Voting, and Mixture-of-Agents Across 67 Frontier Models Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-07-04T14:19:53.611757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 92d66bfd-91bd-42ea-a4a3-242940b3fdc0 · inbound

How Much of the Routing Gap Is Real? Decomposing the Router-to-Oracle Gap into Reproducible Specialist Advantage and Single-Draw Label Noise cites this paper.

How Much of the Routing Gap Is Real? Decomposing the Router-to-Oracle Gap into Reproducible Specialist Advantage and Single-Draw Label Noise Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-12T02:29:58.886380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4478556-1642-4af0-ad24-9a002f7db2fc · inbound

Agon: Competitive Cross-Model RL with Implicit Rival Grading of Reasoning cites this paper.

Agon: Competitive Cross-Model RL with Implicit Rival Grading of Reasoning Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:35:53.863433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e644ca93-f9c8-4749-b528-a1887e91e432 · inbound

Are Diversity Metrics Measuring Diversity? A Capability-Controlled Audit of Majority-Vote Gain in LLM Ensembles cites this paper.

Are Diversity Metrics Measuring Diversity? A Capability-Controlled Audit of Majority-Vote Gain in LLM Ensembles Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-01T09:29:50.407020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba55738c-e6e6-46c6-b77c-84eb57d5b856 · inbound

Adversarial Test-Hardening for AI-Written Code: An Instrument Autopsy and a Pre-Registered Causal Estimate of the Critic Loop cites this paper.

Adversarial Test-Hardening for AI-Written Code: An Instrument Autopsy and a Pre-Registered Causal Estimate of the Critic Loop Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-01T03:57:40.775727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41f6081d-1b0e-410a-a46f-139c9448904b · inbound

Chained Recursive Language Models for Multi-Iteration Reasoning cites this paper.

Chained Recursive Language Models for Multi-Iteration Reasoning Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T04:47:19.451802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2d17029-7de0-4b96-bc3f-6df644aae589 · inbound

Macaron-V1: Towards Open Continual Learning with Self-Improvement and Mixture-of-LoRA cites this paper.

Macaron-V1: Towards Open Continual Learning with Self-Improvement and Mixture-of-LoRA Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-11T10:20:58.826813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.