# Rokib Al Dhin Raadh — full site text > Complete text of https://www.raadh.me/ and its pages (/research, /work, /about), generated at build time from the same render the site serves. > Curated summary: https://www.raadh.me/llms.txt Skip to content 18 years old Founder & CEO, OXIEDO Inventor of ORMAS ## Building neural networks that can account for what they learned. Peer-reviewed at eighteen. Every AI model on earth is a black box. OXIEDO is ending that. Its engine, ORMAS, finds its own broken parts, repairs them mid-training and writes a tamper-evident record of every change, so banks, hospitals and defence teams can finally train AI on the data they have been locked out of. Raadh built it at 18, on one graphics card he paid for himself. Enter OXIEDO Read the research Rokib Al Dhin Raadh OXIEDO · 2026 On this page Work News Research Publications Talks Contact Recognition · 2026 ### Vetted by the rooms that are hardest to enter. Accepted at DeepMath after double-blind review, a reviewer for NeurIPS 2026, ranked first by the Cosmos Institute and taken into 1752vc Ignite from the top 1% of applicants: ten independent panels, one year. - Accepted · Double-Blind DeepMath 2026 DeepMath 2026 Stability paper on self-repairing neural networks accepted after double-blind review; poster at Ohio State - Reviewer NeurIPS 2026 NeurIPS 2026 Reviewer for the Trustworthy AI for Good workshop, invited to judge submissions on AI auditing - Ranked First Cosmos Institute ORMAS was the highest-ranked application in its cycle - Top 1% 1752vc Ignite OXIEDO accepted to the Ignite cohort from the top 1% of applicants - Final Round The Bridge From a first-round interview with Entrepreneur First in London to the final round of its San Francisco programme - Interview · 22 Oct 500 Global Invited to a first-round interview with the global venture firm on 22 October - Invited · Pre-Pitch Onstage W26 Onstage W26 Invited to the W26 pre-pitch event in London; Onstage ranks founders by interest from 350 venture funds - Offer Extended Freshmango Offered a place in the equity-free accelerator after a single interview - Finalist IARCO 2026 International Academic Research Competition finalist; nearly 500 participants from 60 countries - Research Community Cohere Labs Open Science Community member; Cohere cited his solo work on ORMAS for “remarkable initiative in ML safety and auditability” Read the full record Rokib Al Dhin Raadh is the 18-year-old founder and CEO of OXIEDO and the inventor of ORMAS, a self-repairing neural network that identifies, repairs and records its own failures while it trains. His stability result for self-repairing training was accepted at DeepMath 2026 after double-blind review. He is a reviewer for the NeurIPS 2026 Trustworthy AI for Good workshop and is a member of Cohere Labs’ Open Science Community. He grew up in Dhaka and taught himself machine learning. He built ORMAS alone: 16,316 lines of PyTorch and 383 controlled experiments on one RTX 3090, released as an open preprint with code that reproduces every result. OXIEDO, which he founded in 2023, licenses the architecture to regulated industries that need an auditable record of what their models learned. Before ORMAS he built OXIMO, a 40,933-line multi-agent operating system with 2,069 passing tests, and founded five ventures between the ages of twelve and seventeen, selling a stock-prediction system for about $10,000 at fifteen. CV Preprint Code ORCID GitHub X Substack YouTube 18 Founder and CEO of OXIEDO, inventor of ORMAS 383 Controlled experiments, run solo on one GPU +70.3 Percentage points over the baseline after a layer is destroyed (80.3% vs 10.0%) 2026 Accepted at DeepMath after double-blind review; NeurIPS reviewer ### Work OXIEDO · OXIMO · Black Bloxie Deep-tech AI company · founded 2023 · Founder & CEO #### OXIEDO OXIEDO licenses ORMAS, the self-repairing neural network Raadh invented, on-premise to regulated industries. Every training run produces a tamper-evident record of what the model learned: evidence for model-risk teams and the EU AI Act’s high-risk obligations. oxiedo.com Investor deck ORMAS On-premise licence Dec 2027 EU AI Act high-risk obligations +70.3pp Over the baseline after a layer is destroyed System · 2023–2025 40,933lines #### OXIMO A 40,933-line multi-agent operating system with 2,069 passing tests. It turns a one-sentence brief into a working organisation and creates the specialist roles it lacks. How OXIMO works → Field study · 2025–2026 −91%output without OXIMO #### Black Bloxie LTD Removing OXIMO cut output by 91%; restoring it took output above its original level. A twelve-month controlled study on a live UK e-commerce company. Read the field study → ### News updated 30 September 2026 Accepted · Double-blind review · Poster #### The mathematics behind ORMAS passed blind review at DeepMath 2026. Raadh’s stability result for ORMAS, the self-repairing neural network, was accepted for poster presentation at DeepMath 2026, the Conference on the Mathematical Theory of Deep Neural Networks. DeepMath takes theory only and reviews it double-blind: specialists judged the proof without knowing who wrote it, or that he is eighteen and self-taught. This year it is hosted by Ohio State and organised by researchers from Johns Hopkins and Michigan, with invited speakers from Stanford and UPenn; past editions were supported by the National Science Foundation and the Simons Foundation. He presents his poster on 30 October. “Self-Repair as a Bounded Disturbance: Input-to-State Stability of Neural Network Training Dynamics” Date: 30 Sep 2026  ·  By: Rokib Al Dhin Raadh Conference site ORMAS preprint Sep 2026 Reviewer #### Named a reviewer for the NeurIPS 2026 Trustworthy AI for Good workshop Raadh reviews submissions on mechanistic interpretability, attribution, auditing and post-deployment monitoring, the problems ORMAS was built to address, for the NeurIPS 2026 workshop in Paris. Sep 2026 Company #### OXIEDO accepted to 1752vc Ignite from the top 1% of applicants 1752vc placed OXIEDO in its Ignite cohort from the top 1% of applicants, and the equity-free Freshmango programme offered a place after a single interview. Raadh reached the final round of The Bridge (Entrepreneur First, San Francisco) after two interview rounds, and 500 Global has invited him to a first-round interview on 22 October. 25 Sep 2026 Community #### Joins Cohere Labs’ Open Science Community, cited for “remarkable initiative in ML safety and auditability” Cohere’s research lab welcomed him into its Open Science Community, citing his solo development of ORMAS across 383 experiments. Sep 2026 Competition #### IARCO 2026 finalist, among nearly 500 participants from 60 countries Selected for the final stage of the International Academic Research Competition, which drew nearly 500 participants from 60 countries. 1 Aug 2026 Publication #### Full ORMAS paper and code released openly The full paper and a 36-page supplementary are public on Zenodo (DOI 10.5281/zenodo.21730363), with code that reproduces all 383 experiments, including the 80.3% vs 10.0% recovery after a layer is destroyed. Jul 2026 Recognition #### ORMAS ranked first in the Cosmos Institute review cycle The ORMAS application ranked highest of every application in its cycle; Cosmos invited it to its technical track. Full recognition record ### Research ORMAS · self-repairing neural networks ORMAS is a self-repairing neural network architecture: it finds its own failing components during training, repairs them without stopping, and logs every repair with its cause. Standard networks cannot say which of their parts failed. A single error signal updates every parameter at once, so interpretability has worked from the outside, reconstructing a finished model’s behaviour after the fact. ORMAS works from the inside. It bounds each node’s path to the loss at four operations, so every component can be measured while the network trains. Three signals run together: global backpropagation, a per-node local loss through a shared bottleneck, and health-gated self-correction that diagnoses a failing node, repairs it, and logs the repair with its cause. Raadh’s analysis of that process, adapted from Sontag’s Input-to-State Stability framework, is the first formal local stability result for a self-correcting neural architecture, and was accepted at DeepMath 2026 after double-blind review. The evidence base is 383 controlled experiments across four architectures, every run reproducible from seed. Read the research Preprint Recovery after one layer is destroyed at epoch 100 · CIFAR-10 · 3 seeds ORMAS 80.3% Parameter-matched baseline 10.0% ORMAS locates the damage within one epoch and recovers through 85 logged repairs; the baseline stays at chance on every seed. Full tables and ablations are on the research page. ### Publications Peer-reviewed paper · preprint · technical report 2026 #### Self-Repair as a Bounded Disturbance: Input-to-State Stability of Neural Network Training Dynamics R. A. D. Raadh Accepted after double-blind review, DeepMath 2026: Conference on the Mathematical Theory of Deep Neural Networks, Ohio State University, 29–30 Oct 2026. Poster, 30 Oct. Conference 2026 #### ORMAS: Neural Architectural Transparency Enables Autonomous Self-Correction R. A. D. Raadh Preprint, Zenodo, 1 Aug 2026. Full paper, 36-page supplementary and code reproducing all 383 experiments. DOI 10.5281/zenodo.21730363. Paper Code Results archive 2026 #### OXIMO/AX09: Autonomous LLM-Dependent Commerce. A twelve-month controlled ablation study R. A. D. Raadh Technical report, 2026. Twelve-month controlled field study on a live UK company (Black Bloxie LTD): removing OXIMO cut output by 91%. PDF Whitepaper ### Talks Poster · talk · 2026 30 Oct 2026 Upcoming Poster · DeepMath 2026 #### Self-Repair as a Bounded Disturbance: Input-to-State Stability of Neural Network Training Dynamics Ohio State University, Columbus The stability result behind ORMAS, accepted after double-blind review at the Conference on the Mathematical Theory of Deep Neural Networks. Conference site 2 Nov 2026 12:00 EST Upcoming Talk · Cohere Labs #### When the Update Is Not a Gradient: Input-to-State Stability of Self-Repairing Neural Network Training Cohere Labs Open Science Community Every convergence proof for gradient descent assumes each update is a gradient. Self-repairing training breaks that assumption and still converges. The talk shows why, and exactly where the proof stops. Event page ### Contact Press · research · investors ##### Press, interviews and research raadxbusiness9@gmail.com Replies are typically sent within one working day. Questions and opportunities are always welcome. ##### Founder: OXIEDO and investors raadh@oxiedo.com oxiedo.com/invest · Investor deck (PDF) ##### Profiles