🧑‍🔬 Hao Liang

🏛️ Affiliations

🎓Peking UniversityBeijing · China2025–2026 · 6 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. CFBench: A Comprehensive Constraints-Following Benchmark for LLMs (2025 · 30 citations)
   CFBench tackles a gap in how LLMs are tested on following instructions that carry many explicit constraints.
2. MM-Verify: Enhancing Multimodal Reasoning with Chain-of-Thought Verification (2025 · 19 citations)
   The paper targets two gaps in multimodal math reasoning. There is no strong verifier for checking answers from multimodal LLMs, and there is little long chain-of-thought (CoT) training data with images. The authors build two 7B models. MM-Verifier is an outcome reward model that judges whether a solution to an…
3. AgenticRAGTracer: A Hop-Aware Benchmark for Diagnosing Multi-Step Retrieval Reasoning in Agentic RAG (2026 · 1 citation)
   Popular multi-hop QA benchmarks such as HotpotQA, 2WikiMultihopQA and MuSiQue give only a question and its final answer.
4. MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning (2026)
   Static reasoning benchmarks quickly become too easy for frontier models. Many also reward recall of rare facts rather than logical inference. MorphoBench addresses this with a multidisciplinary benchmark whose difficulty can be tuned to the models being tested.
5. Rethinking Text-to-SQL: Dynamic Multi-turn SQL Interaction for Real-world Database Exploration (2026)
   Most Text-to-SQL benchmarks test one-shot translation of a fixed question into a read-only query. Real database work is different: users refine goals over several turns and often need to change data, not just read it. The authors introduce DySQL-Bench to evaluate LLM agents in this setting. It contains 1,072…
6. Training Verifier to Assessing Complex Real-World Tool-Use Trajectories (2026)
   LLM agents that handle multi-turn customer-service style tasks need training dialogues that reflect real user–agent collaboration.

🤝 Co-authors here

12 of 18 co-authors with a page, the most papers together first.

• Wentao Zhang ×4
• Bihui Yu ×3
• Fan Yang ×3
• Jingxuan Wei ×3
• Linzhuang Sun ×3
• Bin Cui ×2
• Tianpeng Li ×2
• Tianyu Guo ×2
• Chenzheng Zhu
• Junbo Niu
• Mingan Lin
• Mingrui Chen

Venues

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

• Findings of the Association for Computational Linguistics ×4: Findings of the Association for Computational Linguistics 2026 ×4
• Annual Meeting of the Association for Computational Linguistics (ACL) ×2: ACL 2025 ×2

🔗 Cited by

Papers of our library cite their works 45 times (of the 149,439 papers whose bibliographies we read); 16 citing papers with a page here →.

Updated 1 November 2026

This page in Markdown → https://md.research.elamur.ai/free/en/authors/hao-liang/