Frame ISA

SAM Instruction Set Architecture - Zero-dependency 6-byte opcode definitions for AI systems.

Overview

Frame ISA defines a compact instruction format for AI output:

[ACT:2 bytes][SUBJ:2 bytes][MOD:2 bytes] = 6 bytes total (big-endian)

ACT (Action): What operation to perform (GREET, RESPOND, CALCULATE, etc.)
SUBJ (Subject): The topic or entity (TIME, USER, WEATHER, RAG reference, etc.)
MOD (Modifier): Style flags (voice, tone, warmth, format, urgency, etc.)

Installation

[dependencies]

frame-isa = "0.1"

Usage

use frame_isa::{Action, Subject, Modifier, Instruction};

// Create an instruction
let instr = Instruction::new(
    Action::RESPOND,
    Subject::TIME,
    Modifier::default(),
);

// Serialize to bytes
let bytes = instr.to_bytes();
assert_eq!(bytes.len(), 6);

// Parse from bytes
let parsed = Instruction::parse_one(&bytes).unwrap();
assert_eq!(instr, parsed);

Using the Builder

use frame_isa::{Action, Subject, InstructionBuilder, Voice, Tone};

let instr = InstructionBuilder::new(Action::GREET)
    .subject(Subject::USER)
    .voice(Voice::Casual)
    .tone(Tone::Positive)
    .build();

Preset Modifiers

use frame_isa::Modifier;

// For crisis situations
let crisis = Modifier::crisis();  // Empathetic, warm, high urgency

// For professional contexts
let pro = Modifier::professional();  // Formal, neutral warmth

// For friendly interactions
let friendly = Modifier::friendly();  // Casual, warm, positive

Opcode Categories

Actions (ACT)

Range	Category	Examples
0x00xx	System	NOP, HALT, ERROR, STATUS
0x01xx	Response	GREET, CONFIRM, DENY, EXPLAIN, RESPOND
0x02xx	Query	ASK, REQUEST, SEARCH, RETRIEVE
0x03xx	Knowledge	DEFINE, DESCRIBE, COMPARE, SUMMARIZE
0x04xx	Skill	CALCULATE, SET_TIMER, KNOWLEDGE_SEARCH
0x05xx	Emotion	EMPATHY, CONCERN, ENCOURAGEMENT
0x06xx	Template	TEMPLATE_LOAD, TEMPLATE_FILL
0x07xx	Chain	CHAIN, FORK, MERGE

Subjects (SUBJ)

Range	Category	Examples
0x00xx	System	NULL, SELF, USER, CONTEXT
0x01xx	Common	WEATHER, TIME, DATE, SCHEDULE
0x02xx	Math/Science	NUMBER, EQUATION, PHYSICS
0x03xx	Technology	COMPUTER, SOFTWARE, AI, API
0x04xx	Knowledge	DOCUMENTATION, CONCEPT
0x05xx	Emotions	FEELINGS, STRESS, ANXIETY
0x06xx	TRM Refs	References to other TRM models
0xE0xx	RAG Refs	Dynamic document lookups

Modifier Bit Layout

Bit:  15  14  13  12  11  10   9   8   7   6   5   4   3   2   1   0
      [--VOICE--] [--TONE--] [-WARM-] [--FORMAT--] [ACCURACY] [URGENCY]

Field	Bits	Values
Voice	15-14	Neutral, Formal, Casual, Technical
Tone	13-12	Neutral, Positive, Empathetic, Cautious
Warmth	11-10	Cold, Neutral, Warm, VeryWarm
Format	9-8	Prose, Bulleted, Numbered, Structured
Accuracy	7-6	Low, Medium, High, Verified
Urgency	5-4	Low, Normal, High, Critical

TRM Integration

This crate is designed for use with TinyRecursiveModels (TRMs) that output opcodes directly.

The factored prediction approach uses 3 heads:

ACT head → Action code
SUBJ head → Subject code
MOD head → Modifier flags

This allows small models (~148K params) to achieve 99%+ accuracy on opcode prediction.

Related Crates

frame-catalog: Vector store and RAG system

License

MIT

frame-isa 0.1.0