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Boyang Xue

Boyang Xue has 5 papers in our library, in 2025–2026, most of them on Reasoning, Language models & pretraining, Summarization; often with Hongru Wang, Rui Wang, Jeff Z. Pan.

5 papers in our library · 2025–2026

OpenAlex · ORCID · Wikidata · h-index 6 · 28 works · 134 citations OpenAlex, 2026-10-14

📄 5 works in our library · 🔗 Cited 22 times in our library · ⭐ Paper Stars ranking: #10117

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Chinese University of Hong KongHong Kong · China2021–2026 · 21 paperscurrent🎓University of Hong KongPok Fu Lam · China2024–2025 · 2 papers

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.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 5 worksArts and Humanities · 4 worksArtificial Intelligence · 5 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 2 worksComputational Theory and Mathematics · 1 workInformation Systems · 2 works

🧭 Topics 3Research subjects and languages the work is about.Reasoning · 3 worksLanguage models & pretraining · 2 worksSummarization · 1 work

 Works by year

2020s

20263 papers20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models (2025 · 16 citations)
    Large Reasoning Models such as OpenAI o1, DeepSeek R1 and QwQ solve hard math and coding problems by producing long chains of thought.
  2. Self-Error-Instruct: Generalizing from Errors for LLMs Mathematical Reasoning (2025 · 5 citations)
    LLMs still make many mistakes on math problems. Earlier error-driven methods build new training data from single failed examples, so the data tends to stay tied to those instances. Self-Error-Instruct (SEI) instead groups failures by error type. First, a target model is run on the GSM8K and MATH training sets to…
  3. WebAggregator: Enhancing Compositional Reasoning Capabilities of Deep Research Agent Foundation Models (2026 · 1 citation)
    Deep Research agents need more than retrieval. They must combine scattered, heterogeneous evidence through multi-step reasoning. Existing training and evaluation resources mostly reward locating a single entity, so agents learn to call tools often instead of reasoning carefully.
  4. Mitigating Context Interference for Reliable and Efficient Search Agents (2026)
    Multi-turn LLM search agents repeatedly query a retriever and add the returned documents to their working context.
  5. Self-Sum: Teaching an Agent to Decide Itself When and What to Summarize (2026)
    Agents that tackle long tasks pile up reasoning steps, actions and observations. Their context grows noisy and expensive to process. Common fixes compress history on a fixed schedule or hand the job to an external model. These approaches ignore where the agent actually is in the task and can discard crucial details.…

 Co-authors here

12 of 34 co-authors with a page, the most papers together first.

Venues

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

 Cited by

Papers of our library cite their works 22 times (of the 149,439 papers whose bibliographies we read); 11 citing papers with a page here →.

Updated

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