xie-et-al-2025-survey-llm-task-planning

license	Apache-2.0
name	xie-et-al-2025-survey-llm-task-planning
description	Comprehensive survey of LLM-based task planning methods including decomposition, search, and execution strategies
category	Research & Academic
tags	["task-planning","llm-agents","survey","decomposition","planning"]
io-contract	{"kind":"none"}
metadata	{"recognition-cues":[],"expectancies":[],"decision-cues":[],"adaptive-workarounds":[],"execution-pattern":"sequential","needs-cdm":true}

SKILL.md: Lyapunov-Based Resilient Control Under Exponential Attacks

When to Use This Skill

Load this skill when designing distributed control systems that must maintain stability under exponentially growing adversarial attacks without relying on attack detection or central coordination.

Activation Triggers:

• Distributed systems facing quantum-era adversaries with exponentially growing attack capabilities
• Control systems requiring provable resilience guarantees (power grids, autonomous vehicles, manufacturing)
• Multi-agent coordination under adversarial conditions with only local observability
• Systems where detection delay exceeds acceptable damage windows

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DECISION POINTS

Primary Decision Tree: Attack Model → Defense Architecture

IF adversarial growth rate IS exponentially unbounded (||attack|| ≤ γ·exp(ρt))
├─ AND system has distributed topology
│  ├─ IF agents have only neighborhood observability
│  │  └─ THEN: Use adaptive neighborhood consensus (this skill)
│  └─ IF global state observable 
│     └─ THEN: Use centralized exponential compensation
├─ AND system has centralized topology
│  └─ THEN: Design centralized adaptive controller with exponential gains
ELSE IF growth rate IS polynomial bounded (||attack|| ≤ γ·t^α)
├─ THEN: Use robust H∞ or sliding mode control
ELSE IF growth rate IS constant bounded (||attack|| ≤ γ)
└─ THEN: Use standard robust control techniques

Lyapunov Function Selection Table

System Dimension	Communication Topology	Recommended Lyapunov Form	Proof Strategy
n < 5	Fully connected	V = ½x^T P x (quadratic)	Direct derivative computation
5 ≤ n < 20	Connected graph	V = V_track + V_adapt (composite)	Block diagonal P matrix
n ≥ 20	Sparse graph	V = Σᵢ Vᵢ(xᵢ) + coupling terms	Distributed Lyapunov approach
Any	Tree topology	V with graph Laplacian structure	Exploit tree properties

Parameter Tuning Decision Process

STEP 1: Estimate attack parameters (γᵢ, ρᵢ)
├─ IF unknown: Use worst-case bounds from physical constraints
└─ IF observable: Estimate from recent consensus error patterns

STEP 2: Select adaptation gains
├─ IF fast dynamics (frequency control): βf = 40-60
├─ IF slow dynamics (voltage control): βv = 20-40
└─ IF mixed timescales: Use βf = 2×βv

STEP 3: Tune threshold parameters
├─ IF high noise environment: λ = 0.10-0.15 (higher threshold)
├─ IF low noise environment: λ = 0.05-0.08 (sensitive response)
└─ IF unknown noise: Start λ = 0.10, adjust based on oscillation

STEP 4: Verify ultimate bound
├─ IF bound > tolerance: Increase β gains by factor 1.5-2.0
├─ IF oscillations appear: Decrease β by factor 0.7, increase η damping
└─ IF stable: Proceed to implementation

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FAILURE MODES

1. Linear Compensation Against Exponential Attacks

Symptoms: System initially stable, then sudden divergence; control effort plateaus while errors grow Diagnosis: Using fixed gains designed for bounded disturbances against exp(ρt) attacks Detection Rule: IF tracking error grows faster than polynomial DESPITE increasing control effort Fix: Replace linear compensation Γ = K·error with exponential form Γ = (error·exp(φ))/(|error| + η)

2. Adaptation Parameter Explosion

Symptoms: φᵢ parameters grow without bound; actuator saturation; system becomes uncontrollable Diagnosis: No damping terms in adaptation law; βᵢ gains too aggressive Detection Rule: IF adaptation parameters φᵢ > 10×initial values AND still growing Fix: Add leakage terms: φ̇ᵢ = βᵢ(|error| - λᵢ) - σᵢφᵢ; implement anti-windup when actuators saturate

3. Graph Topology Vulnerability

Symptoms: System unstable despite correct local control laws; consensus never achieved Diagnosis: Communication graph lacks sufficient connectivity; adversary has isolated critical nodes Detection Rule: IF eigenvalues of graph Laplacian < connectivity threshold Fix: Ensure algebraic connectivity λ₂(L) > minimum value; add redundant communication links

4. Lyapunov Derivative Sign Error

Symptoms: Proof "works" but simulation shows instability; parameters tuned from flawed analysis Diagnosis: V̇ computation error; neglected cross-terms between tracking and adaptation dynamics Detection Rule: IF theoretical bound doesn't match simulation results within 20% Fix: Recompute V̇ including all coupling terms: V̇ = V̇_track + V̇_adapt + V̇_cross

5. False Consensus Under Attack

Symptoms: Agents converge to wrong values; system appears stable but operates at incorrect setpoints Diagnosis: Adversary has compromised enough agents to shift consensus; local compensation insufficient Detection Rule: IF steady-state values drift from references despite stable Lyapunov function Fix: Implement reference tracking alongside consensus; use signed graph approaches for Byzantine tolerance

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WORKED EXAMPLES

Scenario: AC Microgrid Under Exponential FDI Attack

Initial State: 4-bus microgrid with distributed generators, nominal frequency 50 Hz Attack Profile: False data injection on frequency measurements: μf₁(t) = 0.5·exp(0.3t) Hz Goal: Maintain frequency synchronization with ultimate bound < 0.2 Hz

