Paper Citation Record · LEDGER

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems

As of 4 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 1 inbound Pith citation observation for arXiv:2604.10029.

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

pith.paper-citation-record.v1
2604.10029 v2

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T16:24:28.048403Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T02:44:32.617211Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

verified exact19
verified fuzzy24
unresolved0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d53063b9-aecc-4e31-be81-8f8160b9b60f · outbound

This paper cites Self-attentive sequential recommenda- tion.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Self-attentive sequential recommenda- tion

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:31:55.807390Z

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Observation 004673d4-d277-4971-b29c-fba74226ca7d · outbound

This paper cites Lightgcn: Simplifying and powering graph convolution network for recommenda- tion.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Lightgcn: Simplifying and powering graph convolution network for recommenda- tion

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:31:55.802483Z

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Observation e049c78f-75df-454b-b40a-8083d12d1d15 · outbound

This paper cites Efficient bi- level optimization for recommendation denoising.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Efficient bi- level optimization for recommendation denoising

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:31:55.800251Z

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Observation 265aa792-383e-4cc8-8233-2462ee8e66bb · outbound

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

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Agentic feedback loop modeling improves recommendation and user simulation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:31:55.804911Z

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Observation f6a94efa-f04f-4547-884e-ed9b78454830 · outbound

This paper cites iagent: Llm agent as a shield between user and recommender systems.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems iagent: Llm agent as a shield between user and recommender systems

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:31:55.810037Z

Source-reported events for the cited work

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Observation a19e5a89-2daa-4e0d-9bc9-d48983e9e6ad · outbound

This paper cites On generative agents in recommendation.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems On generative agents in recommendation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:31:55.812149Z

Source-reported events for the cited work

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Observation a2014003-e152-4844-8471-6910c3a0d9d7 · outbound

This paper cites Recommender ai agent: Integrating large language models for interactive recommen- dations.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Recommender ai agent: Integrating large language models for interactive recommen- dations

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:31:55.819617Z

Source-reported events for the cited work

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Observation 424c98d2-d34e-440c-91a8-6febe9ff8d0f · outbound

This paper cites Re- flexion: Language agents with verbal reinforcement learning.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Re- flexion: Language agents with verbal reinforcement learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:31:55.824593Z

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Observation bbca6b1a-1446-430e-a2b5-bf9631f2e591 · outbound

This paper cites Entropy guided diversification and preference elicitation in agentic recommendation systems.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Entropy guided diversification and preference elicitation in agentic recommendation systems

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:56:02.127976Z

Source-reported events for the cited work

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Observation 1e6a731c-e948-40f1-926a-1d0f766b3e70 · outbound

This paper cites Memorybank: En- hancing large language models with long-term memory.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Memorybank: En- hancing large language models with long-term memory

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:31:55.827397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e05d95aa-3ffd-4d06-a64a-c59c7d9b43f1 · outbound

This paper cites A-MEM: Agentic Memory for LLM Agents.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems A-MEM: Agentic Memory for LLM Agents

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-11T08:56:02.212756Z

Source-reported events for the cited work

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Observation 92f4dbd6-ee38-4495-ae89-95f6a3f1f76b · outbound

This paper cites Tallrec: An effective and efficient tuning framework to align large language model with recommendation.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Tallrec: An effective and efficient tuning framework to align large language model with recommendation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:31:55.787562Z

Source-reported events for the cited work

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Observation c5bd9325-2b47-4bde-89ee-3988d8ff067d · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-05-11T08:56:02.225700Z

Source-reported events for the cited work

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Observation 027e70ce-192f-4647-becc-138042797b73 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Proximal Policy Optimization Algorithms

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-11T08:56:02.179684Z

Source-reported events for the cited work

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Observation 2035d37c-4579-4a40-a1e1-93e3616bfd03 · outbound

This paper cites Deepseek-r1 incentivizes reasoning in llms through reinforcement learning.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Deepseek-r1 incentivizes reasoning in llms through reinforcement learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:31:55.792482Z

Source-reported events for the cited work

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Observation a91d93f1-b305-491a-b8e3-0a7b3f18dc95 · outbound

This paper cites Amem4rec: Leveraging cross-user similarity for memory evolution in agentic llm recom- menders.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Amem4rec: Leveraging cross-user similarity for memory evolution in agentic llm recom- menders

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:56:02.121845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5baec4f5-324a-4310-8ac3-69bae702ccd3 · outbound

This paper cites A survey on agent-as-a-judge.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems A survey on agent-as-a-judge

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:56:02.220165Z

Source-reported events for the cited work

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

This paper cites RecoWorld: Building Simulated Environments for Agentic Recommender Systems.

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 2026-08-04T06:34:03.388597+00:00.

Observation 9ae63de2-cb16-4583-bad8-6ddbaa637a2f · outbound

This paper cites RuleAgent: Discovering Rules for Recommendation Denoising with Autonomous Language Agents.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems RuleAgent: Discovering Rules for Recommendation Denoising with Autonomous Language Agents

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:56:02.140206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4f33a700-ae2c-4547-8713-6ffdaa565abe · outbound

This paper cites Macrec: A multi- agent collaboration framework for recommendation.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Macrec: A multi- agent collaboration framework for recommendation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:31:55.780685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9664e998-438a-4047-8696-33e059526f36 · outbound

This paper cites arXiv preprint arXiv:2602.02482 , year=.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems arXiv preprint arXiv:2602.02482 , year=

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T08:56:02.173063Z

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Observation 8ccd2e18-9a25-4da2-8a73-d799961d75c3 · outbound

This paper cites Treerl: Llm reinforcement learning with on-policy tree search.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Treerl: Llm reinforcement learning with on-policy tree search

