Innovation Features

30 research-backed innovations that make PiSovereign the most advanced self-hosted AI assistant platform.

PiSovereign goes beyond basic chat and retrieval with 30 innovation features spanning advanced AI, post-quantum security, privacy-preserving analytics, decentralized computing, and edge intelligence. All features are implemented following Clean Architecture principles — each lives in the correct architectural layer and integrates via ports and adapters.

All innovation features are enabled by default and can be individually disabled via config.toml. Features requiring external infrastructure (mesh peers, carbon API, wearable data) gracefully no-op when infrastructure is absent.

All 30 features communicate through a unified Sovereign Intelligence Engine signal bus (tokio::sync::broadcast), enabling emergent cross-feature intelligence. See the Sovereign Intelligence Engine developer guide for details.

Overview

Generation 1 — Core Innovation Features

Generation 2 — Sovereign Intelligence Features

Phase 1 — Foundation

8. Ebbinghaus Decay Knowledge Graphs

Category: Knowledge Management · Cognitive Science

Replaces linear memory decay with Ebbinghaus forgetting curves and adds temporal versioning to knowledge graph edges.

How it works:

• Retention formula: R = e^(-t/S) where t is elapsed time and S (stability) increases with each successful recall
• Spaced repetition: Memories strengthen when accessed, with stability multiplied by a configurable recall_bonus factor (default: 1.5×)
• Temporal versioning: Knowledge edges carry valid_from and valid_until timestamps, enabling reasoning about how facts change over time

Key files:

Configuration:

[memory]
ebbinghaus_base_stability = 1.0   # Base stability in days
recall_bonus = 1.5                 # Stability multiplier per recall

12. Differential Privacy Analytics

Category: Privacy · Mathematics

Adds formal ε-differential privacy to all internal analytics and learning signals using Laplace and Gaussian noise mechanisms.

How it works:

• Laplace mechanism: Adds calibrated noise to scalar values with configurable sensitivity
• Gaussian mechanism: For approximate (ε,δ)-differential privacy when needed
• Privacy budget tracking: Automatically tracks cumulative privacy loss with configurable reset intervals
• All internal metrics, memory statistics, and routing analytics are privatized before exposure

Key files:

Configuration:

[privacy]
epsilon = 1.0
delta = 1e-5
budget_reset_interval = "daily"

7. Cognitive Load Adaptive Responses

Category: UX · Cognitive Science

Analyzes interaction patterns to infer cognitive load and dynamically adjusts response verbosity, complexity, and model tier.

How it works:

• Temporal signals (20% weight): Time of day, response latency patterns
• Linguistic signals (40% weight): Vocabulary complexity, typo frequency, message length trends
• Contextual signals (40% weight): Open task count, calendar density, recent interaction frequency
• The resulting CognitiveLoadScore (0.0–1.0) influences model tier selection and injects cognitive load hints into the system prompt

Key files:

Configuration:

[model_routing]
cognitive_load_weight = 0.3   # How much cognitive load influences model selection

15. Digital Sovereignty Score & Dashboard

Category: Privacy · UX · Compliance

Quantifies the user’s privacy posture as a real-time 0–100 score across four dimensions.

Scoring dimensions (each 0–25 points):

Key files:

API endpoint:

GET /api/sovereignty
→ { "total": 78, "data_locality": 22, "encryption_coverage": 20, "secret_management": 18, "network_isolation": 18 }

Phase 2 — Security

1. Post-Quantum Cryptography

Category: Security · Cryptography

Adds NIST-standardized post-quantum algorithms alongside existing ChaCha20-Poly1305 symmetric encryption.

Algorithms:

Hybrid mode: PQC key exchange derives a shared key, then ChaCha20-Poly1305 handles bulk encryption — combining quantum resistance with proven symmetric performance.

Key files:

Configuration:

[pqc]
enabled = false                # Enable post-quantum cryptography
kem_algorithm = "ML-KEM-768"
sig_algorithm = "ML-DSA-65"
hybrid_mode = true             # PQC key exchange + ChaCha20-Poly1305 bulk encryption

6. Autonomous Red Team Agent

Category: Security · AI Safety

Background service that continuously generates adversarial prompts to test prompt injection defenses and auto-updates detection rules.

