Elamur Research⌕
☰
home / authors / Zachary S. Siegel

Zachary S. Siegel

Zachary S. Siegel has 2 papers in our library, in 2024, most of them on Evaluation & benchmarks, Reasoning, Information retrieval & search; often with Arvind Narayanan, Benedikt Stroebl, Hongjin Su.

2 papers in our library · 2024

OpenAlex · h-index 4 · 8 works · 23 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 166 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #7395

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

🎓Massachusetts Institute of TechnologyCambridge · United States2026 · 1 papercurrent🎓Princeton UniversityPrinceton · United States2022–2024 · 5 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksArts and Humanities · 1 workInformation Systems · 1 workLanguage and Linguistics · 1 work

🧭 Topics 3Research subjects and languages the work is about.Evaluation & benchmarks · 1 workInformation retrieval & search · 1 workReasoning · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Inverted index · 1 work

 Works by year

2020s

20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval (2024 · 77 citations)
    Standard retrieval benchmarks such as BEIR and MTEB mostly test search-engine-style questions. In these, relevant documents share many words or meanings with the query. Many real needs are harder. A programmer may need documentation for a function that is never named in the question, or a student may need a problem…
  2. AI Agents That Matter (2024 · 55 citations)
    Benchmarks steer the development of LLM-based agents. The Princeton authors argue that current evaluation habits reward topping leaderboards rather than building systems that work in practice. They identify four problems:

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive RetrievalarXiv:2407.12883Hongjin Su, Howard Yen, Mengzhou Xia, et al.2024 · 77 citations
  2. AI Agents That MatterarXiv:2407.01502Sayash Kapoor, Benedikt Stroebl, Zachary S. Siegel, et al.2024 · 55 citations
  3. CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark ↗arXiv:2409.11363 · no summary yetZachary S. Siegel et al.2024 · 39 citations
  4. Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluation ↗arXiv:2510.11977 · no summary yetSayash Kapoor et al.2025 · 11 citations

 Co-authors here

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

 Cited by

Papers of our library cite their works 166 times (of the 149,439 papers whose bibliographies we read); 35 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/zachary-s-siegel/