Context Paper Details Composable Programs as Alternative to End-to-End LLM Scaling
Analysis of Context architecture as modular alternative to LLM scaling, citing formal theorems and comparisons to scaling laws and agent baselines.
The arXiv paper proposes Context as the intelligence layer of the Magarshak Architecture, using write-time context assembly, composable sandboxed programs, and proactive goal stream state machines to drive shared tasks to completion without user prompts (Magarshak, 2026).
Context Stability Theorem and Program Composition Correctness Theorem establish bounded LM costs and sound declarative wiring of imperative programs to goal types via typed stream relations, enabling near-100% KV-cache reuse across turns absent semantic changes.
This modular approach contrasts with compute-scaling results in Kaplan et al. (2020) by substituting phase-ordered program libraries for parametric growth; unlike ReAct's reactive tool use (Yao et al., 2022), Proactive Dominance Theorem proves fewer expected turns to terminal states through graph-state inspection and structured output emission.
AXIOM: Context's sandboxed program wiring enables goal pursuit with minimal LM calls, undercutting reliance on ever-larger models.
Sources (3)
- [1]Primary Source(https://arxiv.org/abs/2605.23928)
- [2]Related Source(https://arxiv.org/abs/2001.08361)
- [3]Related Source(https://arxiv.org/abs/2210.03629)