R&D Orchestration Topology (Deterministic Kernels + LLM Interface)

What this is

A framework for reducing LLM variance on technical/scientific tasks by routing through deterministic gates and kernels.

Goal: Ambiguity becomes a first-class outcome (CLARIFY), not a guess
Non-goals: Replacing LLMs; handling arbitrary domains without validation

Core Principle

LLMs: propose / extract / format
Deterministic system: validates / selects / executes / records

Same validated spec → same numeric outputs (D1 determinism)

Quickstart

# Prerequisites
python 3.11+
pip install fastapi uvicorn pyyaml
# Run tests
python tests/test_golden_path.py
# Start gateway
uvicorn gateway.main:app --reload
# Test endpoint
curl -X POST http://localhost:8000/task \
-H "Content-Type: application/json" \
-d '{"user_input": "specific weight of water"}'# Expected: {"status": "clarify", ...}

Repository Map

gateway/ # FastAPI entrypoint, structured logging
router/ # Deterministic task classification
validator/ # Gates consuming policies (schema, units, ambiguity)
kernels/ # Deterministic compute units (units, constants)
registry/ # Kernel registry, quantities, reason codes
schemas/ # JSON Schema contracts
policies/ # YAML policy files
docs/ # Architecture, pipelines, determinism docs
tests/ # Golden path integration tests

Key Files

File	Purpose
`gateway/main.py`	FastAPI routes: /task, /kernel, /registry
`router/classifier.py`	Rule-based domain classification
`validator/gates.py`	Schema, unit, ambiguity gates
`kernels/units.py`	UCUM unit conversion
`kernels/constants.py`	Authoritative physical constants
`tests/test_golden_path.py`	Regression tests

Safety and Side Effects

File writes are patch-only and sandboxed (see docs/FILE_WRITE_POLICY.md)
All requests logged as JSONL for audit
Gates enforce CLARIFY on ambiguous inputs

Example: Ambiguity Detection

# Input: "What is the specific weight of water?"# Router detects:# domain: physics.fluids# ambiguous_terms: ["specific weight"]# Ambiguity gate returns:# decision: CLARIFY# reasons: ["TERM_COLLISION", "DISALLOWED_TERM"]# required_fields: ["specific_weight_disambiguation"]# System asks: "Is 'specific weight' weight density (N/m³) or surface tension (N/m)?"

Status

Implemented: Gateway, router, validator, unit/constants kernels
In progress: Additional domain kernels
Next: LLM integration for spec extraction

R&D Orchestration Topology (Deterministic Kernels + LLM Interface)

What this is

A framework for reducing LLM variance on technical/scientific tasks by routing through deterministic gates and kernels.

Goal: Ambiguity becomes a first-class outcome (CLARIFY), not a guess
Non-goals: Replacing LLMs; handling arbitrary domains without validation

Core Principle

LLMs: propose / extract / format
Deterministic system: validates / selects / executes / records

Same validated spec → same numeric outputs (D1 determinism)

Quickstart

# Prerequisites
python 3.11+
pip install fastapi uvicorn pyyaml
# Run tests
python tests/test_golden_path.py
# Start gateway
uvicorn gateway.main:app --reload
# Test endpoint
curl -X POST http://localhost:8000/task \
-H "Content-Type: application/json" \
-d '{"user_input": "specific weight of water"}'# Expected: {"status": "clarify", ...}

Repository Map

gateway/ # FastAPI entrypoint, structured logging
router/ # Deterministic task classification
validator/ # Gates consuming policies (schema, units, ambiguity)
kernels/ # Deterministic compute units (units, constants)
registry/ # Kernel registry, quantities, reason codes
schemas/ # JSON Schema contracts
policies/ # YAML policy files
docs/ # Architecture, pipelines, determinism docs
tests/ # Golden path integration tests

Key Files

File	Purpose
`gateway/main.py`	FastAPI routes: /task, /kernel, /registry
`router/classifier.py`	Rule-based domain classification
`validator/gates.py`	Schema, unit, ambiguity gates
`kernels/units.py`	UCUM unit conversion
`kernels/constants.py`	Authoritative physical constants
`tests/test_golden_path.py`	Regression tests

Safety and Side Effects

File writes are patch-only and sandboxed (see docs/FILE_WRITE_POLICY.md)
All requests logged as JSONL for audit
Gates enforce CLARIFY on ambiguous inputs

Example: Ambiguity Detection

# Input: "What is the specific weight of water?"# Router detects:# domain: physics.fluids# ambiguous_terms: ["specific weight"]# Ambiguity gate returns:# decision: CLARIFY# reasons: ["TERM_COLLISION", "DISALLOWED_TERM"]# required_fields: ["specific_weight_disambiguation"]# System asks: "Is 'specific weight' weight density (N/m³) or surface tension (N/m)?"

