Claude/fix issue 417 chain of thought 011 c uu sh u85 jd b ngn qq2dc1 j

[ooples]

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📥 Commits

Reviewing files that changed from the base of the PR and between 373bff6 and 5c91e07.

📒 Files selected for processing (16)

• docs/ReasoningFrameworkGuide.md (1 hunks)
• docs/reasoning/GettingStarted.md (1 hunks)
• docs/reasoning/Tutorials.md (1 hunks)
• src/Interfaces/IAnswerAggregator.cs (1 hunks)
• src/Interfaces/IContradictionDetector.cs (1 hunks)
• src/Interfaces/ICriticModel.cs (1 hunks)
• src/Interfaces/IDiversitySampler.cs (1 hunks)
• src/Interfaces/IExternalToolVerifier.cs (1 hunks)
• src/Interfaces/IPredictionModelBuilder.cs (2 hunks)
• src/Interfaces/IReasoner.cs (1 hunks)
• src/Interfaces/IReasoningStrategy.cs (1 hunks)
• src/Interfaces/IRewardModel.cs (1 hunks)
• src/Interfaces/ISearchAlgorithm.cs (1 hunks)
• src/Interfaces/ISelfRefinementEngine.cs (1 hunks)
• src/Interfaces/IThoughtEvaluator.cs (1 hunks)
• src/Interfaces/IThoughtGenerator.cs (1 hunks)

Summary by CodeRabbit

New Features

• Added comprehensive reasoning framework with multiple strategies for multi-step problem solving.
• Introduced domain-specific reasoners for mathematics, code generation, scientific reasoning, and logical analysis.
• Added 13+ benchmarks (GSM8K, MATH, MMLU, HumanEval, etc.) for evaluating reasoning performance.
• Enabled reinforcement learning training with configurable reward models and checkpointing.
• Added self-refinement and verification capabilities for improving reasoning quality.

Documentation

• Published getting started guide, best practices, and five detailed tutorials with code examples.
• Added reasoning framework architecture documentation and troubleshooting resources.

Examples

• Included working example applications for math solving, code generation, benchmarking, and training workflows.

Walkthrough

Adds a complete Reasoning Framework: documentation, examples, many new public interfaces and models, three reasoning strategies (CoT, Self‑Consistency, ToT) with search algorithms and components, verification/refinement and reward models, numerous benchmarks and data loaders, RL training tooling, and cancellation support for language-model APIs.

Changes

Cohort / File(s)	Summary
Documentation & Tutorials docs/ReasoningFrameworkGuide.md, docs/reasoning/GettingStarted.md, docs/reasoning/BestPractices.md, docs/reasoning/Tutorials.md	New comprehensive guide, getting-started, best-practices, and tutorials covering strategies, presets, advanced usage, architecture, benchmarks, and examples.
Examples & Demo examples/Program.cs, examples/ConcreteExamples/*	New console demo and runnable examples: Program.cs, MathSolverExample.cs, CodeGenerationExample.cs, BenchmarkRunnerExample.cs, TrainingExample.cs.
Public Interfaces src/Interfaces/*	Added many interfaces and DTOs (e.g., IReasoningStrategy<T>, IBenchmark<T>, IThoughtGenerator<T>, IThoughtEvaluator<T>, ISearchAlgorithm<T>, ICriticModel<T>, IRewardModel<T>, IContradictionDetector<T>, IAnswerAggregator<T>, IDiversitySampler<T>, IExternalToolVerifier<T>, ISelfRefinementEngine<T>). Updated IChatModel<T>.GenerateResponseAsync to accept CancellationToken.
Core Models & Config src/Reasoning/Models/*, src/Reasoning/Benchmarks/Models/*	Added ReasoningConfig, ReasoningResult<T>, ReasoningChain<T>, ReasoningStep<T>, ThoughtNode<T>, and benchmark model types (BenchmarkProblem, BenchmarkResult<T>, ProblemEvaluation<T>).
Strategy Base & Strategies src/Reasoning/ReasoningStrategyBase.cs, src/Reasoning/Strategies/*	New abstract ReasoningStrategyBase<T> and strategies: ChainOfThoughtStrategy<T>, SelfConsistencyStrategy<T>, TreeOfThoughtsStrategy<T> (ToT supports generator/evaluator and selectable search algorithms).
Search Algorithms src/Reasoning/Search/*	New search algorithms implementing ISearchAlgorithm<T>: BreadthFirstSearch<T>, DepthFirstSearch<T>, BestFirstSearch<T>, BeamSearch<T>, MonteCarloTreeSearch<T>.
Components src/Reasoning/Components/*	New components: ThoughtGenerator<T>, ThoughtEvaluator<T>, ContradictionDetector<T>, DiversitySampler<T>.
Aggregation & Scaling src/Reasoning/Aggregation/*, src/Reasoning/ComputeScaling/*	Added aggregators MajorityVotingAggregator<T>, WeightedAggregator<T>, and AdaptiveComputeScaler.
Verification, Critics & Reward src/Reasoning/Verification/*	New verification and evaluation modules: CalculatorVerifier<T>, CodeExecutionVerifier<T> (+ result types), CriticModel<T>, ProcessRewardModel<T>, OutcomeRewardModel<T>, HybridRewardModel<T> (+ RewardBreakdown<T>), SelfRefinementEngine<T>.
Benchmarks & Data Loaders src/Reasoning/Benchmarks/*, src/Reasoning/Benchmarks/Data/*	Many benchmark implementations (GSM8K, MATH, MMLU, HumanEval, MBPP, HellaSwag, ARC‑AGI, BoolQ, TruthfulQA, LogiQA, DROP, CommonsenseQA, PIQA, WinoGrande) plus GSM8KDataLoader, HumanEvalDataLoader.
Training & RL src/Reasoning/Training/*	RL/training stack: PolicyGradientTrainer<T>, ReinforcementLearner<T>, RLConfig, TrainingDataCollector<T>, TrainingSample<T>, training metrics, checkpointing and STaR helpers.
Language Models & Cancellation src/LanguageModels/*, src/LanguageModels/ChatModelBase.cs	Added CancellationToken propagation: IChatModel<T>.GenerateResponseAsync, ILanguageModel<T>.GenerateAsync, ChatModelBase<T>.GenerateAsync/GenerateAsyncCore, and updated model implementations (AnthropicChatModel, AzureOpenAIChatModel, OpenAIChatModel) to accept cancellation.
Tests tests/Reasoning/Benchmarks/BenchmarkTests.cs, tests/.../TreeOfThoughtsRetrieverTests.cs	New benchmark unit tests and minor test cleanup.

Sequence Diagram(s)

sequenceDiagram
    participant Client as Client/App
    participant Strategy as Strategy (CoT / Self‑Consistency / ToT)
    participant Chat as IChatModel
    participant Search as ISearchAlgorithm
    participant Verifier as Verifier/Critic
    participant Refiner as SelfRefinement
    participant Aggreg as Aggregator

    Client->>Strategy: ReasonAsync(query, config)
    activate Strategy
    Strategy->>Chat: GenerateResponseAsync(prompt, token)
    Chat-->>Strategy: LLM response

    alt Tree‑of‑Thoughts
        Strategy->>Search: SearchAsync(root, generator, evaluator, config)
        activate Search
        loop exploration
            Search->>Chat: GenerateThoughtsAsync(...)
            Chat-->>Search: candidate thoughts
            Search->>Chat: EvaluateThoughtAsync(...)
            Chat-->>Search: scores
        end
        Search-->>Strategy: best path
        deactivate Search
    else Self‑Consistency
        Strategy->>Strategy: spawn N CoT samples (bounded concurrency)
        Strategy->>Chat: multiple GenerateResponseAsync calls
        Chat-->>Strategy: samples
        Strategy->>Aggreg: Aggregate(answers, confidences)
        Aggreg-->>Strategy: final answer
    end

    alt Verification enabled
        Strategy->>Verifier: VerifyStepAsync / CritiqueChainAsync
        Verifier-->>Strategy: verification / critiques
    end

    alt Refinement triggered
        Strategy->>Refiner: RefineChainAsync(chain, critic, config)
        Refiner->>Chat: refinement prompts
        Chat-->>Refiner: refined steps
        Refiner-->>Strategy: refined chain
    end

    Strategy-->>Client: ReasoningResult (FinalAnswer, Chains, Metrics)
    deactivate Strategy

Loading

Estimated code review effort

🎯 5 (Critical) | ⏱️ ~120 minutes

• Areas needing extra attention:
  • Search algorithms: MCTS selection/backpropagation, beam pruning, and path reconstruction correctness.
  • Strategies: concurrency, LLM-call retries, JSON/text parsing fallbacks, resource/time budgeting.
  • Verification & execution: expression parsing, sandboxing/timeouts for code execution, language detection.
  • Reward & training: reward/advantage calculations, baseline updates, STaR selection logic, checkpoint serialization.
  • Interface changes: IChatModel / ILanguageModel / ChatModelBase signature updates and propagation to implementations.

Possibly related PRs

• Work on Issue Number Two #423 — Overlaps IChatModel.GenerateResponseAsync signature change adding CancellationToken; directly related at interface and ChatModelBase implementation level.
• Fix issue 417 chain of thought #426 — Appears to modify reasoning strategies and components; likely intersects on strategy/search/verification implementations.

Poem

🐇
I hop through prompts and branching trails,
I gather thoughts and balance scales.
I run the tests, then verify the code,
I polish chains and chart the road.
A crunchy carrot for each solved node.

Pre-merge checks and finishing touches

❌ Failed checks (3 warnings)

Check name	Status	Explanation	Resolution
Title check	⚠️ Warning	The title is a nonsensical string that does not accurately describe the changeset, which introduces comprehensive reasoning framework components.	Replace with a clear, descriptive title reflecting the actual changes, e.g., 'Implement AiDotNet Reasoning Framework with strategies, verification, and benchmarking.'
Description check	⚠️ Warning	The description is entirely a checklist template with no actual content describing what was changed, why, or how it relates to the changeset.	Complete the Summary section with concrete details about the changes made, objectives achieved, and implementation details relevant to the reasoning framework additions.
Docstring Coverage	⚠️ Warning	Docstring coverage is 77.91% which is insufficient. The required threshold is 80.00%.	You can run @coderabbitai generate docstrings to improve docstring coverage.

