Ishan2364/TeamprojectX--XAI

This project is a nascent research evaluation of the INSIDE framework (ICLR 2024). With 0 stars, 0 forks, and being 0 days old, it currently represents a personal or academic experiment rather than a defensible tool or library. The core value proposition is an audit of an existing hallucination detection technique, specifically finding 'architectural vulnerabilities' via layer-wise ablation. While academically interesting, it lacks a moat; the findings can be easily integrated into broader LLM observability suites like Galileo, Giskard, or Cleanlab. Furthermore, frontier labs (OpenAI, Anthropic) are aggressively developing internal confidence scoring and process-based supervision that would render third-party state-monitoring frameworks like INSIDE—and critiques thereof—obsolete at the API layer. The project is a classic 'feature-not-product' research implementation in a highly saturated and fast-moving niche.