Status

Implemented: Gateway, router, validator, unit/constants kernels
In progress: Additional domain kernels
Next: LLM integration for spec extraction

R&D Orchestration Topology (Deterministic Kernels + LLM Interface)

What this is

A framework for reducing LLM variance on technical/scientific tasks by routing through deterministic gates and kernels.

Goal: Ambiguity becomes a first-class outcome (CLARIFY), not a guess
Non-goals: Replacing LLMs; handling arbitrary domains without validation

Core Principle

LLMs: propose / extract / format
Deterministic system: validates / selects / executes / records

Same validated spec → same numeric outputs (D1 determinism)

Quickstart

# Prerequisites
python 3.11+
pip install fastapi uvicorn pyyaml
# Run tests
python tests/test_golden_path.py
# Start gateway
uvicorn gateway.main:app --reload
# Test endpoint
curl -X POST http://localhost:8000/task \
-H "Content-Type: application/json" \
-d '{"user_input": "specific weight of water"}'# Expected: {"status": "clarify", ...}

Repository Map

gateway/ # FastAPI entrypoint, structured logging
router/ # Deterministic task classification
validator/ # Gates consuming policies (schema, units, ambiguity)
kernels/ # Deterministic compute units (units, constants)
registry/ # Kernel registry, quantities, reason codes
schemas/ # JSON Schema contracts
policies/ # YAML policy files
docs/ # Architecture, pipelines, determinism docs
tests/ # Golden path integration tests

Key Files

File	Purpose
`gateway/main.py`	FastAPI routes: /task, /kernel, /registry
`router/classifier.py`	Rule-based domain classification
`validator/gates.py`	Schema, unit, ambiguity gates
`kernels/units.py`	UCUM unit conversion
`kernels/constants.py`	Authoritative physical constants
`tests/test_golden_path.py`	Regression tests

Safety and Side Effects

File writes are patch-only and sandboxed (see docs/FILE_WRITE_POLICY.md)
All requests logged as JSONL for audit
Gates enforce CLARIFY on ambiguous inputs

Example: Ambiguity Detection

# Input: "What is the specific weight of water?"# Router detects:# domain: physics.fluids# ambiguous_terms: ["specific weight"]# Ambiguity gate returns:# decision: CLARIFY# reasons: ["TERM_COLLISION", "DISALLOWED_TERM"]# required_fields: ["specific_weight_disambiguation"]# System asks: "Is 'specific weight' weight density (N/m³) or surface tension (N/m)?"

Status

Implemented: Gateway, router, validator, unit/constants kernels
In progress: Additional domain kernels
Next: LLM integration for spec extraction

R&D Orchestration Topology (Deterministic Kernels + LLM Interface)

What this is

A framework for reducing LLM variance on technical/scientific tasks by routing through deterministic gates and kernels.

Goal: Ambiguity becomes a first-class outcome (CLARIFY), not a guess
Non-goals: Replacing LLMs; handling arbitrary domains without validation

Core Principle

LLMs: propose / extract / format
Deterministic system: validates / selects / executes / records

Same validated spec → same numeric outputs (D1 determinism)

Quickstart

# Prerequisites
python 3.11+
pip install fastapi uvicorn pyyaml
# Run tests
python tests/test_golden_path.py
# Start gateway
uvicorn gateway.main:app --reload
# Test endpoint
curl -X POST http://localhost:8000/task \
-H "Content-Type: application/json" \
-d '{"user_input": "specific weight of water"}'# Expected: {"status": "clarify", ...}

