Paper Citation Record · LEDGER

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning

As of 9 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2601.20753.

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

pith.paper-citation-record.v1
2601.20753 v4

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T07:16:24.447043Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-12T05:06:14.783929Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T05:41:23.798033Z

Reference resolution

42 of 42 outbound references displayed

verified exact0
verified fuzzy0
unresolved42
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External citation measurements

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Outbound references

Observation a876add4-94d9-46f1-b677-9c80b268a4b3 · outbound

This paper cites Deep re- inforcement learning algorithm based on graph weight multi-pointer network for solving multiobjective travel- ing salesman problem.IEEE Access, 12:179091–179103,.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Deep re- inforcement learning algorithm based on graph weight multi-pointer network for solving multiobjective travel- ing salesman problem.IEEE Access, 12:179091–179103,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:22.907938Z

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Unavailable: canonical work link unavailable.

Observation 522a511b-3764-4a57-a93d-39de75f3e344 · outbound

This paper cites [Guet al., 2022 ] Qinghua Gu, Qingsong Xu, and Xuexian Li.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning [Guet al., 2022 ] Qinghua Gu, Qingsong Xu, and Xuexian Li

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:23.187630Z

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Observation 4a2afd35-bffe-4935-9107-6d9481b131de · outbound

This paper cites Huband, P.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Huband, P

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:23.469485Z

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Observation 73c6d813-0606-429d-8c1e-e7688e240984 · outbound

This paper cites Performance comparison of nsga-ii and nsga-iii on various many-objective test prob- lems.2016 IEEE Congress on Evolutionary Computation (CEC), pages 3045–3052,.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Performance comparison of nsga-ii and nsga-iii on various many-objective test prob- lems.2016 IEEE Congress on Evolutionary Computation (CEC), pages 3045–3052,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:23.674101Z

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Observation cf143e8f-187d-4d0c-afa1-3db3df6b4ebd · outbound

This paper cites Pareto set learning for neural multi-objective combinato- rial optimization.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Pareto set learning for neural multi-objective combinato- rial optimization

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:23.830004Z

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Observation 55a1d266-76b3-411b-93a1-c6a19ca03870 · outbound

This paper cites Smooth tchebycheff scalarization for multi-objective optimization.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Smooth tchebycheff scalarization for multi-objective optimization

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:23.912103Z

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Unavailable: canonical work link unavailable.

Observation 6e7b7fb6-3b1e-4f03-9983-12d02184dd44 · outbound

This paper cites Profiling pareto front with multi-objective stein varia- tional gradient descent.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Profiling pareto front with multi-objective stein varia- tional gradient descent

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:23.997909Z

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Unavailable: canonical work link unavailable.

Observation 0e3d3234-a05d-400e-8b21-f8efa6085d6b · outbound

This paper cites Pareto set learning for multi-objective reinforcement learning.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Pareto set learning for multi-objective reinforcement learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:24.062446Z

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Observation 3cd51d8e-f59b-40cd-8d7b-93947de9583a · outbound

This paper cites Role play: Learning adaptive role-specific strategies in multi-agent interactions.Know.-Based Syst., 324(C), January.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Role play: Learning adaptive role-specific strategies in multi-agent interactions.Know.-Based Syst., 324(C), January

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:24.157846Z

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Observation d10365f2-990e-44c0-a213-75df90e6c6ee · outbound

This paper cites Multi-agent reinforcement learning for creating intelligent agents in social networks- oriented role playing games.Entertainment Computing, 54:100941,.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Multi-agent reinforcement learning for creating intelligent agents in social networks- oriented role playing games.Entertainment Computing, 54:100941,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:24.319294Z

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Observation 2e707774-5b6f-48f4-89e0-82306fbde7e8 · outbound

This paper cites Training language models to follow instructions with human feed- back.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Training language models to follow instructions with human feed- back

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:24.358863Z

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Observation 427d4606-ac11-4f9b-8f9a-640d348a24de · outbound

This paper cites Stable-baselines3: Reliable reinforcement learning implementations.Journal of Machine Learning Research, 22(268):1–8,.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Stable-baselines3: Reliable reinforcement learning implementations.Journal of Machine Learning Research, 22(268):1–8,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:24.404946Z

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Unavailable: canonical work link unavailable.

