Author

Bohao Wang

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Preprint Aug 2026

Astar: Learning to Propose Evolution Directions for Self-Evolving Industrial AI Systems

This work proposes Astar, a training-based approach that learns a specialized evolution-guiding model from the abundant iteration histories of industrial systems, and proposes a pipeline that turns noisy historical commits into a large, clean evolutionary corpus via pairwise sample expansion and noise filtering.

Jin-Xin Hu, Hao Deng, Hai-Bo Xing et al. · 2 citations

#artificial intelligence Preprint Sep 2026

Using Context Is Not Enough: Test-Time Training for Personalized Reward Modeling

Preference-Aligned Test-Time Training (P-TTT) is proposed, which explicitly encodes preference relations conveyed by contextual pairs into user-specific fast weights for personalized reward prediction and outperforms state-of-the-art methods by a large margin.

Bo-Hao Wang, Xiao-Yan Zhao, Yang Zhang et al. · 0 citations

#natural language process... Preprint Sep 2026

TelecomGPT-R1: Unified Post-Training for Reasoning Across Heterogeneous Telecom Tasks

The TelecomGPT-R1 family of open source unified telecom reasoning models structured around four complementary axes: protocol, knowledge, modeling, and fault are introduced, and supervised fine-tuning instills telecom knowledge and evidence-grounded reasoning patterns to overcome the cold start barrier for reinforcement...

Bo-Hao Wang, Chen-Wei Wu, Han Zou et al. · 1 citation

Jul 2026

LBR: Towards Mitigating Length Bias in Large Language Models for Recommendation

LBR is proposed, a lightweight and model-agnostic framework for mitigating length bias in LLM-based recommendation that substantially alleviates length bias while consistently improving recommendation accuracy and fairness, with negligible additional training and inference overhead.

Hongchen Li, Bohao Wang, Jing-Bang Chen et al. · arXiv.org · 0 citations

Preprint Aug 2026

Making Collaborative Signals Count: Graph-Aware Large Language Models for Sequential Recommendation

GALLM constructs a collaborative graph over text tokens and item tokens, and models three types of relations that are transformed into lightweight learnable attention biases and incorporated into the LLM attention mechanism, enabling collaborative-aware token interactions without introducing an additional graph encoder...

Fenglin Yan, Bo-Hao Wang, Jian Zhang et al. · 0 citations

Preprint Aug 2026

SmartGR: Hierarchy and Beam-Aware Knowledge Distillation for Generative Recommendation

SmartGR is proposed, a novel distillation framework that utilizes Hierarchy-Aware SID Distillation to transfer the teacher's modeling capability across the hierarchy and leverages Beam-Aware Ranking Distillation to distill the teacher's ranking preferences during beam search.

Ziheng Zhang, Yu Cui, Bohao Wang et al. · 0 citations