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:31:55.785196Z

Source-reported events for the cited work

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Observation aa9dd6c5-6bd4-4fc7-855d-5344e529ec31 · outbound

This paper cites Supervised pretraining can learn in-context reinforcement learning.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Supervised pretraining can learn in-context reinforcement learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:31:55.773319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3be3a04a-107c-4a9a-886c-3834f5d9d2c6 · outbound

This paper cites Reward Is Enough: LLMs Are In-Context Reinforcement Learners.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Reward Is Enough: LLMs Are In-Context Reinforcement Learners

Reference 24

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Source-reported events for the cited work

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Observation 3348998f-c5a2-4a65-bca7-2bc632d57b7f · outbound

This paper cites Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-13T12:44:27.816864Z

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Observation 491749b6-12b7-4845-92e4-515c786d01c9 · outbound

This paper cites Self-Distillation Enables Continual Learning.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Self-Distillation Enables Continual Learning

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:31:52.723793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c9b4f882-5512-42be-ad90-1f98b2eec84c · outbound

This paper cites Reinforcement Learning via Self-Distillation.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Reinforcement Learning via Self-Distillation

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:29:18.795354Z

Source-reported events for the cited work

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Observation 887d32f4-fa2f-46ef-a8cb-213c931246c8 · outbound

This paper cites Self-Distilled RLVR.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Self-Distilled RLVR

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-05-11T08:56:02.092723Z

Source-reported events for the cited work

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Observation 20a7b5c6-b756-45ab-a252-800e25f92d69 · outbound

This paper cites Second workshop on infor- mation heterogeneity and fusion in recommender systems (hetrec2011).

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Second workshop on infor- mation heterogeneity and fusion in recommender systems (hetrec2011)

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:31:55.775887Z

Source-reported events for the cited work

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Observation 94b0ae1d-bbed-426b-ac67-6784f6ac5fdb · outbound

This paper cites The movielens datasets: History and context.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems The movielens datasets: History and context

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:31:55.778186Z

Source-reported events for the cited work

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Observation e6248ad7-e440-4414-bfd1-9e47d9fef17b · outbound

This paper cites Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-05-11T08:56:02.195798Z

Source-reported events for the cited work

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Observation 25c7840d-4141-441b-a28f-73e075306bd7 · outbound

This paper cites Let me do it for you: Towards llm empowered recommendation via tool learning.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Let me do it for you: Towards llm empowered recommendation via tool learning

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:31:55.782938Z

Source-reported events for the cited work

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Observation 708f2f17-0135-4b9d-8ab7-89c46c7bac65 · outbound

This paper cites Lora: Low-rank adaptation of large language models.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Lora: Low-rank adaptation of large language models

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:31:55.789867Z

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Observation e39cb58a-cef5-4ab6-9188-47a402cb0ef6 · outbound

This paper cites Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:54:31.187112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6dd8baba-c873-45ae-bac6-a8e256312811 · outbound

This paper cites Negotiating the shared agency between humans & ai in the recommender system.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Negotiating the shared agency between humans & ai in the recommender system

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:31:55.795096Z

Source-reported events for the cited work

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Observation bc2b7259-1584-4f12-afc5-494c5223dbe1 · outbound

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

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems User behavior simulation with large lan- guage model-based agents

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:31:55.830002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2153ce36-707b-4465-9fa8-4d405eba717b · outbound

This paper cites Recmind: Large language model powered agent for recommendation.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Recmind: Large language model powered agent for recommendation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:31:55.817428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3d8a3fc9-d1e3-44e3-8ab7-a1ebcbdf9dca · outbound

This paper cites Id-free not risk-free: Llm-powered agents unveil risks in id- free recommender systems.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Id-free not risk-free: Llm-powered agents unveil risks in id- free recommender systems

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:31:55.814834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8b804b5b-c66a-4187-bc16-60ab154bb62a · outbound

This paper cites MemRec: Collaborative Memory-Augmented Agentic Recommender System.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems MemRec: Collaborative Memory-Augmented Agentic Recommender System

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-05-11T08:56:02.160365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8bc6d331-a311-48a7-bd55-f96ea70c9114 · outbound

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

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Agentcf: Collaborative learning with autonomous language agents for recommender systems

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:31:55.821919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 67d6453c-12cb-4168-a772-dfbe4c7c8125 · outbound

This paper cites Agentcf++: Memory-enhanced llm-based agents for popularity-aware cross-domain recommendations.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Agentcf++: Memory-enhanced llm-based agents for popularity-aware cross-domain recommendations

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:31:55.798096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c6c01178-2781-4371-a4e7-aa3f7f6f528f · outbound

This paper cites Multi-agent collaborative filtering: Orchestrating users and items for agentic rec- ommendations.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Multi-agent collaborative filtering: Orchestrating users and items for agentic rec- ommendations

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:56:02.232542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 22b2884f-a43b-4317-89cc-049ce1ff0cfb · outbound

This paper cites Recnet: Self-evolving preference propagation for agentic recommender systems.arXiv preprint arXiv:2601.21609.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems Recnet: Self-evolving preference propagation for agentic recommender systems.arXiv preprint arXiv:2601.21609

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:56:02.204406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation aa7799e6-5e29-4d17-b41e-e2cf8e40e283 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-05-11T08:56:02.187439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Pith citing papers

Observation 5ba483d8-be47-44fc-a746-f91a8801b4f8 · inbound

ODYSSE: Episode-wise Policy Optimization for Personalized Agentic Reasoning cites this paper.

ODYSSE: Episode-wise Policy Optimization for Personalized Agentic Reasoning Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:32.617211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.