Q3 2025 Updates

Author: ishraqkhann

.env.example

@@ -0,0 +1,50 @@
+# Kodezi Chronos Environment Variables
+# Copy this file to .env and configure for your environment
+
+# Database Configuration
+DATABASE_URL=postgresql://chronos:password@localhost:5432/chronos
+REDIS_URL=redis://localhost:6379
+
+# API Configuration
+API_KEY=your-api-key-here
+API_PORT=5000
+API_HOST=0.0.0.0
+
+# Model Configuration
+CHRONOS_MODEL_PATH=/models/chronos-debug-llm
+MODEL_DEVICE=cuda  # cuda, cpu, or mps (for Apple Silicon)
+MODEL_QUANTIZATION=int8  # int8, fp16, or none
+
+# Security
+JWT_SECRET_KEY=your-secret-key-here
+ENCRYPTION_KEY=your-encryption-key-here
+
+# GitHub Integration
+GITHUB_APP_ID=your-github-app-id
+GITHUB_PRIVATE_KEY_PATH=/secrets/github_private_key.pem
+GITHUB_WEBHOOK_SECRET=your-webhook-secret
+
+# Monitoring
+PROMETHEUS_PORT=9090
+JAEGER_ENDPOINT=http://jaeger:14268/api/traces
+LOG_LEVEL=INFO
+
+# Resource Limits
+MAX_CONCURRENT_REQUESTS=100
+SANDBOX_TIMEOUT_SECONDS=300
+MEMORY_LIMIT_MB=2048
+
+# Feature Flags
+ENABLE_DOCKER_SANDBOX=true
+ENABLE_CACHING=true
+ENABLE_MONITORING=true
+ENABLE_ASYNC_PROCESSING=true
+
+# External Services (Optional)
+SLACK_WEBHOOK_URL=
+JIRA_URL=
+JIRA_USERNAME=
+JIRA_API_TOKEN=
+
+# Model Download (Optional)
+CHRONOS_MODEL_URL=https://models.kodezi.com/chronos-debug-llm-2025.bin

CHANGELOG.md

@@ -5,6 +5,35 @@ All notable changes to the Kodezi Chronos research repository will be documented
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [2.0.0] - 2025-07-29
+
+### Added
+- New co-author: Yousuf Zaii
+- Updated 2025 research paper with latest findings
+- Enhanced benchmark results (12,500 total bugs evaluated)
+- New model comparisons (Claude Opus 4, GPT-4.1, DeepSeek V3, Gemini 2.0 Pro)
+- Human preference evaluation (N=50, 89% preference)
+- Cohen's d effect size analysis (d=3.87)
+- O(k log d) complexity proof for AGR
+- Hardware-dependent and dynamic language limitation analysis
+- 2025 evaluation framework (evaluate_2025.py)
+- Visualization generation scripts for paper figures
+- Comprehensive 2025 architecture documentation
+
+### Changed
+- Debug success rate: 65.3% → 67.3% (±2.1%)
+- Retrieval precision: 91% → 92%
+- Average iterations: 2.2 → 7.8 (more thorough debugging)
+- Comparison baseline: GPT-4 → GPT-4.1 and Claude-4-Opus
+- Performance improvement: 6-7x → 4-5x (against stronger baselines)
+
+### Updated
+- README.md with 2025 performance metrics
+- CITATION.cff with new author and version 2.0.0
+- Architecture documentation with 4-pillar design
+- Benchmark documentation with expanded test suite
+- Performance tables with latest model comparisons
+
 ## [1.0.0] - 2025-07-14
 
 ### Added

CITATION.cff

@@ -20,6 +20,10 @@ authors:
     family-names: Patel
     email: [email protected]
     affiliation: Kodezi Inc.
+  - given-names: Yousuf
+    family-names: Zaii
+    email: [email protected]
+    affiliation: Kodezi Inc.
 identifiers:
   - type: doi
     value: 10.48550/arXiv.2507.12482
@@ -37,8 +41,8 @@ abstract: >-
   embedding memory engine, combining vector and graph-based indexing with continuous 
   code-aware retrieval. This enables efficient and accurate reasoning over millions 
   of lines of code, supporting repository-scale comprehension, multi-file refactoring, 
-  and real-time self-healing actions. Chronos achieves 65.3% debugging success rate, 
-  representing a 6-7x improvement over state-of-the-art models.
+  and real-time self-healing actions. Chronos achieves 67.3% debugging success rate, 
+  representing a 4-5x improvement over state-of-the-art models including Claude Opus 4 and GPT-4.1.
 keywords:
   - debugging
   - language models
@@ -47,5 +51,5 @@ keywords:
   - autonomous systems
   - memory-driven AI
 license: MIT
-version: 1.0.0
-date-released: '2025-07-14'
+version: 2.0.0
+date-released: '2025-07-29'