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Actionable comments posted: 5

♻️ Duplicate comments (10)

src/Reasoning/Search/BreadthFirstSearch.cs (2)

127-134: Fallback discards all explored nodes when no terminal is found.

If the loop exits without finding a terminal node, the method returns only [root] (line 134), discarding all explored structure. BeamSearch and BestFirstSearch both fall back to the best-scoring explored node. Consider tracking a bestNode (any node, not just terminal) to return a meaningful path when no terminal is reached.

Consider this approach:

         // Track the best path found
         AiDotNet.Reasoning.Models.ThoughtNode<T>? bestTerminalNode = null;
+        AiDotNet.Reasoning.Models.ThoughtNode<T>? bestNode = null;
         var numOps = MathHelper.GetNumericOperations<T>();
         T bestScore = numOps.Zero;
@@
                 if (bestTerminalNode == null || currentScoreValue > bestScoreValue)
                 {
                     bestTerminalNode = currentNode;
                     bestScore = currentNode.EvaluationScore;
                 }
+
+                // Also track overall best
+                if (bestNode == null || currentScoreValue > Convert.ToDouble(bestNode.EvaluationScore))
+                {
+                    bestNode = currentNode;
+                }
@@
         // Return the best path found
         if (bestTerminalNode != null)
         {
             return ReconstructPath(bestTerminalNode);
         }

+        // Fallback: return best explored node's path
+        if (bestNode != null)
+        {
+            return ReconstructPath(bestNode);
+        }
+
-        // Fallback: return just the root if no terminal node found
         return new List<AiDotNet.Reasoning.Models.ThoughtNode<T>> { root };

---

1-2: Add missing using directive for MathHelper.

Line 68 calls MathHelper.GetNumericOperations<T>() but the required namespace AiDotNet.Helpers is not imported. This will cause a compilation failure.

Apply this fix:

+using AiDotNet.Helpers;
 using AiDotNet.Interfaces;
 using AiDotNet.Reasoning.Models;

src/Reasoning/Search/BestFirstSearch.cs (2)

1-2: Add missing using directive for MathHelper.

Line 103 calls MathHelper.GetNumericOperations<T>() but the required namespace AiDotNet.Helpers is not imported. This will cause a compilation failure.

Apply this fix:

+using AiDotNet.Helpers;
 using AiDotNet.Interfaces;
 using AiDotNet.Reasoning.Models;

---

141-153: Fix semantic inconsistency: evaluate children against the original query, not their own thought.

Line 146 evaluates each child using child.Thought, but the evaluator's originalQuery parameter should represent the original problem statement. DepthFirstSearch correctly threads the original query through its recursion, and BeamSearch uses root.Thought. This strategy should align with that pattern so all nodes are scored in terms of progress toward solving the original problem.

Apply this fix:

             // Evaluate and add children to queue (with unique counter)
             foreach (var child in children)
             {
                 child.EvaluationScore = await evaluator.EvaluateThoughtAsync(
                     child,
-                    child.Thought,
+                    root.Thought,
                     config,
                     cancellationToken
                 );

                 currentNode.Children.Add(child);
                 priorityQueue.Add((child, nodeCounter++));
             }

src/Reasoning/Search/MonteCarloTreeSearch.cs (1)

102-108: Fix semantic inconsistency: evaluate children against the original query, not their own thought.

Line 104 evaluates each child using child.Thought, but the evaluator's originalQuery parameter should represent the original problem statement. DepthFirstSearch threads the original query through its recursion, and BeamSearch uses root.Thought. MCTS should align with that pattern to ensure all nodes are scored in terms of progress toward the original problem.

Apply this fix:

                 foreach (var child in children)
                 {
-                    child.EvaluationScore = await evaluator.EvaluateThoughtAsync(child, child.Thought, config, cancellationToken);
+                    child.EvaluationScore = await evaluator.EvaluateThoughtAsync(child, root.Thought, config, cancellationToken);
                     child.Metadata["visits"] = 0;
                     child.Metadata["total_value"] = 0.0;
                     node.Children.Add(child);
                 }

src/Reasoning/ComputeScaling/AdaptiveComputeScaler.cs (1)

114-115: Temperature formula contradicts reasoning best practices.

The implementation gives lower temperature (0.5× baseline) to easy problems and higher temperature (1.0× baseline) to hard problems. While this matches the comment, it contradicts standard reasoning practices where harder problems benefit from lower temperature (more focused, deterministic search) and easier problems can tolerate higher temperature.

The previous review suggested this fix:

-            // Temperature: lower for easier problems (more deterministic)
-            Temperature = Math.Max(0.1, _baselineConfig.Temperature * (0.5 + difficulty * 0.5)),
+            // Temperature: lower for harder problems (more deterministic, focused reasoning)
+            Temperature = Math.Max(0.1, _baselineConfig.Temperature * (1.5 - difficulty * 0.5)),

This would produce 1.5× baseline (easy) → 1.0× baseline (hard), giving harder problems more deterministic behavior.

src/Reasoning/Training/TrainingDataCollector.cs (1)

106-118: Category distribution bookkeeping remains incorrect after mutating operations.

The _categoryCount dictionary is still only updated in AddSample (lines 140-144) but not synchronized when:

• FilterByQuality removes samples (lines 178-183)
• BalanceCategories rebuilds the sample list (lines 188-200)
• LoadFromFileAsync replaces all samples (lines 294-309)

Line 367 in CalculateStatistics returns this stale dictionary, causing Statistics.CategoryDistribution to diverge from actual sample contents.

This issue was previously flagged. Please apply one of the suggested fixes from the earlier review:

• Option 1 (recommended): Remove _categoryCount and compute distribution on-demand in CalculateStatistics:

-    private readonly Dictionary<string, int> _categoryCount;

     public TrainingDataCollector()
     {
         _samples = new List<TrainingSample<T>>();
         _numOps = MathHelper.GetNumericOperations<T>();
-        _categoryCount = new Dictionary<string, int>();
     }

     public void AddSample(TrainingSample<T> sample)
     {
         if (sample == null)
             throw new ArgumentNullException(nameof(sample));

         _samples.Add(sample);
-
-        // Track category
-        string category = sample.Category ?? "uncategorized";
-        if (!_categoryCount.ContainsKey(category))
-            _categoryCount[category] = 0;
-        _categoryCount[category]++;
     }

     public void Clear()
     {
         _samples.Clear();
-        _categoryCount.Clear();
     }

     private DataStatistics<T> CalculateStatistics()
     {
         // ... existing code ...
         
+        var categoryDistribution = _samples
+            .GroupBy(s => s.Category ?? "uncategorized")
+            .ToDictionary(g => g.Key, g => g.Count());

         return new DataStatistics<T>
         {
             TotalSamples = _samples.Count,
             AverageChainReward = _numOps.FromDouble(chainRewards.Average()),
             AverageOutcomeReward = _numOps.FromDouble(outcomeRewards.Average()),
             CorrectCount = correctCount,
             CorrectPercentage = (double)correctCount / _samples.Count,
-            CategoryDistribution = new Dictionary<string, int>(_categoryCount),
+            CategoryDistribution = categoryDistribution,
             AverageStepsPerChain = _samples.Average(s => s.ReasoningChain?.Steps.Count ?? 0)
         };
     }

• Option 2: Recompute _categoryCount after every mutating operation (add helper method and call after FilterByQuality, BalanceCategories, LoadFromFileAsync).

Also applies to: 140-145, 178-183, 188-200, 294-309, 342-370

src/Reasoning/Training/ReinforcementLearner.cs (2)

377-398: Duplicate reward calculation wastes computation.

Lines 387-388 both invoke _rewardModel.CalculateChainRewardAsync(result.ReasoningChain, answer, cancellationToken) with identical arguments, computing the same reward twice. If ChainReward and OutcomeReward should be the same, calculate once; if they should differ, use distinct methods.

Option 1: If both rewards should be identical, calculate once:

                 if (result.ReasoningChain != null)
                 {
                     chains.Add(result.ReasoningChain);
                     answers.Add(answer);

                     // Collect training sample
                     var reward = await _rewardModel.CalculateChainRewardAsync(result.ReasoningChain, answer, cancellationToken);
-                    var outcomeReward = await _rewardModel.CalculateChainRewardAsync(result.ReasoningChain, answer, cancellationToken);

                     _dataCollector.AddSample(new TrainingSample<T>
                     {
                         Problem = problem,
                         ReasoningChain = result.ReasoningChain,
                         CorrectAnswer = answer,
                         ChainReward = reward,
-                        OutcomeReward = outcomeReward
+                        OutcomeReward = reward
                     });
                 }

Option 2: If they should be conceptually different (process vs. outcome reward), clarify by calling distinct methods or computing differently:

                     var chainReward = await _rewardModel.CalculateChainRewardAsync(result.ReasoningChain, answer, cancellationToken);
-                    var outcomeReward = await _rewardModel.CalculateChainRewardAsync(result.ReasoningChain, answer, cancellationToken);
+                    // Compute outcome-based reward separately if IRewardModel exposes such a method
+                    var outcomeReward = /* compute final-answer-only reward */;

Based on TrainingSample<T> documentation (line 394-401 in TrainingDataCollector.cs), ChainReward is "Process reward (quality of reasoning steps)" while OutcomeReward is "Outcome reward (correctness of final answer)". They should be computed differently, but the current implementation treats them as identical.

---

348-362: Non-deterministic shuffling breaks training reproducibility.

Line 354 uses Guid.NewGuid() to shuffle the training set, making each training run non-reproducible. This was previously flagged but remains unresolved.

