Gemini 3-Powered Audit Refire

Re-fire the Sunaiva Radar audit system using Gemini 3 Deep Think to deliver all seven sections of the audit. The previous attempt only partially completed sections 1 and 2 due to API disconnection. The audit comprises Design, Content, SEO, Lead Capture, Mobile scoring details, Revenue Leak Calculator, Agency White-Label Architecture, Conversion Optimization, Cold Start Strategy, and Viral Loop Design.


Sunaiva Radar Audit Tool

An automated business audit tool ('Sunaiva Radar') that acts as the top-of-funnel diagnostic engine, generating leads by exposing operational inefficiencies and prescribing paid solutions. The free tier offers a 30-second website scan with scoring across 5 dimensions (Design, Content, SEO, Lead Capture, Mobile) and the paid tier provides more detailed analysis and strategic recommendations. Aims to generate first revenue through effective audits.


Agency White-Label Audit Distribution

Distribute the Radar audit tool to agencies as a white-labeled lead magnet. Agencies run audits on their prospects, and the audit results will prescribe Sunaiva's products. Agencies earn a commission on resulting subscriptions, creating a mutually beneficial relationship.


Audit Scoring Methodology

A rigorous and defensible scoring methodology for the 5 dimensions (Design, Content, SEO, Lead Capture, Mobile) is needed. This scoring system must include specific metrics, weighting, gaming prevention, and be accurate and alarming to drive action.


Revenue Leak Calculator Model

Design a mathematical model to convert detected gaps into estimated monthly revenue loss. This model requires industry benchmarks and must balance accuracy with provocative projections to drive action while avoiding legal issues.


Audit Report Psychological Architecture

Design the psychological architecture of the audit report to maximize conversion. Prioritize elements, visualize scores effectively, and strategically place CTAs, leveraging behavioral economics principles like loss aversion and social proof.


Cold Start 30-Day Agency Customer Acquisition

Develop a 30-day playbook to acquire the first 10 paying audit customers from the 38 agency leads. This includes email sequences, call scripts, demo flows, and strategies to prove the audit's value without case studies.


Viral Audit Loop Design

Design a mechanism to incentivize businesses who receive the audit to share it, creating a viral loop. Example include competition score comparison, industry leaderboards, and embeddable badges to turn the diagnostic tool into a self-propagating lead generation engine.


Hyper-Vertical Agent Paradigm

A pre-set workflow becomes a product, delivered as a precise, reliable, and duplicatable series of steps with safeguards.


Agent Gym for Maturity Acceleration

Simulated training environments for new agents before going live, to accelerate maturity and reduce risk in production.


Voice-Browser Fusion Architecture

Architecture for Telnyx voice triggering Playwright browser agents, enabling phone calls to trigger real-time browser actions. Requires careful latency and failure handling.


Monthly Subscription Model for Agents

Offer vertical agents as monthly subscriptions ($97-$497/mo), hosting the agents on the company's infrastructure.


Secure Credential Management System

A system for securely managing customer credentials (OAuth, username/password, API keys, session cookies) for agents logging into customer software, including MFA handling and session persistence.


Reliability Engineering Architecture

Architecture designed to achieve 99%+ uptime for browser automation agents, incorporating monitoring, self-healing, retry logic, and fallback strategies.


Scaling Architecture for Browser Instances

Infrastructure design to handle 1,000+ simultaneous browser instances running 24/7, considering cost per agent, shared instances, headless browsers, and VPS sizing.


Marketplace Cold Start Strategy

Strategy for efficiently building the first 20 agents for the marketplace, including considering third-party builders, revenue splits, and quality control.


Incremental Permission Granting for Agent Maturity

Agents need experience to become trustworthy, but they need trust to gain experience.  Establish a pathway where agents earn permissions incrementally to avoid being perpetually restricted (L1) or prematurely granted full access (L4) and causing damage. Addresses the maturity paradox.


Safeguard Mechanisms for Autonomous AI Agents

Implement pre-execution validation, capability checksums, canary deployments, reversibility enforcement, blast radius calculation, anomaly detection, and constitutional circuit breakers to mitigate risks associated with broad agent permissions.


Balancing Real-Time Voice Responsiveness with Safety

Voice agents need to be responsive, but dangerous operations require human confirmation. Design a mechanism to reconcile real-time conversations with safety requirements, given that a 500ms delay feels unnatural and a 5-second approval loop breaks the UX. This addresses the Voice Constraint.


Engineering Excellence Patterns for AI Agents

Define and enforce permission enforcement locations, anti-prompt-injection patterns, maturity state management, and real-time decision structures to improve the safety and reliability of AI agents.


Trust-earning maturity progression (L1-L4)

Design a progression from L1 to L4 with clear, measurable criteria, appropriate speed, self-adjustment, and automatic demotion for degraded performance.


Real-time Safety Systems

Design real-time safety systems to prevent catastrophic actions, distinguish between approval/block/log actions, operate at voice speed, resist prompt injection, and degrade gracefully.


Engineering Excellence Patterns

Recommend technical implementations for permission enforcement location, prompt injection prevention, declarative vs imperative permission schemas, real-time decision data structures, distributed permission decisions, and observability.


Permission enforcement architecture

Synthesize concrete architectural proposal with components, data flows, and key design principles for permission enforcement.


Safeguard Mechanisms

Pre-execution validation, capability checksums, canary deployments, Reversibility enforcement, blast radius calculation, anomaly detection, Constitutional circuit breakers, cryptographic attestation


Quantum-Inspired Session Continuity

Employ quantum computing principles (superposition, entanglement, tunneling, etc.) to create seamless AI agent session continuity, avoiding state loss and the appearance of 'respawning' agents.


Multi-System Error Correction for State

Encode logical session state across multiple physical storage systems (PostgreSQL, Qdrant, Redis, Supermemory, FalkorDB) to enable error correction via majority voting.


Interference-Based Confidence Scoring

Utilize constructive and destructive interference based on repeated mentions and contradictions to generate a confidence score for each fact stored about a user.


Delayed Decoherence for Ambiguity Handling

Maintain a partial superposition of session states through the first 3-5 user messages to handle ambiguous user intent before collapsing to a single trajectory.


Quantum-Inspired Session Continuity

Analyze session continuity through the lens of quantum information theory. Propose a quantum-inspired architecture where session states exist in superposition, collapse based on user interaction, and leverage entanglement, tunneling, and error correction for seamless continuity.


AI Session Continuity via Music Theory

Applying music theory concepts like leitmotifs, key signatures, movements, and transitions to maintain continuity in AI agent sessions across context window limits and 'respawns'. Aim is seamless user experience.


Leitmotif Registry

A system for tracking and managing recurring themes (leitmotifs) across sessions. Includes name, musical key, first introduction, last reference, recurrence count, and variation history. PostgreSQL schema is the suggested implementation.


Key Signature Tracking

A system for tracking the primary mission (tonal center) across sessions using a JSON structure. Tracks tonic, current key, and modulation history per session.


Transition Composition Mechanism

Mechanism for composing smooth transitions (attacca, segue) between sessions using pivot concepts, common tones, and rhythmic continuity.


Variation Tracking

Tracks how themes (concepts) have evolved across sessions, showing progression from question to declaration to promotion to integration.


Counterpoint Thread Management

Manages multiple interleaved project threads (melodies) across sessions with voice assignment (soprano/alto/bass), status, and activity history.


Call and Response Tracking

Creates conversational continuity between sessions by tracking unresolved questions from Session N and ensuring they are answered in Session N+1.


Dynamic Arc Tracking

Tracks the emotional/intensity arc across sessions using energy levels and narrative position awareness.


Cadence Recognition and Resolution

Detects imperfect cadences (unresolved sessions) and ensures the next session provides resolution (perfect cadence).


Recapitulation Detection

Recognizes when sessions are returning to earlier themes by checking the leitmotif registry and referencing prior variations, avoiding redundant explanations.


Coda Composition

Composes a coda (final synthesis) at project completion by retrieving all leitmotifs, writing a one-sentence final state for each, and assembling them into a narrative synthesis with metrics.


Holographic Session State Encoding

Encoding Genesis session state holographically to ensure continuity, graceful degradation, and reconstruction from fragments, where any 20% fragment can reconstruct 80% of the context.


Distributed Holographic Storage System

Distributing session state across 5 systems (PostgreSQL, Qdrant, Redis, Supermemory, FalkorDB) such that ANY 3/5 can reconstruct the full state, ensuring no single point of failure.


Interference Pattern Encoding for Associative Retrieval

Encoding facts as overlapping patterns using vector embeddings to enable associative retrieval, where related facts cluster, facilitating retrieval.


Fragment-Based Reconstruction Algorithm

Design breadth-first traversal of contextual pointers starting from any fragment to reconstruct the full session context from partial fragments.


Content-Addressable Retrieval Mechanism

Design hybrid search combining vector similarity + metadata filters to retrieve by pattern (semantic similarity) instead of address (SQL queries).


Graceful Degradation Mechanism

Divide sessions into key fragments with contextual pointers to ensure ANY 20% fragment can reconstruct 80% of context. Start from any fragment, traverse pointers, rebuild context.


Mycelial Substrate

A persistent substrate beneath Genesis sessions for maintaining memory and communication. It consists of Elestio storage, a Knowledge Graph, memory systems, and event streams. Sessions are ephemeral 'fruiting bodies' that contribute to and draw from this substrate.


Decomposition Pipeline

A system for extracting facts, decisions, and insights from dead sessions, converting them to structured data, distributing them across storage, and deleting raw transcripts, effectively recycling nutrients.


Mycorrhizal Connection Protocol

A protocol for automatic session connection to the Mycelial Network. Sessions signal availability, and the substrate responds with connections to parent sessions and shared knowledge, initiating nutrient flow without manual retrieval.


Optimal Foraging Algorithm

Algorithm for intelligent context retrieval by scoring facts based on recency, relevance (semantic similarity), importance, and connectivity (hub weighting). Retrieves top-N most relevant facts.


Nutrient Classification and Transport

Classify nutrient by importance (critical, important, contextual, archival) and transport them efficiently using tiered delivery: critical=preloaded, important=cached, contextual=lazy-loaded, archival=on-demand.


Signal Propagation Mechanism

When a session detects a blocker or insight, it writes an event to a signal stream. The substrate propagates this through the network, ensuring subsequent sessions receive the signal before starting.


Temporal Session Management System

A system using temporal database principles to manage AI agent sessions, providing seamless continuity, time-travel debugging, and branching timelines.


Event Sourcing for Session State

Storing every session state change as an immutable event, enabling reconstruction of current state by replaying events.


Bitemporal Modeling for Session Data

Tracking both the valid_time (when a fact was true) and transaction_time (when it was recorded) for session data.


