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Paper Citation Record · LEDGER

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination

As of 15 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2607.03473.

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

pith.paper-citation-record.v1
2607.03473 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-12T02:15:17.079975Z

measured 29 of 29 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

29 of 29 outbound references displayed

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Outbound references

Observation 8ef9cac5-cc4d-469d-8d94-ee0a9e5c3b75 · outbound

This paper cites Multi-agent reinforcement learning for autonomous vehicles: A survey.Autonomous Intelligent Systems, 2 (1):27, 2022.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination Multi-agent reinforcement learning for autonomous vehicles: A survey.Autonomous Intelligent Systems, 2 (1):27, 2022

Reference 1

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Observation 849882da-9f36-472b-84c8-047a78591a04 · outbound

This paper cites an unresolved cited work.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination Unresolved cited work

Reference 2

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Observation 1c0a4742-745f-4c40-abba-3fe79e4f57b7 · outbound

This paper cites Multi-agent reinforcement learning as a rehearsal for decentralized planning.Neurocomputing, 190:82–94, 2016.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination Multi-agent reinforcement learning as a rehearsal for decentralized planning.Neurocomputing, 190:82–94, 2016

Reference 3

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Observation 83fb0e7e-0f24-4fb2-a6f3-7e8ccba2259a · outbound

This paper cites Monotonic value function factorisation for deep multi-agent reinforcement learning.Journal of Machine Learning Research, 21(178):1–51, 2020.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination Monotonic value function factorisation for deep multi-agent reinforcement learning.Journal of Machine Learning Research, 21(178):1–51, 2020

Reference 4

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Observation ee7897d9-a37a-45f6-a64f-d0af49b193a6 · outbound

This paper cites Multi-agent incentive communication via decentralized teammate modeling.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination Multi-agent incentive communication via decentralized teammate modeling

Reference 5

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Observation e7233185-c472-4ee3-8b68-3e4301a7960e · outbound

This paper cites The surprising effectiveness of ppo in cooperative multi-agent games.Advances in neural information processing systems, 35:24611–24624, 2022.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination The surprising effectiveness of ppo in cooperative multi-agent games.Advances in neural information processing systems, 35:24611–24624, 2022

Reference 6

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Observation bed3de85-08ad-4a64-b280-511fa7f94402 · outbound

This paper cites Multi-agent actor-critic for mixed cooperative-competitive environments.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination Multi-agent actor-critic for mixed cooperative-competitive environments

Reference 7

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Observation bac78f20-d2ef-4e13-a0cf-163952b9e686 · outbound

This paper cites Learning to communicate with deep multi-agent reinforcement learning.Advances in neural information processing systems, 29, 2016.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination Learning to communicate with deep multi-agent reinforcement learning.Advances in neural information processing systems, 29, 2016

Reference 8

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Observation 6397cd9b-869e-4353-a443-a941f92b443d · outbound

This paper cites Learning multiagent communication with backpropa- gation.Advances in neural information processing systems, 29, 2016.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination Learning multiagent communication with backpropa- gation.Advances in neural information processing systems, 29, 2016

Reference 9

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Observation 7d8e6884-09e2-48ed-96d9-266f034ec5ee · outbound

This paper cites Learning when to communicate at scale in multiagent cooperative and competitive tasks.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination Learning when to communicate at scale in multiagent cooperative and competitive tasks

Reference 10

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Observation 5011cc1f-05b6-47de-9226-b7bbbbc04f90 · outbound

This paper cites Efficient multi-agent communication via self-supervised information aggregation.Advances in Neural Information Processing Systems, 35:1020–1033, 2022.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination Efficient multi-agent communication via self-supervised information aggregation.Advances in Neural Information Processing Systems, 35:1020–1033, 2022

Reference 11

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Observation ec3ff897-3df1-432f-936f-b8fceca01548 · outbound

This paper cites T2mac: Targeted and trusted multi-agent communication through selective engagement and evidence-driven integration.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination T2mac: Targeted and trusted multi-agent communication through selective engagement and evidence-driven integration

Reference 12

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Observation 1bc60dfe-38a4-439b-bc69-cad298300eb5 · outbound

