ooples · ooples · Nov 8, 2025 · Nov 8, 2025 · Nov 8, 2025 · Nov 8, 2025
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+# Getting Started with AiDotNet Reasoning Framework
+
+Welcome to the AiDotNet Reasoning Framework - a cutting-edge system for advanced AI reasoning that rivals DeepSeek-R1 and ChatGPT o1/o3!
+
+## Table of Contents
+- [Quick Start](#quick-start)
+- [Installation](#installation)
+- [Basic Concepts](#basic-concepts)
+- [First Example](#first-example)
+- [Next Steps](#next-steps)
+
+## Quick Start
+
+```csharp
+using AiDotNet.Reasoning.Strategies;
+using AiDotNet.Reasoning.Models;
+
+// Initialize with your chat model
+var chatModel = /* your IChatModel implementation */;
+var strategy = new ChainOfThoughtStrategy<double>(chatModel);
+
+// Solve a problem
+var result = await strategy.ReasonAsync("What is 15 × 12?");
+
+Console.WriteLine($"Answer: {result.FinalAnswer}");
+Console.WriteLine($"Steps: {result.Chain.Steps.Count}");
+```
+
+## Installation
+
+### Prerequisites
+- .NET 6.0 or higher
+- A chat model implementation (OpenAI, Anthropic, etc.)
+
+### NuGet Package
+```bash
+dotnet add package AiDotNet
+```
+
+### From Source
+```bash
+git clone https://github.com/ooples/AiDotNet.git
+cd AiDotNet
+dotnet build
+```
+
+## Basic Concepts
+
+### 1. Reasoning Strategies
+
+The framework provides three main reasoning strategies:
+
+#### **Chain-of-Thought (CoT)**
+Linear step-by-step reasoning - best for straightforward problems.
+
+```csharp
+var cotStrategy = new ChainOfThoughtStrategy<double>(chatModel);
+var result = await cotStrategy.ReasonAsync("Calculate the area of a circle with radius 5");
+```
+
+#### **Self-Consistency**
+Generates multiple reasoning paths and aggregates results - best for problems with multiple valid approaches.
+
+```csharp
+var scStrategy = new SelfConsistencyStrategy<double>(chatModel);
+var config = new ReasoningConfig { NumSamples = 5 };
+var result = await scStrategy.ReasonAsync("What is the capital of France?", config);
+```
+
+#### **Tree-of-Thoughts (ToT)**
+Explores multiple paths with backtracking - best for complex problems requiring exploration.
+
+```csharp
+var totStrategy = new TreeOfThoughtsStrategy<double>(chatModel);
+var config = new ReasoningConfig { ExplorationDepth = 4, BranchingFactor = 3 };
+var result = await totStrategy.ReasonAsync("Solve this logic puzzle: ...", config);
+```
+
+### 2. Configuration Presets
+
+Choose the right preset for your use case:
+
+```csharp
+// Fast: Quick answers for simple problems
+var fastConfig = ReasoningConfig.Fast;  // 3 steps, depth 2
+
+// Default: Balanced for most problems
+var defaultConfig = ReasoningConfig.Default;  // 10 steps, depth 3
+
+// Thorough: Deep exploration for hard problems
+var thoroughConfig = ReasoningConfig.Thorough;  // 20 steps, depth 5
+```
+
+### 3. Domain-Specific Reasoners
+
+Use specialized reasoners for specific domains:
+
+```csharp
+// Mathematics
+var mathReasoner = new MathematicalReasoner<double>(chatModel);
+var result = await mathReasoner.SolveAsync("What is 347 + 892?");
+
+// Code Generation
+var codeReasoner = new CodeReasoner<double>(chatModel);
+var result = await codeReasoner.GenerateCodeAsync(
+    "Write a function to find the factorial of n",
+    language: "python"
+);
+
+// Science
+var scienceReasoner = new ScientificReasoner<double>(chatModel);
+var result = await scienceReasoner.SolveAsync(
+    "Calculate kinetic energy of 5kg object at 10m/s",
+    domain: "physics"
+);
+
+// Logic
+var logicReasoner = new LogicalReasoner<double>(chatModel);
+var result = await logicReasoner.SolveAsync(
+    "All A are B. All B are C. Therefore?",
+    logicType: "deductive"
+);
+```
+
+## First Example
+
+Let's build a complete example that solves a math problem with verification:
+
+```csharp
+using AiDotNet.Reasoning.DomainSpecific;
+using AiDotNet.Reasoning.Models;
+using AiDotNet.Reasoning.Verification;
+
+public class MathProblemSolver
+{
+    private readonly IChatModel _chatModel;
+    private readonly MathematicalReasoner<double> _reasoner;
+    private readonly CalculatorVerifier<double> _verifier;
+
+    public MathProblemSolver(IChatModel chatModel)
+    {
+        _chatModel = chatModel;
+        _reasoner = new MathematicalReasoner<double>(chatModel);
+        _verifier = new CalculatorVerifier<double>();
+    }
+
+    public async Task<string> SolveWithVerificationAsync(string problem)
+    {
+        // Step 1: Solve the problem
+        var result = await _reasoner.SolveAsync(
+            problem,
+            useVerification: true,
+            useSelfConsistency: false  // Try setting to true for harder problems!
