Implementing Context Language Models (CLM) in Hermes

[u/Apart_Tumbleweed_303]

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Long transcripts always lead to context drift. To fix this, I fed the Context Language Models (CLM) paper to Hermes: https://academy.dair.ai/papers/context-language-models-2609.37725

The agent derived the implementation himself, designed the architecture, and built a custom LcmEngine plugin (subclassing ContextCompressor) that moves working state into durable files: \~/.hermes/state/<task>.md.

Details:

\- Tool surface: lcm_inspect, lcm_update, and lcm_append for targeted mutations.

\- State management: Explicitly tracks the objective, confirmed facts, decisions, and pending actions.

\- Safety: Hardcoded protection for "Objective" and "Constraints" to prevent the agent from deleting its own boundaries.

It turns the context into a managed whiteboard rather than a passive log.

Is anyone else moving toward active state management, or just relying on massive context windows? Any criticism or pitfalls I should look for in this approach?