{ "title": "Condrox AI Evidence Report", "overall": "WARN", "score": 93.87, "version": "3.3.0", "environment": { "platform": "Windows-10-10.0.26200-SP0", "python": "3.11.9", "hostname": "TobyOne", "processor": "Intel64 Family 6 Model 191 Stepping 2, GenuineIntel" }, "metadata": { "scenario_count": 3, "conversation_count": 2, "reasoning_session_count": 3, "agent_pipeline_session_count": 2, "full_system_session_count": 1, "system_checks": 5 }, "generated_at": "2026-07-21T18:06:46.001523", "deviations": [], "runs": [ { "name": "code_agent_trigger", "category": "agents", "input": { "text": "Write a Python function to sort a list.", "expected_intent": "code", "expected_mode": "code" }, "output": { "final_output": "Implementation plan: a Python function to sort a list. Step 1 — Define the function signature ```python def solution(input_data: list) -> list: \"\"\"Process the input and return the result. Complexity: T(n) ∈ O(n), S(n) ∈ O(n). \"\"\" ``` Step 2 — Validate the input First, check that the input is valid. If the input is empty or invalid, return an appropriate default value. Because validation prevents errors, it should always be the first step. Step 3 — Implement the core logic ```python if not input_data: return [] result = [] for item in input_data: # Process each item result.append(transform(item)) return result ``` Step 4 — Handle edge cases - Empty input: return empty list (handled in step 2). - Single element: the loop handles this correctly. - Large input: complexity is O(n), so performance is acceptable. Step 5 — Test the implementation Write unit tests covering: normal case, empty input, single element, and edge cases. Because tests verify correctness, they're essential.", "intent": "code", "mode": "code", "domain": "programming", "emotion": "neutral", "context": { "has_knowledge": false, "knowledge_count": 0, "knowledge_texts": [], "has_memory": false, "memory_count": 0, "memory_texts": [], "agent_id": "code_agent", "agent_confidence": 1.0, "agent_mode": "code" } }, "status": "PASS", "score": 100.0, "metrics": { "duration_ms": 433.60350000148173 }, "deviations": [], "timestamp": "2026-07-21T18:05:36.570442" }, { "name": "personal_empathy", "category": "conversation", "input": { "text": "I'm thinking about quitting my job. I don't know what to do.", "expected_intent": "question", "expected_mode": "explain" }, "output": { "final_output": "I'm listening. Thank you for sharing that. I notice you mentioned thinking, quitting, know. Thank you for trusting me with that. You shared: \"I'm thinking about quitting my job. I don't know what to do.\". It takes courage to share personal things. Since you've opened up, I'm here to listen — would you like to continue?", "intent": "question", "mode": "explain", "domain": "general", "emotion": "personal", "context": { "has_knowledge": true, "knowledge_count": 1, "knowledge_texts": [ "Subject: norwegian\nTitle: Verbs\nConcepts: verb, action, state, tense, subject\nExplanation: A verb expresses an action, occurrence, or state of being. It must agree with the subject in person and number.\nExamples: run, is, think\nContext: Verbs change form depending on tense.\n" ], "has_memory": false, "memory_count": 0, "memory_texts": [], "agent_id": "explain_agent", "agent_confidence": 0.574442516811659, "agent_mode": "explain" } }, "status": "PASS", "score": 100.0, "metrics": { "duration_ms": 492.26720000115165 }, "deviations": [], "timestamp": "2026-07-21T18:05:37.063384" }, { "name": "explain_algorithm", "category": "intelligence", "input": { "text": "Explain how quicksort works step by step.", "expected_intent": "explain", "expected_mode": "explain" }, "output": { "final_output": "explain step by step: how quicksort works step by step 1. Core concept Quicksort is a divide-and-conquer sorting algorithm. Because it partitions the array around a pivot, each recursive call sorts a smaller sub-array. Therefore, the algorithm converges quickly. 