London, UK
Javal Vyas
PhD Researcher • Risk & Reliability in Generative Decision Systems
Risk • Reliability • Uncertainty • Convergence (sequential decision-making in dynamic systems)
I study generative models as stochastic decision policies in dynamic systems—and build mechanisms to make them measurable, controllable, and reliable under constraints. My work focuses on risk-aware validation, uncertainty/entropy diagnostics, and convergence behavior in sequential decision-making, with applications to industrial control and operations.
Core question
When can we trust a stochastic policy?
Risk + reliability for sequential decision-making under constraints
Mechanism
Validation loops + measurable failure metrics
Constraint violation → targeted reprompting / policy shaping
Applied setting
Dynamic systems (control + operations)
Latency constraints, safety envelopes, and action correctness
Selected Work
Systems + experiments that make stochastic policies safer, measurable, and more reliable under constraints.
GraphRAG-powered agentic fault handling for controlled operations. Structured context injection + validator-guided action selection (paper under review).
Theme
Risk-aware recovery
Method
Graph + tools + validation
- Relation-aware retrieval for decision-time grounding
- Action-oriented agents (not just Q&A)
- Designed around constraints, latency, and failure costs
Study on transforming operational information into constraint-consistent actions using agentic LLM workflows.
Goal
Action correctness
Lens
Constraints + safety
- Information → action pipelines with validation
- Failure-mode taxonomy for iteration
- Operator-facing framing for controlled operations
Open-source scheduling package for reproducible process scheduling workflows (first author).
Type
OSS package
Domain
Scheduling
- Clean interfaces for experiments and reuse
- Reproducible scheduling workflows
Optimization + ML surrogate integration to improve efficiency and feasibility handling in scheduling problems.
Angle
Surrogate modeling
Scope
Large OSS
- Surrogate integration for optimization
- Engineering contributions on a large open-source project
Engineering-first bridge into quant: signals + backtesting hygiene + disciplined evaluation to avoid false discoveries.
Theme
Signals + evaluation
Goal
Reliable iteration
- Evaluation hygiene to reduce spurious results
- Clear experiment structure for iteration and ablations
Trajectory-level health metrics (validity/consistency/invalid-transition suppression) that map failures to actionable interventions. Available on request.
- Metrics that map to intervention (not just accuracy)
- Designed to reason about policy health and structural errors
Research
Selected publications + current directions (risk, reliability, and convergence in generative decision systems).
Selected publications
- Autonomous Industrial Control Using an Agentic Framework with Large Language Models IFAC-PapersOnLine (DYCOPS) • 2025 • conference
- Leveraging LLM Agents and Digital Twins for Fault Handling in Process Plants IEEE ETFA • 2025 • conference
- Integration of Plant Scheduling Feasibility with Supply Chain Networks Under Disruptions Using Machine Learning Surrogates ESCAPE • 2024 • conference
- Cut-Based Symbolic Feedback for Suppressing Structural Errors in LLM Planning ICML • 2026 • submitted / under review
- From Detection to Action: Using LLM Agents for Fault-Tolerant Control Journal of Process Control • 2026 • under review
- Small Language Models for Control via GRPO Fine-Tuning preprint • 2026 • submitted
Work in progress
- Reliability envelopes for generative policiesDefine measurable reliability/violation metrics and map them to decision-time interventions (constraints, validation, structured context).
- Risk controllability with statistical guaranteesFault detection (e.g., HMMs) → action proposal (LLMs) → data-driven verification (to avoid model mismatch) → conformal predictors for calibrated guarantees.
- Capability selection without luckDetermine when/which model to use a priori via task structure, failure cost, and empirical capability frontiers ("jagged frontier").
Full publication list: Google Scholar .
Writing
Public writing and research notes on risk, reliability, and decision-time validation.
Writing on risk, reliability, and decision-time validation for AI systems.
GraphRAG + agentic fault handling for safer, constraint-consistent action selection.
Skills
A compact stack for building reliable, testable decision systems.
Core
Probabilistic / Decision Systems
LLMs / Agents
Optimization
Systems
Contact
If you’re working on decision-making under uncertainty (control, ML, or quantitative systems), I’m happy to chat.
Best way to reach me: javalvyas2000@gmail.com