Shreshth Rajan

I study Pure Math and Philosophy at Harvard. I took leave to be the first person on the team at Forge and founding engineer at Pillar.

Research
Envaudit. Audited code RL environments; 28.5% of SWE-bench Verified is reward-hackable. [arxiv]
Arithmetic Intensity Aware Quantization. Quantized by FLOPs per byte for a 1.66x speedup. [arxiv]
Uncertainty Gated Retrieval. Retrieved only when unsure: +11% accuracy, 87% cheaper. [arxiv]
Multiver. Gave verification agents shared memory and beat fine-tuned detectors. [arxiv]
Multi-Agent Code Verification. Proved why weak checkers beat strong ones; 76% of bugs in 200 ms. [arxiv]
Social Contagion and Bank Runs. Modeled the SVB to First Republic cascade with LLM agents. [arxiv]
Test-Time Selection for VLAs. Showed a perfect world model barely beats one sample. [writeup] [github]
Geodesics on Finite 2-Manifolds. Built a discrete analog of geodesic spreading in relativity. [paper]
Mixing Rates of Markov Chains. Bounded how fast a chain forgets where it started. [paper]
Projects
LoopHealth. Caught an RL loop halving its coverage while every metric stayed flat. [writeup] [github]
Harvy. Built an AI academic advisor that over half of Harvard uses daily. [site] [press]
Visdep. Turned large repos into dependency maps you can navigate. [site] [writeup]
STAR. Cut human labeling for code generation by 85% with a self-training loop.
Sisyphus. Built physics-integrated world models for robot RL environments.
Robomerge. Built an autonomous preprocessing pipeline for robot training data. [github]
Writing
Incompleteness and intelligence
Dignāga and conventional objects
Three notes on optimizers and plasticity
Pooled diversity is the wrong thing to log
Search agents have no state and no value
A perfect world model barely helps
Codebase understanding
India's Automation Trap

I'm an investor or advisor to Arda (seed), Etched (seed), Applied Compute (seed), Sable (seed), Transfyr AI (advisor), Advanced Machine Intelligence (seed), and Operating Intelligence (seed).

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