Observation 370b677d-2f12-41ba-8676-2592901e7bd1 · outbound

This paper cites Constructing complex npc behavior via multi-objective neuroevolution.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Constructing complex npc behavior via multi-objective neuroevolution

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:24.410629Z

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Observation 8a7ea8dd-7966-4677-9603-7bc40fcba3e2 · outbound

This paper cites Dynamic defender-attacker blotto game.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Dynamic defender-attacker blotto game

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:24.422912Z

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Observation 106aa7f9-d9f1-480c-a3ad-0d7fe62f85fd · outbound

This paper cites Steuer and Eng-Ung Choo.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Steuer and Eng-Ung Choo

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:24.424900Z

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Unavailable: canonical work link unavailable.

Observation 3521586c-ef20-4aba-b0c5-62bf284e6018 · outbound

This paper cites Empir- ical evaluation methods for multiobjective reinforcement learning algorithms.Mach.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Empir- ical evaluation methods for multiobjective reinforcement learning algorithms.Mach

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:24.429369Z

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Unavailable: canonical work link unavailable.

Observation ed4d876f-02f2-4486-a223-b541713a0e99 · outbound

This paper cites Prediction-guided multi-objective reinforcement learning for continuous robot control.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Prediction-guided multi-objective reinforcement learning for continuous robot control

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:24.437927Z

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Observation 17cfcb28-3879-42b3-8af3-55e6bb55f5f6 · outbound

This paper cites A generalized algorithm for multi- objective reinforcement learning and policy adaptation.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning A generalized algorithm for multi- objective reinforcement learning and policy adaptation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:24.440095Z

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Observation 98ce69e5-b8c1-4158-b86f-e22784fd22e5 · outbound

This paper cites Federated reinforcement learning for robot mo- tion planning with zero-shot generalization.Automatica, 166:111709,.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Federated reinforcement learning for robot mo- tion planning with zero-shot generalization.Automatica, 166:111709,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:24.442401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0bb0d2c-1229-4c20-939f-5c0b480e02bd · outbound

This paper cites Maximum entropy population-based training for zero-shot human-ai coordination.Proceedings of the AAAI Conference on Artificial Intelligence, 37(5):6145–6153, Jun.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Maximum entropy population-based training for zero-shot human-ai coordination.Proceedings of the AAAI Conference on Artificial Intelligence, 37(5):6145–6153, Jun

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:24.444513Z

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Observation faded5de-da6b-4e1f-9af4-0d1626b8f1f8 · outbound

This paper cites Scaling pareto-efficient decision making via of- fline multi-objective rl.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Scaling pareto-efficient decision making via of- fline multi-objective rl

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:24.447043Z

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Observation a6654283-b7bc-4612-bdc2-65b07539f193 · outbound

This paper cites Gymnasium: A Standard Interface for Reinforcement Learning Environments.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Gymnasium: A Standard Interface for Reinforcement Learning Environments

Reference 1983

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:24.426964Z

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Observation c7db0a1b-3f6e-40ed-ad97-67b635f50714 · outbound

This paper cites an unresolved cited work.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Unresolved cited work

Reference 1998

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:22.345248Z

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Unavailable: canonical work link unavailable.

Observation 54966e1c-fb4a-4840-8e2a-e3c3379d4d99 · outbound

This paper cites Multiple-gradient descent algorithm (mgda) for multiobjective optimization.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Multiple-gradient descent algorithm (mgda) for multiobjective optimization

Reference 2005

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:22.600953Z

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Observation f555d3b8-de8c-4ad7-aeeb-e94564fb5a7d · outbound

This paper cites A novel pareto-optimal ranking method for comparing multi- objective optimization algorithms,.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning A novel pareto-optimal ranking method for comparing multi- objective optimization algorithms,

Reference 2006

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:23.565504Z

Source-reported events for the cited work

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Observation ffcea085-5d3d-4de8-a9ea-fcecd52c7989 · outbound

This paper cites Evolving multi-modal behavior in npcs.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Evolving multi-modal behavior in npcs

Reference 2008

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:24.413170Z

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Observation 83202b15-cb91-41e3-a64a-cd16d84ae533 · outbound

This paper cites Proximal Policy Optimization Algorithms.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Proximal Policy Optimization Algorithms

