Paper Citation Record · LEDGER

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits

As of 14 August 2026, this Paper Citation Record lists 67 of 67 outbound references and 1 inbound Pith citation observation for arXiv:2506.18627.

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

pith.paper-citation-record.v1
2506.18627 v1

Coverage vector

measured 67 of 67 reference resolution

Typed states for the displayed outbound observations.

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measured 68 of 68 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T17:57:54.792899Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T18:02:26.956725Z

Reference resolution

67 of 67 outbound references displayed

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External citation measurements

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Outbound references

Observation e065f7c0-fce9-4d93-88e7-23627ae3ae9b · outbound

This paper cites Optuna: A next-generation hyperparameter optimization framework.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Optuna: A next-generation hyperparameter optimization framework

Reference 1

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Observation 96425405-4db0-4f6d-bca0-bb5d33d3131c · outbound

This paper cites Sgd generalizes better than gd (and regularization doesn’t help).

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Sgd generalizes better than gd (and regularization doesn’t help)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:45.964819Z

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Observation f9b53fae-08ba-4c29-91ec-d27eae9b66cf · outbound

This paper cites Inverse design of nanophotonic devices with structural integrity.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Inverse design of nanophotonic devices with structural integrity

Reference 3

Resolution: verified exact; doi, observed 2026-08-06T23:21:31.054749Z

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Observation 10186bd9-92d8-4858-aba6-2cdcbb960cea · outbound

This paper cites Universal design of waveguide bends in silicon-on-insulator photonics platform.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Universal design of waveguide bends in silicon-on-insulator photonics platform

Reference 4

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Observation 5c3caaf5-49fc-4ecf-83d7-6fce965e0be2 · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 5

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Observation caea5e40-bdaf-4a95-977b-e87f51b22334 · outbound

This paper cites Crossq: Batch normalization in deep reinforcement learning for greater sample efficiency and simplicity.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Crossq: Batch normalization in deep reinforcement learning for greater sample efficiency and simplicity

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:45.434554Z

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Observation 7edfee36-1b52-4c23-9a28-3defc1c61603 · outbound

This paper cites Bogdanov, Sergey Makarov, and Yuri Kivshar.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Bogdanov, Sergey Makarov, and Yuri Kivshar

Reference 7

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Observation 2759817e-99d5-426f-9fa0-2f13f24bae29 · outbound

This paper cites JAX : Composable transformations of Python + NumPy programs, 2018.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits JAX : Composable transformations of Python + NumPy programs, 2018

Reference 8

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Observation 35121fc1-57d0-4284-8f72-0e2d70eba1e9 · outbound

This paper cites A universal approach to nanophotonic inverse design through reinforcement learning.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits A universal approach to nanophotonic inverse design through reinforcement learning

Reference 9

Resolution: verified exact; doi, observed 2026-08-06T23:21:30.364747Z

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Observation 3adbf82c-038c-4d04-9374-62e9aa9cd727 · outbound

This paper cites Yang, S.B.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Yang, S.B

Reference 10

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Observation bddf4583-0a5f-4d30-991d-637422002618 · outbound

This paper cites Randomized ensembled double q-learning: Learning fast without a model.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Randomized ensembled double q-learning: Learning fast without a model

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:44.524759Z

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Observation 7a9a86bb-cec3-4ae3-a691-68ba02f6f199 · outbound

This paper cites Soft Actor-Critic for Discrete Action Settings.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Soft Actor-Critic for Discrete Action Settings

Reference 12

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Observation 7f2cb9a0-67ab-4eaa-8627-a29688ec9d91 · outbound

This paper cites Friedrichs, and Hans Lewy.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Friedrichs, and Hans Lewy

Reference 13

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Observation 10e44326-8fc5-4b40-84b8-3c1f2a12265b · outbound

This paper cites Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?

