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Guoxin Chen

Guoxin Chen has 4 papers in our library, in 2024–2025, most of them on Reasoning; often with Chengxi Li, Kai Fan, Minpeng Liao.

4 papers in our library · 2024–2025

OpenAlex · ORCID · Wikidata · h-index 36 · 164 works · 4,632 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 141 times in our library · ⭐ Paper Stars ranking: #5938

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

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 27 more institutions on single bylines are not listed.

 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. AlphaMath Almost Zero: Process Supervision without Process (2024 · 120 citations)
    Most recent work that improves LLM math reasoning fine-tunes models on step-by-step solutions written by human experts or GPT-4, which is costly to produce.
  2. Table-Critic: A Multi-Agent Framework for Collaborative Criticism and Refinement in Table Reasoning (2025 · 25 citations)
    Large language models often fail at multi-step reasoning over tables. A wrong choice early in the chain, such as picking the wrong row or column, tends to carry through to the final answer. Earlier decomposition methods like Binder, Dater and Chain-of-Table have no reliable way to catch and fix these intermediate…
  3. DiReCT: Diagnostic Reasoning for Clinical Notes via Large Language Models (2024 · 20 citations)
    LLMs such as GPT-4 score well on medical exam questions, but those benchmarks use short inputs and say little about whether a model's diagnostic reasoning can be inspected.
  4. ReForm: Reflective Autoformalization with Prospective Bounded Sequence Optimization (2025 · 8 citations)
    Autoformalization turns natural-language math problems into Lean statements that a machine can check. Current LLMs usually produce code that compiles, but the statements often change the problem's meaning. The authors argue this happens because models translate in one shot and never review their own output. ReForm…

 Co-authors here

12 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 141 times (of the 149,439 papers whose bibliographies we read); 44 citing papers with a page here →.

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