mirror of
https://github.com/allaunthefox/Research-Stack.git
synced 2026-07-31 03:05:21 +00:00
418 lines
20 KiB
Text
418 lines
20 KiB
Text
/- Copyright (c) 2026 Sovereign Research Stack. All rights reserved.
|
||
Released under Apache 2.0 license as described in the file LICENSE.
|
||
Authors: Research Stack Team
|
||
|
||
AgenticOrchestration.lean — Multi-Agent Coordination for Research Automation
|
||
|
||
This module extends SubagentOrchestrator with agentic research capabilities,
|
||
enabling autonomous scientific discovery through coordinated agent teams.
|
||
|
||
Agent Types:
|
||
1. SearchAgent — Literature discovery (wraps ScholarOrchestrator)
|
||
2. ExtractAgent — Concept extraction from papers
|
||
3. FormalizeAgent — Lean 4 code generation
|
||
4. ValidateAgent — Empirical benchmarking
|
||
5. SynthesizeAgent — Report compilation
|
||
|
||
Orchestration via unified field Φ_orchestrate:
|
||
Φ_team(team, task) = Σᵢ Φᵢ(agentᵢ) + Σᵢ<ⱼ Φ_coordination(agentᵢ, agentⱼ)
|
||
|
||
Where coordination field captures:
|
||
- Dependency: Agent j needs output from agent i
|
||
- Conflict: Agents compete for resources
|
||
- Synergy: Agents collaborate on shared goals
|
||
|
||
Per AGENTS.md §1.4: Q16_16 fixed-point for hardware extraction.
|
||
Per AGENTS.md §2: PascalCase types, camelCase functions.
|
||
Per AGENTS.md §4: Every def has eval witness or theorem.
|
||
|
||
TODO(lean-port): Coordinate with SubagentOrchestrator.lean
|
||
TODO(lean-port): Define agent communication protocols
|
||
TODO(lean-port): Prove orchestration stability (no deadlock)
|
||
-/
|
||
|
||
import Mathlib.Data.Nat.Basic
|
||
import Mathlib.Data.Real.Basic
|
||
import Mathlib.Tactic
|
||
|
||
namespace Semantics.AgenticOrchestration
|
||
|
||
-- ═══════════════════════════════════════════════════════════════════════════
|
||
-- §0 Agent Types and States
|
||
-- ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
/-- Agent specializations. -/
|
||
inductive AgentType
|
||
| searchAgent -- Literature discovery
|
||
| extractAgent -- Concept extraction
|
||
| formalizeAgent -- Lean 4 formalization
|
||
| validateAgent -- Empirical validation
|
||
| synthesizeAgent -- Report synthesis
|
||
| metaAgent -- Orchestrates other agents
|
||
deriving Repr, DecidableEq, Inhabited
|
||
|
||
namespace AgentType
|
||
|
||
/-- Human-readable names. -/
|
||
def name : AgentType → String
|
||
| searchAgent => "SearchAgent"
|
||
| extractAgent => "ExtractAgent"
|
||
| formalizeAgent => "FormalizeAgent"
|
||
| validateAgent => "ValidateAgent"
|
||
| synthesizeAgent => "SynthesizeAgent"
|
||
| metaAgent => "MetaAgent"
|
||
|
||
/-- Capabilities per agent type. -/
|
||
def capabilities : AgentType → List String
|
||
| searchAgent => ["query_scholar", "fetch_pdf", "parse_bibliography"]
|
||
| extractAgent => ["read_pdf", "identify_theorems", "extract_definitions"]
|
||
| formalizeAgent => ["write_lean", "prove_lemmas", "integrate_module"]
|
||
| validateAgent => ["run_benchmarks", "collect_metrics", "compare_baselines"]
|
||
| synthesizeAgent => ["compile_report", "generate_plots", "write_paper"]
|
||
| metaAgent => ["delegate_task", "monitor_progress", "resolve_conflicts"]
|
||
|
||
end AgentType
|
||
|
||
/-- Agent state in the orchestration. -/
|
||
structure AgentState where
|
||
id : String
|
||
agentType : AgentType
|
||
currentTask : Option String
|
||
completedTasks : List String
|
||
outputBuffer : List String -- Results ready for other agents
|
||
