Paper Citation Record · LEDGER

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems

As of 10 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2607.03324.

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

pith.paper-citation-record.v1
2607.03324 v2

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-12T03:16:38.965369Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

32 of 32 outbound references displayed

verified exact0
verified fuzzy0
unresolved32
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 91a76f49-fa9c-4cf7-9d52-826d026e2be5 · outbound

This paper cites Energy and policy considerations for modern deep learning research,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Energy and policy considerations for modern deep learning research,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8152e502-8f9e-4b54-a2a4-5c1630477071 · outbound

This paper cites Estimating the carbon footprint of BLOOM, a 176B parameter language model,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Estimating the carbon footprint of BLOOM, a 176B parameter language model,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f62a24f2-80ac-476e-959b-0e6e9cc0f4db · outbound

This paper cites Energy and AI 2024,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Energy and AI 2024,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89aea528-f684-44b3-b83a-9dad48157f6d · outbound

This paper cites Electricity Demand and Grid Impacts of AI Data Centers: Challenges and Prospects.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Electricity Demand and Grid Impacts of AI Data Centers: Challenges and Prospects

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eba52267-57d5-46a3-b7e2-efd460d28dd6 · outbound

This paper cites Computation- power coupled modeling for IDCs and collaborative optimiza- tion in ADNs,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Computation- power coupled modeling for IDCs and collaborative optimiza- tion in ADNs,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a589d85c-f80a-470e-8dcf-30ea2e1a949e · outbound

This paper cites Incentive- compatible demand response for spatially coupled Internet data centers in electricity markets,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Incentive- compatible demand response for spatially coupled Internet data centers in electricity markets,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eec735f7-d0d6-49ad-8a97-cfff6ceda706 · outbound

This paper cites Game-based optimization method for geo-distributed data centers under customer directrix load demand response mechanism,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Game-based optimization method for geo-distributed data centers under customer directrix load demand response mechanism,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 428d7f77-5125-4b10-9787-daba222957ac · outbound

This paper cites Supply restoration of data centers in flexible distribution networks with spatial-temporal regulation,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Supply restoration of data centers in flexible distribution networks with spatial-temporal regulation,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fadfef90-0eff-4115-b1a2-8013d5fa108e · outbound

This paper cites Synergising hierarchical data centers and power networks: A privacy-preserving approach,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Synergising hierarchical data centers and power networks: A privacy-preserving approach,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d820040-c708-4fd5-bb7c-e5f963cdb005 · outbound

This paper cites Learning-enabled adaptive power capping scheme for cloud data centers,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Learning-enabled adaptive power capping scheme for cloud data centers,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5a08382-00a8-4065-81d5-10667435946c · outbound

This paper cites Let’s wait awhile: How temporal workload shifting can reduce carbon emissions in the cloud,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Let’s wait awhile: How temporal workload shifting can reduce carbon emissions in the cloud,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c16a044-ae32-4a77-a414-55068e0857fa · outbound

This paper cites Carbon-aware computing for datacen- ters,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Carbon-aware computing for datacen- ters,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a63cd434-3858-4aef-862d-0b65509cf461 · outbound

This paper cites Carbon explorer: A holistic framework for designing carbon aware datacenters,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Carbon explorer: A holistic framework for designing carbon aware datacenters,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1977822-38df-4e48-9b38-0535a0401d42 · outbound

This paper cites Sustainable AIGC workload scheduling of geo-distributed data centers: A multi-agent reinforcement learning approach,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Sustainable AIGC workload scheduling of geo-distributed data centers: A multi-agent reinforcement learning approach,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03fe3f05-66c4-4b4b-8e59-e0ab97c85a61 · outbound

This paper cites Game-theoretic deep reinforcement learning to minimize carbon emissions and energy costs for AI inference workloads in geo-distributed data centers,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Game-theoretic deep reinforcement learning to minimize carbon emissions and energy costs for AI inference workloads in geo-distributed data centers,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 643e5aab-b68b-4e77-af5c-bb05f3095879 · outbound

