Paper Citation Record · LEDGER

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning

As of 21 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2607.10836.

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

pith.paper-citation-record.v1
2607.10836 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T08:54:28.745567Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

46 of 46 outbound references displayed

verified exact1
verified fuzzy0
unresolved45
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 14766d52-753a-4c30-a7b0-62f363c2d073 · outbound

This paper cites Emergent Abilities of Large Language Models,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Emergent Abilities of Large Language Models,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Unavailable: canonical work link unavailable.

Observation cac922d4-2928-4a06-b15e-fe852566747e · outbound

This paper cites AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversations,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversations,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation 046456bc-afe3-4531-9443-9401d052b7dc · outbound

This paper cites MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation c799ed1c-ce8b-47ac-a8ae-d940056d80a2 · outbound

This paper cites CAMEL: Communicative Agents for.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning CAMEL: Communicative Agents for

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation e1193235-ed56-4500-a18a-2b984a997e3a · outbound

This paper cites ChatDev: Communicative Agents for Software Development,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning ChatDev: Communicative Agents for Software Development,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation 4ee937eb-8c25-4769-8cb9-7b3db2088de4 · outbound

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

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Improving Factuality and Reasoning in Language Models through Multiagent Debate,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Unavailable: canonical work link unavailable.

Observation fc23c948-0e57-40ce-9bbe-236009e23d6a · outbound

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

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edd4c73e-3b9a-438d-b3c0-ac557a5dd13f · outbound

This paper cites Self-Refine: Iterative Refinement with Self-Feedback,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Self-Refine: Iterative Refinement with Self-Feedback,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Unavailable: canonical work link unavailable.

Observation b5cb2774-d3d7-46e9-9f07-af0d04f5edfc · outbound

This paper cites ReAct: Synergizing Reasoning and Acting in Language Models,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning ReAct: Synergizing Reasoning and Acting in Language Models,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Unavailable: canonical work link unavailable.

Observation 13614990-8794-474b-b8ef-f16fc2bc8de2 · outbound

This paper cites LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation 421b9df5-1977-4c76-a895-983e5c7f2285 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Self-Consistency Improves Chain of Thought Reasoning in Language Models,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation cd792f5a-52b4-4cdd-8db8-298016971e1b · outbound

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

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Mixture-of-Agents Enhances Large Language Model Capabilities,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation 7095c262-278f-4ffe-9c75-6278f9f61bc3 · outbound

This paper cites EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation 08ea4cea-cbc2-435c-b1c2-029d40d732b9 · outbound

This paper cites Multi-Agent Collaboration via Evolving Orchestration,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Multi-Agent Collaboration via Evolving Orchestration,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation 27c99fae-8f38-4cee-b78a-32db15a301a6 · outbound

This paper cites MAPoRL: Multi-agent post-co-training for collaborative large language models with reinforcement learning,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning MAPoRL: Multi-agent post-co-training for collaborative large language models with reinforcement learning,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation ad63f6ec-3524-4002-a839-362e8c5b3d24 · outbound

This paper cites LLM Collaboration with Multi-Agent Reinforcement Learning,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning LLM Collaboration with Multi-Agent Reinforcement Learning,

Reference 16

Resolution: verified exact; doi, observed 2026-07-14T09:00:22.830314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 551cc81e-9b33-4241-871d-58c02d697a64 · outbound

This paper cites Adaptive Computation Time for Recurrent Neural Networks.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Adaptive Computation Time for Recurrent Neural Networks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation 1d555918-74a6-45c5-8ad6-02de052156e5 · outbound

This paper cites Universal Transformers,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Universal Transformers,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation 0db80d4e-fecf-4c8f-bc42-b35017df48b2 · outbound

This paper cites Depth-Adaptive Transformer,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Depth-Adaptive Transformer,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation 1c381568-52aa-49b6-9488-ba25bb48516b · outbound

This paper cites Confident Adaptive Language Modeling,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Confident Adaptive Language Modeling,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation a913da61-7762-4a13-aad3-c5f30e1f7e51 · outbound

This paper cites Mixture-of-Depths: Dynamically allocating compute in transformer-based language models.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Mixture-of-Depths: Dynamically allocating compute in transformer-based language models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Unavailable: canonical work link unavailable.

Observation d588121d-3a35-4c50-a354-ffbf391c2001 · outbound

This paper cites FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Unavailable: canonical work link unavailable.

Observation aa2a2a9a-c502-45a3-ae9a-27bc8ee74ebd · outbound

This paper cites RouteLLM: Learning to Route LLMs from Preference Data,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning RouteLLM: Learning to Route LLMs from Preference Data,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Unavailable: canonical work link unavailable.