Repository Map

gateway/ # FastAPI entrypoint, structured logging
router/ # Deterministic task classification
validator/ # Gates consuming policies (schema, units, ambiguity)
kernels/ # Deterministic compute units (units, constants)
registry/ # Kernel registry, quantities, reason codes
schemas/ # JSON Schema contracts
policies/ # YAML policy files
docs/ # Architecture, pipelines, determinism docs
tests/ # Golden path integration tests

Key Files

File	Purpose
`gateway/main.py`	FastAPI routes: /task, /kernel, /registry
`router/classifier.py`	Rule-based domain classification
`validator/gates.py`	Schema, unit, ambiguity gates
`kernels/units.py`	UCUM unit conversion
`kernels/constants.py`	Authoritative physical constants
`tests/test_golden_path.py`	Regression tests

Safety and Side Effects

File writes are patch-only and sandboxed (see docs/FILE_WRITE_POLICY.md)
All requests logged as JSONL for audit
Gates enforce CLARIFY on ambiguous inputs

Example: Ambiguity Detection

# Input: "What is the specific weight of water?"# Router detects:# domain: physics.fluids# ambiguous_terms: ["specific weight"]# Ambiguity gate returns:# decision: CLARIFY# reasons: ["TERM_COLLISION", "DISALLOWED_TERM"]# required_fields: ["specific_weight_disambiguation"]# System asks: "Is 'specific weight' weight density (N/m³) or surface tension (N/m)?"

Status

Implemented: Gateway, router, validator, unit/constants kernels
In progress: Additional domain kernels
Next: LLM integration for spec extraction

R&D Orchestration Topology (Deterministic Kernels + LLM Interface)

What this is

A framework for reducing LLM variance on technical/scientific tasks by routing through deterministic gates and kernels.

Goal: Ambiguity becomes a first-class outcome (CLARIFY), not a guess
Non-goals: Replacing LLMs; handling arbitrary domains without validation

Core Principle

LLMs: propose / extract / format
Deterministic system: validates / selects / executes / records

Same validated spec → same numeric outputs (D1 determinism)

Quickstart

# Prerequisites
python 3.11+
pip install fastapi uvicorn pyyaml
# Run tests
python tests/test_golden_path.py
# Start gateway
uvicorn gateway.main:app --reload
# Test endpoint
curl -X POST http://localhost:8000/task \
-H "Content-Type: application/json" \
-d '{"user_input": "specific weight of water"}'# Expected: {"status": "clarify", ...}

Repository Map

gateway/ # FastAPI entrypoint, structured logging
router/ # Deterministic task classification
validator/ # Gates consuming policies (schema, units, ambiguity)
kernels/ # Deterministic compute units (units, constants)
registry/ # Kernel registry, quantities, reason codes
schemas/ # JSON Schema contracts
policies/ # YAML policy files
docs/ # Architecture, pipelines, determinism docs
tests/ # Golden path integration tests

Key Files

File	Purpose
`gateway/main.py`	FastAPI routes: /task, /kernel, /registry
`router/classifier.py`	Rule-based domain classification
`validator/gates.py`	Schema, unit, ambiguity gates
`kernels/units.py`	UCUM unit conversion
`kernels/constants.py`	Authoritative physical constants
`tests/test_golden_path.py`	Regression tests

Safety and Side Effects

File writes are patch-only and sandboxed (see docs/FILE_WRITE_POLICY.md)
All requests logged as JSONL for audit
Gates enforce CLARIFY on ambiguous inputs

Example: Ambiguity Detection

# Input: "What is the specific weight of water?"# Router detects:# domain: physics.fluids# ambiguous_terms: ["specific weight"]# Ambiguity gate returns:# decision: CLARIFY# reasons: ["TERM_COLLISION", "DISALLOWED_TERM"]# required_fields: ["specific_weight_disambiguation"]# System asks: "Is 'specific weight' weight density (N/m³) or surface tension (N/m)?"

Status

Implemented: Gateway, router, validator, unit/constants kernels
In progress: Additional domain kernels
Next: LLM integration for spec extraction

R&D Orchestration Topology (Deterministic Kernels + LLM Interface)

What this is

A framework for reducing LLM variance on technical/scientific tasks by routing through deterministic gates and kernels.

