Introducing Chronos-1
Introducing
Chronos-1
A language model engineered for autonomous bug localization, causal trace analysis,
and test-driven patch generation at repository scale.
A language model engineered for autonomous bug localization, causal trace analysis, and test-driven patch generation at repository scale.
65.3% end-to-end debugging success
Benchmarks*
87.4% root cause localization accuracy
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Benchmarks*
94.6% regression-free validation rate
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Benchmarks*
65.3% end-to-end debugging success
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Benchmarks*
65.3% end-to-end debugging success
Benchmarks*
87.4% root cause localization accuracy
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Benchmarks*
94.6% regression-free validation rate
-
Benchmarks*
65.3% end-to-end debugging success
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Benchmarks*
LLMs Don’t Debug
Static tokens don’t scale with dynamic bugs.
Static tokens don’t scale with dynamic bugs.
Sliding windows miss cross-file dependencies.
Sliding windows miss cross-file dependencies.
High token count ≠ high fix quality.
High token count ≠ high fix quality.
Prediction isn’t understanding.
Prediction isn’t understanding.
Prediction isn’t understanding.
A New Class of Language Model
Chronos-1 is built to debug code at scale.
It moves beyond token prediction to perform structured reasoning across entire codebases.
By tracing logic paths, identifying failure causes, and generating test-passing fixes, Chronos-1 redefines how developers interact with bugs.
Private research is ongoing. Launching in Q4 2025 with Kodezi OS.
[ LLM SYSTEM DESIGN ]
LLM Autonomic Debugging Stack
Chronos operates as a self-healing substrate across source, test, and infrastructure layers, enabling token-efficient, memory-driven software repair at scale.
Memory-Guided Code Retrieval
Chronos traverses a graph-indexed memory of code, tests, logs, and history to extract bug-relevant context.
Memory-Guided Code Retrieval
Chronos traverses a graph-indexed memory of code, tests, logs, and history to extract bug-relevant context.
Memory-Guided Code Retrieval
Chronos traverses a graph-indexed memory of code, tests, logs, and history to extract bug-relevant context.
Multi-Hop Contextual Retrieval
Scales context depth to bug complexity with token-efficient precision.
Multi-Hop Contextual Retrieval
Scales context depth to bug complexity with token-efficient precision.
Multi-Hop Contextual Retrieval
Scales context depth to bug complexity with token-efficient precision.
AGR-Guided Fix Planning
Reconstructs logic state to guide patch synthesis.
AGR-Guided Fix Planning
Reconstructs logic state to guide patch synthesis.
AGR-Guided Fix Planning
Reconstructs logic state to guide patch synthesis.
Test-Validated Fix Generation
Fixes are synthesized and accepted only upon passing full-suite validation.
Test-Validated Fix Generation
Fixes are synthesized and accepted only upon passing full-suite validation.
Test-Validated Fix Generation
Fixes are synthesized and accepted only upon passing full-suite validation.
Autonomous Debugging Feedback Loop
Validated repairs reshape retrieval and context flow.
Autonomous Debugging Feedback Loop
Validated repairs reshape retrieval and context flow.
Autonomous Debugging Feedback Loop
Validated repairs reshape retrieval and context flow.
Purpose-Built for Debugging at Scale
TOKEN-EFFICIENT AUTONOMY
Memory-guided fixes with minimal tokens
TOKEN-EFFICIENT AUTONOMY
Memory-guided fixes with minimal tokens
TOKEN-EFFICIENT AUTONOMY
Memory-guided fixes with minimal tokens
CONTEXTUAL INTEGRATION
Retrieves across files, tests, and logs
CONTEXTUAL INTEGRATION
Retrieves across files, tests, and logs
CONTEXTUAL INTEGRATION
Retrieves across files, tests, and logs
DEBUGGING ENGINEERED FOR YOU
LLM-native patching from learned bugs
DEBUGGING ENGINEERED FOR YOU
LLM-native patching from learned bugs
DEBUGGING ENGINEERED FOR YOU
LLM-native patching from learned bugs
CONTINUOUS LEARNING
Retrains memory after every resolution
CONTINUOUS LEARNING
Retrains memory after every resolution
CONTINUOUS LEARNING
Retrains memory after every resolution
Chronos fits into your existing workflows with zero disruption and full production awareness.
Chronos fits into your existing workflows with zero disruption and full production awareness.
[ RESULTS]
Benchmarks
Scalability of Debugging
Performance by Codebase Size
This comparison demonstrates how effectively different AI models handle various debugging challenges. Chronos achieves 58.3% to 94.2% success rates across all bug categories, while competing models (GPT-4.1, Claude 4 Opus, DeepSeek V3, and Gemini 2.5 Pro) struggle with complex issues, dropping to just 4.5% to 11.3% on concurrency and performance bugs. This 3-10x performance advantage highlights Chronos's superior capabilities through its specialized debugging architecture.
Comprehensive Debugging
Performance Comparison
While frontier models show incremental improvements, Chronos demonstrates 3-10x better performance on complex bug types through debug-specific training. Raw context size alone cannot solve debugging. Chronos's intelligent retrieval and persistent memory enable it to outperform even million-token models by over 5x across all bug categories, from syntax errors to concurrency issues.
Debugging Accuracy
by Bug Type
This evaluation shows Chronos consistently outperforming state-of-the-art models by 3-5x across all debugging dimensions. Chronos achieves 71.2% to 89.2% success rates in critical areas like root cause accuracy and retrieval precision, while competing models (Claude 4 Opus, GPT-4.1, Gemini 2.0 Pro, and DeepSeek V3) struggle at 14% to 40% performance levels. This comprehensive advantage demonstrates that specialized debugging architecture surpasses general-purpose language models across every evaluation metric.
End-to-End Debugging
Pipeline Efficiency
This analysis reveals Chronos's superior efficiency in real-world debugging scenarios. Despite taking 4.2 minutes average fix time, Chronos achieves 65.3% success rate at just $0.18 per bug, making it the most cost-effective solution. Competing systems require 18.7 to 21.3 minutes with only 13.4% to 14.2% success rates while consuming 4-5x more tokens. Human developers achieve 87.2% success but need 35.8 minutes at $29.83 per bug, making Chronos 8x faster and 165x more cost-efficient for automated debugging tasks.
Comparative Debugging
Loop Analysis
This analysis reveals why Chronos achieves superior debugging performance compared to other systems. Chronos performs 7.8 iterations on average with autonomous test execution, persistent memory, and full backtracking support, achieving 65.3% success. In contrast, competing models like Claude 4 Opus and GPT-4.1 manage only 1.2 to 2.1 iterations with session-only memory and no backtracking, resulting in just 13.8% to 14.2% success rates. The combination of deep iteration, test integration, and memory persistence enables Chronos to solve complex bugs that other systems cannot handle.
Retrieval Strategy
Performance Analysis
This comparison reveals the fundamental differences between debugging-first and code-generation tools. Chronos achieves 65.3% debugging success through unlimited context via intelligent retrieval, persistent memory, automated debug loops, and full CI/CD integration. In contrast, IDE-integrated tools like Cursor (4.2%) and code assistants like GitHub Copilot X (5.3%) lack these specialized capabilities, while CLI tools such as Claude Code CLI (6.8%) and Gemini CLI (9.7%) offer larger contexts but still miss the iterative refinement and persistent memory essential for effective debugging.
Retrieval Strategy
Performance Analysis
This evaluation demonstrates the superiority of Adaptive Graph-Guided Retrieval (AGR) over traditional retrieval approaches. Chronos's AGR achieves 65.3% debugging success with 89.2% precision and 84.7% recall by intelligently following code dependencies. In contrast, traditional methods show poor performance: random baseline (13.6%), BM25 text search (18.3%), and even advanced techniques like HyDE (31.2%) and Graph RAG (33.7%) fail to capture the complex relationships needed for debugging. The gap to a theoretical oracle retriever (78.9%) shows AGR captures 82.7% of maximum possible performance while using 27% less context.
Computational Efficiency
and Cost Analysis
Despite higher per-attempt cost, Chronos's high success rate yields the lowest effective cost for debugging. While Chronos takes 134.7 seconds and costs $0.89 per bug attempt, its 65.3% success rate results in an effective cost of just $1.36 per successful fix. Competing models appear cheaper initially ($0.52 to $0.72) but their low success rates (13.8% to 14.2%) drive effective costs to $3.77 to $5.18. Human developers achieve 94.2% success but require 2.4 hours at $180 per bug, making Chronos 132x more cost-efficient for automated debugging.