Observation 35ed2e96-5f59-44d2-ac5c-a7afe539cb4b · outbound

This paper cites Learning Multiagent Communication with Backpropagation,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Learning Multiagent Communication with Backpropagation,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation c8efba77-711d-43bf-a186-9ad5c478dc65 · outbound

This paper cites Learning to Communicate with Deep Multi-Agent Reinforcement Learning,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Learning to Communicate with Deep Multi-Agent Reinforcement Learning,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Unavailable: canonical work link unavailable.

Observation 2325a7af-293a-4517-babc-76d4d0b31406 · outbound

This paper cites Learning Attentional Communication for Multi-Agent Cooperation,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Learning Attentional Communication for Multi-Agent Cooperation,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation d0abeed8-3df7-4e9b-9816-01a612873baa · outbound

This paper cites Individualized Controlled Continuous Communication Model for Multiagent Cooperative and Competitive Tasks,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Individualized Controlled Continuous Communication Model for Multiagent Cooperative and Competitive Tasks,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation 79034a76-c26d-4933-ada1-8cc80017e631 · outbound

This paper cites TarMAC: Targeted Multi-Agent Communication,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning TarMAC: Targeted Multi-Agent Communication,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation 8e389bc5-13fb-4400-a2b8-479906a58cbe · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation 2adc4ae9-2605-4836-9740-13e50c728df6 · outbound

This paper cites Categorical Reparameterization with Gumbel-Softmax,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Categorical Reparameterization with Gumbel-Softmax,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation c16c44db-6866-46f9-ba1c-5164d5d92851 · outbound

This paper cites Training language models to follow instructions with human feedback,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Training language models to follow instructions with human feedback,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation 71393bbe-ffa8-4d29-8e6d-c126655bc63e · outbound

This paper cites Proximal Policy Optimization Algorithms.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Proximal Policy Optimization Algorithms

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation 98fbea01-23ea-479e-90c4-1e3f439f9bd6 · outbound

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

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 33

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation acb15d91-ea7a-44b0-8ee8-a956c166a3af · outbound

This paper cites The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation f7cb2fc0-fb97-453f-9160-8ed667ee1e55 · outbound

This paper cites On Calibration of Modern Neural Networks,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning On Calibration of Modern Neural Networks,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Unavailable: canonical work link unavailable.

Observation 54d2ea48-8afb-42df-972a-32e80db37c10 · outbound

This paper cites Calibration of Pre-trained Transformers,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Calibration of Pre-trained Transformers,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation 59078031-9797-41ec-97f2-bfe1e5a4990f · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Training Verifiers to Solve Math Word Problems

Reference 37

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation 87936b66-db57-4a84-9815-22c1f10a694b · outbound

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

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning MMLU-Pro: A More Robust and Chal- lenging Multi-Task Language Understanding Benchmark,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Observation dc80fc7e-1909-460b-8ebb-28726f02369e · outbound

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

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning GPQA: A Graduate-Level Google-Proof Q&A Benchmark,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Unavailable: canonical work link unavailable.

Observation 174ff51d-5415-4a03-bce0-7b223ae5a067 · outbound

This paper cites Beyond benchmarks: Matharena as an evaluation platform for mathematics with LLMs,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Beyond benchmarks: Matharena as an evaluation platform for mathematics with LLMs,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Unavailable: canonical work link unavailable.

Observation 985b484b-80ac-4712-a9e9-bc825beb715c · outbound

This paper cites Chain-of- Thought Prompting Elicits Reasoning in Large Language Models,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Chain-of- Thought Prompting Elicits Reasoning in Large Language Models,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Unavailable: canonical work link unavailable.

Observation 096fe2c4-3a54-4ec2-89d4-0d7f26de04ad · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Y our Phone,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Phi-3 Technical Report: A Highly Capable Language Model Locally on Y our Phone,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Unavailable: canonical work link unavailable.

Observation f20e2f64-eb8a-4fc2-be10-b06c081432f4 · outbound

This paper cites The Llama 3 Herd of Models.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning The Llama 3 Herd of Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 546f589d-26b0-4f35-8c14-f693d00ceb6a · outbound

This paper cites Mixtral of Experts.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Mixtral of Experts

Reference 44

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Unavailable: canonical work link unavailable.

Observation 2849201e-50e2-433c-9bfe-3c81ec07fd34 · outbound

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

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer,

Reference 45

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8cbc323-1226-43f5-a30d-0ec833959651 · outbound

This paper cites Switch transformers: scaling to trillion parameter models with simple and eﬀicient sparsity,.

Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning Switch transformers: scaling to trillion parameter models with simple and eﬀicient sparsity,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-07-14T08:54:28.745567Z

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Unavailable: canonical work link unavailable.

Pith citing papers

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