Goal: Ambiguity becomes a first-class outcome (CLARIFY), not a guess
Non-goals: Replacing LLMs; handling arbitrary domains without validation

Core Principle

LLMs: propose / extract / format
Deterministic system: validates / selects / executes / records

Same validated spec → same numeric outputs (D1 determinism)

Quickstart

# Prerequisites
python 3.11+
pip install fastapi uvicorn pyyaml
# Run tests
python tests/test_golden_path.py
# Start gateway
uvicorn gateway.main:app --reload
# Test endpoint
curl -X POST http://localhost:8000/task \
-H "Content-Type: application/json" \
-d '{"user_input": "specific weight of water"}'# Expected: {"status": "clarify", ...}

Repository Map

gateway/ # FastAPI entrypoint, structured logging
router/ # Deterministic task classification
validator/ # Gates consuming policies (schema, units, ambiguity)
kernels/ # Deterministic compute units (units, constants)
registry/ # Kernel registry, quantities, reason codes
schemas/ # JSON Schema contracts
policies/ # YAML policy files
docs/ # Architecture, pipelines, determinism docs
tests/ # Golden path integration tests

Key Files

File	Purpose
`gateway/main.py`	FastAPI routes: /task, /kernel, /registry
`router/classifier.py`	Rule-based domain classification
`validator/gates.py`	Schema, unit, ambiguity gates
`kernels/units.py`	UCUM unit conversion
`kernels/constants.py`	Authoritative physical constants
`tests/test_golden_path.py`	Regression tests

Safety and Side Effects

File writes are patch-only and sandboxed (see docs/FILE_WRITE_POLICY.md)
All requests logged as JSONL for audit
Gates enforce CLARIFY on ambiguous inputs

Example: Ambiguity Detection

# Input: "What is the specific weight of water?"# Router detects:# domain: physics.fluids# ambiguous_terms: ["specific weight"]# Ambiguity gate returns:# decision: CLARIFY# reasons: ["TERM_COLLISION", "DISALLOWED_TERM"]# required_fields: ["specific_weight_disambiguation"]# System asks: "Is 'specific weight' weight density (N/m³) or surface tension (N/m)?"

Status

Implemented: Gateway, router, validator, unit/constants kernels
In progress: Additional domain kernels
Next: LLM integration for spec extraction

R&D Orchestration Topology (Deterministic Kernels + LLM Interface)

What this is

A framework for reducing LLM variance on technical/scientific tasks by routing through deterministic gates and kernels.

Goal: Ambiguity becomes a first-class outcome (CLARIFY), not a guess
Non-goals: Replacing LLMs; handling arbitrary domains without validation

Core Principle

LLMs: propose / extract / format
Deterministic system: validates / selects / executes / records

Same validated spec → same numeric outputs (D1 determinism)

Quickstart

# Prerequisites
python 3.11+
pip install fastapi uvicorn pyyaml
# Run tests
python tests/test_golden_path.py
# Start gateway
uvicorn gateway.main:app --reload
# Test endpoint
curl -X POST http://localhost:8000/task \
-H "Content-Type: application/json" \
-d '{"user_input": "specific weight of water"}'# Expected: {"status": "clarify", ...}

Repository Map

gateway/ # FastAPI entrypoint, structured logging
router/ # Deterministic task classification
validator/ # Gates consuming policies (schema, units, ambiguity)
kernels/ # Deterministic compute units (units, constants)
registry/ # Kernel registry, quantities, reason codes
schemas/ # JSON Schema contracts
policies/ # YAML policy files
docs/ # Architecture, pipelines, determinism docs
tests/ # Golden path integration tests

Key Files

File	Purpose
`gateway/main.py`	FastAPI routes: /task, /kernel, /registry
`router/classifier.py`	Rule-based domain classification
`validator/gates.py`	Schema, unit, ambiguity gates
`kernels/units.py`	UCUM unit conversion
`kernels/constants.py`	Authoritative physical constants
`tests/test_golden_path.py`	Regression tests

Safety and Side Effects

File writes are patch-only and sandboxed (see docs/FILE_WRITE_POLICY.md)
All requests logged as JSONL for audit
Gates enforce CLARIFY on ambiguous inputs

Example: Ambiguity Detection

# Input: "What is the specific weight of water?"# Router detects:# domain: physics.fluids# ambiguous_terms: ["specific weight"]# Ambiguity gate returns:# decision: CLARIFY# reasons: ["TERM_COLLISION", "DISALLOWED_TERM"]# required_fields: ["specific_weight_disambiguation"]# System asks: "Is 'specific weight' weight density (N/m³) or surface tension (N/m)?"