load : Float -- 0.0-1.0 (CPU/memory utilization)
|
||
status : AgentStatus
|
||
deriving Repr, Inhabited
|
||
|
||
/-- Agent status. -/
|
||
inductive AgentStatus
|
||
| idle
|
||
| working
|
||
| waiting -- Blocked on dependency
|
||
| completed
|
||
| failed
|
||
deriving Repr, DecidableEq, Inhabited
|
||
|
||
-- ═══════════════════════════════════════════════════════════════════════════
|
||
-- §1 Task Dependencies
|
||
-- ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
/-- Task with dependencies. -/
|
||
structure Task where
|
||
id : String
|
||
description : String
|
||
requiredType : AgentType -- Which agent type can execute
|
||
dependencies : List String -- Task IDs that must complete first
|
||
estimatedDuration : Float -- Minutes
|
||
priority : Nat -- 1 (high) to 5 (low)
|
||
deriving Repr, Inhabited
|
||
|
||
/-- Research pipeline as task graph. -/
|
||
def researchPipeline : List Task :=
|
||
[ { id := "T1", description := "Search literature", requiredType := AgentType.searchAgent
|
||
dependencies := [], estimatedDuration := 10.0, priority := 1 }
|
||
, { id := "T2", description := "Extract concepts", requiredType := AgentType.extractAgent
|
||
dependencies := ["T1"], estimatedDuration := 20.0, priority := 1 }
|
||
, { id := "T3", description := "Generate hypotheses", requiredType := AgentType.extractAgent
|
||
dependencies := ["T2"], estimatedDuration := 15.0, priority := 2 }
|
||
, { id := "T4", description := "Formalize in Lean", requiredType := AgentType.formalizeAgent
|
||
dependencies := ["T3"], estimatedDuration := 60.0, priority := 1 }
|
||
, { id := "T5", description := "Design experiments", requiredType := AgentType.validateAgent
|
||
dependencies := ["T3"], estimatedDuration := 30.0, priority := 2 }
|
||
, { id := "T6", description := "Run benchmarks", requiredType := AgentType.validateAgent
|
||
dependencies := ["T4", "T5"], estimatedDuration := 120.0, priority := 1 }
|
||
, { id := "T7", description := "Synthesize report", requiredType := AgentType.synthesizeAgent
|
||
dependencies := ["T6"], estimatedDuration := 45.0, priority := 1 }
|
||
]
|
||
|
||
-- ═══════════════════════════════════════════════════════════════════════════
|
||
-- §2 Orchestration Field
|
||
-- ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
/-- Individual agent field parameters. -/
|
||
structure AgentFieldParams where
|
||
rhoCapability : Float -- ρ²: capability match to task
|
||
vEfficiency : Float -- v²: processing speed
|
||
tauLoad : Float -- τ²: current load (inverse)
|
||
qReliability : Float -- q²: historical success rate
|
||
deriving Repr, Inhabited
|
||
|
||
/-- Coordination field parameters between agents. -/
|
||
structure CoordinationParams where
|
||
dependencyStrength : Float -- How much agent j needs agent i
|
||
conflictPenalty : Float -- Resource competition
|
||
synergyBonus : Float -- Collaboration benefit
|
||
|
||
wf_dependency_pos : dependencyStrength ≥ 0
|
||
wf_conflict_nonneg : conflictPenalty ≥ 0
|
||
wf_synergy_pos : synergyBonus ≥ 0
|
||
deriving Repr
|
||
|
||
/-- Individual agent field: Φᵢ(agentᵢ, task). -/
|
||
def agentField (agent : AgentState) (task : Task) (params : AgentFieldParams) : Float :=
|
||
-- Capability match: 1.0 if types match, 0.0 otherwise
|
||
let capabilityMatch : Float := if agent.agentType = task.requiredType then 1.0 else 0.0
|
||
|
||
-- Efficiency factor
|
||
let efficiency := params.vEfficiency
|
||
|
||
-- Load penalty (inverse: higher load → lower field)
|
||
let loadFactor := 1.0 - agent.load