Performance on Debugging
Tasks Requiring Extensive Context
This comparison reveals that raw context size alone cannot solve debugging challenges. Despite models having up to 1M tokens (Gemini 2.5 Pro), they achieve only 13.9% average success on complex debugging tasks. Chronos, using unlimited context through intelligent retrieval rather than brute force token expansion, achieves 71.5% success rate across cross-file bugs (71.2%), historical bugs (68.9%), and complex traces (74.3%). This 5x performance advantage demonstrates that specialized debugging architecture and smart retrieval outperform massive context windows.
Average Debug
Cycles to Resolution
This chart demonstrates Chronos's efficiency in reaching successful bug fixes. Chronos requires only 2.2 cycles on average to resolve bugs, significantly outperforming GPT-4.1 (4.8 cycles), Claude 4 Opus (4.5 cycles), and DeepSeek V3 (4.2 cycles). Fewer debugging cycles means faster resolution times and reduced computational costs. This efficiency stems from Chronos's specialized debugging architecture, which learns from each iteration through persistent memory and adaptive retrieval, enabling it to converge on correct solutions more rapidly than general-purpose models.
Scalability of Debugging
Performance by Codebase SizeThis comparison demonstrates how effectively different AI models handle various debugging challenges. Chronos achieves 58.3% to 94.2% success rates across all bug categories, while competing models (GPT-4.1, Claude 4 Opus, DeepSeek V3, and Gemini 2.5 Pro) struggle with complex issues, dropping to just 4.5% to 11.3% on concurrency and performance bugs. This 3-10x performance advantage highlights Chronos's superior capabilities through its specialized debugging architecture.
Comprehensive Debugging
Performance ComparisonWhile frontier models show incremental improvements, Chronos demonstrates 3-10x better performance on complex bug types through debug-specific training. Raw context size alone cannot solve debugging. Chronos's intelligent retrieval and persistent memory enable it to outperform even million-token models by over 5x across all bug categories, from syntax errors to concurrency issues.
Debugging Accuracy
by Bug TypeThis evaluation shows Chronos consistently outperforming state-of-the-art models by 3-5x across all debugging dimensions. Chronos achieves 71.2% to 89.2% success rates in critical areas like root cause accuracy and retrieval precision, while competing models (Claude 4 Opus, GPT-4.1, Gemini 2.0 Pro, and DeepSeek V3) struggle at 14% to 40% performance levels. This comprehensive advantage demonstrates that specialized debugging architecture surpasses general-purpose language models across every evaluation metric.
End-to-End Debugging
Pipeline EfficiencyThis analysis reveals Chronos's superior efficiency in real-world debugging scenarios. Despite taking 4.2 minutes average fix time, Chronos achieves 65.3% success rate at just $0.18 per bug, making it the most cost-effective solution. Competing systems require 18.7 to 21.3 minutes with only 13.4% to 14.2% success rates while consuming 4-5x more tokens. Human developers achieve 87.2% success but need 35.8 minutes at $29.83 per bug, making Chronos 8x faster and 165x more cost-efficient for automated debugging tasks.
Comparative Debugging
Loop AnalysisThis analysis reveals why Chronos achieves superior debugging performance compared to other systems. Chronos performs 7.8 iterations on average with autonomous test execution, persistent memory, and full backtracking support, achieving 65.3% success. In contrast, competing models like Claude 4 Opus and GPT-4.1 manage only 1.2 to 2.1 iterations with session-only memory and no backtracking, resulting in just 13.8% to 14.2% success rates. The combination of deep iteration, test integration, and memory persistence enables Chronos to solve complex bugs that other systems cannot handle.
Retrieval Strategy
Performance AnalysisThis comparison reveals the fundamental differences between debugging-first and code-generation tools. Chronos achieves 65.3% debugging success through unlimited context via intelligent retrieval, persistent memory, automated debug loops, and full CI/CD integration. In contrast, IDE-integrated tools like Cursor (4.2%) and code assistants like GitHub Copilot X (5.3%) lack these specialized capabilities, while CLI tools such as Claude Code CLI (6.8%) and Gemini CLI (9.7%) offer larger contexts but still miss the iterative refinement and persistent memory essential for effective debugging.
Retrieval Strategy
Performance AnalysisThis evaluation demonstrates the superiority of Adaptive Graph-Guided Retrieval (AGR) over traditional retrieval approaches. Chronos's AGR achieves 65.3% debugging success with 89.2% precision and 84.7% recall by intelligently following code dependencies. In contrast, traditional methods show poor performance: random baseline (13.6%), BM25 text search (18.3%), and even advanced techniques like HyDE (31.2%) and Graph RAG (33.7%) fail to capture the complex relationships needed for debugging. The gap to a theoretical oracle retriever (78.9%) shows AGR captures 82.7% of maximum possible performance while using 27% less context.
Computational Efficiency
and Cost AnalysisDespite higher per-attempt cost, Chronos's high success rate yields the lowest effective cost for debugging. While Chronos takes 134.7 seconds and costs $0.89 per bug attempt, its 65.3% success rate results in an effective cost of just $1.36 per successful fix. Competing models appear cheaper initially ($0.52 to $0.72) but their low success rates (13.8% to 14.2%) drive effective costs to $3.77 to $5.18. Human developers achieve 94.2% success but require 2.4 hours at $180 per bug, making Chronos 132x more cost-efficient for automated debugging.
Performance on Debugging
Tasks Requiring Extensive ContextThis comparison reveals that raw context size alone cannot solve debugging challenges. Despite models having up to 1M tokens (Gemini 2.5 Pro), they achieve only 13.9% average success on complex debugging tasks. Chronos, using unlimited context through intelligent retrieval rather than brute force token expansion, achieves 71.5% success rate across cross-file bugs (71.2%), historical bugs (68.9%), and complex traces (74.3%). This 5x performance advantage demonstrates that specialized debugging architecture and smart retrieval outperform massive context windows.
Average Debug
Cycles to ResolutionThis chart demonstrates Chronos's efficiency in reaching successful bug fixes. Chronos requires only 2.2 cycles on average to resolve bugs, significantly outperforming GPT-4.1 (4.8 cycles), Claude 4 Opus (4.5 cycles), and DeepSeek V3 (4.2 cycles). Fewer debugging cycles means faster resolution times and reduced computational costs. This efficiency stems from Chronos's specialized debugging architecture, which learns from each iteration through persistent memory and adaptive retrieval, enabling it to converge on correct solutions more rapidly than general-purpose models.
Scalability of Debugging
Performance by Codebase SizeThis comparison demonstrates how effectively different AI models handle various debugging challenges. Chronos achieves 58.3% to 94.2% success rates across all bug categories, while competing models (GPT-4.1, Claude 4 Opus, DeepSeek V3, and Gemini 2.5 Pro) struggle with complex issues, dropping to just 4.5% to 11.3% on concurrency and performance bugs. This 3-10x performance advantage highlights Chronos's superior capabilities through its specialized debugging architecture.
Comprehensive Debugging
Performance ComparisonWhile frontier models show incremental improvements, Chronos demonstrates 3-10x better performance on complex bug types through debug-specific training. Raw context size alone cannot solve debugging. Chronos's intelligent retrieval and persistent memory enable it to outperform even million-token models by over 5x across all bug categories, from syntax errors to concurrency issues.
Debugging Accuracy
by Bug TypeThis evaluation shows Chronos consistently outperforming state-of-the-art models by 3-5x across all debugging dimensions. Chronos achieves 71.2% to 89.2% success rates in critical areas like root cause accuracy and retrieval precision, while competing models (Claude 4 Opus, GPT-4.1, Gemini 2.0 Pro, and DeepSeek V3) struggle at 14% to 40% performance levels. This comprehensive advantage demonstrates that specialized debugging architecture surpasses general-purpose language models across every evaluation metric.
End-to-End Debugging
Pipeline EfficiencyThis analysis reveals Chronos's superior efficiency in real-world debugging scenarios. Despite taking 4.2 minutes average fix time, Chronos achieves 65.3% success rate at just $0.18 per bug, making it the most cost-effective solution. Competing systems require 18.7 to 21.3 minutes with only 13.4% to 14.2% success rates while consuming 4-5x more tokens. Human developers achieve 87.2% success but need 35.8 minutes at $29.83 per bug, making Chronos 8x faster and 165x more cost-efficient for automated debugging tasks.
Comparative Debugging
Loop AnalysisThis analysis reveals why Chronos achieves superior debugging performance compared to other systems. Chronos performs 7.8 iterations on average with autonomous test execution, persistent memory, and full backtracking support, achieving 65.3% success. In contrast, competing models like Claude 4 Opus and GPT-4.1 manage only 1.2 to 2.1 iterations with session-only memory and no backtracking, resulting in just 13.8% to 14.2% success rates. The combination of deep iteration, test integration, and memory persistence enables Chronos to solve complex bugs that other systems cannot handle.
