Open preprints from the AICumene community — where signals meet minds and machines: neural interfaces, biosignal processing, drug discovery, cognitive enhancement, and efficient AI at the edge.

PAPERS

July 2026

Brain Oscillator Intrinsic Dimension marks consciousness, recovery mode, and failed downshifting in coma

Alexandra Bernadotte, Ivan Menshikov

BOID — the count of active oscillatory modes recovered from EEG by multichannel singular-spectrum analysis and ESPRIT — is high in conscious, resource-demanding states and low in a healthy recovery mode. Across sleep, anesthesia, meditation, and 575 post-anoxic coma patients, poor outcome is a failure to downshift out of pathological high-dimensional activity (fused model AUC 0.81, leakage-free).

Signal processingCognitiveNeural interfacesv1 · HTML · PDF · Supplement · DOI
July 2026

When Do Language Models Reach Nash? Heterogeneous Cross-Family LLM Agents in Classic Economic Games

Alexandra Bernadotte, Ivan Menshikov

Tournaments of cross-family LLM agents in matching pennies, RPS, and public-goods games: convergence to mixed equilibria is governed by family heterogeneity, and persistent over-cooperation survives long horizons.

LLM systemsCognitivev1 · HTML · PDF · RU · DOI
June 2026

Verifiable AI for Law: a legal verifiability compiler with a rule/judgment firewall

Alexandra Bernadotte, Ivan Menshikov

Raw LLMs fail in law by fabrication and premature completion, and a bigger black box stays unauditable. The model proposes; a deterministic compiler disposes — one firewall in code separates bright-line rules (verified, cited) from open-textured judgment (escalated to a human, never auto-asserted).

LLM systemsv1 · HTML · DOI
August 2025

What is Strong AI? Intrinsic Structural Criteria for Strong Intelligence Based on Dimensionality and Topology

Alexandra Bernadotte, Vasilii A. Gromov

A system of instrumental, internally defined criteria that classifies intelligence not by task performance but by intrinsic structural invariants of the data a system generates — intrinsic dimension, topology, and dynamical complexity. It yields a measurable hierarchy — Weak (Level 0), Strong (Level 1), Super-Strong (Level 2) — and a formal operational definition of Strong AI distinct from Strong Natural Intelligence and from AGI.

LLM systemsCognitivev1 · HTML · PDF · DOI

IN PREPARATION

June 2026

Bit-Exact Linear-Attention Decoding on Volta: Megakernel Design at the Load–Store Wall draft

Alexandra Bernadotte, Ivan Menshikov
Edge AILLM systems
June 2026

A Two-Tier Virtual-Screening Funnel on Legacy Datacenter GPUs draft

Alexandra Bernadotte, Ivan Menshikov
Drug discovery
forthcoming

Closed-Loop Motor Decoding from Non-Invasive EEG under a 40 ms Budget in review

Alexandra Bernadotte, AICumene Neural Interfaces group
Neural interfacesSignal processing

LAB NOTES

Short, citable write-ups — negative results, engineering lessons, reproductions. The things that would never clear arXiv moderation but move the field anyway. Each note is versioned and gets its own DOI, exactly like a paper.

July 2026

The heart adds nothing to coma prognosis: a pre-registered negative control

The same intrinsic-dimension and heart-rate-variability machinery applied to simultaneous ECG in the I-CARE cohort — marginal AUC over the EEG-plus-clinical model was indistinguishable from zero across every cardiac descriptor.

July 2026

What LUT dequantization actually costs you on Volta: an autopsy

We tried table-lookup dequant to beat the load–store wall on V100. It loses. Here is the profile that killed it, so you don’t have to rerun it.

June 2026

Gemma-on-Volta numerics: four fixes for coherent generation

Raw RMSNorm, unit scaling, weightless value-norm, proportional RoPE — the minimal patch set that turns garbage into text on sm_70.