# Research Pieces

> Independent technical analyses at the intersection of AI systems, physics, and control theory. By Vishal Verma — CTO at Dehurdle, IIT Delhi alumnus.

## Articles

- [AI's Autonomous Galileo Moment: Discovering the Hidden Hamiltonians of Chaotic Systems](https://vishalvermalabs.com/papers/autonomous-galileo-moment-hamiltonians/): Jun 2026, Machine Learning Theory. Xi and Chen's April 2026 paper proves that equivariant attention networks trained on passive thermal snapshots of frustrated spin glasses can autonomously recover the exact underlying Hamiltonian with 99.7% accuracy. This shatters the assumption that deep learning is restricted to statistical interpolation, establishing an exact algebraic equivalence between generative score fields and conservative thermodynamic force fields.
- [The Fluid Dynamics of Multi-Agent AI: Resolving d'Alembert's Paradox of Generative Workflows](https://vishalvermalabs.com/papers/fluid-dynamics-multi-agent-ai/): May 2026, Multi-Agent Systems. Production multi-agent AI systems fail at rates of 41-87% because they treat natural language as frictionless flow, ignoring the viscous semantic boundary layer where probabilistic agent intents meet deterministic database states. The paper proposes Renormalization Group compression for agent communications, Holographic Invariant Storage for context drift prevention, and Lyapunov-verified control loops for stability guarantees.
- [The Physics of AI: Why the Generative Era is a Computational Dead End](https://vishalvermalabs.com/papers/physics-of-ai-computational-dead-end/): Apr 2026, AI Architecture. Modern generative AI architectures are computational analogues of Laplace's Demon, exhausting resources on microstate reconstruction instead of macrostate prediction. Through the lens of statistical mechanics, gravitational singularities, and quantum degeneracy pressure, this paper demonstrates why JEPA and SIGReg represent the thermodynamically necessary pivot from pixel-level generation to abstract state-space modeling.

## About the Author

Vishal Verma is the Co-Founder & CTO of Dehurdle (https://dehurdle.com), an enterprise AI coaching platform featured as a Harvard Business Publishing case study and trusted by Deloitte, PwC, AB InBev, and 10 global enterprises.

He studied robotics, physics, and systems design at IIT Delhi. After college, he built MVPs for early-stage startups and ran his own ventures. He then spent three years preparing for the Indian Civil Services — studying economics, ethics, governance, history, and public policy — with physics as his optional — which gave him a systems-level understanding of how institutions, incentives, and human behavior actually work.

Today, Vishal is responsible for Dehurdle's entire technology stack — the zero-payload privacy engine, the behavioral intelligence pipeline, the real-time voice simulation infrastructure, and the agentic coaching layer. He writes about research in developmental psychology, computational neuroscience, and AI alignment. He also plays competitive chess.

## Linked Identities

- LinkedIn: https://www.linkedin.com/in/visha1v/
- X / Twitter: https://x.com/v1sha1v
- GitHub: https://github.com/vishal-dehurdle
- Company: https://dehurdle.com
- About (Dehurdle): https://dehurdle.com/about
- Email: vishal.verma@dehurdle.com

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