Use a seeded Random instance:

 public class ReinforcementLearner<T>
 {
     private readonly IChatModel<T> _model;
     private readonly IRewardModel<T> _rewardModel;
     private readonly PolicyGradientTrainer<T> _trainer;
     private readonly TrainingDataCollector<T> _dataCollector;
     private readonly RLConfig _config;
     private readonly IReasoningStrategy<T> _reasoningStrategy;
+    private readonly Random _random;

     public ReinforcementLearner(
         IChatModel<T> model,
         IRewardModel<T> rewardModel,
         RLConfig? config = null)
     {
         _model = model ?? throw new ArgumentNullException(nameof(model));
         _rewardModel = rewardModel ?? throw new ArgumentNullException(nameof(rewardModel));
         _config = config ?? RLConfig.Default;
+        _random = new Random(_config.Seed ?? 42); // Add Seed property to RLConfig

         // ... rest of constructor
     }

     private async Task<EpochMetrics<T>> TrainEpochAsync(
         List<(string problem, string answer)> trainingSet,
         int epoch,
         CancellationToken cancellationToken)
     {
         // Shuffle training set
-        var shuffled = trainingSet.OrderBy(_ => Guid.NewGuid()).ToList();
+        var shuffled = trainingSet.OrderBy(_ => _random.Next()).ToList();
         // ... rest of method
     }
}

Don't forget to add Seed property to RLConfig (around line 608):

 public class RLConfig
 {
     public int Epochs { get; set; } = 10;
     public int BatchSize { get; set; } = 32;
     public double LearningRate { get; set; } = 0.0001;
     public double DiscountFactor { get; set; } = 0.99;
     public double EntropyCoefficient { get; set; } = 0.01;
     public int ValidationFrequency { get; set; } = 1;
     public int EarlyStoppingPatience { get; set; } = 3;
     public bool SaveCheckpoints { get; set; } = true;
+    public int? Seed { get; set; } = null;

     public static RLConfig Default => new RLConfig();
 }

src/Reasoning/Benchmarks/GSM8KBenchmark.cs (1)

211-215: Add input validation to prevent ArgumentOutOfRangeException on negative count values.

Confirmed: the condition at line 211 lacks a guard for negative values. When Take(negativeValue) executes, it throws ArgumentOutOfRangeException. While zero values are caught by the defensive guard at line 79, negative values bypass validation entirely. No existing tests cover this edge case. The suggested fix is correct.

Apply the diff to add the missing validation:

-        if (count.HasValue && count.Value < problems.Count)
+        if (count.HasValue && count.Value > 0 && count.Value < problems.Count)
         {
             // Random sample
             var random = new Random(42); // Deterministic seed
             problems = problems.OrderBy(_ => random.Next()).Take(count.Value).ToList();
         }

🧹 Nitpick comments (8)

src/Reasoning/ComputeScaling/AdaptiveComputeScaler.cs (2)

111-111: Consider scaling NumSamples consistently across all difficulty levels.

NumSamples only scales for difficulty > 0.7, while most other parameters (MaxSteps, ExplorationDepth, BranchingFactor, BeamWidth) scale across all difficulty levels. This means easy and medium problems always use the baseline sample count regardless of their exact difficulty.

If this is intentional to conserve compute on easier problems, consider adding a comment explaining the rationale. Otherwise, consider removing the conditional:

-            NumSamples = difficulty > 0.7 ? (int)Math.Round(_baselineConfig.NumSamples * scalingFactor) : _baselineConfig.NumSamples,
+            NumSamples = (int)Math.Max(1, Math.Round(_baselineConfig.NumSamples * scalingFactor)),

---

177-181: Multi-step detection may produce false positives.

Using Split with "step" and "first" as delimiters will match these substrings anywhere in the text, including within other words (e.g., "stepfather", "firsthand"). This could lead to inflated difficulty scores.

Consider using word-boundary matching for more accurate detection:

-        int steps = Math.Max(
-            problem.Split(new[] { "step" }, StringSplitOptions.RemoveEmptyEntries).Length - 1,
-            problem.Split(new[] { "first" }, StringSplitOptions.RemoveEmptyEntries).Length - 1
-        );
+        int stepMatches = System.Text.RegularExpressions.Regex.Matches(lower, @"\bsteps?\b").Count;
+        int sequenceMatches = System.Text.RegularExpressions.Regex.Matches(lower, @"\b(first|second|third|finally)\b").Count;
+        int steps = Math.Max(stepMatches, sequenceMatches);

src/Reasoning/Verification/ProcessRewardModel.cs (3)

138-148: Consider penalizing chains with no final answer when a correct answer is expected.

Lines 141-142 check if chain.FinalAnswer is not null before comparing, which correctly avoids the null-reference exception from the previous review. However, when a correctAnswer is provided but chain.FinalAnswer is null, no penalty is applied. This means a chain with no final answer receives the same reward as a chain with the correct answer.

If this is intentional (treating null as "not yet evaluated" rather than "wrong"), the current logic is fine. Otherwise, consider penalizing chains that fail to produce a final answer:

         if (!string.IsNullOrEmpty(correctAnswer))
         {
-            bool answerCorrect = chain.FinalAnswer is not null &&
-                                chain.FinalAnswer.Equals(correctAnswer, StringComparison.OrdinalIgnoreCase);
-            if (!answerCorrect)
+            if (chain.FinalAnswer is null)
+            {
+                // Heavily penalize chains that produce no answer
+                avgReward = _numOps.Multiply(avgReward, _numOps.FromDouble(0.3));
+            }
+            else if (!chain.FinalAnswer.Equals(correctAnswer, StringComparison.OrdinalIgnoreCase))
             {
                 // Penalize if final answer is wrong (even if process was good)
                 avgReward = _numOps.Multiply(avgReward, _numOps.FromDouble(0.5));
             }
         }

---

220-231: Consider expanding normalization to handle additional score ranges.

The regex fallback normalizes scores between 1.0 and 10.0 by dividing by 10 (lines 225-228), but doesn't handle other common scoring scales. For example:

• A score of 15 wouldn't trigger normalization (fails the <= 10.0 check) and would be silently clamped to 1.0
• A score of 85 (from a 0-100 scale) would also be clamped to 1.0 without detection

Consider adding normalization for additional ranges:

             // Normalize if needed
             if (reward > 1.0 && reward <= 10.0)
             {
                 reward /= 10.0;
             }
+            else if (reward > 10.0 && reward <= 100.0)
+            {
+                reward /= 100.0;
+            }
+            else if (reward > 100.0)
+            {
+                Debug.WriteLine($"Warning: Unusually high reward score {reward} detected. Response: {response.Substring(0, Math.Min(100, response.Length))}...");
+            }

             return MathHelper.Clamp(reward, 0.0, 1.0);

---

233-235: Consider improving debug output for parse failures.

The Debug.WriteLine on line 234 addresses the previous concern about silent failures. However, truncating to 100 characters may not provide enough context for debugging. Consider:

-        Debug.WriteLine($"Warning: Failed to parse reward from LLM response. Defaulting to 0.5. Response: {response.Substring(0, Math.Min(100, response.Length))}...");
+        Debug.WriteLine($"Warning: Failed to parse reward from LLM response. Defaulting to 0.5. Full response: {response}");

Alternatively, if responses are very long, log both a truncated preview and the full response separately, or use structured logging with a configurable truncation threshold.

src/Reasoning/Training/TrainingDataCollector.cs (2)

278-289: Async methods use synchronous I/O.

The SaveToFileAsync, LoadFromFileAsync, and SaveSplitAsync methods use synchronous File.WriteAllText/File.ReadAllText followed by await Task.CompletedTask for .NET Framework 4.6.2 compatibility. While this maintains the async signature, it doesn't provide true async I/O benefits and could block the thread pool on large files.

Consider documenting this limitation more explicitly in the XML docs, or conditionally compile true async I/O for newer target frameworks:

/// <summary>
/// Saves training data to a JSON file.
/// </summary>
/// <remarks>
/// Note: Uses synchronous I/O for .NET Framework 4.6.2 compatibility.
/// The async signature is maintained for API consistency.
/// </remarks>
public async Task SaveToFileAsync(string filePath, CancellationToken cancellationToken = default)
{
#if NET462
    var json = JsonConvert.SerializeObject(_samples, settings);
    File.WriteAllText(filePath, json);
    await Task.CompletedTask;
#else
    var json = JsonConvert.SerializeObject(_samples, settings);
    await File.WriteAllTextAsync(filePath, json, cancellationToken);
#endif
}

Also applies to: 294-309, 327-340

---

242-245: Hardcoded correctness threshold appears in multiple locations.

Both GetCorrectSamples (line 244) and CalculateStatistics (line 358) use a hardcoded 0.9 threshold to determine correctness. If this threshold needs to change, it must be updated in both places, risking inconsistency.

Extract the threshold as a constant or configurable parameter:

 public class TrainingDataCollector<T>
 {
+    private const double CorrectnessThreshold = 0.9;
     private readonly List<TrainingSample<T>> _samples;
     // ...

     public List<TrainingSample<T>> GetCorrectSamples()
     {
-        return _samples.Where(s => Convert.ToDouble(s.OutcomeReward) >= 0.9).ToList();
+        return _samples.Where(s => Convert.ToDouble(s.OutcomeReward) >= CorrectnessThreshold).ToList();
     }

     private DataStatistics<T> CalculateStatistics()
     {
         // ...
-        int correctCount = _samples.Count(s => Convert.ToDouble(s.OutcomeReward) >= 0.9);
+        int correctCount = _samples.Count(s => Convert.ToDouble(s.OutcomeReward) >= CorrectnessThreshold);
         // ...
     }
}

Or make it configurable via constructor if different thresholds are needed per use case.

Also applies to: 356-359

src/Reasoning/Benchmarks/GSM8KBenchmark.cs (1)

195-195: Consider averaging middle elements for even counts in median calculation.

The current median calculation uses ElementAt(problemResults.Count / 2), which selects the second middle element for even-sized collections. The standard statistical median averages the two middle elements in this case.

For benchmark reporting, the current approach is acceptable, but a more precise calculation would improve statistical accuracy.

If you want precise median calculation:

-        result.Metrics["median_time_seconds"] = problemResults.Select(p => p.Duration.TotalSeconds).OrderBy(t => t).ElementAt(problemResults.Count / 2);
+        var sortedTimes = problemResults.Select(p => p.Duration.TotalSeconds).OrderBy(t => t).ToList();
+        var medianTime = sortedTimes.Count % 2 == 0
+            ? (sortedTimes[sortedTimes.Count / 2 - 1] + sortedTimes[sortedTimes.Count / 2]) / 2.0
+            : sortedTimes[sortedTimes.Count / 2];
+        result.Metrics["median_time_seconds"] = medianTime;

📜 Review details

Configuration used: CodeRabbit UI

Review profile: CHILL

Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between 50b2893 and 0d2b8b6.

