Behavioral NLP

Behavioral NLP: A Reward-Free Foraging Architecture for Language Description This white paper introduces Behavioral NLP, a third paradigm for natural language processing that is fundamentally distinct from both deterministic (rule-based) and statistical (neural/probabilistic) approaches. Core Innovation: Instead of computing outputs from rules or sampling from learned distributions, Behavioral NLP implements a foraging agent that navigates a corpus-derived symbol graph using a viability field φ—with no reward signal, no training loop, and no objective function. Key Features: Reward-free navigation: Policy emerges from habitat geometry, not optimization Single-pass corpus ingestion: Entire navigable environment crystallized into two binary files (~64KB overhead) Persistent metabolic state: Agent maintains viability field φ that accumulates relational coherence Entropic termination: Navigation stops when local coherence drops below metabolic threshold Grounded relational output: 16-bit bitmask tags encode CAUSE, EFFECT, PART, AGENT, CONTRAST, and other relational roles Theoretical Foundation: Direct instantiation of the Axiomatic Theory of Replicating Agents, which proves that viability-field navigation is the necessary and sufficient architecture for any agent that replicates, decides, and persists in a complex environment. Deployment: Runs on consumer hardware with no GPU, no cloud API, no embedding server. Complete stack: raw corpus → binary files → navigation agent → local 8B instruction-tuned LLM for translation. Applications: Education (relational scaffolding), knowledge discovery (anomaly detection via φ perturbation), grounded retrieval (structural traces instead of text chunks). This is not an improved algorithm within the statistical paradigm—it is a different paradigm: foraging rather than optimizing, viability rather than reward, habitat rather than distribution. Keywords: natural language processing, viability theory, autonomous agents, symbol grounding, reward-free learning, metabolic AI, foraging theory, corpus navigation, behavioral systems Related Publication: Axiomatic Theory of Replicating Agents (Zenodo, 2026)