Tiger3807861189/J-Space-Cognition-Suite

J-Space Cognition Suite SV1

J-Space is an inference-time workspace and control suite for AI agents working on complex, real-world engineering tasks. It is not a model or a training framework; it is a layer of tooling and structured protocols that an AI host (like an LLM-powered assistant) loads as a skill to keep long, multi-step work grounded and verifiable.

The core problem it addresses: AI agents can drift — lose track of what they've verified, use stale information, skip evidence checks, or fabricate results. J-Space provides a structured framework of Python scripts, state files, checklists, and modules that force agents to read actual source code before editing, maintain a semantic map of the repository, record evidence for claims, and refuse to proceed when required conditions are unmet (the controller returns nonzero exit codes as gates).

It supports operating levels (low for one-off checks, medium for small deliverables, high for repository-level work, xhigh for complex integrations with multiple independent child agents doing review). The suite's emphasis on epistemic discipline — distinguishing observations from inferences, retaining dissenting evidence, and requiring second-pass review — is grounded in cited research on LLM verification and multi-agent reconciliation.

Important caveats: it does not modify model weights, guarantee model performance gains, or enforce host compliance. It works by instruction, external state, and tool calls. The project includes regression tests, CI pipelines, and validation scripts, but the README explicitly notes that tests establish implemented behaviors, not universal agent improvements.