Early-career researcher in LLM agent benchmark auditing and hidden chain-of-thought detection in large language models
2
papers here
1
reviewed by Evolve
1
first-author
0
corresponding
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works · OpenAlex
0
h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-10-17) and cover the author's entire public record, not just the papers listed here. Reproducibility signal comes from Evolve's own reviews.
Research profile
Output—
Not yet scored: the record shows only h-index 0, i10-index 0, and 2 works dated 2026, which is too thin for a volume assessment. This axis would measure total publication count, venue spread, and productivity trends over time.
Impact—
Not yet scored: with h-index 0, i10-index 0, and just 2 recorded works, citation-based influence cannot be meaningfully measured. This axis would capture total citations, citation velocity, and uptake of the benchmark-audit findings by later evaluation and interpretability work.
Focus100/100
The focus score of 100 reflects an unusually concentrated record. The topic profile centers on topic modeling (2 occurrences), with single appearances of natural language processing techniques, software engineering research, explainable AI, and AI applications, and the field distribution assigns the reviewed work to machine learning (LLM agent benchmark evaluation). Both 2026 papers sit squarely in LLM evaluation and interpretability rather than spreading across unrelated areas.
Collaboration53/100
The collaboration score of 53 sits at mid-range: the OpenAlex record lists 13 distinct coauthors within the recent three-year window, and 12 recurring coauthors appear across the 2026 arXiv papers. This indicates genuine team-based work on benchmark auditing and interpretability, though not yet the wide, multi-institution networks typical of senior researchers.
Activity32/100
The activity score of 32 is modest: countsByYear shows 2 works in 2026 and recent3yWorks is 2, with no earlier years recorded in the snapshot. Both items appeared in the same year, so a publishing cadence and sustained throughput over multiple years cannot yet be demonstrated.
Reproducibility20/100
The reproducibility score of 20 is low and rests on Evolve's review of 1 paper (reproMean 20). The first-author Terminal-Bench audit paper is rated low repro-readiness because its task labels rest on LLM judges and single oracle runs; the hidden chain-of-thought paper has not yet been assessed.
Research summary
Edward Lue Chee Lip is an early-stage researcher working at the intersection of large language model evaluation and interpretability; this profile is name-resolved from OpenAlex bibliographic data plus two 2026 arXiv papers in the Evolve catalog. The record shows an h-index of 0, an i10-index of 0, and 2 works in 2026, so the output and impact axes are unscored, and the activity score of 32 reflects the thin recent volume. The work is sharply concentrated: the focus score reaches 100, topic modeling appears twice in the topic profile alongside natural language processing techniques, software engineering research, explainable AI, and AI applications, and the field distribution places the reviewed work in machine learning (LLM agent benchmark evaluation). The flagship first-author paper audits Terminal-Bench 3, splitting all-fail tasks into 78 certified-unsolved and 47 defective cases; Evolve finds the audit protocol credible but notes the labels rest on LLM judges and single oracle runs, rating repro-readiness low (reproducibility score 20 across 1 paper). A second, middle-author paper proposes detecting hidden chain-of-thought through linguistic, behavioral, and mechanistic indicators. Collaboration is genuine but compact: 13 distinct coauthors in the recent three-year window yield a collaboration score of 53.
Collaboration pattern
Collaboration is team-based but compact. The OpenAlex record lists 13 distinct coauthors in the recent three-year window, and the 2026 papers draw on a recurring group of twelve coauthors, including Boden Moraski, Tim Knappe, Lang Xiong, and Sarvesh Gharat. The mid-range collaboration score of 53 reflects genuine co-authorship without yet a broad multi-institution footprint.
Research keywords
Highlights
Auditing Terminal-Bench 3: separating genuine task hardness from defects
Detecting hidden chain-of-thought with linguistic, behavioral, and mechanistic signals
A 2026 arXiv paper in which the researcher contributes as a middle author, proposing to detect hidden chain-of-thought in large language models through linguistic, behavioral, and mechanistic indicators. The Evolve catalog lists the paper without a full review, keywords, or repro-readiness rating yet.
Timeline
- 2026First-author arXiv paper 'What Makes a Terminal-Bench Task Hard?' audits Terminal-Bench 3, splitting all-fail tasks into 78 certified-unsolved vs 47 defective.
- 2026Middle-author arXiv paper proposes detecting hidden chain-of-thought in LLMs with linguistic, behavioral, and mechanistic indicators.
- 2026OpenAlex snapshot records 2 works in 2026 with 13 distinct coauthors; h-index and i10-index both stand at 0.
Papers in the Evolve catalog
Frequent co-authors
About this profile
This profile is AI-structured by Evolve.Pub from public bibliographic data (OpenAlex) and Evolve's own paper reviews. It is not written or endorsed by the researcher unless the profile is claimed. Radar dimensions are computed from real bibliometrics with fixed formulas — raw figures are shown alongside; missing dimensions mean insufficient data, never zero. Errors? Claim the profile to correct or hide it.
- Bibliometrics come from OpenAlex only; the record shows h-index 0, i10-index 0, and 2 works in 2026, so the output and impact axes are unscored.
- No institution, country, or ORCID is attached to this record; the profile is name-resolved from public bibliographic data.
- Only one of the two catalog papers carries an Evolve review verdict and repro-readiness rating; the hidden chain-of-thought paper is listed without a full review.
- The name may be shared by multiple authors; identity rests on the OpenAlex record and the coauthor set shown here.