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On Wednesday, 24 September 2025 at 08:22:45 UTC-7 [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/a5d33513-a5e1-4711-8269-600008a48141n%40googlegroups.com.