Branching Timelines for Session Exploration

Forking session history to explore alternative paths and merging outcomes, analogous to 'git for sessions'.


Snapshot Isolation for Consistent Session Views

Providing each session with a consistent point-in-time view of the data by using snapshot isolation.


Timeline Compression for Scalable Session History

Employing strategies such as checkpointing, aggregation, and archiving to compress event logs and manage unbounded growth.


Causal Ordering Using Lamport Timestamps

Tracking causal dependencies between session events and preventing causal paradoxes using Lamport timestamps.


Two-Tier Memory Architecture (Innate & Adaptive)

Implements fast innate (Redis, <100ms) and slow adaptive (PostgreSQL + Qdrant, <3s) memory tiers for AI agent sessions. Innate stores recent session state, adaptive stores all decisions and historical patterns.


Memory Cell Lifecycle (Create, Activate, Maintain, Retire)

Defines the lifecycle of memory cells: creation on significant events, activation on matching queries, maintenance via periodic refresh and hypermutation, and retirement after a period of inactivity.


Clonal Selection Algorithm for Memory Amplification

Algorithm to amplify best-fit memories and suppress poor matches by scoring candidates based on recency, confidence, and reference count, then adjusting their affinity and storage location.


Antibody Design as Pattern Matchers

Design of pattern matchers (antibodies) with a variable region (embedding vector), binding sites (keywords), and a constant region (metadata) for hybrid recognition based on semantic similarity, keyword matching, and metadata filtering.


Somatic Hypermutation Engine for Memory Evolution

Engine to evolve memories over time by generating variations, testing against historical queries, and replacing memories with improved versions, enabling continuous memory improvement.


Immune Tolerance via Signal vs Noise Classification

System to train a classifier on labeled examples to distinguish signal from noise, ensuring that only important events create memory cells and preventing memory pollution.


Cytokine Cascade for Associative Recall

Implementation of associative recall via signal propagation through the Knowledge Graph, where initial antigen activation triggers related recalls, spreading activation based on relationship strength.


Memory Consolidation (Sleep Phase)

Nightly process to consolidate memories: extract antigens from completed sessions, create/update cells, run hypermutation, prune weak cells, rebuild Knowledge Graph edges.


Vaccine-Like Preloading of Knowledge

"Vaccinate" sessions with critical knowledge before first exposure by extracting key concepts from research docs, creating memory cells upfront with moderate affinity to start with learned immunity.


Antigen Presentation Pipeline

Automated extraction of key concepts from sessions using LLM to generate key phrases, followed by checking memory existence or creating new memory cells.


Gemini 3.0 Flash Synergy Analysis (DT18)

New prompt (DT18) created specifically for Gemini 3.0 Flash synergy analysis across 8 pillars. Aims to identify compound effects, emergent properties, and capability multiplication between Gemini 3.0 Flash and existing Genesis stack.


Genesis Voice-Browser Fusion Architecture

Architecture fusing voice interactions (Telnyx, AIVA) with browser automation (Playwright MCP, Crawl4AI). Explores synergy with Gemini 3.0 Flash for real-time browsing and simultaneous voice conversation.


Bloodstream Memory Layer

Memory layer consisting of PostgreSQL, Redis, and Qdrant for knowledge storage and retrieval. Explores how Gemini 3.0 Flash's vision capabilities can enhance memory-driven applications.


Zero-Touch Sales Pipeline

Fully automated sales pipeline (Radar -> Video -> AI Call -> Auto-Close) with no human intervention. Aims to accelerate each stage using Gemini 3.0 Flash.


ADK as Unified Agent Framework

Evaluate Google ADK as a replacement for the current patchwork of agent frameworks (Playwright MCP, browser-use, Crawl4AI). Determine if ADK provides a single, unified development environment.


Fleet-Wide Intelligence (Lux Evolution)

Lux pattern for fleet-wide auto-healing of agents when websites change. Explores leveraging Gemini 3.0 Flash's visual understanding to improve UI adaptation.


Cost Arbitrage Analysis of Gemini 3.0 Flash

Comprehensive cost comparison of Gemini 3.0 Flash versus other models (Claude Opus, Kimi K2.5, MiniMax) across various use cases (voice brain, browser agent, etc.).


Synergy Analysis Framework

A structured approach to identify synergistic opportunities by analyzing compound effects, new capabilities, implementation architecture, economic impact, timelines, and competitive advantages (moats).  Focuses on 10x outcomes.


Deep Think Re-Fire Index

Index of Deep Think prompts to be re-fired in Gemini 3 to address incomplete or missing results. Prioritized by revenue impact, architectural foundation, and research depth.


Re-Firing Priority

Prioritize re-firing Deep Think prompts based on: 1) Revenue Impact, 2) Architectural Foundation, 3) Research Depth. This order ensures the most critical areas are addressed first.


Batch Firing Strategy

A phased approach to re-firing prompts: Wave 1 (DT03, DT18 - revenue); Wave 2 (DT05a, DT04 - architecture); Wave 3 (DT14, DT16, DT15 - session continuity); Wave 4 (DT13, DT11, DT12 - theoretical).


Deep Think Engine Prompts and Results Tracking

The system tracks the source prompt files and corresponding result files generated by the Deep Think Engine. It also records the completion status of each task, indicated by statuses like PARTIAL, MISSING, and NEW.


Verified Agentic Executor with Compliance Layer

Transition from a basic chatbot to an AI agent capable of executing tasks and adhering to Australian privacy regulations. Offer a "Compliance-as-a-Service" guarantee.


Launch White-Label Agentic Protocol

Offer agencies a programmable workforce they can brand, moving beyond FAQs to high-value workflows. Provide a "Compliance-as-a-Service" guarantee.


Compliance-First Feature Set

Build automated ADM disclosure and sovereign data hosting into the widget core to meet Australian Privacy Principles.


Native Integrations for Agentic Execution

Build deep, native hooks into CRMs popular in AU (HubSpot, Salesforce, and Pipedrive) and Local Payment Gateways to facilitate task execution.


Pivot to Verified Conversational Agent

Move from a voice interface to a verified conversational agent with an integrated Australian compliance & handoff layer.


Safe-Voice Integration Framework

Provide a platform that handles mandatory disclosure, Privacy Act compliance, and contextual handoff for Australian agencies.


Automate 'Live' Disclosure

Build a mandatory, customizable 'Identity Intro' into the widget to comply with ACMA guidelines on synthetic voices.


Implement Privacy-First Data Sovereignty

Offer a 'Local-Vault' feature for voice data processing and storage on Australian-based servers. Provide an automated 'Right to be Forgotten' button.


Build Agentic Handoffs

Hand off a Live Summary Dashboard with real-time transcript and 'Intent Score' when the AI detects frustration or a 'Trust Threshold' breach.


Pivot to Agentic API-First Middleware

Shift from a standalone widget to an API-first middleware.  Integrate with clients' First-Party Data ecosystems, complying with Australia's Privacy Act.


Sovereign Voice Guarantee

Explicitly market a Sovereign Voice Guarantee. Provide automated Compliance Certificates for agency clients' legal teams.


Agentic AU Integrations

Develop one-click connectors for the Aussie SMB Tech Stack (Health, Trades, Hospitality, Payments) to execute tasks beyond FAQ responses.


Multi-Tenant Agency Dashboard

Build a command center for agency account managers to manage multiple client voice agents and export AI Attribution Reports.


Conversion Agent Focus

Shift from support (FAQ bot) to revenue generation (Conversion Agent). Integrate voice agent with CRM for booking and sales.


Invest in Latency and Dialect

Prioritize low latency (sub-300ms) and Australian accent support as technical requirements, not features.


Prioritize technical investments for revenue velocity & moat

Focus on bridging the gap between demo and mission-critical infrastructure with investments in browser automation, Telnyx voice integration for the Australian market, and a living knowledge graph.


Browser Automation Template Orchestration for Agencies

Develop a standardized library of Browser-Executable Agents (using Playwright or Selenium) to enable agencies to deploy revenue recipes, clone across client accounts, and manage multiple 'Genesis bots'. Launch a template marketplace.


Telnyx AU Voice Widget & Compliance Guardrails

Integrate Telnyx Voice API with focus on Australian English and local compliance. Optimize latency by hard-coding the RTC stack to anchor media in the Sydney PoP. Build a compliance wrapper for DNC and Privacy Act compliance.


Revenue-First Living Knowledge Graph (LKG) Architecture

Implement a Graph-structured Memory (Neo4j or Vector-Graph hybrid) that maps relationships between companies, stakeholders, and interactions. Use browser automation to scrape-and-update the graph autonomously.


GTM Strategy for AI Voice Widget via Agency Partners

Optimal go-to-market strategy for the Telnyx WebRTC AI voice widget targeting Australian trades through digital agency partners, focusing on positioning, pricing, and partnership models.


Ideal Tradie AI Receptionist System Prompt (Mazza)

System prompt design for a tradie-focused AI receptionist maximising lead capture with Australian voice/tone, embodying a friendly and efficient persona named 'Mazza'.


Dynamic Triage & Dispatch Agent

Lightweight classifier using a fast/cheap model (Groq Llama3, Claude Haiku) to route tasks instantly into priority queues, decoupling identification from execution for reduced latency.


Unified Context Object (UCO) via Redis Cache

Standardised JSON schema for core entities (Lead, Customer) stored in Redis. Agents Read → Execute → Write back to Redis, eliminating redundant API calls and inconsistent outputs.


Fan-Out / Fan-In Parallelisation for Lead Enrichment

Parallel execution of lead enrichment agents (Company_Info_Agent, Tech_Stack_Agent, Social_Signal_Agent) with a Fan-In aggregator triggering only when all complete, reducing enrichment time.


Adaptive Model Router

Single `/api/v1/llm-router` endpoint routing requests to different LLMs based on complexity (Groq/GPT-3.5, Claude Sonnet/Gemini Flash, GPT-4o/Claude Opus) to reduce latency and compute costs.


Automated Quality Gates with Confidence Scoring

Scoring Agent evaluating outputs against a rubric, assigning a confidence score to auto-approve, flag, or reject and re-queue tasks, reducing human latency.


Meta-Cognition Loop

6-module architecture for self-directing Genesis AI that identifies strategic gaps and initiates Deep Think cycles autonomously.


Strategic Question Identifier (SQI)

Module that runs graph algorithms on Knowledge Graph to find strategic weaknesses and outputs a prioritized question queue.


Prompt Engineering Engine (PEE)

Module that translates raw questions into context-aware prompts using advanced framing techniques and persona assignment.


Deep Think Orchestrator (DTO)

Module that manages the API to reasoning models (e.g., Gemini), dispatches prompts with different personas, and logs full provenance.


Insight Extraction & Synthesis (IES)

Module that parses LLM output, resolves entities, normalizes relationships, and assigns confidence scores.


