How much information about an agent's underlying values can be recovered from its observable behavior? This question matters for any approach that infers agent properties from action sequences, yet remains empirically open at scale. We address it through controlled experiments: LLM-based agents (Llama 3.1-8B) assigned one of 36 behavioral profiles (9 belief systems x 4 motivations) generate over 1.5 million behavioral sequences in grid-world environments, providing ground truth unavailable in human behavioral studies. After filtering, classifiers train and evaluate on a shared canonical dataset of 10,338 episodes and 1,200,834 sequences. A fundamental asymmetry emerges in both magnitude and structure. Motivations achieve 98-100% accuracy and recover 97% of available mutual information across all architectures. Belief systems plateau at 24% for LSTMs regardless of capacity, and even transformers reach only 34.0%, recovering 16.3% of available information, a 6.1x asymmetry in extraction efficiency. Per-alignment accuracy ranges from 23.2% (Lawful Neutral) to 59.4% (Chaotic Evil). Confusion analysis maps the failure structure: a neutral zone of behavioral ambiguity centers on True Neutral, absorbing misclassified samples from adjacent Neutral and Good alignments whose prosocial or balance-keeping behavior lacks distinctive signal. Combined inference yields 12.2x improvement over random baseline for full 36-class profile classification, with the bottleneck located entirely in belief system inference. Signal enhancement and explanatory queries yield only marginal LSTM gains (+3.8%), confirming the recurrent ceiling is architectural rather than data-limited. Whether the transformer's 34.0% ceiling reflects a similar architectural-class limit or a more fundamental bound remains open. These results characterize what behavioral observation can and cannot reveal about LLM agent values.

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