Retrieval Strategy
Performance AnalysisThis comparison reveals the fundamental differences between debugging-first and code-generation tools. Chronos achieves 65.3% debugging success through unlimited context via intelligent retrieval, persistent memory, automated debug loops, and full CI/CD integration. In contrast, IDE-integrated tools like Cursor (4.2%) and code assistants like GitHub Copilot X (5.3%) lack these specialized capabilities, while CLI tools such as Claude Code CLI (6.8%) and Gemini CLI (9.7%) offer larger contexts but still miss the iterative refinement and persistent memory essential for effective debugging.
Retrieval Strategy
Performance AnalysisThis evaluation demonstrates the superiority of Adaptive Graph-Guided Retrieval (AGR) over traditional retrieval approaches. Chronos's AGR achieves 65.3% debugging success with 89.2% precision and 84.7% recall by intelligently following code dependencies. In contrast, traditional methods show poor performance: random baseline (13.6%), BM25 text search (18.3%), and even advanced techniques like HyDE (31.2%) and Graph RAG (33.7%) fail to capture the complex relationships needed for debugging. The gap to a theoretical oracle retriever (78.9%) shows AGR captures 82.7% of maximum possible performance while using 27% less context.
Computational Efficiency
and Cost AnalysisDespite higher per-attempt cost, Chronos's high success rate yields the lowest effective cost for debugging. While Chronos takes 134.7 seconds and costs $0.89 per bug attempt, its 65.3% success rate results in an effective cost of just $1.36 per successful fix. Competing models appear cheaper initially ($0.52 to $0.72) but their low success rates (13.8% to 14.2%) drive effective costs to $3.77 to $5.18. Human developers achieve 94.2% success but require 2.4 hours at $180 per bug, making Chronos 132x more cost-efficient for automated debugging.
Performance on Debugging
Tasks Requiring Extensive ContextThis comparison reveals that raw context size alone cannot solve debugging challenges. Despite models having up to 1M tokens (Gemini 2.5 Pro), they achieve only 13.9% average success on complex debugging tasks. Chronos, using unlimited context through intelligent retrieval rather than brute force token expansion, achieves 71.5% success rate across cross-file bugs (71.2%), historical bugs (68.9%), and complex traces (74.3%). This 5x performance advantage demonstrates that specialized debugging architecture and smart retrieval outperform massive context windows.
Average Debug
Cycles to ResolutionThis chart demonstrates Chronos's efficiency in reaching successful bug fixes. Chronos requires only 2.2 cycles on average to resolve bugs, significantly outperforming GPT-4.1 (4.8 cycles), Claude 4 Opus (4.5 cycles), and DeepSeek V3 (4.2 cycles). Fewer debugging cycles means faster resolution times and reduced computational costs. This efficiency stems from Chronos's specialized debugging architecture, which learns from each iteration through persistent memory and adaptive retrieval, enabling it to converge on correct solutions more rapidly than general-purpose models.
Scalability of Debugging
Performance by Codebase SizeThis comparison demonstrates how effectively different AI models handle various debugging challenges. Chronos achieves 58.3% to 94.2% success rates across all bug categories, while competing models (GPT-4.1, Claude 4 Opus, DeepSeek V3, and Gemini 2.5 Pro) struggle with complex issues, dropping to just 4.5% to 11.3% on concurrency and performance bugs. This 3-10x performance advantage highlights Chronos's superior capabilities through its specialized debugging architecture.
Comprehensive Debugging
Performance ComparisonWhile frontier models show incremental improvements, Chronos demonstrates 3-10x better performance on complex bug types through debug-specific training. Raw context size alone cannot solve debugging. Chronos's intelligent retrieval and persistent memory enable it to outperform even million-token models by over 5x across all bug categories, from syntax errors to concurrency issues.
Debugging Accuracy
by Bug TypeThis evaluation shows Chronos consistently outperforming state-of-the-art models by 3-5x across all debugging dimensions. Chronos achieves 71.2% to 89.2% success rates in critical areas like root cause accuracy and retrieval precision, while competing models (Claude 4 Opus, GPT-4.1, Gemini 2.0 Pro, and DeepSeek V3) struggle at 14% to 40% performance levels. This comprehensive advantage demonstrates that specialized debugging architecture surpasses general-purpose language models across every evaluation metric.
End-to-End Debugging
Pipeline EfficiencyThis analysis reveals Chronos's superior efficiency in real-world debugging scenarios. Despite taking 4.2 minutes average fix time, Chronos achieves 65.3% success rate at just $0.18 per bug, making it the most cost-effective solution. Competing systems require 18.7 to 21.3 minutes with only 13.4% to 14.2% success rates while consuming 4-5x more tokens. Human developers achieve 87.2% success but need 35.8 minutes at $29.83 per bug, making Chronos 8x faster and 165x more cost-efficient for automated debugging tasks.
Comparative Debugging
Loop AnalysisThis analysis reveals why Chronos achieves superior debugging performance compared to other systems. Chronos performs 7.8 iterations on average with autonomous test execution, persistent memory, and full backtracking support, achieving 65.3% success. In contrast, competing models like Claude 4 Opus and GPT-4.1 manage only 1.2 to 2.1 iterations with session-only memory and no backtracking, resulting in just 13.8% to 14.2% success rates. The combination of deep iteration, test integration, and memory persistence enables Chronos to solve complex bugs that other systems cannot handle.
Retrieval Strategy
Performance AnalysisThis comparison reveals the fundamental differences between debugging-first and code-generation tools. Chronos achieves 65.3% debugging success through unlimited context via intelligent retrieval, persistent memory, automated debug loops, and full CI/CD integration. In contrast, IDE-integrated tools like Cursor (4.2%) and code assistants like GitHub Copilot X (5.3%) lack these specialized capabilities, while CLI tools such as Claude Code CLI (6.8%) and Gemini CLI (9.7%) offer larger contexts but still miss the iterative refinement and persistent memory essential for effective debugging.
Retrieval Strategy
Performance AnalysisThis evaluation demonstrates the superiority of Adaptive Graph-Guided Retrieval (AGR) over traditional retrieval approaches. Chronos's AGR achieves 65.3% debugging success with 89.2% precision and 84.7% recall by intelligently following code dependencies. In contrast, traditional methods show poor performance: random baseline (13.6%), BM25 text search (18.3%), and even advanced techniques like HyDE (31.2%) and Graph RAG (33.7%) fail to capture the complex relationships needed for debugging. The gap to a theoretical oracle retriever (78.9%) shows AGR captures 82.7% of maximum possible performance while using 27% less context.
Computational Efficiency
and Cost AnalysisDespite higher per-attempt cost, Chronos's high success rate yields the lowest effective cost for debugging. While Chronos takes 134.7 seconds and costs $0.89 per bug attempt, its 65.3% success rate results in an effective cost of just $1.36 per successful fix. Competing models appear cheaper initially ($0.52 to $0.72) but their low success rates (13.8% to 14.2%) drive effective costs to $3.77 to $5.18. Human developers achieve 94.2% success but require 2.4 hours at $180 per bug, making Chronos 132x more cost-efficient for automated debugging.
Performance on Debugging
Tasks Requiring Extensive ContextThis comparison reveals that raw context size alone cannot solve debugging challenges. Despite models having up to 1M tokens (Gemini 2.5 Pro), they achieve only 13.9% average success on complex debugging tasks. Chronos, using unlimited context through intelligent retrieval rather than brute force token expansion, achieves 71.5% success rate across cross-file bugs (71.2%), historical bugs (68.9%), and complex traces (74.3%). This 5x performance advantage demonstrates that specialized debugging architecture and smart retrieval outperform massive context windows.
Average Debug
Cycles to ResolutionThis chart demonstrates Chronos's efficiency in reaching successful bug fixes. Chronos requires only 2.2 cycles on average to resolve bugs, significantly outperforming GPT-4.1 (4.8 cycles), Claude 4 Opus (4.5 cycles), and DeepSeek V3 (4.2 cycles). Fewer debugging cycles means faster resolution times and reduced computational costs. This efficiency stems from Chronos's specialized debugging architecture, which learns from each iteration through persistent memory and adaptive retrieval, enabling it to converge on correct solutions more rapidly than general-purpose models.
[ RESULTS]
Benchmarks
Scalability of Debugging
Performance by Codebase SizeThis comparison demonstrates how effectively different AI models handle various debugging challenges. Chronos achieves 58.3% to 94.2% success rates across all bug categories, while competing models (GPT-4.1, Claude 4 Opus, DeepSeek V3, and Gemini 2.5 Pro) struggle with complex issues, dropping to just 4.5% to 11.3% on concurrency and performance bugs. This 3-10x performance advantage highlights Chronos's superior capabilities through its specialized debugging architecture.