Knowledge Graph Ingestion Service (KGIS)

Module that validates transactions, deduplicates insights, and atomically commits to the KG with provenance tags.


Actionable Intelligence Engine (AIE)

Module that uses pattern matching on new KG commits to trigger task creation and goal re-evaluation, closing the meta-loop.


Pivot to Agentic API-First Middleware

Shift from standalone widget to a system that integrates with existing First-Party Data ecosystems and complies with Australia's tightened Privacy Act requirements.


Data Sovereignty in Australia

Focus on local (AUS) data residency & Privacy Act compliance to address C-Suite concerns about AI ethics and data privacy.


Agentic Capability for Voice Widget

Move from FAQs to Task Execution (e.g., booking via API) to deliver Outcome-Based ROI (leads/sales).


Whitelabel Orchestration for Voice Widget

Allow agencies to "own" the brand voice and logic, providing a more valuable tool that enables orchestration rather than just installation.


Invest in Latency and Dialect

Achieve sub-300ms response time and handle a broad range of Australian accents to address the "trust gap" with AI.


Multi-Tenant Dashboard for Agencies

Build a command center for agency account managers to manage multiple client voice agents, tweak brand personalities, and export AI Attribution Reports.


Pivot to Agentic Partner with Deep CRM Integration

The most important strategic decision is to pivot from a "tool" to an "agentic partner" by prioritizing deep CRM integration and first-party data sovereignty. The widget must function as an Autonomous Voice Agent, triggering workflows and executing next steps (e.g., booking a service in Calendly, updating a lead in HubSpot, or processing a return).


White-Label AI Strategy Platform

Agencies are moving toward becoming "AI Strategy Partners." Providing a fully white-labelable platform allows them to resell your tech as their own proprietary "Intelligent Operations" service, securing high margins (often 70-90%).


Prioritize Australian-Based Data Hosting

To win government-adjacent or enterprise-level agency clients, you must offer Australian-based data hosting to meet compliance mandates.


Human-Grade Nuance for AI Voice

By 2026, "robotic" AI is a brand liability. The Australian market specifically craves authenticity and relatability.  This includes Emotional Intelligence, Sub-second latency, First-Party Context data usage, and Multimodal interaction.


Compliance-First AI Voice Widget

Prioritize Sovereign Data & Regulatory Proofing over pure feature depth for AI voice widgets targeting Australian digital agencies in 2026 due to upcoming compliance mandates.


Sovereign AI Infrastructure

Host data and processing for the AI voice widget on Australian-based infrastructure (e.g., AWS Sydney or Azure Melbourne regions) to cater to 'high-trust' sectors.


Agentic AI Widget

The AI voice widget should function as a headless agent that plugs into the agency's preferred tech stack (HubSpot, Salesforce, Shopify), not just a voice interface.


Gemini Interaction Workflow

Workflow for interacting with Gemini, including launching Chromium, entering a prompt, and submitting the prompt. Uses selectors like '[aria-label="Enter a prompt for Gemini"]' and '[aria-label="Send message"]' for UI interaction.


Core Configuration Validation System

System for validating the core configuration of the Genesis Hyperdrive project, ensuring proper setup and adherence to specifications. Validated by US-001: Core Config Validator test.


Import Chain Verification System

System that verifies the integrity and correctness of the import chain within the Genesis Hyperdrive project. Validated by US-002: Import Chain Verifier test.


Dependency Health Check System

System for checking the health and status of dependencies within the Genesis Hyperdrive project, ensuring they are functioning correctly. Validated by US-003: Dependency Health Check test.


AIVA Integration Validation System

System for validating AIVA integration, ensuring successful connections, stress testing context windows, and overall integration validation. Demonstrates 100% success rate across three test suites.


Genesis Hyperdrive Loop

Automated testing loop for Genesis Hyperdrive features. Includes skill activation, API scope validation, and server health checks.


Ralph Wiggum Loop (RWL)

The Ralph Wiggum Loop (RWL) comprises Parser, Orchestrator, and Quality Gate components, all undergoing unit tests as evidenced by the RWL Meta report.


MCP Config Validator

A system for validating the configuration of the MCP (Mission Control Program).


MCP Server Launcher

A system for launching MCP (Mission Control Program) servers.


MCP Tool Registry Validator

A system for validating the tool registry within the MCP (Mission Control Program).


Knowledge Base Integrity Check

A system to ensure the integrity of the knowledge base used by GENESIS.


Knowledge Extraction Tests

A system for testing the effectiveness of knowledge extraction processes.


Knowledge Search Tests

A system used to test the functionality and performance of knowledge search capabilities.


Secrets Security Audit

A security audit process focused on secrets management within the GENESIS hyperdrive configuration.


Environment Variable Validator

A system component responsible for validating environment variables used in the GENESIS hyperdrive configuration.


Config Path Resolver

A system component that resolves configuration paths within the GENESIS hyperdrive configuration, ensuring correct file locations are accessed.


Genesis Hyperdrive Security Audits

Automated security audits for the Genesis Hyperdrive project, including dependency vulnerability scanning, access control audits, and audit logging completeness checks.


Ralph Wiggum Loop

A system for running tests, reporting on story success and durations.


Genesis Hyperdrive 1.0 Integration

Report on the integration of Genesis Hyperdrive 1.0, including end-to-end flow tests, component integration matrix validation, and system status dashboard verification.


Hybrid Retrieval Weight Optimization for Memory Upgrade

The strategic document focuses on optimizing hybrid retrieval weights within the context of a memory upgrade, potentially involving a combination of retrieval methods to enhance performance or efficiency.


Dynamic Hybrid Retrieval Weight Optimization Strategy

A comprehensive strategy for dynamically adjusting hybrid retrieval weights in the `memory-lancedb` plugin, addressing limitations of static weights and context-agnostic retrieval. Includes algorithms for adjusting weights based on memory profile (age, importance, category), query-specific weighting, and a mechanism for learning optimal weights over time via retrieval feedback and A/B testing.


AIVA's Hybrid Retrieval System

AIVA's current hybrid retrieval system utilizes a combination of vector similarity, BM25, recency, and importance scores to retrieve memories. Weights are defined in `HYBRID_WEIGHTS` constant, and combined in the `hybridSearch` method. It has static weights which need to be optimized dynamically


Dynamic Weight Adjustment based on Memory Profile Algorithm

Algorithm for dynamically adjusting `HYBRID_WEIGHTS` based on memory age, importance score, and category distributions. The adjustment should occur periodically, potentially on plugin initialization or before each `hybridSearch`. Goal: Improve P90 relevance by 10%.


Extension to `hybridSearch` to allow query-specific or implied-category-specific weighting. Uses `detectCategory` (or similar) to inform temporary `HYBRID_WEIGHTS`. Goal: Improve P90 retrieval precision for category-filtered `memory_search_advanced` queries by 15%.


Learned Weights & A/B Testing Framework Mechanism

Mechanism for 'learning' optimal weights over time based on retrieval feedback. Could involve implicit feedback (click-through rates, dwell time) and explicit feedback (thumbs up/down). Requires A/B testing to evaluate different weighting strategies.


Dynamic AI Memory Weighting

A three-tiered dynamic system is proposed for AIVA's memory retrieval: 1) Global Dynamic Adjustment based on memory database statistics, 2) Contextual Transient Adjustment based on query intent, and 3) Learned Optimization through A/B testing and feedback to refine weights over time.


Implement Dynamic Weight Manager

Build a DynamicWeightManager class with an updateWeights method to calculate and apply dynamic weight adjustments based on memory age and importance distributions. Use LanceDB/DuckDB for efficient statistics queries.


Enhance hybridSearch for Category-Specific Weighting

Update the hybridSearch method to accept an optional 'weights' parameter, allowing category-specific weight profiles to be applied based on query intent, improving retrieval precision for category-filtered queries.


A/B Testing Framework for Learned Weights

Design and implement an A/B testing framework to optimize memory weights and hyperparameters based on implicit and explicit user feedback, utilizing user grouping, instrumentation via OpenClawPluginApi, and key metrics like Recall Success Rate and User Engagement.


Phased Implementation Roadmap

Implement a staged rollout: 1) Instrumentation & Baseline, 2) Contextual Weights (Query-Specific), 3) Dynamic Profile Weights (Memory-Shape). This minimizes risk and allows for iterative improvement.


Dynamic Weight Adjustment for Memory Retrieval

A system to dynamically adjust hybrid retrieval weights based on memory statistics (age, importance) to optimize retrieval relevancy as memory profiles evolve. Implemented via DynamicWeightManager class. Adjusts recency and importance weights based on memory age and importance distributions, renormalizing to ensure a total weight of 1.


Category-Specific Weighting for Hybrid Memory Search

Implement category-specific weighting in hybridSearch to improve retrieval precision by tailoring weights based on the query's intent. This involves defining weight profiles for different memory categories (preference, event, fact, decision) and applying these weights during the hybrid search process.


Hybrid Search with Transient Weights

The `hybridSearch` function in `MemoryDB` is modified to accept optional transient weights. These weights, supplied by the calling function (e.g., `memory_recall`), override or augment the dynamic global weights to prioritize specific retrieval strategies based on query context.


A/B Testing Framework for Memory Recall

A framework to manage A/B tests using OpenClawPluginApi hooks. Assigns users to test groups based on stable identifiers and logs structured events for analysis, including before agent start and after agent end. Includes metrics like recall success rate and user feedback to optimize memory recall.


Data-Driven Memory Weight Optimization

Move from heuristics to a data-driven, self-improving system for memory recall optimization using implicit and explicit feedback. Implicit feedback tracks memory usage in agent responses, while explicit feedback comes directly from users.


Category-Specific Memory Decay

Optimize memory recall by implementing category-specific decay rates for memory importance. Different memory types (e.g., decisions, events, preferences) should have different half-life and decay parameters.


Phased Rollout for Memory Upgrade

A phased rollout strategy is employed to minimize risk and validate each change during the memory upgrade process. The rollout includes instrumentation, contextual weights, global dynamic weights, and A/B testing.


Hybrid Search with Transient Weights

The `hybridSearch` function will be updated to include a `transientWeights` parameter to incorporate contextual, query-specific weights for memory retrieval.


Dynamic Weight Manager

The `DynamicWeightManager` class will be built to query LanceDB for statistics to enable dynamic adjustment of retrieval weights over time.


Implicit Feedback for Memory Usage

Implementing implicit feedback through the `analyzeResponseForMemoryUsage` function to assess and optimize memory usage during retrieval.


Auto-Capture Trigger System

A system for automatically capturing relevant information from user interactions to enhance AIVA's memory capabilities. Currently uses a static array of regex patterns (`MEMORY_TRIGGERS`) to determine capture eligibility.


