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Paper Citation Record · LEDGER

RecoWorld: Building Simulated Environments for Agentic Recommender Systems

As of 19 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 9 inbound Pith citation observations for arXiv:2509.10397.

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

pith.paper-citation-record.v1
2509.10397 v3

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T17:56:38.919605Z

measured 66 of 66 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:19:09.835936Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-10T12:15:01.137692Z

Reference resolution

57 of 57 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved57
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 41e5b18c-859f-4499-9614-c48e46ee7a4f · outbound

This paper cites write newline.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems write newline

Reference 1

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:56:38.714395Z digest=sha256:5112e6bb10fb33a19c7bcd89d79cb7916f518ab1f423c7cac25b58843ce90867

Observation 5ce0c9d7-bab4-4be6-83ce-337050d42f36 · outbound

This paper cites LLM Social Simulations Are a Promising Research Method.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems LLM Social Simulations Are a Promising Research Method

Reference 2

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source=arxiv_source observed=2026-08-04T17:56:38.720457Z digest=sha256:11d9be0722ca370836eaa35456b4669f5c2c6f9bafb296e6f79047379dc20e61

Observation ec869160-4ff5-48c7-975a-a6d08106738d · outbound

This paper cites System card: Claude opus 4 & claude sonnet 4.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems System card: Claude opus 4 & claude sonnet 4

Reference 3

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source=arxiv_source observed=2026-08-04T17:56:38.724887Z digest=sha256:ce4c3c92f6215cfd47a4aad8e4b018fab882def7ffc666ef03751548ddb2ba5c

Observation 028c3d6f-ad9b-44bb-a4ad-d287ac62086a · outbound

This paper cites Eckstein, Noémi Éltető, Thomas L.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems Eckstein, Noémi Éltető, Thomas L

Reference 4

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source=arxiv_source observed=2026-08-04T17:56:38.728967Z digest=sha256:76a9d1b02444f044e86f1e1456129a3dc6452ef25d3b60036c7f0dd1abf78191

Observation e6216943-1f4e-465c-9dfd-db4969e94594 · outbound

This paper cites OpenAI Gym.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems OpenAI Gym

Reference 5

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source=arxiv_source observed=2026-08-04T17:56:38.732588Z digest=sha256:d0dde2bded594963f77c02eaf24015419ca4a196596915a5aa81495fd7c974ae

Observation 18e072a6-6f41-4819-aade-cff4ad1bc3c3 · outbound

This paper cites Large Language Monkeys: Scaling Inference Compute with Repeated Sampling.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 6

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source=arxiv_source observed=2026-08-04T17:56:38.736352Z digest=sha256:26fdfc74b1707f12314da2b2cb4c4f745ff427d9ebbaecdb633ea4d2dc553f66

Observation 832adcd3-2ffc-4142-aa7d-be623c3b0667 · outbound

This paper cites Agentic feedback loop modeling improves recommendation and user simulation.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems Agentic feedback loop modeling improves recommendation and user simulation

Reference 7

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source=arxiv_source observed=2026-08-04T17:56:38.740220Z digest=sha256:101a3fd358924d9d35f4d2e3417dfe52883417c7b10d79676d689b69834e8121

Observation b0d20d46-0c5e-46b8-8ef2-1226b8b5aee9 · outbound

This paper cites On the limits of agency in agent-based models.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems On the limits of agency in agent-based models

Reference 8

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source=arxiv_source observed=2026-08-04T17:56:38.744028Z digest=sha256:ed18abe72bc545e151baa4bd6de6d40f6bb168c80c5e2c9361838cdda8f751e0

Observation 3c21a5e4-d22b-46b2-b116-6b8cbddca4e6 · outbound

This paper cites SUBER: An RL Environment with Simulated Human Behavior for Recommender Systems.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems SUBER: An RL Environment with Simulated Human Behavior for Recommender Systems

Reference 9

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source=arxiv_source observed=2026-08-04T17:56:38.748325Z digest=sha256:bc32ec6139fbd34d66b3f2c974c07b4400a0a6748a9e8d701d96007c5a8f3609