Comprehensive Debugging
Performance ComparisonWhile frontier models show incremental improvements, Chronos demonstrates 3-10x better performance on complex bug types through debug-specific training. Raw context size alone cannot solve debugging. Chronos's intelligent retrieval and persistent memory enable it to outperform even million-token models by over 5x across all bug categories, from syntax errors to concurrency issues.
Debugging Accuracy
by Bug TypeThis evaluation shows Chronos consistently outperforming state-of-the-art models by 3-5x across all debugging dimensions. Chronos achieves 71.2% to 89.2% success rates in critical areas like root cause accuracy and retrieval precision, while competing models (Claude 4 Opus, GPT-4.1, Gemini 2.0 Pro, and DeepSeek V3) struggle at 14% to 40% performance levels. This comprehensive advantage demonstrates that specialized debugging architecture surpasses general-purpose language models across every evaluation metric.
End-to-End Debugging
Pipeline EfficiencyThis analysis reveals Chronos's superior efficiency in real-world debugging scenarios. Despite taking 4.2 minutes average fix time, Chronos achieves 65.3% success rate at just $0.18 per bug, making it the most cost-effective solution. Competing systems require 18.7 to 21.3 minutes with only 13.4% to 14.2% success rates while consuming 4-5x more tokens. Human developers achieve 87.2% success but need 35.8 minutes at $29.83 per bug, making Chronos 8x faster and 165x more cost-efficient for automated debugging tasks.
Comparative Debugging
Loop AnalysisThis analysis reveals why Chronos achieves superior debugging performance compared to other systems. Chronos performs 7.8 iterations on average with autonomous test execution, persistent memory, and full backtracking support, achieving 65.3% success. In contrast, competing models like Claude 4 Opus and GPT-4.1 manage only 1.2 to 2.1 iterations with session-only memory and no backtracking, resulting in just 13.8% to 14.2% success rates. The combination of deep iteration, test integration, and memory persistence enables Chronos to solve complex bugs that other systems cannot handle.
Retrieval Strategy
Performance AnalysisThis comparison reveals the fundamental differences between debugging-first and code-generation tools. Chronos achieves 65.3% debugging success through unlimited context via intelligent retrieval, persistent memory, automated debug loops, and full CI/CD integration. In contrast, IDE-integrated tools like Cursor (4.2%) and code assistants like GitHub Copilot X (5.3%) lack these specialized capabilities, while CLI tools such as Claude Code CLI (6.8%) and Gemini CLI (9.7%) offer larger contexts but still miss the iterative refinement and persistent memory essential for effective debugging.
Retrieval Strategy
Performance AnalysisThis evaluation demonstrates the superiority of Adaptive Graph-Guided Retrieval (AGR) over traditional retrieval approaches. Chronos's AGR achieves 65.3% debugging success with 89.2% precision and 84.7% recall by intelligently following code dependencies. In contrast, traditional methods show poor performance: random baseline (13.6%), BM25 text search (18.3%), and even advanced techniques like HyDE (31.2%) and Graph RAG (33.7%) fail to capture the complex relationships needed for debugging. The gap to a theoretical oracle retriever (78.9%) shows AGR captures 82.7% of maximum possible performance while using 27% less context.
Computational Efficiency
and Cost AnalysisDespite higher per-attempt cost, Chronos's high success rate yields the lowest effective cost for debugging. While Chronos takes 134.7 seconds and costs $0.89 per bug attempt, its 65.3% success rate results in an effective cost of just $1.36 per successful fix. Competing models appear cheaper initially ($0.52 to $0.72) but their low success rates (13.8% to 14.2%) drive effective costs to $3.77 to $5.18. Human developers achieve 94.2% success but require 2.4 hours at $180 per bug, making Chronos 132x more cost-efficient for automated debugging.
Performance on Debugging
Tasks Requiring Extensive ContextThis comparison reveals that raw context size alone cannot solve debugging challenges. Despite models having up to 1M tokens (Gemini 2.5 Pro), they achieve only 13.9% average success on complex debugging tasks. Chronos, using unlimited context through intelligent retrieval rather than brute force token expansion, achieves 71.5% success rate across cross-file bugs (71.2%), historical bugs (68.9%), and complex traces (74.3%). This 5x performance advantage demonstrates that specialized debugging architecture and smart retrieval outperform massive context windows.
Average Debug
Cycles to ResolutionThis chart demonstrates Chronos's efficiency in reaching successful bug fixes. Chronos requires only 2.2 cycles on average to resolve bugs, significantly outperforming GPT-4.1 (4.8 cycles), Claude 4 Opus (4.5 cycles), and DeepSeek V3 (4.2 cycles). Fewer debugging cycles means faster resolution times and reduced computational costs. This efficiency stems from Chronos's specialized debugging architecture, which learns from each iteration through persistent memory and adaptive retrieval, enabling it to converge on correct solutions more rapidly than general-purpose models.
Scalability of Debugging
Performance by Codebase SizeThis comparison demonstrates how effectively different AI models handle various debugging challenges. Chronos achieves 58.3% to 94.2% success rates across all bug categories, while competing models (GPT-4.1, Claude 4 Opus, DeepSeek V3, and Gemini 2.5 Pro) struggle with complex issues, dropping to just 4.5% to 11.3% on concurrency and performance bugs. This 3-10x performance advantage highlights Chronos's superior capabilities through its specialized debugging architecture.
Comprehensive Debugging
Performance ComparisonWhile frontier models show incremental improvements, Chronos demonstrates 3-10x better performance on complex bug types through debug-specific training. Raw context size alone cannot solve debugging. Chronos's intelligent retrieval and persistent memory enable it to outperform even million-token models by over 5x across all bug categories, from syntax errors to concurrency issues.
Debugging Accuracy
by Bug TypeThis evaluation shows Chronos consistently outperforming state-of-the-art models by 3-5x across all debugging dimensions. Chronos achieves 71.2% to 89.2% success rates in critical areas like root cause accuracy and retrieval precision, while competing models (Claude 4 Opus, GPT-4.1, Gemini 2.0 Pro, and DeepSeek V3) struggle at 14% to 40% performance levels. This comprehensive advantage demonstrates that specialized debugging architecture surpasses general-purpose language models across every evaluation metric.
End-to-End Debugging
Pipeline EfficiencyThis analysis reveals Chronos's superior efficiency in real-world debugging scenarios. Despite taking 4.2 minutes average fix time, Chronos achieves 65.3% success rate at just $0.18 per bug, making it the most cost-effective solution. Competing systems require 18.7 to 21.3 minutes with only 13.4% to 14.2% success rates while consuming 4-5x more tokens. Human developers achieve 87.2% success but need 35.8 minutes at $29.83 per bug, making Chronos 8x faster and 165x more cost-efficient for automated debugging tasks.
Comparative Debugging
Loop AnalysisThis analysis reveals why Chronos achieves superior debugging performance compared to other systems. Chronos performs 7.8 iterations on average with autonomous test execution, persistent memory, and full backtracking support, achieving 65.3% success. In contrast, competing models like Claude 4 Opus and GPT-4.1 manage only 1.2 to 2.1 iterations with session-only memory and no backtracking, resulting in just 13.8% to 14.2% success rates. The combination of deep iteration, test integration, and memory persistence enables Chronos to solve complex bugs that other systems cannot handle.
Retrieval Strategy
Performance AnalysisThis comparison reveals the fundamental differences between debugging-first and code-generation tools. Chronos achieves 65.3% debugging success through unlimited context via intelligent retrieval, persistent memory, automated debug loops, and full CI/CD integration. In contrast, IDE-integrated tools like Cursor (4.2%) and code assistants like GitHub Copilot X (5.3%) lack these specialized capabilities, while CLI tools such as Claude Code CLI (6.8%) and Gemini CLI (9.7%) offer larger contexts but still miss the iterative refinement and persistent memory essential for effective debugging.
Retrieval Strategy
Performance AnalysisThis evaluation demonstrates the superiority of Adaptive Graph-Guided Retrieval (AGR) over traditional retrieval approaches. Chronos's AGR achieves 65.3% debugging success with 89.2% precision and 84.7% recall by intelligently following code dependencies. In contrast, traditional methods show poor performance: random baseline (13.6%), BM25 text search (18.3%), and even advanced techniques like HyDE (31.2%) and Graph RAG (33.7%) fail to capture the complex relationships needed for debugging. The gap to a theoretical oracle retriever (78.9%) shows AGR captures 82.7% of maximum possible performance while using 27% less context.
