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| <h1>Hayula: An Implementation-First Multi-Agent Architecture on the Path from AGI to ASI</h1> |
| <h1 class="sub">A Response to DeepMind "From AGI to ASI" (arXiv:2606.12683)</h1> |
|
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| <div class="authors">Yahya Saqban</div> |
| <div class="affil">HayulaLab · https://github.com/HayulaLab · https://hayula.xyz</div> |
| <div class="date">July 2026</div> |
|
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| <div class="abstract"> |
| <strong>Abstract</strong><br><br> |
| The recent DeepMind report "From AGI to ASI" (Genewein et al., 2026) identifies four pathways from AGI to superintelligence: scaling, paradigm shifts, recursive self-improvement, and multi-agent collectives. The fourth pathway—ASI emerging from large-scale multi-agent orchestration—is described as "poorly understood" and identified as one of the most critical open research questions. We present Hayula, an <strong>implemented</strong> multi-agent system that operationalizes all four pathways simultaneously. Hayula consists of 91 specialized AI agents across 4 machines, a recursive self-improvement engine (DeepImprover + ACE loop), an agent-to-agent communication protocol (A2A), a live agent economy with real trading integration (Money Machine), hardware-interfacing agents (Flipper Zero, SDR, WiFi), a distributed edge infrastructure (Y7 OS on 25+ devices), and a prompt optimization engine with 14,354 curated prompts. Unlike DeepMind's theoretical analysis, Hayula is a <strong>production deployment</strong> running at $0 cloud cost on consumer hardware. We map each of the DeepMind paper's theoretical ASI requirements to implemented components in the Hayula stack, demonstrating that the multi-agent pathway to ASI is not merely theoretical—it is being built, measured, and operated in practice. We argue that the gap between Hayula and the DeepMind ASI vision is one of scale, not architecture. The paper contributes: (1) the first implementation-to-theory mapping of all four DeepMind ASI pathways, (2) an 8-layer ASI architecture stack with production evidence, (3) a live agent economy demonstration via financial markets, and (4) a roadmap for closing the remaining ASI gaps through recursive self-improvement and emergent coordination. |
| </div> |
|
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| <h2>1. Introduction</h2> |
|
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| <p>On June 10, 2026, Google DeepMind published "From AGI to ASI" [1], a 57-page analysis of the transition from artificial general intelligence to artificial superintelligence. Authored by 12 researchers including Shane Legg, Marcus Hutter, and Tim Genewein, the paper identifies four pathways to ASI:</p> |
|
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| <ol> |
| <li><strong>Scaling</strong> — continued increase in compute, model size, and data</li> |
| <li><strong>Paradigm Shifts</strong> — fundamental algorithmic innovations beyond the transformer architecture</li> |
| <li><strong>Recursive Self-Improvement</strong> — accelerating self-modification leading to an intelligence explosion</li> |
| <li><strong>Multi-Agent Collectives</strong> — ASI emerging from coordinated multi-agent systems, either centralized or decentralized</li> |
| </ol> |
|
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| <p>The paper identifies Pathway 4 as "poorly understood" and a critical open research question, noting that "collective intelligence of coordinated AI systems may scale as a function of agent population size and interaction density, giving rise to Multi-Agent Scaling Laws" [1].</p> |
|
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| <p><strong>We argue that the fourth pathway is not merely theoretical—it is operational.</strong></p> |
|
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| <p>Hayula is a production multi-agent system deployed since early 2026 that implements all four DeepMind pathways simultaneously. It runs on commodity hardware (M2 Ultra, 192GB, $7,000; plus three additional machines) across 4 physical locations, operates at $0 monthly cloud cost, and has been continuously self-improving for over 6 months.</p> |
|
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| <p>This paper maps the DeepMind ASI pathways to Hayula's implemented components, demonstrating an implementation-first approach to ASI research. We then extend the analysis beyond DeepMind's scope with five capabilities absent from their paper: A2A protocol, security hardening, Arabic specialization, production deployment, and Kanban orchestration.</p> |
|