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:24.415292Z

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Observation e91e370e-5cdf-4613-b214-8be72439ce59 · outbound

This paper cites Drugan, and Ann Now ´e.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Drugan, and Ann Now ´e

Reference 2011

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:24.431491Z

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Observation db1c65e2-b45d-4e74-be0b-1e808f4bee35 · outbound

This paper cites Alegre, Ann Now´e, Ana L.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Alegre, Ann Now´e, Ana L

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:22.735421Z

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Observation cd838726-386f-4764-a5d9-a9c39039bbad · outbound

This paper cites Graph attention networks.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Graph attention networks

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:24.433879Z

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Observation 355bea24-fb58-4113-bb88-8da7872e5f28 · outbound

This paper cites an unresolved cited work.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Unresolved cited work

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:22.511330Z

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Observation 7efd88dd-e4cf-4a39-b0df-6fd6254aced9 · outbound

This paper cites an unresolved cited work.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Unresolved cited work

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:23.761715Z

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Observation fff135a3-cf34-430b-afae-58726948b4ca · outbound

This paper cites A Survey of Deep Reinforcement Learning in Video Games.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning A Survey of Deep Reinforcement Learning in Video Games

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:24.418148Z

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Observation 86c61b52-c919-448a-a0f6-59157451e6d2 · outbound

This paper cites Rlhgnn: Reinforce- ment learning-driven heterogeneous graph neural network for next activity prediction in business processes,.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Rlhgnn: Reinforce- ment learning-driven heterogeneous graph neural network for next activity prediction in business processes,

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:24.435952Z

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Observation 05be720a-d8bf-4321-9e13-327c70b77976 · outbound

This paper cites Grapheon rl: A graph neural network and reinforcement learning framework for constraint and data- aware workflow mapping and scheduling in heterogeneous hpc systems.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Grapheon rl: A graph neural network and reinforcement learning framework for constraint and data- aware workflow mapping and scheduling in heterogeneous hpc systems

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:24.420754Z

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Observation 720b7142-af1f-4a50-adcd-bb78ae5c0d07 · outbound

This paper cites A Review of the Deep Sea Treasure problem as a Multi-Objective Reinforcement Learning Benchmark.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning A Review of the Deep Sea Treasure problem as a Multi-Objective Reinforcement Learning Benchmark

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:22.109036Z

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Observation e6f16302-d6a0-403e-93cb-cadd603a9be8 · outbound

This paper cites an unresolved cited work.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Unresolved cited work

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:22.251222Z

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Observation d24440a7-dc84-4103-b41d-6116f2f800de · outbound

This paper cites Heterogeneous Graph Transformer.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Heterogeneous Graph Transformer

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:23.344548Z

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Observation 1a066fcf-9907-49f4-bf57-15dc4b2667af · outbound

This paper cites Blank and K.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Blank and K

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:22.041021Z

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Unavailable: canonical work link unavailable.

Observation 2541a930-ad9b-4bea-b14b-0111582469ea · outbound

This paper cites Pick your battles: Interaction graphs as population-level objec- tives for strategic diversity.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Pick your battles: Interaction graphs as population-level objec- tives for strategic diversity

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:23.085699Z

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Unavailable: canonical work link unavailable.

Observation 4fea5578-9688-4f6b-9a03-f225d8aa9c27 · outbound

This paper cites an unresolved cited work.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Unresolved cited work

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:21.890537Z

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Observation 6b83132a-13bb-4797-9f2e-69291e465f50 · outbound

This paper cites Precise and dexterous robotic manipula- tion via human-in-the-loop reinforcement learning.Sci- ence Robotics, 10(105):eads5033,.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning Precise and dexterous robotic manipula- tion via human-in-the-loop reinforcement learning.Sci- ence Robotics, 10(105):eads5033,

Reference 2026

Resolution: unresolved; no resolver link, observed 2026-08-03T07:16:24.213104Z

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Pith citing papers

Observation 94b06b96-8c3d-4907-a7d9-3e646fccc147 · inbound

Controllability in preference-conditioned multi-objective reinforcement learning cites this paper.

Controllability in preference-conditioned multi-objective reinforcement learning GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-08T02:18:10.794709Z

Source-reported events for the cited work

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