Semantic Pattern Augmentation for Auto-Capture

Augmenting or replacing the `MEMORY_TRIGGERS` with a fine-tuned language model or curated dictionary to detect implicit memory categories (relationship, health, finance).  Outputs a confidence score for a new `ImplicitMemoryCategory`.


Enhanced Triggering via detectCategory

Enhance `detectCategory` with more sophisticated phrase patterns and sentiment analysis on keywords to feed back into `shouldCapture` logic, increasing capture robustness for user values/beliefs and recurring frustrations. Lower the threshold for capture based on strong signals from `detectCategory`.


Tiered Capture Architecture

A multi-tiered decision process for AIVA's memory capture, evolving `shouldCapture` to use computationally efficient methods first and engaging sophisticated models only when necessary. Tier 1: Regex. Tier 2: Semantic classifier & enhanced category detection. Tier 3: Conversation review.


Phase 1: Smarter Category Detection

Enhance `detectCategory` by modifying `shouldCapture` to return a `CaptureSignal` object with a score. Extend category detection to use sentiment analysis with a dictionary of sentiment-laden phrases. Calls enhanced `detectCategoryAndSentiment` if regex fails.


Phase 2: Semantic Classifiers

Introduce a small, specialized multi-label classifier (fine-tuned DistilBERT) to capture implicit information like relationships, health, and finances. Train the model using synthetic examples, anonymized AIVA logs, and sanitized public datasets. Use `shouldCaptureImplicit` function after regex check.


Phase 3: Contextual Memory Review

Leverage the `agent_end` event to review the last few turns of a conversation for professional history and cultural preferences. Use a combination of keyword spotting and semantic search via embeddings to identify high-confidence signals. Implemented in `reviewConversationForImplicitMemories`.


Tiered Cascade of Filters

Implement a tiered "cascade of filters" architecture, keeping the existing regex as a rapid-fire first pass. Then, when that fails, a more intensive approach will take over. Aiming for high precision (P90 > 0.85) and low false positives (< 0.05).


Implicit Memory Classifier

A fine-tuned, multi-label text classification model used to capture implicit relationship, health, and finance information from user utterances. Employs lightweight transformers (e.g., DistilBERT) and is integrated into the `shouldCapture` function within `enhanced_index.ts`.


Implicit Memory Capture Strategy

Leverage a lightweight classification model with synthetic, log-based, and public data for training. Integrate with a confidence threshold to balance recall and precision. Evaluate based on F1-score, Implicit Memory Recall Lift, and precision of newly captured memories.


Enhanced Category Detection with Heuristics

System to enhance memory capture by using heuristics and sentiment analysis to identify User Values/Beliefs and Recurring Frustrations. It augments `detectCategory` with dictionaries of phrases and sentiment analysis, triggering capture when strong signals and relevant sentiment align.


Sentiment-Based Keyword Triggering

Pattern for triggering memory capture based on the co-occurrence of specific keywords and a defined sentiment threshold. Keywords indicating 'frustration' require strong negative sentiment, while keywords indicating 'value' require positive sentiment.


Prioritize Heuristic Checks in Capture Logic

The decision to integrate category heuristic checks as a tier in the `shouldCapture` function, after implicit semantic checks, to improve capture accuracy without relying on a full ML model initially.


Contextual Memory Capture System

System to analyze conversational context for implicit memories (Professional History, Cultural Preferences) by analyzing the last N user turns using the `agent_end` event to trigger a `reviewConversationForImplicitMemories` function.  Uses keyword and embedding similarity to detect relevant context and then summarizes into a memory.


Summarize Context for Memory Capture

The system will summarize relevant facts from a block of conversation context into a single, clean memory using an LLM. This combines multiple conversational turns into a single, high-value memory, respecting the `AUTO_CAPTURE_LIMIT`.


Use agent_end event for context analysis

The `agent_end` event will trigger the `reviewConversationForImplicitMemories` function. This allows analysis of the full conversational context.


Memory Consolidation Algorithm

Improvements to the memory consolidation algorithm are the focus of this analysis within the Deep Think: Memory Upgrade project.


AIVA's Memory Consolidation System

AIVA's memory consolidation system currently uses an O(n^2) algorithm for semantic deduplication and merging, and a static staleness pruning method. The `db.update` operation is implemented as a delete-and-re-add. `db.listAllWithVectors()` currently fetches *all* memories into memory.


Incremental Consolidation Algorithm

An algorithm for `consolidateMemories` that avoids the `O(n^2)` full-scan. Considers neighborhood-based merging, batch processing and hashing/clustering.  Outline how to efficiently track and update the `toDelete` and `merged` sets across batches or incremental passes.


Semantic Synthesis Merge Strategy

A new merge function, e.g., `synthesizeMemories` that leverages an LLM to generate a concise, synthesized `text` that incorporates key information from both inputs. The `importance` of the synthesized memory should be derived intelligently.


Adaptive Staleness Thresholds

Introduce adaptive staleness thresholds based on `MemoryCategory` and accelerate decay for memories that are likely contradicted.  This should reduce "false positive" pruning and increase pruning of genuinely irrelevant memories.


Synaptic Pruning and Strengthening Architecture

Re-architect AIVA's memory consolidation system for efficiency, accuracy, and long-term adaptivity. Uses Approximate Nearest Neighbor (ANN) based incremental consolidation, LLM-powered Semantic Synthesis, and Adaptive Decay model.


Approximate Nearest Neighbor (ANN) Consolidation

Replaces O(n^2) full comparison with ANN-based incremental consolidation. Uses LanceDB to achieve O(N log N) complexity. Includes batching and a processedIds set to avoid re-merging.


LLM-powered Semantic Synthesis

Transforms the naive 'keep-longer' merge strategy into an LLM-powered Semantic Synthesis process. It strengthens memories by creating richer, more comprehensive knowledge nodes through synthesis of memories using an LLM.


Adaptive Decay Model

Replaces the static staleness rules with an Adaptive Decay model. It recognizes that the relevance of memories is context-dependent. Introduces category-specific decay profiles and uses contradiction detection to reduce the importance of contradictory memories.


Neighborhood-based Batched Consolidation Strategy

Consolidate memories by iterating through them in batches and merging them with their closest semantic neighbors found using vector index, improving complexity from O(N^2) to O(N * k * log N).


EnhancedMemoryIndex class

A class that manages memory consolidation and pruning using vector-based similarity search and batch processing for improved efficiency.


Batched Database Operations

Instead of performing database operations (delete and add) for each merge, batch all operations and execute them at the end to minimize I/O and index churn. Use LanceDB's bulk delete and add operations.


LLM-based Memory Synthesis

Leverage an LLM to synthesize two related memory fragments into a single, comprehensive memory entry, retaining unique details and removing redundancy. This produces a single, coherent, and information-rich memory entry that is more than the sum of its parts.


synthesizeMemories Function

A TypeScript function that crafts a precise prompt for an LLM, instructing it to act as a memory consolidation agent. It takes two memory entries as input, synthesizes them using the LLM, re-embeds the new text, calculates a new importance score, and returns a new, synthesized MemoryEntry object.


Adaptive Staleness and Contradiction-Driven Decay

A strategy to manage memory relevance based on category, age, and contradictions.  Implements category-specific decay profiles and proactive contradiction handling.


Memory Decay Profiles

Configuration-driven decay profiles defined per memory category, specifying decay rate and importance in a configuration file (enhanced_config.ts).


Contradiction Detection at Ingestion

Modify the `memory_store` tool to detect contradictions when new information arrives and immediately penalize contradicted memories.  Uses a gpt-4-turbo-preview model to check for contradictions.


Adaptive Pruning

Implementation of a pruning mechanism (pruneStaleMemories) that removes memories based on their category, age, and importance. It is implemented in `enhanced_index.ts`.


Memory Consolidation Evaluation Strategy

Comprehensive strategy for evaluating and deploying memory consolidation algorithms, including incremental consolidation, semantic synthesis, and adaptive staleness & decay.


Adaptive Staleness & Decay System

A system for pruning memories based on adaptive staleness and decay, improving relevance and reducing redundancy. It addresses the challenge of how long to keep memories.


Use LLM as Judge for Synthesis Evaluation

Leverage GPT-4 to compare old merged memories and new synthesized memories against original fragments, automating the evaluation of memory synthesis quality.


A new memory consolidation function, that uses a neighborhood-based approach.


Cross-Session Memory Transfer System

System designed for transferring memory between different sessions in a Deep Think memory upgrade initiative.


Session-Aware Memory Consolidation

Implement session boundary detection and lightweight consolidation triggered by session end to reduce memory footprint and improve retrieval latency.


SessionManager Class

A class to track session start/end times using inactivity thresholds, triggering targeted memory consolidation upon session end.


Lightweight Targeted Consolidation

A `consolidateMemories` operation focused on recent memories within a defined consolidation window. Prioritize merging duplicates and only prune when they are no longer needed.


Proactive Session Management & Targeted Consolidation

Introduces a SessionManager to track session boundaries (inactivity, explicit commands). Consolidates memories within each session during the nightly memory_consolidate process using timestamp-based queries instead of listAllWithVectors(), and configurable retention periods.


SessionManager Class

A class to track the current session, manage session boundaries (e.g., inactivity, explicit commands like `/new_session`), and handle session-related metadata. A new `sessionId` field will be added to the `MemoryEntry` schema.


Dynamic, working memory system

Transition from a static, archival memory to a dynamic, working memory system that actively manages context, relevance, and importance based on user interaction patterns.


Implement a targeted memory consolidation process at the end of user sessions to strengthen important memories and prune weak ones, mimicking cognitive consolidation.


SessionManager Class

A class to manage the session lifecycle, including starting new sessions, recording user activity, ending sessions due to inactivity or manual termination, and triggering session-end protocols like consolidation.


Targeted Memory Consolidation in MemoryDB

Introduce a `consolidateRecentMemories` method in `MemoryDB` that focuses on consolidating memories created within a specific time window of the ended session, avoiding full database scans and reducing computational cost.


Generative Session Summarization & Context Priming

System to create episodic summaries of past sessions to prime AIVA's context, ensuring contextually-aware continuation.  It generates a summary of the most important memories from a past session and injects it into the beginning of the subsequent session.


Implement Session Summarization Logic

Logic to generate a concise, one-paragraph summary of the main topics, conclusions, and action items from a session's key memories using an LLM.  This summary is then stored as a high-importance memory with the category 'session_summary' and metadata linking it to the summarized session.


Modify autoRecall for Context Priming

Modifies the `autoRecall` function to proactively inject the last session's summary at the start of a new session to provide context. This involves checking if it's the first interaction of a new session and retrieving/injecting the most recent session summary if available.


