Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging | Paper Citation Record

Observation fc62d903-0c84-4893-8164-4ee5b62d3bf5 · outbound

This paper cites L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning.

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning

Reference 1

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Observation f730ce83-77d7-4ce1-bc43-f5c5b36b08cb · outbound

This paper cites Aytes, Jinheon Baek, and Sung Ju Hwang.

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Aytes, Jinheon Baek, and Sung Ju Hwang

Reference 2

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This paper cites A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems.

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems

Reference 3

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Unresolved cited work

Reference 4

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This paper cites 2009.Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit.

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging 2009.Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit

Reference 5

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Observation bcdec77e-ddf7-43cc-a15f-22a675bab619 · outbound

This paper cites Breaking the Aggregation Bottleneck in Federated Recommendation: A Personalized Model Merging Approach.

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Breaking the Aggregation Bottleneck in Federated Recommendation: A Personalized Model Merging Approach

Reference 6

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Observation 75767dd9-0881-4cc6-81b7-8981e800ea3c · outbound

This paper cites Reason4Rec: Deliberative User Preference Alignment of Large Language Models for Recommendation.

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Reason4Rec: Deliberative User Preference Alignment of Large Language Models for Recommendation

Reference 7

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Observation a1e116f7-3d3b-4966-be1e-d032f81a16bf · outbound

This paper cites Reinforced Preference Optimization for Reasoning-Augmented Recommendations.

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Reinforced Preference Optimization for Reasoning-Augmented Recommendations

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-15T14:24:18.495697Z

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Observation 1a927998-cf27-42d2-8474-e2fe91c9b910 · outbound

This paper cites Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5).

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)

Reference 9

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This paper cites Transformer Feed-Forward Layers Are Key-Value Memories.

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Transformer Feed-Forward Layers Are Key-Value Memories

Reference 10

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Unresolved cited work

Reference 11

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This paper cites Token-Budget-Aware LLM Reasoning.

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Token-Budget-Aware LLM Reasoning

Reference 12

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Observation c655de7e-6c70-4012-bb8f-fce87539d74e · outbound

This paper cites ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning.

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning

Reference 13

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Unresolved cited work

Reference 14

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

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging LoRA: Low-Rank Adaptation of Large Language Models

Reference 15

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Unresolved cited work

Reference 16

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Observation e4bcf1b3-0c40-4113-bfa8-2d4a05512f74 · outbound

This paper cites Editing Models with Task Arithmetic.

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Editing Models with Task Arithmetic

Reference 17

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Unresolved cited work

Reference 18

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Observation 2942d480-75e0-4a02-9e20-47cbddb649e4 · outbound

This paper cites 2011.Thinking, Fast and Slow.

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging 2011.Thinking, Fast and Slow

Reference 19

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Unresolved cited work

Reference 20

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Unresolved cited work

Reference 21

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Unresolved cited work

Reference 22

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This paper cites Limitations of the Empirical Fisher Approximation for Natural Gradient Descent.

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Limitations of the Empirical Fisher Approximation for Natural Gradient Descent

Reference 23

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Unresolved cited work

Reference 24

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging LLaRA: Large Language-Recommendation Assistant

Reference 25

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Observation af4594a3-6590-43ca-94bd-0069a9d7c49a · outbound

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

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging How Can Recommender Systems Benefit from Large Language Models: A Survey

Reference 26

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Unresolved cited work

Reference 27

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This paper cites CoT-Valve: Length-Compressible Chain-of-Thought Tuning.

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging CoT-Valve: Length-Compressible Chain-of-Thought Tuning

Reference 28

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This paper cites Sens-Merging: Sensitivity-Guided Parameter Balancing for Merging Large Language Models.

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Sens-Merging: Sensitivity-Guided Parameter Balancing for Merging Large Language Models

Reference 29

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Merging Models with Fisher-Weighted Averaging

Reference 30

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging New insights and perspectives on the natural gradient method

Reference 31

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Observation d60be6e2-3dca-4f79-b074-9eae2a278c4e · outbound

This paper cites Thinking Sparks!: Emergent Attention Heads in Reasoning Models During Post Training.

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Thinking Sparks!: Emergent Attention Heads in Reasoning Models During Post Training

Reference 32

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Unresolved cited work

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This paper cites Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models.

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models

Reference 34

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Fisher-Weighted Merge of Contrastive Learning Models in Sequential Recommendation

Reference 35

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned

Reference 36

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Large Language Models Enhanced Collaborative Filtering

Reference 37

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 38

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This paper cites RDRec: Rationale Distillation for LLM-based Recommendation.

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging RDRec: Rationale Distillation for LLM-based Recommendation

Reference 39

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Willmott and Kenji Matsuura

Reference 40

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Reference 41

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This paper cites Unlocking Efficient Long-to-Short LLM Reasoning with Model Merging.

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Unlocking Efficient Long-to-Short LLM Reasoning with Model Merging

Reference 42

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Reference 43

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Unresolved cited work

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging A Survey on Large Language Models for Recommendation

Reference 45

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Chain of Draft: Thinking Faster by Writing Less

Reference 46

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging TIES-Merging: Resolving Interference When Merging Models

Reference 47

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This paper cites arXiv:2502.12067 [cs.CL] https://arxiv.org/abs/2502.12067.

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging arXiv:2502.12067 [cs.CL] https://arxiv.org/abs/2502.12067

Reference 49

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Unresolved cited work

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities

Reference 52

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Unresolved cited work

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Unresolved cited work

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This paper cites Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch.

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch

Reference 56

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Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach

Reference 57

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Observation 29195fc9-c692-4200-94ae-aaacc4473852 · outbound

This paper cites Moreover, 96.6% of these heads reappear in the bootstrap top-5% set in at least 900 of the 1,000 draws.

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Moreover, 96.6% of these heads reappear in the bootstrap top-5% set in at least 900 of the 1,000 draws

Reference 576

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:24:18.669881Z

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Observation 2e5d1988-2bef-48a6-9b2f-af5cc683a795 · outbound

This paper cites Retrieval Head Mechanistically Explains Long-Context Factuality.

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging Retrieval Head Mechanistically Explains Long-Context Factuality

Reference 2024

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Observation 4a631723-7d6a-4d04-9491-a689a02faa92 · outbound

This paper cites arXiv:2510.26546 [cs.IR] https://arxiv.org/abs/ 2510.26546.

Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging arXiv:2510.26546 [cs.IR] https://arxiv.org/abs/ 2510.26546

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-15T14:24:17.030173Z

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