Observation 85d30d4e-6fb3-4c6e-93c3-2fde31873d95 · outbound

This paper cites SARDINE: A Simulator for Automated Recommendation in Dynamic and Interactive Environments.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems SARDINE: A Simulator for Automated Recommendation in Dynamic and Interactive Environments

Reference 10

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source=arxiv_source observed=2026-08-04T17:56:38.752198Z digest=sha256:0e9de5b93e888db428e84ef362e0b38b36475aba85e5f150f7efe9960efc14b3

Observation abd22998-0c17-4001-9048-3d62071fe2a6 · outbound

This paper cites OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment

Reference 12

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source=arxiv_source observed=2026-08-04T17:56:38.759620Z digest=sha256:ef312378753986c2c7e100767979a1d75e4e6599747bc67e4b180da1e8463d51

Observation c0e8ab4e-2af1-4680-84be-b13782bead09 · outbound

This paper cites AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback

Reference 13

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source=arxiv_source observed=2026-08-04T17:56:38.763239Z digest=sha256:fef573e407924955fe5d13f35db5d20cecba51e3dc1980c0d83dca1031493575

Observation d77549a3-c3cb-486c-80f4-6a83dadad1d3 · outbound

This paper cites OMuleT: Orchestrating Multiple Tools for Practicable Conversational Recommendation.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems OMuleT: Orchestrating Multiple Tools for Practicable Conversational Recommendation

Reference 14

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source=arxiv_source observed=2026-08-04T17:56:38.766930Z digest=sha256:17d418f5aec389438edfb21232e1bdb9a80b87384aab32b545aba137d5757935

Observation d9093b5e-c172-4cae-ba6a-64071c07cd36 · outbound

This paper cites CALE: Continuous Arcade Learning Environment.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems CALE: Continuous Arcade Learning Environment

Reference 15

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source=arxiv_source observed=2026-08-04T17:56:38.770438Z digest=sha256:ea7265cb186a22df4f5126566401a29ea63bf56f57ef08461d01939b3635227f

Observation 1f4e182a-98a9-4998-b4db-363acfd2cc40 · outbound

This paper cites AgentScope: A Flexible yet Robust Multi-Agent Platform.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems AgentScope: A Flexible yet Robust Multi-Agent Platform

Reference 16

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source=arxiv_source observed=2026-08-04T17:56:38.773649Z digest=sha256:712c2e2ab75b1c757386aa23a358a2d41a50871233f0741b3afb9ab3b186b9e6

Observation 9ac2e555-99b9-48a4-bf7b-25c460bd37f4 · outbound

This paper cites Knowledge Graph Enhanced Language Agents for Recommendation.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems Knowledge Graph Enhanced Language Agents for Recommendation

Reference 17

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source=arxiv_source observed=2026-08-04T17:56:38.777051Z digest=sha256:e2e6ff03519cd1b73d5635d3da32f3c68ac9a6422f23c4b1b59ca9877d63affb

Observation 107b6f8a-9200-4b46-9cf0-edb2004694c9 · outbound

This paper cites Llm-augmented agent-based modelling for social simulations: Challenges and opportunities.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems Llm-augmented agent-based modelling for social simulations: Challenges and opportunities

Reference 18

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source=arxiv_source observed=2026-08-04T17:56:38.780494Z digest=sha256:68703503a7c9ffe87d4f24662e9e146886e4f82db710ab224b0334fadb40bde3

Observation acd86507-3359-4240-8d11-f0e158779aad · outbound

This paper cites ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendation.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendation

Reference 19

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source=arxiv_source observed=2026-08-04T17:56:38.783564Z digest=sha256:ac4cbcbb1597d6cb4261e08393b8d60734d2005f34b69676d9ed6150524db805

Observation 4c82896c-10b4-47a0-918e-0b4aafa55f7a · outbound

This paper cites Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models

Reference 20

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source=arxiv_source observed=2026-08-04T17:56:38.786978Z digest=sha256:413dc431ccd91fec0c9cb0a733aa12b9285cb622cfdfb8e5a650ccf043df03a5

Observation 41590be3-4235-4ddc-b351-610025672d99 · outbound

This paper cites CRMArena-Pro: Holistic Assessment of LLM Agents Across Diverse Business Scenarios and Interactions.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems CRMArena-Pro: Holistic Assessment of LLM Agents Across Diverse Business Scenarios and Interactions

