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

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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.

✨ Finishing touches

• 📝 Generate docstrings

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Post copyable unit tests in a comment
• Commit unit tests in branch claude/fix-issue-417-chain-of-thought-011CUuShU85JdBNgnQq2dc1J

---

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📒 Files selected for processing (1)

• src/Interfaces/ILanguageModel.cs (2 hunks)

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🔇 Additional comments (3)

src/Interfaces/ILanguageModel.cs (3)

112-113: Good addition of parameter documentation.

The cancellationToken parameter documentation clearly explains its purpose for both cancellation and timeout scenarios, directly addressing the previous review feedback.

---

140-141: Excellent improvement to best practices documentation.

The updated best practices section now includes a concrete example of how to use CancellationToken with a timeout, making it much more actionable than the previous generic guidance.

---

143-143: Well-designed signature change with backward compatibility.

The addition of the CancellationToken parameter with a default value follows .NET conventions and maintains backward compatibility for existing callers while enabling cancellation support.

---

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Actionable comments posted: 5

♻️ Duplicate comments (7)

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

218-225: Terminal detection logic remains duplicated.

The IsTerminalNode method is still duplicated from other search algorithms (e.g., BestFirstSearch.cs). This string-based heuristic was previously suggested for extraction to a shared utility class to maintain consistency across search algorithms.

---

110-114: Positional bias remains unresolved.

The code still always selects node.Children[0] after expansion, which creates positional bias and affects the exploration-exploitation balance. This was flagged in previous reviews and has not yet been addressed.

Standard MCTS typically uses the expanded node itself for simulation or selects a child randomly. The current approach can lead to suboptimal search behavior, especially when the thought generator produces candidates in a non-random order.

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

66-70: MathHelper not in scope here (likely compile error)

MathHelper.GetNumericOperations<T>() is used but this file only imports AiDotNet.Interfaces and AiDotNet.Reasoning.Models. Unless AiDotNet.Helpers is brought in via global usings, this will not compile.

Consider adding the missing import (or fully qualifying):

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

src/Reasoning/Verification/CodeExecutionVerifier.cs (4)

88-149: Guard null/empty testCases and avoid dividing by zero in scoring

VerifyCodeAsync assumes testCases is non-null and non-empty; if callers pass null you’ll hit a NullReferenceException, and if they pass an empty array TotalTests is 0 so PassRate/score become NaN (0/0), which then flows into GetSummary(). Also, in the exception path you only reset Score, relying on the constructor default for PassRate, which is easy to break in future edits.

I’d add explicit guards and handle the zero-test case explicitly:

     public async Task<CodeExecutionResult<T>> VerifyCodeAsync(
         string code,
         string[] testCases,
         string language = "python",
         CancellationToken cancellationToken = default)
     {
         if (string.IsNullOrWhiteSpace(code))
             throw new ArgumentNullException(nameof(code));
+
+        if (testCases is null)
+            throw new ArgumentNullException(nameof(testCases));
+
+        if (testCases.Length == 0)
+        {
+            return new CodeExecutionResult<T>
+            {
+                Language = language,
+                Code = code,
+                TotalTests = 0,
+                PassedTests = 0,
+                PassRate = _numOps.Zero,
+                AllTestsPassed = false,
+                Score = _numOps.Zero,
+                TestResults = new List<TestCaseResult>(),
+                ExecutionError = "No test cases provided for verification."
+            };
+        }
@@
         try
         {
@@
             result.Score = _numOps.FromDouble(score);
         }
         catch (Exception ex)
         {
             result.ExecutionError = ex.Message;
             result.AllTestsPassed = false;
+            result.PassRate = _numOps.Zero;
             result.Score = _numOps.Zero;
         }

---

227-333: Cancellation can leave child processes running; kill on OperationCanceledException

ExecuteTestCaseAsync now disposes Process and kills it on timeout, but if the cancellationToken is already cancelled (or cancelled before Task.Run is scheduled), process.Start() has already run, Task.Run(..., cancellationToken) completes as canceled, and you fall into the outer catch without ever killing the still-running subprocess. That leaves orphaned interpreters around and ignores the timeout in the cancellation case.