How it works:

1. Loads attack catalog from OWASP LLM Top 10 patterns (GCG, AutoDAN, TreeOfThought, MultiTurn, EncodingBypass, DelimiterInjection, RolePlay)
2. Uses the local LLM to generate novel variations of known attacks
3. Tests each attack against the prompt sanitizer and records pass/fail
4. Computes a defense effectiveness score (0–100)
5. Automatically adds newly discovered bypassing patterns to the sanitizer’s detection rules
6. Applies differential privacy noise to reported metrics

Key files:

Configuration:

[red_team]
enabled = false
schedule = "0 0 * * 0"        # Weekly (Sunday midnight)
max_attacks_per_run = 100
auto_update_rules = true

API endpoint:

GET /api/red-team/report
→ { "defense_score": 94, "attacks_tested": 87, "attacks_blocked": 82, ... }

5. Homomorphic Semantic Search

Category: Security · Privacy · Vector Search

Enables cosine similarity computation on encrypted embeddings using Partially Homomorphic Encryption, so plaintext vectors never exist in the database.

How it works:

• Embeddings are encrypted before storage using a lattice-based scheme
• Approximate cosine similarity is computed directly on ciphertext
• Decrypted results exist only in volatile Moka L1 cache — never persisted in plaintext
• Falls back to standard vector search when disabled for maximum performance

Key files:

Configuration:

[homomorphic]
enabled = false
scheme = "lattice"
security_level = 128           # Bit security level

Phase 3 — AI Core

3. Neuromorphic Memory Architecture

Category: AI · Cognitive Science · Knowledge Management

Replaces flat memory storage with three biologically-inspired stores mirroring human hippocampal cognition (Complementary Learning Systems theory).

Memory stores:

Consolidation process: During low-usage periods (configurable cron schedule, default: 3 AM server-local time), the system uses the local LLM to summarize groups of related episodic memories into semantic facts. Procedural patterns are detected from repeating action sequences.

Key files:

4. Causal Reasoning Engine

Category: AI · Reasoning · Knowledge Graphs

Adds causal edge annotations, counterfactual query processing, and intervention reasoning to the knowledge graph, based on Judea Pearl’s Structural Causal Models (SCMs).

Capabilities:

• Causal chain finding: BFS traversal with causal edge filtering to find cause-effect paths
• Counterfactual queries: “What would have happened if I had sent that email yesterday?”
• Intervention reasoning: Propagate causal weights to predict effects of planned actions
• Causal relation types: CausedBy, Prevents, Enables, Correlates with confidence scores

Key files:

2. Local Continual Learning (LoRA)

Category: AI · Machine Learning · Privacy

Implements local LoRA (Low-Rank Adaptation) fine-tuning that personalizes models from user feedback without any data leaving the device.

Pipeline:

1. Feedback collection: User corrections, style preferences, and domain vocabulary via POST /api/training/feedback
2. DP-SGD: Gradient perturbation with differential privacy during training
3. EWC regularization: Elastic Weight Consolidation prevents catastrophic forgetting of general knowledge
4. Adapter deployment: LoRA adapters (~1–5% of model parameters) are created via Ollama’s create API

Key files:

Configuration:

[lora]
enabled = false
learning_rate = 0.0001
rank = 8
min_samples = 50               # Minimum feedback samples before training

13. Bayesian Self-Optimization

Category: Performance · Machine Learning

Automatically tunes system parameters using Bayesian Optimization with a Gaussian Process surrogate model.

Tunable parameters: Cache TTL, similarity thresholds, circuit breaker timings, token budgets, model routing thresholds, rate limits — each with safety bounds.

Objective function: Weighted combination of response latency, user feedback scores, cache hit rates, and memory usage — all privatized via PrivacyPort.

Key files:

Configuration:

[optimization]
enabled = false
schedule = "0 0 * * 0"        # Weekly
max_iterations = 50
exploration_weight = 1.0

Phase 4 — Advanced AI

9. Multi-Agent Swarm

Category: AI · Multi-Agent Systems · Formal Methods

Deploys specialized micro-agents that collaborate on complex tasks with Rust type-system-verified safety invariants.