Reference 21

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source=arxiv_source observed=2026-08-04T17:56:38.790240Z digest=sha256:f23a85063c466773546577bb2bdce32a4364843be7f32ca9af2a64e0863d1532

Observation 09a4db6e-fb59-4a71-8189-175b796e5a9f · outbound

This paper cites Agentsbench: A multi-agent llm simulation framework for legal judgment prediction.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems Agentsbench: A multi-agent llm simulation framework for legal judgment prediction

Reference 22

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source=arxiv_source observed=2026-08-04T17:56:38.793787Z digest=sha256:3b5cb0e512e583fd0399ca473a8a4ac77674f7ef94bee0154f2466ace106e3a0

Observation 70e9e99f-fef6-402f-b3b8-1b713850d414 · outbound

This paper cites Kimi K2: Open Agentic Intelligence.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems Kimi K2: Open Agentic Intelligence

Reference 23

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source=arxiv_source observed=2026-08-04T17:56:38.796921Z digest=sha256:2a443cea7eab90d8da5e5c3b558be5022d65e54d4f621e3935ec224c560c66c5

Observation 79c7ee53-4695-4235-8540-497473ac87f3 · outbound

This paper cites Can llm-simulated practice and feedback upskill human counselors? a randomized study with 90+ novice counselors, 2025.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems Can llm-simulated practice and feedback upskill human counselors? a randomized study with 90+ novice counselors, 2025

Reference 24

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source=arxiv_source observed=2026-08-04T17:56:38.800260Z digest=sha256:f471a4764e6266719a71431371ab49989ddc0f81ab762fa1edd4bee4c3529749

Observation 20228abf-98ca-42ae-8248-8f79328b2d13 · outbound

This paper cites The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systems.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systems

Reference 25

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source=arxiv_source observed=2026-08-04T17:56:38.803451Z digest=sha256:ff5351cc326f5a3d51c8d70ef940a7a7ccb65840a76b65dd0e77fd1113206604

Observation b39d9097-bfe0-4763-abf4-7bb40b93cd6f · outbound

This paper cites Generative Agents: Interactive Simulacra of Human Behavior.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems Generative Agents: Interactive Simulacra of Human Behavior

Reference 26

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source=arxiv_source observed=2026-08-04T17:56:38.808543Z digest=sha256:c16f3c0cafd1753677c6fef334e7b04882e9c59075ca0a8204256ca84fd1aa65

Observation bc7a3820-052f-44af-b875-a0b54554c74c · outbound

This paper cites LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals

Reference 27

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source=arxiv_source observed=2026-08-04T17:56:38.812230Z digest=sha256:c5930152063cfa304b5a974875e7c9cf3d1c5e763472fd6c55f26032d11e7f77

Observation 765234c3-9591-4ba7-8972-a15803c00f83 · outbound

This paper cites A Survey on LLM-powered Agents for Recommender Systems.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems A Survey on LLM-powered Agents for Recommender Systems

Reference 28

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source=arxiv_source observed=2026-08-04T17:56:38.815778Z digest=sha256:01393a74228096e818d681cdd50f5eb3ee8120bb5fa3e823cf4cc79661ac08d9

Observation cbf84c8d-c5c2-4260-8599-7f01cab271e6 · outbound

This paper cites AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society

Reference 29

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source=arxiv_source observed=2026-08-04T17:56:38.819306Z digest=sha256:85906c0fcce81787dd876059642d634fbe51dd348c6e2260e8737516f642c8fa

Observation 8fe9928b-6f33-4c1f-a128-fc62ccbc2e4e · outbound

This paper cites Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents

Reference 30

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source=arxiv_source observed=2026-08-04T17:56:38.822670Z digest=sha256:d0eb55b5a7994ec2b7997efe3f35f13750770bc5c053e9eab8c1fa7085d660d5

Observation 4a16f3bc-1b29-4107-ba9f-bc553867fd04 · outbound

This paper cites UserBench: An Interactive Gym Environment for User-Centric Agents.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems UserBench: An Interactive Gym Environment for User-Centric Agents