Step 1: Model the Attack

• Exponential coefficient: γ₁ = 0.5 Hz
• Growth rate: ρ₁ = 0.3 rad/s
• Attack grows from 0.5 Hz at t=0 to 5.0 Hz at t=8s without compensation

Step 2: Design Lyapunov Function

V = ½∑ᵢ(δfᵢ - δf*)² + ½∑ᵢ(φfᵢ - φf*)²/βfᵢ

Where δfᵢ = frequency deviation, φfᵢ = adaptation parameter

Step 3: Compute Neighborhood Consensus Errors For agent 1 connected to agents 2,4:

ξf₁ = a₁₂(δf₁ - δf₂) + a₁₄(δf₁ - δf₄)

Under attack: ξ̃f₁ = ξf₁ + μf₁(t) = ξf₁ + 0.5·exp(0.3t)

Step 4: Apply Adaptive Compensation

Γf₁ = (ξ̃f₁ · exp(φf₁))/(|ξ̃f₁| + ηf₁)
φ̇f₁ = βf₁(|ξ̃f₁| - λf₁)

Parameters: βf₁ = 50, λf₁ = 0.08, ηf₁ = 0.01

Step 5: Verify Lyapunov Derivative

V̇ ≤ -α∑ᵢξfᵢ² + β∑ᵢγᵢexp(ρᵢt)

Key insight: Adaptation parameter φf₁ grows to ensure α·ξf₁² dominates β·γ₁exp(ρ₁t)

Step 6: Calculate Ultimate Bound From Lyapunov analysis: ||δf||∞ ≤ √(2γ₁/(α·λf₁)) = √(2×0.5/(2.5×0.08)) = 1.58 Hz

What Expert Catches vs. Novice Misses:

• Expert: Recognizes that φf₁ must grow exponentially to compensate for exp(0.3t) attack; tunes βf₁ large enough to ensure φ̇f₁ > 0.3
• Novice: Tries fixed gain compensation; system diverges when attack exceeds initial gain sizing
• Expert: Verifies communication graph has λ₂ > 0 (connectivity) and designs redundant paths
• Novice: Assumes local control laws will work regardless of graph topology

Validation Results:

• Simulation confirms frequency deviations remain below 0.15 Hz (better than theoretical 1.58 Hz bound due to conservative analysis)
• Attack magnitude reaches 20 Hz at t=15s, but compensation tracks successfully
• No attack detection required; system responds purely to symptom (consensus error)

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QUALITY GATES

Validation checklist for Lyapunov-based resilient control implementation:

Lyapunov Function Construction:

• V(x) ≥ 0 for all x (positive definite or positive semi-definite)
• V(0) = 0 (zero at desired equilibrium)
• V(x) → ∞ as ||x|| → ∞ (radially unbounded for global results)

Derivative Analysis:

• V̇ computed including all cross-coupling terms between tracking and adaptation dynamics
• V̇ ≤ -α||x||² + β outside ultimate bound region (negative definite form verified)
• Constants α, β explicitly calculated in terms of system parameters

Parameter Validation:

• Adaptation gains βᵢ chosen to ensure φ̇ᵢ > ρᵢ (faster than attack growth rate)
• Threshold parameters λᵢ prevent response to measurement noise (λᵢ > 3σ_noise)
• Damping parameters ηᵢ prevent division by zero in compensation formula

Ultimate Bound Verification:

• Theoretical bound calculated: ||error||∞ ≤ f(γᵢ, ρᵢ, βᵢ, λᵢ)
• Bound meets system requirements (< specified tolerance)
• Simulation confirms practical bound within 20% of theoretical

Graph Topology Requirements:

• Communication graph strongly connected (path exists between any two agents)
• Algebraic connectivity λ₂(L) > minimum threshold for convergence rate
• Edge weights aᵢⱼ satisfy doubly stochastic or balanced conditions

Physical Implementation:

• Actuator saturation limits exceed maximum required compensation signal
• Anti-windup mechanisms prevent adaptation parameter growth during saturation
• Computational complexity per agent scales as O(neighbors), not O(total_agents)

Robustness Checks:

• System remains stable with ±20% parameter variations
• Performance degrades gracefully if communication links fail
• No deadlock states where adaptation stops but attacks continue

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NOT-FOR Boundaries

Do NOT use this skill for:

Detection-based security systems: For intrusion detection, malware classification, or signature-based defense → use adversarial-ml-defense skill instead

Bounded disturbance scenarios: For systems facing only constant-bounded or polynomial-growing disturbances → use robust-h-infinity-control or sliding-mode-control skills instead

Centralized architectures: For systems with reliable central coordination and global state observability → use centralized-adaptive-control skill instead

Performance optimization: For maximizing tracking accuracy under normal conditions → use optimal-control-theory or model-predictive-control skills instead

Byzantine fault tolerance: For systems requiring resilience against arbitrary malicious behavior (not just false data injection) → use byzantine-consensus-protocols skill instead

Real-time systems with hard deadlines: For control loops requiring deterministic response times → use real-time-control-systems skill instead

Unknown system dynamics: For systems where plant model is completely unknown → use reinforcement-learning-control or neural-adaptive-control skills instead

This skill specifically targets the intersection of:

• Distributed topology (no central coordinator)
• Exponentially unbounded attacks (beyond traditional robust control assumptions)
• Known system dynamics (model-based approach)
• Soft real-time requirements (ultimate boundedness vs. hard deadlines)
• False data injection attacks (not arbitrary Byzantine behavior)