This paper cites Learning Nearly Decomposable Value Functions Via Communication Minimization.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination Learning Nearly Decomposable Value Functions Via Communication Minimization

Reference 13

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Observation 09800af9-affd-4a16-b8a9-704df264eb4a · outbound

This paper cites Learning efficient multi-agent communication: An information bottleneck approach.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination Learning efficient multi-agent communication: An information bottleneck approach

Reference 14

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Observation 3d29be34-18d8-455b-b1f5-fa0021df469b · outbound

This paper cites Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey

Reference 15

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Observation 1b82fd81-a5aa-460d-b97a-c86b3ec3c674 · outbound

This paper cites Efficient multi-agent communication via shapley message value.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination Efficient multi-agent communication via shapley message value

Reference 16

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Observation 289b044f-c6a8-45fd-8fd6-0b0ca7d3349d · outbound

This paper cites A value for n-person games.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination A value for n-person games

Reference 17

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Observation accb3c0f-7a18-4f02-93b7-9a2da24ad25f · outbound

This paper cites Dop: Off- policy multi-agent decomposed policy gradients.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination Dop: Off- policy multi-agent decomposed policy gradients

Reference 18

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Observation 5581224f-578d-4dfd-b409-6843865abd1e · outbound

This paper cites Towards making systems forget with machine unlearning.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination Towards making systems forget with machine unlearning

Reference 19

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Observation b212d9c4-80ba-4af5-8af5-2fedb16c5a8d · outbound

This paper cites Eternal sunshine of the spotless net: Selective forgetting in deep networks.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination Eternal sunshine of the spotless net: Selective forgetting in deep networks

Reference 20

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Observation 3f048548-be12-4c9d-b03e-56eb8bb59859 · outbound

This paper cites A survey of machine unlearning.ACM Transactions on Intelligent Systems and Technology, 16(5):1–46, 2025.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination A survey of machine unlearning.ACM Transactions on Intelligent Systems and Technology, 16(5):1–46, 2025

Reference 21

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Observation 9fe177db-5ad1-48f7-8817-20fe29f0892f · outbound

This paper cites Smacv2: An improved benchmark for cooperative multi-agent reinforcement learning.Advances in Neural Information Processing Systems, 36:37567–37593, 2023.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination Smacv2: An improved benchmark for cooperative multi-agent reinforcement learning.Advances in Neural Information Processing Systems, 36:37567–37593, 2023

Reference 22

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Observation 9cb369d8-820c-4f37-939d-063f19639ed1 · outbound

This paper cites Tarmac: Targeted multi-agent communication.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination Tarmac: Targeted multi-agent communication

Reference 23

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Observation c95abbc4-b1ab-4f7b-ac38-71685df78c5c · outbound

This paper cites Towards true lossless sparse communication in multi-agent systems.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination Towards true lossless sparse communication in multi-agent systems

Reference 24

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Observation 7477245e-cfc7-410c-ac1d-3e1ddf8c2443 · outbound

This paper cites Rescom: Reward- shaped curriculum for efficient multi-agent communication learning.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination Rescom: Reward- shaped curriculum for efficient multi-agent communication learning

Reference 25

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Observation 8b9ea464-7017-41f7-acb4-309681c64e2a · outbound

This paper cites Counterfactual multi-agent policy gradients.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination Counterfactual multi-agent policy gradients

Reference 26

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Observation 71e7966f-1acc-407b-81b4-5085e449c57d · outbound

This paper cites Trust region policy optimisation in multi-agent reinforcement learning.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination Trust region policy optimisation in multi-agent reinforcement learning

Reference 27

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Observation ea45f994-0eeb-4786-97e1-617e52ebf61b · outbound

This paper cites Rgmcomm: Return gap minimization via discrete communications in multi-agent reinforcement learning.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination Rgmcomm: Return gap minimization via discrete communications in multi-agent reinforcement learning

Reference 28

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Observation cf92c0e7-4391-40aa-8a3d-442ae9554ec6 · outbound

This paper cites surrounded and reflect.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination surrounded and reflect

Reference 29

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Pith citing papers

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