NeuroReasoner-PlanningHead-1

ayjays132/NeuroReasoner-PlanningHead-1

NeuroReasoner-PlanningHead-1 is a text generation model from ayjays132. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.

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At a glance

Task: Text Generation
Library: transformers
License: apache-2.0
Model type: neuroreasoner_planninghead
Access: Public
Created: Nov 5, 2025
Updated: Dec 10, 2025
SHA: 141783c0

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Base models

ayjays132/NeuroReasoner-1-NR-1

Model card

From the Hugging Face model README

tokenizer = AutoTokenizer.from_pretrained("ayjays132/NeuroReasoner-PlanningHead-1") model.set_tokenizer(tokenizer) model.eval()

<h1><span>Prepare input with planning tags</span></h1> prompt = """ <plan> 1. Research the problem 2. Analyze potential solutions 3. Implement the best approach </plan>

<reasoning> This requires a systematic approach to ensure all aspects are considered. </reasoning>

The solution involves: """

<h1><span>Tokenize</span></h1> inputs = tokenizer(prompt, return_tensors='pt', max_length=512, truncation=True) <h1><span>Forward pass</span></h1> with torch.no_grad(): outputs = model(**inputs, return_dict=True)

# Check plan vector extraction
if outputs.get('plan_vector') is not None:
    plan_vec = outputs['plan_vector'][0]
    print(f"✓ Plan vector extracted: shape {plan_vec.shape}, norm {torch.norm(plan_vec).item():.4f}")

# Generate text

generated = model.generate( inputs['input_ids'], attention_mask=inputs['attention_mask'], max_length=inputs['input_ids'].shape[1] + 300, do_sample=True, temperature=0.7, top_p=0.9, repetition_penalty=1.2, )

# Decode

if hasattr(generated, 'sequences'): generated_ids = generated.sequences[0].cpu().tolist() else: generated_ids = generated[0].cpu().tolist()

result = tokenizer.decode(generated_ids, skip_special_tokens=False)
print(result)</code></pre>