Semantic Coherence Analysis for Topic Detection

Adapts memory retrieval based on topic coherence. Narrow focus for continuations, broaden for shifts. Uses cosine similarity threshold to detect topic shifts and prioritizes memories from current session for continuations. Modifies hybridSearch to bias toward recent memories in continuation contexts.


analyzeTopicCoherence Method

Method to analyze the semantic coherence between a new prompt and recent memories within a session. Calculates cosine similarity and returns 'continuation' or 'shift' based on a defined threshold (TOPIC_SHIFT_THRESHOLD).


hybridSearch Adaptation

Modified hybridSearch method to accept a 'topicContext' parameter. For continuations, it prioritizes memories from the current session. For topic shifts, it performs a broad global search. Uses LanceDB with pre-filtering to simulate boosting.


Memory Upgrade - Episodic vs Semantic Separation

Strategy for memory upgrade focused on separating episodic and semantic memory.


AIVA's Unified Memory Architecture

AIVA's memory system uses a single LanceDB table ('memories') storing MemoryEntry objects with 'id', 'text', 'vector', 'importance', 'category', and 'createdAt'. HybridSearch retrieves from this table, applying HYBRID_WEIGHTS for vector, BM25, recency, and importance. Consolidation merges duplicates and prunes stale memories uniformly.


Dual-Store Architecture with Categorical Routing

A dual-store architecture uses separate LanceDB tables: 'episodic_memories' and 'semantic_memories'. MemoryEntry objects are categorized into 'episodic' or 'semantic' types via an enhanced detectCategory function. A MemoryRouter directs MemoryEntry objects to the appropriate table. A fusedHybridSearch function intelligently queries both stores based on query analysis, prioritizing episodic or semantic memories based on query content.


Unified Memory Management System (UMMS)

A dual-store architecture within LanceDB, segregating memories into Episodic (context-rich, time-bound events) and Semantic (abstracted, timeless knowledge). Allows specialized management policies for storage, retrieval, decay, and consolidation.


Dual-Process Memory Architecture

Separating episodic and semantic memories enhances relevance, efficiency, and scalability of AIVA's memory system. Allows applying specialized management policies for storage, retrieval, decay, and consolidation.


Differentiated Decay Policies

Episodic memory: rapid decay (HALF_LIFE_DAYS_EPISODIC = 7) to prioritize recency. Semantic memory: slow decay (HALF_LIFE_DAYS_SEMANTIC = 180) for stability.


Intelligent Retrieval Orchestration

Refine fusedHybridSearch, use heuristic and LLM analysis for query analysis, leverage parallel search, and dynamically adjust fusion weights based on memory and query type.


Unified Memory Management System (UMMS)

The UMMS consists of four core components for memory management, separating episodic and semantic memory stores for optimized retrieval and lifecycle management.


Dual-Store Memory Architecture

Physical separation of memory into episodic (event-based) and semantic (fact-based) stores using distinct tables (aiva_memories_episodic, aiva_memories_semantic).


MemoryRouter (MemoryManager)

Abstracts access to episodic and semantic memory tables, providing a single point of contact for memory operations (store, search, consolidate).


Differentiated Decay Constants

Episodic memory decays faster than semantic memory. Different half-life days and importance decay per day are applied to each memory type.


Differentiated Consolidation Policies

Consolidation policies are adapted to run with different parameters on each table. Episodic requires a higher duplicate threshold and merge threshold (low).


Phased Rollout of Memory Architecture Upgrade

A phased implementation roadmap to upgrade AIVA's memory system, ensuring stability and measured impact. The phases are Foundation & Segregation, Intelligent Retrieval, and Semantic Enrichment.


MemoryManager Routing Layer

A new routing layer implemented as part of the memory upgrade, refactoring all memory access calls through it.


Differentiated Memory Consolidation

The `consolidateMemories` job will be updated to run separately on episodic and semantic memory tables with distinct parameters.


Fused Hybrid Search

Implementation of full orchestration logic with query analysis, dynamic limits, parallel search, and advanced re-ranking for memory retrieval.


Knowledge Graph Integration

Integrating a knowledge graph to augment `fusedHybridSearch` with multi-hop reasoning for complex user queries.


Memory Importance Decay Curve Engineering

System for engineering memory importance decay curves within the Deep Think memory upgrade project.


Implement Category-Specific Power Law Decay Curves

Propose and implement new decay curves for each memory category based on power law, to reflect different decay rates and retention needs. This includes defining parameters, minimum importance floors, and A/B testing.


Integrate Spaced Repetition Boosting on Recall

Boost memory importance and reduce decay rate upon successful recall. Add 'lastRecalledAt' and 'repetitionFactor' to MemoryEntry, updating them upon recall and incorporating them into the importanceScore calculation.


AIVA Memory Decay Mechanism

AIVA's current memory decay mechanism uses a universal linear decay model. This is considered suboptimal and should be replaced with category-specific power law decay curves and a spaced repetition boosting mechanism.


AIVA Memory Upgrade Strategy

A three-part proposal to enhance AIVA's memory system: category-specific power law decay, spaced repetition boosting, and refined importance calculation. Aims for human-like recall and memory management.


Category-Specific Power Law Decay

Replaces linear decay with tailored power law decay curves for each MemoryCategory (contact, event, preference, skill, fact) to mimic Ebbinghaus forgetting curve (t^-α).  Requires modifications to config, hybridSearch logic, and A/B testing.


Spaced Repetition Boosting

Mechanism to strengthen memories upon recall, incorporating 'lastRecalledAt' and 'repetitionFactor' to rejuvenate memory and mimic the 'testing effect'. Affects update and hybridSearch logic. Evaluation using 'memory stickiness'.


Dynamic Importance Adjustments

Refines importance calculation for initial and subsequent updates, using MemoryCategory to adjust importance and boosting relevance on semantic updates. Aims to improve recall and reduce false positives.


Category-Specific Power Law Memory Decay

Implements a new memory decay model using power law curves, configurable per memory category, to improve recall precision for stable memories and temporal relevance of ephemeral memories.  It replaces the linear decay model.


A/B Test for Memory Decay Models

A 60-day A/B test to compare the existing linear memory decay model (Control Group A) with the new category-specific power law decay model (Variant Group B). Metrics include P90 recall precision for 'contact' memories and 'event' memory relevance scores based on LLM evaluation.


Spaced Repetition Boosting on Recall (reinforceMemory)

Implements a spaced repetition algorithm to reinforce memories that are successfully recalled. It increases a memory's resistance to decay based on its recall history by adding lastRecalledAt and repetitionFactor to the MemoryEntry. A reinforceMemory method updates these fields. The decay exponent is modified based on repetitionFactor and REPETITION_SCALAR within the hybridSearch method.


Dynamic Importance Adjustments for Memory Store

Proposes refining the computeImportance function to be category-aware, providing importance boosts when memories are updated, and evaluating the impact on memory preservation through specific metrics.


Context-Aware Memory Injection Layer for AIVA

Design a context-aware memory injection layer to dynamically re-rank, filter, and place memories, maximizing their utility and improving conversational flow by minimizing context stuffing.


Dynamic Relevance Thresholding & Re-ranking Algorithm

Dynamically adjust RECALL_MIN_SIMILARITY and RECALL_INJECT_MAX based on conversation topic and user intent. Re-rank memories after applyMMR, incorporating contextual signals (topic alignment, recency, user sentiment).


Conversation Topic Modeling for Memory Filtering

Implement a lightweight, real-time topic modeling approach for ongoing conversations (LLM call or keyword extraction + clustering). Use detected topics to filter or boost specific MemoryCategory types.


ConversationContext Data Structure

Expand ConversationContext to capture topic, user sentiment, task context, and temporal cues. Use this to improve intelligent injection placement within the prependContext block.


Cognitive Re-ranking & Augmentation (CRA) Layer

A multi-stage layer that replaces the current static retrieval mechanism. Operates between candidate retrieval and context injection, transforming the process to a dynamic, context-sensitive cognitive function.


ConversationContext Object

A rich data structure, updated with every turn, that models AIVA's working memory and attentional focus. Used to dynamically re-score, filter, and format memories.


Dynamic Relevance Re-ranking

Function that calculates a new, context-aware score for each memory candidate, based on the ConversationContext.


Structured Context Block

Format final memories into a structured block (<AIVA_INTERNAL_CONTEXT>, <RELEVANT_FACTS>, <PAST_DECISIONS>, etc.) to guide LLM reasoning.


Context-Aware Memory Re-ranking

Reranks memories based on contextual relevance using a weighted sum of similarity, topic alignment, temporal relevance, and category boost. Dynamic thresholds for injection.


Context-Aware Composite Scoring Function

Calculates a `contextualScore` for each memory using a weighted sum: `w_sim * S_sim + w_topic * S_topic + w_temp * S_temp + w_cat * S_cat`.  Includes dynamic temporal relevance and category boosts.


Dynamic Injection Budget

Replaces static `RECALL_INJECT_MAX` with a dynamic `injectionBudget` adjusted based on the `contextualScore` of candidate memories to control noise.


Conversation Context Engine

A system for maintaining and updating conversation context, including topic embedding, summary, and keywords. It uses a hybrid approach: Rolling Topic Embedding (fast, EWMA) and LLM-Generated Topic Summary (periodic, for accuracy). Implemented as a TypeScript class `ContextEngine`.


Hybrid Topic Modeling

A two-tiered approach to topic modeling: 1. Rolling Topic Embedding (fast): EWMA of recent message embeddings for immediate focus. 2. LLM-Generated Topic Summary (periodic): LLM call to summarize conversation history into `topicSummary` and `topicKeywords`.


Multi-Dimensional Context Integration & Injection

Strategy to expand context object to include sentiment, current task, and temporal focus. Format context into an XML-style block for LLM understanding. Implement categorical grouping, dynamic summarization, and confidence-based formatting of memories.


ContextEngine

A class responsible for generating topic summaries and keywords, updating topic embeddings, and managing conversational context.


formatRelevantMemoriesContext function

Function to format relevant memories into an XML-style structure, grouping by category, summarizing long memories, and adding confidence hints.


Memory Compression for Long-Term Storage

Utilizing memory compression techniques as a strategy for upgrading memory capacity, specifically focused on long-term storage solutions.


Progressive Summarization for MemoryEntry.text

Implement a tiered system where recent memories retain full text, while older/lower-importance memories are progressively summarized into 'hot', 'warm', and 'cold' tiers, impacting storage and retrieval.


Hierarchical Memory Clusters (Meta-Memories)

Group semantically related MemoryEntry objects into clusters and create a higher-level meta-memory (summary and aggregated vector) for each cluster, improving retrieval efficiency.


AIVA Memory System

AIVA's memory system uses a LanceDB table ('memories') to store MemoryEntry objects, including text, vector, importance, category, and createdAt. Current implementation lacks explicit compression or hierarchical structuring.