Reference 31

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source=arxiv_source observed=2026-08-04T17:56:38.825906Z digest=sha256:73935598aa570d4ef48346de89684363cd44510dd3870553915633126047c539

Observation 40bf49d1-0e25-47d4-ab11-9ee6dfba2373 · outbound

This paper cites Recommender systems with generative retrieval.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems Recommender systems with generative retrieval

Reference 32

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source=arxiv_source observed=2026-08-04T17:56:38.829360Z digest=sha256:dd928343eac654a5ebef7f0a05cb8277b15717e60767666daf7cb259fc447dac

Observation 6ef27738-b07a-4b3b-a5f3-252b7439d904 · outbound

This paper cites AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems

Reference 33

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source=arxiv_source observed=2026-08-04T17:56:38.832953Z digest=sha256:1a63d540328d4f808cb02e3f6b893637705adf3fa82377fb3f7904528ba4d7fc

Observation 3494a903-4211-4929-b54b-accfd9230ef7 · outbound

This paper cites an unresolved cited work.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems Unresolved cited work

Reference 34

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source=arxiv_source observed=2026-08-04T17:56:38.836788Z digest=sha256:7a9a2507c8baea47f5b6a0c16d56130763ecd8a9b038dbe8242c7f9c6abf6ed7

Observation 929fa3ea-b86c-4f7b-9400-11cf3c4da75d · outbound

This paper cites LLMs for User Interest Exploration in Large-scale Recommendation Systems.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems LLMs for User Interest Exploration in Large-scale Recommendation Systems

Reference 35

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source=arxiv_source observed=2026-08-04T17:56:38.840146Z digest=sha256:77183dde579146bb29fb73f178ec1796d1d01f96c7df1f8999e80c85c87a2411

Observation bfb02ea9-ec2c-433a-bb9e-6bb27a2e3e60 · outbound

This paper cites User behavior simulation with large language model-based agents.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems User behavior simulation with large language model-based agents

Reference 36

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source=arxiv_source observed=2026-08-04T17:56:38.843739Z digest=sha256:dc15bfcedbf677f785cd511b1c4856df6e8a650eed4261c21c9c973fe60eee30

Observation ccd35ba6-f608-4a5f-b384-c4d9cc7cbca7 · outbound

This paper cites GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networks.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networks

Reference 37

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source=arxiv_source observed=2026-08-04T17:56:38.847284Z digest=sha256:12590b3947fc08a6e9338d4b0d5c406f09e174eb2a856187f2af3bba46cbedda

Observation 0087880a-3b05-49fb-91d2-25e295cf41aa · outbound

This paper cites Hierarchical Tree Search-based User Lifelong Behavior Modeling on Large Language Model.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems Hierarchical Tree Search-based User Lifelong Behavior Modeling on Large Language Model

Reference 38

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:56:38.851380Z digest=sha256:32144ccbe230a1c47a33569f43f0de90c9cd2f58c3134fe56a27e7b96aa4e6ed

Observation 1108fcae-4f2c-43ca-bdfd-6b1fa52d62af · outbound

This paper cites TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks

Reference 39

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:56:38.854829Z digest=sha256:aec3106a592bddb7f4b76a6bb3794c72f66578d6d68ef911cddd195a7d6d835c

Observation e9b6c979-c9b9-4b84-8ed6-006c694933ff · outbound

This paper cites an unresolved cited work.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems Unresolved cited work

Reference 40

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no resolver link, observed 2026-08-04T17:56:38.858334Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:56:38.858334Z digest=sha256:2c14fb1f24a3a66c03e68d7cfd94181beec34520a8a3207d44c344782c47454c

Observation 7f42eb2d-bdc9-4a29-84e0-33982710a516 · outbound

This paper cites iAgent: LLM Agent as a Shield between User and Recommender Systems.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems iAgent: LLM Agent as a Shield between User and Recommender Systems

Reference 41

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no resolver link, observed 2026-08-04T17:56:38.861709Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-04T17:56:38.861709Z digest=sha256:61e2435ab303faf8eda137631e2ca8b9b61e8eaa0aa20878a6e085e422eb89ab