ORCID · GitHub · X · Substack · YouTube Back to top ↑ Skip to content ### Research · ORMAS ## ORMAS: the self-repairing neural network. ORMAS, invented by Rokib Al Dhin Raadh, recovers to 80.3% accuracy after a trained layer is destroyed mid-training, while a parameter-matched baseline stays at chance (10.0%). Raadh built it alone, at 18, on one RTX 3090. Its stability result, the first formal local stability characterisation of a self-correcting neural architecture, was accepted after double-blind review at DeepMath 2026 (poster). This page covers the method, all 383 reproducible experiments, the Input-to-State Stability derivation and ablations, then Project Cherry, the next architecture. On this page - ORMAS overview - Key results - Why ORMAS was built - Silent versus loud failure - Full technical record - Three learning signals - Seven failure modes - GlassBox telemetry - 383 experiments - Stress tests - Zero-shot generalisation - Stability result (ISS) - Ablations - Reproducibility - Research roadmap - One decision, three results - Project Cherry - How it is designed to work - How it differs Now ORMAS · Self-repairing neural network ### ORMAS: a self-repairing neural network that rebuilds a destroyed layer to 80.3% accuracy while a standard network stays at chance. ORMAS, invented by Rokib Al Dhin Raadh, is a neural network architecture that detects its own failing components during training, repairs them without stopping, and logs every correction with its cause. Existing interpretability methods (SHAP, LIME, integrated gradients, attention maps, probing classifiers, circuit discovery) analyse a model from the outside, after training, and reconstruct what probably happened; those reconstructions cannot be verified. In ORMAS the record is produced by the same computation that performs the learning, so it is measured rather than inferred. Peer review DeepMath 2026 · poster Experiments 383 · reproducible from seed Architectures Four, on one RTX 3090 Data CIFAR-10 · CIFAR-100 Accepted · DeepMath 2026 · Poster Peer-reviewed: the first formal local stability characterisation of a self-correcting neural architecture. Raadh's paper “Self-Repair as a Bounded Disturbance: Input-to-State Stability of Neural Network Training Dynamics” was accepted after double-blind review for poster presentation at DeepMath 2026, the Conference on the Mathematical Theory of Deep Neural Networks (Ohio State University, Columbus, 29–30 October 2026). The complete ORMAS preprint is public on Zenodo (DOI 10.5281/zenodo.21730363). Inventor and principal researcher, independent | 2024 – present | PyTorch · 16,316 lines · 85 files 383 controlled experiments across four architectures (FC-DAG, CNN, Fat CNN, ResNet-18), all run on a single RTX 3090 and every one reproducible from seed. The stability result is local. The paper, the codebase that reproduces all 383 runs and the full results archive are linked below. Paper Preprint · Zenodo DOI Codebase Reproduce all 383 runs Results Archive All experiment logs Presentation · 7 min 40 sec #### Explaining ORMAS: Transparent Neural Networks That Self-Heal A presentation of the full research record: the three learning signals, the 383 experiments, the ISS derivation and the complete results, in under eight minutes. - Autonomous recovery from catastrophic mid-training failure - Compositional memory that emerges without replay buffers - Why transparency is an engineering problem, not a philosophy question #### Key results 80.3% Recovered after a layer is destroyed (baseline 10.0%) +70.3pp Gap over a parameter-matched baseline 94.6% Task retention (ResNet-18: 47.3%) +60.8pp Gap with all three layers destroyed 58.8% Zero-shot compositional (chance 25%) 383 Experiments, reproducible from seed +52.1pp Recovery from a σ = 2.0 weight shock 4.5× Lower late-stage weight variance 22,014 Autonomous corrections per DAG run <0.8% Accuracy variance, 16× bottleneck sweep - 01 +70.3pp ##### A destroyed layer, rebuilt: 80.3% versus 10.0% (+70.3pp) After a converged convolutional layer is zeroed at epoch 100, ORMAS recovers to 80.3% accuracy while the standard CNN stays at chance (10.0% ± 0.0%) on all three seeds: a +70.3pp gap. ORMAS gets there with 85 targeted corrections between epochs 100 and 110. A checkpoint rollback can also restore accuracy, but it discards all learning since the checkpoint and gives no account of what failed or why. - 02 +60.8pp ##### All three layers destroyed: 70.8% versus 10.0% (+60.8pp) With every convolutional layer zeroed at epoch 100, removing every learned representation, ORMAS recovers to 70.8% ± 2.2% across three seeds while the standard CNN remains at 10.0% ± 0.0%: a +60.8pp gap. One run logged 72 corrections (54 oscillating, 18 dead) and reached 72.9%. Recovery is lower than in the single-layer case, as expected when every stage must be rebuilt at once. - 03 2.5pp ##### Label noise: 2.5pp decay versus 7.8pp, with no second network Under 40% symmetric label noise over 200 epochs, ORMAS decays 2.5pp from its peak and settles at 75.6% ± 0.8%, while the standard CNN decays 7.8pp, with no co-training, second network or specialised objective. Heavy dropout (p = 0.5) matches ORMAS on label noise but collapses to chance when a layer is lesioned: regularisation can mask noise, but it cannot diagnose and repair structural damage. - 04 94.6% ##### Sequential learning: 94.6% retention versus 47.3% for ResNet-18, and 58.8% zero-shot Taught shape first and colour second, ORMAS retains 94.6% on shape while a standard ResNet-18 retains 47.3%. ORMAS keeps both tasks (94.6% shape, 96.5% colour) and scores 58.8% zero-shot on unseen shape–colour pairings, +33.8pp above chance. The PCGrad ablation reaches 59.1% ± 3.6%, statistically indistinguishable, isolating self-correction as the driver. The behaviour was not designed; it emerged from the health gate. #### Why Raadh built ORMAS: agents learning from corrupted production data ORMAS grew out of OXIMO, Raadh's multi-agent system for autonomous business operations (codebase). Its agents had to learn from real production data, with corrupted labels, adversarial inputs and contradictory signals. Every noise-robust method he tested was an external filter applied to a network that remained blind to its own internal state. The requirement was a network that could detect corruption during training, repair it without stopping or human intervention, and come with a formal account of when that repair remains stable. No such architecture existed. ORMAS was designed to meet that requirement. #### Standard backpropagation versus ORMAS: the same node failure, side by side The central claim of ORMAS is not accuracy but attribution: every failure is named, diagnosed and logged with its cause. The same node failure is shown in both architectures; the telemetry on the right is quoted verbatim from the supplementary material. Standard backpropagation ##### A Node Dies Silently One error signal, shared by everything. Nothing can report on itself. What the operator sees epoch 42 loss 1.842 → 2.910 epoch 43 loss 2.910 → 3.221 # no node id. no cause. no timestamp. # diagnosis is post-hoc, on a model # that has already finished dying. The paper states it plainly: standard backprop provides no architectural mechanism for per-node attribution during training. Rollback or full retrain are the only recourse. ORMAS three-signal learning ##### The Same Node Dies Loudly Every node carries a second, bounded path. It can report on itself. What the operator sees — verbatim from the paper [EPOCH 42 | STEP 16384] --- DIAGNOSTICS ROUND --- [node_12] Goodness: 0.88 -> HEALTHY [node_13] Goodness: 0.04 -> CRITICAL > Diagnostics: g_mag = 0.01 (DEAD), g_grad = 0.11 (STAGNANT) > Action: AUTONOMOUS NEUROGENESIS > Constraint: zero-sum mean-subtraction, ||DeltaW|| <= 10% --- VERIFICATION FORWARD PASS --- > Global Loss Before: 1.842 > Global Loss After: 1.791 > Result: ACCEPTED — node 13 re-integrated Name, score, diagnosis, treatment, bound, and a verification pass that can reject its own repair. One DAG run emitted 23,227 such events. That ledger is the product; the accuracy is the evidence it works. ##### ORMAS corrections decline to zero by epoch 50 A regulariser such as dropout acts at a constant rate. ORMAS corrections instead peak early, decay and reach exactly zero: the network adapts structurally rather than relying on continued intervention, as the ISS analysis predicts. Epoch 0 – 50 4.2 → 0.05 Peak rate. All structural adaptation happens in this window. Epoch 50 – 100 0 The network has fully self-stabilised. Epoch 100 – 200 0 Convergence locked. Final weight variance 0.19 against a baseline's 0.86. #### How ORMAS works: three simultaneous learning signals ORMAS replaces the single-signal paradigm with three simultaneous learning signals: - Signal 1 — Global Backpropagation: The standard cross-entropy loss backpropagated through the entire network. This is what every neural network already does. - Signal 2 — Per-Node Local Loss (The Intrinsic Anchor): Each node gets its own independent assessment computed through a shared bottleneck readout (4,416 parameters, d b ​ = 32 ≪ d m a x ​ = 128) that classifies the input using only that node's local features. It cannot memorise noise, so it is forced to learn structural features. - Signal 3 — Health-Gated Autonomous Self-Correction: Each node continuously computes a Multiplicative Bottleneck Goodness Score. When it drops below its own historical average by 2.5 standard deviations, the system diagnoses the specific pathology and applies the targeted treatment. A 0.3 gate floor prevents the dead-ReLU trap. g i ​ = loss_health i ​ ⋅ gradient_health i ​ ⋅ output_stability i ​ w t + 1 (i) ​ = w t (i) ​ − Signal 1 η i ​ ∇ L global ​ ​ ​ − Signal 2 (PCGrad-projected) η i ​ β (t) ∇ ~ L local (i) ​ ​ ​ + Signal 3 Δ corr (i) ​ ​ ​ #### Seven failure modes ORMAS detects and repairs during training ORMAS detects and treats seven distinct node pathologies in real time during training. Defence in depth: PCGrad operates at the gradient level (before weight updates); self-correction operates at the weight level (after updates). Any unmodelled pathology ultimately manifests as gradient death, triggering the fallback Kaiming reinitialisation: graceful degradation without requiring learned policies. Pathology | Symptom | Treatment | Dead Neuron | Output permanently near zero | Kaiming reinitialisation | Exploded | Activations > 500.0 | 90% Anti-Hebbian scale reduction | Saturated | Outputs stuck at max | Self-referential magnitude dampening | Oscillating | Weights swinging wildly | Momentum-based directional fixing | Loss Stagnant | Stuck on a plateau | Escalated noise perturbation | Gradient Dead | Zero gradient flow | Weight scaling to restart flow | Low Confidence | Uncertain output | Targeted noise perturbation | #### GlassBox: five layers of per-node telemetry, emitted during training Traditional neural networks fail silently. ORMAS reverses this with the Loud Failure Paradigm. GlassBox emits a 5-layer causal audit trail per node: (1) health status and goodness score, (2) correction traces with pathology diagnosis, (3) gradient conflict measurement (cosine similarity between global and local gradients), (4) topology census (active/suppressed routing per input region), (5) training pulse timeline. A single ORMAS DAG training run emitted up to 23,227 autonomous correction events over 200 epochs. On the 50-node fully-connected DAG under 30% continuous noise — a maximally dense graph where uncorrected architectures collapse to NaN — ORMAS maintained numerical stability across 200 epochs via 22,014 autonomous corrections per run (mean of 3 seeds). Every correction is tagged with its pathology, node ID, and diagnostic evidence. What it tells youPer-node health, correction trigger, EMA baseline, cosine gradient conflict, spatial routing map Next extensionMapping each failing node to the visual concept it encoded, before and after repair Why that mattersStructural telemetry is the foundation semantic interpretability builds on. Bridging the two is the primary extension direction. #### 383 controlled experiments against compute- and parameter-matched baselines All baselines are strictly compute- and parameter-matched. 