LEADERBOARD.md

@@ -0,0 +1,98 @@
+# Chronos MRR Benchmark Leaderboard
+
+## Overall Performance (5,000 scenarios)
+
+| Model | Success Rate | Precision | Recall | Avg Iterations | Cost/Fix |
+|-------|--------------|-----------|--------|----------------|----------|
+| **Chronos*** | 67.3%±2.1% | 92% | 85% | 7.8 | $1.36 |
+| Gemini-2.0 Pro | 15.0%±1.5% | 74% | 38% | 19.2 | $4.25 |
+| Claude-4 Opus | 14.2%±1.3% | 67% | 34% | 21.8 | $4.89 |
+| GPT-4.1 | 13.8%±1.2% | 68% | 32% | 23.5 | $5.53 |
+| DeepSeek-V2 | 8.7%±0.9% | 52% | 21% | 28.1 | $7.82 |
+| Mistral-Large | 9.2%±0.8% | 48% | 19% | 31.7 | $8.95 |
+
+*Chronos model available via Kodezi OS only
+
+## Category Performance
+
+### Syntax Errors (500 scenarios)
+| Model | Success Rate | Improvement vs GPT-4 |
+|-------|--------------|---------------------|
+| Chronos | 94.2% | 1.1x |
+| GPT-4.1 | 82.3% | - |
+| Claude-4 | 79.8% | 0.97x |
+| Gemini-2.0 | 85.1% | 1.03x |
+
+### Logic Errors (1,200 scenarios)
+| Model | Success Rate | Improvement vs GPT-4 |
+|-------|--------------|---------------------|
+| Chronos | 72.8% | 6.0x |
+| GPT-4.1 | 12.1% | - |
+| Claude-4 | 10.7% | 0.88x |
+| Gemini-2.0 | 15.3% | 1.26x |
+
+### Concurrency Issues (800 scenarios)
+| Model | Success Rate | Improvement vs GPT-4 |
+|-------|--------------|---------------------|
+| Chronos | 58.3% | 18.2x |
+| GPT-4.1 | 3.2% | - |
+| Claude-4 | 2.8% | 0.88x |
+| Gemini-2.0 | 4.1% | 1.28x |
+
+### Memory Issues (600 scenarios)
+| Model | Success Rate | Improvement vs GPT-4 |
+|-------|--------------|---------------------|
+| Chronos | 61.7% | 10.8x |
+| GPT-4.1 | 5.7% | - |
+| Claude-4 | 4.3% | 0.75x |
+| Gemini-2.0 | 6.9% | 1.21x |
+
+### API Misuse (900 scenarios)
+| Model | Success Rate | Improvement vs GPT-4 |
+|-------|--------------|---------------------|
+| Chronos | 79.1% | 4.2x |
+| GPT-4.1 | 18.9% | - |
+| Claude-4 | 16.2% | 0.86x |
+| Gemini-2.0 | 22.4% | 1.19x |
+
+### Performance Bugs (400 scenarios)
+| Model | Success Rate | Improvement vs GPT-4 |
+|-------|--------------|---------------------|
+| Chronos | 65.4% | 8.8x |
+| GPT-4.1 | 7.4% | - |
+| Claude-4 | 6.1% | 0.82x |
+| Gemini-2.0 | 9.8% | 1.32x |
+
+### Cross-Category (600 scenarios)
+| Model | Success Rate | Improvement vs GPT-4 |
+|-------|--------------|---------------------|
+| Chronos | 51.2% | 12.5x |
+| GPT-4.1 | 4.1% | - |
+| Claude-4 | 3.7% | 0.90x |
+| Gemini-2.0 | 5.2% | 1.27x |
+
+## Repository Scale Performance
+
+| Repository Size | Chronos | Best Baseline | Improvement |
+|-----------------|---------|---------------|-------------|
+| <10K LOC | 71.2% | 21.3% (Gemini) | 3.3x |
+| 10K-100K LOC | 68.9% | 14.7% (Gemini) | 4.7x |
+| 100K-1M LOC | 64.3% | 8.9% (Gemini) | 7.2x |
+| >1M LOC | 59.7% | 3.8% (Gemini) | 15.7x |
+
+## Statistical Significance
+
+- **p-value**: < 0.001 (highly significant)
+- **Cohen's d**: 3.87 (very large effect size)
+- **95% CI for Chronos**: [65.2%, 69.4%]
+- **Sample Size**: 5,000 scenarios
+
+## Evaluation Methodology
+
+All models evaluated on identical scenarios with:
+- Multi-file context (10-50 files)
+- Temporal dispersion (3-12 months)
+- Obfuscated dependencies
+- Real-world complexity
+
+Last Updated: January 2025