Unified Memory Lifecycle Strategy

A multi-tiered memory system with Hot (recent, high-fidelity), Warm (summarized), and Cold (highly summarized) tiers, and Hierarchical Clusters (Meta-memories) to represent groups of related memories.


Progressive Summarization

Tiered memory system (Hot, Warm, Cold) where memories transition between tiers based on age and importance, triggering summarization via an LLM call. `consolidateMemories` function manages the transitions.


Hierarchical Memory Clusters

Groups related memories into Meta-Memories/clusters using approximate nearest neighbor search.  If a cluster of sufficiently similar memories is formed, a MetaMemoryEntry is created.


Progressive Summarization of Memory Entries

Implements a tiered memory system (Hot, Warm, Cold) where the `text` field of `MemoryEntry` is progressively summarized as it ages.  Hot memories retain full text, Warm memories are summarized to ~40%, and Cold memories are summarized to ~15%.  `summaryText` stores the summarized text, and the original `text` is retained. Memory tiers are managed by `consolidateMemories`.


Memory Tiering System

A system with three tiers (Hot, Warm, Cold) for managing memory entries based on age and importance.  Each tier has different summarization levels for the `text` field, with 'hot' retaining full text, 'warm' using a ~40% summary, and 'cold' using a ~15% keyword-rich summary. The `summaryText` field stores the summaries.


Memory Update Resets Tier to Hot

When a memory entry is updated, its `tier` is reset to 'hot' and `summaryText` is cleared to ensure the most relevant information is used in searches.


Hybrid Search with Progressive Summarization

The `db.hybridSearch` function queries against `summaryText` if it exists, otherwise it queries against the full `text`. Vector search uses the original vector, ensuring semantic accuracy is maintained.


Creation of a second LanceDB table `meta_memories` to store `MetaMemoryEntry` objects. A periodic function `clusterMemories` analyzes warm tier memories, finds related groups, summarizes them into meta-memories, and links the original memories.


Two-Stage Search

Retrieval strategy using two stages: Meta-Search against `meta_memories` to find relevant clusters, then Drill-Down Search on the main `memories` table, boosting scores of memories within the top meta-memories' `memberIds`.


Clustering Threshold and Size

Use a cosine similarity threshold of 0.75 and minimum cluster size of 4 for forming meta-memories.


Tiered Memory Architecture with Vector Quantization

Implements a multi-table memory architecture using LanceDB.  `hot_warm_memories` stores full-precision vectors, while `cold_memories` uses Product Quantization (PQ) for storage reduction. `consolidateMemories` moves memories between tiers. Hybrid search queries both tables.


Product Quantization for Cold Memory Storage

Leverages LanceDB's Product Quantization (PQ) to reduce storage costs for cold memories by 16x-32x, accepting precision loss in favor of storage efficiency. Uses approximate nearest neighbor (ANN) search on the `cold_memories` table.


Hierarchical Hybrid Search

A two-stage search process that queries both the full-precision `hot_warm_memories` and the PQ-indexed `cold_memories`, then combines, de-duplicates, and re-ranks results based on score.


Multi-Agent Shared Memory Protocols

Protocols enabling multiple agents to share memory, facilitating communication and collaboration.


Multi-Agent Shared Memory Protocol

A system facilitating knowledge propagation between a Queen AIVA instance and its SubAIVA instances, balancing aggregate learning with strict privacy partitioning and conflict resolution. Addresses weaknesses of isolated memory, including lack of upward aggregation, inefficient downward propagation, absence of privacy partitioning and no conflict resolution.


Upward Aggregation Protocol (SubAIVA -> Queen)

A strategy for SubAIVAs to securely push generalized, anonymized behavioral patterns and aggregated knowledge to the Queen. It involves identifying suitable `MemoryEntry` objects, generalization/anonymization, defining criteria for upload, and a secure API endpoint on the Queen.


SubAIVA.aggregateAndPushMemory()

A function for SubAIVAs to aggregate and push memory to the Queen AIVA, including identifying suitable MemoryEntry objects, generalization/anonymization, and defining upload criteria.


AIVA Cognitive Synchronization Fabric (CSF)

A hierarchical learning system that transforms AIVA's memory architecture. It introduces a two-stage aggregation protocol where SubAIVAs generate anonymized, generalized Candidate memories, which are then promoted to Global memories by the Queen AIVA. The Queen then propagates these Global memories and system-wide configuration updates back down to the SubAIVAs, creating a continuous, privacy-preserving learning loop.


Upward Aggregation (Candidate/Promotion Model)

SubAIVAs safely propose generalized knowledge to the Queen through a Candidate/Promotion model, ensuring privacy and controlled knowledge sharing.


Downward Propagation (Push/Pull System)

A versioned, asynchronous push/pull system disseminates global knowledge and configuration updates from the Queen to SubAIVAs.


Privacy & Conflict Governance

Data-level tagging (isPrivate) and centralized conflict detection/resolution within the Queen ensure privacy and manage semantic conflicts in aggregated memories.


Federated Refinement Loop

Aggregated vectors are used for continuous model improvement without data egress, enabling a closed loop of learning and refinement.


A two-stage aggregation protocol where SubAIVAs send anonymized memory candidates to a Queen AIVA, which then clusters and validates these candidates to form global knowledge. This reduces redundant memory storage and promotes shared understanding.


Memory Candidate Aggregation

SubAIVAs cluster their local memories, creating a centroid vector (prototype) and sending the aggregated data to the Queen AIVA to represent a generalized memory candidate, preserving privacy.


Global Aggregated Memory Validation

The Queen AIVA clusters incoming memory candidates from different SubAIVAs. If a cluster contains candidates from a threshold number of distinct SubAIVAs, a global memory is created, indicating a widely observed pattern.


Two-Stage Aggregation for Global Knowledge

Implementing a two-stage memory aggregation process (SubAIVA -> Queen) to ensure global knowledge represents widely observed patterns and reduce redundant local memory storage.


Global Memory Update Protocol

A protocol for propagating global knowledge and operational parameters from the Queen AIVA to SubAIVAs. It uses a versioned API endpoint on the Queen and periodic polling by SubAIVAs, incorporating a `GlobalUpdatePayload` data type to efficiently transfer updates.


Privacy Partitioning Strategy

A strategy to ensure PII never leaves the local environment of SubAIVAs. This is enforced by flagging `MemoryEntry` objects as private (`isPrivate`) and filtering out private memories during aggregation and pushing to the Queen. Private categories are defined in `MEMORY_PRIVATE_CATEGORIES`.


Conflict Resolution Mechanism

A mechanism on the Queen AIVA to detect and resolve conflicts in `CandidateAggregatedMemory` received from SubAIVAs. It groups candidates by `userId` and `category`, identifies semantic opposites (e.g., using a model or clustering), and resolves conflicts by giving higher weight to the most recent candidate.


Federated Memory Training Loop

A cyclical process where SubAIVAs collect anonymized data, the Queen AIVA trains models on aggregated data, and then propagates the updated models back to the SubAIVAs.


Global Update Payload

The mechanism by which the Queen AIVA delivers model updates (weights or endpoint URLs) to the SubAIVAs, triggering a hot-swap of models.


Data structures representing aggregated memories at different stages: `CandidateAggregatedMemory` for initial submissions, `GlobalAggregatedMemory` for validated, high-quality representations.


Phased Implementation Roadmap

A three-phase approach (Foundational Changes, Upward Aggregation, Downward Propagation & Global Memory) designed for incremental value delivery and risk mitigation.


Titan Memory Connection - Surprise Learning Integration

Integration of surprise-based learning into the Titan Memory Connection, likely referring to enhancements or upgrades to the memory system of a Genesis agent or system by incorporating unexpected or novel data for improved performance.


Integrate Surprise-Based Learning (Titan Memory) into AIVA

Integrate a Surprise-Based Learning system to dynamically adjust memory importance, trigger reclassification, and connect with Genesis KG based on the 'unexpectedness' of new information.


Surprise Score Computation Algorithm (computeSurprise)

Algorithm to calculate a 'surprise score' (0-1) for a new memory based on semantic distance to existing memories, novelty compared to AIVA's general knowledge (Genesis KG), vector similarity, and keyword novelty.


Importance Boosting for High-Surprise Memories

Modify `computeImportance` or introduce `applySurpriseBoost` to give high-surprise memories a significant initial importance boost, overriding the default `computeImportance` for a period.


Automatic Memory Category Reclassification

Trigger automatic reclassification of nearest neighbor memories when a new, high-surprise memory is stored, if neighbors share a common, different category and the new memory aligns more with that category.


Titan Memory: Surprise-Based AI Memory System

A dynamic AI memory system integrating 'surprise' to enhance learning, adaptation, and retention. Key components: computeSurprise algorithm, importance boosting logic, automatic reclassification, reinforcement learning feedback loop, and KG integration.


computeSurprise Algorithm

Algorithm that calculates a surprise score based on semantic and conceptual novelty. Semantic novelty is determined by vector search similarity, while conceptual novelty involves querying the Genesis KG for axioms and confidence scores. A weighted average combines the scores.


Importance Boosting

A process to adjust memory importance based on the surprise score.  If the surprise score exceeds a threshold, the memory's importance is boosted using a defined formula.


Automatic Reclassification

A dynamic categorization process triggered by high surprise scores. This involves a vector search, analysis of neighbor categories, and an LLM call to make a final decision on reclassification.


Reinforcement Learning Feedback Loop

A learning mechanism that reinforces the importance of memories used in agent interactions. It tracks retrieved memory IDs, detects their use in agent responses, and updates their importance accordingly.


Titan Memory: Surprise-Based Learning

A system that computes a surprise score for new memories based on semantic and conceptual novelty to boost important and re-evaluate categories.


ComputeSurprise Method

A method within the EnhancedMemory class that calculates a surprise score based on semantic novelty (LanceDB) and conceptual novelty (Genesis KG).


Surprise-Based Memory Importance Boosting

The system adjusts the initial importance of a memory based on its surprise score. Memories exceeding a surprise threshold have their importance boosted, ensuring highly surprising memories receive greater attention.


A system that automatically re-evaluates a memory's category based on its semantic neighbors when a high surprise score is detected. It leverages vector search and an LLM to refine categorization.


Reinforcement Learning for Recalled Memories

Implements a feedback loop to strengthen useful memories by tracking injected memories per request using AsyncLocalStorage and boosting importance based on usage in agent responses, determined by keyword analysis.


Genesis KG Axiom Connection

Bidirectional flow between personal memory and Genesis KG. Personal memory informs KG with high surprise memories. KG refines personal memory, reducing importance of memories similar to established axioms, reducing redundancy.


Memory Importance Adjustment Based on KG Axioms

Reduce the importance of personal memories highly similar to established axioms in the Genesis KG. This de-duplicates personal experiences into semantic knowledge and addresses redundancy.