Status

Implemented: Gateway, router, validator, unit/constants kernels
In progress: Additional domain kernels
Next: LLM integration for spec extraction

R&D Orchestration Topology (Deterministic Kernels + LLM Interface)

What this is

A framework for reducing LLM variance on technical/scientific tasks by routing through deterministic gates and kernels.

Goal: Ambiguity becomes a first-class outcome (CLARIFY), not a guess
Non-goals: Replacing LLMs; handling arbitrary domains without validation

Core Principle

LLMs: propose / extract / format
Deterministic system: validates / selects / executes / records

Same validated spec → same numeric outputs (D1 determinism)

Quickstart

# Prerequisites
python 3.11+
pip install fastapi uvicorn pyyaml
# Run tests
python tests/test_golden_path.py
# Start gateway
uvicorn gateway.main:app --reload
# Test endpoint
curl -X POST http://localhost:8000/task \
-H "Content-Type: application/json" \
-d '{"user_input": "specific weight of water"}'# Expected: {"status": "clarify", ...}

Repository Map

gateway/ # FastAPI entrypoint, structured logging
router/ # Deterministic task classification
validator/ # Gates consuming policies (schema, units, ambiguity)
kernels/ # Deterministic compute units (units, constants)
registry/ # Kernel registry, quantities, reason codes
schemas/ # JSON Schema contracts
policies/ # YAML policy files
docs/ # Architecture, pipelines, determinism docs
tests/ # Golden path integration tests

Key Files

File	Purpose
`gateway/main.py`	FastAPI routes: /task, /kernel, /registry
`router/classifier.py`	Rule-based domain classification
`validator/gates.py`	Schema, unit, ambiguity gates
`kernels/units.py`	UCUM unit conversion
`kernels/constants.py`	Authoritative physical constants
`tests/test_golden_path.py`	Regression tests

Safety and Side Effects

File writes are patch-only and sandboxed (see docs/FILE_WRITE_POLICY.md)
All requests logged as JSONL for audit
Gates enforce CLARIFY on ambiguous inputs

Example: Ambiguity Detection

# Input: "What is the specific weight of water?"# Router detects:# domain: physics.fluids# ambiguous_terms: ["specific weight"]# Ambiguity gate returns:# decision: CLARIFY# reasons: ["TERM_COLLISION", "DISALLOWED_TERM"]# required_fields: ["specific_weight_disambiguation"]# System asks: "Is 'specific weight' weight density (N/m³) or surface tension (N/m)?"

Status

Implemented: Gateway, router, validator, unit/constants kernels
In progress: Additional domain kernels
Next: LLM integration for spec extraction

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
baseline		baseline
docs		docs
eval		eval
gateway		gateway
indexes/train/documents		indexes/train/documents
ingest		ingest
kernels		kernels
models		models
partitions		partitions
pipelines		pipelines
plugins		plugins
policies		policies
registry		registry
router		router
runtime		runtime
schemas		schemas
scripts		scripts
services/bootstrap		services/bootstrap
sources		sources
telemetry		telemetry
tests		tests
validator		validator
.DS_Store		.DS_Store
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CONTRIBUTING.md		CONTRIBUTING.md
Init_outline_convo.md		Init_outline_convo.md
README.md		README.md
compose.yaml		compose.yaml
failure_taxonomy.md		failure_taxonomy.md
mission.md		mission.md
modes.md		modes.md
pyproject.toml		pyproject.toml
rd_orchestration_topology (2).md		rd_orchestration_topology (2).md

