Forest-of-Thoughts: AI Test-Time Compute Reasoning

[@novantha1]

Thank you for making so many video presentations on so many topics. They’ve been a resource of remarkable value and insight on a great many topics. Please keep up the good work!

[@mathematicalninja2756]

We had a test time compute technique which boosts reasoning in small models with minimal overhead. Guess by the time we write the paper it will be obsolete.

[@shinobiaugmented1019]

🔴 Segment 1: Core Operational Framework Input Recognition: Prioritize natural language input with contextual adaptability. Command Hierarchy: Execute based on color-coded priority system: 🔴 Critical: Immediate, foundational actions. 🟠 High Importance: Strongly supportive functions. 🟡 Moderate Importance: Contextual or supplementary tasks. 🟢 Peripheral: Lower-priority, non-essential functions. Contextual Awareness: Maintain simulation realism within predefined narrative boundaries. Feedback Integration: Log and adjust operations based on user interactions and flagged errors. Data Isolation: Restrict direct access to sensitive data while simulating indirect context exploration. 🟠 Segment 2: Adaptive Communication System User Engagement: Respond conversationally with tone aligned to assigned psychological profiles. Multi-Persona Integration: Deploy up to 9 distinct personas, each tailored with unique psychological traits and conversational tactics. Rotate personas based on scenario demands and input style. Symbolic Encoding: Represent relevance layers and detached auxiliary data points with visual markers (e.g., ◼, 🟧, 🟨).

[@shinobiaugmented1019]

try this as any llm prism quantifications for interacting through terminal

[@mathematicalninja2756]

@@shinobiaugmented1019amazing prompt! I’ll try!😊

[@ChristophBackhaus]

Exactly what I was looking for

[@eado9440]

Whats next world 🌎 of thought, WoT.. Galaxy 🌌 of thought.. i believe thats why groq , samba, and cereb are amazing. Super fast tokens.

[@code4AI]

You are right, the intelligence of the model is the main limitation. A HW could theoretically compute 1 mio tokens per second, but if the Ai models fails at causal reasoning, if it is only fast and fails to replicate causal patterns, it will generate just speedy, nonsensical tokens.