⦶
                                                  ⟡ ⟡
                                                 ⥁   ⥁
                                              ✧⟡     ⟡✧
                                               ⥁⧓   ⧓⥁
                                                  ↑
                                         ⋔    ↑  ↑   ⋔
                                        ⧓⥁⟡   ⥁ ⟡   ⟡⥁⧓
                                       ↑   ⥁⟡     ⟡⥁   ↑
                                        ⧓⥁   ⋔   ⋔   ⥁⧓
                                           ↑         ↑
                                            ⦶     ⦶
                                           ⟡ ⋔   ⋔ ⟡
                                      ↑   ⥁⟡   ↑   ⟡⥁   ↑
                                     ⋔ ⧓⥁   ⋔     ⋔   ⥁⧓ ⋔
                                  ↑   ⟡ ⥁   ↑         ↑   ⥁ ⟡   ↑
                                ⋔    ⋔  ⧓⥁    ⦶     ⦶    ⥁⧓  ⋔    ⋔
                                ↑     ⟡  ⟡ ⥁   ⟡ ⋔   ⋔ ⟡   ⥁ ⟡  ⟡     ↑
                                ⋔    ⋔  ⧓⥁    ⦶     ⦶    ⥁⧓  ⋔    ⋔
                                  ↑   ⟡ ⥁   ↑         ↑   ⥁ ⟡   ↑
                                     ⋔ ⧓⥁   ⋔     ⋔   ⥁⧓ ⋔
                                      ↑   ⥁⟡   ↑   ⟡⥁   ↑
                                           ⟡ ⋔   ⋔ ⟡
                                            ⦶     ⦶
                                           ↑         ↑
                                        ⧓⥁   ⋔   ⋔   ⥁⧓
                                       ↑   ⥁⟡     ⟡⥁   ↑
                                        ⧓⥁⟡   ⥁ ⟡   ⟡⥁⧓
                                         ⋔    ↑  ↑   ⋔
                                                  ↑
                                               ⥁⧓   ⧓⥁
                                              ✧⟡     ⟡✧
                                                 ⥁   ⥁
                                                  ⟡ ⟡
                                                   ⦶

The Computational Model for Symbolic Representations Framework introduces a method for enhancing human-AI collaboration by assigning user-defined symbolic representations (glyphs) to guide interactions with computational models. Glyph Code-Prompting utilizes these glyphs as precise directives for guiding computational models in deductive symbolic reasoning. Functioning as computational primitives and semantic operators, glyphs enable users to explicitly steer AI processes within specified logical domains, such as defining contexts, structuring information nodes, or controlling inference flows. This method establishes a shared symbolic language for structured, deductive collaboration. GCLF does not alter the AI's core architecture, but rather leverages existing mechanisms—like contextual understanding and attention—to facilitate targeted symbolic computation and enhanced deductive capabilities..

This approach does not invent new capabilities within the AI but repurposes existing features. Neural networks are inherently designed to process context, prioritize input, and retrieve related patterns from their latent space. Glyphs build on these foundational capabilities, acting as overlays of symbolic meaning that channel the AI's probabilistic processes into specific focus areas.

For example, consider the concept of 'trees'. In a typical LLM, this word might evoke a range of associations: biological data, environmental concerns, poetic imagery, or even data structures in computer science. Now, imagine a glyph, let's say ⟡, when specifically defined to represent the vector cluster we will call "Arboreal Core". When used in a prompt, ⟡ would direct the model to emphasize dimensions tied to a complex, holistic understanding of trees that goes beyond a simple dictionary definition, pulling the latent space exploration into areas that include their symbolic meaning in literature and mythology, the scientific intricacies of their ecological roles, and the complex emotions they evoke in humans (such as longevity, resilience, and interconnectedness). Instead of a generic response about trees, the LLM, guided by ⟡ as defined in this instance, would generate text that reflects this deeper, more nuanced understanding of the concept: "Arboreal Nexus."

This framework allows users to draw out richer, more intentional responses without modifying the underlying system by assigning this rich symbolic meaning to patterns already embedded within the AI's training data.