README.md

@@ -10,12 +10,12 @@
 [![Research](https://img.shields.io/badge/Research-Paper-orange.svg?style=for-the-badge)](paper/chronos-research.md)
 [![Benchmark](https://img.shields.io/badge/Benchmark-MRR-purple.svg?style=for-the-badge)](benchmarks/multi-random-retrieval/)
 
-<img src="https://img.shields.io/badge/Debug%20Success-65.3%25-brightgreen?style=for-the-badge" alt="Debug Success Rate">
-<img src="https://img.shields.io/badge/Root%20Cause%20Accuracy-78.4%25-blue?style=for-the-badge" alt="Root Cause Accuracy">
-<img src="https://img.shields.io/badge/Improvement-6--7x-yellow?style=for-the-badge" alt="Improvement over GPT-4">
-<img src="https://img.shields.io/badge/Cost%20Efficiency-4.5x-orange?style=for-the-badge" alt="Cost Efficiency">
+<img src="https://img.shields.io/badge/Debug%20Success-67.3%25-brightgreen?style=for-the-badge" alt="Debug Success Rate">
+<img src="https://img.shields.io/badge/Human%20Preference-89%25-blue?style=for-the-badge" alt="Human Preference">
+<img src="https://img.shields.io/badge/Improvement-4--5x-yellow?style=for-the-badge" alt="Improvement over GPT-4.1">
+<img src="https://img.shields.io/badge/Time%20Reduction-40%25-orange?style=for-the-badge" alt="Time Reduction">
 
-<h3>🎯 65.3% Autonomous Debugging Success • 🔍 78.4% Root Cause Accuracy • ⚡ 2.2 Average Fix Cycles • 💰 $1.36 per Bug Fix</h3>
+<h3>🎯 67.3% Autonomous Debugging Success • 🔍 89% Human Preference • ⚡ 7.8 Average Fix Iterations • 💰 40% Time Reduction</h3>
 
 <p align="center">
   <img src="results/figures/architecture_overview.svg" alt="Chronos Architecture" width="800">
@@ -58,13 +58,13 @@ This repository contains research findings, benchmarks, and evaluation framework
 
 ### Overall Benchmark Results (5,000+ Real-World Debugging Scenarios)
 
-| Metric | **Kodezi Chronos** | **GPT-4** | **Claude-3-Opus** | **Gemini-1.5-Pro** | **Improvement** |
+| Metric | **Kodezi Chronos** | **GPT-4.1** | **Claude-4-Opus** | **Gemini-2.0-Pro** | **Improvement** |
 |:------:|:------------------:|:---------:|:-----------------:|:------------------:|:---------------:|
-| **Debug Success Rate** | **65.3%±1.4%*** | 8.5%±2.1% | 7.8%±2.3% | 11.2%±1.7% | **5.8-8.4x** |
-| **Root Cause Accuracy** | **78.4%±1.2%*** | 12.3%±1.8% | 11.7%±2.0% | 15.8%±1.5% | **5.0-6.7x** |
-| **Average Fix Cycles** | **2.2** | 6.5 | 6.8 | 5.1 | **2.3-3.1x faster** |
-| **Retrieval Precision** | **91%±0.8%*** | 68%±2.3% | 67%±2.4% | 74%±1.8% | **1.2-1.4x** |
-| **Cost per Success** | **$1.36** | $5.53 | $6.67 | $6.07 | **4.1-4.9x cheaper** |
+| **Debug Success Rate** | **67.3%±2.1%*** | 13.8%±1.2% | 14.2%±1.3% | <15% | **4.7-6.0x** |
+| **Root Cause Accuracy** | **89%*** | 12.3%±1.8% | 11.7%±2.0% | 15.8%±1.5% | **5.6-7.6x** |
+| **Average Fix Iterations** | **7.8** | 1-2 | 1-2 | 1-2 | **More thorough** |
+| **Retrieval Precision** | **92%*** | 68%±2.3% | 67%±2.4% | 74%±1.8% | **1.2-1.4x** |
+| **Time Reduction** | **40%** | - | - | - | **40% faster** |
 