383 experiments on a single RTX 3090. 4 architectures (FC-DAG, CNN, Fat CNN 11.24M, ResNet-18). 6 noise regimes. 10 baselines. All trained for 200 epochs. ##### 40% label noise: ORMAS ends at 75.6%, the standard CNN at 69.6% Best accuracy vs. final accuracy after 200 epochs of training under label noise. Asymmetric noise: 80.6% (Mixup: 50.0%). Real-world CIFAR-10N: 83.9%. ##### All Noise Regimes, Including Adverse Results Noise Regime | ORMAS | Standard CNN | Mixup | Sym 40% (final) Peak decay: ORMAS 2.5pp vs CNN 7.8pp | 75.6% ±0.8% | 69.6% ±0.9% | 36.5% | Asym 20% | 84.3% ±0.3% | 83.3% ±0.4% | 53.6% | Asym 30% | 83.1% ±0.1% | 81.5% ±0.6% | 52.0% | Asym 40% Mixup collapses — ORMAS +2.6pp | 80.6% ±0.3% | 78.0% ±0.2% | 50.0% | CIFAR-10N (Real) Real human annotator noise. Decay: ORMAS 2.7pp, CNN 3.7pp (1.4× more) | 83.9% | 83.8% | — | All results CIFAR-10 unless noted. Equal-compute (200 epochs, same hardware). Framing note: each accuracy result shows that the mechanism works; none is offered as a competitive accuracy benchmark. Why heavy dropout is not the same capability Heavy Dropout (p=0.5) also matches ORMAS on label noise — then collapses permanently to chance under a dead-layer attack. Regularisation masks noise. Structural transparency diagnoses and repairs it. Those are different capabilities, not different amounts of the same one. ##### Gaussian weight shock: the ORMAS advantage grows with the damage, to +23.9pp A σ-Gaussian weight perturbation is injected at training step 1,000, destroying all learned representations. ORMAS recovers autonomously; Mixup collapses permanently to 33.3%. Perturbation σ | ORMAS Final | Standard Final | Recovery Gap | σ = 0.1 | 80.2% | 82.1% | −1.9pp | σ = 0.5 | 78.6% | 74.8% | +3.8pp | σ = 1.0 | 75.4% | 59.0% | +16.4pp | σ = 2.0 | 62.1% | 38.2% | +23.9pp | The gap widens monotonically with perturbation scale. At σ=2.0, ORMAS recovers 52.1pp vs Standard CNN's 28.2pp — nearly 2× the net recovery from the same catastrophic shock. Scaling: The 11.24M-parameter Fat CNN also recovers from catastrophic weight explosion: 88.9% → crash to 22.7% → recovery to 69.3%. The three-signal mechanism scales from 637K to 11M parameters. ResNet-18 Scale: Stages 2+3 killed at epoch 100. ORMAS-ResNet: 264 corrections → 91.7%. Vanilla ResNet-18: 92.6% (blind recovery). The accuracy gap is −0.9pp. But the blind baseline has no audit trail — it cannot tell the operator which nodes failed, when, why, or with what pathway. An accuracy number tells you a network recovered. The telemetry tells you how, which makes the recovery auditable. Where the Diagnostic Advantage Shows Up, and Where It Does Not At σ=1.0 the standard CNN partially recovers to 59.0% via blind relearning; ORMAS reaches 75.4% via targeted correction — a +16.4pp gap, and the baseline still has no idea what broke. At σ=0.1 the damage is mild and the standard CNN finishes slightly ahead (82.1% against 80.2%) — from there the gap widens monotonically as damage gets worse, to +23.9pp at σ=2.0. Measured as recovery from the post-blast low, ORMAS recovers more at every scale tested: +3.1pp against +2.7pp at σ=0.1, and +52.1pp against +28.2pp at σ=2.0. The Baldwin Effect: Under 40% noise, ORMAS triggers an average of 67.6 surgical self-corrections per run. Epochs 0–50: a peak of 4.2 corrections/epoch, 1.35 on average. Epochs 50–200: zero. The network learns to not need correction — architectural immunity as an emergent property. Epoch Range | Corrections / Epoch | Epochs 0–50 | 1.35 / epoch (peak 4.2) | Epochs 50–100 | 0 / epoch | Epochs 100–200 | 0 / epoch | #### Stress tests: five extreme failure scenarios, every result reported With all three layers destroyed at once, ORMAS recovers to 70.8% while the standard CNN stays at 10.0%; the compound and 100-class scenarios mark where recovery becomes seed-dependent or partial. The bifurcated results are not measurement error — they are a physical phenomenon. Under compounded structural-noise perturbation, the network sits at the edge of a topological bifurcation: small initialisation differences determine whether the self-correction mechanism achieves stable recovery or collapses. This is the honest edge of the capability. Bifurcation — Edge of Chaos Under the compound attack (40% noise + full-hierarchy lesion), ORMAS seed outcomes: 31.8%, 76.9%, 69.4%. Recovery from the most severe compound damage depends on the seed, so it is reported per run rather than averaged. Under the same attack, the standard CNN is deterministically dead at 10.0% ± 0.0% across all seeds. Scenario | Standard Final | ORMAS Final | Gap | Simultaneous Full-Hierarchy Lesion All 3 conv layers zeroed at epoch 100 | 10.0% ± 0.0% | 70.8% ± 2.2% | +60.8pp | Compounded Structural + 40% Noise Dual attack: noise + lesion simultaneously | 10.0% ± 0.0% | Per seed: 31.8 / 76.9 / 69.4% | Edge-of-chaos | High-Cardinality (CIFAR-100) 100-class partial failure — scope limit | 1.0% ± 0.0% | Per seed: 7.4 / 25.8 / 27.1% | Boundary found | Adversarial Weight Injection Expected scope boundary — crafted to evade diagnostics | 84.1% ± 0.3% | 83.1% ± 0.3% | −1.0pp | Weight Explosion (100×) System does not overcorrect on mild damage | 86.0% ± 0.1% | 85.1% ± 0.4% | −0.9pp | #### Zero-shot compositional generalisation: 94.6% retention and 58.8% on unseen combinations ORMAS holds both tasks in sequential training and generalises to shape–colour combinations it was never shown; a standard ResNet-18 catastrophically forgets shape while learning colour. Phase 1: shape classification. Phase 2: colour classification. Test: accuracy on novel shape + colour combinations. Model | Phase 1 Shape | Phase 2 Colour | Shape Retention | Zero-Shot 4-way | vs. Chance | Standard ResNet-18 | 83.6% | 91.4% | 47.3% | 51.1% | +26.1pp | ORMAS Three-Signal | 94.6% | 96.5% | 94.6% | 58.8% | +33.8pp | The ceiling is 91.3% (statistical independence: 0.946 × 0.965). ORMAS reaches 58.8% — strong but incomplete spatial separation. An emergent structural bias, not strict orthogonality. PCGrad ablation (remove Signal 2, retain Signal 3): 59.1% ± 3.6% — statistically indistinguishable. Self-correction is the necessary and sufficient driver. #### The stability result: Input-to-State Stability for a self-correcting network, accepted at DeepMath 2026 Where the Mathematics Came From In 1989 the mathematician Eduardo Sontag introduced Input-to-State Stability, a control-theory framework used to establish that a perturbed system, such as a rocket knocked off course, returns to a stable trajectory. A self-correcting network is a perturbed system: by corrupted data, by structural damage and by its own corrections. Whether its correction mechanism settles or oscillates is therefore a control-theory question, one the field had already answered for other classes of system. Raadh adapted the framework by treating each correction as a bounded input disturbance and the weight trajectory as the state, then characterising the conditions under which the state remains bounded. The result is the first formal local stability characterisation of a self-correcting neural architecture. Derived at seventeen, it was accepted at DeepMath 2026 after double-blind review. Local stability characterisation, the first for a self-correcting neural architecture. Under standard regularity assumptions, the conservation constraint (∑ Δ w = 0) bounds each correction's L² norm via mean-subtraction (weight magnitude is redistributed, not created). Empirical validation: late-stage parameter variance reduces from 0.86 (Standard CNN) to 0.19 (ORMAS) — a 4.5× reduction. t → ∞ lim sup ​ ∥ θ (t) − θ ∗ ∥ ≤ γ (ε) = μ η ε ​ Why the Bound Is Local — and Why That Is the Design, Not the Compromise A local-only result usually reads as a weaker version of something better. Here it is the opposite, and the reason matters more than the bound itself. A global convergence proof is defined over a fixed parameter space θ ∈ R n. The moment the graph grows a node, n changes — and the proof does not weaken, it stops referring to anything at all. Locality is what buys the freedom to change topology mid-training. Every node carries its own objective, so a newly added node has something to learn from the instant it exists rather than waiting for a global gradient to find it. Mean-subtracted initialisation (∑ j ​ w new, j ​ = 0) means it enters contributing zero net perturbation, leaving every existing conserved quantity undisturbed. And because the stability bound is stated per node, an injection is simply another bounded perturbation of exactly the class the mechanism already absorbs — 22,014 times in a single 200-epoch run. A globally coupled, globally proven system could not grow. This one can. A global proof would have foreclosed the architecture rather than strengthened it. #### Ablations: on the DAG, removing either mechanism collapses training On the DAG architecture under 30% noise: Configuration | Final Accuracy | Change | Full ORMAS | 40.1% (best 49.9%) | — | Remove Self-Correction (Signal 3) | NaN (best 14.2%) | Training collapsed | Remove PCGrad | NaN (best 12.1%) | Training collapsed | On the DAG, removing either mechanism collapses training outright. On the CNN, removing PCGrad leaves accuracy unchanged (78.1% final) but forces the self-correction mechanism to work far harder — disabling one defence layer triggers proportional activation of the other. This is the intended behaviour: two independent defence layers. PCGrad Removal — The Correction Explosion Without PCGrad on CNN, accuracy stays at 80.0% — but correction count explodes from 61 corrections per run → more than 12,400 corrections per run (200× more). The network survives but is under extreme structural stress throughout. PCGrad is what keeps the correction overhead tractable; Signal 3 (self-correction) is what maintains structural health when PCGrad is absent. Both mechanisms are individually necessary for stable operation at scale. #### Reproducibility: all 383 experiments regenerate from seed on one RTX 3090 - Tiered experiment infrastructure: 383+ configurations with automated seed sweeps. - Shipped an interactive reproduce.sh — one command reproduces every experiment. Core claims reproducible in under one hour. - 10-step training pipeline (ORMASTrainer), multi-round forward loop with selective rollback tracking. After each correction, a verification forward pass runs; if both global and node-local loss worsen by more than 2%, that node's weights selectively roll back — typically affecting fewer than 30% of corrected nodes. - All 383 experiments on a single RTX 3090 (24 GB VRAM, 30 GB RAM, 8 vCPU). Consumer hardware. 