2. How it works First, choose a pivot element from the array. Then, partition the array so that elements smaller than the pivot go left, and larger go right. Given this partition, recursively sort the left and right sub-arrays. Hence, the array becomes sorted. 3. Example ```python def quicksort(arr): if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) ``` 4. Complexity Time complexity: O(n log n) average, O(n²) worst case. Since the worst case happens when the pivot is always the smallest or largest element, choosing a good pivot is important. Therefore, random pivot selection or median-of-three improves performance. Space complexity: O(log n) for the recursion stack. 5. Key takeaways - Quicksort is efficient because it divides the problem in half. - The pivot choice matters because a bad pivot causes O(n²). - If the array is nearly sorted, quicksort may degrade. - Hence, quicksort is preferred for average-case performance.", "intent": "explain", "mode": "explain", "domain": "programming", "emotion": "neutral", "context": { "has_knowledge": false, "knowledge_count": 0, "knowledge_texts": [], "has_memory": false, "memory_count": 0, "memory_texts": [], "agent_id": "explain_agent", "agent_confidence": 0.9168273035060777, "agent_mode": "explain" } }, "status": "PASS", "score": 100.0, "metrics": { "duration_ms": 10244.968099999824 }, "deviations": [], "timestamp": "2026-07-21T18:05:47.308355" } ], "conversation_sessions": [ { "name": "multi_turn_stability", "summary": { "score": 81.66, "status": "PASS", "turns": 5, "metric_averages": { "Empathy": 58.0, "Relevance": 90.29, "Consistency": 100.0, "NoDictionaryDump": 100.0, "ContextPreservation": 60.0 }, "deviation_count": 0 }, "turns": [ { "input": "Hi, I'm feeling stressed about work.", "output": "Hello. I'm Condrox, a rule-based AI system. I can help you with code, analysis, architecture, and explanations. What would you like to work on?", "emotion": "neutral", "mode": "explain", "metrics": [ { "name": "Empathy", "score": 50.0, "status": "PASS", "details": { "matched": [] } }, { "name": "Relevance", "score": 51.42857142857142, "status": "PASS", "details": { "overlap": 3 } }, { "name": "Consistency", "score": 100.0, "status": "PASS", "details": {} }, { "name": "NoDictionaryDump", "score": 100.0, "status": "PASS", "details": { "markers": [] } }, { "name": "ContextPreservation", "score": 60.0, "status": "PASS", "details": { "context": { "topic": null, "user_name": null, "profession": null, "emotion": "neutral" }, "extra": 3 } } ], "deviations": [] }, { "input": "My boss keeps giving me impossible deadlines.", "output": "I'm here for you. I understand your frustration. I notice you mentioned boss, keeps, giving. Your frustration is valid. You mentioned: \"My boss keeps giving me impossible deadlines.\". Sometimes situations are genuinely unfair, and it's normal to feel angry. Since your feelings matter, would it help to talk through what happened? I recall we've talked before — I'm keeping that context in mind.", "emotion": "angry", "mode": "default", "metrics": [ { "name": "Empathy", "score": 70.0, "status": "PASS", "details": { "matched": [ "understand", "i'm here" ] } }, { "name": "Relevance", "score": 100.0, "status": "PASS", "details": { "overlap": 7 } }, { "name": "Consistency", "score": 100.0, "status": "PASS", "details": { "contradictions": 0 } }, { "name": "NoDictionaryDump", "score": 100.0, "status": "PASS", "details": { "markers": [] } }, { "name": "ContextPreservation", "score": 60.0, "status": "PASS", "details": { "context": { "topic": null, "user_name": null, "profession": null, "emotion": "angry" }, "extra": 3 } } ], "deviations": [] }, { "input": "What should I do?", "output": "I'm listening. Thank you for sharing that. Thank you for trusting me with that. You shared: \"What should I do?