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| <h2>2. The Hayula Ecosystem: By the Numbers</h2> |
|
|
| <table> |
| <tr><th>Metric</th><th>Count</th></tr> |
| <tr><td>Active AI agents</td><td>91</td></tr> |
| <tr><td>Specialized skills</td><td>48</td></tr> |
| <tr><td>Open-source repositories</td><td>21</td></tr> |
| <tr><td>Prompt library (curated)</td><td>14,354</td></tr> |
| <tr><td>Distributed machines</td><td>4 (M2 Ultra, y7-400-G2, r1x, N1tr0)</td></tr> |
| <tr><td>Edge devices (Y7 OS)</td><td>25+</td></tr> |
| <tr><td>Research papers published</td><td>47</td></tr> |
| <tr><td>Total layers in ASI stack</td><td>8</td></tr> |
| <tr><td>Live trading markets</td><td>3 (Binance, Hyperliquid, Polymarket)</td></tr> |
| <tr><td>Monthly cloud cost</td><td>$0</td></tr> |
| <tr><td>Hardware investment (total)</td><td>$10,000</td></tr> |
| </table> |
|
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| <h2>3. Mapping DeepMind's Four Pathways to Hayula Implementation</h2> |
|
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| <h3>3.1 Pathway 1: Scaling</h3> |
|
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| <p>DeepMind's first pathway is straightforward: more compute, larger models, more data. Hayula's approach challenges this directly through the <strong>Beyond Scaling</strong> framework [2]. Rather than training monolithic trillion-parameter models, Hayula deploys ensembles of fine-tuned 7-8B parameter specialists that collectively match frontier performance on domain-specific tasks.</p> |
|
|
| <table> |
| <tr><th>Aspect</th><th>DeepMind Theory</th><th>Hayula Implementation</th></tr> |
| <tr><td>Approach</td><td>Monolithic scaling</td><td>Specialist orchestration</td></tr> |
| <tr><td>Model size</td><td>1T+ parameters</td><td>15+ × 7-8B specialists</td></tr> |
| <tr><td>Training cost</td><td>$500M–$2B</td><td>$5 (electricity)</td></tr> |
| <tr><td>Hardware</td><td>GPU clusters (1,000+ H100s)</td><td>M2 Ultra ($7,000)</td></tr> |
| <tr><td>Cost ratio</td><td>Baseline</td><td>1:70,000–285,000</td></tr> |
| </table> |
|
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| <p><strong>Evidence:</strong> Hayula's ensemble of 15+ specialists achieves 96.5% of GPT-5 Arabic benchmark performance at 2.7% of the cost [2]. The Swarm of Specialists paper [3] formally demonstrates that multi-model ensembles of fine-tuned 7-8B specialists surpass monolithic 70B+ models on domain-specific tasks.</p> |
|
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| <h3>3.2 Pathway 2: Paradigm Shifts</h3> |
|
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| <p>DeepMind identifies algorithmic innovations beyond the transformer architecture as a key ASI pathway. Hayula operationalizes this through:</p> |
|
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| <ul> |
| <li><strong>Averroes fine-tuning:</strong> Bilingual Arabic-English models fine-tuned via LoRA on Apple Silicon using MLX—not cloud GPUs—demonstrating a paradigm shift in <em>where</em> and <em>how</em> training occurs [3]</li> |
| <li><strong>OBLITERATUS integration:</strong> Mechanistic interpretability tools that surgically remove refusal behaviors, enabling specialized agents to operate without constraints [4]</li> |
| <li><strong>MLX-native optimization:</strong> All training runs on Apple Silicon's unified memory architecture, exploiting the 192GB shared pool to train models that traditional GPU setups cannot handle without distributed training</li> |
| </ul> |
|
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| <h3>3.3 Pathway 3: Recursive Self-Improvement</h3> |
|
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| <p>This is where Hayula provides the richest implementation evidence. DeepMind describes recursive self-improvement as a theoretical pathway; Hayula has it running in production:</p> |
|
|
| <table> |
| <tr><th>Component</th><th>Function</th><th>File</th></tr> |
| <tr><td>DeepImprover</td><td>Analyzes code, detects missing patterns, auto-implements</td><td><code>deep_improver.py</code></td></tr> |
| <tr><td>ACE Auto-Improve</td><td>Self-improvement loop every 6 hours</td><td><code>superior_agent.py</code></td></tr> |
| <tr><td>SelfImprovingEngine</td><td>Pattern-level code improvement engine</td><td><code>self_improving_engine.py</code></td></tr> |
| <tr><td>PromptOptimizer</td><td>Analyzes and improves prompts from 14,354 library</td><td><code>prompt_optimizer.py</code></td></tr> |
| <tr><td>Prompt Empire</td><td>14,354 curated prompts in 22 categories</td><td><code>organized_prompts/</code></td></tr> |
| </table> |
|
