Early-career researcher working across LLM-agent fine-tuning data curation and egocentric navigation benchmarking
2
papers here
2
reviewed by Evolve
1
first-author
0
corresponding
—
works · OpenAlex
0
h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-09-19) 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—
No output score yet: the OpenAlex record counts only one work (2026) with an h-index of 0, so publication volume cannot be assessed reliably. This axis would measure total output and productivity over time once more works are indexed.
Impact—
Impact is not scored: with an h-index and i10-index of 0 and preprints too recent to accrue citations, community uptake cannot be measured yet. This axis would capture citation influence, downstream adoption, and recognition as citation data matures.
Focus0/100
The focus score is 0, reflecting an even, undifferentiated spread rather than a concentrated agenda: the field distribution splits 0.5/0.5 between natural language processing (LLM agents) and computer vision / embodied AI, and each of the three indexed topics — robotics and sensor-based localization, multimodal machine learning applications, spatial cognition and navigation — carries a single paper. With only two cataloged works, thematic concentration cannot yet be established.
Collaboration36/100
Collaboration scores 36 on a compact footprint: five distinct coauthors (Zhen Zhang, Xinyu Wang, Kewei Tu, Yang Zhao, Xubo Yang) appear across the record. Zhuo Chen holds first authorship on the trajectory-refinement paper and a middle position on EgoPathBench, with neither role as corresponding author — consistent with a small shared working group spanning both LLM-agent and embodied-AI work.
Activity20/100
Activity scores 20: the counts-by-year record shows a single indexed work in 2026, and recent three-year output is 1. Both cataloged papers are dated 2026, so the measurable publication window is effectively just opening; the low score reflects a very short observable record rather than a demonstrated slowdown.
Reproducibility55/100
Reproducibility averages 55 across the two reviewed papers, both rated medium readiness. The verdicts temper confidence: the trajectory-refinement paper's headline margins lack statistical support and its checkpoint-selection protocol favors the proposed methods, while EgoPathBench shows strong evaluator validation but SFT gains without control baselines — a mixed but partially reusable methodological record.
Research summary
Zhuo Chen is an early-stage researcher whose public bibliographic footprint — resolved from OpenAlex data, with no institutional affiliation or ORCID attached in the current snapshot — centers on two 2026 arXiv preprints reviewed in the Evolve catalog. The first, where Zhuo Chen is first author, recasts multi-turn LLM-agent supervised fine-tuning data cleanup as deterministic editing of a round-level dependency DAG, reporting accuracy and efficiency gains. The second, EgoPathBench, is a geometry-grounded benchmark showing that the strongest vision-language models reach only 28.3/100 on zero-shot egocentric waypoint decision-making. Together these place the work at an even split between natural language processing (LLM agents) and computer vision / embodied AI (field distribution 0.5/0.5), with indexed topics spanning robotics and sensor-based localization, multimodal machine learning applications, and spatial cognition and navigation. The bibliometric record is thin: h-index and i10-index are both 0, one work is counted for 2026, recent three-year output is one work, and five distinct coauthors appear. Evolve's reproducibility assessment averages 55 across the two papers, both rated medium readiness; its verdicts flag missing statistical support, a checkpoint-selection protocol favoring the proposed method, and absent control baselines for SFT gains. This is a name-resolved profile built solely from these sources; Zhuo Chen is a common name, so identity may not be unique.
Collaboration pattern
The collaboration footprint is compact: five distinct coauthors are recorded (Zhen Zhang, Xinyu Wang, Kewei Tu, Yang Zhao, Xubo Yang), yielding a collaboration score of 36. Zhuo Chen leads as first author on the trajectory-refinement paper and contributes in a middle position on EgoPathBench, with no corresponding-author role in the catalog. The recurring co-author pool across both an LLM-agent and an embodied-AI paper suggests a small, closely shared working group rather than a wide network.
Research keywords
Highlights
Recasting trajectory cleanup as deterministic DAG editing
EgoPathBench: VLMs still fail zero-shot egocentric route decisions
Timeline
- 2026Publishes first-author arXiv preprint on dependency-aware trajectory refinement for efficient multi-turn agent fine-tuning.
- 2026Contributes as a middle author to EgoPathBench, a zero-shot egocentric waypoint decision-making benchmark for vision-language models.
- 2026OpenAlex record opens: one indexed work in 2026, five distinct coauthors, and topics spanning localization, multimodal learning, and spatial navigation.
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.
- Zhuo Chen is a common name; this profile is resolved from public OpenAlex bibliographic data and may not uniquely identify the individual.
- Bibliometrics come from OpenAlex only; the h-index (0), i10-index (0), and single 2026 work reflect a sparse, very recent record rather than measured impact.
- No institution, country, or ORCID is attached to this record, and the output and impact dimensions lack sufficient data to score.
- Both cataloged papers are 2026 arXiv preprints; peer-reviewed venue status is unknown.