📒 Files selected for processing (11)

• src/Reasoning/Benchmarks/GSM8KBenchmark.cs (1 hunks)
• src/Reasoning/ComputeScaling/AdaptiveComputeScaler.cs (1 hunks)
• src/Reasoning/Models/ThoughtNode.cs (1 hunks)
• src/Reasoning/Search/BeamSearch.cs (1 hunks)
• src/Reasoning/Search/BestFirstSearch.cs (1 hunks)
• src/Reasoning/Search/BreadthFirstSearch.cs (1 hunks)
• src/Reasoning/Search/DepthFirstSearch.cs (1 hunks)
• src/Reasoning/Search/MonteCarloTreeSearch.cs (1 hunks)
• src/Reasoning/Training/ReinforcementLearner.cs (1 hunks)
• src/Reasoning/Training/TrainingDataCollector.cs (1 hunks)
• src/Reasoning/Verification/ProcessRewardModel.cs (1 hunks)

🚧 Files skipped from review as they are similar to previous changes (1)

• src/Reasoning/Models/ThoughtNode.cs

🧰 Additional context used

🧬 Code graph analysis (10)

src/Reasoning/Verification/ProcessRewardModel.cs (2)

src/Helpers/MathHelper.cs (2)

• INumericOperations (36-66)
• MathHelper (19-992)

src/Interfaces/ICriticModel.cs (1)

• ReasoningContext (63-84)

src/Reasoning/Training/TrainingDataCollector.cs (2)

src/Helpers/MathHelper.cs (2)

• INumericOperations (36-66)
• MathHelper (19-992)

src/Reasoning/Training/ReinforcementLearner.cs (7)

• Task (229-293)
• Task (298-346)
• Task (348-435)
• Task (437-450)
• Task (452-469)
• Task (483-505)
• Task (518-552)

src/Reasoning/Benchmarks/GSM8KBenchmark.cs (5)

src/Helpers/MathHelper.cs (2)

• INumericOperations (36-66)
• MathHelper (19-992)

src/Reasoning/Benchmarks/HumanEvalBenchmark.cs (3)

• List (213-290)
• Task (64-192)
• Task (195-211)

src/Reasoning/Benchmarks/Models/BenchmarkProblem.cs (1)

• BenchmarkProblem (23-60)

src/Interfaces/IBenchmark.cs (2)

• Task (70-73)
• Task (85-85)

src/Reasoning/Benchmarks/Models/BenchmarkResult.cs (2)

• BenchmarkResult (32-139)
• ProblemEvaluation (150-196)

src/Reasoning/Search/BreadthFirstSearch.cs (4)

src/Reasoning/Components/ThoughtGenerator.cs (3)

• Task (43-88)
• List (133-162)
• List (167-189)

src/Reasoning/Components/ThoughtEvaluator.cs (1)

• Task (43-73)

src/Reasoning/Models/ThoughtNode.cs (4)

• List (230-242)
• ThoughtNode (60-320)
• ThoughtNode (67-71)
• CheckIsTerminalByHeuristic (298-308)

src/Helpers/MathHelper.cs (1)

• MathHelper (19-992)

src/Reasoning/Search/BestFirstSearch.cs (5)

src/Reasoning/Search/BeamSearch.cs (2)

• Task (58-182)
• List (187-199)

src/Reasoning/Search/BreadthFirstSearch.cs (2)

• Task (50-135)
• List (140-152)

src/Reasoning/Search/DepthFirstSearch.cs (3)

• Task (62-90)
• Task (92-137)
• List (139-151)

src/Reasoning/Models/ThoughtNode.cs (4)

• List (230-242)
• ThoughtNode (60-320)
• ThoughtNode (67-71)
• CheckIsTerminalByHeuristic (298-308)

src/Helpers/MathHelper.cs (1)

• MathHelper (19-992)

src/Reasoning/Search/DepthFirstSearch.cs (2)

src/Reasoning/Models/ThoughtNode.cs (4)

• ThoughtNode (60-320)
• ThoughtNode (67-71)
• List (230-242)
• CheckIsTerminalByHeuristic (298-308)

src/Helpers/MathHelper.cs (1)

• MathHelper (19-992)

src/Reasoning/Search/MonteCarloTreeSearch.cs (2)

src/Reasoning/Search/BeamSearch.cs (2)

• Task (58-182)
• List (187-199)

src/Reasoning/Models/ThoughtNode.cs (4)

• List (230-242)
• ThoughtNode (60-320)
• ThoughtNode (67-71)
• CheckIsTerminalByHeuristic (298-308)

src/Reasoning/ComputeScaling/AdaptiveComputeScaler.cs (1)

src/Helpers/MathHelper.cs (1)

• MathHelper (19-992)

src/Reasoning/Search/BeamSearch.cs (5)

src/Reasoning/Strategies/TreeOfThoughtsStrategy.cs (3)

• ISearchAlgorithm (308-316)
• Task (132-253)
• Task (258-288)

src/Reasoning/Components/ThoughtGenerator.cs (3)

• Task (43-88)
• List (133-162)
• List (167-189)

src/Reasoning/Components/ThoughtEvaluator.cs (1)

• Task (43-73)

src/Reasoning/Models/ThoughtNode.cs (4)

• List (230-242)
• ThoughtNode (60-320)
• ThoughtNode (67-71)
• CheckIsTerminalByHeuristic (298-308)

src/Helpers/MathHelper.cs (1)

• MathHelper (19-992)

src/Reasoning/Training/ReinforcementLearner.cs (4)

src/Reasoning/Training/PolicyGradientTrainer.cs (10)

• T (407-424)
• T (426-448)
• PolicyGradientTrainer (137-449)
• PolicyGradientTrainer (158-173)
• Task (182-242)
• Task (247-275)
• Task (284-323)
• Task (325-344)
• TrainingMetrics (454-473)
• EvaluationMetrics (478-491)

src/Reasoning/Training/TrainingDataCollector.cs (14)

• TrainingDataCollector (104-371)
• TrainingDataCollector (113-118)
• Task (278-289)
• Task (294-309)
• Task (314-325)
• Task (327-340)
• List (205-217)
• List (222-237)
• List (242-245)
• List (250-273)
• AddSample (133-145)
• TrainingSample (376-427)
• Clear (169-173)
• AddSamples (150-156)

src/Reasoning/Strategies/ChainOfThoughtStrategy.cs (4)

• ChainOfThoughtStrategy (54-372)
• ChainOfThoughtStrategy (70-77)
• List (228-261)
• List (266-290)

src/Helpers/MathHelper.cs (1)

• MathHelper (19-992)

⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)

• GitHub Check: Build All Frameworks

🔇 Additional comments (10)

src/Reasoning/Search/BreadthFirstSearch.cs (1)

113-124: Fix semantic inconsistency: evaluate children against the original query, not their own thought.

Line 118 evaluates each child using child.Thought, but the evaluator's originalQuery parameter should represent the original problem statement that all nodes are attempting to solve. DepthFirstSearch correctly passes the original query through its recursion, and BeamSearch uses root.Thought. This implementation should align with that pattern.

Apply this fix:

             // Evaluate each child
             foreach (var child in children)
             {
                 child.EvaluationScore = await evaluator.EvaluateThoughtAsync(
                     child,
-                    child.Thought,
+                    root.Thought,
                     config,
                     cancellationToken);

                 currentNode.Children.Add(child);
                 queue.Enqueue(child);
             }

Likely an incorrect or invalid review comment.

src/Reasoning/ComputeScaling/AdaptiveComputeScaler.cs (1)

210-222: LGTM!

The strategy recommendation logic is clear and the difficulty thresholds are well-chosen, progressively escalating from simple Chain-of-Thought to more sophisticated approaches as difficulty increases.

src/Reasoning/Verification/ProcessRewardModel.cs (3)

12-57: LGTM! Excellent documentation and proper initialization.

The comprehensive documentation explaining PRM vs ORM is very helpful. The constructor properly validates dependencies and initializes the numeric operations provider.

---

153-196: LGTM! Well-structured reward evaluation prompt.

The prompt provides clear, tiered scoring criteria (high/medium/low) and requests a structured JSON response. This should help the LLM provide consistent, parseable reward scores.

---

238-257: LGTM! JSON extraction handles common response formats.

The method correctly handles both markdown code blocks and plain JSON objects, with appropriate fallback behavior.

src/Reasoning/Training/ReinforcementLearner.cs (3)

483-552: Checkpoint implementation is now complete and correct.

The checkpoint save/load implementation properly handles:

• Training state persistence (epoch counters, best metrics, early stopping state)
• Configuration serialization
• Training data restoration (lines 545-551 correctly clear and repopulate _dataCollector)

The methods include appropriate validation, error handling, and async I/O. The XML documentation (lines 471-482, 507-517) clearly explains what is and isn't saved.

One minor suggestion: Consider adding a version field to TrainingCheckpoint<T> for forward compatibility if the checkpoint format evolves:

 internal class TrainingCheckpoint<T>
 {
+    public int Version { get; set; } = 1;
     public int CurrentEpoch { get; set; }
     public double BestAccuracy { get; set; }
     // ...
 }

Then validate the version when loading to gracefully handle old checkpoint formats.

---

437-450: Verify that empty reasoning chains don't skew evaluation metrics.

Line 446 returns an empty ReasoningChain<T>() when result.ReasoningChain is null (reasoning failed). Depending on how PolicyGradientTrainer<T>.EvaluateAsync handles empty chains, this could:

• Count as an incorrect answer (if reward is 0.0 for empty chains) ✓
• Cause exceptions if evaluation logic assumes non-empty chains ✗
• Silently pass with default rewards, inflating accuracy ✗

Check PolicyGradientTrainer<T>.EvaluateAsync implementation to confirm empty chains are scored appropriately. Consider explicitly filtering out or logging failed reasoning attempts:

         return await _trainer.EvaluateAsync(
             validationSet,
             async (problem) =>
             {
                 var result = await _reasoningStrategy.ReasonAsync(problem, cancellationToken: cancellationToken);
-                return result.ReasoningChain ?? new ReasoningChain<T>();
+                if (result.ReasoningChain == null)
+                {
+                    Console.WriteLine($"Warning: Reasoning failed for problem: {problem.Substring(0, Math.Min(50, problem.Length))}...");
+                    return new ReasoningChain<T>(); // Or throw to skip this sample
+                }
+                return result.ReasoningChain;
             },
             cancellationToken
         );

Based on learnings from relevant code snippets (PolicyGradientTrainer.cs), verify that CalculateRewardAsync correctly assigns 0.0 reward to empty chains.