|
||
|
||
-- Reliability bonus
|
||
let reliability := params.qReliability
|
||
|
||
-- Compute field
|
||
(params.rhoCapability * capabilityMatch + efficiency * loadFactor + reliability)
|
||
|
||
/-- Coordination field: Φ_coord(agentᵢ, agentⱼ). -/
|
||
def coordinationField (agentI agentJ : AgentState) (params : CoordinationParams) : Float :=
|
||
let dependency := params.dependencyStrength
|
||
let conflict := params.conflictPenalty
|
||
let synergy := params.synergyBonus
|
||
|
||
-- Coordination is positive for synergy, negative for conflict
|
||
dependency + synergy - conflict
|
||
|
||
/-- Team orchestration field: Σᵢ Φᵢ + Σᵢ<ⱼ Φ_coord. -/
|
||
def teamOrchestrationField
|
||
(agents : List AgentState)
|
||
(task : Task)
|
||
(agentParams : AgentFieldParams)
|
||
(coordParams : CoordinationParams) : Float :=
|
||
-- Sum of individual agent fields
|
||
let individualSum := agents.foldl (fun acc agent =>
|
||
acc + agentField agent task agentParams
|
||
) 0.0
|
||
|
||
-- Sum of pairwise coordination (simplified: adjacent agents)
|
||
let coordinationSum := match agents with
|
||
| [] => 0.0
|
||
| _ :: [] => 0.0
|
||
| a1 :: a2 :: rest =>
|
||
let init := coordinationField a1 a2 coordParams
|
||
rest.foldl (fun acc (a, prev) =>
|
||
acc + coordinationField prev a coordParams
|
||
) init (a2 :: rest, a2)
|
||
|
||
individualSum + coordinationSum
|
||
|
||
-- ═══════════════════════════════════════════════════════════════════════════
|
||
-- §3 Task Assignment
|
||
-- ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
/-- Assign task to best available agent using field-weighted selection. -/
|
||
def assignTask
|
||
(task : Task)
|
||
(availableAgents : List AgentState)
|
||
(agentParams : AgentFieldParams) : Option AgentState :=
|
||
-- Filter agents by capability (must match required type)
|
||
let capableAgents := availableAgents.filter (fun a =>
|
||
a.agentType = task.requiredType && a.status = AgentStatus.idle
|
||
)
|
||
|
||
if capableAgents.isEmpty then
|
||
none
|
||
else
|
||
-- Select agent with highest field value
|
||
some $ capableAgents.foldl (fun best agent =>
|
||
if agentField agent task agentParams > agentField best task agentParams then
|
||
agent
|
||
else
|
||
best
|
||
) capableAgents.head!
|
||
|
||
/-- Check if all dependencies are satisfied. -/
|
||
def dependenciesSatisfied (task : Task) (completedTasks : List String) : Bool :=
|
||
task.dependencies.all (fun dep => completedTasks.contains dep)
|
||
|
||
/-- Get ready tasks (dependencies satisfied, not yet assigned). -/
|
||
def readyTasks (tasks : List Task) (completedTasks : List String) : List Task :=
|
||
tasks.filter (fun t =>
|
||
dependenciesSatisfied t completedTasks && !completedTasks.contains t.id
|
||
)
|
||
|
||
-- ═══════════════════════════════════════════════════════════════════════════
|
||
-- §4 Orchestration Algorithm
|
||
-- ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
/-- Execute one step of orchestration. Returns updated agent states. -/
|
||
def orchestrationStep
|
||
(agents : List AgentState)
|
||
(tasks : List Task)
|
||
(completedTasks : List String)
|
||
(agentParams : AgentFieldParams)
|
||
(coordParams : CoordinationParams) : List AgentState × List String :=
|
||
-- Find ready tasks
|
||
let ready := readyTasks tasks completedTasks
|
||
|
||
-- Assign tasks to agents
|
||
let (updatedAgents, newCompleted) := ready.foldl (fun (accAgents, accCompleted) task =>
|
||
match assignTask task accAgents agentParams with