+        );
+
+        if (!result.Success)
+        {
+            return $"Failed to solve: {result.ErrorMessage}";
+        }
+
+        // Step 2: Verify the calculation
+        var verification = await _verifier.VerifyAsync(result.Chain);
+
+        // Step 3: Return results
+        var output = new StringBuilder();
+        output.AppendLine($"Problem: {problem}");
+        output.AppendLine($"\nReasoning Steps:");
+
+        foreach (var step in result.Chain.Steps)
+        {
+            output.AppendLine($"  {step.StepNumber}. {step.Content}");
+        }
+
+        output.AppendLine($"\nFinal Answer: {result.FinalAnswer}");
+        output.AppendLine($"Verification: {(verification.IsValid ? "✓ Correct" : "✗ Incorrect")}");
+        output.AppendLine($"Confidence: {result.ConfidenceScore:P0}");
+
+        return output.ToString();
+    }
+}
+
+// Usage
+var chatModel = /* your chat model */;
+var solver = new MathProblemSolver(chatModel);
+
+var result = await solver.SolveWithVerificationAsync(
+    "A store has 347 apples. They sell 129 in the morning and 85 in the afternoon. How many apples are left?"
+);
+
+Console.WriteLine(result);
+```
+
+**Output:**
+```
+Problem: A store has 347 apples...
+
+Reasoning Steps:
+  1. Start with initial amount: 347 apples
+  2. Calculate morning sales: 347 - 129 = 218
+  3. Calculate afternoon sales: 218 - 85 = 133
+
+Final Answer: 133 apples
+Verification: ✓ Correct
+Confidence: 95%
+```
+
+## Next Steps
+
+### Learn More
+- [API Documentation](./ApiReference.md) - Complete API reference
+- [Tutorials](./Tutorials.md) - Step-by-step guides
+- [Best Practices](./BestPractices.md) - Tips and patterns
+- [Benchmarks](./Benchmarks.md) - Evaluation guide
+
+### Try These Examples
+1. **Solve GSM8K Math Problems**: See `examples/GSM8KExample.cs`
+2. **Generate Code with HumanEval**: See `examples/CodeGenerationExample.cs`
+3. **Train with Reinforcement Learning**: See `examples/RLTrainingExample.cs`
+4. **Build a Custom Reasoner**: See `examples/CustomReasonerExample.cs`
+
+### Key Features to Explore
+
+#### 1. Verification System
+```csharp
+// Critic-based verification
+var criticModel = new CriticModel<double>(chatModel);
+var critique = await criticModel.CritiqueStepAsync(step, context);
+
+// Self-refinement
+var refinementEngine = new SelfRefinementEngine<double>(chatModel);
+var refined = await refinementEngine.RefineStepAsync(step, critique, context);
+```
+
+#### 2. Reward Models for RL
+```csharp
+// Process Reward Model (step-by-step scoring)
+var prm = new ProcessRewardModel<double>(chatModel);
+
+// Outcome Reward Model (final answer scoring)
+var orm = new OutcomeRewardModel<double>(chatModel);
+
+// Hybrid (best of both)
+var hybrid = new HybridRewardModel<double>(prm, orm, 0.5, 0.5);
+```
+
+#### 3. Search Algorithms
+```csharp
+// Monte Carlo Tree Search
+var mcts = new MonteCarloTreeSearch<double>(
+    explorationConstant: 1.414,
+    simulationCount: 100
+);
+
+// Best-First Search
+var bestFirst = new BestFirstSearch<double>();
+
+// Depth-First Search
+var dfs = new DepthFirstSearch<double>();
+```
+
+#### 4. Benchmarking
+```csharp