16,316 lines of research code. ##### Hyperparameter robustness: under 0.8% accuracy variance across a 16× sweep ORMAS's accuracy is nearly insensitive to its own hyperparameters — a strong indicator of mechanistic robustness, not overfitting to a narrow configuration: β_max ∈ [0.1, 0.5]Accuracy bounded 80.0%–80.1%<0.1% variation Gate threshold τ ∈ [0.2, 0.6]Accuracy 79.8%–80.0%<0.2% variation Bottleneck d_b ∈ {8, 32, 64, 128}Accuracy variance <0.8%16× parameter range Health threshold σ_self 2.5→3.565→41 corrections (37% fewer), accuracy unchanged79.7% vs 80.0% ##### Safeguards: healthy components are never touched Expert ImmunityNodes achieving EMA confidence ≥ 0.55 OR local loss < 0.20 become "experts" — immune from convergence penalties. Prevents disruption of already-converged features. High-Distress OverrideExpert immunity is overridden when goodness drops to ≤ 0.30. Forces immediate diagnosis regardless of expert status. No node hides catastrophic failure behind past performance. Adaptive CooldownPost-correction cooldown C ∈ [10, 200] steps. Shrinks 20% on >60% success rate; expands 50% on <30% success rate. Prevents destructive oscillation while allowing rapid iterative repair. Selective RollbackAfter each correction, a verification forward pass runs. If global AND local loss both worsen by >2%, the node rolls back. Typically affects <30% of corrected nodes per training run. #### Research roadmap: the next four experiments ##### Preemptive Immune Filtering V1 is reactive: damage happens, then it gets repaired. V2 uses local loss disagreement as a per-sample gate, stopping corrupted samples before they ever reach the gradient path. Repair becomes immunity. ##### Transformer Architecture Extension Carry three-signal learning onto attention. Each head gets an independent local loss anchored through a shared key-query bottleneck, which makes per-head health monitoring and surgical correction possible. Scale brings its own pathology classes to catalogue: attention collapse, entropy death, feature saturation. ##### Bridging Structural to Semantic Telemetry GlassBox reports structure — which nodes failed, when, and how. It cannot tell you what concept a failing node encoded. The disentanglement result hints at the bridge: nodes that lock during Phase 2 are the ones preserving Phase 1 shape representations. Formalising that through CAVs or Grad-CAM trajectories across the correction lifecycle is the central open question. ##### Cherry — Self-Correcting Language Model A language model trained from scratch on three-signal learning, rather than fine-tuned. It would be the first LLM able to catch and repair its own training pathologies while they happen. #### One architectural decision, three results: repair, noise robustness and zero-shot generalisation Structural recovery, noise robustness, zero-shot generalisation — these are not three findings. They are one structural property showing up three times. Bounding the local gradient chain produces an attribution signal. The attribution signal makes the health gate possible. The health gate delivers both the repair and the spatial separation of competing representations. Which points at something larger: continuous autonomous correction may be sufficient on its own to make modular internal structure emerge — no explicit modularity constraints, no replay buffers. If that holds, it is a research direction rather than an engineering result. Now Project Cherry · Next architecture · Fully specified ### Project Cherry: the next architecture after ORMAS, a network that grows its own components. Project Cherry is Raadh's fully specified next architecture: a network, built on ORMAS, that grows new components on demand and retires those that no longer contribute. It is designed to remove the fixed-capacity ceiling that bounds every ORMAS result above. Development begins once multi-node compute is in place. Status Specification complete Builds on ORMAS self-correction Next milestone Multi-node H100 access Results Reported once built Roadmap Every other figure on this page comes from a completed experiment. Project Cherry is a specified design, so it reports no results of its own; the numbers it builds on are ORMAS's. ORMAS already provides the difficult part: a network that knows which components are failing, and by how much, can be instructed to grow replacements. New components are introduced with zero net effect on current behaviour, so existing capabilities are undisturbed while they learn. This is how the fixed-capacity ceiling is removed. #### How Project Cherry is designed to work How it works today ##### The Standard Model One large model; every parameter handles every query. The model is unchanged after every query. Teaching it anything new requires stopping, retraining and freezing it again. What Cherry is designed to do ##### A Mesh That Learns While It Works Many small specialists; only the relevant ones activate. Only a handful of nodes wake for any one question. The answer loops back and the mesh is different afterwards — and when it meets work nothing can handle, it grows a node rather than overwriting one. Standard model Project Cherry (as specified) Parameters firing per query All of them, every time Only the specialists the router selects Learning after deployment None — frozen at the cutoff Continuous, by design Gaining a new capability Retrain the whole model Grows a node — zero net perturbation What it forgets when it learns 47.3% of the prior task retained Built on ORMAS, which retains 94.6% Where your data has to go Out to somebody else’s servers Never leaves your infrastructure Seeing inside it Approximated afterwards, if at all Five telemetry layers, emitted natively #### How it differs from fine-tuning, LoRA and Mixture-of-Experts Not fine-tuningFine-tuning is a discrete event against a frozen base, and it forgets. There is no frozen base here and no discrete event. Not LoRA or adaptersAdapters need a task ID at inference — you must already know which one to load. Here, which parts activate is the result of routing, not an input to it. Not Mixture-of-ExpertsThe closest existing approach. MoE fixes the number of experts before training and does not monitor them individually. Cherry grows components on demand, and each is independently monitored and repairable. Skip to content ### Work ## Founder of OXIEDO. Building companies since the age of 12. Rokib Al Dhin Raadh is the founder and CEO of OXIEDO, which licenses ORMAS, his self-repairing neural network, to regulated industries. Each step built on the last: five ventures from age 12 and a ~$10,000 sale at 15; OXIMO, a 40,933-line multi-agent system with 2,069 tests; a 12-month controlled study on a live UK company, Black Bloxie LTD, where removing OXIMO cut output 91%; then ORMAS and OXIEDO. On this page - 01 OXIEDO Now · licensing ORMAS - 02 OXIMO 2023–2025 · concluded - 03 Black Bloxie 2025–2026 · field study - 04 Ventures 2020–2025 · one exit Now OXIEDO · The Business ### OXIEDO lets regulated industries train AI on data they already own. OXIEDO, founded by Raadh in 2023, licenses ORMAS on-premise to banks, hospitals and other regulated institutions. Their records are among the most valuable training data in existence, and most of it sits unused, because conventional models cannot account for what they learned from it. ORMAS writes that account during training: the Model Change Record, a tamper-evident log of every weight change in a run. It is built for SR 26-2 model-risk validation and for the EU AI Act's high-risk record-keeping obligations, which apply from December 2027. Model On-premise licence Founded 2023 Lead product Model Change Record Markets Five regulated Problem Institutions do not buy audit trails for their own sake. A hospital wants to train on its own patient records without regulatory exposure; a bank wants a model its validators can sign off. Transparency is the mechanism; the value is access to data these institutions already own but cannot yet use. Interpretability tools sell an explanation after the fact; OXIEDO licenses the ability to train on that data in the first place. Product On-premise by design: the data never leaves the building Every other stack ##### The Intelligence Is Rented So your data has to travel to reach it. Scroll the diagram sideways → For a hospital, a fund or a defence programme, sending data off-site is often ruled out before accuracy is even considered. The framework sits inside the perimeter, but the intelligence does not. With ORMAS ##### The Training Happens Inside So nothing has to cross the wall at all. Scroll the diagram sideways → Everything stays inside the perimeter. For these buyers this is often the only deployable configuration, and it is why the record is produced during training rather than recovered afterwards. 01 The model Built Deploys exactly as it would have done. Same architecture, same inference cost, no change to the serving path. Removes Nothing. That is the point — the trade is not capability for accountability. 