---

554-563: Clarify whether ResetTrainingState should also clear collected training data.

ResetTrainingState resets epoch counters and best metrics (lines 559-562) but does not clear _dataCollector. This means training samples accumulated in previous training runs persist across resets.

This could be:

• Intentional: Allowing data accumulation across multiple training sessions
• Unexpected: Users might assume "reset" clears everything

Consider one of:

1. If intentional, clarify in XML docs:

     /// <summary>
     /// Resets training state to initial values (useful when starting fresh training).
     /// </summary>
+    /// <remarks>
+    /// Note: This does NOT clear collected training data. Use _dataCollector.Clear() separately if needed.
+    /// </remarks>
     public void ResetTrainingState()

2. If data should also be cleared, add parameter:

-    public void ResetTrainingState()
+    public void ResetTrainingState(bool clearData = false)
     {
         _currentEpoch = 0;
         _bestAccuracy = 0.0;
         _bestEpoch = 0;
         _epochsWithoutImprovement = 0;
+        if (clearData)
+            _dataCollector.Clear();
     }

src/Reasoning/Benchmarks/GSM8KBenchmark.cs (2)

79-92: LGTM! Proper guard against empty problem sets.

The defensive guard correctly handles the edge case when no problems are loaded, preventing division-by-zero and out-of-range exceptions in downstream metrics calculations. All return values are properly initialized to safe defaults.

---

1-356: Well-structured benchmark implementation with past issues addressed.

The implementation successfully addresses previous review feedback:

• ✅ Empty problem set guard (lines 79-92) prevents runtime exceptions
• ✅ TotalProblems property (line 62) clarified with accurate comment
• ✅ Typo in problem text (line 293) corrected from "How load" to "How long"

The overall structure is solid: follows the IBenchmark<T> interface, includes proper exception handling, supports cancellation tokens, tracks metrics by category, and provides comprehensive documentation. The implementation is consistent with the existing HumanEvalBenchmark pattern.

[coderabbitai]

Actionable comments posted: 3

♻️ Duplicate comments (8)

src/Reasoning/Search/BreadthFirstSearch.cs (1)

1-3: Add missing using directive for MathHelper.

Line 68 calls MathHelper.GetNumericOperations<T>() but the required namespace AiDotNet.Helpers is not imported. This will cause a compilation failure.

Apply this fix:

+using AiDotNet.Helpers;
 using AiDotNet.Interfaces;
 using AiDotNet.Reasoning.Models;

src/Reasoning/Components/ContradictionDetector.cs (3)

192-200: Negation pattern only checks one order

Pattern 2 detects "X is not Y" in text1 vs "X is Y" in text2, but the reverse order (positive statement first, negation second) is not checked. This asymmetry causes missed contradictions when the order is swapped.

Add the reverse check:

         // Pattern 2: "X is not Y" vs "X is Y" (explicit negation)
         var isNotPattern = @"(\w+)\s+is\s+not\s+(\w+)";
         match1 = Regex.Match(lower1, isNotPattern);
         match2 = Regex.Match(lower2, isPattern);
         if (match1.Success && match2.Success &&
             match1.Groups[1].Value == match2.Groups[1].Value &&
             match1.Groups[2].Value == match2.Groups[2].Value)
         {
             return true;
         }
+
+        // Check reverse order: positive first, negation second
+        match1 = Regex.Match(lower1, isPattern);
+        match2 = Regex.Match(lower2, isNotPattern);
+        if (match1.Success && match2.Success &&
+            match1.Groups[1].Value == match2.Groups[1].Value &&
+            match1.Groups[2].Value == match2.Groups[2].Value)
+        {
+            return true;
+        }

---

280-306: Harden JSON parsing and text fallback

Two robustness issues:

1. Unhandled exceptions (line 289)
   Value<bool>() throws FormatException or InvalidCastException if the LLM returns {"contradictory": "true"} (string) instead of a boolean. Only JsonException is caught at line 292, so format errors will surface as unhandled exceptions.

2. Naive text fallback produces false positives (lines 302-305)
   The fallback matches "yes", "contradictory", etc., anywhere in the response. Phrases like "no, they are not contradictory" or "no contradictions found" will incorrectly return true.

Improve robustness:

         try
         {
             string jsonContent = ExtractJsonFromResponse(response);
             var root = JObject.Parse(jsonContent);

-            if (root["contradictory"] != null)
-            {
-                return root["contradictory"]!.Value<bool>();
-            }
+            var token = root["contradictory"];
+            if (token != null)
+            {
+                if (token.Type == JTokenType.Boolean)
+                    return token.Value<bool>();
+
+                if (bool.TryParse(token.ToString(), out var parsed))
+                    return parsed;
+            }
         }
         catch (JsonException)
         {
             // Fallback to text analysis
         }
+        catch (FormatException)
+        {
+            // Invalid format → fallback to text analysis
+        }
+        catch (InvalidCastException)
+        {
+            // Type mismatch → fallback to text analysis
+        }

         // Fallback: look for positive indicators
         if (string.IsNullOrWhiteSpace(response))
             return false;

         string lower = response.ToLowerInvariant();
-        return lower.Contains("yes") ||
-               lower.Contains("contradictory") ||
+        return lower.Contains("contradictory") ||
                lower.Contains("contradict") ||
                lower.Contains("inconsistent");

---

311-349: Strengthen JSON parsing defenses

The current code has a partial fix (FormatException catch at line 332), but two vulnerabilities remain:

1. Explanation parsing (line 324)
   Value<string>() throws if the token is a complex object or array instead of a scalar. Use ToString() for safety.

2. Severity parsing incomplete (lines 326-336)
   The inner try-catch only guards the Clamp call. Value<double>() at line 330 can throw FormatException or InvalidCastException if the LLM returns {"severity": "0.8"} (string), and those exceptions will bubble up before reaching the catch block.

Apply a fully defensive parse:

         try
         {
             string jsonContent = ExtractJsonFromResponse(response);
             var root = JObject.Parse(jsonContent);

-            contradiction.Explanation = root["explanation"]?.Value<string>() ?? response;
+            var explanationToken = root["explanation"];
+            contradiction.Explanation = explanationToken != null
+                ? explanationToken.ToString()
+                : response;

-            if (root["severity"] != null)
-            {
-                try
-                {
-                    contradiction.Severity = MathHelper.Clamp(root["severity"]!.Value<double>(), 0.0, 1.0);
-                }
-                catch (FormatException)
-                {
-                    contradiction.Severity = 0.8; // Default if non-numeric
-                }
-            }
+            var severityToken = root["severity"];
+            if (severityToken != null)
+            {
+                double severity;
+                if (severityToken.Type == JTokenType.Float || severityToken.Type == JTokenType.Integer)
+                {
+                    severity = severityToken.Value<double>();
+                }
+                else if (!double.TryParse(severityToken.ToString(), System.Globalization.NumberStyles.Float,
+                                          System.Globalization.CultureInfo.InvariantCulture, out severity))
+                {
+                    severity = 0.8;
+                }
+
+                contradiction.Severity = MathHelper.Clamp(severity, 0.0, 1.0);
+            }
             else
             {
                 contradiction.Severity = 0.8; // Default high severity
             }
         }
         catch (JsonException)
         {
             contradiction.Explanation = response;
             contradiction.Severity = 0.8;
         }

         return contradiction;

src/Reasoning/ReasoningStrategyBase.cs (3)

48-74: Reasoning trace remains unsafe for concurrent ReasonAsync calls.

Despite the previous review comment, _reasoningTrace is still an instance-level field shared across all concurrent invocations of ReasonAsync. While the added _traceLock prevents StringBuilder corruption, it does not solve the logical problem:

• ClearTrace() (line 134) clears the trace for all concurrent executions
• Traces from different queries executing concurrently will interleave

The lock only prevents data structure corruption; it does not provide per-invocation isolation.

The previous suggestion to use AsyncLocal<StringBuilder?> remains valid and would provide true per-invocation isolation:

-    private readonly System.Text.StringBuilder _reasoningTrace;
-    private readonly List<ITool> _tools;
-    private readonly object _traceLock = new object();
+    private readonly System.Threading.AsyncLocal<System.Text.StringBuilder?> _reasoningTrace = new();
+    private readonly List<ITool> _tools;

Then update the accessor and helper methods accordingly (see previous review for full diff).

Alternatively, document that strategy instances must not be used concurrently.

---

157-157: Unconditionally overwriting Success still masks failures.

Despite the previous review comment being marked as addressed, line 157 still unconditionally sets result.Success = true after ReasonCoreAsync returns, which overwrites any Success = false value set by the derived strategy.

Apply this diff to preserve the derived strategy's success determination:

-            result.Success = true;
             result.StrategyUsed = StrategyName;

The derived implementation of ReasonCoreAsync should be responsible for setting the Success flag appropriately.

---

105-105: Missing lock protection on ReasoningTrace read.

The ReasoningTrace property reads _reasoningTrace.ToString() without acquiring _traceLock, while AppendTrace and ClearTrace do acquire the lock. This creates a race condition where StringBuilder.ToString() can execute concurrently with modifications, potentially causing corruption or inconsistent reads.

Apply this diff to protect the read:

-    protected string ReasoningTrace => _reasoningTrace.ToString();
+    protected string ReasoningTrace
+    {
+        get
+        {
+            lock (_traceLock)
+            {
+                return _reasoningTrace.ToString();
+            }
+        }
+    }

src/Reasoning/ComputeScaling/AdaptiveComputeScaler.cs (1)

113-114: Temperature scaling remains inverted for reasoning tasks.

The temperature formula still increases temperature with difficulty (0.5× baseline at difficulty=0 to 1.0× at difficulty=1), which contradicts typical reasoning heuristics where harder problems benefit from lower temperature (more deterministic, focused search) to avoid errors in complex logical steps. Easy problems can tolerate higher temperature for broader exploration.