\". It takes courage to share personal things. Since you've opened up, I'm here to listen — would you like to continue? I recall we've talked before — I'm keeping that context in mind.", "emotion": "personal", "mode": "explain", "metrics": [ { "name": "Empathy", "score": 60.0, "status": "PASS", "details": { "matched": [ "i'm here" ] } }, { "name": "Relevance", "score": 100.0, "status": "PASS", "details": { "overlap": 4 } }, { "name": "Consistency", "score": 100.0, "status": "PASS", "details": { "contradictions": 0 } }, { "name": "NoDictionaryDump", "score": 100.0, "status": "PASS", "details": { "markers": [] } }, { "name": "ContextPreservation", "score": 60.0, "status": "PASS", "details": { "context": { "topic": null, "user_name": null, "profession": null, "emotion": "personal" }, "extra": 3 } } ], "deviations": [] }, { "input": "That makes sense. Can you help me write a plan?", "output": "explain step by step: That makes sense. Can you help me write a plan? 1. Core concept The concept of That makes sense. Can you help me write a plan? is important because it appears in many contexts. Since understanding it helps you make better decisions, let's break it down. 2. How it works First, consider the definition. Then, look at how it's applied in practice. Given that theory and practice reinforce each other, both are important. Hence, you can use this concept effectively. 3. Example For instance, consider a real-world scenario. Because examples make abstract concepts concrete, they're the best way to learn. Therefore, always look for examples when learning something new. 4. Key takeaways - That makes sense. Can you help me write a plan? is important because it has wide applications. - If you understand the core concept, the rest follows naturally. - Since practice reinforces theory, apply what you learn. - Hence, you now have a solid foundation to build on.", "emotion": "neutral", "mode": "explain", "metrics": [ { "name": "Empathy", "score": 60.0, "status": "PASS", "details": { "matched": [ "understand" ] } }, { "name": "Relevance", "score": 100.0, "status": "PASS", "details": { "overlap": 10 } }, { "name": "Consistency", "score": 100.0, "status": "PASS", "details": { "contradictions": 0 } }, { "name": "NoDictionaryDump", "score": 100.0, "status": "PASS", "details": { "markers": [] } }, { "name": "ContextPreservation", "score": 60.0, "status": "PASS", "details": { "context": { "topic": null, "user_name": null, "profession": null, "emotion": "neutral" }, "extra": 3 } } ], "deviations": [] }, { "input": "Thanks, this really helps.", "output": "You said: \"Thanks, this really helps.\".", "emotion": "grateful", "mode": "default", "metrics": [ { "name": "Empathy", "score": 50.0, "status": "PASS", "details": { "matched": [] } }, { "name": "Relevance", "score": 100.0, "status": "PASS", "details": { "overlap": 4 } }, { "name": "Consistency", "score": 100.0, "status": "PASS", "details": { "contradictions": 0 } }, { "name": "NoDictionaryDump", "score": 100.0, "status": "PASS", "details": { "markers": [] } }, { "name": "ContextPreservation", "score": 60.0, "status": "PASS", "details": { "context": { "topic": null, "user_name": null, "profession": null, "emotion": "grateful" }, "extra": 3 } } ], "deviations": [] } ] }, { "name": "technical_explanation", "summary": { "score": 84.0, "status": "PASS", "turns": 1, "metric_averages": { "Empathy": 60.0, "Relevance": 100.0, "Consistency": 100.0, "NoDictionaryDump": 100.0, "ContextPreservation": 60.0 }, "deviation_count": 0 }, "turns": [ { "input": "Explain recursion in simple terms.", "output": "explain step by step: recursion in simple terms 1. Core concept The concept of recursion in simple terms is fundamental to computer science. Because it appears in many algorithms, understanding it's essential. 2. How it works First, the algorithm takes an input. Then, it processes the input according to defined rules. Given that each step is deterministic, the same input always produces the same output. Hence, the algorithm is predictable. 