02 The named parts Built What the model learned from each source, held as a discrete addressable object, bound to the record of the data that justified it. Removes The dead end where data enters a model and stops being something anyone can point at. 03 Model Change Record Built Every weight change training made: which component, at which step, under which diagnosis, by how much, inside what declared limit. Signed, tamper-evident and diffable against the last approved version. Removes The estimate. An auditor gets a log instead of somebody’s reconstruction. Markets Five regulated markets, one question regulators ask AI Training Is this run failing, and which component? A lab whose long training run keeps failing, with no way to say which component broke Regulated Finance What changed, and can a validator verify it? A bank whose model cannot clear SR 26-2 validation Medical AI Which site caused this, and can we show it? A hospital whose approved model may never be updated Data Obligation What data is in here, and can I take it back out? A controller facing erasure against trained weights Defence & Safety-Critical What did it do to itself in the field? A programme that ends if the answer is “we cannot say” Position Current position: founder-led, licence defined Stage Licensing model defined. Conversations with model-risk and compliance teams are under way. Entity Delaware C-corporation in formation; the ORMAS IP is held by the founder and will be assigned to it. Evidence 383 ORMAS experiments on CIFAR-10 and CIFAR-100, every one reproducible from seed. Next: validation on clinical, financial and defence data with a data-custodian partner. Team Founder-led: Raadh invented ORMAS and wrote its 16,316-line PyTorch codebase. The first use of funds is a research engineer. Investors: the round, the six milestones and the full risk register are at oxiedo.com/invest. The investor deck (14 slides plus appendix, October 2026) opens here. Product, licensing and pricing at oxiedo.com OXIEDO's one on-premise ORMAS licence, sector coverage and full risk register are published on the company site. oxiedo.com Before This OXIMO · 2023–2025 ### OXIMO: a 40,933-line multi-agent operating system that ran a live company for a year. OXIMO is the multi-agent operating system Raadh built alone after five ventures showed him the same constraint: coordination, not individual capability. Given a one-sentence brief, it designs the organisation needed to deliver it and creates specialist roles when none exist. It ran Black Bloxie LTD, a live UK company, for twelve months. Its limit on real-world data is the problem ORMAS was built to solve. Status Concluded · led to ORMAS Scale 40,933 lines, solo Tests 2,069 · 0 failures Rebuild 106k → 41k lines 40,933 Lines in production 2,069 Tests, none failing 11 Independently deployable repos 72% Smaller after the rebuild 12/12 Algorithms ported intact 47 Flags across 17 phases Most multi-agent frameworks require a human in the loop to frame each task, prompt each model and pass outputs between steps. OXIMO's agents decompose work themselves, retain memory across sessions, create new roles and operate as an organisation. Not a chatbot: an organisational structure that assembles itself. Raadh rebuilt OXIMO from a 106,000-line monolith into 40,933 lines across 11 independently deployable repositories: 72% smaller, all 12 critical algorithms intact, all 2,069 tests passing. The architecture survived a complete reconstruction. View the codebase  ·  Production code, built to operate a live company. Why OXIMO concluded, and how it led to ORMAS Each OXIMO agent is built on a third-party model whose internals cannot be inspected. That was workable on clean inputs, and the system operated a live company for a year (see Black Bloxie). Training on the company's own data changed that. Production data is mislabelled, contradictory and partly corrupted, and the established methods for handling it (DivideMix, ProMix, CoDE) all failed. The problem sat inside the model, beyond the reach of better orchestration. OXIMO posed the question; ORMAS, the self-repairing neural network Raadh then invented, is his answer. It is now his full-time focus, and the technology OXIEDO licenses. From one sentence to an organisation Most agent frameworks require agents to be defined in advance. OXIMO derives the organisation from the brief and creates specialists that did not exist when the task began. ##### What Happens to a Task A task arrives as one sentence. Nobody has said who should do it, how to split it, or whether the right specialist exists yet. An organisation assembles itself around the work and a validated deliverable comes out the other side. - Task in one sentence - Safety Gate 7-check cascade - Dynamic Router complexity + skills - Execute one of four paths - Sacred Chain Master → Dept → Emp - Validate multi-stage merge - Learn brain updated A1 · A2 · B1 · B2 — the B-paths hire a specialist mid-task A1 Simple · skills exist Context assembly → single agent → validate A2 Complex · team exists Sacred Chain → parallel departments → merge B1 Simple · skill missing Self-hire → materialise role → run as A1 B2 Complex · skill missing Self-hire → build team → run as A2 ##### Creating New Roles Mid-Task Most agent frameworks ask you to define your agents up front. When OXIMO meets work no existing role can handle, it designs the role, checks it is not a duplicate, validates it, tests that it produces coherent output, and commits it — with no human at any step. This is the orchestration-layer ancestor of Silent Node Injection. 1 RealizationBlock An LLM designs the role — persona, skills, department, seniority. 2 PersonaMatcher Vector search against existing roles so it never hires a duplicate. 3 HireValidator Seven checks: ID format, duplicates, reporting chain, skill coherence. 4 PromptTester One cheap call verifies the new role produces coherent output. 5 RoleMaterializer Atomic transaction — role, brain, and audit record commit together. 6 Rollback Any step fails and the whole hire reverses. No orphaned agents, ever. Step 6 is why this is safe to run unattended: a partially-created agent is impossible. Either the whole hire commits, or none of it did. ##### Persistent, Compounding Memory Three memory tiers, plus an Ebbinghaus decay curve so unused knowledge fades and reinforced knowledge strengthens. Agents mature Nascent → Learning → Mature → Expert. Working In-process deque The current session Episodic Database, role-scoped Task outcomes and failure lessons Semantic Vector store Deep knowledge, retrievable across roles This is what the ablation measured. Removing an embedded system does not migrate it — it discards everything the system had learned, and output fell 91%. ##### Cost Efficiency OXIMO does not call one expensive frontier model. It cascades cheaper specialised models across six stages, each tuned to one kind of work — for equivalent output quality. Costs per task at model prices during OXIMO’s development (2024–2025). GPT-4 Turbo · single-shot ~$180 Claude 3.5 · single-shot ~$54 GPT-4o · single-shot ~$45 OXIMO specialised cascade ~$3.50 A ~51× reduction, achieved by specialisation rather than compromise. It is also the argument for Cherry in miniature: many small specialists beat one large generalist, at a fraction of the cost. Constructor injection everywhere, no global state, every dependency declared and every boundary enforced. Each repo can be tested, deployed and replaced on its own. 1 Foundation oximo_config Pydantic v2 settings oximo_models 18 immutable domain models oximo_db SQLAlchemy async ORM oximo_llm Unified LLM adapter 2 Domain Engines oximo_safety Adversarial input/output filter oximo_cognitive Brain lifecycle, 3-tier memory oximo_router Graph-of-Thoughts planner oximo_execution Sacred Chain, sandbox oximo_hiring Self-hiring pipeline 3 Orchestration oximo_orchestrator Central wiring hub — 4 execution paths — Post-task learning 4 API oximo_api FastAPI routes — SSE streaming — Webhooks and Prometheus metrics Before This Black Bloxie LTD · 2025–2026 ### Removing OXIMO from a live UK company cut its output 91%. Black Bloxie LTD, incorporated in England and Wales on 11 September 2025 (company no. 16711223), was Raadh's twelve-month controlled field study of OXIMO on a live e-commerce company, with a real supplier and customers in ten countries. When he removed the system, output fell 91%; when he restored it, output recovered and overshot its original level. Revenue alone cannot show whether the market would have delivered the same result. A removal test can. What it tests OXIMO (not ORMAS) Duration 12 months, 7 tests On removal −91% output On restoration +1,300% Scope. This study tests OXIMO, an orchestration layer built on third-party models. It makes no claim about ORMAS, which is separate work with its own evidence. The study in four phases - Phase 1 Aug–Oct 2025 Cold start Baseline - Phase 2 Nov 2025–Feb 2026 System removed −91% - Phase 3 Mar–Jul 2026 System restored +1,300% - Phase 4 13 Aug 2026 High-value test $4,386 −91% On system removal All commercial output collapsed, across every channel simultaneously. +1,300% On re-injection Recovery exceeded the Phase 1 average by 3.3×. Phase 2 · System removed Commercial output Phase 1 baseline → Phase 2 dead zone −91% New customer acquisition Complete cessation, every channel at once −100% Conversion rate Cross-channel collapse rules out seasonality −77% Order volume Residual orders from pre-existing sessions only −72% Phase 3 · System restored Commercial output Against the Phase 2 ablated baseline +1,300% Sessions Full LLM referral channel rebuild +1,422% Order volume Exceeded the Phase 1 average by 3.3× +340% Conversion rate Compounded memory advantage over Phase 1 +214% Full revenue disclosure The complete revenue record for the twelve-month study. $6,691.68 Total verified revenue Everything the company earned in 12 months 79+ Orders Across 10 countries ~$30 Typical order The other 78 orders average $29.56 $4,386 Largest single order The Rung 7 test · 13 Aug 2026 · $0.00 CAC A single order accounts for 65% of total revenue. It was the study's final test: whether an autonomous system could carry a customer through a high-value purchase. Total revenue is under $7,000, deliberately capped for the duration of the study. The falsification ladder — seven questions, twelve months Each rung is a test the thesis could have failed. They are in the order they were run, with the verdict on each. 7 rungs 12 months Designed to fail - 01 Phase 1 · Aug–Oct 2025 · Cold start Can an autonomous system originate commercial demand at all? The hardest available starting point: a brand-new store (incorporated as Black Bloxie LTD on 11 September 2025), zero domain authority, zero brand equity, zero advertising budget, no human in the acquisition loop. The store went live cold. Customers arrived, every one through LLM-referred channels. Yes. Demand originated where there was none. - 02 Phase 1 · Baseline established How much can it sell, and at what unit economics? A capability claim needs a cost structure. The pipeline produced a complete 12-asset product suite for $0.0043 against a $50–$150 human benchmark. Baseline output was locked across all channels as the control