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Paper Citation Record · LEDGER

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations

As of 17 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2507.21274.

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

pith.paper-citation-record.v1
2507.21274 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T13:06:32.504261Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

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  • verified fuzzy21
  • unresolved14
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External citation measurements

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Outbound references

Observation 9527e20e-14bb-4f47-890d-93febf486a30 · outbound

This paper cites Llama 3 model card.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Llama 3 model card

Reference 1

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Observation 31fce3d4-00b5-477b-a939-fa271ee711c5 · outbound

This paper cites The claude 3 model family: Opus, sonnet, haiku.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations The claude 3 model family: Opus, sonnet, haiku

Reference 2

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Observation 6cf12dc9-72f4-42f0-b329-8c7c7fafc310 · outbound

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

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Tallrec: An effective and efficient tuning framework to align large language model with recommendation

Reference 3

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Observation 86ea9218-6a7d-4a89-b1ee-fe433c9d6f35 · outbound

This paper cites Adversarial model for offline reinforcement learning.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Adversarial model for offline reinforcement learning

Reference 4

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Observation c39dd834-a376-4763-9a45-3242aab78d80 · outbound

This paper cites Stochastic approximation with two time scales.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Stochastic approximation with two time scales

Reference 5

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Source-reported events for the cited work

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Observation a7b8571b-43b6-4aff-b1c8-3a6987727fee · outbound

This paper cites When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities

Reference 6

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Observation 2fe45c2c-ab35-4408-b387-13fb4afe38b6 · outbound

This paper cites Adversarially trained actor critic for offline reinforcement learning.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Adversarially trained actor critic for offline reinforcement learning

Reference 7

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 235ee978-ba42-4412-b5bf-4161837b600c · outbound

This paper cites On the Properties of Neural Machine Translation: Encoder-Decoder Approaches.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations On the Properties of Neural Machine Translation: Encoder-Decoder Approaches

Reference 8

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Observation 631a51b8-7791-4f5e-b835-bf11c20585b5 · outbound

This paper cites A review of modern recommender systems using generative models (gen-recsys).

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations A review of modern recommender systems using generative models (gen-recsys)

Reference 9

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Observation ead6fcdf-e467-46ea-ae23-9cd1aded9f74 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 10

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Observation 9efe23b4-678d-427b-b898-80ecc37f7feb · outbound

This paper cites Recommender Systems in the Era of Large Language Models (LLMs).

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Recommender Systems in the Era of Large Language Models (LLMs)

Reference 11

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Observation d4bc672e-c5d1-43b7-bafe-1f786eff552b · outbound

This paper cites Addressing function approxi- mation error in actor-critic methods.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Addressing function approxi- mation error in actor-critic methods

Reference 12

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 8510f530-5fd6-422b-8d3e-cf9fd20045bd · outbound

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

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Soft actor- critic: Off-policy maximum entropy deep reinforcement learning with a stochas- tic actor

Reference 13

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Observation ab507f5d-99fd-42ba-992a-c16053591d26 · outbound

This paper cites Maxwell Harper and Joseph A.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Maxwell Harper and Joseph A

Reference 14

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Observation c7d85c18-6e52-439e-b37e-2632fd564f0a · outbound

This paper cites Large lan- guage models as zero-shot conversational recommenders.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Large lan- guage models as zero-shot conversational recommenders

Reference 15

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 76edea6e-06dc-44e9-8ad0-8bf3260464d8 · outbound

This paper cites Session-based Recommendations with Recurrent Neural Networks.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Session-based Recommendations with Recurrent Neural Networks

Reference 16

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Observation 44a34131-597b-490d-a7c3-db8d9ab19b74 · outbound

This paper cites Towards universal sequence representation learning for recommender systems.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Towards universal sequence representation learning for recommender systems

Reference 17

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Observation 2b46a6e7-e9f2-4988-968a-1200f7ca45dc · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations LoRA: Low-Rank Adaptation of Large Language Models

Reference 18

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Observation b2bf7143-86ce-49bc-ae97-f31aca2c4e00 · outbound

This paper cites Human-centric dialog training via offline reinforcement learning.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Human-centric dialog training via offline reinforcement learning

Reference 19

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Source-reported events for the cited work

