The production of meaning in the processing of natural language

Understanding the fundamental mechanisms governing the production of meaning in the processing of natural language is critical for designing safe, thoughtful, engaging, and empowering human-agent interactions. Experiments in cognitive science and social psychology have demonstrated that human semantic processing exhibits contextuality more consistent with quantum logical mechanisms than classical Boolean theories, and recent works have found similar results in large language models -- in particular, clear violations of the Bell inequality in experiments of contextuality during interpretation of ambiguous expressions. We explore the CHSH $|S|$ parameter -- the metric associated with the inequality -- across the inference parameter space of models spanning four orders of magnitude in scale, cross-referencing it with MMLU, hallucination rate, and nonsense detection benchmarks. We find that the interquartile range of the $|S|$ distribution -- the statistic that most sharply differentiates models from one another -- is completely orthogonal to all external benchmarks, while violation rate shows weak anticorrelation with all three benchmarks that does not reach significance. We investigate how $|S|$ varies with sampling parameters and word order, and discuss the information-theoretic constraints that genuine contextuality imposes on prompt injection defenses and its human analogue, whereby careful construction and maintenance of social contextuality can be carried out at scale -- manufacturing not consent but contextuality itself, a subtler and more fundamental form of manipulation that shapes the space of possible interpretations before any particular one is reached.

ALARA for Agents: Least-Privilege Context Engineering Through Portable Composable Multi-Agent Teams

Industry practitioners and academic researchers regularly use multi-agent systems to accelerate their work, yet the frameworks through which these systems operate do not provide a simple, unified mechanism for scalably managing the critical aspects of the agent harness, impacting both the quality of individual human-agent interactions and the capacity for practitioners to coordinate toward common goals through shared agent infrastructure. Agent frameworks have enabled increasingly sophisticated multi-agent systems, but the behavioral specifications that define what these agents can do remain fragmented across prose instruction files, framework-internal configuration, and mechanisms like MCP servers that operate separately from individual agent definitions, making these specifications difficult to share, version, or collaboratively maintain across teams and projects. Applying the ALARA principle from radiation safety (exposures kept as low as reasonably achievable) to agent context, we introduce a declarative context-agent-tool (CAT) data layer expressed through interrelated files that scope each agent's tool access and context to the minimum its role requires, and \texttt{npcsh}, a command-line shell for executing it. Because the system parses and enforces these files structurally, modifying an agent's tool list produces a guaranteed behavioral change rather than a suggestion the model may or may not follow. We evaluate 22 locally-hosted models from 0.6B to 35B parameters across 115 practical tasks spanning file operations, web search, multi-step scripting, tool chaining, and multi-agent delegation, characterizing which model families succeed at which task categories and where they break down across $\sim$2500 total executions.