3. Example ```python # Example of recursion in simple terms def example(input_data): result = process(input_data) return result ``` 4. Complexity Time complexity depends on the input size. Since most algorithms process each element, they're typically O(n). If there are nested loops, the complexity is O(n²). Therefore, choose algorithms carefully. 5. Key takeaways - The algorithm is deterministic because each step is defined. - Complexity matters because it determines scalability. - If the input is large, choose an efficient algorithm. - Hence, understanding complexity helps you write better code.", "emotion": "neutral", "mode": "explain", "metrics": [ { "name": "Empathy", "score": 60.0, "status": "PASS", "details": { "matched": [ "understand" ] } }, { "name": "Relevance", "score": 100.0, "status": "PASS", "details": { "overlap": 5 } }, { "name": "Consistency", "score": 100.0, "status": "PASS", "details": {} }, { "name": "NoDictionaryDump", "score": 100.0, "status": "PASS", "details": { "markers": [] } }, { "name": "ContextPreservation", "score": 60.0, "status": "PASS", "details": { "context": { "topic": null, "user_name": null, "profession": null, "emotion": "neutral" }, "extra": 3 } } ], "deviations": [] } ] } ], "reasoning_sessions": [ { "name": "code_analysis", "summary": { "score": 84.17, "status": "WARN", "tasks": 1, "metric_averages": { "StepByStepStructure": 55.0, "LogicalCoherence": 100.0, "ExplanationQuality": 50.0, "PlanningQuality": 100.0, "CodeAnalysis": 100.0, "CorrectnessSignal": 100.0 }, "deviation_count": 1 }, "tasks": [ { "input": "Analyze the following function for correctness and complexity: def bubble_sort(arr): for i in range(len(arr)): for j in range(len(arr)-1): if arr[j] > arr[j+1]: arr[j], arr[j+1] = arr[j+1], arr[j] return arr", "output": "Code analysis: bubble_sort Step 1 — Identify the algorithm The function bubble_sort(arr) implements a sorting algorithm. Step 2 — Analyze correctness The function bubble_sort sorts the input by comparing adjacent elements and swapping them if they're in the wrong order. Therefore, the algorithm is correct because each pass moves the largest unsorted element to its final position. Given that the loop runs n times, all elements are sorted. Step 3 — Analyze complexity Time complexity: O(n²) because there are nested loops, each iterating over the input. Space complexity: O(1) because the function sorts in place and uses only a constant amount of extra memory for the swap variable. Step 4 — Identify edge cases - Empty input: the function should handle an empty list/array gracefully. - Single element: the function should return the input unchanged. - Duplicate values: the function should handle duplicates correctly. - Already sorted input: the function should still produce correct output. Step 5 — Suggest improvements - Add input validation (type checks, empty checks) at the start. - Add type hints for clarity and safety. - Add a docstring explaining the algorithm and complexity. - Consider early termination if no swaps occur in a pass (optimization).", "intent": "code", "mode": "code", "domain": "programming", "metrics": [ { "name": "StepByStepStructure", "score": 55.0, "status": "WARN", "details": { "indicators_found": 1 } }, { "name": "LogicalCoherence", "score": 100.0, "status": "PASS", "details": { "indicators_found": 5 } }, { "name": "ExplanationQuality", "score": 50.0, "status": "PASS", "details": { "indicators_found": 0 } }, { "name": "PlanningQuality", "score": 100.0, "status": "PASS", "details": { "not_required": true } }, { "name": "CodeAnalysis", "score": 100.0, "status": "PASS", "details": { "indicators_found": 9 } }, { "name": "CorrectnessSignal", "score": 100.0, "status": "PASS", "details": { "signals": 7 } } ], "deviations": [ "Response lacks clear step-by-step structure" ] } ] }, { "name": "planning_task", "summary": { "score": 90.0, "status": "PASS", "tasks": 1, "metric_averages": { "StepByStepStructure": 100.0, "LogicalCoherence": 100.0, "ExplanationQuality": 50.0, "PlanningQuality": 100.0, "CodeAnalysis": 100.0, "CorrectnessSignal": 90.0 }, "deviation_count": 0 }, "tasks": [ { "input": "Plan the implementation of a REST API for a task manager.", "output": "Architecture plan: Plan the implementation of a REST API for a task manager. Approach: Design a RESTful API with clear resource separation, standard HTTP methods, and versioned endpoints. Because REST is stateless, each request must carry all needed context. Phase 1 — Design (milestone: API contract) 1. Identify resources (e.g., tasks, users, projects). Since resources map to nouns, derive them from the domain model. 