Bake AI’s Épi agent reports 120 record results with GPT-6 Astra
Including a narrower n-queens constant and Cohn–Elkies non-sharpness in dimensions 10 and 11
Awaiting review
The takeaway
Bake AI’s technical report (arXiv 2610.24944, 18 Oct 2026; authors also from Notre Dame, Scale AI, NVIDIA, Ohio State and others) describes Épi, a persistent research agent run “over a few days using OpenAI frontier models (GPT-5.6 Sol and GPT-6-Astra)”.
Status
- Claim
Awaiting review
- Our reporting
- Medium confidence
- Verification
- Preprint only
- Importance
- 3 of 5
- Last verified
- 24 October 2026
Your AI and this story
- GPT-6 Astra171 days after its cutoff
- Claude Opus 5.5110 days after its cutoff
- Gemini 4 Argon201 days after its cutoff
- Grok 4.7140 days after its cutoff
None of these four assistants can know about it. The closest, Claude Opus 5.5, stops 110 days before it.
Key facts
- Agent: persistent research history, hierarchical memory, branching and independent review; experiments run across model sessions and ‘a fixed verifier evaluates their outputs’. Models: ‘We ran Épi over a few days using OpenAI frontier models (GPT-5.6 Sol and GPT-6-Astra)’
- Claimed: 120 record-setting results, 20 numerical bound records across 12 families and 100 construction records across 8 families (packings etc.), compared with literature and registries as of 16 Sept 2026; the results repo says ‘106 improved constructions, 15 mathematical results, and their certificates’
- n-queens constant α (Q(n) = (n e^{−α+o(1)})^n): interval narrowed from Nobel et al. (2023) [1.944000752, 1.944001082] to 1.9440010223327 ≤ α ≤ 1.9440010262694 (width 3.30×10⁻⁷ → 3.94×10⁻⁹, ≈84×), each endpoint with a retained witness
- Cohn–Elkies: new dual lower bounds on the LP optimum in dimensions 9, 10, 11 and 13; in dimensions 10 and 11 they exceed the packing upper bounds of Cohn et al. (2022) by ~0.35% and ~1.92%, so ‘Even exact optimization of this formulation cannot recover the true packing density’ there
- Quartic Feigenbaum attractor dimension: a rigorous interval more than 3.6-million-fold narrower (claimed)
- Tuza’s conjecture (1981, τ△ ≤ 2ν△): computer-assisted proof for split graphs with an eight-vertex clique part and at most three triangle-active neighbourhood types, arbitrary multiplicities (a special case, not the conjecture)
- Épi-turbo: on 30 FrontierOR tasks, a 5.4% higher total score than Codex (with GPT-6 Astra) at 64% of its model cost, same model and reasoning effort
- Status: technical report; certificates public on GitHub (BakeLab/Epi-results, created 7 Sept); no independent check found on 24 Oct
What happened
Bake AI and co-authors from several universities and labs released a technical report on Épi, an agent built to keep long investigations going across many model sessions, with memory, branching and independent review, and a fixed verifier that scores every output. Running on GPT-5.6 Sol and GPT-6 Astra for a few days, it is credited with 120 records against the literature as of mid-September 2026.
The most mathematical claims are an ~84× narrower interval for the n-queens constant, dual certificates showing that the Cohn–Elkies linear-programming bound for sphere packing cannot be sharp in dimensions 10 and 11, a much narrower interval for the quartic Feigenbaum attractor dimension, and a computer-assisted proof of Tuza’s conjecture for a restricted class of split graphs.
Why it matters
It is one more of the AlphaEvolve-style record-hunting systems, built on OpenAI models by a third party. The n-queens interval and the Cohn–Elkies separations come with witnesses anyone can check, so they are easy to confirm. The non-sharpness claims depend on comparing with Cohn et al.’s 2022 upper bounds, and nobody had checked them independently by 24 Oct. The blog post was not read in detail; facts here come from the arXiv PDF and the GitHub repo description.
What is disputed or not yet verified
| Verification | Unrefereed technical report with public certificates (dual vectors, witnesses); not independently checked |
|---|
Sources
3 sources from 3 sites. Numbers match the chips in the text.
3 sources: 3 primary
Primary
- arXiv 2610.24944: Épi: Towards a Persistent, Self-Improving Agent for Autonomous Researcharxiv.org, paper
- Bake AI blog: Épibakeai.inc, official
- GitHub BakeLab/Epi-results: constructions, results and certificatesgithub.com, code
Changes
- Filed from the arXiv PDF