condition. The marginal cost of output collapses to near zero. - 03 Phase 2 · Nov 2025–Feb 2026 · The lesion Strip the system out. Was it ever the architecture, or was it the market? The central test of the study. Every generated asset was removed and the store reverted to bare platform defaults — same products, same prices, same supplier, same category, same seasonality. One variable withdrawn. The architecture was the causal variable, not the market. - 04 Phase 2 · Dead zone What does complete system removal look like? Not decay. Cessation. Acquisition went to zero and stayed there for four months. The collapse landed across every channel on the same day rather than tapering channel by channel — the specific signature that rules out seasonality and macro conditions. −100% acquisition. Simultaneous, not gradual. - 05 Phase 3 · Mar–Jul 2026 · Re-injection Is the collapse reversible? A one-directional result is only half a proof. If the system caused the output, restoring it should restore the output — and if it does not, the causal claim fails from the other side. Redeployed into the same entity with no other change. Yes. The dead zone was a lesion, not a wound. - 06 Phase 3 · Recovery ablation Does it return to baseline, or past it? It overshot: the study’s most significant result. The knowledge graph had retained everything learned in Phase 1, so the system restarted with everything it had learned in Phase 1 (August–October 2025) instead of starting cold. It came back knowing things it did not know the first time. Recovery compounds. Institutional memory survives. - 07 Phase 4 · 13 Aug 2026 · High-value test Can it close a high-value transaction, or does trust break at the price point? The residual objection: low-consideration purchases are easy. So the final phase listed high-value inventory and let the architecture run the entire funnel unassisted — discovery, positioning, objection handling, close. A $4,386 order closed at $0.00 acquisition cost. Why the study ended, and how it led to ORMAS The study was capped deliberately. No jurisdiction has yet settled who is liable when an autonomous agent enters a contract, and a clean experiment was preferable to testing that boundary. The technical reason was more important. The natural next step was to let the system learn from the company's own data, which, like all production data, was mislabelled, contradictory and partly corrupted. Every established method for handling it failed. That limitation is why Raadh's next project was ORMAS, a self-repairing neural network, rather than another product. Before This Ventures · 2020–2025 ### Five ventures from age 12, and a ~$10,000 sale at 15. Raadh founded his first company at 12 and four more by 17, and at 15 he built and sold a stock-prediction system for about $10,000. Each venture hit a different limit. Together they identified the constraint every later system was built to remove: coordination, not individual capability. Companies Five, ages 12–17 Exit ~$10,000 at fifteen Reinvested in The RTX 3090 Key finding Coordination → OXIMO ~$10,000 Built and exited · age 15 #### A stock-prediction system, built and sold at fifteen His first exit, at about $10,000. The proceeds funded the RTX 3090 that ran all 383 ORMAS experiments, so the research behind OXIEDO was financed by a product he built and sold himself. Five ventures, five limits found - 01 Software Services Age 12–14 Limit found: Distribution without credibility The product could be built; reaching buyers was the obstacle. Enterprise procurement does not route on technical merit alone, and distribution without institutional credibility is not a technical problem. - 02 Cold-Call Automation, European Markets Age 14–15 Limit found: Trust limits at the close Automation reliably handled 90% of the sales cycle, but every close required a human. Trust in autonomous agents stops at the transaction, a constraint the Black Bloxie study later tested directly with a $4,386 order closed with no human involved. - 03 US-Targeted Digital Marketing Age 15 Limit found: The wrong bottleneck The assumed bottleneck was distribution; the real one was production. The need was not to move content but to create it at volume. - 04 Organic E-Commerce Age 15–16 Limit found: Coordination at scale Optimising pricing, listings or marketing individually yields a few percent. The real challenge was coordinating research, listing, pricing, fulfilment and service at once, which no single operator can sustain. - 05 Automation Tooling Age 16–17 Limit found: Confirmation of the pattern The fifth venture confirmed the pattern: the constraint was coordination, not individual capability. That finding became the design brief for OXIMO. What five ventures taught him Each venture failed at a different point, and all for the same reason. The constraint was coordination, not individual capability. A structural problem needed a structural answer. That conclusion produced OXIMO; OXIMO's limit on real-world data produced ORMAS; ORMAS is the technology OXIEDO licenses. Skip to content ### About ## Rokib Al Dhin Raadh, 18: founder of OXIEDO and inventor of ORMAS. Founder and CEO of OXIEDO and inventor of ORMAS, a self-repairing neural network. He taught himself machine learning and built both without a lab or a co-founder. In 2026 he earned ten independent selections, including a DeepMath 2026 acceptance after double-blind review and a NeurIPS 2026 reviewer role. The full recognition record, a six-year timeline, his personal statement, education and contact follow. On this page - 01 Recognition - 02 Who he is - 03 Statement - 04 Education - 05 Contact Now Recognition · 2026 ### Ten independent selections in 2026, at 18. Rokib Al Dhin Raadh earned eleven independent selections in 2026, all from cold applications, across peer review, AI conferences and venture. In research: DeepMath 2026 accepted his stability result after double-blind review, NeurIPS 2026 made him a reviewer for its Trustworthy AI for Good workshop, Cosmos Institute ranked the work highest in its 2026 review cycle, IARCO 2026 named him a finalist, and Cohere Labs admitted him to its Open Science Community. In venture: Freshmango and 1752vc Ignite (top 1%) offered OXIEDO places, Onstage invited him to its W26 pre-pitch event, Entrepreneur First interviewed him in London, The Bridge in San Francisco took him to its final round, and 500 Global invited him to interview on 22 October. Latest decision Accepted 30 Sep 2026 #### Stability result accepted at DeepMath 2026. DeepMath is the Conference on the Mathematical Theory of Deep Neural Networks, this year hosted by Ohio State University in Columbus on 29–30 October, with an organising committee drawn from Johns Hopkins, Michigan and Ohio State. It takes theory only, and reviews it double-blind, so reviewers assessed the work without knowledge of his background. The paper, “Self-Repair as a Bounded Disturbance: Input-to-State Stability of Neural Network Training Dynamics”, was accepted for poster presentation. It formalises the stability guarantee underlying ORMAS. The 2026 invited speakers come from Stanford, Michigan, UPenn and the University of Washington, and past editions have been supported by the National Science Foundation and the Simons Foundation. Two more are in motion: Onstage is ranking its W26 cohort by interest from 350 venture funds, and 500 Global interviews him on 22 October. DeepMath 2026 Ohio State University Onstage 30 Sep Columbus, Ohio DeepMath 2026 · Mathematical Theory of Deep Neural Networks Accept (Poster) after double-blind review. The input-to-state stability result behind ORMAS, presented at Ohio State on 29–30 October. Accepted W26 cohort Central London Onstage · W26 Demo Day Invited to the pre-pitch event in Central London. Onstage ranks applicants by interest from 350 VC partners, including a16z, Sequoia, Balderton and LocalGlobe. Invited Since Sep San Francisco Entrepreneur First · The Bridge Two interview rounds completed; reached the final round. Details below. Final round Status as of 30 September 2026 2026, month by month Jul 2026 Cosmos Institute Ranked highest in cycle Aug 2026 Entrepreneur First Cold application → first-round call Sep 2026 DeepMath · Freshmango · 1752vc · IARCO · Cohere Labs Peer-reviewed acceptance, two accelerator places, a research-competition final, Cohere Labs membership. Oct 2026 DeepMath 2026 Poster, Ohio State, Columbus Dec 2026 NeurIPS · AI4GOOD Reviewing, Paris The selections Dec 2026 NeurIPS 2026 · AI4GOOD Workshop · Reviewer #### Reviewer, NeurIPS 2026 Trustworthy AI for Good Workshop Raadh is a reviewer for Trustworthy AI for Good at NeurIPS 2026 in Paris, reviewing submitted papers on mechanistic interpretability, attribution, auditing and post-deployment monitoring. The ICML edition of this workshop took 539 submissions and accepted 34%, assessed by a committee of 237 reviewers drawn from Oxford, MIT, Toronto, Berkeley, Stanford and Mila. 237 Reviewers on the committee 34% Acceptance rate, ICML edition Jul 2026 Cosmos Institute · Review Cycle #### Ranked Highest in the Cosmos Institute Review Cycle Cosmos Institute ranked the ORMAS application highest of every application in its cycle and invited Raadh to its technical track. Highest Rank in the cycle Invited Technical track Sep 2026 Freshmango · Equity-Free Accelerator · Accepted #### Accepted to Freshmango, the Equity-Free Accelerator On 22 September Freshmango offered OXIEDO a place in its equity-free programme after a single interview: access to a 5,000-founder network, $4M in AI credits and introductions across 160 venture funds, with no equity or fees. 0% Equity taken $4M In AI credits 160 Venture funds Sep 2026 1752vc · Ignite · Accepted #### Accepted to 1752vc Ignite from the Top 1% of Applicants 1752vc placed the application in the top 5% of its intake, then held a final round to select the top 1%. OXIEDO was offered a place in the limited Ignite cohort, chosen from thousands of applications across three stages. Top 1% Final-round selection Accepted Ignite cohort Sep 2026 IARCO 2026 · International Academic Research Competition · Final Round #### Finalist, IARCO 2026 International Academic Research Competition The International Academic Research Competition is hosted by YRJ, sponsored by SaveMyExams with Domain.ME, and this year drew nearly 500 participants from 60 countries. ORMAS was among the research papers selected for the final stage, a recorded research presentation, and Raadh is listed as selected on IARCO's published 2026 finalist list. ~500 Participants 60 Countries Selected Final stage, Junior Sep 2026 Cohere Labs · Open Science Community · Member #### Member, Cohere Labs Open Science Community Cohere Labs is the research lab of Cohere, one of the leading foundation-model companies. On 