Kevin Yandoka Denamganai
Compositional Learning Behaviours as a Necessary Condition for Olympiad-Level Formal Theorem Proving
Anju Chhetri
Nicole Casanova
Validating a cross-model behavioral benchmark — 7 models, 1,050 conversations, Cohen's d=1.08 — with no cost to factual honesty.
Ryan Ingosi
An independent safety score for AI agents you can verify — deterministic, reproducible, auditable, and it never needs your private data.
John Greer
Aashka Patel
AI Nutrition Labels For Everyday Consumers & AI Agents: Travel + POC Grant
Oleksii Simon
Deterministic, no-LLM-judge benchmark for how faithfully AI tracks changing beliefs. Funding v1.1: a new ambivalence metric + 20 cross-domain scenarios.
Ahmed
A fast, comprehensive directory of the people and orgs in AI safety: search, filter, and match.
Gate proves agents engage coupled dynamics before grid spend. Publish-either-way: pass → theory confirmed; fail → clean negative; gate-fail → report.
lucas bailey
Open source, cross-framework benchmark for AI agents
Guenin Nicolas
A working infrastructure for auditing AI behavior without access to weights
Roman Stirskyi
Forcing living networks of brain cells to act as a self-organizing, ultra-efficient computational substrate that bypasses the GPU scaling trap.
Pedro Bentancour Garin
One year of bootstrapped development, four patent filings, seeking support to continue.
Iman Schrovk
Open, formally-verified proof of which named human approved an irreversible AI-agent action before it ran — so the model isn't the crumple zone.
Conor Plunkett
Benchmark for agent safety when spending users money. How often do they violate user intent and rules?
Marian Dorobantu
A governance layer that lets a person refuse AI output that drifts from who they are — and measures the drift. Validated on one case; now scaling to many.
Riyane El Qoqui
Deterministic, constant memory, continuous learning. Building the alternative to transformers
Saurav Panigrahi
Accepted ICML 2026 workshop paper on cross-constitution drift in LLMs; seeking $2,050 travel support to present in Seoul and gather research feedback.
Petya Sarafova
A user's engagement style leaves a measurable signature in a language model's attribution graphs. This ask reimburses the completed study's costs.
Shaun Srirangam