Aarav Singh
International Institute of Information Technology · India
Early-career NLP researcher at IIIT: benchmark-validity critique and clinician-in-the-loop diagnostic AI design
2
papers here
1
reviewed by Evolve
1
first-author
1
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 bibliometric record lacks a reliable total output count for this author, so the axis cannot be computed. Output would measure the volume and cadence of publications and preprints across the research career.
Impact—
Not yet scored: the record lists h-index 0 and i10-index 0, reflecting a career at its start rather than measured influence, so no score is given. Impact would measure citations, uptake, and influence on follow-up work.
Focus100/100
The focus score of 100 reflects an exceptionally concentrated record: the OpenAlex field distribution attributes this author's indexed work to a single field, natural language processing (computational argumentation). Both reviewed papers stay within NLP — a fallacy-benchmark critique in computational argumentation and a clinical NLP design framework — though topic counts (topic modeling 2, AI in healthcare and education 2) hint at applied breadth inside that core.
Collaboration22/100
The collaboration score of 22 sits low, matching a sparse co-author network: the raw record lists only 2 distinct co-authors, with Navyansh Singh and Animesh Pathak as the recurring names. Both reviewed papers are small-team efforts — the NSIDDx framework was first-authored with corresponding authorship — suggesting early-career work done in compact groups rather than large consortia.
Activity40/100
The activity score of 40 reflects a short but current record: all 3 indexed works fall in 2026 (countsByYear 2026: 3), and recent3yWorks is 3, so publishing is recent and concentrated rather than sustained across years. Both cataloged papers are 2026 arXiv preprints, consistent with a researcher whose output history is only beginning to accumulate.
Reproducibility55/100
The reproducibility score of 55 is an average over just 1 scored paper (reproMean 55, reproPapers 1), so it should be read as a single data point rather than a track record. Evolve rates the NSIDDx design framework's reproducibility readiness as high — the paper documents LLM+RAG failure analysis honestly — while the fallacy-benchmark critique is rated medium.
Research summary
Aarav Singh is an early-career researcher at the International Institute of Information Technology (India) whose indexed work sits squarely in natural language processing, with a focus score of 100 reflecting a single-field OpenAlex distribution (computational argumentation). The record is short but current: all 3 indexed works date to 2026, and the h-index and i10-index are 0, consistent with a career just beginning. Two Evolve-reviewed papers define the profile. The first, "Fallacy Benchmarks Measure Scheme Recognition, Not Fallacy Detection" (2026, arXiv), where Singh is last author, argues that reported false-positive rates on fallacy benchmarks are artifacts of how the valid class is constructed — controlled evidence models track argumentation-scheme identity rather than genuine validity — a claim Evolve judges largely convincing. The second, "NSIDDx" (2026, arXiv), is a first-authored, corresponding-author position paper proposing a neuro-symbolic, clinician-in-the-loop framework for differential diagnosis in low-resource settings; Evolve credits its honest LLM+RAG failure analysis but notes the framework is never quantitatively evaluated and the thesis rests on one case, while rating its reproducibility readiness high. Topic counts (topic modeling 2; AI in healthcare and education 2) and only 2 distinct co-authors — Navyansh Singh and Animesh Pathak — sketch a compact, emerging collaboration pattern.
Collaboration pattern
The raw record lists only 2 distinct co-authors, and the collaboration score is 22, indicating a small early-career network. Navyansh Singh and Animesh Pathak are the recurring collaborators. Both cataloged papers are compact-team efforts: the fallacy-benchmark critique lists Singh as last author, while the NSIDDx framework is first-authored with corresponding authorship. No large consortium work appears yet.
Research keywords
Highlights
Argumentation-scheme recognition masquerades as fallacy detection
NSIDDx: a clinician-in-the-loop design framework for low-resource differential diagnosis
Timeline
- 2026Last author on "Fallacy Benchmarks Measure Scheme Recognition, Not Fallacy Detection" (arXiv), a benchmark-validity critique in computational argumentation.
- 2026First and corresponding author of the NSIDDx position paper (arXiv), proposing a neuro-symbolic, clinician-in-the-loop design framework for differential diagnosis in low-resource settings.
- 2026Bibliometric record shows all 3 indexed works dated to 2026 (countsByYear 2026: 3), spanning computational argumentation and clinical NLP.
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.
- Bibliometric metrics come from OpenAlex identity resolution only (no ORCID linked), so name ambiguity cannot be fully excluded for this common name.
- h-index and i10-index are 0 and all 3 indexed works are 2026 outputs, so impact indicators are too early to interpret.
- The reproducibility score averages a single scored paper (reproPapers: 1) and should be treated as one data point, not a track record.
- Only the two Evolve-reviewed papers are profiled in depth; a third 2026 work appears in the bibliometric counts but is not reviewed here.