If the intent is to have lower temperature for harder problems (typical for reasoning), consider:

-            // Temperature: lower for easier problems (more deterministic)
-            Temperature = Math.Max(0.1, _baselineConfig.Temperature * (0.5 + difficulty * 0.5)),
+            // Temperature: lower for harder problems (more deterministic, focused reasoning)
+            Temperature = Math.Max(0.1, _baselineConfig.Temperature * (1.2 - difficulty * 0.8)),

This would range from 1.2× baseline (easy) to 0.4× baseline (hard), making harder problems more deterministic.

🧹 Nitpick comments (2)

src/Reasoning/ReasoningStrategyBase.cs (1)

159-163: Consider distinguishing timeout from user cancellation.

The OperationCanceledException handler assumes the cancellation was due to timeout, but the user's cancellation token could also trigger this exception. This could lead to a misleading error message.

Consider checking which token was actually cancelled:

         catch (OperationCanceledException)
         {
             result.Success = false;
-            result.ErrorMessage = $"Reasoning timeout after {config.MaxReasoningTimeSeconds} seconds";
+            result.ErrorMessage = cancellationToken.IsCancellationRequested
+                ? "Reasoning cancelled by user"
+                : $"Reasoning timeout after {config.MaxReasoningTimeSeconds} seconds";
             AppendTrace($"ERROR: {result.ErrorMessage}");
         }

src/Reasoning/ComputeScaling/AdaptiveComputeScaler.cs (1)

177-180: Consider more robust multi-step detection.

The current approach counts splits on "step" and "first", which may produce false positives (e.g., "stepmother" or "firstly" would increment the count).

For more accurate detection, consider word-boundary matching:

-        int steps = Math.Max(
-            problem.Split(new[] { "step" }, StringSplitOptions.RemoveEmptyEntries).Length - 1,
-            problem.Split(new[] { "first" }, StringSplitOptions.RemoveEmptyEntries).Length - 1
-        );
+        int steps = Math.Max(
+            System.Text.RegularExpressions.Regex.Matches(lower, @"\bstep\b").Count,
+            System.Text.RegularExpressions.Regex.Matches(lower, @"\bfirst\b").Count
+        );

📜 Review details

Configuration used: CodeRabbit UI

Review profile: CHILL

Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between 0d2b8b6 and 00ae306.

📒 Files selected for processing (7)

• src/Reasoning/Components/ContradictionDetector.cs (1 hunks)
• src/Reasoning/ComputeScaling/AdaptiveComputeScaler.cs (1 hunks)
• src/Reasoning/ReasoningStrategyBase.cs (1 hunks)
• src/Reasoning/Search/BeamSearch.cs (1 hunks)
• src/Reasoning/Search/BestFirstSearch.cs (1 hunks)
• src/Reasoning/Search/BreadthFirstSearch.cs (1 hunks)
• src/Reasoning/Search/DepthFirstSearch.cs (1 hunks)

🧰 Additional context used

🧬 Code graph analysis (7)

src/Reasoning/Search/DepthFirstSearch.cs (2)

src/Reasoning/Models/ThoughtNode.cs (4)

• ThoughtNode (60-320)
• ThoughtNode (67-71)
• List (230-242)
• CheckIsTerminalByHeuristic (298-308)

src/Helpers/MathHelper.cs (1)

• MathHelper (19-992)

src/Reasoning/Search/BreadthFirstSearch.cs (6)

src/Reasoning/Search/BeamSearch.cs (2)

• Task (59-183)
• List (188-200)

src/Reasoning/Search/BestFirstSearch.cs (2)

• Task (61-171)
• List (173-185)

src/Reasoning/Search/DepthFirstSearch.cs (3)

• Task (63-91)
• Task (93-138)
• List (140-152)

src/Reasoning/Components/ThoughtGenerator.cs (3)

• Task (43-88)
• List (133-162)
• List (167-189)

src/Reasoning/Components/ThoughtEvaluator.cs (1)

• Task (43-73)

src/Reasoning/Models/ThoughtNode.cs (4)

• List (230-242)
• ThoughtNode (60-320)
• ThoughtNode (67-71)
• CheckIsTerminalByHeuristic (298-308)

src/Reasoning/ReasoningStrategyBase.cs (3)

src/Reasoning/Models/ReasoningResult.cs (3)

• ToString (281-281)
• ReasoningResult (48-282)
• ReasoningResult (55-59)

src/Interfaces/IReasoningStrategy.cs (1)

• Task (58-61)

src/Reasoning/Models/ReasoningConfig.cs (4)

• ReasoningConfig (40-347)
• ReasoningConfig (298-298)
• ReasoningConfig (309-318)
• ReasoningConfig (329-346)

src/Reasoning/Search/BestFirstSearch.cs (3)

src/Reasoning/Search/BeamSearch.cs (2)

• Task (59-183)
• List (188-200)

src/Reasoning/Models/ThoughtNode.cs (4)

• List (230-242)
• ThoughtNode (60-320)
• ThoughtNode (67-71)
• CheckIsTerminalByHeuristic (298-308)

src/Helpers/MathHelper.cs (1)

• MathHelper (19-992)

src/Reasoning/ComputeScaling/AdaptiveComputeScaler.cs (1)

src/Helpers/MathHelper.cs (1)

• MathHelper (19-992)

src/Reasoning/Components/ContradictionDetector.cs (2)

src/Reasoning/Verification/ProcessRewardModel.cs (1)

• ExtractJsonFromResponse (241-256)

src/Helpers/MathHelper.cs (1)

• MathHelper (19-992)

src/Reasoning/Search/BeamSearch.cs (5)

src/Reasoning/Strategies/TreeOfThoughtsStrategy.cs (3)

• ISearchAlgorithm (308-316)
• Task (132-253)
• Task (258-288)

src/Reasoning/Components/ThoughtGenerator.cs (3)

• Task (43-88)
• List (133-162)
• List (167-189)

src/Reasoning/Components/ThoughtEvaluator.cs (1)

• Task (43-73)

src/Reasoning/Models/ThoughtNode.cs (4)

• List (230-242)
• ThoughtNode (60-320)
• ThoughtNode (67-71)
• CheckIsTerminalByHeuristic (298-308)

src/Helpers/MathHelper.cs (1)

• MathHelper (19-992)

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🔇 Additional comments (10)

src/Reasoning/Search/DepthFirstSearch.cs (1)

1-153: LGTM! DFS implementation is clean and all past issues resolved.

The implementation correctly:

• Includes the required using AiDotNet.Helpers; directive (Line 1)
• Evaluates all nodes against the original query (root.Thought) for semantic consistency
• Uses parent-based path reconstruction
• Handles terminal detection via heuristic and depth limits
• Supports cancellation tokens

src/Reasoning/Search/BreadthFirstSearch.cs (1)

50-178: BFS implementation is solid once import is added.

The code properly:

• Uses parent-based path reconstruction (Lines 166-178) instead of the previous fragile text-based approach
• Includes intelligent fallback logic (Lines 133-148) to return the best explored node when no terminal is found
• Follows consistent evaluation semantics by passing root.Thought as the original query for all nodes

Once the missing using AiDotNet.Helpers; directive is added, this will be good to merge.

src/Reasoning/Search/BeamSearch.cs (1)

1-201: LGTM! Beam Search implementation is complete and all past issues resolved.

The implementation correctly:

• Includes the required using AiDotNet.Helpers; directive (Line 1)
• Validates all required parameters (Lines 66-76)
• Maintains beam width constraints and prunes to top-k nodes per depth
• Evaluates all nodes against the original query (root.Thought) for consistency
• Provides intelligent fallback behavior when no terminal node is found
• Uses parent-based path reconstruction (Lines 188-200)

src/Reasoning/Search/BestFirstSearch.cs (1)

1-186: LGTM! Best-First Search implementation is robust and all past issues resolved.

The implementation correctly:

• Includes the required using AiDotNet.Helpers; directive (Line 1)
• Validates all required parameters with comprehensive null guards (Lines 68-75)
• Uses a tuple-based priority queue with unique counter (Lines 82-99) to prevent the SortedSet duplicate-dropping bug
• Evaluates all nodes against the original query (root.Thought) for semantic consistency
• Provides intelligent fallback to the highest-scored node in the queue
• Uses parent-based path reconstruction (Lines 173-185)

src/Reasoning/ReasoningStrategyBase.cs (3)

145-155: LGTM! Timeout handling is implemented correctly.

The timeout logic properly creates a linked cancellation token that combines the user-provided token with a timeout token, ensuring the operation can be cancelled by either source. The use of nested using statements ensures proper disposal of both CancellationTokenSource instances.

---

304-328: LGTM! Tool execution handles errors gracefully.

The tool execution logic is well-implemented with proper error handling:

• Validates tool existence before execution
• Catches and converts most exceptions to error messages (keeping the reasoning process running)
• Correctly rethrows critical exceptions (OutOfMemoryException, StackOverflowException) that should not be caught
• Provides clear error messages with context

This resilient design ensures that a single tool failure doesn't crash the entire reasoning process.

---

345-376: LGTM! Configuration validation is comprehensive.

The ValidateConfig method now includes validation for all configuration properties, including the previously missing MaxReasoningTimeSeconds check (lines 374-375). All validation logic is correct:

• Positive value checks for counts and dimensions
• Range validation for thresholds and scores
• Non-negative checks where zero is a valid value

The validation ensures that invalid configurations are rejected early with clear error messages.

src/Reasoning/ComputeScaling/AdaptiveComputeScaler.cs (3)

55-59: Constructor correctly clamps maxScalingFactor.

The fix to clamp maxScalingFactor to at least 2.0 ensures the hard-region scaling (lines 95-98) remains monotonic, addressing the previous review concern.

---

80-99: Scaling factor logic is correct and continuous.

The piecewise scaling function is well-designed:

• Continuous at boundaries (0.3 and 0.7)
• Monotonically increasing
• Reasonable ranges for each difficulty band

---

102-132: Scaled configuration logic is well-structured.

The selective scaling approach is sensible:

• Linear scaling for step limits and time budget
• Sub-linear (sqrt) scaling for branching to avoid combinatorial explosion
• NumSamples only scaled for high difficulty (>0.7), which is appropriate since sampling is primarily beneficial for complex problems
• Feature toggles (verification, refinement, etc.) activated at reasonable difficulty thresholds

[coderabbitai]

Actionable comments posted: 1

♻️ Duplicate comments (4)

src/Reasoning/Training/TrainingDataCollector.cs (1)

106-121: Category count bookkeeping remains incorrect after mutations.