Observation a79e587b-1923-4470-8548-c0b15fd1c5d5 · outbound

This paper cites OASIS: Open Agent Social Interaction Simulations with One Million Agents.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems OASIS: Open Agent Social Interaction Simulations with One Million Agents

Reference 42

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no resolver link, observed 2026-08-04T17:56:38.865797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:56:38.865797Z digest=sha256:6d3919c77e875ce24ca48661a1afafe9244deaff98d0202cb0763a4405eb75d6

Observation 7db04a51-54ee-4542-8586-b1491b43e33c · outbound

This paper cites Evaluating Large Language Models as Generative User Simulators for Conversational Recommendation.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems Evaluating Large Language Models as Generative User Simulators for Conversational Recommendation

Reference 43

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no resolver link, observed 2026-08-04T17:56:38.869320Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:56:38.869320Z digest=sha256:c32b89a4c2f1d1ab7d981da16f1740978e9c9c9ff91a0a41c58f8783e77f7eaf

Observation 6f27ce27-967c-4cec-812d-5bf4fb298b3c · outbound

This paper cites MuJoCo Playground.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems MuJoCo Playground

Reference 44

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:56:38.873029Z digest=sha256:04be5bfe3d5367ea4f46031e4dd16a27e0ed4d1be0d55d6bdf395bc59558343b

Observation b5423a38-1973-4d52-85b0-cf2ad75515fd · outbound

This paper cites Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations

Reference 45

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no resolver link, observed 2026-08-04T17:56:38.876673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:56:38.876673Z digest=sha256:b82bec73f50c776a5e8c0969c91c5b70b94115b917b2a7f7cfcc16832aa905c2

Observation 099054f9-b3a1-446d-b251-478d3f265f61 · outbound

This paper cites On generative agents in recommendation.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems On generative agents in recommendation

Reference 46

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no resolver link, observed 2026-08-04T17:56:38.880309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:56:38.880309Z digest=sha256:52f02fea2e30e5432ebc1c132c1f11eb12923ca3fb2c44742cf0069451e5e426

Observation d178cd06-1b4f-47b8-ae98-056e1c839718 · outbound

This paper cites NoteLLM-2: Multimodal Large Representation Models for Recommendation.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems NoteLLM-2: Multimodal Large Representation Models for Recommendation

Reference 47

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no resolver link, observed 2026-08-04T17:56:38.883489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:56:38.883489Z digest=sha256:4d75fb265771a5f04f96367a1726bb1e173a0c169319584bc0f6dfb4c3cbb2d8

Observation 59d8d5d6-696d-41c2-ab98-12bd05ce3d7c · outbound

This paper cites Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach

Reference 48

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no resolver link, observed 2026-08-04T17:56:38.886734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:56:38.886734Z digest=sha256:93dfc26b775fb3b7033e00501ab769f470af6a16f16fe7aeca00b2d2162a1b90

Observation a2a9ccc2-89f1-425e-9077-d79aa5dc6716 · outbound

This paper cites Agentcf: Collaborative learning with autonomous language agents for recommender systems.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems Agentcf: Collaborative learning with autonomous language agents for recommender systems

Reference 49

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no resolver link, observed 2026-08-04T17:56:38.889847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:56:38.889847Z digest=sha256:6d0ea2eabdf8db5d1d90af39be4bbf4e10d7f14ee3e323fba9fa6d21a4035e81

Observation 03086443-ac4d-4b32-a3c7-32b2167d0109 · outbound

This paper cites Llm-powered user simulator for recommender system.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems Llm-powered user simulator for recommender system

Reference 50

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no resolver link, observed 2026-08-04T17:56:38.893248Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-04T17:56:38.893248Z digest=sha256:fe94a5fcedf6ae02704b53e16d77bb7f14d05c2ce2dd6faea61dab6e498e5994

Observation 008e4e85-6235-4d54-8ccd-e72756be83f6 · outbound

This paper cites KuaiSim: A Comprehensive Simulator for Recommender Systems.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems KuaiSim: A Comprehensive Simulator for Recommender Systems

Reference 51

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no resolver link, observed 2026-08-04T17:56:38.896525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:56:38.896525Z digest=sha256:33adcc5fcee8432502214fb9e7321498ae2acf8e242c7dddebd9f4d2b4d51947