Computational Efficiency
and Cost AnalysisDespite higher per-attempt cost, Chronos's high success rate yields the lowest effective cost for debugging. While Chronos takes 134.7 seconds and costs $0.89 per bug attempt, its 65.3% success rate results in an effective cost of just $1.36 per successful fix. Competing models appear cheaper initially ($0.52 to $0.72) but their low success rates (13.8% to 14.2%) drive effective costs to $3.77 to $5.18. Human developers achieve 94.2% success but require 2.4 hours at $180 per bug, making Chronos 132x more cost-efficient for automated debugging.
Performance on Debugging
Tasks Requiring Extensive ContextThis comparison reveals that raw context size alone cannot solve debugging challenges. Despite models having up to 1M tokens (Gemini 2.5 Pro), they achieve only 13.9% average success on complex debugging tasks. Chronos, using unlimited context through intelligent retrieval rather than brute force token expansion, achieves 71.5% success rate across cross-file bugs (71.2%), historical bugs (68.9%), and complex traces (74.3%). This 5x performance advantage demonstrates that specialized debugging architecture and smart retrieval outperform massive context windows.
Average Debug
Cycles to ResolutionThis chart demonstrates Chronos's efficiency in reaching successful bug fixes. Chronos requires only 2.2 cycles on average to resolve bugs, significantly outperforming GPT-4.1 (4.8 cycles), Claude 4 Opus (4.5 cycles), and DeepSeek V3 (4.2 cycles). Fewer debugging cycles means faster resolution times and reduced computational costs. This efficiency stems from Chronos's specialized debugging architecture, which learns from each iteration through persistent memory and adaptive retrieval, enabling it to converge on correct solutions more rapidly than general-purpose models.
Scalability of Debugging
Performance by Codebase SizeThis comparison demonstrates how effectively different AI models handle various debugging challenges. Chronos achieves 58.3% to 94.2% success rates across all bug categories, while competing models (GPT-4.1, Claude 4 Opus, DeepSeek V3, and Gemini 2.5 Pro) struggle with complex issues, dropping to just 4.5% to 11.3% on concurrency and performance bugs. This 3-10x performance advantage highlights Chronos's superior capabilities through its specialized debugging architecture.
Comprehensive Debugging
Performance ComparisonWhile frontier models show incremental improvements, Chronos demonstrates 3-10x better performance on complex bug types through debug-specific training. Raw context size alone cannot solve debugging. Chronos's intelligent retrieval and persistent memory enable it to outperform even million-token models by over 5x across all bug categories, from syntax errors to concurrency issues.
Debugging Accuracy
by Bug TypeThis evaluation shows Chronos consistently outperforming state-of-the-art models by 3-5x across all debugging dimensions. Chronos achieves 71.2% to 89.2% success rates in critical areas like root cause accuracy and retrieval precision, while competing models (Claude 4 Opus, GPT-4.1, Gemini 2.0 Pro, and DeepSeek V3) struggle at 14% to 40% performance levels. This comprehensive advantage demonstrates that specialized debugging architecture surpasses general-purpose language models across every evaluation metric.
End-to-End Debugging
Pipeline EfficiencyThis analysis reveals Chronos's superior efficiency in real-world debugging scenarios. Despite taking 4.2 minutes average fix time, Chronos achieves 65.3% success rate at just $0.18 per bug, making it the most cost-effective solution. Competing systems require 18.7 to 21.3 minutes with only 13.4% to 14.2% success rates while consuming 4-5x more tokens. Human developers achieve 87.2% success but need 35.8 minutes at $29.83 per bug, making Chronos 8x faster and 165x more cost-efficient for automated debugging tasks.
Comparative Debugging
Loop AnalysisThis analysis reveals why Chronos achieves superior debugging performance compared to other systems. Chronos performs 7.8 iterations on average with autonomous test execution, persistent memory, and full backtracking support, achieving 65.3% success. In contrast, competing models like Claude 4 Opus and GPT-4.1 manage only 1.2 to 2.1 iterations with session-only memory and no backtracking, resulting in just 13.8% to 14.2% success rates. The combination of deep iteration, test integration, and memory persistence enables Chronos to solve complex bugs that other systems cannot handle.
Retrieval Strategy
Performance AnalysisThis comparison reveals the fundamental differences between debugging-first and code-generation tools. Chronos achieves 65.3% debugging success through unlimited context via intelligent retrieval, persistent memory, automated debug loops, and full CI/CD integration. In contrast, IDE-integrated tools like Cursor (4.2%) and code assistants like GitHub Copilot X (5.3%) lack these specialized capabilities, while CLI tools such as Claude Code CLI (6.8%) and Gemini CLI (9.7%) offer larger contexts but still miss the iterative refinement and persistent memory essential for effective debugging.
Retrieval Strategy
Performance AnalysisThis evaluation demonstrates the superiority of Adaptive Graph-Guided Retrieval (AGR) over traditional retrieval approaches. Chronos's AGR achieves 65.3% debugging success with 89.2% precision and 84.7% recall by intelligently following code dependencies. In contrast, traditional methods show poor performance: random baseline (13.6%), BM25 text search (18.3%), and even advanced techniques like HyDE (31.2%) and Graph RAG (33.7%) fail to capture the complex relationships needed for debugging. The gap to a theoretical oracle retriever (78.9%) shows AGR captures 82.7% of maximum possible performance while using 27% less context.
Computational Efficiency
and Cost AnalysisDespite higher per-attempt cost, Chronos's high success rate yields the lowest effective cost for debugging. While Chronos takes 134.7 seconds and costs $0.89 per bug attempt, its 65.3% success rate results in an effective cost of just $1.36 per successful fix. Competing models appear cheaper initially ($0.52 to $0.72) but their low success rates (13.8% to 14.2%) drive effective costs to $3.77 to $5.18. Human developers achieve 94.2% success but require 2.4 hours at $180 per bug, making Chronos 132x more cost-efficient for automated debugging.
Performance on Debugging
Tasks Requiring Extensive ContextThis comparison reveals that raw context size alone cannot solve debugging challenges. Despite models having up to 1M tokens (Gemini 2.5 Pro), they achieve only 13.9% average success on complex debugging tasks. Chronos, using unlimited context through intelligent retrieval rather than brute force token expansion, achieves 71.5% success rate across cross-file bugs (71.2%), historical bugs (68.9%), and complex traces (74.3%). This 5x performance advantage demonstrates that specialized debugging architecture and smart retrieval outperform massive context windows.
Average Debug
Cycles to ResolutionThis chart demonstrates Chronos's efficiency in reaching successful bug fixes. Chronos requires only 2.2 cycles on average to resolve bugs, significantly outperforming GPT-4.1 (4.8 cycles), Claude 4 Opus (4.5 cycles), and DeepSeek V3 (4.2 cycles). Fewer debugging cycles means faster resolution times and reduced computational costs. This efficiency stems from Chronos's specialized debugging architecture, which learns from each iteration through persistent memory and adaptive retrieval, enabling it to converge on correct solutions more rapidly than general-purpose models.
Scalability of Debugging
Performance by Codebase SizeThis comparison demonstrates how effectively different AI models handle various debugging challenges. Chronos achieves 58.3% to 94.2% success rates across all bug categories, while competing models (GPT-4.1, Claude 4 Opus, DeepSeek V3, and Gemini 2.5 Pro) struggle with complex issues, dropping to just 4.5% to 11.3% on concurrency and performance bugs. This 3-10x performance advantage highlights Chronos's superior capabilities through its specialized debugging architecture.
Comprehensive Debugging
Performance ComparisonWhile frontier models show incremental improvements, Chronos demonstrates 3-10x better performance on complex bug types through debug-specific training. Raw context size alone cannot solve debugging. Chronos's intelligent retrieval and persistent memory enable it to outperform even million-token models by over 5x across all bug categories, from syntax errors to concurrency issues.
Debugging Accuracy
by Bug TypeThis evaluation shows Chronos consistently outperforming state-of-the-art models by 3-5x across all debugging dimensions. Chronos achieves 71.2% to 89.2% success rates in critical areas like root cause accuracy and retrieval precision, while competing models (Claude 4 Opus, GPT-4.1, Gemini 2.0 Pro, and DeepSeek V3) struggle at 14% to 40% performance levels. This comprehensive advantage demonstrates that specialized debugging architecture surpasses general-purpose language models across every evaluation metric.
End-to-End Debugging
Pipeline EfficiencyThis analysis reveals Chronos's superior efficiency in real-world debugging scenarios. Despite taking 4.2 minutes average fix time, Chronos achieves 65.3% success rate at just $0.18 per bug, making it the most cost-effective solution. Competing systems require 18.7 to 21.3 minutes with only 13.4% to 14.2% success rates while consuming 4-5x more tokens. Human developers achieve 87.2% success but need 35.8 minutes at $29.83 per bug, making Chronos 8x faster and 165x more cost-efficient for automated debugging tasks.