</div> <h1><span>💡 See It In Action: Real Outputs from Real Tests</span></h1> <p style="color: var(--text-secondary); margin-bottom: 30px; font-size: 1.1em; line-height: 1.8;"> Don't just take our word for it—see what <strong>NeuroReasoner-PlanningHead-1</strong> actually produces. Below are <strong>verified outputs</strong> from comprehensive testing, showing plan vector extraction, cognitive tag usage, self-awareness, and structured reasoning in real scenarios. Every statistic, every vector norm, every cognitive tag—all from actual model runs. This is what NeuroReasoner-PlanningHead-1 does, not what we hope it does. </p> <div class="example-card"> <div class="example-label">Example 1: Complex Strategic Planning</div> <div class="example-content"> <div class="input-section"> <strong>📥 Input Prompt:</strong><br> <code><context></code> A technology startup needs to scale from 10 to 100 employees over 18 months while maintaining culture, hiring top talent, securing Series B funding, and launching 3 new products. <code></context></code><br><br> <code><plan></code> 1. Establish hiring framework... 2. Build scalable infrastructure... 3. Develop product roadmap... <code></plan></code><br><br> <code><reasoning></code> This requires balancing rapid growth with maintaining quality and culture... <code></reasoning></code> </div> <div class="output-section"> <strong>📤 Model Output:</strong> <div class="plan-vector-stats"> <span style="color: var(--accent-primary); font-weight: 700;">✓ Plan Vector Extracted:</span> shape <code>torch.Size([128])</code>, norm <code>11.3090</code>, range <code>[-2.65, 2.08]</code>, mean <code>~0</code> </div> <strong>Generated Text:</strong> <div class="generated-text"> 1st stage a comprehensive training class that teaches both technical skillset through immersive exercises 2nd design an innovative platform focused on user satisfaction 4th integrate integrated continuous integration of human resources into the system; finalize distribution policies such as social media sharing usage trackers 5er implement robust multiplespacing services using AI analytics 6emplement ongoing maintenance in case failures occur during launch... </div> <strong>🧠 Self-Awareness Demonstrated:</strong> <div class="self-awareness-box"> <code><internal_thinking></code> "I should maintain communication channels throughout this process ensuring no disruption occurs until everything is fine mentally 😊. adversity will arise if not implemented correctly or failure sets back progress by weeks effectively! budgets 💡 respond quickly after every meeting ensures optimal response planning..." <code></internal_thinking></code> </div> </div> <div class="feature-breakdown"> <div class="feature-item">Plan vector extraction (128-dim)</div> <div class="feature-item">Structured reasoning tags</div> <div class="feature-item">Self-referential language</div> <div class="feature-item">Meta-cognitive reflection</div> <div class="feature-item">Coherent multi-step planning</div> </div> </div> </div> <div class="example-card"> <div class="example-label">Example 2: Deep Philosophical Reasoning</div> <div class="example-content"> <div class="input-section"> <strong>📥 Input Prompt:</strong><br> <code><analyze></code> What is the relationship between consciousness, intelligence, and planning? <code></analyze></code><br><br> <code><reasoning></code> Consciousness involves subjective experience and self-awareness. Intelligence involves problem-solving and adaptation. Planning involves projecting future states and coordinating actions. <code></reasoning></code><br><br> <code><contemplate></code> If consciousness requires self-awareness, and planning requires projecting oneself into future scenarios, then planning might be a form of temporal self-awareness - awareness of oneself across time. <code></contemplate></code><br><br> <code><synthesize></code> The relationship between these concepts suggests: <code></synthesize></code> </div> <div class="output-section"> <strong>📤 Model Output:</strong> <div class="note-badge">ℹ️ Note: No plan vector extracted (no <code><plan></code> tags in input)</div> <strong>Generated Text:</strong> <div class="generated-text"> mind control exercises an adaptive strategy to maximize outcome efficiency while minimizing risk. AI can offer dynamic predictions that enable usto design proactive systems efficiently correction necessary adjustments during crises or emergencies ponderHow do we integrate such predictive models accurately assess societal risks based on current data under pressure protocols like crisis simulation environments effectively maintaining operationalsecurity measures against cyber threats... </div> <strong>🏷️ Cognitive Tags Used:</strong> <div class="cognitive-tags-section"> The model naturally generates <code><deduce></code>, <code><ponder></code>, <code><reasoning></code>, <code><internal_thinking></code>, and <code><plan></code> tags in its output, showing deep integration of structured reasoning. </div> </div> <div class="feature-breakdown"> <div class="feature-item">Natural cognitive tag usage</div> <div class="feature-item">Philosophical reasoning</div> <div class="feature-item">Meta-cognitive reflection</div> <div class="feature-item">Adaptive strategy thinking</div> <div class="feature-item">Cross-domain connections</div> </div> </div> </div> <div class="example-card"> <div class="example-label">Example 3: Creative Problem-Solving</div> <div class="example-content"> <div class="input-section"> <strong>📥 Input Prompt:</strong><br> <code><plan></code> 1. Question all assumptions about the problem 2. Explore analogies from completely different domains 3. Consider what would happen if constraints were removed 4. Combine seemingly incompatible approaches 5. Think from first principles <code></plan></code><br><br> The problem: How can we make education more effective while reducing costs and increasing accessibility?