VANSH RAMANI / SELECTED WORK Building ramAIn YC W26

Vansh Ramani.

Co-founder & CTO, ramAIn ↗

I work on computer-use agents, efficient learning,
and reasoning systems.

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ramAIn

Y W26

Computer-use agents.

We build AI agents that operate across web portals and legacy systems. Describe the work in natural language; ramAIn executes it through the software your team already uses.

Visit ramain.ai ↗
COMPUTER USE ORCHESTRATION AUTOMATION
Layered application windows within the Observe, Reason, Act, Verify computer-use loopView poster ↗
Observe → Reason → Act → Verify ↺

Research.

Graphs, geometry, molecules, and reasoning.
Selected work from 2024–2026.

PUBLICATION INDEX
01
arXiv · 2026 revision / FAISS

Panorama: Fast-Track Nearest Neighbors

Panorama research figure

Fast search starts with doing less work. PANORAMA uses spectral decay to evaluate distances incrementally and prune candidates early. The July 2026 revision reports up to 28.9× end-to-end speedups and integration into FAISS.

Vansh Ramani, Alexis Schlomer, Akash Nayar, Sayan Ranu, Jignesh M. Patel, Panagiotis Karras

02
ICML 2026

DISSOLVR: An Interpretable and Fast Framework for Aqueous and Organic Solubility Prediction

DISSOLVR research figure

An interpretable and fast framework for predicting solubility in aqueous and organic solvents.

Vansh Ramani, A. Arora, D. Kuchhal, Sayan Ranu, Tarak Karmakar

03
ICML 2026 · Spotlight

Position: Graph Condensation Needs a Reset — Move Beyond Full-dataset Training and Model-Dependence

Graph condensation research figure

A position paper arguing for graph condensation methods that move beyond full-dataset training and model dependence.

Mridul Gupta, Samyak Jain, Vansh Ramani, Hariprasad Kodamana, Sayan Ranu

04
EIML · ICML 2026

ReasonBENCH: Benchmarking the (In)Stability of LLM Reasoning

ReasonBENCH research figure

Investigating the stability of large language model reasoning: how reliably do reasoning systems behave when their inputs change?

Vansh Ramani, N. Potamitis, H. A. Arora, D. Kuchhal, L. H. Klein, A. Arora

05
ICLR 2025

Bonsai: Gradient-free Graph Distillation for Node Classification

Bonsai research figure

Graph distillation without the expensive full-dataset training loop. Representative computation trees capture the structure that matters for node classification.

Mridul Gupta, Samyak Jain, Vansh Ramani, Hariprasad Kodamana, Sayan Ranu

06
NeurIPS 2026

SC3: The Multi-Solvent Solubility Challenge and Benchmark

SC3 research figure

A multi-solvent challenge and benchmark for evaluating solubility prediction beyond a single solvent.

Vansh Ramani, H. A. Arora, D. Kuchhal, S. Tatarin, L. Krasnov, Sayan Ranu, Tarak Karmakar

07
JCTC 2024

Graph Neural Networks for Predicting Solubility in Diverse Solvents Using MolMerger Incorporating Solute–Solvent Interactions

MolMerger research figure

Modeling solute–solvent interactions with a merged-molecule graph representation, rather than treating each molecule in isolation.

Vansh Ramani, Tarak Karmakar

7 WORKS / 2024–2026

03 / ABOUT EXPERIENCE & NEWS
Portrait of Vansh Ramani
VANSH RAMANIPORTRAIT / 01

COMPUTER SCIENCE · RESEARCH

A bit about me.

I'm a computer science undergraduate at IIT Delhi, currently on a break to build ramAIn.

My research spans graph learning and distillation, high-dimensional similarity search, molecular prediction, machine unlearning, and the stability of LLM reasoning.

I've worked with researchers at IIT Delhi, Carnegie Mellon, the University of Copenhagen, and Aarhus. Outside of work, I like jazz, random facts, and exploring new places.

PEOPLE & PLACES

Research experience

Four institutions
across my research

05 / Contact

Vansh Ramani

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Project poster