Reference 14

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Observation 41e6c40e-4fe9-4ac8-a887-21d05be283e0 · outbound

This paper cites Trends in ai inference energy consumption: Beyond the performance-vs-parameter laws of deep learning.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Trends in ai inference energy consumption: Beyond the performance-vs-parameter laws of deep learning

Reference 15

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Observation 73d74a28-8387-4c28-88b5-fb109f9735f7 · outbound

This paper cites Inverse-designed diamond photonics.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Inverse-designed diamond photonics

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:43.677725Z

Source-reported events for the cited work

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Observation 57f593c5-feed-42f1-85c6-b9be6d7b991d · outbound

This paper cites Incorporating Nesterov Momentum into Adam.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Incorporating Nesterov Momentum into Adam

Reference 17

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Observation b01b532d-30b0-4f78-8542-821bccebe984 · outbound

This paper cites Tidy3d: hardware-accelerated electromagnetic solver for fast simulations at scale.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Tidy3d: hardware-accelerated electromagnetic solver for fast simulations at scale

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:43.015197Z

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Observation 4ff1da92-327a-4cb2-ab45-38e1e005ac74 · outbound

This paper cites Counterfactual multi-agent policy gradients.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Counterfactual multi-agent policy gradients

Reference 19

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This paper cites Pygad: An intuitive genetic algorithm python library.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Pygad: An intuitive genetic algorithm python library

Reference 20

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Observation 5ee41b61-eef2-458c-be3d-1f0e5c8c0406 · outbound

This paper cites Mathematical games.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Mathematical games

Reference 21

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Observation 3a987fe8-71ce-4c67-9089-fe79ed93ae6a · outbound

This paper cites Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 22

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Observation 1a00cfd9-1473-4563-9101-8c99e23141c8 · outbound

This paper cites The next generation of deep learning hardware: Analog computing.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits The next generation of deep learning hardware: Analog computing

Reference 23

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Observation f236cc1f-7c6a-494d-95a3-6f284a706292 · outbound

This paper cites Metal-assisted chemical etching of silicon and nanotechnology applications.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Metal-assisted chemical etching of silicon and nanotechnology applications

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:41.754754Z

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Observation 1c237a62-3fad-4a59-a29f-ea9a9f0841f5 · outbound

This paper cites Rance, Gustavo F.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Rance, Gustavo F

Reference 25

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Observation f3416355-63c0-4409-adb7-07c28a1963e1 · outbound

This paper cites Forward-mode differentiation of maxwell’s equations.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Forward-mode differentiation of maxwell’s equations

Reference 26

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Observation 9fd95de7-bbe6-4f7c-aea3-3c81e0d26fa7 · outbound

This paper cites Focused ion beam machining of silicon.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Focused ion beam machining of silicon

Reference 27

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Observation b740a246-5ed6-4a99-bc18-7c24b66d016c · outbound

This paper cites A reinforcement learning method for optical thin-film design.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits A reinforcement learning method for optical thin-film design

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:40.603844Z

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Observation 490bd285-e511-4565-9031-9b1307bd1212 · outbound

This paper cites Otf gym: A set of reinforcement learning environment of layered optical thin film inverse design.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Otf gym: A set of reinforcement learning environment of layered optical thin film inverse design

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:40.339610Z

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Observation 375fce24-9d45-4120-a6e0-f285b56a0bf8 · outbound

This paper cites Evolutionary Algorithms, pp.\ 49--71.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Evolutionary Algorithms, pp.\ 49--71

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:40.114894Z

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Observation a5d7493d-79ed-472e-8838-13317c51bfc9 · outbound

This paper cites Numerical solution of initial boundary value problems involving maxwell's equations in isotropic media.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Numerical solution of initial boundary value problems involving maxwell's equations in isotropic media

Reference 31

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Observation 5b58707e-3116-4fb3-927f-8cbf9524469f · outbound

This paper cites Optical computing: Status and perspectives.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Optical computing: Status and perspectives

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:39.650336Z

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Observation 19fa1721-6cb2-4365-9311-dc69d8699fb1 · outbound

This paper cites Kingma and Jimmy Ba.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Kingma and Jimmy Ba

Reference 33

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Observation 2f22f79b-1d41-4287-b075-127c29fd2f00 · outbound