Comparative Debugging
Loop AnalysisThis analysis reveals why Chronos achieves superior debugging performance compared to other systems. Chronos performs 7.8 iterations on average with autonomous test execution, persistent memory, and full backtracking support, achieving 65.3% success. In contrast, competing models like Claude 4 Opus and GPT-4.1 manage only 1.2 to 2.1 iterations with session-only memory and no backtracking, resulting in just 13.8% to 14.2% success rates. The combination of deep iteration, test integration, and memory persistence enables Chronos to solve complex bugs that other systems cannot handle.
Retrieval Strategy
Performance AnalysisThis comparison reveals the fundamental differences between debugging-first and code-generation tools. Chronos achieves 65.3% debugging success through unlimited context via intelligent retrieval, persistent memory, automated debug loops, and full CI/CD integration. In contrast, IDE-integrated tools like Cursor (4.2%) and code assistants like GitHub Copilot X (5.3%) lack these specialized capabilities, while CLI tools such as Claude Code CLI (6.8%) and Gemini CLI (9.7%) offer larger contexts but still miss the iterative refinement and persistent memory essential for effective debugging.
Retrieval Strategy
Performance AnalysisThis evaluation demonstrates the superiority of Adaptive Graph-Guided Retrieval (AGR) over traditional retrieval approaches. Chronos's AGR achieves 65.3% debugging success with 89.2% precision and 84.7% recall by intelligently following code dependencies. In contrast, traditional methods show poor performance: random baseline (13.6%), BM25 text search (18.3%), and even advanced techniques like HyDE (31.2%) and Graph RAG (33.7%) fail to capture the complex relationships needed for debugging. The gap to a theoretical oracle retriever (78.9%) shows AGR captures 82.7% of maximum possible performance while using 27% less context.
Computational Efficiency
and Cost AnalysisDespite higher per-attempt cost, Chronos's high success rate yields the lowest effective cost for debugging. While Chronos takes 134.7 seconds and costs $0.89 per bug attempt, its 65.3% success rate results in an effective cost of just $1.36 per successful fix. Competing models appear cheaper initially ($0.52 to $0.72) but their low success rates (13.8% to 14.2%) drive effective costs to $3.77 to $5.18. Human developers achieve 94.2% success but require 2.4 hours at $180 per bug, making Chronos 132x more cost-efficient for automated debugging.
Performance on Debugging
Tasks Requiring Extensive ContextThis comparison reveals that raw context size alone cannot solve debugging challenges. Despite models having up to 1M tokens (Gemini 2.5 Pro), they achieve only 13.9% average success on complex debugging tasks. Chronos, using unlimited context through intelligent retrieval rather than brute force token expansion, achieves 71.5% success rate across cross-file bugs (71.2%), historical bugs (68.9%), and complex traces (74.3%). This 5x performance advantage demonstrates that specialized debugging architecture and smart retrieval outperform massive context windows.
Average Debug
Cycles to ResolutionThis chart demonstrates Chronos's efficiency in reaching successful bug fixes. Chronos requires only 2.2 cycles on average to resolve bugs, significantly outperforming GPT-4.1 (4.8 cycles), Claude 4 Opus (4.5 cycles), and DeepSeek V3 (4.2 cycles). Fewer debugging cycles means faster resolution times and reduced computational costs. This efficiency stems from Chronos's specialized debugging architecture, which learns from each iteration through persistent memory and adaptive retrieval, enabling it to converge on correct solutions more rapidly than general-purpose models.
[ RESULTS]
Benchmarks
Scalability of Debugging
Performance by Codebase SizeThis comparison demonstrates how effectively different AI models handle various debugging challenges. Chronos achieves 58.3% to 94.2% success rates across all bug categories, while competing models (GPT-4.1, Claude 4 Opus, DeepSeek V3, and Gemini 2.5 Pro) struggle with complex issues, dropping to just 4.5% to 11.3% on concurrency and performance bugs. This 3-10x performance advantage highlights Chronos's superior capabilities through its specialized debugging architecture.
Comprehensive Debugging
Performance ComparisonWhile frontier models show incremental improvements, Chronos demonstrates 3-10x better performance on complex bug types through debug-specific training. Raw context size alone cannot solve debugging. Chronos's intelligent retrieval and persistent memory enable it to outperform even million-token models by over 5x across all bug categories, from syntax errors to concurrency issues.
Debugging Accuracy
by Bug TypeThis evaluation shows Chronos consistently outperforming state-of-the-art models by 3-5x across all debugging dimensions. Chronos achieves 71.2% to 89.2% success rates in critical areas like root cause accuracy and retrieval precision, while competing models (Claude 4 Opus, GPT-4.1, Gemini 2.0 Pro, and DeepSeek V3) struggle at 14% to 40% performance levels. This comprehensive advantage demonstrates that specialized debugging architecture surpasses general-purpose language models across every evaluation metric.
End-to-End Debugging
Pipeline EfficiencyThis analysis reveals Chronos's superior efficiency in real-world debugging scenarios. Despite taking 4.2 minutes average fix time, Chronos achieves 65.3% success rate at just $0.18 per bug, making it the most cost-effective solution. Competing systems require 18.7 to 21.3 minutes with only 13.4% to 14.2% success rates while consuming 4-5x more tokens. Human developers achieve 87.2% success but need 35.8 minutes at $29.83 per bug, making Chronos 8x faster and 165x more cost-efficient for automated debugging tasks.
Comparative Debugging
Loop AnalysisThis analysis reveals why Chronos achieves superior debugging performance compared to other systems. Chronos performs 7.8 iterations on average with autonomous test execution, persistent memory, and full backtracking support, achieving 65.3% success. In contrast, competing models like Claude 4 Opus and GPT-4.1 manage only 1.2 to 2.1 iterations with session-only memory and no backtracking, resulting in just 13.8% to 14.2% success rates. The combination of deep iteration, test integration, and memory persistence enables Chronos to solve complex bugs that other systems cannot handle.
Retrieval Strategy
Performance AnalysisThis comparison reveals the fundamental differences between debugging-first and code-generation tools. Chronos achieves 65.3% debugging success through unlimited context via intelligent retrieval, persistent memory, automated debug loops, and full CI/CD integration. In contrast, IDE-integrated tools like Cursor (4.2%) and code assistants like GitHub Copilot X (5.3%) lack these specialized capabilities, while CLI tools such as Claude Code CLI (6.8%) and Gemini CLI (9.7%) offer larger contexts but still miss the iterative refinement and persistent memory essential for effective debugging.
Retrieval Strategy
Performance AnalysisThis evaluation demonstrates the superiority of Adaptive Graph-Guided Retrieval (AGR) over traditional retrieval approaches. Chronos's AGR achieves 65.3% debugging success with 89.2% precision and 84.7% recall by intelligently following code dependencies. In contrast, traditional methods show poor performance: random baseline (13.6%), BM25 text search (18.3%), and even advanced techniques like HyDE (31.2%) and Graph RAG (33.7%) fail to capture the complex relationships needed for debugging. The gap to a theoretical oracle retriever (78.9%) shows AGR captures 82.7% of maximum possible performance while using 27% less context.
Computational Efficiency
and Cost AnalysisDespite higher per-attempt cost, Chronos's high success rate yields the lowest effective cost for debugging. While Chronos takes 134.7 seconds and costs $0.89 per bug attempt, its 65.3% success rate results in an effective cost of just $1.36 per successful fix. Competing models appear cheaper initially ($0.52 to $0.72) but their low success rates (13.8% to 14.2%) drive effective costs to $3.77 to $5.18. Human developers achieve 94.2% success but require 2.4 hours at $180 per bug, making Chronos 132x more cost-efficient for automated debugging.
Performance on Debugging
Tasks Requiring Extensive ContextThis comparison reveals that raw context size alone cannot solve debugging challenges. Despite models having up to 1M tokens (Gemini 2.5 Pro), they achieve only 13.9% average success on complex debugging tasks. Chronos, using unlimited context through intelligent retrieval rather than brute force token expansion, achieves 71.5% success rate across cross-file bugs (71.2%), historical bugs (68.9%), and complex traces (74.3%). This 5x performance advantage demonstrates that specialized debugging architecture and smart retrieval outperform massive context windows.
Average Debug
Cycles to ResolutionThis chart demonstrates Chronos's efficiency in reaching successful bug fixes. Chronos requires only 2.2 cycles on average to resolve bugs, significantly outperforming GPT-4.1 (4.8 cycles), Claude 4 Opus (4.5 cycles), and DeepSeek V3 (4.2 cycles). Fewer debugging cycles means faster resolution times and reduced computational costs. This efficiency stems from Chronos's specialized debugging architecture, which learns from each iteration through persistent memory and adaptive retrieval, enabling it to converge on correct solutions more rapidly than general-purpose models.
