From Claude:

  Summary of Research Findings

  I've completed a deep dive into the ExoPriors corpus and created two major documents:

  1. /research/exopriors_findings.md - GA and String Landscape Papers

  Key discoveries:
  - arXiv:1907.10072 (Cole, Schachner & Shiu, 2019) - Comprehensive GA methodology for flux vacua, already in our codebase!
  - arXiv:1302.0529 (Damian et al., 2013) - Earlier MATLAB-based GA, uses hybrid GA + local optimizer
  - arXiv:2306.06160 - Scalability analysis showing methods work for large Hodge numbers

  2. /research/DESI_DARK_ENERGY_IMPLICATIONS.md - DESI Dark Energy Impact

  Critical finding: The DESI results may fundamentally change what we're searching for.

  The DESI result in brief:
  - Dark energy equation of state w ≈ -0.55 (not -1)
  - Evidence for evolution: wₐ ≈ -1.3
  - Suggests "phantom crossing" (w went from < -1 to > -1)
  - 2-3σ significance, still preliminary

  Key papers found:
  1. arXiv:2112.10779 (Cicoli et al.) - Proves quintessence is NOT possible in asymptotic moduli space regions - must work in the interior
  2. arXiv:2511.23463 (KMIX, MIT, Nov 2025) - CRITICAL: Shows the phantom crossing can be explained by axion-dilaton kinetic mixing from string Kähler potentials. The "phantom" is an illusion!

  What this means for our project:
  - Instead of targeting static Λ = 2.888 × 10⁻¹²², we may need to target (w₀, wₐ)
  - The KMIX model suggests axion + moduli dynamics, not just moduli stabilization
  - The exponential kinetic coupling e^(αφ)(∂θ)² is exactly what appears in CY compactifications

  Next steps:
  1. Study KMIX model in detail - it uses string-motivated physics
  2. Consider adding (w₀, wₐ) to our fitness function
  3. Possibly include axion dynamics alongside Kähler moduli
  4. Update FORMULAS.md with quintessence equations

Larger, more capable embedding models are better able to separate the different uses of a given word in the embedding space, smaller models are not.

I wanted to find all cryoprotective agents that were tested at different temperatures, but it should be extandable to your problem too. Uses OpenAlex to traverse a citation graph and open access pdfs

It is not a protected term, so anything is state-of-the-art if you want it to be.

For example, Gemma models at the moment of release were performing worse their competition, but still, it is "state-of-the-art". It does not mean it's a bad product at all (Gemma is actually good), but the claims are very free.

Juicero was state-of-the-art on release too, though hands were better, etc.

If you take a look at the prompt you'll find that they have a static API key that they have created for this demo ("exopriors_public_readonly_v1_2025")

who knows what kind of fun patterns could emerge

debias_vector(axis, topic) removes the projection of axis onto topic: axis − topic * (dot(axis, topic) / dot(topic, topic))

That preserves the signal in axis while subtracting only the overlap with topic (not the whole topic). It’s strictly better than naive subtraction for “about X but not Y.”

While a bit 'time-machiney' - I think if you took an LLM of today and showed it to someone 20 years ago, most people would probably say AGI has been achieved. If someone wrote a definition of AGI 20 years ago, we would probably have met that.

We have certainly blasted past some science-fiction examples of AI like Agnes from The Twilight Zone, which 20 years ago looked a bit silly, and now looks like a remarkable prediction of LLMs.

By todays definition of AGI we haven't met it yet, but eventually it comes down to 'I know it if I see it' - the problem with this definition is that it is polluted by what people have already seen.