This paper cites Deep reinforcement learning empowers automated inverse design and optimization of photonic crystals for nanoscale laser cavities.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Deep reinforcement learning empowers automated inverse design and optimization of photonic crystals for nanoscale laser cavities

Reference 34

Resolution: verified exact; doi, observed 2026-08-06T23:21:30.005518Z

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Observation 41675a91-8b33-49c4-8a81-38286cbd938d · outbound

This paper cites SGDR: stochastic gradient descent with warm restarts.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits SGDR: stochastic gradient descent with warm restarts

Reference 35

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Observation 7e172b08-179b-4850-b6f4-534e5d961595 · outbound

This paper cites Merging automatic differentiation and the adjoint method for photonic inverse design.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Merging automatic differentiation and the adjoint method for photonic inverse design

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:39.304717Z

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Observation 4a6fb3a7-0bce-45e4-b6a0-0b938821fee8 · outbound

This paper cites Mastering zero-shot interactions in cooperative and competitive simultaneous games.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Mastering zero-shot interactions in cooperative and competitive simultaneous games

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:39.093437Z

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Observation c78f88fe-bc6d-4a0f-a324-841b7c3ed5d2 · outbound

This paper cites A flexible framework for large-scale fdtd simulations: open-source inverse design for 3d nanostructures.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits A flexible framework for large-scale fdtd simulations: open-source inverse design for 3d nanostructures

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:38.856069Z

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Observation 0f960f17-a948-4a7b-8bb6-aeee758b6c81 · outbound

This paper cites Multifunctional 2.5d metastructures enabled by adjoint optimization.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Multifunctional 2.5d metastructures enabled by adjoint optimization

Reference 39

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Observation 442c6a88-f1e9-4dc2-8721-e5a239033f77 · outbound

This paper cites Limits on fundamental limits to computation.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Limits on fundamental limits to computation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:38.620182Z

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Observation 662bf554-72c5-4a3f-9d14-f28eb4e7e6c5 · outbound

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Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Unresolved cited work

Reference 41

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Observation 392cd458-6965-48e5-96fd-1062029638d7 · outbound

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Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Unresolved cited work

Reference 42

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Observation e7a0cefc-3648-455f-984c-0123226db4ef · outbound

This paper cites ma-n 400 and ma-n 1400 - negative tone photoresists.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits ma-n 400 and ma-n 1400 - negative tone photoresists

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:37.837557Z

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Observation 8843a8f1-f62d-42de-a5a7-c63dc4b40879 · outbound

This paper cites Piggott, Weiliang Jin, Jelena Vuckovi \'c , and Alejandro W.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Piggott, Weiliang Jin, Jelena Vuckovi \'c , and Alejandro W

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:37.666624Z

Source-reported events for the cited work

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Observation 26cfe23f-fb06-4e1d-a75b-f8f911e3a91f · outbound

This paper cites The primacy bias in deep reinforcement learning.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits The primacy bias in deep reinforcement learning

Reference 45

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Observation f3267312-8816-4458-a524-4380575984e7 · outbound

This paper cites Two-photon polymerization: Fundamentals, materials, and chemical modification strategies.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Two-photon polymerization: Fundamentals, materials, and chemical modification strategies

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:37.468030Z

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Observation d6c29e28-6ca3-4b79-b4d6-d32d2cb87872 · outbound

This paper cites Jung, Juho Park, Dongjin Seo, Yongha Kim, Chanhyung Park, Chan Y.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Jung, Juho Park, Dongjin Seo, Yongha Kim, Chanhyung Park, Chan Y

Reference 47

Resolution: verified exact; doi, observed 2026-08-06T23:21:28.873549Z

Source-reported events for the cited work

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Observation 80669957-1eaf-4de9-aef0-88de12f0ff3e · outbound

This paper cites Alan Roden and Stephen D.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Alan Roden and Stephen D

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:37.171449Z

Source-reported events for the cited work

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Observation c63279ee-daaa-47ea-a113-e7e029601fbe · outbound

This paper cites JaxMARL: Multi-Agent RL Environments and Algorithms in JAX.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:23.594750Z

