arXiv AI August 11, 2026 By Juncheng Dong, Ding Tong, Ishan Gupta, Yuyan Wang

LLM Reasoning for Subjective Tasks: Failure Modes, Mitigation, and Dynamic Reasoning Routing

arXiv:2608. 08889v1 Announce Type: new Abstract: Recommendation systems thrive on personalization, where ''correctness'' is rarely a binary truth but a matter of subjective human preference.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 4

Aligning Deep Implicit Preferences by Learning to Reason Defensively

arXiv:2510. 11194v3 Announce Type: replace Abstract: Personalized alignment is crucial for enabling Large Language Models (LLMs) to engage effectively in user-centric interactions.

By Peiming Li, Zhiyuan Hu, Yang Tang, Shiyu Li, Xi Chen

llms reinforcement-learning benchmarks safety

arXiv AI

Jul 29

ODYSSE: Episode-wise Policy Optimization for Personalized Agentic Reasoning

arXiv:2607. 25369v1 Announce Type: new Abstract: Agentic systems have rapidly advanced in their ability to interact with real-world environments, leverage external tools, and provide services for users.

By Jiaqi Zhang, Tong Chen, Junliang Yu, Quoc Viet Hung Nguyen, Hongzhi Yin

agents fine-tuning

arXiv AI

Jul 8

Mathematical Reasoning in Large Language Models: Benchmarks, Architectures, Evaluation, and Open Challenges

arXiv:2605. 19723v2 Announce Type: replace-cross Abstract: Mathematical reasoning is essential for problem-solving in education, science, and industry, serving as a crucial benchmark for evaluating artificial intelligence systems.

By Husnain Amjad, Raja Khurram Shahzad, Aamir Shahzad, Mehwish Fatima

llms fine-tuning benchmarks

arXiv AI

Aug 12

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

arXiv:2608. 10447v1 Announce Type: cross Abstract: Large language model-based recommender systems are increasingly adopting slow-thinking models that generate step-by-step reasoning before making predictions, often achieving higher accuracy than fast-thinking models that predict directly.

By Linh Dieu Le, Tong Chen, Shazia Sadiq, Hongzhi Yin, Ming Jin, Junliang Yu

llms benchmarks

arXiv Machine Learning

Jun 30

Reward Modeling for Reinforcement Learning-Based LLM Reasoning: Design, Challenges, and Evaluation

arXiv:2602. 09305v2 Announce Type: replace Abstract: Large Language Models (LLMs) demonstrate transformative potential, yet their reasoning remains inconsistent and unreliable.

By Pei-Chi Pan, Yingbin Liang, Sen Lin

llms reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jun 9

Generative Reasoning Re-ranker

arXiv:2602. 07774v5 Announce Type: replace-cross Abstract: Recent studies increasingly explore Large Language Models (LLMs) as a new paradigm for recommendation systems due to their scalability and world knowledge.

By Mingfu Liang, Yufei Li, Jay Xu, Kavosh Asadi, Xi Liu, Shuo Gu, Kaushik Rangadurai, Frank Shyu, Shuaiwen Wang, Song Yang, Zhijing Li, Jiang Liu, Mengying Sun, Fei Tian, Xiaohan Wei, Chonglin Sun, Jacob Tao, Shike Mei, Wenlin Chen, Santanu Kolay, Sandeep Pandey, Hamed Firooz, Luke Simon

llms rag nlp reinforcement-learning fine-tuning benchmarks

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Aligning Deep Implicit Preferences by Learning to Reason Defensively

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Mathematical Reasoning in Large Language Models: Benchmarks, Architectures, Evaluation, and Open Challenges

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

Reward Modeling for Reinforcement Learning-Based LLM Reasoning: Design, Challenges, and Evaluation

Generative Reasoning Re-ranker