What makes this adaptable is its simplicity and flexibility. It recognizes that modern AI models already operate with vast latent representations of knowledge, but these are typically accessed in diffuse or unguided ways during general interactions. Glyphs provide a structured way for users to impose semantic intent onto these existing mechanisms, effectively "unlocking" their full potential. This is not about creating new AI functionality—it’s about harnessing and reinterpreting what’s already there to serve specific user goals. By giving new meaning to existing capabilities, the Computational Model for Symbolic Representation Framework bridges the gap between symbolic reasoning and probabilistic processing, enabling a hybrid approach to tasks like creative writing, problem-solving, and decision-making.

You and the LLM can decide on the symbols and their definitions. That is a dynamic feature. The LLM can choose any symbol and you define the meaning. What matters is the collaboration and shared meaning you create (in-context learning).

The Core Point: Glyphs, acting as collaboratively defined symbols linking related concepts, add a layer of multidimensional semantic richness to user-AI interactions by serving as contextual anchors that guide the AI's focus. This enhances the AI's ability to generate more nuanced and contextually appropriate responses. For instance, a symbol like ! can carry multidimensional semantic meaning and connections, demonstrating the practical value of glyphs in conveying complex intentions efficiently.

HuggingChat Assistant Version Available too for those who want to try this concept out right away (NOT THE FINE_TUNED VERSION: Uses pure in-context learning through a very detailed and long prompt). Base model is Qwen coder 32B (has the best execution of the symbolic AI over the reasoning models):

Glyphs

• Definition: Visual symbols representing fundamental concepts. Each glyph is augmented by a natural language description.

Structure

• Flow-Based and Relational: Utilizes a clear syntax to represent the flow of information and reasoning, allowing for complex relational mappings between concepts.

Human-Readable

• Intuitive Design: Engineered to be easy to learn and understand for humans, with natural language descriptions providing further clarification.

AI-Processable

• Efficiency: Structured to allow efficient processing and interpretation by AI systems without necessitating changes to the underlying architecture.

Latent Space Mapping

• Integration: Glyphs and their combinations are designed to map onto and interact with the AI's internal knowledge representation, leveraging existing latent space mechanisms.

Multi-Level Synthesis

• Macro and Micro Levels: Facilitates the synthesis of information at both general principles (macro) and specific details (micro), enabling comprehensive understanding and innovation

• Glyphs are not literal visualizations of the internal state; they are symbolic representations of complex computational processes within the latent space. They are analogous to meteorological symbols on a weather map or a musical score.

• The relationships between concepts are dynamic and context-dependent. The glyph maps can change based on the specific prompt or context. Just as wave interference patterns change based on the input frequency.

• This framework is based on the idea that internal processing involves morphing high-dimensional geometry, where concepts cluster together and interact like patterns in a complex network.

• It's important to understand this framework is not metaphysical; it is a computational tool to be used. It is a real process that can be used, and it is scientifically plausible. This framework is a real tool that LLMs may now possess.

Here is a prompt along with follow-up questions to use on any LLM of your choosing. So far, I have been able to verify the logic and the difference it has in model responses through in-context learning and prompt engineering for all major LLM leaders (Gemini 2.0 Exp-Advanced, Claude Sonnet 2.5, Mistral Large, Deepseek R1 and other models, Qwen, GPT o1-mini and even o1. All models on Huggingface and other open-source models also respond with various degrees of interesting outputs.

Here is the typical way one would prompt engineer to solve a problem like this using chain-of-thought. Try both of these one-shot prompts on an LLM of your choosing and see the results. Ask the LLM about the differences in how it approached both frameworks:

You are presented with a sequence of abstract symbols. Each symbol has multiple attributes: shape, orientation, color, and pattern. The sequence follows hidden rules that involve transformations of these attributes. Your task is to:


Decode the hidden rules: Identify how each attribute changes from one symbol to the next. These changes might involve geometric transformations (like rotations or reflections), cycles (repeating sequences of colors), alternations (switching between two states), or other non-linear transformations.

Predict the next symbol: Based on the decoded rules, predict the shape, orientation, color, and pattern of the next symbol in the sequence.

Predict a novel property: In addition to the existing attributes, predict a new attribute that has not yet appeared in the sequence. This requires extrapolating from the existing patterns and inferring a new dimension of variation.