Source-reported events for the cited work

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Observation 8d04c26a-3f3c-4180-8d7e-2214c1f9a988 · outbound

This paper cites Learned fourier bases for deep set feature extractors in automotive reinforcement learning.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Learned fourier bases for deep set feature extractors in automotive reinforcement learning

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:36.929715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 89fe521c-d280-42f5-97f1-d88a06788f13 · outbound

This paper cites Explainable reinforcement learning via dynamic mixture policies.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Explainable reinforcement learning via dynamic mixture policies

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:36.684751Z

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Observation f34dfc02-cde7-499c-a46e-0f49f4047a45 · outbound

This paper cites Quantized inverse design for photonic integrated circuits.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Quantized inverse design for photonic integrated circuits

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:36.435624Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0265f35b-00dd-4ef2-a3fb-521f31cd0200 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Proximal Policy Optimization Algorithms

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:24.102690Z

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Observation da5ee9e3-8675-4bc7-8c1a-1d88865615f4 · outbound

This paper cites Park, and Min Seok Jang.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Park, and Min Seok Jang

Reference 54

Resolution: verified exact; doi, observed 2026-08-06T23:21:28.065063Z

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Observation 5dcf3288-d9cd-4990-a4f5-f43bb90ca369 · outbound

This paper cites Deep transfer reinforcement learning in nanophotonics: A multi-objective inverse design approach.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Deep transfer reinforcement learning in nanophotonics: A multi-objective inverse design approach

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:36.134831Z

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Observation 42f86998-25bb-4bb3-a4ee-55213daba23d · outbound

This paper cites Snyder and J.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Snyder and J

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:35.944082Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a9fa58a5-8c8b-45ab-a2a2-403bd303bcd0 · outbound

This paper cites an unresolved cited work.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:21:35.621026Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 777e6849-d97d-49a5-86c2-f0dbb6f099f0 · outbound

This paper cites an unresolved cited work.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Unresolved cited work

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:25.735810Z

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Observation f033ce82-df37-41b9-a311-cffa1d5b63e1 · outbound

This paper cites Multi-agent reinforcement learning: Independent vs.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Multi-agent reinforcement learning: Independent vs

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:35.314746Z

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Observation 8b463e71-391c-4cdf-818a-fdbcdeb66e62 · outbound

This paper cites Time reversal differentiation of fdtd for photonic inverse design.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Time reversal differentiation of fdtd for photonic inverse design

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:35.085460Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a5fc25f7-0fdf-491b-891b-7d4ea1c14392 · outbound

This paper cites Attention is all you need.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Attention is all you need

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:26.025050Z

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Observation b218e02e-3302-45f3-961f-e2646e8134d5 · outbound

This paper cites Munchausen reinforcement learning.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Munchausen reinforcement learning

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:34.645277Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a529a61e-fa9c-4209-8623-a580e25ffe91 · outbound

This paper cites Reinforcement learning for photonic component design.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Reinforcement learning for photonic component design

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:34.222316Z

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Observation 9bc9c929-efd1-4a26-ac50-c4f00312c9a7 · outbound

This paper cites Overcoming the spectral bias of neural value approximation.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Overcoming the spectral bias of neural value approximation

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:33.434743Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5656b20b-cf1c-4798-bb8e-32b96b43fe03 · outbound

This paper cites The surprising effectiveness of ppo in cooperative multi-agent games.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits The surprising effectiveness of ppo in cooperative multi-agent games

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:33.144823Z

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Observation a1951c10-ffc0-4556-a354-5cecdb58a6ef · outbound

This paper cites Inverse design of high-q topological corner states nanocavities based on deep reinforcement learning.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits Inverse design of high-q topological corner states nanocavities based on deep reinforcement learning

Reference 66

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T23:21:31.402772Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e995893b-8e20-417c-8398-5a7267e5c479 · outbound

This paper cites write newline.

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits write newline

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:27.259057Z

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Pith citing papers

Observation c9b4d8c9-44eb-49d7-8992-e72772a04747 · inbound

Autonomous agentic design for photonics cites this paper.

Autonomous agentic design for photonics Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-06-28T18:02:26.958424Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.