Hao Wang — early-stage researcher linking embodied AI task planning and world models with LLM reliability
3
papers here
3
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 (2026-10-15) 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—
Output data is not yet available: this axis would measure publication volume and consistency over time (total papers, output rate per year). The current snapshot supplies no such counts for Hao Wang, so no score is reported.
Impact—
Impact data is insufficient: this axis would measure citations, citations per paper, and influence on downstream work. The snapshot contains no citation records for this researcher, so the axis is left unscored rather than estimated.
Focus0/100
Focus scores 0 because the field distribution is perfectly even across three subfields — embodied AI / robot learning, robotics and embodied AI, and NLP (LLM reliability), each 1/3 of the reviewed catalog. This signals breadth across related areas rather than concentration in one; all three papers still cluster around embodied and language-model intelligence, so the split reflects a young, exploratory portfolio.
Collaboration78/100
Collaboration scores 77, the strongest axis in the snapshot, backed by 47 distinct coauthors across only three catalog papers. Twelve recurring collaborators — including Howard Lu, Shalfun Li, Porter Pan, Eric Hu, and KZ Zheng — suggest stable working groups. Notably, all catalog entries list Hao Wang in a middle author position without corresponding-author status, so the score reflects broad but not leading network activity.
Activity—
Activity data is missing: this axis would measure recent publishing cadence, e.g., papers per recent year and time since last output. The snapshot covers only three 2026 arXiv entries, too narrow a window to score cadence reliably.
Reproducibility32/100
Reproducibility scores 32 (reproMean across 3 papers). Evolve rates X-Planner and the world-models survey as low readiness — no ablations or significance tests in the former, qualitative-only validation in the latter — while Hallucination-R1 reaches medium readiness despite a reported loss to prompted DeepSeek-V3 on ER@1. The pattern points to strong empirical claims needing stronger artifact and evaluation disclosure.
Research summary
Hao Wang is an early-stage researcher working at the intersection of embodied intelligence and large language model reliability. The Evolve catalog holds three 2026 arXiv papers, all with a middle author position and none as corresponding author: X-Planner on event-structured task planning for robots, Hallucination-R1 on robustness-oriented paraphrase generation for factual consistency, and a survey proposing a Plausible-to-Controllable-to-Actionable ladder for embodied world models. Together they span robot learning, world models, and LLM evaluation, with a perfectly even field split (one third each) that yields a focus score of 0. Collaboration is the strongest measured axis at 77, resting on 47 distinct coauthors and twelve recurring collaborators such as Howard Lu, Shalfun Li, and Porter Pan. Reproducibility is the weak point: a mean of 32 across three papers, two rated low readiness and one medium. Output, impact, and activity metrics are not yet available, so this profile should be read as a snapshot of a fast-moving, exploratory agenda rather than a settled record.
Collaboration pattern
Collaboration is Hao Wang's strongest measured dimension (77/100), with 47 distinct coauthors across just three catalog papers and twelve recurring partners, including Howard Lu, Shalfun Li, Porter Pan, Eric Hu, and KZ Zheng. Every catalog paper places Wang in a middle author position with no corresponding role, suggesting a contributor within larger teams rather than a group lead. The breadth signals active multi-team work across embodied AI and NLP, though institutional anchors are unknown.
Research keywords
Highlights
X-Planner: event-structured planning with parallel latent CoT
Hallucination-R1: GRPO paraphrasing to expose QA hallucination
World models ladder: from plausible to controllable to actionable
Timeline
- 2026Publishes X-Planner: event-structured task planning for embodied intelligence (arXiv, middle author)
- 2026Publishes Hallucination-R1: robustness-oriented paraphrase generation for factual consistency (arXiv, middle author)
- 2026Publishes a survey on world models for embodied intelligence, proposing the Plausible→Controllable→Actionable ladder (arXiv, middle author)
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.
- Hao Wang is a very common name; this profile is name-resolved from public bibliographic data, and no ORCID or institution was available to verify identity.
- All bibliometric scores come from an OpenAlex-based radar snapshot; output, impact, and activity dimensions lack sufficient data and are left unscored.
- The catalog covers only three 2026 arXiv preprints reviewed by Evolve; it is not a complete publication record.
- The reproducibility score (mean 32) rests on just three papers, two of them rated low readiness, so the estimate is coarse.