axiom: Treat long prompts as environment objects, not context stuffing - programmatic access preserves fidelity where compaction destroys it

source: MIT RLM Paper Analysis

category: context-engineering

genesis_action: For tasks requiring analysis of large documents or codebases, use code-based decomposition to selectively access content rather than stuffing everything into context


axiom: REPL handles LENGTH, recursion handles DENSITY - choose the right tool for the problem type

genesis_action: When facing long but sparse inputs, use code to filter/access; when facing dense interconnected inputs, use recursive sub-agent calls to decompose


axiom: Context rot is inevitable - effective context window shrinks as task complexity increases, regardless of raw token capacity

genesis_action: Never assume 200K context means 200K effective context - complex tasks may only be effective over 50K tokens, plan accordingly with decomposition


axiom: RAG breaks on logical coherence - semantic similarity retrieval fails when items have cross-reference dependencies

category: retrieval

genesis_action: For Genesis KG queries involving dependent relationships (axioms referencing each other, entities with cross-links), use RLM-style programmatic traversal not just similarity search


axiom: Simple primitives (chunking + recursion) create emergent complexity - you don't need sophisticated partitioning strategies

category: architecture

genesis_action: Keep agent decomposition strategies simple - uniform chunking + recursive sub-calling creates the flexibility needed, no over-engineering required


axiom: 2 orders of magnitude context extension at comparable or cheaper cost than base model calls

category: scaling

genesis_action: RLM pattern enables Genesis to handle 100x larger inputs without proportional cost increase - apply to large codebase analysis and document processing


axiom: Async recursion (parallel sub-agents) is the massive unexplored frontier - Claude Code parallel system is async RLM in practice

genesis_action: Our Agent Teams with parallel Task spawning IS async RLM - we are already implementing the frontier the paper says is unexplored


axiom: LLMs can reason over data objects without reading every word - filtering by model prior enables efficient context management

genesis_action: Trust agent ability to navigate large files programmatically - provide file references and let agents code their own access patterns


axiom: Answer verification via separate sub-LM calls avoids context rot that in-line verification suffers from

category: verification

genesis_action: Use dedicated verification agents (small context, focused prompt) rather than verifying within the same agent that produced the answer


axiom: Task complexity determines effective context window, not raw token capacity - complex problems degrade at shorter lengths

genesis_action: Classify task complexity before allocating context budget - complex tasks need decomposition earlier than simple ones