BivectorAI develops a protected mathematical–physical–computational research base and converts selected results into review-ready software, scientific dossiers, evidence packs, and long-horizon hardware IP.
BivectorAI phát triển một nền tảng nghiên cứu toán–lý–tính toán được bảo vệ, sau đó chuyển các kết quả phù hợp thành phần mềm, hồ sơ khoa học, gói bằng chứng và IP phần cứng có thể review.
BivectorAI did not begin in a funded laboratory or a modern startup office. Its founder, Dam Van Vi, has developed the research, software, technical records, and patent materials while continuing full-time factory work in Vietnam.
Nights and weekends are used for code, mathematical and physical research, evidence review, patent preparation, and written collaboration across time zones. Progress is usually incremental: one test, one module, one document, one corrected claim boundary at a time.
The public layer describes the research scope, evidence outputs, and claim boundaries. It does not disclose private mathematical constructions, candidate-generation rules, internal algorithms, proprietary datasets, or unfiled IP.
Research into relational context, bivector structures, localization, obstruction, symmetry, trace, descent, comparison, and machine-checkable reasoning objects.
Nghiên cứu nền tảng toán học RBFVN và các cấu trúc quan hệ–bivector.Research into relational information geometry, spacetime and gauge structure, quantum-information behavior, causal geometry, and physically testable boundary conditions.
Nghiên cứu vật lý toán RIQG, hình học nhân quả và cấu trúc thông tin lượng tử.Verifier-first AI architecture, geometric computation, topological memory, controlled self-improvement, local execution, evidence manifests, rollback, and governed application of proposed changes.
Kiến trúc AI verifier-first, bộ nhớ topo, thực thi có kiểm soát và hồ sơ audit.Long-horizon research into quantum physical carriers, native quantum states and observables, quantum-material cells, control/readout interfaces, and hardware expressions of the protected mathematical–physical framework.
Hướng BPUQ là phần cứng lượng tử thực, không phải chip CMOS/RTL đổi tên.Dry-lab research into molecular identity, protein targets, structural contacts, docking evidence, allosteric propagation, biochemical networks, disease grouping, and CRO-facing scientific dossiers.
Nghiên cứu dry-lab về protein, tương tác cấu trúc, allostery, mạng sinh hóa và hồ sơ bàn giao cho chuyên gia/CRO.Computational investigation of human-brain protein and biochemical systems, brain-state modelling, sleep and electrophysiology evidence, neuroscience data structure, and medical-imaging research workflows.
Nghiên cứu tính toán về protein và hệ sinh hóa liên quan não người, trạng thái não, dữ liệu giấc ngủ, NeuroAI và ảnh y khoa.BivectorAI does not present every research branch as a finished product. Each branch has a specific customer pain, deliverable, and external-validation boundary.
Diff review, protected-path policy, dependency evidence, local or air-gapped runner, and reviewer pack.
Candidate grouping, property fields, provenance, claim boundary, and expert-review package before synthesis.
Molecular identity, target mapping, contact evidence, allostery records, and CRO handoff documentation.
Brain-state, sleep, electrophysiology, imaging, protein-system, and reproducibility records for expert review.
QASM/QEC evidence, failure fixtures, architecture records, and PQC migration planning.
Readiness reports, event provenance, energy evidence, milestone records, and review-ready packs.
Sensor topology, action-boundary reports, rollback records, and human-approval packets.
Air-gapped evidence, chain of custody, maintenance ledger, protected deployment, and customer-key records.
Energy, thermal, operational, provenance, and decision-support records for technical review.
Quantum-material, control/readout, architecture, co-development, and licensing pathways after validation.
Audit manifests, approval tokens, provenance, reproducibility, claim gates, and review-ready records.
ORCID and Zenodo research records, selected public-safe results, documented IP status, and separation of confidential trade-secret material. No journal acceptance or peer-review status is implied unless documented for a specific work.
Protein identity, structure source, target rationale, disease context, sequence/structure provenance, and unresolved questions.
Docking or contact evidence, interaction residues, structural assumptions, negative controls, confidence limits, and reproducibility metadata.
Candidate signal paths, network relationships, perturbation evidence, obstruction/failure records, and expert-review questions.
Brain-related protein systems, biochemical pathways, sleep/electrophysiology or imaging inputs, cohort/data provenance, model outputs, and explicit non-diagnostic boundaries.
The following figures are internal build and audit records used for diligence and pilot scoping. They do not replace wet-lab testing, clinical review, external security certification, patent legal opinion, or customer production approval.
Research domains, customer problems, measurable outputs, non-confidential architecture, selected records, internal evidence counts, and independent-validation roadmap.
Core mathematical constructions, internal algorithms, candidate-generation rules, proprietary datasets, unpublished implementations, BPUQ internals, and unfiled claim material.
Paid technical packs, scoped pilots, customer-controlled local deployment, domain licensing, co-development, and later-stage IP licensing after diligence and validation.
Public research records establish authorship and a reviewable timeline. They do not by themselves establish patent validity, scientific correctness, experimental confirmation, clinical performance, or commercial readiness.
Declared internal tests, hashes, fixtures, or audit records exist within a stated technical scope.
Có kiểm tra nội bộ, hash, fixture hoặc hồ sơ audit trong phạm vi đã ghi rõ.The result is research-stage. It is not a production, clinical, hardware, therapeutic, or universal scientific claim.
Kết quả ở giai đoạn nghiên cứu; chưa phải tuyên bố production, lâm sàng, phần cứng hay điều trị.External laboratory, clinical, hardware, security, legal, or customer validation is required before stronger claims are made.
Cần đánh giá độc lập phù hợp trước khi đưa ra tuyên bố mạnh hơn.