about
Ayodeji Adesegun

ayodeji adesegun

software engineer, AI researcher and founder.

now
founder ancyble, cylinadvocate qiskit
before
fellow non-trivial, rooteresearch intern airlabsoftware intern gpi, inits, tranter it
open to
internships, collaborations
projects
scratch is all you need screenshot

scratch is all you need

a 29,665-parameter decoder-only transformer implemented and trained entirely in mit scratch 3.0

scratchpythonturbowarp
cpufromscratch screenshot

cpufromscratch

a cycle-accurate cpu emulator in mit scratch 3.0 supporting subleq, chip-8, and a full mos 6502 core

scratchpython6502
wev screenshot

wev

in-browser local slm calibration and entropy-based safety gating via onnx runtime web & wasm

typescriptnext.jsonnx
github activity, last 12 months

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skills
languages
pythontypescriptjavascriptmovejava
frameworks
nextjsreact nativefastapipytorchscikit learn
tools
postgresqlgitdockercloudflare

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research

no single signal separates agreement from parallel conduct among llm sellers

published

no single signal reliably separated real agreement from parallel pricing among LLM sellers.

details

tests whether auditors can tell real agreement between llm sellers from parallel pricing, using no-channel markets as decoys. planted agreements raised the collusion index well above baseline, but no single signal reliably separated agreement from parallel conduct.

AI safetymulti-agentcollusionauditing

failing without refusing: effective yield of open-weight models under forensic artifact load

published

open-weight models rarely refuse forensic work but silently fail on 17.3% of prompts.

details

tests whether self-hostable open-weight models can take over forensic analysis that hosted models refuse. across 2,758 graded prompts on two models, refusals were rare (0.15%) but 17.3% of prompts got silent non-answers, and partial redaction gave the best effective yield (60.2% against 38.6% for raw artifacts).

AI safetyincident responseopen-weight models

distress representations in language models are referent-specific

published

a model's own distress is represented separately from distress attributed to others.

details

welfare evaluations of language models often confuse internal distress directions with the sentiment of the text or with distress attributed to others. varying only the referent (the model itself, another model, or a fictional android), self-referential and other-referential distress representations separate across six open-weight models, and self-reported valence tracks the self-specific component.

digital mindsinterpretabilityAI safety
interests
mechanistic interpretability, machine learning theory, AI policy, AI safety, AI governance, theoretical computer science

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writing
reading

reading now

god: an anatomyfrancesca stavrakopoulou
the alignment problembrian christian

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contact

open to internships and collaborations,