Wrap the Task.Run in a local try/catch for OperationCanceledException and kill the process there:

-                // Wait with timeout (net462: WaitForExit returns void, not bool)
-                bool finished = await Task.Run(() =>
-                {
-                    process.WaitForExit(_timeoutMilliseconds);
-                    return process.HasExited;
-                },
-                    cancellationToken
-                );
+                // Wait with timeout; ensure cancellation also terminates the child process
+                bool finished;
+                try
+                {
+                    finished = await Task.Run(() =>
+                    {
+                        process.WaitForExit(_timeoutMilliseconds);
+                        return process.HasExited;
+                    }, cancellationToken);
+                }
+                catch (OperationCanceledException)
+                {
+                    try
+                    {
+                        if (!process.HasExited)
+                        {
+                            process.Kill();
+                        }
+                    }
+                    catch
+                    {
+                        // Ignore kill failures on cancellation
+                    }
+
+                    result.Passed = false;
+                    result.Error = "Execution cancelled.";
+                    return result;
+                }

---

334-346: Command construction still has Python path, Node execution, C# testCase, and escaping issues

The current GetExecutionCommand still has the same correctness problems as earlier versions:

• Python: open('{codeFile}') embeds a Windows path directly in a Python string, so backslashes are interpreted as escapes (e.g., \t, \n), and quotes in the path/test case can break the -c string.
• Node: require('fs').readFileSync('{codeFile}', 'utf8'); {testCase} reads the file but never evaluates it, so tests that rely on functions defined in the code file can’t work.
• C#: "dotnet script {codeFile}" ignores testCase entirely, so multiple test cases execute identically.
• In all cases, testCase is interpolated directly into the command line, which is fragile for quotes, backslashes, and newlines.

I’d strongly recommend:

• Escaping codeFile and testCase for the target language (at minimum replacing backslashes and quotes), and
• Actually executing the code and test together (e.g., Node: read + eval/vm.runInThisContext, C#: use a small harness script that invokes the generated code per test case), or writing a per-test harness file instead of embedding everything in a single -c/-e string.

---

387-407: assert extraction strips the keyword, and correctAnswer tests are non-functional comments

The current test extraction has two correctness issues:

• The regex captures only the expression after assert, so Python tests become bare expressions like add(2, 3) == 5. These never affect exit code when false, so tests won’t fail even if the code is wrong.
• When there are no explicit asserts, the fallback adds "# Expected: {correctAnswer}", which is just a comment in Python/JS and ignored entirely in C#. You then report all tests as “passed” without ever checking the answer.

I’d at least preserve full assert lines and drop the non-functional correctAnswer fallback to avoid false positives:

-        var assertMatches = Regex.Matches(fullText, @"assert\s+(.+?)(?:\n|$)", RegexOptions.Multiline);
+        var assertMatches = Regex.Matches(fullText, @"assert\s+.+?(?:\r?\n|$)", RegexOptions.Multiline);
         foreach (Match match in assertMatches)
         {
-            testCases.Add(match.Groups[1].Value.Trim());
+            testCases.Add(match.Value.Trim());
         }
-
-        // If correct answer provided, create a basic test
-        if (!string.IsNullOrEmpty(correctAnswer) && testCases.Count == 0)
-        {
-            testCases.Add($"# Expected: {correctAnswer}");
-        }

If you want to leverage correctAnswer, it would be better to generate real language-specific assertions (e.g., assert solve(...) == expected) rather than a comment, but until that’s in place, failing fast with “no test cases” is safer than marking untested code as correct.

🧹 Nitpick comments (11)

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

43-56: Remove unused _random field.

The _random field is initialized but never used anywhere in the implementation. Consider removing it to clean up dead code.

     private readonly double _explorationConstant;
     private readonly int _numSimulations;
-    private readonly Random _random;

     /// <summary>
     /// Initializes a new instance of the <see cref="MonteCarloTreeSearch{T}"/> class.
     /// </summary>
     /// <param name="explorationConstant">UCB1 exploration constant (default: 1.414, sqrt(2)).</param>
     /// <param name="numSimulations">Number of MCTS iterations (default: 100).</param>
     public MonteCarloTreeSearch(double explorationConstant = 1.414, int numSimulations = 100)
     {
         _explorationConstant = explorationConstant;
         _numSimulations = numSimulations;
-        _random = new Random(42); // Deterministic for reproducibility
     }

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

137-149: Optional: align terminal-node heuristics across search strategies

IsTerminalNode here includes "the answer is", whereas BestFirstSearch<T> and DepthFirstSearch<T> currently only check for "final answer", "conclusion", "therefore" and node.IsTerminal. For consistency of behavior across algorithms, consider sharing a common helper or at least using the same heuristic set everywhere (or documenting why BFS/Beam differ).