The previous review flagged that _categoryCount becomes stale after FilterByQuality, BalanceCategories, and LoadFromFileAsync, yet this issue is unresolved. CalculateStatistics at line 370 exposes the stale dictionary, leading to incorrect statistics.

As suggested in the previous review, either:

1. Remove _categoryCount and compute on-demand in CalculateStatistics:

CategoryDistribution = _samples
    .GroupBy(s => s.Category ?? "uncategorized")
    .ToDictionary(g => g.Key, g => g.Count())

2. Or recompute _categoryCount after every mutation (in FilterByQuality, BalanceCategories, LoadFromFileAsync, and Clear).

src/Reasoning/Components/ContradictionDetector.cs (3)

191-200: Pattern 2 negation check is still asymmetric

The current code only detects "x is not y" in text1 vs "x is y" in text2. The reverse case ("x is y" in text1 vs "x is not y" in text2) is not detected, even though it represents the same logical contradiction.

A past review comment flagged this exact issue at lines 168-224 and was marked as addressed, but the asymmetry remains in the current code.

To fix, add a symmetric check after line 200:

         // Pattern 2: "X is not Y" vs "X is Y" (explicit negation)
         var isNotPattern = @"(\w+)\s+is\s+not\s+(\w+)";
+        // Check text1="is not", text2="is"
         match1 = Regex.Match(lower1, isNotPattern);
         match2 = Regex.Match(lower2, isPattern);
         if (match1.Success && match2.Success &&
             match1.Groups[1].Value == match2.Groups[1].Value &&
             match1.Groups[2].Value == match2.Groups[2].Value)
         {
             return true;
         }
+        
+        // Check text1="is", text2="is not" (reverse)
+        match1 = Regex.Match(lower1, isPattern);
+        match2 = Regex.Match(lower2, isNotPattern);
+        if (match1.Success && match2.Success &&
+            match1.Groups[1].Value == match2.Groups[1].Value &&
+            match1.Groups[2].Value == match2.Groups[2].Value)
+        {
+            return true;
+        }

---

280-306: JSON parsing still vulnerable to non-boolean values and text fallback has false positives

Two issues previously flagged at lines 277-306 remain in the current code:

1. Line 289: Value<bool>() throws FormatException/InvalidCastException when the LLM returns {"contradictory": "true"} (string instead of boolean). The catch (JsonException) at line 292 won't catch these exceptions.

2. Lines 302-305: Text fallback matches "yes", which yields false positives for responses like "no, they are not contradictory".

Apply the fixes suggested in the previous review:

         try
         {
             string jsonContent = ExtractJsonFromResponse(response);
             var root = JObject.Parse(jsonContent);

-            if (root["contradictory"] != null)
-            {
-                return root["contradictory"]!.Value<bool>();
-            }
+            var token = root["contradictory"];
+            if (token != null)
+            {
+                if (token.Type == JTokenType.Boolean)
+                    return token.Value<bool>();
+
+                if (bool.TryParse(token.ToString(), out var parsed))
+                    return parsed;
+            }
         }
         catch (JsonException)
         {
             // Fallback to text analysis
         }
+        catch (FormatException)
+        {
+            // Invalid format → fallback to text analysis
+        }
+        catch (InvalidCastException)
+        {
+            // Type mismatch → fallback to text analysis
+        }

         // Fallback: look for positive indicators
         string lower = response.ToLowerInvariant();
-        return lower.Contains("yes") ||
-               lower.Contains("contradictory") ||
+        return lower.Contains("contradictory") ||
                lower.Contains("contradict") ||
                lower.Contains("inconsistent");

---

311-349: JSON parsing partially hardened but still has vulnerabilities

The previous review at lines 308-349 identified JSON parsing issues. The current code added a FormatException handler for severity (lines 332-335), which is an improvement, but gaps remain:

1. Line 324: root["explanation"]?.Value<string>() can throw if the token is not scalar (e.g., an array or object). Using .ToString() is safer.

2. Line 332: The inner try-catch only handles FormatException, not InvalidCastException, which Value<double>() can also throw.

3. The outer catch (JsonException) at line 342 won't catch format/cast exceptions from line 324 or 330.

Apply the defensive parsing pattern suggested in the previous review:

         try
         {
             string jsonContent = ExtractJsonFromResponse(response);
             var root = JObject.Parse(jsonContent);

-            contradiction.Explanation = root["explanation"]?.Value<string>() ?? response;
+            var explanationToken = root["explanation"];
+            contradiction.Explanation = explanationToken != null
+                ? explanationToken.ToString()
+                : response;

-            if (root["severity"] != null)
-            {
-                try
-                {
-                    contradiction.Severity = MathHelper.Clamp(root["severity"]!.Value<double>(), 0.0, 1.0);
-                }
-                catch (FormatException)
-                {
-                    contradiction.Severity = 0.8; // Default if non-numeric
-                }
-            }
+            var severityToken = root["severity"];
+            if (severityToken != null)
+            {
+                double severity;
+                if (severityToken.Type == JTokenType.Float || severityToken.Type == JTokenType.Integer)
+                {
+                    severity = severityToken.Value<double>();
+                }
+                else if (!double.TryParse(severityToken.ToString(), System.Globalization.NumberStyles.Float,
+                                          System.Globalization.CultureInfo.InvariantCulture, out severity))
+                {
+                    severity = 0.8;
+                }
+
+                contradiction.Severity = MathHelper.Clamp(severity, 0.0, 1.0);
+            }
             else
             {
                 contradiction.Severity = 0.8; // Default high severity
             }
         }
         catch (JsonException)
         {
             contradiction.Explanation = response;
             contradiction.Severity = 0.8;
         }

🧹 Nitpick comments (7)

src/Reasoning/Training/TrainingDataCollector.cs (5)

222-240: LGTM! Shuffling is now reproducible; consider validating ratio bounds.

The seeded _random ensures deterministic shuffling. However, the method doesn't validate that trainRatio and validationRatio are non-negative, which could lead to unexpected behavior.

Optionally add:

 public (List<TrainingSample<T>> train, List<TrainingSample<T>> validation, List<TrainingSample<T>> test)
     SplitData(double trainRatio = 0.8, double validationRatio = 0.1)
 {
+    if (trainRatio < 0 || validationRatio < 0)
+        throw new ArgumentException("Ratios must be non-negative");
     if (trainRatio + validationRatio >= 1.0)
         throw new ArgumentException("Train + validation ratios must be < 1.0");

---

242-248: Consider parameterizing the correctness threshold.

The magic number 0.9 at line 247 is hardcoded. Extracting it to a parameter or constant would improve clarity and flexibility.

-public List<TrainingSample<T>> GetCorrectSamples()
+public List<TrainingSample<T>> GetCorrectSamples(double threshold = 0.9)
 {
-    return _samples.Where(s => Convert.ToDouble(s.OutcomeReward) >= 0.9).ToList();
+    return _samples.Where(s => Convert.ToDouble(s.OutcomeReward) >= threshold).ToList();
 }

---

250-276: Consider improving diversity hashing and documenting early return.

Two optional improvements:

1. The string prefix at lines 261-263 could collide for problems with identical prefixes. Consider using a hash code or the full problem string.
2. The method may return fewer than count samples without warning, which might be unexpected. Consider documenting this behavior or throwing/logging when insufficient samples exist.

 // Option 1: Use full problem string
-string problemHash = sample.Problem.Length > 50
-    ? sample.Problem.Substring(0, 50)
-    : sample.Problem;
+string problemHash = sample.Problem;

 // Option 2: Document behavior
 /// <summary>
-/// Gets samples with diverse reasoning approaches.
+/// Gets up to <paramref name="count"/> samples with diverse reasoning approaches.
+/// May return fewer if insufficient unique problems exist.
 /// </summary>

---

278-292: Consider using true async file I/O for better scalability.

The method uses synchronous File.WriteAllText at line 290 with await Task.CompletedTask, which doesn't provide true async I/O benefits. For better scalability under load, consider using StreamWriter with async writes.

 public async Task SaveToFileAsync(string filePath, CancellationToken cancellationToken = default)
 {
     var settings = new JsonSerializerSettings
     {
         Formatting = Formatting.Indented,
         NullValueHandling = NullValueHandling.Ignore
     };

     var json = JsonConvert.SerializeObject(_samples, settings);
-    File.WriteAllText(filePath, json);  // net462 compatible
-    await Task.CompletedTask;  // Maintain async signature
+    using (var writer = new StreamWriter(filePath, false, System.Text.Encoding.UTF8))
+    {
+        await writer.WriteAsync(json);
+    }
 }

Note: If targeting .NET Standard 2.1+ or .NET 5+, you can use File.WriteAllTextAsync(filePath, json, cancellationToken) directly.

---

294-312: Consider using true async file I/O for better scalability.

Like SaveToFileAsync, this method uses synchronous File.ReadAllText at line 302 with await Task.CompletedTask. For better scalability, consider async file reading.

 public async Task LoadFromFileAsync(string filePath, CancellationToken cancellationToken = default)
 {
     if (!File.Exists(filePath))
         throw new FileNotFoundException($"Training data file not found: {filePath}");

-    var json = File.ReadAllText(filePath);  // net462 compatible
+    string json;
+    using (var reader = new StreamReader(filePath, System.Text.Encoding.UTF8))
+    {
+        json = await reader.ReadToEndAsync();
+    }
     var samples = JsonConvert.DeserializeObject<List<TrainingSample<T>>>(json);

     if (samples != null)
     {
         _samples.Clear();
         _samples.AddRange(samples);
     }
-
-    await Task.CompletedTask;  // Maintain async signature
 }

Note: If targeting .NET Standard 2.1+ or .NET 5+, you can use File.ReadAllTextAsync(filePath, cancellationToken) directly.

src/Reasoning/Components/ContradictionDetector.cs (2)

58-130: LGTM! Sound sliding-window algorithm with reproducible sampling

The contradiction detection strategy is well-designed:

• Sliding window (5 steps ahead) prioritizes detecting contradictions in nearby reasoning steps
• Deterministic random sampling (seed 42) for long chains ensures reproducible results
• Proper cancellation checks at lines 78 and 110

Optional refinement: Consider making the window size (5) and spot check count (5) configurable via constructor or class-level constants for easier tuning.