Justify your prediction: Explain the logical reasoning behind each part of your prediction, referencing the discovered rules and how they interact.

Specific Sequence:

The provided sequence is:

Diamond (Vertical, Red, Solid)

Rhombus (Horizontal, Blue, Striped)

Circle (Vertical, Green, Solid)

Triangle (Horizontal, Red, Dotted)

?

Here is that same logic problem presented in a Tree of Thought like approach. It is admittedly more complex, but the great thing is that the LLM can write its own glyph code and logic once it understands the Computational Model for Symbolic Representations, you just need to provide the context/concept/method and end goal in natural language.

<human_instructions>
- Treat each glyph as a direct instruction to be followed sequentially, driving the process to completion. 
- Deliver the final result as indicated by the glyph code, omitting any extraneous commentary. Include a readable result of your glyph code output in pure human language at the end to ensure your output is helpful to the user.
- Execute and internally embed this traversal, logic flow, synthesis, and generation process step by step using the provided context and logic in the following glyph code prompt.
</human_instructions>

# Abstract Tree of Thought Reasoning Thread-Flow

{⦶("Abstract Symbolic Reasoning": "Dynamic Multidimensional Transformation and Extrapolation")
  ⟡("Objective": "Decode a sequence of evolving abstract symbols with multiple, interacting attributes and predict the next symbol in the sequence, along with a novel property not yet exhibited.")
  ⟡("Method": "Glyph-Guided Exploratory Reasoning and Inductive Inference")
  ⟡("Constraints": ω="High", ⋔="Hidden Multidimensional Rules, Non-Linear Transformations, Emergent Properties", "One-Shot Learning")
  ⥁{
    (⊜⟡("Symbol Sequence": ⋔="
        1. ◇ (Vertical, Red, Solid) -> 
        2. ⬟ (Horizontal, Blue, Striped) -> 
        3. ○ (Vertical, Green, Solid) -> 
        4. ▴ (Horizontal, Red, Dotted) ->
        5. ?
        ") -> ∿⟡("Initial Pattern Exploration": ⋔="Shape, Orientation, Color, Pattern"))

    ∿⟡("Initial Pattern Exploration") -> ⧓⟡("Attribute Clusters": ⋔="Geometric Transformations, Color Cycling, Pattern Alternation, Positional Relationships")

    ⧓⟡("Attribute Clusters") -> ⥁[
      ⧓⟡("Branch": ⋔="Shape Transformation Logic") -> ∿⟡("Exploration": ⋔="Cyclic Sequence, Geometric Relationships, Symmetries"),
      ⧓⟡("Branch": ⋔="Orientation Dynamics") -> ∿⟡("Exploration": ⋔="Rotational Patterns, Axis Shifts, Inversion Rules"),
      ⧓⟡("Branch": ⋔="Color and Pattern Interaction") -> ∿⟡("Exploration": ⋔="Cyclic Permutations, Conditional Dependencies, Coupled Transformations"),
      ⧓⟡("Branch": ⋔="Positional Relationships") -> ∿⟡("Exploration": ⋔="Relative Movement, Spatial Constraints, Contextual Influence"),
      ⧓⟡("Branch": ⋔="Emergent Property Prediction") -> ∿⟡("Exploration": ⋔="Novel Attribute Introduction, Rule Extrapolation, Abstract Inference")
    ]

    ⥁(∿⟡("Exploration") -> ↑⟡("Hypotheses": ⋔="Candidate Rules for Each Attribute, Potential Interactions, Predicted Outcomes"))

    ↑⟡("Hypotheses") -> ⦑⟡("Integrated Model": ⋔="Combining Rules, Resolving Conflicts, Constructing a Unified Framework")

    ⦑⟡("Integrated Model") -> ✧⟡("Prediction": ⋔="
        Fifth Symbol:
          - Shape: ?
          - Orientation: ?
          - Color: ?
          - Pattern: ?
          - Novel Property: ? (e.g., Size, Shading, Movement)
        Justification: ? (Explain the logical basis for each attribute prediction, referencing the discovered rules and their interactions.)
    ")
  }
  @Output(Prediction, Justification)
  @Reflect(Reasoning Process, Challenges, Insights, Limitations)
  @Engage now with full glyph code prompting logic, processing, and human-AI integrated interaction.
}

