🧑‍🔬 Bibo Cai

🏛️ Affiliations

🎓Harbin Institute of TechnologyHarbin · China2026 · 4 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. MAESTRO: Meta-learning Adaptive Estimation of Scalarization Trade-offs for Reward Optimization (2026 · 1 citation)
   GRPO aligns LLMs well when answers can be checked automatically, as in math or code. Open-ended generation has no such check, and its goals often conflict, for example creativity versus factuality. Existing fixes either rely on costly LLM judges or combine cheap heuristic rewards (perplexity, entropy, format) using…
2. Large Language Models Are Still Misled by Simple Bias Ensembles (2026)
   Recent LLMs handle individual shortcut cues better than before. This paper argues that the real weakness appears when several such cues occur together in one input, which is common in real-world text. Most existing bias benchmarks inject only one bias per sample, and a few inject two. The authors therefore built…
3. MDC-Bench: A Multidisciplinary Causal Benchmark Based on Causal Structures for Evaluating Large Language Models (2026)
   Most existing causal benchmarks for LLMs use everyday commonsense scenarios with short, single-hop cause–effect links.

All works

5 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

1. MAESTRO: Meta-learning Adaptive Estimation of Scalarization Trade-offs for Reward OptimizationAnnual Meeting of the Association for Computational Linguistics (ACL)Yang Zhao, Hepeng Wang, Xiao Ding, et al.2026 · 1 citation
2. Large Language Models Are Still Misled by Simple Bias EnsemblesFindings of the Association for Computational LinguisticsZhouhao Sun, Zhiyuan Kan, Xiao Ding, et al.2026
3. MDC-Bench: A Multidisciplinary Causal Benchmark Based on Causal Structures for Evaluating Large Language ModelsFindings of the Association for Computational LinguisticsPeng Wang, Yuxiong Yan, Xiao Ding, et al.2026
4. TinyJudge: Unverifiable Constraint Alignment via Lightweight Specialist Ensembles ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetYirong Zeng et al.2026
5. Consolidation or Adaptation? PRISM: Disentangling SFT and RL Data via Gradient Concentration ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetYang Zhao et al.2026

🤝 Co-authors here

11 co-authors with a page, the most papers together first.

• Bing Qin ×3
• Ting Liu ×3
• Xiao Ding ×3
• Kai Xiong ×2
• Li Du ×2
• Yang Zhao ×2
• Zhouhao Sun ×2
• Chao Peng
• Dandan Tu
• Peng Wang
• Yutai Hou

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Annual Meeting of the Association for Computational Linguistics (ACL) ×3: ACL 2026
• Findings of the Association for Computational Linguistics ×2: Findings of the Association for Computational Linguistics 2026 ×2

🔗 Cited by

Papers of our library cite their works 1 time (of the 149,439 papers whose bibliographies we read).

Updated 1 November 2026

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