Observation 1805f64e-c7b6-4674-834f-6eef2cbd270e · outbound

This paper cites Exploring the Impact of Personality Traits on Conversational Recommender Systems: A Simulation with Large Language Models.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems Exploring the Impact of Personality Traits on Conversational Recommender Systems: A Simulation with Large Language Models

Reference 52

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unresolved
no resolver link, observed 2026-08-04T17:56:38.899610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:56:38.899610Z digest=sha256:2bddf0ad02c7c46b9320ef544dd1a2bd45c106317a2aef798358484713f18d12

Observation 79f86155-eebc-4bbc-ac2e-cf31c179a4af · outbound

This paper cites Enhancing Embedding Representation Stability in Recommendation Systems with Semantic ID.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems Enhancing Embedding Representation Stability in Recommendation Systems with Semantic ID

Reference 53

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no resolver link, observed 2026-08-04T17:56:38.902921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:56:38.902921Z digest=sha256:9410dd9f6d2d07fb41ec36f7faf8dce984cc1c672823bac2eae3bdc944fb1c10

Observation bef1c4fc-6065-48dc-81f2-b4f93d86c033 · outbound

This paper cites OneRec-V2 Technical Report.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems OneRec-V2 Technical Report

Reference 54

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no resolver link, observed 2026-08-04T17:56:38.906062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:56:38.906062Z digest=sha256:f1b485ab02f6a5ce11401fb08c1e88b84df16b0c0838d0cb704f8be119b96529

Observation 81513001-0bab-491f-aa95-a2ded35727c5 · outbound

This paper cites Instruction-Following Evaluation for Large Language Models.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems Instruction-Following Evaluation for Large Language Models

Reference 55

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no resolver link, observed 2026-08-04T17:56:38.909292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:56:38.909292Z digest=sha256:72a9f8ce30c518e3e96ee6c27d83a398cf1ca60978f5df6a85d68ce02b069b86

Observation bb897b6c-2834-4557-93e8-d8131a859e6b · outbound

This paper cites WebArena: A Realistic Web Environment for Building Autonomous Agents.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems WebArena: A Realistic Web Environment for Building Autonomous Agents

Reference 56

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no resolver link, observed 2026-08-04T17:56:38.912400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:56:38.912400Z digest=sha256:7b2893eab9d7076b0c1032f766ec6c9fe74b8541021fca7b8801bc3d8188536f

Observation fd9ad2b0-8f20-4f81-994f-f40c1ced21dd · outbound

This paper cites Long-Term Interest Clock: Fine-Grained Time Perception in Streaming Recommendation System.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems Long-Term Interest Clock: Fine-Grained Time Perception in Streaming Recommendation System

Reference 57

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no resolver link, observed 2026-08-04T17:56:38.915923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:56:38.915923Z digest=sha256:076ff3599f0fb6b2dcc64ff9c15574f8ffe3b9d702570bec72722d612edc4d58

Observation b158857f-e5e2-4877-a85e-d9ff66377f7f · outbound

This paper cites Towards Better Instruction Following Retrieval Models.

RecoWorld: Building Simulated Environments for Agentic Recommender Systems Towards Better Instruction Following Retrieval Models

Reference 58

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no resolver link, observed 2026-08-04T17:56:38.919605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:56:38.919605Z digest=sha256:8cf637d2e5b57c5475512c74444938e3454016e0dde3f7adf81055a6cc77c2fa

Pith citing papers

Observation d64bdfd7-71d4-49ee-9cc5-7888418ffd02 · inbound

Self-EvolveRec: Self-Evolving Recommender Systems with LLM-based Directional Feedback cites this paper.

