Continual learning researcher bridging brain-inspired modularity and embodied multi-view video perception.
2
papers here
2
reviewed by Evolve
0
first-author
0
corresponding
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works · OpenAlex
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h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-10-09) 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—
The output axis is unscored: this snapshot contains no publication counts, venue totals, or full output history — only 2 reviewed 2026 arXiv papers — so productivity volume and outlet mix cannot yet be measured.
Impact—
Impact is unscored: no citation counts, h-index, or uptake metrics are available in this snapshot, so this axis — which would measure how the two 2026 papers influence later work — cannot be estimated.
Focus0/100
The radar scores focus at 0, and the raw field distribution shows why: the reviewed record splits evenly at 0.5 / 0.5 between machine learning (NeuroAI / continual learning) and computer vision / machine learning (continual learning for embodied multi-view video perception). In substance this is one continuous program — continual learning — pursued from both an algorithm-design angle and an embodied video benchmark angle, but the even split keeps the concentration score low.
Collaboration51/100
Collaboration scores 51 on the radar, based on 12 distinct coauthors recorded in the snapshot. The frequent-collaborator list is led by Hongwei Yan, Kanglei Zhou, Liyuan Wang and Yuchen Liu, alongside Qingyu Shi, Qi Cheng, Weiyi Dong, Chunyan Lan, Guanglong Sun, Jun Zhou and Qian Li. On both reviewed 2026 papers Yi Zhong holds a middle author position without corresponding-author status.
Activity—
Activity is unscored: the snapshot has no per-year publication frequency or submission cadence data, so research rhythm over time cannot be measured; the only dated records are the two 2026 arXiv papers.
Reproducibility55/100
Reproducibility scores 55, computed as the mean over 2 reviewed papers (reproMean 55, reproPapers 2), each rated medium reproducibility readiness. Evolve's verdicts note strong empirics in the brain-inspired modularity paper but informal theory, and find the CE⁴L benchmark largely convincing while flagging router overclaim and seed variance — consistent with a mid-range, medium-readiness score.
Research summary
Yi Zhong, affiliated with the Center for Life Sciences in the available records, is a machine learning researcher whose work centers on continual learning — learning new tasks without catastrophic forgetting. The reviewed snapshot splits evenly (0.5 / 0.5) between two strands: NeuroAI-inspired continual learning algorithms, and continual learning for embodied multi-view video perception in computer vision. Two 2026 arXiv papers, both reviewed in the Evolve catalog with Yi Zhong in a middle author position, define the visible record. The first, Brain-Inspired Hierarchical Modularity for General Continual Learning, applies Drosophila mushroom-body-inspired mixture-of-experts and ensemble modularity; Evolve's verdict credits large continual-learning gains across vision and robotics with strong empirics, while noting informal theory and replay-competitive results. The second, CE⁴L: Continual Ego, Exo, and Ego-Exo Learning, contributes a benchmark for continual egocentric, exocentric and ego-exo multi-view video learning plus training-free subspace-routed adapters; it is judged largely convincing, with router overclaim and seed variance flagged. Radar scores reproducibility at 55 (mean over 2 papers, both medium readiness) and collaboration at 51, with 12 distinct coauthors recorded; the focus score is 0, reflecting the even split across two adjacent subfields. Output, impact and activity lack underlying data and remain unscored, so this should be read as an early-stage profile anchored on the 2026 record.
Collaboration pattern
Yi Zhong works within co-author teams rather than solo: the snapshot records 12 distinct coauthors, and the radar's collaboration score is 51. Both reviewed 2026 arXiv papers list Yi Zhong in a middle author position without corresponding-author status, suggesting a contributing role within larger groups. Recurring collaborators include Hongwei Yan, Kanglei Zhou, Liyuan Wang, Yuchen Liu, Qingyu Shi, Qi Cheng, Weiyi Dong, Chunyan Lan, Guanglong Sun, Jun Zhou and Qian Li.
Research keywords
Highlights
Drosophila-inspired modular architecture for general continual learning
Continual ego-exo multi-view video learning benchmark
One program bridging brain-inspired algorithms and embodied perception
Across both reviewed papers, the same continual-learning thread runs from NeuroAI-inspired modularity to embodied multi-view video: the field distribution splits evenly (0.5 / 0.5), and both works rely on parameter-efficient adaptation — fine-tuning in one, subspace-routed adapters in the other.
Timeline
- 2026Co-authored Brain-Inspired Hierarchical Modularity for General Continual Learning (arXiv), a Drosophila mushroom-body-inspired mixture-of-experts and ensemble design for general continual learning.
- 2026Co-authored CE⁴L: Continual Ego, Exo, and Ego-Exo Learning (arXiv), a continual ego/exo multi-view video benchmark with training-free subspace-routed adapters.
- 2026Both 2026 papers enter the Evolve catalog with medium reproducibility readiness (reproMean 55 across 2 papers); the snapshot records 12 distinct coauthors.
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 and radar scores derive from OpenAlex-based snapshots only; no citation counts, h-index, or venue history are included.
- The profile rests on only 2 Evolve-reviewed 2026 arXiv preprints; the broader publication record is not captured.
- No country or ORCID is available; 'Yi Zhong' is a common name, so identity is resolved from public bibliographic data and may be ambiguous.
- Output, impact and activity axes lack underlying data and are left unscored rather than estimated.