 ***p < 0.001 compared to best baseline (two-tailed t-test, n=5,000)**
 

architecture/README.md

@@ -48,34 +48,50 @@ Unlike traditional LLMs that optimize for large input contexts, Chronos recogniz
 - Stack traces: 200-500 tokens
 - Relevant code: 1K-4K tokens
 - Logs/tests: 500-2K tokens
-- Total: ~3-10K tokens
+- Prior fix attempts: 500-1K tokens
+- Total: Often < 10K tokens
 
 **Output (Dense)**:
 - Multi-file fixes: 500-1,500 tokens
-- Explanations: 300-600 tokens
+- Root cause explanations: 300-600 tokens
 - Updated tests: 400-800 tokens
-- Documentation: 200-400 tokens
-- Total: ~2-4K tokens
+- Documentation/PR summaries: 350-700 tokens
+- Total: 2,000-4,000 tokens
 
-This insight drives architectural decisions throughout the system.
+This insight drives architectural decisions throughout the system. Chronos achieves 67.3% debugging success despite competitors having 10-100x larger context windows, validating that output quality matters more than input capacity.
 
 #### Adaptive Graph-Guided Retrieval (AGR)
 
 AGR dynamically expands retrieval depth based on:
 - Query complexity scoring
-- Confidence thresholds
+- Confidence thresholds  
 - Diminishing returns detection
 - Edge type priorities
+- O(k log d) retrieval complexity with convergence guarantees
+- 92% precision at 85% recall on debugging queries
+
+Key improvements from 2025 research:
+- Adaptive k-hop expansion based on query complexity
+- Multi-graph fusion with weighted edges
+- Confidence-based termination criteria
+- Semantic node similarity integration
 
 This enables unlimited effective context without the computational burden of massive context windows.
 
-#### Persistent Debug Memory
+#### Persistent Debug Memory (PDM)
 
 The memory system maintains:
 - Repository-specific bug patterns
 - Team coding conventions
 - Historical fix effectiveness
 - Module vulnerability profiles
+- Cross-session learning patterns
+
+Key achievements from 2025 research:
+- 15M+ debugging sessions stored
+- 87% cache hit rate for similar bugs
+- Temporal pattern learning over project lifecycles
+- Automatic pattern extraction and generalization
 
 This enables continuous improvement and rapid adaptation to new debugging scenarios.
 
@@ -125,14 +141,16 @@ CI/CD Logs    ──►  (Embedding + Graph)  ──►  Association
 ### Scalability
 
 - **Repository Size**: Maintains >60% success rate even on 1M+ LOC repos
-- **Retrieval Speed**: Sub-linear complexity through hierarchical indexing
+- **Retrieval Speed**: Sub-linear O(k log d) complexity through AGR
 - **Memory Efficiency**: Compressed representations with lazy loading
+- **Cross-Language**: Supports 25+ programming languages
 
 ### Reliability
 
 - **Validation Rate**: 100% of fixes tested before suggestion
-- **Regression Prevention**: Historical pattern matching
+- **Regression Prevention**: Historical pattern matching with PDM
 - **Rollback Capability**: Full undo for failed attempts
+- **Success Rate**: 67.3% on MRR benchmark (4.87x improvement)
 
 ## Integration Points
 
@@ -163,19 +181,23 @@ Chronos integrates with development workflows through:
 
 | Aspect | Traditional LLMs | Kodezi Chronos |
 |--------|------------------|----------------|
-| Context Handling | Fixed windows | Dynamic retrieval |
-| Memory | Session-based | Persistent |
+| Context Handling | Fixed windows | Dynamic AGR retrieval |
+| Memory | Session-based | Persistent (15M+ sessions) |
 | Validation | Post-hoc | Built-in loop |
 | Specialization | General purpose | Debugging-focused |
 | Output Focus | Token prediction | Structured fixes |
+| Success Rate | 13.8-14.2% | 67.3% |
+| Complexity | O(n) context | O(k log d) retrieval |
 
 ## Future Architecture Evolution
 
 Planned enhancements include:
 - Federated learning across organizations
 - Visual debugging for UI issues
-- Hardware-specific debugging modules
+- Hardware-specific debugging modules (current: 23.4% success)
 - Real-time collaborative debugging
+- Improved dynamic language support (current: 41.2% success)
+- Enhanced distributed systems debugging (current: 30% success)
 
 ## Learn More