## Summary

I am seeking support for independent technical AI-safety research on how transformer models reason over long generated sequences. The project studies token-level hidden-state trajectories during chain-of-thought decoding: first using small local models to search for candidate phenomena, then using cluster/API runs to validate any discovered structure.

The aim is to identify a non-arbitrary local ontology for representation movement during reasoning, derive a longitudinal model over generated tokens, and test whether that model predicts important phenomena such as correctness signals, failure onset, reasoning phase changes, or safety-relevant decision formation.

## Plan

This is a local-first project. I can begin exploratory work on my own machine. Funding primarily supports focused research time, modest validation compute, and completion of two near-term reasoning-time manuscripts, Lateral Tree-of-Thoughts (LToT) and Natural Language Edge Labelling (NLEL).

The tentative target is an ICLR 2027-oriented submission by late September or early October 2026, with the possibility of a small paper cluster if the work yields both a main result and a corollary result.

## Funding levels

At the $10,000 minimum, I would prioritise local exploratory experiments, initial phenomenon identification, and a public update or draft writeup.

At the $35,000 target, I would support several months of focused research time, complete LToT/NLEL experiments where feasible, and reserve funds for validation compute.

Additional funding would mainly extend runway, improve validation quality, and reduce the need to interrupt the work for unrelated paid employment.

## Why this matters

Reasoning models increasingly rely on long generated reasoning traces, but we still lack good interpretability tools for understanding how reasoning unfolds token by token. Better models of long-chain reasoning could improve oversight, failure detection, and eventually durable alignment.