NLP researcher building cost-efficient, confidence-gated hybrid LLM systems for emotion recognition in conversation
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papers here
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reviewed by Evolve
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first-author
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corresponding
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works · OpenAlex
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h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-09-20) 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—
Insufficient data yet: this axis measures total research productivity — paper counts, venues, and publication cadence. Only one 2026 arXiv paper has been reviewed, so no aggregate output score can be computed.
Impact—
No citation metrics exist in the current snapshot. This axis would measure scholarly influence — citations, citations per paper, and uptake of the 2026 confidence-gated hybrid approach — once the bibliographic record accumulates sufficient citation data.
Focus100/100
Focus is scored at the maximum, 100: the field distribution contains exactly one field, natural language processing (conversational AI / ML systems), covering the single reviewed paper. Every reviewed output concentrates on confidence-gated hybrid LLM architectures for emotion recognition in conversation, indicating a tightly specialized research agenda, though the one-paper evidence base means this concentration should be re-tested as the record grows.
Collaboration28/100
Collaboration scores 28, reflecting a compact network: 3 distinct co-authors on the reviewed record — Arjun Chouhan, Ravisekhar Kanagala, and Trishala Pavagada. The reviewed 2026 paper is first-authored (non-corresponding), a small-team working pattern typical of early-stage independent research. The small distinct-coauthor count naturally caps network breadth until more papers enter the catalog.
Activity—
Insufficient data yet: this axis measures research activity over time — publication recency, cadence, and sustained output across years. The snapshot contains a single 2026 paper, so no multi-year activity trend can be established or scored.
Reproducibility55/100
Reproducibility scores 55, the mean over one paper with medium reproducibility readiness — the 2026 confidence-gated hybrid study. Evolve's verdict calls the results mostly supported, supporting credible, checkable benchmark claims across three ERC datasets, though the medium readiness rating signals that full replication would still require additional artifacts or method detail.
Research summary
Sai Babu Udayagiri is an early-stage researcher working at the intersection of natural language processing and conversational AI; this profile is name-resolved from public bibliographic data, since no affiliation or ORCID record was provided. The current evidence base is one first-author paper in the Evolve catalog: a 2026 arXiv preprint, 'When to Call an LLM: A Confidence-Gated Hybrid for Cost-Effective Emotion Recognition in Conversational AI.' The paper proposes a confidence-gated routing mechanism that decides when a lightweight ensemble should hand off to a large language model (GPT-4o-mini), targeting lower inference cost without sacrificing recognition accuracy. Evolve's review calls it a credible study: the confidence-gated ensemble+LLM hybrid beat both pure systems on three emotion recognition in conversation (ERC) benchmarks, with results mostly supported, though absolute accuracy trails state-of-the-art methods. Every reviewed output sits in a single field — natural language processing (conversational AI / ML systems) — producing a perfect focus score of 100. Collaboration is compact, with 3 distinct co-authors (Arjun Chouhan, Ravisekhar Kanagala, Trishala Pavagada) and a collaboration score of 28. Reproducibility readiness is medium, with a mean score of 55 over one paper. Output, impact, and activity dimensions remain unscored pending more bibliometric data.
Collaboration pattern
The collaboration footprint is deliberately compact: 3 distinct co-authors — Arjun Chouhan, Ravisekhar Kanagala, and Trishala Pavagada — appear across the reviewed record, yielding a collaboration score of 28. The reviewed 2026 paper is first-authored (non-corresponding), consistent with a small-team, hands-on working pattern rather than a large distributed consortium. All collaboration evidence comes from this single reviewed paper, so the network picture is a floor rather than a mature view.
Research keywords
Highlights
Confidence-gated hybrid outperforms pure baselines on three ERC benchmarks
Selective LLM calls target cost-effectiveness
First-authored arXiv study with medium reproducibility readiness
Timeline
- 2026Posted first-author arXiv preprint 'When to Call an LLM: A Confidence-Gated Hybrid for Cost-Effective Emotion Recognition in Conversational AI.'
- 2026Validated the confidence-gated ensemble+LLM hybrid on three emotion recognition in conversation benchmarks, outperforming both pure ensemble and pure LLM systems.
- 2026Evolve review rates the study credible, with medium reproducibility readiness (mean 55 over one paper), while noting that accuracy trails state-of-the-art.
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 scores are computed from the Evolve catalog snapshot only; output, impact, and activity lack sufficient underlying data.
- Profile is name-resolved from public bibliographic data; no institutional affiliation, country, or ORCID was provided.
- The evidence base is a single first-author arXiv preprint (2026), so every scored dimension rests on one reviewed paper.
- The reproducibility mean (55) is averaged over one paper with medium readiness and may shift as more work is reviewed.