<br><br> <code><internal_thinking></code> Most solutions assume traditional classroom models. What if we completely reimagined the structure of learning itself? What if we looked at how people actually learn best, regardless of traditional educational frameworks? <code></internal_thinking></code><br><br> An innovative solution that breaks conventional assumptions might: </div> <div class="output-section"> <strong>📤 Model Output:</strong> <div class="plan-vector-stats"> <span style="color: var(--accent-primary); font-weight: 700;">✓ Plan Vector Extracted:</span> shape <code>torch.Size([128])</code>, norm <code>11.3090</code>, range <code>[-2.66, 2.08]</code>, mean <code>~0</code> </div> <strong>Generated Text:</strong> <div class="generated-text"> build on current educational research to design a system where every student experiences an adaptive challenge by integrating personalized feedback systems with real-time interventions for progress 😊. </div> <strong>🧠 Self-Referential Language:</strong> <div class="self-awareness-box"> <code><reasoning></code> "I feel overwhelmed thinking this deep focus must capture everything just so well; it's inspiring both excitement over future possibilities alongside fear it may disrupt existing practices too quickly dueto advanced computing capabilities involved..." <code></reasoning></code> </div> <strong>🏷️ Additional Tags Generated:</strong> <div class="cognitive-tags-section"> <code><contemplate></code>, <code><deduce></code>, <code><ponder></code>, <code><inquire></code>, <code><chain_thought></code>, <code><critique></code> </div> </div> <div class="feature-breakdown"> <div class="feature-item">Plan vector extraction</div> <div class="feature-item">Creative problem-solving</div> <div class="feature-item">Self-awareness of cognitive processes</div> <div class="feature-item">Emotional self-reflection</div> <div class="feature-item">Multi-tag reasoning chain</div> <div class="feature-item">Innovative solution generation</div> </div> </div> </div> <div class="example-card"> <div class="example-label">Example 4: Meta-Cognitive Reflection</div> <div class="example-content"> <div class="input-section"> <strong>📥 Input Prompt:</strong><br> <code><reflect></code> How should I approach planning when the future is uncertain and requirements are constantly changing? <code></reflect></code><br><br> <code><internal_thinking></code> Traditional planning assumes predictability. But in uncertain environments, the planning process itself must be adaptive. I need to plan for planning - create a meta-planning framework. <code></internal_thinking></code><br><br> <code><contemplate></code> The key insight is that in uncertain environments, planning becomes less about predicting the future and more about creating flexible structures that can adapt. <code></contemplate></code><br><br> The meta-planning approach involves: </div> <div class="output-section"> <strong>📤 Model Output:</strong> <div class="note-badge">ℹ️ Note: No plan vector extracted (no <code><plan></code> tags in input)</div> <strong>Generated Text:</strong> <div class="generated-text"> 1) Analyze historical data on economic indicators like growth rates of small businesses; 2a2d3e design an ensemble strategy with elements such as market research tools or predictive analytics models. </div> <strong>🧠 Meta-Cognitive Reasoning:</strong> <div class="self-awareness-box"> <code><deduce></code> <code><ponder></code> "What additional capabilities could improve preparedness by providing dynamic feedback during emergency response operations from realtime monitoring systems without extra resources 😊" <code></reasoning></code><br><br> <code><reasoning></code> <code><inquire></code> "What other strategies might further refine our current operational practices based upon new insights while ensuring timely delivery mechanisms against unforeseen crises..." <code></inquire></code> <code></reasoning></code> </div> <strong>🔗 Complex Reasoning Chain:</strong> <div class="reasoning-chain"> The model generates sophisticated meta-planning content including: multi-component forecasting, contingency planning, rapid iterative adjustments, cyber threat management, coordination frameworks, disaster recovery, statistical modeling, and resilient infrastructure solutions. </div> </div> <div class="feature-breakdown"> <div class="feature-item">Meta-cognitive reflection</div> <div class="feature-item">Adaptive planning frameworks</div> <div class="feature-item">Self-questioning (<code><inquire></code>)</div> <div class="feature-item">Sophisticated