Hi all, I am Vincent Chong.
I’ve spent the past few weeks building and refining a control framework called Language Construct Modeling (LCM) — a modular semantic system that operates entirely within language, without code, plugins, or internal function rewrites. This post isn’t about announcing a product. It’s about sharing a framework I believe solves one of the most fundamental problems in working with LLMs today:
We rely on prompts to instruct LLMs, but we don’t yet have a reliable way to architect internal behavior through those prompts alone.
LCM attempts to address this by rethinking what a prompt is — not just a request, but a semantic module capable of instantiating logic, recursive structure, and state behavior inside the LLM. Think of it like building a modular system using language alone, where each prompt can trigger, call, or even regenerate other prompt structures.
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What LCM Tries to Solve:
• Fragile Prompt Behavior → LCM stabilizes reasoning chains by embedding modular recursion into the language structure itself.
• Lack of Prompt Reusability → Prompts become semantic units that can be reused, layered, and re-invoked across contexts.
• Hard-coded control logic → Replaces external tuning / API behavior with nested, semantically-activated control layers.
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How It Works (Brief): • Uses Meta Prompt Layering (MPL) to recursively define semantic layers
• Defines a Regenerative Prompt Tree structure to allow prompts to re-invoke other prompt chains dynamically • Operates via language-native intent structuring rather than tool-based triggers or plugin APIs ⸻
Why It Matters:
Right now, most frameworks treat prompts as static instructions. LCM treats them as semantic control units, meaning that your “prompt” can become a framework in itself. That opens doors for: • Structured memory management (without external vector DBs)
• Behavior modulation purely through language • Scalable, modular prompt design patterns • Internal agent-like architectures that don’t require function calling or tool-use integration ⸻
I’ve just published the first formal white paper (v1.13), along with appendices, a regenerative prompt chart, and full hash-sealed verification via OpenTimestamps. This is just the foundational framework —a larger system is coming.
LCM is only the beginning.
I’d love feedback, criticism, and especially — if any devs or researchers are curious — collaboration.
Here’s the release post with link to the full repo: https://www.reddit.com/r/PromptEngineering/s/1J56dvdDdu
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Read the full paper (open access):
LCM v1.13 White Paper • GitHub: https://github.com/chonghin33/lcm-1.13-whitepaper • OSF (timestamped & hash verified): https://doi.org/10.17605/OSF.IO/4FEAZ
Licensed under CC BY-SA 4.0 ——————
Let me know if this idea makes sense to anyone else.
— Vincent
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