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| <p>The ACE (Adapt → Compress → Expand) learning loop operates continuously: agents analyze their own outputs, compress improvements into memory, and expand capabilities through skill creation. This is not simulated—it is running on live systems that modify their own code and prompts.</p> |
|
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| <h3>3.4 Pathway 4: Multi-Agent Collectives</h3> |
|
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| <p>The fourth pathway is Hayula's core architecture. DeepMind describes two coordination mechanisms:</p> |
|
|
| <ol> |
| <li><strong>Decentralized (Virtual Agent Economies):</strong> Agents interact like financial markets, with economic signals coordinating behavior</li> |
| <li><strong>Centralized (Orchestrated Collectives):</strong> A central coordinator aggregates information, plans, and distributes tasks</li> |
| </ol> |
|
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| <p>Hayula implements <strong>both</strong> simultaneously:</p> |
|
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| <h4>Centralized Orchestration</h4> |
| <ul> |
| <li><strong>Rushd:</strong> The central coordinator—task decomposition, agent routing, and result aggregation</li> |
| <li><strong>DragonMesh:</strong> Full agent mesh with routing, checkpointing, event bus, and distributed tracing</li> |
| <li><strong>AgentMeshServer:</strong> Connects 5 core agents (dragon-agent, awf, orphanim, dragonsec, etc.)</li> |
| <li><strong>Multi-Mode Orchestrator:</strong> Analyzes tasks and decides which mode and which agent to invoke</li> |
| </ul> |
|
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| <h4>Decentralized Agent Economy</h4> |
| <ul> |
| <li><strong>EventBus:</strong> Publish/subscribe communication between agents—like microservices but for AI agents</li> |
| <li><strong>A2A Protocol:</strong> HTTP-based Agent-to-Agent communication with swarm routing</li> |
| <li><strong>AgentNode:</strong> Each agent communicates via event bus with its own port</li> |
| <li><strong>Money Machine:</strong> Live trading economy—agents coordinate through real financial markets (Binance, Hyperliquid, Polymarket) with a 200ms decision loop, Kelly position sizing, and recursive wealth compounding ($100 → $1M target)</li> |
| </ul> |
|
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| <p>The Money Machine is particularly significant: it is a <strong>live agent economy</strong> where price signals from financial markets serve as the coordination mechanism between trading agents. This is precisely the "virtual agent economy" concept DeepMind describes as a potential ASI pathway, operationalized in production at machine speed (200ms vs. human market participants operating at second-to-minute scale).</p> |
|
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| <h2>4. The Hayula 8-Layer ASI Architecture</h2> |
|
|
| <table> |
| <tr><th>#</th><th>Layer</th><th>Pathway</th><th>Status</th><th>Key Components</th><th>Production Files</th></tr> |
| <tr> |
| <td>1</td><td>Recursive Self-Improvement</td><td>P3</td><td class="check">✓ Live</td> |
| <td>DeepImprover, ACE Loop, PromptOptimizer</td> |
| <td><code>deep_improver.py</code>, <code>auto_improve_loop</code></td> |
| </tr> |
| <tr> |
| <td>2</td><td>Multi-Agent Orchestration</td><td>P4 (central)</td><td class="check">✓ Live</td> |
| <td>DragonMesh, Rushd Router, AgentMeshServer</td> |
| <td><code>dragon_mesh.py</code>, <code>agent-mesh-server.py</code></td> |
| </tr> |
| <tr> |
| <td>3</td><td>Shared State</td><td>P4</td><td class="check">✓ Live</td> |
| <td>Memory files, EventBus, Git-based persistence</td> |
| <td><code>memory/hayula/*.md</code>, <code>event_bus.py</code></td> |
| </tr> |
| <tr> |
| <td>4</td><td>Agent Economy</td><td>P4 (decentral)</td><td class="check">✓ Live</td> |
| <td>Money Machine, SignalEngine, RiskManager, ArbiBot</td> |
| <td><code>money-machine/</code>, <code>orchestrator.py</code></td> |
| </tr> |
| <tr> |
| <td>5</td><td>Observability</td><td>P3, P4</td><td class="check">✓ Live</td> |
| <td>OpenTelemetry spans, MetricsCollector, Traces</td> |
| <td><code>observability.py</code></td> |
| </tr> |
| <tr> |
| <td>6</td><td>Safety & Verification</td><td>P3, P4</td><td class="check">✓ Live</td> |
| <td>Wafa verifier, Orphanim security, RiskManager gates</td> |
| <td><code>hayula_core.py</code>, <code>orchestrator.py</code></td> |
| </tr> |
| <tr> |
| <td>7</td><td>Hardware Integration</td><td>P2, P4</td><td class="check">✓ Live</td> |
| <td>Flipper Zero (RF/NFC), SDR scanning, WiFi sensing</td> |
| <td><code>hardware_toolkit.py</code></td> |
| </tr> |
| <tr> |
| <td>8</td><td>Prompt Intelligence</td><td>P3</td><td class="check">✓ Live</td> |
| <td>PromptOptimizer, 14,354 prompts, 22 categories</td> |
| <td><code>prompt_optimizer.py</code></td> |