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Observation b2791e8f-824f-4d4b-9c64-2545aa38da62 · outbound

This paper cites Cumulated gain-based evaluation of ir techniques.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Cumulated gain-based evaluation of ir techniques

Reference 20

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Observation 40441afe-eff5-4cbe-9117-4ad73561fa0f · outbound

This paper cites Kingma and Jimmy Ba.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Kingma and Jimmy Ba

Reference 21

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation d9bb156e-7077-4a25-b77e-4046b99350b9 · outbound

This paper cites How Can Recommender Systems Benefit from Large Language Models: A Survey.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations How Can Recommender Systems Benefit from Large Language Models: A Survey

Reference 22

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Observation 0aa9d40c-b56d-47cb-b921-156523c8a3a5 · outbound

This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing

Reference 23

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Observation 3c55693a-53a4-4bff-a68f-1f4d78cc7583 · outbound

This paper cites Diversity-promoting deep reinforcement learning for interactive recommendation.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Diversity-promoting deep reinforcement learning for interactive recommendation

Reference 24

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 7a19ab21-727d-43e0-912d-dc2aefe06833 · outbound

This paper cites Convergent temporal-difference learning with arbitrary smooth function approximation.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Convergent temporal-difference learning with arbitrary smooth function approximation

Reference 25

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 557e0e8c-9c13-45e2-bb4d-54bb07b0ebd7 · outbound

This paper cites Recent advances in natural language processing via large pre-trained language models: A survey.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Recent advances in natural language processing via large pre-trained language models: A survey

Reference 26

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 1254bda8-8018-46c8-a2a2-f9a3e33fc42c · outbound

This paper cites Training language models to follow instructions with human feedback.Advances in Neural Information Processing Systems , 35:27730–27744, 2022.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Training language models to follow instructions with human feedback.Advances in Neural Information Processing Systems , 35:27730–27744, 2022

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation c5381e4b-f6c2-4ae3-bc1a-c2fbe106b12e · outbound

This paper cites Proximal Policy Optimization Algorithms.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Proximal Policy Optimization Algorithms

Reference 28

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Observation 257ccca1-7c6c-4e00-bdda-02925ad0f94e · outbound

This paper cites Choosing the best of both worlds: Diverse and novel recom- mendations through multi-objective reinforcement learning.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Choosing the best of both worlds: Diverse and novel recom- mendations through multi-objective reinforcement learning

Reference 29

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation ba59590b-e618-4bec-baca-a6ae722e81ac · outbound

This paper cites Learning to summarize with human feedback.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Learning to summarize with human feedback

Reference 30

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 1cfc025b-b6c0-4950-a669-f6b8aeeb1eac · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations LLaMA: Open and Efficient Foundation Language Models

Reference 31

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Observation f60b4162-d92c-456d-a661-93715900ebbe · outbound

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Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Unresolved cited work

Reference 32

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation b5e0e357-3cea-4b17-9434-9ac56afaef47 · outbound

This paper cites Transrec: Learning transferable recommendation from mixture-of-modality feedback.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Transrec: Learning transferable recommendation from mixture-of-modality feedback

Reference 33

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation d51c539b-01bf-43af-8f05-39fdd9744ee8 · outbound

This paper cites Behavior Regularized Offline Reinforcement Learning.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Behavior Regularized Offline Reinforcement Learning

Reference 34

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Observation 4095c89b-7e48-4600-bee7-e7563e198836 · outbound

This paper cites A Survey of Large Language Models.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations A Survey of Large Language Models

Reference 35

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source=pdf_text observed=2026-08-06T13:06:32.497705Z digest=sha256:a6457b2df3665ea14f8f8ec2d6630ffbc4aafd1b31c8dce7b2d582b1ebf7c556

Observation 3bd8393f-bddc-4a8e-b59c-d6c18f435ca0 · outbound

This paper cites Drn: A deep reinforcement learning framework for news recommendation.

Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations Drn: A deep reinforcement learning framework for news recommendation

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:06:32.857292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:06:32.504261Z digest=sha256:7f5b6d5e300fd1545e4eaea3b771cb4904beb22362d34c87850a3d7dd44e3329

Pith citing papers

No inbound Pith citation observations are available.