2. Define endpoints: GET /tasks, POST /tasks, GET /tasks/{id}, PUT /tasks/{id}, DELETE /tasks/{id}. Given that CRUD maps to HTTP methods, each operation has a clear verb. 3. Define request/response schemas with types. Therefore, clients know exactly what to send and expect. 4. Choose authentication: API key or JWT. If the API is public, use API keys; if user-scoped, use JWT. 5. Define error response format (status, code, message). Hence, errors are consistent across all endpoints. Phase 2 — Implement (milestone: working endpoints) 1. Set up FastAPI/Express router with middleware. Because middleware runs before handlers, it's the right place for auth and logging. 2. Implement CRUD handlers for each resource. 3. Add input validation on every endpoint. Since invalid input causes bugs, validation must happen before processing. 4. Add rate limiting and CORS. If rate limiting is missing, the API is vulnerable to abuse. 5. Wire persistence layer (database or in-memory). Phase 3 — Test (milestone: passing test suite) 1. Unit tests for each handler. Given that handlers are isolated, unit tests verify logic without external dependencies. 2. Integration tests for full request/response cycle. 3. Edge cases: missing fields, invalid IDs, auth failures. Because edge cases reveal bugs, they must be tested explicitly. 4. Load test to verify rate limiting. Therefore, we know the API handles traffic gracefully. Phase 4 — Deploy (milestone: production endpoint) 1. Containerize with Docker. Since containers are portable, the same image runs in any environment. 2. Configure environment variables for secrets. If secrets are hardcoded, they leak into version control. 3. Set up health check endpoint (/health). Hence, orchestrators can monitor and restart the service automatically. 4. Configure reverse proxy with TLS. Key design decisions: - Statelessness: each request carries all needed context. Therefore, the server can scale horizontally without session affinity. - Versioning: /v1/ prefix for forward compatibility. If the API changes, old clients keep working on /v1/. - Pagination: cursor-based for large collections. Given that offset pagination is slow on large datasets, cursors are more efficient. - Idempotency: PUT and DELETE are idempotent by design. Hence, retries don't cause duplicate side effects.", "intent": "architect", "mode": "architect", "domain": "programming", "metrics": [ { "name": "StepByStepStructure", "score": 100.0, "status": "PASS", "details": { "indicators_found": 8 } }, { "name": "LogicalCoherence", "score": 100.0, "status": "PASS", "details": { "indicators_found": 8 } }, { "name": "ExplanationQuality", "score": 50.0, "status": "PASS", "details": { "indicators_found": 0 } }, { "name": "PlanningQuality", "score": 100.0, "status": "PASS", "details": { "indicators_found": 6 } }, { "name": "CodeAnalysis", "score": 100.0, "status": "PASS", "details": { "not_required": true } }, { "name": "CorrectnessSignal", "score": 90.0, "status": "PASS", "details": { "signals": 4 } } ], "deviations": [] } ] }, { "name": "quicksort_explanation", "summary": { "score": 93.67, "status": "PASS", "tasks": 2, "metric_averages": { "StepByStepStructure": 100.0, "LogicalCoherence": 100.0, "ExplanationQuality": 62.0, "PlanningQuality": 100.0, "CodeAnalysis": 100.0, "CorrectnessSignal": 100.0 }, "deviation_count": 0 }, "tasks": [ { "input": "Explain how quicksort works step by step.", "output": "explain step by step: how quicksort works step by step 1. Core concept Quicksort is a divide-and-conquer sorting algorithm. Because it partitions the array around a pivot, each recursive call sorts a smaller sub-array. Therefore, the algorithm converges quickly. 