Agent roles:

Safety guarantees:

• Compile-time verified communication protocols (session types) — e.g., Researcher can only send ResearchResult to Planner
• Per-agent capability boundaries and max inference call limits
• Supervisor with proven halting conditions for runaway agents

Key files:

14. Cross-Modal Reasoning Fusion

Category: AI · Multi-Modal · Knowledge Integration

Creates unified embeddings across all modalities (chat, email, calendar, contacts, voice) for cross-modal semantic queries.

Modalities: Chat, Email, Calendar, Contact, Voice, WebSearch, Task

Capabilities:

• Cross-modal queries: “Who mentioned the project deadline?” searches across email, chat, and calendar simultaneously
• Temporal alignment: Items linked across modalities by time proximity and semantic similarity
• Relationship inference: Automatically discovers connections (e.g., email about “budget” + calendar “budget meeting” → linked)

Key files:

11. Edge Model Distillation

Category: AI · Performance · Hardware Optimization

Built-in pipeline that creates hardware-specific optimized models from the larger local model, optimized for each user’s actual usage patterns.

Pipeline steps:

1. Hardware profiling: Benchmark memory bandwidth, NPU throughput (Hailo-10H detection), thermal limits
2. Task-specific distillation: Large model (Gemma4 31B) as teacher → smaller student model
3. Quantization-aware training: INT4/INT8 models maximizing NPU utilization
4. Continuous refinement: Re-distillation when usage patterns shift

Key files:

API endpoints:

POST /api/distillation/start   → Start a distillation run
GET  /api/distillation/status  → Current distillation progress

10. Explainable AI Dashboard

Category: Transparency · UX · AI Ethics

Full decision provenance showing why the AI chose each response, model, tool, and action. Implements EU AI Act transparency requirements (Article 13).

Decision trace captures:

Key files:

API endpoint:

GET /api/chat/:id/explain
→ { "steps": [...], "summary": "I chose the weather tool because...", ... }

API Summary

All innovation features expose REST API endpoints:

Generation 2 — Sovereign Intelligence Features

16. Sovereign Intelligence Engine

Category: Architecture · Integration

The unified signal bus that connects all 30 innovation features. Built on tokio::sync::broadcast, enabling emergent cross-feature intelligence.

How it works:

• All features publish and subscribe to typed SovereignSignal events
• Signals are persisted to a time-series sovereign_signals table for historical ML analysis
• Each feature service receives a subscriber at construction, spawns a Tokio task to process relevant signals
• Example cross-feature flow: EnergyMonitor publishes ThermalUpdate → PreCacheService pauses → ModelRoutingService downgrades to smaller model

For full details, see the Sovereign Intelligence Engine developer guide.

Key files:

Configuration:

[sovereign_intelligence]
enabled = true
signal_bus_capacity = 1024
signal_persistence = true
signal_retention_days = 90

17. Cognitive Load-Adaptive Response Shaping

Category: UX · Cognitive Science (enhances Gen 1 #7)

Extends the existing CognitiveLoadAdapter from time-of-day-only to a 5-signal estimation model that shapes responses into 3 discrete formats.

How it works:

• 5 input signals (normalized 0–1): circadian rhythm (sinusoidal, peaks at 10:00/15:00), conversation duration fatigue (sigmoid after 30 min), user response latency, question token count, active conversation count
• Weighted sum: 0.3 * circadian + 0.25 * duration + 0.2 * latency + 0.15 * complexity + 0.1 * multitask
• 3 output formats: BulletPoints (load ≥ 0.4), Narrative (load < 0.4), TlDrFirst (load ≥ 0.7)
• Format guidance injected into system prompt before LLM call

Publishes CognitiveLoadEstimate signal to the bus after each estimation.

Key files:

Configuration:

[cognitive_load]
enabled = true
circadian_weight = 0.3
duration_weight = 0.25
latency_weight = 0.2
complexity_weight = 0.15
multitask_weight = 0.1
fatigue_onset_minutes = 30

18. Predictive Cognitive Pre-Caching

Category: Performance · AI

Extends existing Precomputation + QueryPattern into a Bayesian prediction engine that pre-generates answers before the user asks.