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
baseline		baseline
docs		docs
eval		eval
gateway		gateway
indexes/train/documents		indexes/train/documents
ingest		ingest
kernels		kernels
models		models
partitions		partitions
pipelines		pipelines
plugins		plugins
policies		policies
registry		registry
router		router
runtime		runtime
schemas		schemas
scripts		scripts
services/bootstrap		services/bootstrap
sources		sources
telemetry		telemetry
tests		tests
validator		validator
.DS_Store		.DS_Store
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CONTRIBUTING.md		CONTRIBUTING.md
Init_outline_convo.md		Init_outline_convo.md
README.md		README.md
compose.yaml		compose.yaml
failure_taxonomy.md		failure_taxonomy.md
mission.md		mission.md
modes.md		modes.md
pyproject.toml		pyproject.toml
rd_orchestration_topology (2).md		rd_orchestration_topology (2).md

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
baseline		baseline
docs		docs
eval		eval
gateway		gateway
indexes/train/documents		indexes/train/documents
ingest		ingest
kernels		kernels
models		models
partitions		partitions
pipelines		pipelines
plugins		plugins
policies		policies
registry		registry
router		router
runtime		runtime
schemas		schemas
scripts		scripts
services/bootstrap		services/bootstrap
sources		sources
telemetry		telemetry
tests		tests
validator		validator
.DS_Store		.DS_Store
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CONTRIBUTING.md		CONTRIBUTING.md
Init_outline_convo.md		Init_outline_convo.md
README.md		README.md
compose.yaml		compose.yaml
failure_taxonomy.md		failure_taxonomy.md
mission.md		mission.md
modes.md		modes.md
pyproject.toml		pyproject.toml
rd_orchestration_topology (2).md		rd_orchestration_topology (2).md

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
baseline		baseline
docs		docs
eval		eval
gateway		gateway
indexes/train/documents		indexes/train/documents
ingest		ingest
kernels		kernels
models		models
partitions		partitions
pipelines		pipelines
plugins		plugins
policies		policies
registry		registry
router		router
runtime		runtime
schemas		schemas
scripts		scripts
services/bootstrap		services/bootstrap
sources		sources
telemetry		telemetry
tests		tests
validator		validator
.DS_Store		.DS_Store
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CONTRIBUTING.md		CONTRIBUTING.md
Init_outline_convo.md		Init_outline_convo.md
README.md		README.md
compose.yaml		compose.yaml
failure_taxonomy.md		failure_taxonomy.md
mission.md		mission.md
modes.md		modes.md
pyproject.toml		pyproject.toml
rd_orchestration_topology (2).md		rd_orchestration_topology (2).md

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
baseline		baseline
docs		docs
eval		eval
gateway		gateway
indexes/train/documents		indexes/train/documents
ingest		ingest
kernels		kernels
models		models
partitions		partitions
pipelines		pipelines
plugins		plugins
policies		policies
registry		registry
router		router
runtime		runtime
schemas		schemas
scripts		scripts
services/bootstrap		services/bootstrap
sources		sources
telemetry		telemetry
tests		tests
validator		validator
.DS_Store		.DS_Store
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CONTRIBUTING.md		CONTRIBUTING.md
Init_outline_convo.md		Init_outline_convo.md
README.md		README.md
compose.yaml		compose.yaml
failure_taxonomy.md		failure_taxonomy.md
mission.md		mission.md
modes.md		modes.md
pyproject.toml		pyproject.toml
rd_orchestration_topology (2).md		rd_orchestration_topology (2).md

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R&D Orchestration Topology (Deterministic Kernels + LLM Interface)

What this is

Core Principle

Quickstart

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Safety and Side Effects

Example: Ambiguity Detection

Status

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R&D Orchestration Topology (Deterministic Kernels + LLM Interface)

What this is

Core Principle

Quickstart

Repository Map

Key Files

Safety and Side Effects

Example: Ambiguity Detection

Status

About

Resources

Contributing

Stars

Watchers

Forks

Releases

Packages

Contributors

Languages

Latest commit

History

Folders and files

Repository files navigation

R&D Orchestration Topology (Deterministic Kernels + LLM Interface)

What this is

Core Principle

Quickstart

Repository Map

Key Files

Safety and Side Effects

Example: Ambiguity Detection

Status

About

Resources

Contributing

Stars

Watchers

Forks

Releases

Packages

Contributors

Languages

Latest commit

History

Folders and files

Repository files navigation

R&D Orchestration Topology (Deterministic Kernels + LLM Interface)

What this is

Core Principle

Quickstart

Repository Map

Key Files

Safety and Side Effects