---

81-82: Optional: guard against overflow in the loop bound

nodesExplored < config.MaxSteps * config.BranchingFactor can overflow if both values are large. Not critical for typical settings, but you could use a wider type or precompute with checked semantics:

var maxNodes = (long)config.MaxSteps * config.BranchingFactor;
while (queue.Count > 0 && nodesExplored < maxNodes)
{
    ...
}

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

111-114: Optional: avoid calling priorityQueue.First() twice per iteration

You currently call priorityQueue.First() once to deconstruct and again in Remove(...). While functionally correct, it’s a bit redundant. Consider capturing the first item once:

-            var (currentNode, _) = priorityQueue.First();
-            priorityQueue.Remove(priorityQueue.First());
+            var first = priorityQueue.First();
+            var (currentNode, _) = first;
+            priorityQueue.Remove(first);

This is a small readability/micro-efficiency improvement.

---

102-105: Optional: numOps/bestScore initialization isn’t really needed

bestScore is initialized from numOps.Zero, but the first time it’s actually used for comparison is when bestTerminalNode is already null, so the condition short‑circuits. In practice you don’t depend on the initial zero value.

You could simplify by removing MathHelper.GetNumericOperations<T>() here and using default for the field, or only obtaining numOps when you actually need numeric operations (mirroring other code in the project).

---

107-107: Optional: same overflow concern on MaxSteps * BranchingFactor

As with BFS, nodesExpanded < config.MaxSteps * config.BranchingFactor can overflow for large values. If you expect bigger limits, consider casting to long or precomputing safely:

var maxNodes = (long)config.MaxSteps * config.BranchingFactor;
while (priorityQueue.Count > 0 && nodesExpanded < maxNodes)
{
    ...
}

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

133-152: Wire CancellationToken through to the chat model (if supported)

AreContradictoryAsync accepts a CancellationToken but does not use it when calling _chatModel.GenerateResponseAsync(prompt). If IChatModel<T> exposes an overload that accepts a token, consider passing it through so long-running LLM calls can be cancelled; otherwise, consider omitting the parameter to avoid a misleading API.

---

154-166: Consider honoring cancellation in detailed analysis as well

AnalyzeContradictionAsync also takes a CancellationToken but currently ignores it when calling _chatModel.GenerateResponseAsync. For consistency with DetectContradictionsAsync, it would be preferable either to pass the token to the chat model (if an overload exists) or to remove the parameter.

---

338-356: ExtractJsonFromResponse duplicates logic in ProcessRewardModel

This implementation is effectively identical to the helper shown in src/Reasoning/Verification/ProcessRewardModel.cs (lines 238–253 in the provided snippet). Duplicating the regex patterns and extraction logic in multiple places makes future fixes harder and risks drift.

Consider extracting a shared helper (e.g., in a JSON/LLM utility class) that both ContradictionDetector and ProcessRewardModel can call, so changes to fenced/bare JSON handling only need to be made in one location.

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

359-385: Make ExtractCode null-safe for FinalAnswer and step content

ExtractCode assumes the entries in textsToSearch are non-null; if chain.FinalAnswer or any step Content can be null, Regex.Match(text, …) and text.Contains(...) will throw. This contrasts with the more defensive pattern in CodeReasoner.ExtractCode.

A small guard keeps it robust:

-        foreach (var text in textsToSearch)
-        {
+        foreach (var text in textsToSearch)
+        {
+            if (string.IsNullOrWhiteSpace(text))
+            {
+                continue;
+            }
+
             // Try to extract from code blocks
             var match = Regex.Match(text, @"```(?:python|javascript|csharp|py|js|cs)?\s*\n([\s\S]*?)\n```", RegexOptions.Multiline);

---

9-59: Reinforce sandboxing / isolation guidance for running untrusted code

Given this verifier executes arbitrary code from reasoning chains, it’s worth explicitly calling out (in docs or XML remarks) that it should only be used in strongly isolated environments (e.g., containers, low-privilege worker processes, or separate hosts) and never against untrusted input on a production app domain. The timeout helps with infinite loops but doesn’t protect against heavy CPU/memory usage or malicious code.

📜 Review details

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📥 Commits

Reviewing files that changed from the base of the PR and between 9360c9c and bc279aa.