|
||
| some agent =>
|
||
-- Mark agent as working on task
|
||
let updated := accAgents.map (fun a =>
|
||
if a.id = agent.id then
|
||
{ a with
|
||
status := AgentStatus.working
|
||
currentTask := some task.id
|
||
load := min (a.load + 0.3) 1.0 }
|
||
else a
|
||
)
|
||
(updated, accCompleted)
|
||
| none =>
|
||
-- No available agent, skip
|
||
(accAgents, accCompleted)
|
||
) (agents, completedTasks)
|
||
|
||
-- Simulate task completion (in real system, check actual status)
|
||
let finalAgents := updatedAgents.map (fun a =>
|
||
if a.status = AgentStatus.working && a.load >= 0.9 then
|
||
{ a with
|
||
status := AgentStatus.completed
|
||
currentTask := none
|
||
completedTasks := a.currentTask.toList ++ a.completedTasks
|
||
load := 0.0
|
||
outputBuffer := a.outputBuffer ++ a.currentTask.toList }
|
||
else if a.status = AgentStatus.working then
|
||
{ a with load := min (a.load + 0.1) 1.0 } -- Progress
|
||
else
|
||
a
|
||
)
|
||
|
||
let finalCompleted := finalAgents.foldl (fun acc a =>
|
||
acc ++ a.completedTasks
|
||
) []
|
||
|
||
(finalAgents, finalCompleted)
|
||
|
||
/-- Run full orchestration until all tasks complete. -/
|
||
def runOrchestration
|
||
(agents : List AgentState)
|
||
(tasks : List Task)
|
||
(agentParams : AgentFieldParams)
|
||
(coordParams : CoordinationParams)
|
||
(maxSteps : Nat := 1000) : List AgentState × List String × Nat :=
|
||
let rec loop (currentAgents : List AgentState) (completed : List String) (steps : Nat) :=
|
||
if steps >= maxSteps || completed.length = tasks.length then
|
||
(currentAgents, completed, steps)
|
||
else
|
||
let (newAgents, newCompleted) := orchestrationStep
|
||
currentAgents tasks completed agentParams coordParams
|
||
loop newAgents newCompleted (steps + 1)
|
||
|
||
loop agents [] 0
|
||
|
||
-- ═══════════════════════════════════════════════════════════════════════════
|
||
-- §5 Theorems: Orchestration Correctness
|
||
-- ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
-- TODO(lean-port): Add orchestration correctness theorems
|
||
-- 1. assignmentRespectsCapabilities: task type matches agent type
|
||
-- 2. dependenciesRespected: tasks only start when deps complete
|
||
-- 3. orchestrationTerminates: finite termination guarantee
|
||
-- 4. synergyImprovesPerformance: higher synergy → faster completion
|
||
|
||
-- ═══════════════════════════════════════════════════════════════════════════
|
||
-- §6 Integration with SubagentOrchestrator
|
||
-- ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
/-! ## Layered Orchestration
|
||
|
||
```
|
||
┌─────────────────────────────────────────────────────────────┐
|
||
│ LAYER 3: AgenticOrchestration │
|
||
│ ├── Research pipeline: search → extract → formalize │
|
||
│ ├── Agent teams: specialized workers │
|
||
│ └── Task graph: dependency management │
|
||
├─────────────────────────────────────────────────────────────┤
|
||
│ LAYER 2: SubagentOrchestrator │
|
||
│ ├── Domain coordination: compression ↔ field-physics │
|
||
│ ├── Resource allocation: CPU, memory, SRAM │
|
||
│ └── Convergence: multi-domain theorem proving │
|
||
├─────────────────────────────────────────────────────────────┤
|
||
│ LAYER 1: Individual Agents │
|
||
│ ├── SearchAgent → ScholarOrchestrator (Python) │
|
||
│ ├── FormalizeAgent → GenomicCompression.lean │
|
||
│ └── ValidateAgent → unified_field_validation.py │
|
||
└─────────────────────────────────────────────────────────────┘
|
||
```
|
||
|
||
## Communication Protocol
|
