What the LLM Should Not Say: Boundary-Aware Context Grounding for A Seven-Channel EEG Agent

• URL: http://arxiv.org/abs/2606.26519v2
• Date: Fri, 26 Jun 2026 03:13:16 GMT
• Title: What the LLM Should Not Say: Boundary-Aware Context Grounding for A Seven-Channel EEG Agent
• Abstract summary: Large language models (LLMs) can make scientific software easier to use.<n>We present NeuraDock Agent, an open-source architecture that separates a deterministic local EEG engine from a hardware-aware language layer.
• Score: 8.617463707951673
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) can make scientific software easier to use. However, a general model does not automatically know which measurements a particular sensor can support, which algorithms are implemented in the current software, or which conclusions are justified by a computed result. These distinctions are especially important for low-channel electroencephalography (EEG), where sparse spatial coverage and variable signal quality make plausible but unsupported interpretations easy to produce. We present NeuraDock Agent, an open-source architecture that separates a deterministic local EEG engine from a hardware-aware language layer. The numerical engine parses recordings, performs quality control, executes reviewed spectral workflows, and writes machine-readable artifacts. The LLM receives only a compact, allowlisted summary and a versioned context pack. The context describes the seven-channel hardware, reviewed workflows, result fields, implementation boundaries, scientific limits, and reference cases. Raw EEG and dense per-sample arrays remain local We evaluate the system at three levels. First, 12 recordings produced identical structured results over ten numerical repetitions, and a complete Rest/Task run produced identical result, report, and figure hashes over three repetitions. Second, request-capture and failure-injection experiments confirmed the tested data boundary and preservation of local artifacts under HTTP, malformed-output, and connection failures. Third, a boundary-awareness benchmark tested 36 ordinary and adversarial questions under four context ablations and two LLMs, yielding 288 outputs.These results support hardware- and implementation-aware grounding as a practical mechanism for calibrating what an EEG agent accepts, qualifies, or refuses; they do not establish clinical validity or a validated absolute cognitive-load index.

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