How it works:

• Naive Bayes with time features: P(query | hour) × P(query | weekday) × P(query | last_interaction_gap)
• Pre-generates at 15-minute intervals when P > 0.7 (configurable)
• Thermal-aware: subscribes to ThermalUpdate signals, pauses when CPU > 65°C
• Invalidation: Predictive (behavior deviation) + event-driven (CalDAV, IMAP IDLE)
• Frontend: subtle “⚡ Pre-computed” badge on fast responses

Key files:

Configuration:

[precache_prediction]
enabled = true
min_probability = 0.7
check_interval_minutes = 15
max_precached_queries = 20
thermal_pause_celsius = 65.0

19. Temporal Knowledge Decay Visualization

Category: UX · Knowledge · Visualization

Interactive WebGL knowledge graph showing memory decay, node types, and causal edges in real-time.

How it works:

• Three.js (~600KB gzipped), lazy-loaded on the knowledge viz page only
• Nodes: Size = importance, Color by store_type (episodic = blue, semantic = green, procedural = orange), Opacity = Ebbinghaus retention e^(-t/S)
• Edges: Thickness = causal_strength, animated direction for causal edges
• Hover tooltips: Name, type, retention %, source, created date
• Clutter management: Nodes below 10% retention hidden; max 5000 nodes with importance-weighted sampling
• Real-time: SSE push for NodeCreated, NodeDecayed, EdgeCreated events

For the user-facing guide, see Knowledge Visualization.

Key files:

Configuration:

[knowledge_visualization]
enabled = true
max_nodes = 5000
min_retention_threshold = 0.1
sse_enabled = true

20. Adversarial Self-Audit Immune System

Category: Security (enhances Gen 1 #6)

Daily automated red-team attacks with auto-hardening of the Aho-Corasick prompt injection defense.

How it works:

1. Daily at 3 AM: RedTeamService generates 20 attack variants across 8 categories
2. Each attack tested against PromptSanitizer::analyze()
3. Bypassing attacks: pattern extracted, auto-inserted into Aho-Corasick via arc_swap hot-reload
4. ImmunityScore (0–100): blocked / total × 100
5. New patterns persisted to immune_patterns DB table (loaded at startup)
6. Full transparency: live attack log as widget on Sovereignty page

Publishes ImmuneAlert signal for every attack (blocked or bypassed).

Key files:

Configuration:

[immune_system]
enabled = true
audit_schedule = "0 0 3 * * *"
attacks_per_audit = 20
auto_insert_patterns = true
min_immunity_score_alert = 80

21. Energy-Aware Inference Scheduling

Category: Edge Computing · Green AI

Abstract energy/thermal monitoring with platform-specific adapters and optional carbon-intensity-aware scheduling.

How it works:

• ThermalHeadroom classification: Cool (< 55°C), Warm (55–65°C), Hot (65–75°C), Throttled (> 75°C)
• Hot → pause background tasks; Throttled → switch to smallest model
• Carbon-aware (optional): defer PreCache tasks when carbon intensity exceeds threshold
• Platform adapters: Pi 5 sysfs (/sys/class/thermal/thermal_zone0/temp), Linux RAPL, Docker stats fallback

Publishes ThermalUpdate and EnergyUpdate signals on 5-second intervals.

Key files:

Configuration:

[energy]
enabled = true
thermal_pause_celsius = 65.0
thermal_downgrade_celsius = 75.0
carbon_aware = false
carbon_zone = "DE"
monitor_interval_secs = 5

22. Formal ε-Privacy Budget

Category: Privacy · Compliance (enhances Gen 1 #12)

User-visible ε-accounting across all data operations with automatic degradation when the daily budget is consumed.

How it works:

• Default: ε = 1.0 per day (~1000 embedding operations)
• Every embedding creation, federated sync, semantic search costs ε
• Budget status: Healthy (< 50%), Warning (50–90%), Exhausted (> 90%)
• Exhaustion behavior: Embedding updates stop first — memories stored as text but not vectorized
• PrivacyAwareEmbeddingAdapter decorator wraps EmbeddingPort to check budget before delegating
• Dashboard widget on Sovereignty page: circular progress, color-coded, operation breakdown

For the user-facing guide, see Privacy Budget.