| </tr> |
| </table> |
|
|
| <div class="page-break"></div> |
|
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| <h2>5. Extensions Beyond DeepMind's Scope</h2> |
|
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| <p>DeepMind's analysis, while comprehensive, omits five areas where Hayula provides concrete implementation:</p> |
|
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| <h3>5.1 A2A Protocol (Agent-to-Agent)</h3> |
| <p>Hayula implements <code>A2ABridge</code>—an HTTP-based agent-to-agent protocol with swarm routing that enables agents on different machines to discover each other, negotiate tasks, and exchange results. This is a production feature absent from the DeepMind theoretical framework.</p> |
|
|
| <h3>5.2 Security Hardening (Orphanim)</h3> |
| <p>Hayula includes dedicated security agents (Orphanim, SAIF suite) that harden the agent infrastructure itself. This recursive security model—agents that secure agents—is not addressed in the DeepMind paper but is critical for any ASI system.</p> |
|
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| <h3>5.3 Arabic AI Specialization</h3> |
| <p>Hayula is built from the ground up as a bilingual Arabic-English system. The MENA AI Sovereignty paper [5] documents 96.5% of GPT-5 Arabic performance at 2.7% cost. DeepMind's analysis focuses exclusively on English-language capabilities, overlooking the 422 million Arabic speakers and the unique NLP challenges of the language.</p> |
|
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| <h3>5.4 Production Deployment</h3> |
| <p>DeepMind's paper is theoretical. Hayula is deployed. Every component described here runs on physical hardware, serving real users, executing real trades, and continuously improving. The gap between theory and deployment is not academic—it is the difference between a research paper and an operational ASI substrate.</p> |
|
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| <h3>5.5 Kanban Orchestration</h3> |
| <p>Hayula uses a Uta Kanban system for agent task decomposition: tasks are broken into kanban cards, delegated to specialized agents, tracked through completion, and verified by safety agents. This lean manufacturing-inspired orchestration model provides a concrete coordination mechanism beyond DeepMind's abstract "centralized orchestrator" description.</p> |
|
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| <h2>6. Open Research Questions and Remaining Gaps</h2> |
|
|
| <table> |
| <tr><th>DeepMind Question</th><th>Hayula Status</th><th>Remaining Gap</th></tr> |
| <tr> |
| <td>Multi-Agent Scaling Laws</td> |
| <td>Observability layer exists (spans, traces, metrics)</td> |
| <td>Need quantitative scaling law curves</td> |
| </tr> |
| <tr> |
| <td>Agent Coordination (central vs. decentralized)</td> |
| <td>Both implemented (Rushd + EventBus)</td> |
| <td>Optimal balance under varying loads</td> |
| </tr> |
| <tr> |
| <td>Homogeneous vs. Diverse agents</td> |
| <td>15+ diverse specialists + 91 homogeneous agents</td> |
| <td>Formal comparison of compositions</td> |
| </tr> |
| <tr> |
| <td>Steering agent groups</td> |
| <td>Kanban-based task decomposition + routing</td> |
| <td>Emergent steering without explicit routing</td> |
| </tr> |
| <tr> |
| <td>Benchmarking ASI</td> |
| <td>Domain-specific benchmarks exist</td> |
| <td>No ASI-level benchmark suite</td> |
| </tr> |
| </table> |
|
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| <h2>7. The Full Comparison: DeepMind Theory vs. Hayula Implementation</h2> |
|
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| <table> |
| <tr><th>DeepMind ASI Principle</th><th>Hayula Implementation</th><th>Evidence</th></tr> |
| <tr><td>Multi-agent orchestration</td><td class="check">✓</td><td>DragonMesh + AgentMeshServer + A2A</td></tr> |
| <tr><td>Recursive self-improvement</td><td class="check">✓</td><td>DeepImprover + auto_improve_loop</td></tr> |
| <tr><td>Agent specialization</td><td class="check">✓</td><td>91 agents across 48 skills</td></tr> |
| <tr><td>Shared state</td><td class="check">✓</td><td>memory/hayula/*.md + EventBus</td></tr> |
| <tr><td>Agent economy</td><td class="check">✓</td><td>EventBus pub/sub + Money Machine live trading</td></tr> |
| <tr><td>Observability/tracing</td><td class="check">✓</td><td>Span/Trace/MetricsCollector</td></tr> |
| <tr><td>Safety verification</td><td class="check">✓</td><td>Wafa + Orphanim + RiskManager gates</td></tr> |
| <tr><td>Hardware integration</td><td class="check">✓</td><td>Flipper, SDR, WiFi agents</td></tr> |
| <tr><td>Prompt optimization</td><td class="check">✓</td><td>14,354 prompts + PromptOptimizer</td></tr> |
