Thank you for replying Linas. I am working on a demo, I am calling it StoryMind. I am growing it into The Cognitive Strategy System. I wis wanting to bridge the systems, make them compatible. I know its early. You are right though, I need working code and I'm working on that now. Unfortunately Iam still designing, but I have a lot of help. LLMs are wonderful.
On Wednesday, September 24, 2025 at 6:40:42 PM UTC-4 Linas Vepstas wrote: > I believe that the best way to find out if an idea is any good is to > go out and try to implement it: write code for it; make it happen. > That helps avoid some 19th-century style flapping-wing flying machine > designs. Sounds good on paper; couldn't build one that actually flew. > > At the end, you ask "how to integrate with opencog?" Let me describe > what OpenCog is -- at its heart, it is the > https://wiki.opencog.org/w/Atomese abstract graph representation > system. Graphs are used to represent computations. You can store those > graphs in a database, called the https://wiki.opencog.org/w/AtomSpace > Examples of computations are, for example, > https://wiki.opencog.org/w/Formulas > > If you want to use an LLM to help you design your system, the > https://wiki.opencog.org/w/CogServer provides MCP (Model Context > Protocol) interfaces that allow LLM's to view, interact with and > control the AtomSpace contents. The toolset includes an extensive > description of what the AtomSpace is, and how to use it, in such a way > that the LLM's clearly understand what this is all about. Works great > with Claude! > > Myself, I'm doing some basic research into sensorimotor systems, > trying to understand things "in the world out there", as opposed to > the qualia of the "things in here" of the subjective inner experience. > The goal is to understand "embodied cognition" or "basal cognition" > and how consciousness arises when pieces-parts combine. This is > super-duper low-level work, down "at the bottom of things", highly > abstract and mathematical. It is backed by running code (so not just > theory) but is so incredibly low-level and basic that you will find > the actual examples underwhelming and disappointing. But I think it's > ... well, I find it interesting. Difficult, though. Lots of > roadblocks in every direction. Oh well. > > -- Linas > > On Wed, Sep 24, 2025 at 10:22 AM [email protected] > <[email protected]> wrote: > > > > A Request for Comment: A Vision for a Strategy-Native Neural System > > What I Mean by NLP in This Context > > > > When I say (NLP) neuro-linguistic programming here, I’m not speaking of > machine NLP, but of the older, more psychological frame that modeled how > humans think and act. Out of that tradition, I take a few clean and useful > ideas. > > > > Strategies: Human beings run internal programs for tasks. We switch into > a math strategy when solving equations, a persuasion strategy when making > an argument, a motivation strategy when driving ourselves forward. Each is > a flow of steps triggered by context. > > > > Modalities: Strategies draw on representational channels — visual, > auditory, kinesthetic, and language. In machines, this translates less > literally, but the principle holds: different channels or flows combine to > shape different behaviors. > > > > TOTE (Test → Operate → Test → Exit): This is the backbone of strategy. > We test our current state, operate to move closer to a goal, test again, > and either exit (done) or loop back for another attempt. It is feedback > incarnate. > > > > Intensity/Desire: Not all goals burn equally. Some pull with urgency, > others linger in the background. Intensity rises and falls with context and > progress, shaping which strategies are chosen and when. > > > > This is the essence of NLP that I want to carry forward: strategies, > feedback, and desire. > > > > Executive Summary > > > > I propose a strategy-native neural architecture. At its center is a > controller transformer orchestrating a library of expert transformers, each > one embodying a strategy. Every strategy is structured as a TOTE loop — it > tests, it operates, it tests again, and it exits or adjusts. > > > > The Goal Setter is itself a strategy. It tests for needs like survival > assurance, operates by creating new goals and behaviors, assigns an > intensity (a strength of desire), and passes them to the controller. The > controller then selects or creates the implementing strategies to pursue > those goals. > > > > This whole system rests on a concept network: the token embeddings and > attention flows of a pretrained transformer. With adapters, controller > tags, gating, and concept annotations, this substrate becomes partitionable > and reusable — a unified field through which strategies carve their paths. > > > > The system is extended with tools for action and RAG memory for > freshness. It grows by scheduled fine-tuning, consolidating daily > experience into long-term weights. > > > > I offer this vision as a Request for Comment — a design to be discussed, > critiqued, and evolved. > > > > The Strategy System > > Controller and Expert Strategies > > > > The controller transformer is the orchestrator. It looks at goals and > context and decides which strategies to activate. The expert transformers — > the strategy library — are adapters or fine-tuned specialists: math, > planning, persuasion, motivation, survival, creativity. Each is structured > as a TOTE loop: > > > > Test: measure current state. > > > > Operate: call sub-strategies, tools, memory. > > > > Test again: check progress. > > > > Exit or adjust: finish or refine. > > > > Strategies are not just black boxes; they are living feedback cycles, > managed and sequenced by the controller. > > > > Goal Generation with Desire and TOTE > > > > The Goal Setter is a special strategy. Its test looks for overarching > needs. Its operate step generates candidate goals with behaviors attached. > Its test again evaluates them against constraints and context. Its exit or > adjust finalizes goals and assigns intensity — the desire to act. > > > > These goals are passed into a Goal Queue, where the controller schedules > them based on intensity, value, urgency, and safety. This is how the system > sets its own direction, not just waiting passively for prompts. > > > > Tools and RAG > > > > The strategies reach outward through tools: calculators, code execution, > simulators, APIs, even robotics. They also reach into retrieval-augmented > generation (RAG): an external vector memory holding documents, experiences, > and notes. > > > > Tools are the system’s hands. RAG is its short-term recall. Together, > they keep the strategies connected to the world. > > > > Daily Consolidation > > > > At the end of each day, the system consolidates. It takes the most > important RAG material, the traces of successful strategies, and runs > scheduled fine-tuning on the relevant experts. This is long-term memory: > the system learns from its own actions. RAG covers freshness, fine-tuning > covers consolidation. The strategies sharpen day by day. > > > > The Substrate: A Concept Network of Tokens > > > > A pretrained transformer is already a concept network: > > > > Tokens are mapped to vectors in a meaning space. > > > > Attention layers connect tokens, forming weighted edges that shift with > context. > > > > By the later layers, tokens are transformed into contextualized vectors, > embodying concepts shaped by their neighbors. > > > > This is a unified substrate, but raw it doesn’t separate strategies. To > make it strategy-native, I propose: > > > > Adapters: LoRA or prefix modules that bias the substrate toward > particular strategy flows. > > > > Controller Tags: prompt tokens like [MATH] or [PLANNING] to activate the > right flows. > > > > Gating and Attention Masks: to route or separate flows, allowing > strategies to partition without isolating. > > > > Concept Annotations: clusters and labels over embeddings, marking areas > as “narrative,” “mathematical,” “social,” so strategies can claim, reuse, > and combine them. > > > > This makes the transformer not just a black box but a living concept > network with pathways carved by strategies. > > > > Safety and Reflection > > > > Every strategy’s TOTE includes policy tests. Unsafe plans are stopped or > restructured. Uncertainty checks trigger escalation or deferral. Logs are > signed and auditable, so the system’s actions can be replayed and verified. > Meta-strategies monitor performance, spawn new strategies when failures > cluster, and adjust intensity rules when needed. > > > > This keeps the growth of the system accountable. > > > > Conclusion: A Call for Comment > > > > This is my vision: a strategy-native neural system that does not merely > respond but calls strategies like a mind does. > > > > Every strategy is a TOTE loop, not just the Goal Setter. > > > > Goals carry intensity, giving the system direction and drive. > > > > The controller orchestrates expert strategies, tools, and memory. > > > > A concept network underlies it all — a transformer substrate refined > with adapters, tags, gating, and annotations. > > > > RAG and tools extend its reach. > > > > Scheduled fine-tuning ensures it grows daily from its own experience. > > > > I put this forward as a Request for Comment. What breaks here? What’s > missing? How do we measure intensity best? Which strategies deserve to be > trained first? Where are the risks in daily consolidation? How should > gating be engineered for efficiency? > > > > This is not just an assistant design. It is a sketch of a mind: one that > sets goals, desires outcomes, tests and operates with feedback, reaches > outward for tools and memory, and grows stronger with each cycle. > > > > I welcome input, critique, and imagination. Together we can refine it — > a mind of strategies carved into a unified network of concepts, guided by > goals that pull with desire. > > > > How can this be interfaced with, joined with Opencog? > > > > -- > > You received this message because you are subscribed to the Google > Groups "opencog" group. > > To unsubscribe from this group and stop receiving emails from it, send > an email to [email protected]. > > To view this discussion visit > https://groups.google.com/d/msgid/opencog/b41659bc-7f3a-4383-83d2-fffd35bea4abn%40googlegroups.com > . > > > > -- > Patrick: Are they laughing at us? > Sponge Bob: No, Patrick, they are laughing next to us. > -- You received this message because you are subscribed to the Google Groups "opencog" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion visit https://groups.google.com/d/msgid/opencog/a78caec9-d05f-43d3-b5f6-7bc5a38774abn%40googlegroups.com.