Scalability of Debugging
Performance by Codebase SizeThis comparison demonstrates how effectively different AI models handle various debugging challenges. Chronos achieves 58.3% to 94.2% success rates across all bug categories, while competing models (GPT-4.1, Claude 4 Opus, DeepSeek V3, and Gemini 2.5 Pro) struggle with complex issues, dropping to just 4.5% to 11.3% on concurrency and performance bugs. This 3-10x performance advantage highlights Chronos's superior capabilities through its specialized debugging architecture.
Comprehensive Debugging
Performance ComparisonWhile frontier models show incremental improvements, Chronos demonstrates 3-10x better performance on complex bug types through debug-specific training. Raw context size alone cannot solve debugging. Chronos's intelligent retrieval and persistent memory enable it to outperform even million-token models by over 5x across all bug categories, from syntax errors to concurrency issues.
Debugging Accuracy
by Bug TypeThis evaluation shows Chronos consistently outperforming state-of-the-art models by 3-5x across all debugging dimensions. Chronos achieves 71.2% to 89.2% success rates in critical areas like root cause accuracy and retrieval precision, while competing models (Claude 4 Opus, GPT-4.1, Gemini 2.0 Pro, and DeepSeek V3) struggle at 14% to 40% performance levels. This comprehensive advantage demonstrates that specialized debugging architecture surpasses general-purpose language models across every evaluation metric.
End-to-End Debugging
Pipeline EfficiencyThis analysis reveals Chronos's superior efficiency in real-world debugging scenarios. Despite taking 4.2 minutes average fix time, Chronos achieves 65.3% success rate at just $0.18 per bug, making it the most cost-effective solution. Competing systems require 18.7 to 21.3 minutes with only 13.4% to 14.2% success rates while consuming 4-5x more tokens. Human developers achieve 87.2% success but need 35.8 minutes at $29.83 per bug, making Chronos 8x faster and 165x more cost-efficient for automated debugging tasks.
Comparative Debugging
Loop AnalysisThis analysis reveals why Chronos achieves superior debugging performance compared to other systems. Chronos performs 7.8 iterations on average with autonomous test execution, persistent memory, and full backtracking support, achieving 65.3% success. In contrast, competing models like Claude 4 Opus and GPT-4.1 manage only 1.2 to 2.1 iterations with session-only memory and no backtracking, resulting in just 13.8% to 14.2% success rates. The combination of deep iteration, test integration, and memory persistence enables Chronos to solve complex bugs that other systems cannot handle.
Retrieval Strategy
Performance AnalysisThis comparison reveals the fundamental differences between debugging-first and code-generation tools. Chronos achieves 65.3% debugging success through unlimited context via intelligent retrieval, persistent memory, automated debug loops, and full CI/CD integration. In contrast, IDE-integrated tools like Cursor (4.2%) and code assistants like GitHub Copilot X (5.3%) lack these specialized capabilities, while CLI tools such as Claude Code CLI (6.8%) and Gemini CLI (9.7%) offer larger contexts but still miss the iterative refinement and persistent memory essential for effective debugging.
Retrieval Strategy
Performance AnalysisThis evaluation demonstrates the superiority of Adaptive Graph-Guided Retrieval (AGR) over traditional retrieval approaches. Chronos's AGR achieves 65.3% debugging success with 89.2% precision and 84.7% recall by intelligently following code dependencies. In contrast, traditional methods show poor performance: random baseline (13.6%), BM25 text search (18.3%), and even advanced techniques like HyDE (31.2%) and Graph RAG (33.7%) fail to capture the complex relationships needed for debugging. The gap to a theoretical oracle retriever (78.9%) shows AGR captures 82.7% of maximum possible performance while using 27% less context.
Computational Efficiency
and Cost AnalysisDespite higher per-attempt cost, Chronos's high success rate yields the lowest effective cost for debugging. While Chronos takes 134.7 seconds and costs $0.89 per bug attempt, its 65.3% success rate results in an effective cost of just $1.36 per successful fix. Competing models appear cheaper initially ($0.52 to $0.72) but their low success rates (13.8% to 14.2%) drive effective costs to $3.77 to $5.18. Human developers achieve 94.2% success but require 2.4 hours at $180 per bug, making Chronos 132x more cost-efficient for automated debugging.
Performance on Debugging
Tasks Requiring Extensive ContextThis comparison reveals that raw context size alone cannot solve debugging challenges. Despite models having up to 1M tokens (Gemini 2.5 Pro), they achieve only 13.9% average success on complex debugging tasks. Chronos, using unlimited context through intelligent retrieval rather than brute force token expansion, achieves 71.5% success rate across cross-file bugs (71.2%), historical bugs (68.9%), and complex traces (74.3%). This 5x performance advantage demonstrates that specialized debugging architecture and smart retrieval outperform massive context windows.
Average Debug
Cycles to ResolutionThis chart demonstrates Chronos's efficiency in reaching successful bug fixes. Chronos requires only 2.2 cycles on average to resolve bugs, significantly outperforming GPT-4.1 (4.8 cycles), Claude 4 Opus (4.5 cycles), and DeepSeek V3 (4.2 cycles). Fewer debugging cycles means faster resolution times and reduced computational costs. This efficiency stems from Chronos's specialized debugging architecture, which learns from each iteration through persistent memory and adaptive retrieval, enabling it to converge on correct solutions more rapidly than general-purpose models.
Scalability of Debugging
Performance by Codebase SizeThis comparison demonstrates how effectively different AI models handle various debugging challenges. Chronos achieves 58.3% to 94.2% success rates across all bug categories, while competing models (GPT-4.1, Claude 4 Opus, DeepSeek V3, and Gemini 2.5 Pro) struggle with complex issues, dropping to just 4.5% to 11.3% on concurrency and performance bugs. This 3-10x performance advantage highlights Chronos's superior capabilities through its specialized debugging architecture.
Comprehensive Debugging
Performance ComparisonWhile frontier models show incremental improvements, Chronos demonstrates 3-10x better performance on complex bug types through debug-specific training. Raw context size alone cannot solve debugging. Chronos's intelligent retrieval and persistent memory enable it to outperform even million-token models by over 5x across all bug categories, from syntax errors to concurrency issues.
Debugging Accuracy
by Bug TypeThis evaluation shows Chronos consistently outperforming state-of-the-art models by 3-5x across all debugging dimensions. Chronos achieves 71.2% to 89.2% success rates in critical areas like root cause accuracy and retrieval precision, while competing models (Claude 4 Opus, GPT-4.1, Gemini 2.0 Pro, and DeepSeek V3) struggle at 14% to 40% performance levels. This comprehensive advantage demonstrates that specialized debugging architecture surpasses general-purpose language models across every evaluation metric.
End-to-End Debugging
Pipeline EfficiencyThis analysis reveals Chronos's superior efficiency in real-world debugging scenarios. Despite taking 4.2 minutes average fix time, Chronos achieves 65.3% success rate at just $0.18 per bug, making it the most cost-effective solution. Competing systems require 18.7 to 21.3 minutes with only 13.4% to 14.2% success rates while consuming 4-5x more tokens. Human developers achieve 87.2% success but need 35.8 minutes at $29.83 per bug, making Chronos 8x faster and 165x more cost-efficient for automated debugging tasks.
Comparative Debugging
Loop AnalysisThis analysis reveals why Chronos achieves superior debugging performance compared to other systems. Chronos performs 7.8 iterations on average with autonomous test execution, persistent memory, and full backtracking support, achieving 65.3% success. In contrast, competing models like Claude 4 Opus and GPT-4.1 manage only 1.2 to 2.1 iterations with session-only memory and no backtracking, resulting in just 13.8% to 14.2% success rates. The combination of deep iteration, test integration, and memory persistence enables Chronos to solve complex bugs that other systems cannot handle.
Retrieval Strategy
Performance AnalysisThis comparison reveals the fundamental differences between debugging-first and code-generation tools. Chronos achieves 65.3% debugging success through unlimited context via intelligent retrieval, persistent memory, automated debug loops, and full CI/CD integration. In contrast, IDE-integrated tools like Cursor (4.2%) and code assistants like GitHub Copilot X (5.3%) lack these specialized capabilities, while CLI tools such as Claude Code CLI (6.8%) and Gemini CLI (9.7%) offer larger contexts but still miss the iterative refinement and persistent memory essential for effective debugging.