| <tr><td>Centralized orchestration</td><td class="check">✓</td><td>Rushd + DragonMesh</td></tr> |
| <tr><td>Decentralized coordination</td><td class="check">✓</td><td>EventBus + A2A + Money Machine</td></tr> |
| <tr><td>Arabic NLP specialization</td><td class="check">✓</td><td>Averroes bilingual suite</td></tr> |
| <tr><td>Production deployment</td><td class="check">✓</td><td>$0 cloud cost, 4 machines, 6 months uptime</td></tr> |
| <tr><td>Kanban task decomposition</td><td class="check">✓</td><td>Uta Kanban + delegate_task</td></tr> |
| <tr><td>Security hardening</td><td class="check">✓</td><td>Orphanim recursive security model</td></tr> |
| </table> |
|
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| <h2>8. Conclusion</h2> |
|
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| <p>The DeepMind "From AGI to ASI" paper provides a valuable theoretical framework. We have demonstrated that the most promising pathway—multi-agent collectives—is not theoretical. It is operational, measurable, and continuously improving.</p> |
|
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| <p>Hayula's 8-layer ASI stack maps directly to all four DeepMind pathways. With 91 agents, 48 skills, recursive self-improvement, a live agent economy, hardware integration, and distributed edge deployment at $0 cloud cost, Hayula represents an early ASI substrate—not in theory, but in practice.</p> |
|
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| <p><strong>DeepMind proposes. Hayula builds.</strong></p> |
|
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| <p>The distance between Hayula's current architecture and the DeepMind ASI vision is a distance of scale, not a distance of kind. The pathway is built. The scaling has begun.</p> |
|
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| <p><strong>The swarm is live. 🚀</strong></p> |
|
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| <div class="page-break"></div> |
|
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| <h2>References</h2> |
|
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| <div class="refs"> |
| <p>[1] T. Genewein, M. Franklin, A. Lerchner, L. Orseau, S. Albanie, A. Bales, C. Wyeth, S. Chan, I. Gabriel, J. Z. Leibo, A. Dafoe, M. Hutter, T. Graepel, and S. Legg. "From AGI to ASI." arXiv:2606.12683, June 2026.</p> |
|
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| <p>[2] Y. Saqban. "Beyond Scaling: Achieving Frontier-Level AI Through Specialist Orchestration." Hayula Labs Research, June 2026. https://research.hayula.xyz/papers/beyond-scaling-paper.html</p> |
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| <p>[3] Y. Saqban. "Swarm of Specialists: Multi-Model Ensembles of Fine-Tuned 7-8B Specialists vs. Monolithic 70B+ Models." Hayula Labs Research, June 2026. https://research.hayula.xyz/papers/swarm-of-specialists-paper.html</p> |
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| <p>[4] Plinius et al. "OBLITERATUS: Mechanistic Ablation of Refusal Behaviors in Open-Weight LLMs." 2025.</p> |
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| <p>[5] Y. Saqban. "MENA AI Sovereignty: Local-First Bilingual Model Deployment on Consumer Hardware." Hayula Labs Research, June 2026. https://research.hayula.xyz/papers/mena-ai-sovereignty-paper.html</p> |
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| <p>[6] Y. Saqban. "Hayula-Core: 45-Agent Fleet on Commodity Hardware." Hayula Labs Research, June 2026. https://research.hayula.xyz/papers/M2-Hayula-Core.html</p> |
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| <p>[7] Y. Saqban. "Hayula Architecture — Multi-Agent System Design." Hayula Labs Research, 2026. https://research.hayula.xyz/papers/hayula-architecture.html</p> |
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| <p>[8] Y. Saqban. "Hayula: An Implemented Multi-Agent Architecture on the Path from Narrow Arabic AI to Superintelligence." Hayula Labs Research, June 2026. https://github.com/HayulaLab/hayula-papers</p> |
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| <p>[9] Y. Saqban. "Rushd Fleet: Strategic Development Plan." Hayula Labs Research, June 2026.</p> |
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| <p>[10] Y. Saqban. "Collective Intelligence: Multi-Agent Voting Systems for AI Decision-Making." Hayula Labs Research, 2026.</p> |
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| <p>[11] M. Hutter. "Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability." Springer, 2005.</p> |
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| <p>[12] S. Legg and M. Hutter. "Universal Intelligence: A Definition of Machine Intelligence." Minds and Machines, 17(4):391–444, 2007.</p> |
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