🤝 Top Collaborators
.png&w=640&q=75)
🤓 Latest Submissions
ContextForge
ContextForge is a context-intelligence system designed to improve AI-assisted software debugging, particularly for multi-step debugging workflows. Modern AI coding agents can access large amounts of source code, logs, test results, tool outputs, and other information, but providing more context does not necessarily provide better context. Irrelevant, stale, duplicated, or poorly prioritized information can make debugging less efficient and cause an agent to repeatedly investigate the same failed hypotheses. ContextForge addresses this problem by maintaining a structured representation of debugging context and continuously determining which evidence is most useful for the current debugging task. Its core pipeline follows the flow of Evidence → Normalization → Retrieval → Ranking → Selection → Token Budgeting → Context Compilation. Each context item can contain provenance, importance, freshness, verification status, and relationships to other evidence. A deterministic ranking system evaluates candidates using relevance, freshness, importance, verification, source reliability, and task relationship, after which ContextForge selects the most useful evidence within a defined context budget. Unlike a simple retrieval system, ContextForge also maintains debugging state. Hypotheses, actions, observations, and rejected hypotheses can persist across the debugging process, allowing previously investigated paths to remain part of the debugging history and reducing unnecessary repetition. ContextForge is demonstrated using TaskFlow, a reproducible debugging environment containing five intentionally constructed software bugs covering authentication, database concurrency, API validation, pagination, and caching. The canonical demonstration focuses on an authentication failure, showing the bug being reproduced, relevant evidence being processed by ContextForge, a structured debugging context package being generated, the issue being fixed, and the resulting tests passing. The current IBM Bob 2.0 workflow uses a file-based context handoff, where ContextForge generates a structured debugging context package that can be supplied to Bob. Programmatic Bob integration is not currently implemented, and simulated portions of the demonstration are explicitly documented as such. The project is intentionally built around a deterministic and reproducible foundation rather than requiring embeddings, vector databases, expensive cloud infrastructure, or a learned ranking model. This provides a transparent baseline that can later be evaluated against more advanced approaches. ContextForge's central idea is simple: don't give the AI more context; give it the right context.
27 Sep 2026