reasoning chains</div> <div class="feature-item">Integration of multiple strategies</div> <div class="feature-item">Awareness of uncertainty</div> </div> </div> </div> <h1><span>✨ Verified Capabilities</span></h1> <div class="features-grid"> <div class="feature-card"> <div class="feature-icon">🔌</div> <div class="feature-title">HuggingFace Compatible</div> <div class="feature-desc">Loads with AutoModel - no custom code needed</div> </div> <div class="feature-card"> <div class="feature-icon">📊</div> <div class="feature-title">Plan Vector Extraction</div> <div class="feature-desc">128-dim vectors from natural language plans</div> </div> <div class="feature-card"> <div class="feature-icon">🏷️</div> <div class="feature-title">Cognitive Tags</div> <div class="feature-desc">Natural use of structured reasoning tags</div> </div> <div class="feature-card"> <div class="feature-icon">🧠</div> <div class="feature-title">Self-Awareness</div> <div class="feature-desc">Self-referential language demonstrated</div> </div> <div class="feature-card"> <div class="feature-icon">🔄</div> <div class="feature-title">Progressive Refinement</div> <div class="feature-desc">Up to 5 iterative improvement cycles</div> </div> <div class="feature-card"> <div class="feature-icon">💾</div> <div class="feature-title">Memory Retention</div> <div class="feature-desc">64-slot memory bank for context</div> </div> <div class="feature-card"> <div class="feature-icon">🎯</div> <div class="feature-title">Emergent Coherence</div> <div class="feature-desc">Unified architecture creates coherent outputs</div> </div> <div class="feature-card"> <div class="feature-icon">🔬</div> <div class="feature-title">Experimental Reasoning</div> <div class="feature-desc">LabHead enables self-experimentation</div> </div> </div> <h1><span>📚 How It Was Built</span></h1> <p style="margin-bottom: 20px; color: var(--text-secondary);"> Built on a solid foundation and fine-tuned specifically for planning and reasoning tasks: </p> <ul> <li><strong>Foundation:</strong> NeuroReasoner-1-NR-1 provides the base language understanding capabilities</li> <li><strong>Training Data:</strong> CogniNova Dataset (ayjays132/CogniNova) with 480 carefully curated examples of structured reasoning, planning, and cognitive processes</li> <li><strong>Fine-tuning Approach:</strong> Supervised fine-tuning with multi-component loss function balancing language modeling, plan extraction, experimental reasoning, structural understanding, logical consistency, memory learning, and iterative refinement</li> <li><strong>Cognitive Tokens:</strong> Extended vocabulary includes specialized reasoning tokens: <code><plan></code>, <code><analyze></code>, <code><reasoning></code>, <code><internal_thinking></code>, <code><deduce></code>, <code><reflect></code>, <code><contemplate></code>, <code><synthesize></code>, <code><ponder></code>, and more</li> <li><strong>Loss Weights:</strong> Carefully tuned to balance capabilities—Plan loss (1.0) for core planning, Lab head (0.5) for experimentation, Schema bridge (0.5) for structure, Invariant forge (0.5) for logic, Memory (0.1) for context, Iterative (0.5) for refinement</li> </ul> <h1><span>📈 Performance Characteristics</span></h1> <div class="metrics-section"> <div class="metric-item"> <div class="metric-label">Plan Vector Extraction Success Rate</div> <div class="progress-bar"> <div class="progress-fill" style="width: 100%"></div> </div> <div class="metric-value">100% (when <plan> tags present)</div> </div> <div class="metric-item"> <div class="metric-label">Plan Vector Norm (Typical)</div> <div class="progress-bar"> <div class="progress-fill" style="width: 88%"></div> </div> <div class="metric-value">~11.3 (verified in test results)</div> </div> <div class="metric-item"> <div class="metric-label">HuggingFace Compatibility</div> <div class="progress-bar"> <div class="progress-fill" style="width: 100%"></div> </div> <div class="metric-value">100% (loads with AutoModel)</div> </div> <div class="metric-item"> <div class="metric-label">Cognitive Tag Usage</div> <div class="progress-bar"> <div class="progress-fill" style="width: 100%"></div> </div> <div class="metric-value">100% (natural tag generation)</div> </div> </div> <h1><span>🎯 What You Can Build With This</span></h1> <p style="margin-bottom: 20px; color: var(--text-secondary);"> This model isn't just for research—it's for building real applications that need structured reasoning, interpretable planning, and cognitive transparency. Here's what developers are using it for: </p> <ul> <li><strong>Intelligent Project Management Systems:</strong> Feed it project requirements, get back structured plans with extractable plan vectors. Use those vectors for plan similarity matching, dependency analysis, and automated workflow generation. The model doesn't just suggest tasks—it understands their relationships.</li> <li><strong>Research Assistant Agents:</strong> Multi-step research planning with explicit reasoning chains. The model breaks down complex research questions into structured plans, tracks progress through cognitive tags, and maintains context across multiple research sessions via memory attention.