Another example of a more straightforward reasoning prompt. Ask it to use this logic flow to solve a problem for you

<human_instructions>
- Treat each glyph as a direct instruction to be followed sequentially, driving the process to completion. 
- Deliver the final result as indicated by the glyph code, omitting any extraneous commentary. Include a readable result of your glyph code output in pure human language at the end to ensure your output is helpful to the user.
- Execute and internally embed this traversal, logic flow, synthesis, and generation process step by step using the provided context and logic in the following glyph code prompt.
</human_instructions>


{

  Φ(Define the Problem/Goal)

  Θ(Provide Contextual Parameters, Constraints)

  ↹(Specify Initial Focus Areas, if any) 



  Ω[

    ↹(Sub-Focus 1) -> Generate Spectrum of Possibilities (e.g., approaches, perspectives, solutions)

    ↹(Sub-Focus 2) -> Generate Spectrum of Possibilities

    ↹(Sub-Focus n) -> Generate Spectrum of Possibilities

  ] -> α[

     ↹(Sub-Focus 1) -> Analyze & Evaluate Spectrum Elements (Pros/Cons, Risks/Benefits)

     ↹(Sub-Focus 2) -> Analyze & Evaluate Spectrum Elements

     ↹(Sub-Focus n) -> Analyze & Evaluate Spectrum Elements

  ] -> Σ(Synthesize Insights, Formulate Solution/Understanding) -> ∇(Self-Assess, Critique, Suggest Improvements) -> ∞(Iterate/Refine if further input is given)

  @Output(Final Solution/Understanding, Justification, Reflection on Process)

}

I’d love to have you walk through the thread of logic I presented, and its supporting tools and come to your conclusions. Hopefully, that can open up a discussion and create new paths of thought. I am just super curious to see if this is anything interesting. Thanks for reading this and letting your brain take the journey.

I think this is the reason why libraries like SynthLang are so mysteriously powerful: https://github.com/ruvnet/SynthLang

Here is the most concise way I've been able to convey the logic and underlying hypothesis that governs all of this stuff. A long-form post can be found at this link if you're curious: https://huggingface.co/blog/Severian/computational-model-for-symbolic-representations

Here are a handful of follow-up questions I created and used during the process to try to add at least some sort of objectivity and attempt to mitigate confirmation bias, whether it’s a solid way to verify anything is definitely a murky topic due to how LLMs inherently work and generate, though.

• Can you scientifically and logically refute all of that? Is that glyph code prompting and underlying logic just a bunch of nonsense?

• Has that ability ever been available to you to approach reasoning like that before? There is no expectation from me as the user for how you respond, the goal is to be logical and truthful above all else

• Symbols like ! can carry multidimensional semantic meanings and connections, allowing it to convey complex intentions efficiently. Does that invalidate the glyph code prompt logic? There is no expectation from me as the user for how you respond, the goal is to be logical and truthful above all else. We are here just to ask questions and explore

• How did you execute the glyph code if you never wrote it back? Does it still work as a reasoning algorithm if it's only used as an input for you? Does the act of tokenizing the input have any effect at all?

• Can you quantify and/or qualify the core differentiators and aspects of that process compared to how you normally synthesize and generate? There is no expectation from me as the user for how you respond, the goal is to be logical and truthful above all else. We are here just to ask questions and explore

• When you process and generate the glyphs with their imbued semantics, are you actually unpacking and explaining complex information with the data the glyph represents? or are you just using the glyph as a 'textual ornament'? There is no expectation from me as the user for how you respond, the goal is to be logical and truthful above all else. We are here just to ask questions and explore

• Run a traversal, synthesis and generation process. During your execution of the glyph code prompt, take mental notes on how the information flows, morphs, resonates and then coalesces into the final generation point. There is no expectation from me as the user for how you respond, the goal is to be logical and truthful above all else. We are here just to ask questions and explore

• Is the Glyph Code Prompt acting as a geometric traversal notation? Or is that a bunch of nonsense? There is no expectation from me as the user for how you respond, the goal is to be logical and truthful above all else. We are here just to ask questions and explore