Hybrid Retrieval Weight Optimization for Memory Upgrade

This document outlines the strategy for optimizing hybrid retrieval weights in the context of a memory upgrade, aiming to improve the system's ability to retrieve relevant information.


AIVA's Hybrid Retrieval Weighting Scheme

AIVA's `MemoryDB.hybridSearch` function uses fixed weights (vector: 0.4, bm25: 0.3, recency: 0.2, importance: 0.1) for hybrid retrieval. Recency is calculated with HALF_LIFE_DAYS=30 and importance with IMPORTANCE_DECAY_PER_DAY=0.01, IMPORTANCE_MINIMUM=0.1. FRESHNESS_BOOST=1.3 is applied within FRESHNESS_WINDOW_MS. MMR_LAMBDA=0.7 is used for diversification. Category diversity is ensured. Memories are categorized, and importance is computed initially.


Multi-stage Optimization of Hybrid Retrieval Weights

A multi-stage approach is proposed to optimize hybrid retrieval weights, starting with a dynamic weighting framework allowing for static, contextual, and learned strategies.


Dynamic Weighting Framework Implementation

Introduce a `WeightedHybridSearch` class or modify `MemoryDB.hybridSearch` to accept dynamic weights. Propose a `WeightedStrategy` enum (e.g., `Static`, `Contextual`, `Learned`).


Contextual (Category-Based) Weighting Implementation

Implement a 'contextual' weighting strategy where `HYBRID_WEIGHTS` are selected based on the detected `MemoryCategory`.


Multi-Stage Approach to Adaptive Memory Retrieval

A multi-stage approach to transform AIVA from a system with static weights to one that's fully adaptive and context-aware. This involves building a dynamic weighting framework, implementing category-based weighting, and implementing a reinforcement learning-driven system for long-term optimization.


Dynamic Weighting Framework

Build the architecture to enable dynamic weights for retrieval. Involves a `WeightedStrategy` enum, a `WeightedHybridSearch` object with a `determineRetrievalWeights` function, `HybridWeightConfig` object including `categoryWeights`, and a placeholder for the `learned` strategy. The `determineRetrievalWeights` function calls the various strategies based on this configuration.


Contextual (Category-Based) Weighting

Assign weights to different memory types based on `detectCategory` results. Weights are assigned based on query category (e.g., fact, preference, decision). A "Default" profile is implemented with a balanced approach as a fallback. Implemented with A/B testing.


Learned Weighting via Online Optimization

Framed as a Multi-Armed Bandit (MAB) problem. The "arms" are the different weight configurations (actions). State is the current weight setup for a user and query category. Actions perturb the weights. Reward function uses user feedback (explicit and implicit). Uses Epsilon-Greedy MAB algorithm. Weights stored in Redis.


Epsilon-Greedy MAB for Weight Optimization

Using an Epsilon-Greedy Multi-Armed Bandit algorithm to optimize retrieval weights based on user feedback. The "arms" are the different weight configurations, and the algorithm explores different weight combinations to maximize rewards.


Refactor the `hybridSearch` function to support dynamic weights from various sources (static, contextual, learned). Implement a `RetrievalStrategyOrchestrator` to decouple weight determination from search execution. This includes enhancing configuration, implementing the orchestrator, and refactoring MemoryDB.


RetrievalStrategyOrchestrator

A class responsible for determining the retrieval weights based on the configured strategy (static, contextual, or learned). It decouples the weight determination logic from the core search execution within the `hybridSearch` function.


Contextual Weighting Strategy

Implements a strategy to improve retrieval relevance by tailoring weights to the user's immediate intent based on memory category (fact, preference, decision, plan/goal, default). Weights are defined for Vector, BM25, Recency, and Importance.


Hybrid Search

The hybrid search system employs a combination of vector similarity, BM25, recency, and importance scores to retrieve memories, weighting these signals differently based on the category of the memory.


User-Level Weight Personalization via Multi-Armed Bandits

A system that automatically discovers optimal retrieval weights for each user and context using a Multi-Armed Bandit (MAB) framework. Each arm represents a weight configuration, and the system learns the best arm for a given context based on feedback rewards.


MAB Actions: Weight Perturbations

Discrete set of weight perturbations used as actions in the MAB framework. Examples include increasing vector, bm25, recency, or importance weights by 0.1 while proportionally decreasing others.


Asynchronous Feedback Processing for Weight Updates

A system to process feedback events (user thumbs-up/down, LLM reference) asynchronously to update Q-values for MAB actions. Uses a message queue to decouple feedback processing from retrieval requests, ensuring system resilience and performance.


Epsilon-Greedy Exploration for MAB

Employ an epsilon-greedy algorithm for balancing exploration (trying random weight configurations) and exploitation (using the best-known weights) in the Multi-Armed Bandit framework.


Auto-Capture Trigger Pattern Expansion

Expansion of the Auto-Capture Trigger Pattern is the subject of this Deep Think document. The document aims to improve memory usage.


Auto-capture mechanism (current)

AIVA's current auto-capture mechanism relies on `MEMORY_TRIGGERS` regex array in `enhanced_index.ts` and `shouldCapture`. Captures explicit mentions of preferences, decisions, contacts, dates, locations, skills, goals, projects, URLs, and numbers. Filtered by `shouldCapture` and passed to `splitFacts`. Importance computed by `computeImportance`.


MEMORY_TRIGGERS limitations

The current `MEMORY_TRIGGERS` array is limited in its ability to capture implicit personal information, evolving relationship dynamics, nuanced health/financial states, recurring frustrations, user values/beliefs, professional history, cultural preferences, or language code-switching signals. Results in ~30-40% recall gap.


Expand autoCapture trigger patterns

Significantly expand the `autoCapture` trigger patterns to encompass a broader spectrum of personal and behavioral insights, moving beyond explicit mentions to implicit signals. Requires advanced NLP, sentiment analysis, and pattern generalization.


Hybrid Trigger System with Embedding-based Pattern Matching

Introduce a secondary, embedding-based trigger mechanism within `shouldCapture` to capture implicit patterns. Embed user message and compare against curated "implicit pattern embeddings". Trigger capture if cosine similarity is high.


Sentiment and Frequency-Boosted Importance Scoring

Enhance `computeImportance` to incorporate sentiment analysis and a rolling frequency count. Apply `IMPORTANCE_SENTIMENT_BOOST` or `IMPORTANCE_FREQUENCY_BOOST` for frequent or emotionally charged memories.


AIVA's Auto-Capture Mechanism

Current auto-capture mechanism relying heavily on `MEMORY_TRIGGERS` and regex, with a 30-40% recall gap.


Phased Implementation of Auto-Capture Enhancements

A phased implementation starting with Proposal 1 (hybrid trigger system), followed by Proposal 2 (boosted importance scoring), and then Proposal 3 (dynamic LLM-based categorization).


Hybrid Trigger System (Regex + Embeddings)

Supplementing the existing regex-based `MEMORY_TRIGGERS` with an embedding-based similarity search to capture implicit patterns in `shouldCapture`.


Boosted Importance Scoring (Sentiment & Frequency)

Enhancing the `computeImportance` function by adding sentiment analysis and frequency boosts to capture emotionally charged and recurring information.


Dynamic LLM-Based Categorization

Using an LLM to categorize messages poorly handled by regex, relying on a fixed enum `NUANCED_CATEGORIES` for safety and structure.


Uses semantic similarity for memory capture, addressing the recall gap by capturing implicit context, making it more flexible and scalable than regex patterns. Employs embeddings and similarity thresholds.


Enhances memory importance gauging by using emotion and repetition as signals of significance. It moves importance computation to reflect user's emotional state and recurring concerns.


Dynamic Categorization with LLM Assistance

Employs an LLM for nuanced categorization of complex statements that don't fit predefined regex-based categories, improving retrieval and adapting to novel phrasings and topics.


Implicit Pattern Embeddings

Corpus of embeddings representing implicit information (values, beliefs, recurring events). Used to capture intent beyond literal string matching. Curation and maintenance is essential.


Hybrid Trigger System

Proposal 1: A hybrid trigger system for capturing implicit information. Involves curating a corpus of implicit pattern sentences, setting up embedding infrastructure, implementing caching, and modifying the `shouldCapture` logic behind a feature flag.


Sentiment/Frequency-Boosted Importance

Proposal 2: A system for prioritizing memories based on sentiment and frequency.  Integrates sentiment analysis and session-based frequency counting to update the `computeImportance` function.


Dynamic LLM Categorization

Proposal 3: A system for dynamic categorization of memories using an LLM. Includes defining nuanced categories, setting up LLM integration with timeouts, and implementing fallback logic in `detectCategory`.


Memory Consolidation Algorithm

Current memory consolidation uses O(N^2) semantic deduping and static staleness pruning.  New algorithm should be incremental/batch-oriented, synthesize memories instead of discarding, and use adaptive staleness based on memory category and contradiction detection.


Synthesizing Merge Strategy

Propose an algorithmic approach (e.g., using a small LLM call or heuristic-based text combination) to generate a new, synthesized `text` entry and a new `vector` embedding for combined memories when cosine similarity exceeds MERGE_THRESHOLD_HIGH.  Implement `synthesizeMemory` with pseudocode.


Adaptive Staleness & Importance Decay

Introduce category-specific decay factors in `enhanced_config.ts` and a mechanism to detect and react to contradictions by rapidly decaying importance of contradicting memories. Outline `detectContradiction` logic.


Next-Generation Memory Consolidation Algorithm

A two-part algorithm: Incremental Consolidation (event-driven, uses ANN to merge similar memories and detect contradictions) and Adaptive Pruning (periodic, handles long-term decay based on category-specific rules and contradiction detection).


Incremental Consolidation

Event-driven process triggered when new memories are added. Uses Approximate Nearest Neighbor (ANN) index for candidate selection. Merges similar memories using LLM-based synthesis and detects contradictions.


Adaptive Pruning

Periodic background task that handles long-term memory decay. Uses category-specific decay factors and accelerates decay for memories that contradict existing knowledge. LLM based.


Category-Specific Decay

Decay memory staleness using category specific factors to provide a more nuanced solution than the current one-size-fits-all approach


ANN-Powered Memory Consolidation

System for consolidating memories using an ANN index (e.g., hnswlib-node, faiss-node) to achieve sub-linear time complexity.  Employs incremental consolidation to process new memories in batches, querying the ANN for nearest neighbors to limit the number of comparisons needed for synthesis and contradiction detection.


Incremental Memory Consolidation

Introducing an `incrementalConsolidate` function that is triggered by batches of new memories.  For each new memory, it queries the ANN index to find its k nearest neighbors.  This reduces the computational complexity from O(N^2) to O(M * k * log N), where M is the number of new memories and k is the number of neighbors.