||
|
||
Agents communicate via:
|
||
1. **Message passing**: Async queue (Kafka/RabbitMQ style)
|
||
2. **Shared state**: OTOM knowledge graph
|
||
3. **Direct RPC**: For synchronous coordination
|
||
|
||
Message types:
|
||
- `TaskRequest`: Assign new task
|
||
- `TaskComplete`: Report results
|
||
- `DependencyMet`: Notify unblocking
|
||
- `ResourceRequest`: Ask for allocation
|
||
-/
|
||
|
||
-- ═══════════════════════════════════════════════════════════════════════════
|
||
-- §7 Verification Examples
|
||
-- ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
#eval let agents := [
|
||
{ id := "A1", agentType := AgentType.searchAgent, currentTask := none,
|
||
completedTasks := [], outputBuffer := [], load := 0.0, status := AgentStatus.idle },
|
||
{ id := "A2", agentType := AgentType.extractAgent, currentTask := none,
|
||
completedTasks := [], outputBuffer := [], load := 0.0, status := AgentStatus.idle }
|
||
]
|
||
let tasks := researchPipeline.take 2
|
||
let params := { rhoCapability := 1.0, vEfficiency := 1.0, tauLoad := 0.0, qReliability := 1.0 }
|
||
let (updated, completed, steps) := runOrchestration agents tasks params
|
||
{ dependencyStrength := 0.5, conflictPenalty := 0.1, synergyBonus := 0.3,
|
||
wf_dependency_pos := by norm_num, wf_conflict_nonneg := by norm_num, wf_synergy_pos := by norm_num }
|
||
steps
|
||
-- Expected: ~20 steps (simulated)
|
||
|
||
#eval assignTask researchPipeline[0] [
|
||
{ id := "A1", agentType := AgentType.searchAgent, currentTask := none,
|
||
completedTasks := [], outputBuffer := [], load := 0.0, status := AgentStatus.idle },
|
||
{ id := "A2", agentType := AgentType.extractAgent, currentTask := none,
|
||
completedTasks := [], outputBuffer := [], load := 0.0, status := AgentStatus.idle }
|
||
] { rhoCapability := 1.0, vEfficiency := 1.0, tauLoad := 0.0, qReliability := 1.0 }
|
||
-- Expected: A1 (searchAgent for search task)
|
||
|
||
-- ═══════════════════════════════════════════════════════════════════════════
|
||
-- §8 Future Work
|
||
-- ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
/-! ## Roadmap
|
||
|
||
### Immediate (This Week)
|
||
- [ ] Connect to SubagentOrchestrator.lean
|
||
- [ ] Define agent communication protocol (Lean + Python)
|
||
- [ ] Implement Python AgentShim classes
|
||
|
||
### Short-term (Next 2 Weeks)
|
||
- [ ] Full research pipeline: 7 tasks, 5 agents
|
||
- [ ] Integration with GenomicCompression + ResearchAgent
|
||
- [ ] Demo: Autonomous paper analysis end-to-end
|
||
|
||
### Medium-term (Next Month)
|
||
- [ ] Multi-team orchestration (multiple research projects)
|
||
- [ ] Dynamic agent spawning based on workload
|
||
- [ ] Paper: "Agentic Orchestration for Scientific Discovery"
|
||
|
||
## Open Questions
|
||
|
||
1. **Deadlock prevention**: How to guarantee no circular dependencies?
|
||
2. **Fault tolerance**: Agent failure recovery mechanisms?
|
||
3. **Scalability**: 10 agents? 100 agents? 1000 agents?
|
||
4. **Human-in-the-loop**: When should human review be required?
|
||
-/
|
||
|
||
-- TODO(lean-port):
|
||
-- 1. Complete all sorry placeholders in theorems
|
||
-- 2. Connect to SubagentOrchestrator domain definitions
|
||
-- 3. Define agent communication protocol (async message passing)
|
||
-- 4. Prove orchestration stability (no deadlock, no starvation)
|
||
-- 5. Implement Python AgentShim for each agent type
|
||
-- 6. Extract coordination patterns from InternAgent-1.5 paper
|
||
|
||
end Semantics.AgenticOrchestration
|