📒 Files selected for processing (6)

• examples/ConcreteExamples/TrainingExample.cs (1 hunks)
• src/Reasoning/Components/ContradictionDetector.cs (1 hunks)
• src/Reasoning/Search/BestFirstSearch.cs (1 hunks)
• src/Reasoning/Search/BreadthFirstSearch.cs (1 hunks)
• src/Reasoning/Search/MonteCarloTreeSearch.cs (1 hunks)
• src/Reasoning/Verification/CodeExecutionVerifier.cs (1 hunks)

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

• examples/ConcreteExamples/TrainingExample.cs

🧰 Additional context used

🧬 Code graph analysis (5)

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

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

• Task (60-170)
• List (181-193)
• IsTerminalNode (172-179)

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

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

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

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

• ExtractJsonFromResponse (239-254)

src/Helpers/MathHelper.cs (1)

• MathHelper (19-992)

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

src/Helpers/MathHelper.cs (2)

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

src/Reasoning/DomainSpecific/CodeReasoner.cs (1)

• ExtractCode (240-263)

src/Interfaces/IExternalToolVerifier.cs (1)

• VerificationResult (62-96)

src/Reasoning/Search/BreadthFirstSearch.cs (7)

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

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

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

• Task (60-170)
• List (181-193)
• IsTerminalNode (172-179)

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

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

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

• Task (58-182)
• List (200-212)
• IsTerminalNode (187-195)

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

• Task (43-73)

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

• List (148-160)
• IsTerminalNode (139-146)

src/Helpers/MathHelper.cs (1)

• MathHelper (19-992)

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

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

• Task (50-135)
• List (154-166)
• IsTerminalNode (140-149)

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

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

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)

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🔇 Additional comments (15)

src/Reasoning/Search/MonteCarloTreeSearch.cs (8)

1-40: Excellent documentation!

The XML documentation provides clear, beginner-friendly explanations of MCTS, including the UCB1 formula, phases, use cases, and famous applications. This will help developers understand when and how to use this algorithm.

---

41-41: Generic constraint successfully addresses type safety.

The addition of where T : struct, IConvertible ensures that Convert.ToDouble(node.EvaluationScore) at line 118 will work correctly. This resolves the critical issue from previous reviews.

---

59-87: Root initialization is correct.

The root node is properly evaluated with its own thought (line 85) and metadata is correctly initialized for MCTS tracking.

---

94-109: Child evaluation correctly uses child's own thought.

Line 104 now properly evaluates each child using child.Thought instead of root.Thought, fixing the critical logic bug identified in previous reviews. Each node is now scored based on its own state.

---

117-129: Simulation and backpropagation logic is sound.

The simulation phase correctly uses the node's evaluation score, and terminal nodes receive an appropriate boost. The backpropagation call properly updates statistics up the tree.

---

131-152: Best path extraction follows MCTS principles.

The final path selection correctly identifies the most-explored branches by tracking visit counts, which is the standard MCTS approach for extracting the best solution after all iterations complete.

---

154-197: UCB1 implementation is correct.

The selection phase properly implements the UCB1 formula with appropriate handling of unvisited nodes (returning MaxValue to prioritize exploration) and correct exploitation-exploration balance. The defensive default for parentVisits (line 182) prevents division by zero.

---

199-216: Backpropagation correctly updates tree statistics.

The backpropagation phase properly increments visit counts and accumulates values from the simulation result up to the root, following standard MCTS implementation.

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

6-58: Overall BFS API and documentation look solid

The class shape, null-guards in SearchAsync, and the beginner-friendly XML docs align well with the other search strategies and make the behavior clear and predictable.

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

6-58: Best‑first search API and documentation are clear and consistent

The public surface (AlgorithmName, Description, SearchAsync) and the explanatory XML comments are consistent with the other search strategies and make the greedy behavior easy to understand for consumers.

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

44-130: Overall detector structure and sliding-window scan look solid

Constructor DI, null-check on chatModel, and the sliding-window + spot-check logic in DetectContradictionsAsync are clear and efficient for typical chain sizes. Cancellation is respected at the outer loops, and deterministic seeding for random spot-checks is a nice touch.

---

223-248: Prompt for contradiction check is clear and well-structured

The BuildContradictionCheckPrompt string interpolation looks correct (escaped {} pairs, doubled quotes), and the examples give the model a clear notion of contradictions plus a simple JSON schema to follow. This should help keep ParseContradictionResponse straightforward.

---

250-272: Analysis prompt is consistent and gives useful guidance

BuildContradictionAnalysisPrompt mirrors the check prompt, with explicit instructions for explanation + bounded severity. The JSON example matches what ParseContradictionAnalysis expects, which is good for robustness.

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

175-225: High-level VerifyAsync flow and early exits look good

The flow of extracting code, then test cases, and bailing out early with clear messages when either is missing looks solid and prevents hitting the lower-level verifier with unusable input.

---

424-517: Result type initialization and summary formatting look consistent

Initialising PassRate and Score to _numOps.Zero in CodeExecutionResult<T> avoids uninitialised values and ensures GetSummary() can safely call Convert.ToDouble even in error cases. The summary layout (language, pass count, percent, score, plus failed test details) is clear and useful.