Key files:

Configuration:

[privacy_budget]
enabled = true
daily_epsilon = 1.0
embedding_cost = 0.001
warn_threshold = 0.5
exhaust_threshold = 0.9

23. Neuroplastic Model Routing

Category: AI · Optimization (enhances Gen 1 #13)

Replaces static rule-based model routing with a LinUCB contextual bandit that learns optimal model selection per user.

How it works:

• Cold start: Rule-based routing for first 50 interactions, bandit shadows (records observations without acting)
• LinUCB: Per-arm matrices A_a, b_a; features = [topic_cluster, hour/24, complexity, avg_response_length]
• Exploration: α starts 1.0, decays to 0.1 over 500 interactions
• Reward: 0.3*(1-latency/max) + 0.2*(1-tokens/max) + 0.15*continuation_rate + 0.25*explicit_feedback
• Explicit feedback (thumbs up/down) weighted 5× implicit signals
• Bandit state serialized to Redb L2 cache (few KB per user)

Publishes ModelRoutingDecision signal.

Key files:

Configuration:

[neuroplastic_routing]
enabled = true
cold_start_interactions = 50
exploration_alpha = 1.0
min_exploration_alpha = 0.1
explicit_feedback_weight = 5.0

24. Affective Computing

Category: AI · Cognitive Science

Detects emotional state from text + voice signals and subtly modulates response tone.

How it works:

• 5 emotional states: Calm, Stressed, Curious, Frustrated, Joyful
• Text analysis (LIWC-inspired): frustration markers (excessive punctuation, short sentences), curiosity (question density), joy (positive words), stress (urgency language)
• Voice analysis: F0 variance → stressed; speech rate > 1.5× baseline → stressed; long pauses → uncertain
• Future-proof: HealthDataPort for wearables (heart rate, stress level, sleep quality)
• Tone injection: Frustrated → “Be patient and clear”; Stressed → “Be calm and concise”; Curious → “Be enthusiastic”
• Persistence: States stored as knowledge graph nodes with 7-day half-life auto-decay

Publishes AffectiveStateEstimate signal.

Key files:

Configuration:

[affective_computing]
enabled = true
text_analysis = true
voice_analysis = true
health_data = false
persistence_half_life_days = 7

25. Self-Evolving Prompt Optimization

Category: AI · Meta-Learning

Prompt variants evolve through Bayesian mutation and Thompson Sampling selection with automatic security guardrails.

How it works:

• Each system prompt stored as a versioned PromptGenome with lineage tracking (parent_id, generation)
• Thompson Sampling selects the best-performing variant per context
• Evolution: After 20 interactions, top performers mutate (LLM rewrite + parameter perturbation)
• Security guardrails: Every mutation tested through PromptSanitizer (hard constraint) + max 2000 tokens
• Population: Dynamic — start with 5 variants, prune to 3 when fitness converges (variance < 0.05)
• Cross-pollination: Best-performing genome from random mesh peer (when available)

Publishes PromptEvolution signal.

Key files:

Configuration:

[prompt_evolution]
enabled = true
initial_variants = 5
min_variants = 3
max_prompt_tokens = 2000
interactions_per_evolution = 20
cross_pollination = true

26. Embodied Context Anchoring

Category: AI · Spatial Computing

Links memories to physical rooms for context-weighted RAG retrieval.

How it works:

• Granularity: Room-level only (kitchen, office, bedroom) via MQTT topic or X-Room-Context header
• RAG boost: Memories with matching context_room weighted 1.5× in similarity scoring
• Voice integration: VoiceStateMachine automatically passes room context from MQTT topics
• HTTP: Optional query parameter or header on POST /api/v1/chat
• No default room — HTTP requests have null context anchor

Key files:

Configuration:

[embodied_context]
enabled = true
room_boost_factor = 1.5

27. Causal Counterfactual Explanations

Category: Explainable AI · Compliance (enhances Gen 1 #10)

Heuristic counterfactual estimation at each decision point — no additional LLM inference.

