Jaypee Institute of Information Technology
Bachelor of Technology in Computer Science · CGPA: 3.3 / 4.0
Noida, India
Aug 2021 – May 2025
Education
Research Interests
Natural Language Processing
Large Language Models
Uncertainty Quantification
Reliability
Reasoning
Trustworthy AI
AI Safety / Security
Publications & Preprints
Proper Scoring Rules for Agentic Uncertainty Quantification
- Introduced a family of strictly proper trajectory-level scoring rules for evaluating uncertainty in LM agents, with a censored-trace extension and negative results establishing that standard trajectory-level calibration metrics (ECE, Brier) are not strictly proper in the agentic setting.
SELFDOUBT: Uncertainty Quantification for Reasoning LLMs via the Hedge-to-Verify Ratio
- Proposed an O(1) black-box uncertainty framework that extracts behavioral hedge/verify signals from reasoning traces, significantly outperforming Semantic Entropy on discrimination (p=0.001) at 10× lower cost; a zero-hedge gate achieves 96.1% precision across 7 models and 3 benchmarks.
Don't Blink: Evidence Collapse during Multimodal Reasoning
- Identified a universal evidence collapse phenomenon in reasoning VLMs, observing visual attention drops up to 90.8% during generation and discovering a task-conditional failure regime where confident but visually disengaged predictions are hazardous on sustained visual reference tasks but benign on symbolic tasks.
Repair of Thought: Advancing Automated Program Repair through a Dual-Model Reasoning Framework
- Introduced a function-level APR framework achieving a SOTA 83.1% plausible repair rate on Defects4J, with an automated verification pipeline combining AST alignment, control-flow symbolic analysis, and semantic checks.
Research Experience
University of California, Santa Cruz · AIEA Lab
Research Assistant · Advisor: Prof. Leilani H. Gilpin
Remote / Santa Cruz, CA
Jun 2024 – Aug 2024
- Investigated trustworthy logical reasoning in LLMs by translating natural language outputs into Prolog-based logic constraints to mathematically verify consistency and detect hallucinations.
- Designed a Modular RAG architecture to decouple knowledge retrieval from reasoning, reducing false positive hallucination detection by integrating external ground-truth validation.
Jaypee Institute of Information Technology
Undergraduate Researcher · Advisor: Prof. Sandeep Kumar Singh
Noida, India
Aug 2023 – May 2024
- Conducted initial experiments on parameter-efficient fine-tuning (Unsloth/QLoRA) which revealed limitations in complex logic repair, motivating a strategic pivot from training-based optimization to an inference-time reasoning framework that became the core contribution of the Repair of Thought publication.
Professional Experience
VFS Global
AI Research Engineer
New Delhi, India
Dec 2024 – Present
- Investigated dynamic execution paths for multi-agent frameworks. Designed a complexity-based routing mechanism that differentiates between deterministic tasks handled by parsers and ambiguous instances routed to reasoning-capable VLMs, optimizing the trade-off between inference cost and reasoning accuracy.
- Integrated a two-stage confidence pipeline: token-level logit probabilities aggregated via Shannon entropy serve as a lightweight first-pass filter for extraction uncertainty, with high-entropy predictions escalated to a reasoning VLM-as-a-Judge for cross-modal extraction verification.
- Designed a trace-guided re-extraction mechanism for cascade-rejected fields that fail both entropy screening and judge validation. The judge's reasoning traces are converted into corrective context for a secondary extraction model, enabling targeted recovery from extraction errors such as character misreads, boundary confusion, and semantic misattribution.
- Designed a multi-level verification framework for biometric photo validation, separating signal-level quality assessment from semantic-level attribute verification with fine-tuned VLMs. The staged design isolated distinct failure modes and improved the interpretability of rejection decisions.