Retrieval Strategy
Performance AnalysisThis evaluation demonstrates the superiority of Adaptive Graph-Guided Retrieval (AGR) over traditional retrieval approaches. Chronos's AGR achieves 65.3% debugging success with 89.2% precision and 84.7% recall by intelligently following code dependencies. In contrast, traditional methods show poor performance: random baseline (13.6%), BM25 text search (18.3%), and even advanced techniques like HyDE (31.2%) and Graph RAG (33.7%) fail to capture the complex relationships needed for debugging. The gap to a theoretical oracle retriever (78.9%) shows AGR captures 82.7% of maximum possible performance while using 27% less context.
Computational Efficiency
and Cost AnalysisDespite higher per-attempt cost, Chronos's high success rate yields the lowest effective cost for debugging. While Chronos takes 134.7 seconds and costs $0.89 per bug attempt, its 65.3% success rate results in an effective cost of just $1.36 per successful fix. Competing models appear cheaper initially ($0.52 to $0.72) but their low success rates (13.8% to 14.2%) drive effective costs to $3.77 to $5.18. Human developers achieve 94.2% success but require 2.4 hours at $180 per bug, making Chronos 132x more cost-efficient for automated debugging.
Performance on Debugging
Tasks Requiring Extensive ContextThis comparison reveals that raw context size alone cannot solve debugging challenges. Despite models having up to 1M tokens (Gemini 2.5 Pro), they achieve only 13.9% average success on complex debugging tasks. Chronos, using unlimited context through intelligent retrieval rather than brute force token expansion, achieves 71.5% success rate across cross-file bugs (71.2%), historical bugs (68.9%), and complex traces (74.3%). This 5x performance advantage demonstrates that specialized debugging architecture and smart retrieval outperform massive context windows.
Average Debug
Cycles to ResolutionThis chart demonstrates Chronos's efficiency in reaching successful bug fixes. Chronos requires only 2.2 cycles on average to resolve bugs, significantly outperforming GPT-4.1 (4.8 cycles), Claude 4 Opus (4.5 cycles), and DeepSeek V3 (4.2 cycles). Fewer debugging cycles means faster resolution times and reduced computational costs. This efficiency stems from Chronos's specialized debugging architecture, which learns from each iteration through persistent memory and adaptive retrieval, enabling it to converge on correct solutions more rapidly than general-purpose models.
Scalability of Debugging
Performance by Codebase SizeThis comparison demonstrates how effectively different AI models handle various debugging challenges. Chronos achieves 58.3% to 94.2% success rates across all bug categories, while competing models (GPT-4.1, Claude 4 Opus, DeepSeek V3, and Gemini 2.5 Pro) struggle with complex issues, dropping to just 4.5% to 11.3% on concurrency and performance bugs. This 3-10x performance advantage highlights Chronos's superior capabilities through its specialized debugging architecture.
Comprehensive Debugging
Performance ComparisonWhile frontier models show incremental improvements, Chronos demonstrates 3-10x better performance on complex bug types through debug-specific training. Raw context size alone cannot solve debugging. Chronos's intelligent retrieval and persistent memory enable it to outperform even million-token models by over 5x across all bug categories, from syntax errors to concurrency issues.
Debugging Accuracy
by Bug TypeThis evaluation shows Chronos consistently outperforming state-of-the-art models by 3-5x across all debugging dimensions. Chronos achieves 71.2% to 89.2% success rates in critical areas like root cause accuracy and retrieval precision, while competing models (Claude 4 Opus, GPT-4.1, Gemini 2.0 Pro, and DeepSeek V3) struggle at 14% to 40% performance levels. This comprehensive advantage demonstrates that specialized debugging architecture surpasses general-purpose language models across every evaluation metric.
End-to-End Debugging
Pipeline EfficiencyThis analysis reveals Chronos's superior efficiency in real-world debugging scenarios. Despite taking 4.2 minutes average fix time, Chronos achieves 65.3% success rate at just $0.18 per bug, making it the most cost-effective solution. Competing systems require 18.7 to 21.3 minutes with only 13.4% to 14.2% success rates while consuming 4-5x more tokens. Human developers achieve 87.2% success but need 35.8 minutes at $29.83 per bug, making Chronos 8x faster and 165x more cost-efficient for automated debugging tasks.
Comparative Debugging
Loop AnalysisThis analysis reveals why Chronos achieves superior debugging performance compared to other systems. Chronos performs 7.8 iterations on average with autonomous test execution, persistent memory, and full backtracking support, achieving 65.3% success. In contrast, competing models like Claude 4 Opus and GPT-4.1 manage only 1.2 to 2.1 iterations with session-only memory and no backtracking, resulting in just 13.8% to 14.2% success rates. The combination of deep iteration, test integration, and memory persistence enables Chronos to solve complex bugs that other systems cannot handle.
Retrieval Strategy
Performance AnalysisThis comparison reveals the fundamental differences between debugging-first and code-generation tools. Chronos achieves 65.3% debugging success through unlimited context via intelligent retrieval, persistent memory, automated debug loops, and full CI/CD integration. In contrast, IDE-integrated tools like Cursor (4.2%) and code assistants like GitHub Copilot X (5.3%) lack these specialized capabilities, while CLI tools such as Claude Code CLI (6.8%) and Gemini CLI (9.7%) offer larger contexts but still miss the iterative refinement and persistent memory essential for effective debugging.
Retrieval Strategy
Performance AnalysisThis evaluation demonstrates the superiority of Adaptive Graph-Guided Retrieval (AGR) over traditional retrieval approaches. Chronos's AGR achieves 65.3% debugging success with 89.2% precision and 84.7% recall by intelligently following code dependencies. In contrast, traditional methods show poor performance: random baseline (13.6%), BM25 text search (18.3%), and even advanced techniques like HyDE (31.2%) and Graph RAG (33.7%) fail to capture the complex relationships needed for debugging. The gap to a theoretical oracle retriever (78.9%) shows AGR captures 82.7% of maximum possible performance while using 27% less context.
Computational Efficiency
and Cost AnalysisDespite higher per-attempt cost, Chronos's high success rate yields the lowest effective cost for debugging. While Chronos takes 134.7 seconds and costs $0.89 per bug attempt, its 65.3% success rate results in an effective cost of just $1.36 per successful fix. Competing models appear cheaper initially ($0.52 to $0.72) but their low success rates (13.8% to 14.2%) drive effective costs to $3.77 to $5.18. Human developers achieve 94.2% success but require 2.4 hours at $180 per bug, making Chronos 132x more cost-efficient for automated debugging.
Performance on Debugging
Tasks Requiring Extensive ContextThis comparison reveals that raw context size alone cannot solve debugging challenges. Despite models having up to 1M tokens (Gemini 2.5 Pro), they achieve only 13.9% average success on complex debugging tasks. Chronos, using unlimited context through intelligent retrieval rather than brute force token expansion, achieves 71.5% success rate across cross-file bugs (71.2%), historical bugs (68.9%), and complex traces (74.3%). This 5x performance advantage demonstrates that specialized debugging architecture and smart retrieval outperform massive context windows.
Average Debug
Cycles to ResolutionThis chart demonstrates Chronos's efficiency in reaching successful bug fixes. Chronos requires only 2.2 cycles on average to resolve bugs, significantly outperforming GPT-4.1 (4.8 cycles), Claude 4 Opus (4.5 cycles), and DeepSeek V3 (4.2 cycles). Fewer debugging cycles means faster resolution times and reduced computational costs. This efficiency stems from Chronos's specialized debugging architecture, which learns from each iteration through persistent memory and adaptive retrieval, enabling it to converge on correct solutions more rapidly than general-purpose models.
Get Access to Chronos-1
Chronos-1 will be released in Q4 2025 as part of the Kodezi OS.
Join the waitlist for early access.
Get Access to Chronos-1
Chronos-1 will be released in Q4 2025 as part of the Kodezi OS.
Join the waitlist for early access.
Get Access to Chronos-1
Chronos-1 will be released in Q4 2025 as part of the Kodezi OS.
Join the waitlist for early access.
[CHRONICLE]
Journal
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Research
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Why We Spent 4 Years on Debugging
What we got wrong about LLMs, what we learned from failure, and why Chronos became necessary
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Research
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Why We Spent 4 Years on Debugging
What we got wrong about LLMs, what we learned from failure, and why Chronos became necessary
[
Research
]
Why We Spent 4 Years on Debugging
What we got wrong about LLMs, what we learned from failure, and why Chronos became necessary
Kodezi Team
Jul 18, 2025
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Research
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How Real Bugs Taught Chronos More Than Any Dataset
What we thought we were teaching the model, and what it ended up learning from us instead.
[
Research
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How Real Bugs Taught Chronos More Than Any Dataset
What we thought we were teaching the model, and what it ended up learning from us instead.
[
Research
]
How Real Bugs Taught Chronos More Than Any Dataset
What we thought we were teaching the model, and what it ended up learning from us instead.
Kodezi Team
Jul 20, 2025