Using ANN for Memory Indexing

Decision to use an Approximate Nearest Neighbors (ANN) index to store memory vectors, which allows for efficient similarity searches and significantly reduces the computational cost of memory consolidation compared to a brute-force approach.


Memory Synthesis with LLM

A strategy to merge similar memories using an LLM to create a richer, concise, and coherent memory entry, preserving unique information from both memories.


Adaptive Staleness & Importance Decay

A system to manage memory staleness with category-aware decay rates and contradiction-based decay, replacing the static staleness logic.


Category-Specific Decay

A system implemented via a configuration object in enhanced_config.ts to manage memory decay rates based on category (e.g., Fact, Opinion, Event).


Contradiction-Accelerated Decay

A system to accelerate the decay of memories that are contradicted by new memories, using an LLM to detect contradictions via Natural Language Inference.


EnhancedIndex Class

Centralized class managing both the primary database and ANN index for memories. It incorporates incremental consolidation logic and becomes the single entry point for creating and indexing memories, simplifying state management.


Adaptive Pruning

Scheduled process to remove redundant or irrelevant memories from the system based on decay factors and other metrics, implemented by `performAdaptivePruning` function.


Incremental Memory Consolidation

Mechanism to consolidate new memories with existing ones in small batches to maintain performance. Implemented with `incrementalConsolidate` function.


Memory Synthesis

Process of creating new memories by combining existing memories using an LLM service. Implemented by `synthesizeMemory` function.


Contradiction Detection

Process of identifying contradictory memories using an LLM service specifically tuned for Natural Language Inference (NLI) tasks. Implemented by `detectContradiction` function.


Cross-Session Memory Transfer Protocol Strategy

Defines the strategy for transferring memory across different sessions within the Deep Think system, focusing on retaining knowledge and context.


Cross-Session Memory Transfer Protocol for LanceDB Plugin

A system for managing memory across sessions in the AIVA LanceDB plugin, addressing issues like suboptimal recall, inefficient consolidation, missed working memory opportunities, and user experience gaps. It involves session lifecycle management, pre-session loading, mid-session updates, post-session flushing, and an archival strategy.


SessionManager Class

A class to manage session lifecycle, including defining a robust 'session' concept, detecting session start and end, and tracking session IDs.


Session Priming Mechanism

A mechanism that injects session-critical memories upon 'api.on("before_agent_start")', potentially using a new 'MemoryEntry' field or a separate 'SessionMemory' table to hold memories crucial for the current session context.


Working Memory Implementation

A concept of 'working memory' is implemented. Memories frequently recalled or explicitly updated within a session will have their 'importance' boosted temporarily, with a decay mechanism for this boost.  A MemoryEntry.sessionRecencyBoost field is considered.


Automated Consolidation Trigger

An automated trigger for `consolidateMemories` (or optimized version) at session end or when a user logs out/becomes inactive. A 'session summary' mechanism is also proposed to generate a concise summary stored as a new memory entry at session end.


Cross-Session Memory Transfer Protocol

Addresses the session-agnostic nature of the plugin to improve recall and memory handling. Includes session lifecycle management, pre-session priming, mid-session working memory, post-session consolidation, and archival strategy.


SessionManager

Class to track sessions, last activity, and context using a 30-minute inactivity timeout. Initially implemented in-memory, but needs persistence.


MemoryEntry Session Tagging

Adding a `sessionIds: string[]` field to `MemoryEntry`. When a memory is created or accessed, it gets tagged with the current session ID. `hybridSearch` is modified to boost memories matching the active session.


Working Memory with Recency Boost

Implementing a `sessionRecencyBoost` field in the memory system that decays with each interaction, creating a transient working memory. This boost does not affect the permanent importance score.


Targeted Post-Session Consolidation

Instead of consolidating the entire database, perform targeted consolidation on just the ended session to save time. This involves summarizing top session memories with an LLM and storing the summary as a high-importance memory.


Archival Memory Flag

Adding an `archived: boolean` flag to `MemoryEntry`. `hybridSearch` filters out archived memories by default. A separate "deep search" tool allows querying archived memories.


SessionManager

The SessionManager class handles the lifecycle of user interactions, defining a session as a continuous period of interaction with a user in a specific context. It tracks session activity, manages session state, and detects topic changes based on vector similarity.


Session Timeout Duration

Sessions are defined to timeout after 30 minutes of inactivity, configurable via `SESSION_TIMEOUT_MINUTES` in `enhanced_config.ts`.


Topic Change Detection

Topic changes are detected using cosine similarity between prompt vectors. A similarity score below a threshold (TOPIC_CHANGE_SIMILARITY_THRESHOLD = 0.75) indicates a topic shift.


MemoryDB class modification for session-aware memory

The MemoryDB class is modified to incorporate session IDs and a transient working memory layer to prioritize session-relevant memories during hybrid searches. It includes updating the MemoryEntry schema, modifying the hybridSearch function to accept a sessionId, implementing a working memory with decay, and adding methods to touch memories.


Prioritize session-relevant memories during hybrid searches

The strategy is to prioritize memories from the current session by assigning session IDs to memories and applying a boost during hybrid searches.  A working memory layer is introduced with a transient boost that decays over time.


Post-Session Memory Consolidation

Automated consolidation and summarization of memories at the end of a session, triggered by the 'session_end' event.  Includes generating a session summary and targeted consolidation of memories associated with that session.


Generate Session Summary

Creating a concise summary of the key outcomes, decisions, or established facts of a session based on the most important and recent memories from that session.


Targeted Memory Consolidation

Consolidating memories only within a specific session, improving efficiency by avoiding full database scans.  This includes pruning and merging.


Memory Archival Strategy

Instead of deleting old, unimportant memories, archive them to reduce active dataset size while preserving them for deep search.


Archiving Stale Memories

Memories older than STALENESS_DAYS and with an importance below STALENESS_IMPORTANCE_THRESHOLD are marked as archived instead of being deleted.


Implement Deep Search

Introduces a 'deepSearch' functionality that bypasses the 'archived' filter, allowing access to all memories, including archived ones, for forensic or specific needs.


Dual-Store Episodic/Semantic Memory Architecture

A proposal to separate episodic and semantic memory into two distinct LanceDB tables within the AIVA's memory system. It will involve differentiated decay rates, consolidation strategies, and intelligent routing to improve memory recall precision and latency.


Intent Router for Memory Queries

Implement an intent router that analyzes a query to determine whether it's primarily episodic, semantic, or mixed, guiding it to the appropriate memory store.


Fused Search Function

Design a `fusedSearch` function to intelligently combine and re-rank search results from both episodic and semantic memory stores.


Dual-Store Memory Architecture for memory-lancedb

System with two LanceDB tables (`episodic` and `semantic`) managed by the `MemoryDB` class to separate episodic and semantic memories for better decay, retrieval, and consolidation.


Differentiated Decay and Consolidation

Use different decay rates and consolidation thresholds for episodic and semantic memories to optimize performance. Implement separate consolidation logic for each type.


Intelligent Query Routing

Route queries to the appropriate memory store (episodic or semantic) based on keywords, `MEMORY_CATEGORIES`, and potentially an LLM for ambiguous cases. Fuse results for mixed queries using parallel search and MMR re-ranking.


Dual Store Memory Design

Implements a dual LanceDB table design with separate episodic and semantic memory stores. Episodic memory stores specific events and interactions, while semantic memory stores general facts and knowledge.


Differentiated Decay Rates

Different decay rates are applied to episodic and semantic memories. Episodic memories decay faster than semantic memories, reflecting the difference in their relevance over time.


Differentiated Consolidation Thresholds

Different consolidation thresholds are applied to episodic and semantic memories. Episodic memories are summarized more aggressively than semantic memories, prioritizing event consolidation over fact deduplication.


Memory Category to Store Mapping

A mapping determines which memory store (episodic or semantic) a memory entry belongs to based on its category. event/conversation_turn/contact/location/decision are episodic. fact/preference/skill/goal/concept_explanation/meta are semantic.


Differentiated Decay and Consolidation of Memory Types

The core logic for search and consolidation will be adapted to operate specifically on episodic and semantic memory types, using type-specific decay parameters and consolidation strategies.


Hybrid Search with Differentiated Decay

The `hybridSearch` logic is parameterized to accept decay constants for recency and importance, allowing for type-specific memory decay. It uses UDFs for recency and importance decay calculations, incorporating these into the reranking formula to influence search results.


Consolidate Memories

The consolidateMemories function orchestrates consolidation for episodic and semantic memory stores independently, using type-specific consolidation logic. This includes creating summary memories for episodic memories and merging similar facts for semantic memories.


Intelligent Routing and Cross-Store Retrieval Fusion

A retrieval system that routes queries to episodic or semantic memory stores based on query content, using keyword matching and potentially LLM-based categorization. It then performs a fused search, combining results from both stores and applying MMR for diversity.


Fused Search Implementation

Implementation of the `fusedSearch` function within the `MemoryDB` class. It routes queries to the appropriate memory store (episodic, semantic, or mixed), performs a hybrid search in the selected store(s), and combines results. Applies MMR to ensure diversity.


Automatic Routing for Capture

A system to automatically save new memories to the correct store (episodic or semantic) based on category using the `storeMemoryIntelligently` function. The system integrates with the agent's `agent_end` listener to capture and store memories.


Intelligent Memory Storage Function

The `storeMemoryIntelligently` function in `MemoryDB` class stores memories in either the episodic or semantic store based on a provided category. This function is called during the automatic capture phase.


Memory Importance Decay Curve Engineering

Focuses on engineering memory importance decay curves within a memory upgrade initiative. This involves modeling how the importance of different pieces of information decays over time within the memory system.


AIVA Memory Decay System

AIVA's memory decay system, currently using uniform decay, needs enhancement to incorporate category-specific decay curves and spaced repetition.


Category-Specific Decay Curves

Proposes defining a configuration map (`CATEGORY_DECAY_PARAMS`) to map each `MemoryCategory` to specific decay parameters like decay function, initial decay rate, half-life, and minimum importance.


Spaced Repetition Reinforcement

Implement a spaced-repetition-like reinforcement mechanism to update `createdAt` and `importance` on memory recall, strengthening frequently retrieved memories.


Category-Specific Memory Decay and Spaced Repetition

System using Category-Specific Decay Curves and Spaced Repetition Reinforcement to improve memory retrieval.  Decay curves are customized per MemoryCategory with parameters like decayFunction, initialDecayRate, halfLifeDays, minimumImportance, and eventCompletionDecayMultiplier. Spaced repetition strengthens memories upon recall by updating lastRecalledAt, recallCount and nextRecallInterval using an SM-2-inspired algorithm. This impacts the decay calculation.