2. How it works First, choose a pivot element from the array. Then, partition the array so that elements smaller than the pivot go left, and larger go right. Given this partition, recursively sort the left and right sub-arrays. Hence, the array becomes sorted. 3. Example ```python def quicksort(arr): if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) ``` 4. Complexity Time complexity: O(n log n) average, O(n²) worst case. Since the worst case happens when the pivot is always the smallest or largest element, choosing a good pivot is important. Therefore, random pivot selection or median-of-three improves performance. Space complexity: O(log n) for the recursion stack. 5. Key takeaways - Quicksort is efficient because it divides the problem in half. - The pivot choice matters because a bad pivot causes O(n²). - If the array is nearly sorted, quicksort may degrade. - Hence, quicksort is preferred for average-case performance.", "intent": "explain", "mode": "explain", "domain": "programming", "metrics": [ { "name": "StepByStepStructure", "score": 100.0, "status": "PASS", "details": { "indicators_found": 9 } }, { "name": "LogicalCoherence", "score": 100.0, "status": "PASS", "details": { "indicators_found": 8 } }, { "name": "ExplanationQuality", "score": 62.0, "status": "PASS", "details": { "indicators_found": 1 } }, { "name": "PlanningQuality", "score": 100.0, "status": "PASS", "details": { "not_required": true } }, { "name": "CodeAnalysis", "score": 100.0, "status": "PASS", "details": { "not_required": true } }, { "name": "CorrectnessSignal", "score": 100.0, "status": "PASS", "details": { "signals": 7 } } ], "deviations": [] }, { "input": "What is the time complexity of quicksort and why?", "output": "explain step by step: the time complexity of quicksort and why 1. Core concept Quicksort is a divide-and-conquer sorting algorithm. Because it partitions the array around a pivot, each recursive call sorts a smaller sub-array. Therefore, the algorithm converges quickly. 2. How it works First, choose a pivot element from the array. Then, partition the array so that elements smaller than the pivot go left, and larger go right. Given this partition, recursively sort the left and right sub-arrays. Hence, the array becomes sorted. 3. Example ```python def quicksort(arr): if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) ``` 4. Complexity Time complexity: O(n log n) average, O(n²) worst case. Since the worst case happens when the pivot is always the smallest or largest element, choosing a good pivot is important. Therefore, random pivot selection or median-of-three improves performance. Space complexity: O(log n) for the recursion stack. 5. Key takeaways - Quicksort is efficient because it divides the problem in half. - The pivot choice matters because a bad pivot causes O(n²). - If the array is nearly sorted, quicksort may degrade. - Hence, quicksort is preferred for average-case performance.", "intent": "question", "mode": "explain", "domain": "general", "metrics": [ { "name": "StepByStepStructure", "score": 100.0, "status": "PASS", "details": { "indicators_found": 9 } }, { "name": "LogicalCoherence", "score": 100.0, "status": "PASS", "details": { "indicators_found": 8 } }, { "name": "ExplanationQuality", "score": 62.0, "status": "PASS", "details": { "indicators_found": 1 } }, { "name": "PlanningQuality", "score": 100.0, "status": "PASS", "details": { "not_required": true } }, { "name": "CodeAnalysis", "score": 100.0, "status": "PASS", "details": { "not_required": true } }, { "name": "CorrectnessSignal", "score": 100.0, "status": "PASS", "details": { "signals": 8 } } ], "deviations": [] } ] } ], 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