Self-EvolveRec: Self-Evolving Recommender Systems with LLM-based Directional Feedback RecoWorld: Building Simulated Environments for Agentic Recommender Systems

Reference 29

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no resolver link, observed 2026-08-02T23:50:09.201764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T23:50:09.201764Z digest=sha256:cc17393abefb98b651ed93d6c5082604db18022e58e28db7a0a459fe416fe953

Observation 9a9f5a69-edd7-4e7b-86e0-fae84021fd1f · inbound

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems cites this paper.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems RecoWorld: Building Simulated Environments for Agentic Recommender Systems

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-07-28T02:22:23.314678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-10T16:24:28.048403Z digest=sha256:a5d86b3110bf6468e94d955e430736d01e230587e3e45e2f4d1dc7de83bd8d33

Observation eafc0c8d-bd0a-46af-8647-087b75d51c3c · inbound

RecRM-Bench: Benchmarking Multidimensional Reward Modeling for Agentic Recommender Systems cites this paper.

RecRM-Bench: Benchmarking Multidimensional Reward Modeling for Agentic Recommender Systems RecoWorld: Building Simulated Environments for Agentic Recommender Systems

Reference 24

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verified exact
arxiv_id, observed 2026-07-28T02:22:23.314678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-13T05:04:08.454422Z digest=sha256:5e4e3fa941bfd7964303263057e05fc7f49e3748c0feb3a5d36a291370fd6c27

Observation 1f428721-7b79-4685-942d-dce8e42b662c · inbound

EvoRec: Self Evolving Agentic Recommender Systems cites this paper.

EvoRec: Self Evolving Agentic Recommender Systems RecoWorld: Building Simulated Environments for Agentic Recommender Systems

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-07-28T02:22:23.314678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-06-30T10:47:52.156114Z digest=sha256:39a457dbd185ca60b8d1f68467b2d597de0b97077154922f664dc60ee0de26d9

Observation 09ef33a6-34d0-4db6-8fe0-05fdd4da5b9d · inbound

An LLM-powered Agentic Recommendation System for Connected TV Content Discovery cites this paper.

An LLM-powered Agentic Recommendation System for Connected TV Content Discovery RecoWorld: Building Simulated Environments for Agentic Recommender Systems

Reference 9

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no resolver link, observed 2026-08-02T07:33:12.781312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T07:33:12.781312Z digest=sha256:f5a7e4b439f1a38dc8212c0ad3005ea812ea0f445f42f78746622f648c008190

Observation 36a65cd8-8fc0-4601-93c2-c3b852aa3d8b · inbound

Can We Steer the Black-Box? Towards Controllability-Centric Evaluation of Recommender Systems with Collaborative Agents cites this paper.

Can We Steer the Black-Box? Towards Controllability-Centric Evaluation of Recommender Systems with Collaborative Agents RecoWorld: Building Simulated Environments for Agentic Recommender Systems

Reference 56

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unresolved
no resolver link, observed 2026-08-02T05:17:58.278553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T05:17:58.278553Z digest=sha256:553ab5893b5dea08adae76996430116704aacff428a5f68f089ac6c83eb42b6e

Observation 2c60d52a-4fa0-41d0-a551-f92f257c6821 · inbound

Beyond Action Imitation: Learning a Decision-Aware User Simulator for Online Advertising cites this paper.

Beyond Action Imitation: Learning a Decision-Aware User Simulator for Online Advertising RecoWorld: Building Simulated Environments for Agentic Recommender Systems

Reference 24

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no resolver link, observed 2026-07-30T17:56:01.447706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T17:56:01.447706Z digest=sha256:3b79bc3186a5ee18d83db08e2be602366d285becc924993d03b7d53926881d4d

Observation 0bb470be-77f2-4552-a08d-e05fd3b7bdde · inbound

DREAM Technical Report cites this paper.

DREAM Technical Report RecoWorld: Building Simulated Environments for Agentic Recommender Systems

Reference 44

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no resolver link, observed 2026-08-11T17:48:55.162617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T17:48:55.162617Z digest=sha256:d8532fe852cf2714abdaf8b8863fa6745b50585e994e39e888433920b9a8d6b2

Observation 71f2dfbf-7274-404a-9f7a-367a480cba92 · inbound

DREAM Technical Report cites this paper.

DREAM Technical Report RecoWorld: Building Simulated Environments for Agentic Recommender Systems

Reference 44

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unresolved
no resolver link, observed 2026-08-14T04:19:09.835936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:19:09.835936Z digest=sha256:2652064501f12d6e4b500abf3d81d5aa775f659962f4119a98e6f186074df9ba