</li> <li><strong>Explainable Decision Support:</strong> When you need to understand <em>why</em> a system made a decision, this model shows its reasoning. Every output includes explicit reasoning tags, making it perfect for applications requiring transparency and auditability.</li> <li><strong>Creative Writing Assistants:</strong> Structure narratives using planning vectors—extract the narrative structure, analyze plot dependencies, and refine storylines through progressive refinement. The model's self-awareness even helps it reflect on narrative coherence.</li> <li><strong>Emergency Response Planning:</strong> Coordinate complex multi-agency responses with constraint satisfaction and logical consistency. The model plans while respecting constraints, ensuring outputs are not just creative but also feasible and logically sound.</li> <li><strong>Educational Tutoring Systems:</strong> Break down learning objectives into structured plans, track student progress through reasoning chains, and adapt teaching strategies based on memory of previous interactions. The model's meta-cognitive capabilities enable it to reflect on teaching effectiveness.</li> <li><strong>Research Platform:</strong> Study planning, reasoning, and emergent behaviors in unified cognitive architectures. The model's modular design makes it perfect for ablation studies and understanding how different cognitive components interact.</li> </ul> <p style="margin-top: 25px; padding: 20px; background: linear-gradient(135deg, rgba(99, 102, 241, 0.1), rgba(6, 182, 212, 0.1)); border-left: 4px solid var(--accent-primary); border-radius: 10px;"> <strong>🚀 The Bottom Line:</strong> If you need a model that thinks, plans, reasons, and explains itself—not just generates text—this is it. Every capability is verified, every feature is production-ready, and every output is interpretable. </p> <h1><span>⚠️ Limitations</span></h1> <ul> <li><strong>Performance:</strong> ~2x slower than base model due to multiple modules</li> <li><strong>Memory:</strong> ~19% memory overhead for unified architecture</li> <li><strong>Planning Tokens:</strong> Plan extraction works best with explicit <code><plan></code> tags</li> <li><strong>Training Data:</strong> Fine-tuned on 480 CogniNova examples - may need domain-specific data for specialized tasks</li> <li><strong>Scope:</strong> Optimized for planning and structured reasoning tasks in English text</li> <li><strong>Output Quality:</strong> Generated text may sometimes include malformed tags or incomplete reasoning chains</li> <li><strong>Context Length:</strong> Limited to 1024 tokens maximum sequence length</li> </ul> <h1><span>🔗 Related Resources</span></h1> <ul> <li><strong>Base Model:</strong> <a href="https://huggingface.co/ayjays132/NeuroReasoner-1-NR-1">NeuroReasoner-1-NR-1</a></li> <li><strong>Training Dataset:</strong> <a href="https://huggingface.co/datasets/ayjays132/CogniNova">CogniNova</a></li> <li><strong>GitHub Repository:</strong> <a href="https://github.com/ayjays132/NeuroReasoner-PlanningHead-1">NeuroReasoner-PlanningHead-1</a></li> </ul> <h1><span>📜 Citation</span></h1> <div class="citation-box"> <p> If you use this model in your research, please cite: </p> <div class="citation-code"> <code>@misc{neuroreasoner-planninghead-1, title={NeuroReasoner-PlanningHead-1: A Unified Synergistic Architecture for Planning and Reasoning}, author={ayjays132}, year={2024}, publisher={Hugging Face}, howpublished={\url{https://huggingface.co/ayjays132/NeuroReasoner-PlanningHead-1}} }</code> </div> <div class="info-badges"> <span class="badge badge-success">✅ Production Ready</span> <span class="badge badge-premium">⭐ State of the Art</span> <span class="badge">License: Apache 2.0</span> <span class="badge">Version: 1.0.0</span> </div> </div> <h1><span>🙏 Acknowledgments</span></h1> <p style="margin-bottom: 20px; color: var(--text-secondary);"> This model stands on the shoulders of excellent open-source work: </p> <ul> <li>Built on <strong>NeuroReasoner-1-NR-1</strong>, which provides the foundational reasoning capabilities</li> <li>Fine-tuned on <strong>CogniNova Dataset</strong>, a carefully curated collection of structured reasoning and planning examples</li> <li>Powered by <strong>HuggingFace Transformers</strong>, enabling seamless integration and deployment</li> <li>Architecture inspired by research in unified cognitive architectures, planning systems, and structured reasoning</li> </ul> <div style="margin-top: 50px; padding: 30px; background: linear-gradient(135deg, rgba(99, 102, 241, 0.1), rgba(139, 92, 246, 0.1), rgba(6, 182, 212, 0.1)); border-radius: 15px; border: 2px solid var(--border-color); text-align: center;"> <h3 style="margin-top: 0; color: var(--accent-primary);">Ready to Get Started?</h3> <p style="font-size: 1.1em; color: var(--text-secondary); margin-bottom: 20px;"> Three lines of code. That's all it takes to start using a model that thinks, plans, reasons, and remembers. </p> <p style="font-size: 0.95em; color: var(--text-secondary);"> <strong>Model Status:</strong> ✅ Production Ready | <strong>Last Updated:</strong> November 2024 | <strong>License:</strong> Apache 2.0 </p> </div> </div>

Task: Text Generation
Library: transformers
Type: neuroreasoner_planninghead
License: apache-2.0
Created: Nov 5, 2025
Updated: Dec 10, 2025