Research Foundations

Academic papers and design influences that shaped the AI Assistant architecture.

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Core Influences

Generative Agents (Park et al., 2023)

Paper: "Generative Agents: Interactive Simulacra of Human Behavior" Authors: Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, Michael S. Bernstein Link: https://arxiv.org/abs/2304.03442

Key Concepts Adopted

1. Reflection System

   • Cumulative importance threshold (default: 150)
   • Automatic reflection during sleep/dreaming phase
   • Insight generation from recent experiences
   • Implementation: generative_reflection.py:run_reflection()

2. Importance Scoring

   • Heuristic scoring with LLM upgrade during sleep
   • Keyword-based initial scores
   • Batch LLM scoring in score_pending_entries()
   • Implementation: importance_scoring.py

3. Episodic Memory Pruning

   • Age + importance based pruning
   • Default: prune entries with importance ≤ 3, older than 30 days
   • Preserves consolidated entries
   • Implementation: helpers/episodic_index.py:prune_low_importance_entries()

4. Memory Retrieval

   • Hybrid scoring: recency + importance + relevance
   • Time decay for recency weighting
   • Implementation: helpers/episodic_index.py:search_episodic_memory()

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O-Mem: Observation Memory (Zhang et al., 2024)

Paper: "O-MEM: Observation Memory for Continual Learning in Streaming Data" Concept: Separating raw observations (Pf) from consolidated attributes (Pa)

Key Concepts Adopted

1. Entity Profiles Structure

   entity_profiles[entity_id] = {
       "observations": [...],   # Pf - raw experiences
       "attributes": {...},     # Pa - consolidated knowledge
       "relationship": {...}    # Derived from observations
   }
   

2. Observation → Attribute Consolidation

   • LLM synthesizes multiple observations into stable attributes
   • Runs during sleep/dreaming phase
   • Min 5 observations before consolidation
   • Implementation: helpers/entity_context.py:run_entity_consolidation_batch()

3. Dunbar Relationship Thresholds

   State	Favorability Range
   stranger	< 0.25
   acquaintance	0.25 - 0.50
   friend	0.50 - 0.75
   ally	≥ 0.75

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A-MEM: Agentic Memory (2024)

Concept: Memory link generation for associative recall

Key Concepts Adopted

1. Memory Links

   • Semantic connections between related memories
   • Generated during dreaming phase
   • Links stored in character.db.memory_links
   • Implementation: sleep/dreaming.py:run_dreaming_tick()

2. Orphaned Link Cleanup

   • Removes links to deleted memories
   • Prevents unbounded growth
   • Implementation: sleep/dreaming.py:prune_orphaned_memory_links()

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ReAct Pattern (Yao et al., 2022)

Paper: "ReAct: Synergizing Reasoning and Acting in Language Models" Authors: Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, Yuan Cao Link: https://arxiv.org/abs/2210.03629

Key Concepts Adopted

1. Reasoning + Acting Loop

   • LLM reasons about task, then selects action
   • Iterates until terminal condition
   • Implementation: tool_execution.py:execute_react_loop()

2. Multi-Action Execution

   • Chain multiple tool calls in single tick
   • Termination conditions: TERMINAL tool, DANGEROUS tool, noop, max iterations
   • Implementation: See Data-Flow-02-ReAct-Loop

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Design Patterns

Three-Layer Execution Pattern

Original design for restrictive composition:

Layer 1 (Static Config) ⊇ Layer 2 (Context Config) ⊇ Layer 3 (LLM Assessment)

Each layer can only restrict, never expand limits set by higher layers.

Influences:

• Unix permission inheritance
• Kubernetes RBAC
• Principle of least privilege

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Sleep/Wake Cycle

Biological inspiration combined with computer science concepts:

1. Sleep Phases

   • Compacting (memory consolidation) → Dreaming (reflection, cleanup)
   • Inspired by human REM/non-REM cycles

2. Light vs Deep Sleep

   • Light: wake on urgent events (interrupts)
   • Deep: timer-only wake (batch processing)

3. Cooldown Period

   • Prevents rapid cycling
   • Inspired by circuit breaker patterns

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Tool Categories

Security-influenced design for tool safety:

Category	Behavior	Rationale
SAFE_CHAIN	Chain freely	Information gathering, no side effects
TERMINAL	Ends loop	Awaits human response, prevents overtalking
DANGEROUS	Single per tick	State modification requires deliberation
ASYNC_REQUIRED	Special handling	Network calls need timeout management

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Token Advisory System

Graceful degradation pattern for context limits:

Level	Threshold	Action
MODERATE	50-70%	Suggest wrapping up
HIGH	70-80%	Complete current task
CRITICAL	80%+	Force termination

Influences:

• Disk space warnings
• Memory pressure handlers
• Garbage collection triggers

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Academic Context

Relevant Fields

1. Human-Computer Interaction (HCI)

   • Generative Agents work from Stanford HCI Group
   • Focus on believable, interactive AI characters

2. Cognitive Architecture

   • Working memory, long-term memory separation
   • Episodic vs semantic memory distinction

3. Multi-Agent Systems

   • Sub-agent delegation patterns
   • Principal-delegate relationships

4. Software Architecture

   • Event sourcing for auditability
   • Circuit breaker for resilience
   • Mixin patterns for modularity

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Implementation Mapping

Research Concept	Implementation File	Key Function/Class
Generative reflection	generative_reflection.py	run_reflection()
Importance scoring	importance_scoring.py	score_pending_entries()
Entity profiles	helpers/entity_profiles.py	create_entity_profile()
Observation consolidation	helpers/entity_context.py	run_entity_consolidation_batch()
Episodic pruning	helpers/episodic_index.py	prune_low_importance_entries()
Hybrid memory search	helpers/episodic_index.py	search_episodic_memory()
Memory links	sleep/dreaming.py	run_dreaming_tick()
ReAct loop	tool_execution.py	execute_react_loop()
Sleep phases	operating_mode.py	transition_mode()
Sleep tick orchestration	sleep/__init__.py	run_sleep_tick()
Context compaction	sleep/compaction.py	compact_conversation_history()
Memory consolidation	sleep/consolidation.py	run_sleep_consolidation()
Tool categories	tools/base.py	ToolCategory enum

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Further Reading

Papers

• Park et al. (2023) - Generative Agents arXiv:2304.03442
• Yao et al. (2022) - ReAct arXiv:2210.03629
• Wei et al. (2022) - Chain of Thought arXiv:2201.11903

Books

• Kahneman, D. (2011) - Thinking, Fast and Slow (dual-process theory)
• Dunbar, R. (1998) - Grooming, Gossip, and the Evolution of Language (relationship limits)

Technical

• OpenAI Function Calling Documentation
• Anthropic Claude Tool Use Documentation
• Mem0 Project Documentation

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Last updated: 2025-12-09