This paper cites DCcluster-Opt: Benchmarking dynamic multi-objective optimization for geo-distributed data center workloads,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems DCcluster-Opt: Benchmarking dynamic multi-objective optimization for geo-distributed data center workloads,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a14e776-87ac-449e-a90d-da9df4f59e9d · outbound

This paper cites Hierarchical Reinforcement Learning for Power Network Topology Control.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Hierarchical Reinforcement Learning for Power Network Topology Control

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23a17536-fdd4-4b6a-89ec-564988c10c4a · outbound

This paper cites Distributed hierarchical deep reinforcement learning for large-scale grid emergency control,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Distributed hierarchical deep reinforcement learning for large-scale grid emergency control,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18681775-99be-4c16-8d14-0684a4f6382a · outbound

This paper cites Distributed online dispatch for microgrids using hierarchical reinforcement learning embedded with operation knowledge,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Distributed online dispatch for microgrids using hierarchical reinforcement learning embedded with operation knowledge,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e608d31-5bbb-4b73-b3d2-b8a9b1a7a077 · outbound

This paper cites Multi- agent hierarchical deep reinforcement learning for HV AC control with flexible DERs,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Multi- agent hierarchical deep reinforcement learning for HV AC control with flexible DERs,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34b75457-99ce-421c-9ce2-d5dd5fcc8282 · outbound

This paper cites Carbon emission flow from generation to demand: A network-based model,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Carbon emission flow from generation to demand: A network-based model,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 893ba28b-effb-4bb0-8f2f-97538cc30193 · outbound

This paper cites Carbon emission flow oriented tri-level planning of integrated electricity–hydrogen– gas system with hydrogen vehicles,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Carbon emission flow oriented tri-level planning of integrated electricity–hydrogen– gas system with hydrogen vehicles,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d01fdd9-95a9-48f5-b9bd-a85de9698425 · outbound

This paper cites Carbon- aware peer-to-peer energy trading in an unbalanced distribution network via a nash equilibrium discovery deep reinforcement learning approach,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Carbon- aware peer-to-peer energy trading in an unbalanced distribution network via a nash equilibrium discovery deep reinforcement learning approach,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bf616fa-34c8-4279-8445-952cfae796f4 · outbound

This paper cites An augmented lagrangian- based safe reinforcement learning algorithm for carbon-oriented optimal scheduling of EV aggregators,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems An augmented lagrangian- based safe reinforcement learning algorithm for carbon-oriented optimal scheduling of EV aggregators,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8fbcbae-2f5b-4597-8780-688ab2f070da · outbound

This paper cites Multi-Agent Reinforcement Learning is a Sequence Modeling Problem.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Multi-Agent Reinforcement Learning is a Sequence Modeling Problem

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b127298-7ac6-4ee0-9635-b13db47fd84e · outbound

This paper cites Settling the variance of multi-agent policy gradients,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Settling the variance of multi-agent policy gradients,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 569fff3e-3047-4328-b0a6-20b9a45c998a · outbound

This paper cites High-Dimensional Continuous Control Using Generalized Advantage Estimation.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems High-Dimensional Continuous Control Using Generalized Advantage Estimation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a81da27-804b-4445-995c-bbdef4093a6a · outbound

This paper cites Multi-agent reinforcement learning is a sequence modeling problem,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Multi-agent reinforcement learning is a sequence modeling problem,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1df001e-2630-4d8f-9a15-096324a56632 · outbound

This paper cites Carbon-aware optimal power flow,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Carbon-aware optimal power flow,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2a15fd0-e300-48fd-bc22-57e2c0bf1fef · outbound

This paper cites SustainDC: Benchmarking for sustainable data center control,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems SustainDC: Benchmarking for sustainable data center control,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74a04af3-612f-40a5-8c6d-f9aa26671652 · outbound

This paper cites Radial distribution test feeders,.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Radial distribution test feeders,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd6e2980-7f41-443b-8c07-642918c8aa5c · outbound

This paper cites Scaling Laws for Neural Language Models.

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems Scaling Laws for Neural Language Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-12T03:16:38.965369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.