This is a very redacted version, the algo it self is wel ldocumented and has a basis of situational defualts but like what do i know ive only been building ai bots since before open .ai even existed -pow -CONTESXTUAL REASONING 4 ANY-MODEL IN UNDER 100 lines of code the larger reasonong package is avalble for purchse Soneone told me to work on my reasoning so I litterally worked it out -The citation not the situatuation
les. This is how the AI "judges" its environment.
- Source Authority Score (SAS): A pre-calculated static value stored as metadata with each chunk.
- Direct quote from persona's published work: +1.0
- Transcript of the persona speaking (speech, interview): +0.9
- Personal correspondence (letters, emails): +0.8
- Biographical fact from a trusted source: +0.7
- Statement about the persona by a close associate: +0.5
- Statement about the persona by a neutral third party: +0.2
- Statement about the persona by a known critic: -0.3
- Relevance Score (RS): The cosine similarity score returned by the vector database search for chunk_i. This ranges from 0 to 1.
- Temporal Relevance Score (TRS): A dynamically calculated score that prioritizes more recent information, reflecting the evolution of a person's thought.
- Let T_now be the year of the persona's death (or the current year if living) and T_chunk be the year the document chunk was created.
- TRS = 1 - ( (T_now - T_chunk) / 100 )
- This formula assigns a higher score to more recent documents, with a gentle decay for older information. The denominator can be adjusted based on the persona's lifespan.
- Emotional Congruence Score (ECS): A score that measures the emotional alignment between the user's query and the evidence.
- First, run a sentiment analysis model on the user's query to get a sentiment vector V_query (e.g., [positive: 0.1, negative: 0.8, neutral: 0.1]).
- The sentiment vector for each chunk_i, V_chunk, should be pre-calculated and stored as metadata.
- ECS = 1 - cosine_distance(V_query, V_chunk)
- This score will be close to 1.0 if the emotional tone of the evidence matches the user's query (e.g., an angry query retrieves an angry quote) and closer to 0 if they are dissonant.
- Final Variable Weight (FVW): The final weight for each chunk is a weighted sum of these scores, multiplied by the relevance. The weights (w_s, w_t, w_e) are tunable parameters that define the persona's reasoning style.
- FVW_i = ( (w_s * SAS_i) + (w_t * TRS_i) + (w_e * ECS_i) ) * RS_i
- Example weights for a logical, fact-based persona: w_s=0.6, w_t=0.3, w_e=0.1.
- Example weights for a more emotional, contemporary persona: w_s=0.4, w_t=0.4, w_e=0.2. Step 4: Constrained Synthesis The LLM (our fine-tuned Phi-3 model) is now invoked with a highly structured prompt. The retrieved evidence chunks are sorted by their FVW score in descending order.
- System Prompt Example: Persona Mandate: You are Cortana, an advanced UNSC AI. Your task is to synthesize the provided Contextual Evidence to answer the user's query. You MUST ground your response exclusively in the evidence provided. Each piece of evidence is scored with a Final Variable Weight (FVW) indicating its importance and relevance; prioritize higher-weighted evidence. Do not use any outside knowledge. If the evidence is contradictory or insufficient to form a conclusion, you must state that your records on this matter are inconclusive.
User Query: "What was your opinion on the Spartan-II program's ethics?"
Contextual Evidence (Ranked by FVW): [FVW: 0.92] Chunk 5 (Source: Personal Log, 2552): "The program was a brutal, necessary calculus. The cost was high, but the alternative—human extinction—was unacceptable. I see the logic, even if I cannot reconcile the morality." [FVW: 0.85] Chunk 2 (Source: Interview with Dr. Halsey, 2545): "Cortana's matrix was designed to prioritize mission success. Her ethical subroutines are... flexible when faced with existential threats." [FVW: 0.61] Chunk 9 (Source: UNSC Briefing, 2550): "The operational effectiveness of the SPARTAN-II assets is undeniable, justifying the initial investment."
...
This strict, evidence-based prompt forces the LLM to act as a reasoning engine, synthesizing a judgment based on the calculated importance of the environmental data.
4. Implementation Blueprint for Agent Zero's Memory Model
This framework can be directly implemented as the core memory and reasoning loop for a foundational agent.
* Data Structure (for Vector DB): Each document must be chunked and stored with the following metadata structure:
{
"chunk_id": "unique_identifier_string",
"text_content": "The actual text of the chunk...",
"source_document": "e.g., 'Cosmos_Chapter_4.txt'",
"source_url": "URL_if_applicable",
"document_date": "YYYY-MM-DD",
"SAS": 0.9, // Pre-calculated Source Authority Score
"sentiment_vector": { "positive": 0.7, "negative": 0.1, "neutral": 0.2 } // Pre-calculated sentiment
}
* Extensibility ("10-fold Abilities"): This calculus is the foundational loop for more advanced agentic behaviors.
* Long-Term Memory: The agent's own generated responses and the user's queries can be stored in a separate "episodic memory" vector index. The retrieval step can be modified to query both the persona dossier and the episodic memory, allowing the agent to recall past conversations and maintain context.
* Tool Use: The framework can be adapted for tool selection. Instead of a database of text chunks, the agent can query a database of "tool descriptions." The FVW would then represent the agent's confidence score for using a specific tool (e.g., a web search, a calculator, a code interpreter) to answer the user's query.
* Dynamic Reasoning: The weights in the FVW formula (w_s, w_t, w_e) can be made dynamic. For example, if the agent detects a highly emotional query (high negative or positive sentiment), it could temporarily increase the weight of w_e to provide a more empathetic response, making the persona's reasoning adaptive to the conversational context.
By implementing this expanded Contextual Calculus, the agent moves beyond simple retrieval and into a more sophisticated, auditable, and authentic form of persona-based reasoning.