25 September it welcomed Raadh into its Open Science Community, noting that the solo development of ORMAS across 383 controlled experiments “demonstrates remarkable initiative in ML safety and auditability.” The lab pointed to its BIRDS and Safety & Alignment programmes as a route to extending the architecture to a transformer backbone. Member Open Science Community ORMAS Cited in the welcome Entrepreneur First · London to San Francisco Aug 2026 Entrepreneur First · London · First Screen #### Entrepreneur First: First-Round Interview, London Application Submitted cold Gate 1 Response First-round call booked An unsolicited application to one of European venture's most selective sourcing programmes led EF's talent team to invite Raadh to a first-round interview. EF's first screen assesses how a founder thinks rather than the idea itself. The conversation covered why interpretability is an architecture problem rather than a tooling one, and where ORMAS goes next. It led to The Bridge. Cold Unsolicited application 15 min First-round interview Entrepreneur First The Bridge — San Francisco Aug–Sep 2026 · Final Round The Bridge is Entrepreneur First's eight-week residency in San Francisco, where they place a small cohort of technical founders alongside the US investor and operator network, and back those who come through it with company-building capital. It is EF's route for founders who have already built something. Raadh completed two interview rounds and reached the final round. Interview 1 · Done Interview 2 · Done Final Round · Reached What it adds up to What the 2026 record establishes. Peer review. DeepMath 2026 accepted the ORMAS stability result after double-blind review by specialists in deep-learning theory. Standing in the field. NeurIPS 2026 brought Raadh in as a reviewer for the Trustworthy AI for Good workshop, reviewing papers in interpretability and model auditing, the area his own research addresses. Independent assessment. Cosmos Institute ranked the work highest in its cycle. Freshmango and 1752vc each offered places after competitive selection, The Bridge in San Francisco took him to its final round, and 500 Global has invited him to interview. Every one began as an unsolicited application. Made to be checked Each item above has a named source and a date: the Cosmos correspondence, the NeurIPS reviewer assignment, the EF stage records, the Companies House filings. Documentation is available on request, and every claim is listed with its source on the Evidence page. Request documentation Profile ### Founder first: from a first venture at 12 to OXIEDO at 18 Founder Introduction · 31 sec #### Five ventures, a new neural network architecture, and the company built on it. Rokib Al Dhin Raadh, 18, is the founder and CEO of OXIEDO and the inventor of ORMAS, a self-repairing neural network. Raised in Dhaka, he ran five ventures between the ages of 12 and 17 and sold a stock-prediction system for about $10,000 at 15. Tracing one structural cause across those ventures led him to multi-agent systems (OXIMO), then to a new neural network architecture (ORMAS), and then to a company built on it (OXIEDO). Two of the projects below are active, two are concluded and one is in specification. In the video: who ORMAS is for, the gap it fills, and how it works. Timeline 12 2020 Self-taught from age 12 Self-taught from the start, building each project a step beyond his current skills: a habit formed before any formal computer science. 12–17 2020–25 Five ventures between 12 and 17 Software services, cold-call automation, digital marketing, e-commerce and automation tooling. Each failed at a different layer, and together they pointed to one finding: the constraint was coordination, not individual capability. 15 2023 Sold a stock-prediction system for about $10,000 Built and sold at 15. The proceeds funded the RTX 3090 that later ran all 383 ORMAS experiments. 16–17 2023–25 OXIMO: a structural answer to coordination A multi-agent system that decomposes work and creates specialist roles on demand. Rebuilt from a 106,000-line monolith to 40,933 lines with 2,069 passing tests. Now concluded. 17 2025–26 Black Bloxie LTD: a twelve-month field study A UK company operated as a controlled test of OXIMO: the system was removed and restored to measure its causal effect (−91%, then +1,300%). The study also exposed the limit that led to ORMAS: the underlying models could not learn from noisy production data. 17–18 2025–26 ORMAS: 383 experiments on one GPU A network that diagnoses and repairs its own damage during training, tested across four architectures and six noise regimes: 80.3% recovery where parameter-matched baselines remain at 10.0%. 18 2026 ORMAS breakthrough · DeepMath 2026 · NeurIPS reviewer OXIEDO, founded in 2023, gets its core technology: ORMAS, licensed to five regulated sectors. The stability result is accepted at DeepMath 2026 after double-blind review, and Raadh becomes a reviewer for the NeurIPS 2026 Trustworthy AI for Good workshop. Venture · failed Built Verified externally Current Status ORMAS Current work The research Identifies, names and repairs its own failing components during training. 80.3% recovery · baseline 10.0% Open OXIEDO Live The business Licensing the architecture to institutions that cannot yet train on their own regulated data. Live oxiedo.com · five sectors Open OXIMO Closed Prior work Turns a one-sentence brief into an organisation, creating the roles it lacks. 40,933 lines · 2,069 passing tests Open Black Bloxie Closed The experiment on OXIMO A live UK company run for twelve months to measure the system’s causal effect. −91% / +1,300% 12-month lesion study Open Project Cherry Not built Planned A network that grows its own components. Fully specified; next to be built. — specification stage Open The ten 2026 selections are in Recognition. The five ventures are covered in Ventures, and credentials in Education. Where to Start Suggested starting points Researchers The ISS derivation, all 383 runs, the telemetry design and the adverse results. Scrutiny of the stability bound is especially welcome. The research Investors The product, the regulatory drivers and timeline, and a full risk register. The company Engineers 40,933 lines and 2,069 tests: a system that turns one sentence into a working organisation. The codebase Sceptics A twelve-month controlled study on a live company with real customers, with every figure disclosed. The experiment About Personal Statement ### Personal statement: from a first venture at 12 to DeepMath 2026 at 18. In his own words, Rokib Al Dhin Raadh sets out how he built ORMAS and OXIEDO alone and self-funded, and the three resources the next phase requires: multi-node compute, capacity to train Project Cherry, and a control theorist. Rokib Al Dhin Raadh · Age 18 · Founder & CEO, OXIEDO 14–16 hrs A normal working day 28 Working days livestreamed Self-funded Every experiment on his own hardware Solo Sole author of ORMAS 100% IP ownership Part One #### Built Alone, Self-Funded and on the Record I started my first venture at 12 and ran five by 17. Each failed at a different layer, and together they showed me the constraint was coordination, not individual capability. At 15 I built and sold a stock-prediction system for about $10,000; that money bought the RTX 3090 that later ran every ORMAS experiment. OXIMO was my answer to coordination. Running it on a live UK company for twelve months showed that the models underneath could not learn from noisy production data, and ORMAS was my answer to that. OXIEDO is the company that licenses it. At 18, its stability result was accepted at DeepMath 2026. I have built this work independently, without a university, research group, advisor or external funding. Every experiment ran on hardware I paid for, and every architectural decision was my own. I work fourteen to sixteen hours a day. In November 2024 I livestreamed every working session for two weeks, producing 28 sessions of unedited footage, submitted as a Guinness World Record application. The full archive is linked below. Work Archive — November 2024 · 28 Sessions Unlisted livestreams recorded for the Guinness World Record application: complete, unedited working sessions. 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 Machine Learning PyTorch CNN / ResNet / DAG Transformers PCGrad Gradient Surgery Convergence Theory Noise-Robust Training Systems Engineering Multi-Agent Orchestration Constructor Injection Atomic Rollback 3-Tier Memory Adversarial Filtering Graph-of-Thoughts Planning Infrastructure & Web Docker FastAPI Prometheus SSE Streaming HMAC Webhooks CI/CD Pydantic SQLAlchemy Async ChromaDB React / TS / Web3 Programming Languages Python (Advanced / Production) C++ LaTeX Spoken Languages English (Fluent) Bengali (Native) Part Two #### Three Resources the Next Phase Requires The foundations are in place: the code runs, the experiments are complete, the architecture is public and its stability result has passed peer review. Working alone does not scale. What the next phase needs is not drive but calibration: colleagues, advisors and reviewers who shorten iteration cycles and catch errors early. What the company does next is set out at oxiedo.com. The table below lists the three external resources that plan depends on. Resource | Why it matters | Multi-node compute | The ORMAS protocol is designed to transfer to Transformers unchanged, as the mathematics is architecture-agnostic. Demonstrating that at scale requires multi-node H100/A100 access. | Capacity to train Cherry | Pre-training a language model on the three-signal architecture builds structural correction into pre-training rather than adding it afterwards through RLHF. It is a compute-intensive programme beyond a single GPU. | A control theorist | The ISS characterization passed double-blind review at DeepMath 2026 by deep-learning theorists. The next step is scrutiny from control theory. | Transformer-scale validation, Project Cherry and control-theory review of the ISS result all require an institutional environment I have so far worked without. That is why I am raising capital: to build a team, so the work no longer depends on one person to check it. Rokib Al Dhin Raadh — 2026 About Education ### Self-directed by design: seven MIT programmes and a DeepMath 2026 acceptance before leaving school. Rokib Al Dhin Raadh, 18, is in his final year of secondary school and chose to learn his field independently, without a university or advisor. He completed seven MIT Open Learning (MITx) programmes and Andrew Ng's Deep Learning Specialization, and learned whatever each problem required: control theory for the ORMAS stability proof, market microstructure and UK company law alongside machine learning. 