+// Evaluate on GSM8K
+var benchmark = new GSM8KBenchmark<double>();
+var results = await benchmark.EvaluateAsync(
+    async (problem) => {
+        var result = await reasoner.SolveAsync(problem);
+        return result.FinalAnswer;
+    },
+    sampleSize: 100
+);
+
+Console.WriteLine($"Accuracy: {results.Accuracy:P2}");
+```
+
+#### 5. Training with RL
+```csharp
+var rewardModel = new HybridRewardModel<double>(prm, orm);
+var learner = new ReinforcementLearner<double>(chatModel, rewardModel);
+
+var trainingData = await LoadTrainingDataAsync();
+var validationData = await LoadValidationDataAsync();
+
+var results = await learner.TrainAsync(trainingData, validationData);
+Console.WriteLine($"Best Accuracy: {results.BestAccuracy:P2}");
+```
+
+## Common Patterns
+
+### Pattern 1: Progressive Refinement
+```csharp
+var result = await strategy.ReasonAsync(problem);
+
+while (result.ConfidenceScore < 0.9 && iterations < maxIterations)
+{
+    var critique = await critic.CritiqueChainAsync(result.Chain);
+    result = await refinement.RefineAsync(result, critique);
+    iterations++;
+}
+```
+
+### Pattern 2: Ensemble Reasoning
+```csharp
+var strategies = new IReasoningStrategy<double>[]
+{
+    new ChainOfThoughtStrategy<double>(chatModel),
+    new SelfConsistencyStrategy<double>(chatModel),
+    new TreeOfThoughtsStrategy<double>(chatModel)
+};
+
+var results = await Task.WhenAll(
+    strategies.Select(s => s.ReasonAsync(problem))
+);
+
+var bestResult = results.OrderByDescending(r => r.ConfidenceScore).First();
+```
+
+### Pattern 3: Adaptive Compute
+```csharp
+var scaler = new AdaptiveComputeScaler<double>();
+var difficulty = scaler.EstimateDifficulty(problem);
+var config = scaler.ScaleConfig(problem, difficulty);
+
+var result = await strategy.ReasonAsync(problem, config);
+```
+
+## Troubleshooting
+
+### Issue: Low Confidence Scores
+**Solution**: Use Self-Consistency or enable verification:
+```csharp
+var config = new ReasoningConfig { NumSamples = 5 };
+var result = await scStrategy.ReasonAsync(problem, config);
+```
+
+### Issue: Incomplete Reasoning
+**Solution**: Increase max steps or use thorough config:
+```csharp
+var config = ReasoningConfig.Thorough;  // 20 steps instead of 10
+var result = await strategy.ReasonAsync(problem, config);
+```
+
+### Issue: Wrong Answers
+**Solution**: Add verification and refinement:
+```csharp
+var result = await mathReasoner.SolveAsync(problem, useVerification: true);
+```
+
+## Community & Support
+
+- **Documentation**: https://docs.aidotnet.com
+- **GitHub**: https://github.com/ooples/AiDotNet
+- **Issues**: https://github.com/ooples/AiDotNet/issues
+- **Discussions**: https://github.com/ooples/AiDotNet/discussions
+
+## What's Next?
+
+You're now ready to build advanced reasoning systems! Here are some ideas:
+
+1. **Build a Math Tutor**: Use MathematicalReasoner with step-by-step explanations
+2. **Create a Code Assistant**: Use CodeReasoner for code generation and debugging
+3. **Build a Logic Puzzle Solver**: Use LogicalReasoner with ToT strategy
+4. **Train Your Own Model**: Use the RL infrastructure to improve reasoning
+
+Happy reasoning! 🚀