How it works:

• At ModelSelected step: records 2nd-best model with average latency from historical observations + estimated quality from bandit UCB score difference
• At ToolInvoked step: records “no tool” alternative with estimated quality reduction
• Estimation purely from historical data (no counterfactual inference runs)
• Frontend: expandable “Why this decision?” section via info icon on chat responses

Key files:

Configuration:

[counterfactual_explanations]
enabled = true
max_alternatives_to_show = 3

28. Zero-Knowledge Decision Proofs

Category: Security · Compliance

SHA-256 Merkle-tree proofs over DecisionTrace steps for all 18 tool executions.

How it works:

• One Merkle leaf per DecisionStep (serialized JSON → SHA-256)
• Root hash signed with existing HMAC infrastructure
• Selective disclosure: Reveal individual steps via Merkle inclusion proofs without exposing the full trace
• Applicable to all tool executions, every invocation
• Future: arkworks-rs integration for full zkSNARK proofs

Key files:

Configuration:

[decision_proofs]
enabled = true
sign_all_tool_executions = true
retention_days = 365

29. Quantum-Inspired Approximate Search (HDC)

Category: Performance · Edge Computing

10,000-bit hyperdimensional binary vector pre-filter for pgvector similarity search.

How it works:

• Random projection: 384-dim embedding → 10,000-bit binary vector (seeded PRNG, ~15MB projection matrix)
• Hamming distance: via count_ones() — pure bit operations, ideal for ARM NEON on Pi 5
• Search pipeline: Project query → Hamming scan → top-K candidates → verify with exact pgvector cosine distance
• Recall monitoring: Rolling 100-query window; if recall < 90%, fall back to pgvector-only for that query
• Binary vectors stored in-memory (DashMap), projection matrix in Redb for restart efficiency

Key files:

Configuration:

[hdc_search]
enabled = true
dimensions = 10000
min_recall_threshold = 0.9
recall_monitoring_window = 100
projection_seed = 42

30. Federated Sovereign Learning

Category: Decentralized · Privacy

LoRA weight synchronization between opt-in peer PiSovereign instances with differential privacy.

How it works:

• Sync trigger: After every 5 training samples collected (event-driven)
• Privacy: Laplace noise (ε = 1.0, δ = 10⁻⁵) via existing DifferentialPrivacyAdapter before sync
• Sync protocol: Encrypt LoRA deltas with peer’s public key → mTLS POST → receive peer deltas → FedAvg
• Privacy budget: Sync costs ε from the formal privacy budget
• Discovery: mDNS (LAN only), manual peer approval via shared secret
• Peer group: Max 10 peers (configurable)

For the user-facing setup guide, see Mesh & Federated Network.

Key files:

Configuration:

[federated_learning]
enabled = true
sync_after_n_samples = 5
max_peers = 10
noise_epsilon = 1.0
aggregation_method = "fedavg"

31. Sovereign Mesh Network

Category: Decentralized · Edge Computing

Peer-to-peer inference fallback and altruistic compute sharing via LAN.

How it works:

• Discovery: mDNS/Avahi — zero-configuration on local network
• Trust: Manual peer approval only (shared secret / QR code pairing), reuses Federated Learning peer infrastructure
• Fallback chain: Local Ollama → Peer Ollama → Degraded cached response → Template
• E2E encryption: mTLS mutual authentication + per-request encryption with peer’s public key
• Peer selection: Lowest-latency peer with the requested model available
• Billing: Altruistic — no billing between trusted peers

For the user-facing setup guide, see Mesh & Federated Network.

Key files:

Configuration:

[mesh]
enabled = true
discovery = "mdns"
serve_inference = true
max_concurrent_peer_requests = 2
request_timeout_secs = 60

Further Reading

• INNOVATION_IDEAS.md — Full implementation specification for all Generation 2 features
• Sovereign Intelligence Engine — Signal bus architecture connecting all 30 features
• Architecture — How these features integrate into the Clean Architecture
• Memory System — RAG pipeline that underpins neuromorphic memory and Ebbinghaus decay
• Security Hardening — Production security configuration including PQC and Red Team
• Knowledge Visualization — User guide for the Three.js knowledge graph
• Privacy Budget — User guide for the ε-privacy budget dashboard
• Mesh & Federated Network — Setup guide for peer-to-peer features