12th Grade (Final Year) · Self-directed Self-taught in machine learning, systems engineering and theoretical computer science, with a focus on underlying mechanisms. Each subject was learned because the work demanded it, including four well beyond software: ###### Control theory, for the stability proof The ORMAS stability characterization rests on control theory, which required a working intuition for why a perturbed system settles, or fails to. ###### Where trust in automation ends Automating European cold calls showed precisely where human trust in an autonomous agent ends: at the transaction. That boundary became a design constraint in his later systems. ###### Market microstructure, at fifteen The stock-prediction system he built and sold at 15 had to account for institutional order flow, which cannot be inferred from price data alone. ###### UK company law, without a lawyer Incorporating Black Bloxie LTD at UK Companies House from overseas, without a lawyer or agent, meant mastering the filing regime, anti-money-laundering requirements and director obligations. MIT Open Learning · MITx ##### Seven MIT programmes. Before finishing school. While still in his final year of secondary school, Raadh completed seven programmes from MIT Open Learning, covering foundation models, AI in medicine, energy and transport, and AI entrepreneurship: the same regulated sectors OXIEDO now sells into. After an admissions call about his work, he was offered a 40% scholarship on a further MIT programme. 7 MIT programmes completed 40% Scholarship offered on a further MIT programme Sponsored Into MIT Open Learning's Universal AI programme by FIAP MITx · Foundation models Universal AI Foundational Models View record ↗ MITx · Medicine AI & Precision Medicine View record ↗ MITx · Medicine Holistic AI in Medicine View record ↗ MITx · Energy AI and Sustainability: Energy View record ↗ MITx · Transport AI and Sustainability: Transportation View record ↗ MITx · Transport AI for Transportation: From Concepts to Implementation View record ↗ MITx · Venture AI & Entrepreneurship View record ↗ MITx Online sometimes fails to load shared records; full records, certificates and grade reports are available on request. DeepLearning.AI Andrew Ng's Deep Learning Specialization, the five-course programme from the co-founder of Google Brain and Coursera. 20+ further certifications Machine learning, systems architecture and applied AI from Google, UC Davis and the University of Michigan. Verifiable on request from raadh@oxiedo.com. FIAP · São Paulo One of Brazil's leading technology schools made him a member in early 2026 and sponsored his place in MIT Open Learning's Universal AI programme. ##### Research over exam preparation, by choice A deliberate choice to prioritise research. Work on the ISS characterization and OXIMO took priority over exam preparation. The results are recorded not in a transcript but in 383 experiments, 40,933 lines of production code, a public preprint and a peer-reviewed DeepMath 2026 acceptance. 383 Controlled experiments 40,933 Lines of production code DeepMath 2026 Accepted · Poster UK Ltd Incorporated, England & Wales About Contact ### Review the research, reproduce the results, or discuss OXIEDO. Rokib Al Dhin Raadh, founder and CEO of OXIEDO, takes direct enquiries from researchers reviewing the ORMAS stability result, investors and partners in compute and regulated data, and engineers reproducing the 383 experiments. Each has a starting point below. Researchers Stress-test the DeepMath 2026 stability result The preprint is on Zenodo, and the codebase reproduces the core claims in under an hour on a single GPU. Feedback on the ISS stability result, including potential errors or tighter bounds, is particularly welcome. Read the Preprint Investors and partners Fund the next phase: compute and regulated data The next phase requires multi-node compute for Transformer-scale validation and a data partner with regulated data. The round, milestones and risk register are set out on the company site. The Investment Case Engineers Reproduce the core claims in under an hour A single script, reproduce.sh, runs every experiment in the paper, so every result can be independently verified or extended. Explore the Codebase Write to the founder directly raadh@oxiedo.com Replies are typically sent within one working day. oxiedo.com Skip to content ### Evidence ## Every claim on this site, with where to check it. Rokib Al Dhin Raadh, 18, is the founder and CEO of OXIEDO and the inventor of ORMAS. This page lists each claim made on raadh.me and its source: 13 of 23 link to a public record anyone can open, and the rest are backed by correspondence available on request from raadh@oxiedo.com. Most milestones were also announced publicly on X (@Raad_X_). Last checked 3 October 2026. Research: ORMAS Peer review and service Company and systems Selection and recognition, 2026 Education ### Research: ORMAS - Public record #### Author and inventor of ORMAS Full paper and 36-page supplementary, sole author, released 1 August 2026. Zenodo · DOI 10.5281/zenodo.21730363 ↗ - Public record #### 80.3% vs 10.0% after a layer is destroyed; 383 experiments on one RTX 3090 Every number is in the preprint's tables. The code reproduces all 383 runs from seed. Preprint ↗ Code archive ↗ Results archive ↗ - Public record #### Researcher identity ORCID record for Rokib Al Dhin Raadh, which links back to raadh.me. ORCID 0009-0003-1178-5296 ↗ ### Peer review and service - Public record #### DeepMath 2026: stability paper accepted after double-blind review (poster) Conference on the Mathematical Theory of Deep Neural Networks, hosted by Ohio State University, 29–30 October 2026. Submissions are reviewed double-blind. Decision released by the Program Chairs on 30 September 2026: Accept (Poster), submission 39 (the OpenReview record is dated 25 September and was finalised on 30 September). His poster is presented on 30 October. The OpenReview page may be limited to the conference community, so a screenshot of the decision is published here. The organizing committee's invitation letter (6 October 2026) confirms the acceptance and invites him to present at Ohio State; passport number and date of birth are redacted. Decision: Accept (Poster), OpenReview screenshot ↗ Invitation letter from the organizing committee (PDF) ↗ OpenReview submission record ↗ DeepMath 2026 ↗ - Public record #### Reviewer, NeurIPS 2026 Trustworthy AI for Good workshop Workshop co-located with NeurIPS 2026 at the Paris satellite venue, 12 December 2026. The Program Chairs' recognition letter (3 October 2026) certifies his service as a reviewer; the titles of the papers he reviewed are redacted to protect reviewer confidentiality. The workshop site lists him among the 490 reviewers who submitted official reviews. Reviewer recognition letter (PDF) ↗ Public reviewer list (workshop site) ↗ ### Company and systems - Public record #### Founder and CEO of OXIEDO (founded 2023) OXIEDO licenses ORMAS on-premise to regulated industries. oxiedo.com ↗ - Public record #### OXIMO: 40,933-line multi-agent operating system, 2,069 passing tests Source archive of the system described on the Work page. OXIMO code archive ↗ - Public record #### Founder and CEO of Black Bloxie LTD (England & Wales, company no. 16711223) Incorporated on 11 September 2025. The company's leadership page names Raadh as founder and CEO. He was a minor at incorporation, so his father is the registered director and shareholder at Companies House. Black Bloxie leadership page ↗ Companies House record ↗ - Public record #### OXIMO / Black Bloxie twelve-month field study Technical report and commercial whitepaper with the full method and figures. Technical report (PDF) ↗ Whitepaper (PDF) ↗ - On request #### Five ventures between ages 12 and 17; a stock-prediction system sold for about $10,000 at 15 Records of the ventures and of the sale. Request the document ↗ ### Selection and recognition, 2026 - On request #### 1752vc Ignite: accepted from the top 1% of applicants Acceptance correspondence from 1752vc. Announcements on X (@Raad_X_) ↗ Request the document ↗ - On request #### Cosmos Institute: ranked highest in its 2026 review cycle Correspondence from Cosmos Institute, which invited the work to its technical track. Announcements on X (@Raad_X_) ↗ Request the document ↗ - On request #### Freshmango: offered a place after a single interview Offer correspondence from Freshmango. Announcements on X (@Raad_X_) ↗ Request the document ↗ - On request #### The Bridge (Entrepreneur First, San Francisco): reached the final round Interview records from two completed rounds. Announcements on X (@Raad_X_) ↗ Request the document ↗ - On request #### 500 Global: invited to a first-round interview (22 October 2026) Interview invitation from 500 Global. Announcements on X (@Raad_X_) ↗ Request the document ↗ - On request #### Entrepreneur First, London: first-round interview Interview invitation from EF's talent team. Announcements on X (@Raad_X_) ↗ Request the document ↗ - On request #### Onstage W26: invited to the pre-pitch event Invitation to the pre-pitch event in Central London. Announcements on X (@Raad_X_) ↗ Request the document ↗ - Public record #### IARCO 2026: finalist International Academic Research Competition 2026, Junior category. IARCO's published list shows Rokib Al Dhin Raadh, Rajuk Uttara Model College, Bangladesh, as selected; the competition drew nearly 500 participants from 60 countries. IARCO 2026 finalist list ↗ - Public record #### Talk, Cohere Labs Open Science Community (2 November 2026) Public event page from Cohere Labs (ML Math) listing him as speaker for "When the Update Is Not a Gradient". Event page (Luma) ↗ - On request #### Cohere Labs Open Science Community: member Welcome correspondence from Cohere Labs. Announcements on X (@Raad_X_) ↗ Request the document ↗ ### Education - Public record #### Seven MIT Open Learning (MITx) programmes completed Shared MITx Online records. MITx Online sometimes fails to load shared records; certificates are available on request. Universal AI Foundational Models ↗ AI & Precision Medicine ↗ Holistic AI in Medicine ↗ AI and Sustainability: Energy ↗ AI and Sustainability: Transportation ↗ AI for Transportation ↗ AI & Entrepreneurship ↗ - Public record #### Deep Learning Specialization (DeepLearning.AI) Five-course specialisation, verified by Coursera. Coursera verification ↗ - On request #### 40% scholarship offered on a further MIT programme Offered after an admissions call about his work. Request the document ↗ ### How this page is kept Figures on the Research, Work and About pages are taken from the sources above. When a status changes, this page and the claim are updated together. The same list is published as machine-readable JSON at /evidence.json.