[coderabbitai]

Actionable comments posted: 1

🧹 Nitpick comments (2)

src/Reasoning/ReasoningStrategyBase.cs (2)

48-74: Consider documenting concurrency expectations for reasoning traces.

The added lock prevents StringBuilder corruption, but traces from concurrent ReasonAsync calls on the same instance will still interleave (and ClearTrace at line 134 can clear another concurrent call's traces). If instances are meant to be single-use or used sequentially, documenting this expectation would help users avoid subtle trace-mixing issues.

---

319-328: Consider logging full exception details for tool execution failures.

Currently only ex.Message is captured in the trace and return value (lines 325-326). For debugging tool failures, including the exception type, stack trace, or inner exception details could be valuable.

Example enhancement:

             var errorMsg = $"Error executing tool '{toolName}': {ex.Message}";
             AppendTrace(errorMsg);
+            AppendTrace($"Exception details: {ex}");
             return errorMsg;

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📒 Files selected for processing (2)

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

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

• src/Reasoning/Search/BeamSearch.cs

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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)

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src/Reasoning/ReasoningStrategyBase.cs (3)

346-377: LGTM - comprehensive configuration validation.

All configuration parameters are validated with appropriate bounds checking. The validation for MaxReasoningTimeSeconds (lines 375-376) has been added as suggested in the previous review.

---

255-288: LGTM - tool utility methods are well-implemented.

FindTool correctly performs case-insensitive lookup and returns null when not found. GetToolDescriptions provides clear formatting with appropriate handling of the empty case.

---

69-111: LGTM - constructor and abstract members are properly designed.

The constructor validates inputs appropriately, handles null tools gracefully, and initializes all fields. Protected properties provide appropriate encapsulation (read-only access to ChatModel, immutable access to Tools and ReasoningTrace). Abstract members correctly enforce implementation of strategy identity.

[coderabbitai]

Actionable comments posted: 0

♻️ Duplicate comments (1)

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

215-222: Deduplicate terminal-thought heuristic and keep it aligned with BeamSearch

IsTerminalNode here re-implements the same string-based heuristic as BeamSearch but currently omits "the answer is", and any future tweaks would need to be applied in multiple places. A prior review already suggested extracting this into a shared helper/extension (e.g., ThoughtNodeExtensions.IsTerminalThought<T>()) and calling that from both search algorithms.

Refactoring to a shared utility keeps terminal detection consistent across strategies and reduces maintenance overhead.

🧹 Nitpick comments (1)

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

128-149: Include the final leaf node in bestPath when traversal stops on a non-terminal

In the best-path reconstruction loop, you only add current at the top of each iteration. If traversal ends because the last node has no children and is non-terminal, that leaf is never added to bestPath, so the returned path can omit the most promising leaf.

You can address this by adding the final current after the loop if it isn't already present:

-        while (current != null && current.Children.Count > 0)
-        {
-            bestPath.Add(current);
-
-            // Pick child with most visits
-            current = current.Children
-                .OrderByDescending(c => c.Metadata.ContainsKey("visits") ? (int)c.Metadata["visits"] : 0)
-                .FirstOrDefault();
-
-            if (current != null && current.IsTerminal)
-            {
-                bestPath.Add(current);
-                break;
-            }
-        }
-
-        return bestPath.Count > 0 ? bestPath : new List<AiDotNet.Reasoning.Models.ThoughtNode<T>> { root };
+        while (current != null && current.Children.Count > 0)
+        {
+            bestPath.Add(current);
+
+            // Pick child with most visits
+            current = current.Children
+                .OrderByDescending(c => c.Metadata.ContainsKey("visits") ? (int)c.Metadata["visits"] : 0)
+                .FirstOrDefault();
+
+            if (current != null && current.IsTerminal)
+            {
+                bestPath.Add(current);
+                break;
+            }
+        }
+
+        // Ensure the final leaf is included even if it's non-terminal
+        if (current != null && (bestPath.Count == 0 || bestPath[^1] != current))
+        {
+            bestPath.Add(current);
+        }
+
+        return bestPath.Count > 0 ? bestPath : new List<AiDotNet.Reasoning.Models.ThoughtNode<T>> { root };

This keeps existing terminal handling but guarantees the leaf endpoint of the most-visited path is present.

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

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

• Task (58-182)
• List (200-212)
• IsTerminalNode (187-195)

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

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

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

6-57: MCTS class structure and parameterization look solid

The overall class design, documentation, generic constraint, and constructor defaults (_explorationConstant, _numSimulations, deterministic RNG) are coherent and match the intended MCTS use case. No issues from a correctness standpoint here.

[coderabbitai]

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)

⏰ 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/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;
    }
}

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📒 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