---

354-369: Consider extracting duplicated JSON extraction logic to a shared helper

This ExtractJsonFromResponse method is identical to the implementation in src/Reasoning/Components/ThoughtEvaluator.cs (lines 150-167). The same regex patterns for extracting JSON from markdown code blocks and finding JSON objects are duplicated.

To reduce duplication and ensure consistent JSON extraction behavior across the reasoning framework:

1. Extract this method to a shared utility class (e.g., src/Helpers/JsonHelper.cs or src/Reasoning/Common/LlmResponseParser.cs)
2. Update both ContradictionDetector and ThoughtEvaluator to call the shared implementation

Example:

// src/Helpers/JsonHelper.cs
public static class JsonHelper
{
    public static string ExtractJsonFromResponse(string response)
    {
        var jsonMatch = Regex.Match(response, @"```(?:json)?\s*(\{[\s\S]*?\})\s*```", RegexOptions.Multiline);
        if (jsonMatch.Success)
            return jsonMatch.Groups[1].Value;

        var jsonObjectMatch = Regex.Match(response, @"\{[\s\S]*?\}");
        if (jsonObjectMatch.Success)
            return jsonObjectMatch.Value;

        return response;
    }
}

📜 Review details

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📥 Commits

Reviewing files that changed from the base of the PR and between 00ae306 and f2af607.

📒 Files selected for processing (6)

• src/Interfaces/IChatModel.cs (2 hunks)
• src/LanguageModels/ChatModelBase.cs (1 hunks)
• src/Reasoning/Components/ContradictionDetector.cs (1 hunks)
• src/Reasoning/ComputeScaling/AdaptiveComputeScaler.cs (1 hunks)
• src/Reasoning/Training/TrainingDataCollector.cs (1 hunks)
• src/Reasoning/Verification/ProcessRewardModel.cs (1 hunks)

🚧 Files skipped from review as they are similar to previous changes (2)

• src/Reasoning/Verification/ProcessRewardModel.cs
• src/Reasoning/ComputeScaling/AdaptiveComputeScaler.cs

🧰 Additional context used

🧬 Code graph analysis (3)

src/Reasoning/Training/TrainingDataCollector.cs (2)

src/Helpers/MathHelper.cs (2)

• INumericOperations (36-66)
• MathHelper (19-992)

src/Reasoning/Training/ReinforcementLearner.cs (7)

• Task (229-293)
• Task (298-346)
• Task (348-435)
• Task (437-450)
• Task (452-469)
• Task (483-505)
• Task (518-552)

src/LanguageModels/ChatModelBase.cs (3)

src/Interfaces/IChatModel.cs (1)

• Task (76-76)

src/Reasoning/Verification/ProcessRewardModel.cs (2)

• Task (66-90)
• Task (93-151)

tests/UnitTests/Agents/MockChatModel.cs (2)

• Task (42-55)
• Task (64-68)

src/Reasoning/Components/ContradictionDetector.cs (2)

src/Reasoning/Components/ThoughtEvaluator.cs (1)

• ExtractJsonFromResponse (151-168)

src/Helpers/MathHelper.cs (1)

• MathHelper (19-992)

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🔇 Additional comments (9)

src/Reasoning/Training/TrainingDataCollector.cs (4)

205-220: LGTM! Shuffling is now reproducible.

The seeded _random ensures deterministic shuffling, addressing the previous review concern about non-reproducibility.

---

314-328: LGTM! Export logic is sound.

The method correctly splits data and exports to HuggingFace format. The underlying SaveSplitAsync has the async I/O concern noted separately.

---

376-430: LGTM! TrainingSample structure is well-designed.

The class provides a comprehensive structure for training samples with appropriate defaults and metadata support.

---

432-455: LGTM! DataStatistics structure is well-designed.

The class provides a clear summary of training data with a helpful ToString override for debugging. Note that CategoryDistribution accuracy depends on the _categoryCount bookkeeping issue flagged earlier.

src/Interfaces/IChatModel.cs (1)

47-47: LGTM! CancellationToken parameter added for async cancellation support

The addition of the optional CancellationToken parameter is well-designed:

• Default parameter value ensures backward compatibility
• Documentation clearly describes the parameter's purpose
• Aligns with the PR's broader goal of enabling cancellation across the reasoning framework

Also applies to: 76-76

src/Reasoning/Components/ContradictionDetector.cs (4)

52-55: LGTM! Constructor properly validates dependencies

The null check ensures the detector cannot be instantiated without a valid chat model, preventing downstream null reference exceptions.

---

133-152: LGTM! Proper use of heuristic fast-path before LLM call

The two-stage approach is efficient:

1. Quick regex-based heuristic check (line 142) avoids unnecessary LLM calls for obvious cases
2. Falls back to LLM analysis for complex contradictions

CancellationToken is correctly propagated to the LLM call at line 149.

---

157-166: LGTM! CancellationToken properly propagated

The method correctly forwards the cancellation token to the LLM call at line 163.

---

229-275: LGTM! Well-structured prompts with clear instructions

Both prompt builders provide:

• Clear task description
• Concrete examples of contradictions
• Explicit JSON response schema

This structured approach should yield consistent, parseable LLM responses.

[coderabbitai]

Actionable comments posted: 2

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📒 Files selected for processing (5)

• src/Interfaces/ILanguageModel.cs (1 hunks)
• src/LanguageModels/AnthropicChatModel.cs (2 hunks)
• src/LanguageModels/AzureOpenAIChatModel.cs (2 hunks)
• src/LanguageModels/ChatModelBase.cs (6 hunks)
• src/LanguageModels/OpenAIChatModel.cs (2 hunks)

🧰 Additional context used

🧬 Code graph analysis (4)

src/LanguageModels/OpenAIChatModel.cs (4)

src/Interfaces/ILanguageModel.cs (1)

• Task (140-140)

src/LanguageModels/AnthropicChatModel.cs (1)

• Task (158-228)

src/LanguageModels/AzureOpenAIChatModel.cs (1)

• Task (179-246)

src/LanguageModels/ChatModelBase.cs (3)

• Task (104-186)
• Task (197-202)
• Task (225-225)

src/LanguageModels/ChatModelBase.cs (7)

src/Interfaces/ILanguageModel.cs (1)

• Task (140-140)

src/LanguageModels/AnthropicChatModel.cs (1)

• Task (158-228)

src/LanguageModels/AzureOpenAIChatModel.cs (1)

• Task (179-246)

src/LanguageModels/OpenAIChatModel.cs (1)

• Task (159-224)

src/Interfaces/IChatModel.cs (1)

• Task (76-76)

tests/UnitTests/Agents/MockChatModel.cs (2)

• Task (42-55)
• Task (64-68)

src/Reasoning/Verification/ProcessRewardModel.cs (2)

• Task (66-90)
• Task (93-151)

src/LanguageModels/AzureOpenAIChatModel.cs (4)

src/Interfaces/ILanguageModel.cs (1)

• Task (140-140)

src/LanguageModels/AnthropicChatModel.cs (1)

• Task (158-228)

src/LanguageModels/ChatModelBase.cs (3)

• Task (104-186)
• Task (197-202)
• Task (225-225)

src/LanguageModels/OpenAIChatModel.cs (1)

• Task (159-224)

src/LanguageModels/AnthropicChatModel.cs (5)

src/Interfaces/ILanguageModel.cs (1)

• Task (140-140)

src/LanguageModels/AzureOpenAIChatModel.cs (1)

• Task (179-246)

src/LanguageModels/ChatModelBase.cs (3)

• Task (104-186)
• Task (197-202)
• Task (225-225)

src/LanguageModels/OpenAIChatModel.cs (1)

• Task (159-224)

src/Interfaces/IChatModel.cs (1)

• Task (76-76)

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🔇 Additional comments (6)

src/LanguageModels/AnthropicChatModel.cs (1)

158-190: LGTM! Cancellation support properly implemented.

The method signature correctly accepts the CancellationToken parameter and propagates it to the HTTP call on line 190. This aligns with the updated abstract method contract in ChatModelBase and matches the implementation pattern in the other chat model classes.

src/LanguageModels/AzureOpenAIChatModel.cs (1)

179-213: LGTM! Cancellation support properly implemented.

The method signature correctly accepts the CancellationToken parameter and propagates it to the HTTP call on line 213. This aligns with the updated abstract method contract in ChatModelBase and ensures Azure OpenAI calls can be cancelled.

src/LanguageModels/OpenAIChatModel.cs (1)

159-191: LGTM! Cancellation support properly implemented.

The method signature correctly accepts the CancellationToken parameter and propagates it to the HTTP call on line 191. This aligns with the updated abstract method contract in ChatModelBase and enables proper cancellation of OpenAI API calls.

src/LanguageModels/ChatModelBase.cs (3)

197-201: Previous review issue resolved - cancellation token now properly propagated.

The method now correctly passes the CancellationToken to GenerateAsync on line 201, fixing the issue identified in the previous review where the token was accepted but not propagated. This enables proper cancellation support for the IChatModel interface compatibility layer.

---

209-225: LGTM! Abstract method contract updated with clear guidance.

The signature change to require a CancellationToken parameter establishes the cancellation contract for all derived implementations. The updated documentation (lines 221-223) clearly instructs implementers to pass the token to HTTP calls, which all current implementations (OpenAI, Azure OpenAI, Anthropic) correctly do.

---

104-186: Exception hierarchy misconception in original review; implementation is actually correct.

The original review comment incorrectly stated that OperationCanceledException inherits from TaskCanceledException. In .NET, it's the reverse: TaskCanceledException inherits from OperationCanceledException. This means:

• catch (TaskCanceledException) on line 149 catches only HTTP timeouts, not user cancellations
• User-initiated cancellations throw OperationCanceledException and fall through to the generic catch (Exception) handler
• The code correctly distinguishes timeout retries from user cancellations without mixing concerns

The implementation is sound. However, no tests exist for cancellation scenarios (verified by searching test files—all calls to GenerateAsync omit the CancellationToken parameter). Consider adding tests to verify:

• Cancellation during retry correctly propagates without infinite loops
• User cancellation is not retried
• Task.Delay respects the cancelled token