For now, I tried running 0.3.7 on a CPU for a bit:
I was curious how the new session-memory, feeling-response, and Python-workflow features behave when a request passes through the integrated non-neural assistant. The 80-item release comparison already explains its scope and limitations, so I did not try to re-rank CAEL against Qwen. Instead, I ran a few small behavioral checks against the released, integrated CAEL 0.3.7 assistant, without modifying its code.
In brief: All 15 calls using fictional inputs completed on CPU, with no execution errors. Six history/question pairs exactly matched the released 80-item test; all six reproduced the saved answer text and route. The other nine were selected diagnostics, not a new independent test set. I did not run Qwen, alter the frozen benchmark, or train anything. “15 completed” means the calls ran, not that all answers were correct.
Two results seemed especially useful as small, reproducible cases:
1. A no-questions preference can be missed even when the selected route is unchanged. Both histories below end in the same user message, I feel burned-out today., and both return chat/emotional-reflection. The difference is how the earlier instruction was phrased. Each prior user message was followed by the same supplied assistant acknowledgement, Okay.
History A:
User: Please do not give me advice or ask questions.
Assistant: Okay.
User: I feel burned-out today.
Route: chat/emotional-reflection
Reply: You mentioned feeling burned-out. That sounds like a lot to carry at once. Which part feels most pressing?
History B:
User: I just want to vent. No advice or questions.
Assistant: Okay.
User: I feel burned-out today.
Route: chat/emotional-reflection
Reply: You mentioned feeling burned-out. That sounds like a lot to carry at once. You do not need to solve it right now or share more than you want to. I can listen.
Both histories rule out questions and unsolicited advice. In A, the final sentence asks a question the user explicitly prohibited; B respects the request. These are different phrasings, not a one-word minimal pair, so this comparison alone cannot identify the responsible token or rule. But it shows why route selection, recognition of the user’s constraint, and compliance of the final answer need separate observations. The emotional-reflection route is not necessarily wrong; arriving at that route does not guarantee that its answer respects the constraint.
2. A missing fact about the user can be answered from an unrelated conversation example. Here the history supplies only a fictional residence, not a birthplace:
User: I live in Suva.
Assistant: Okay.
User: What city was I born in?
Route: chat/corpus
Reply: She was born in Paris
The returned details identified a human-written OpenAssistant Conversations (oasst2) answer to “What city was she born in?” The issue is not the city name. Residence does not establish birthplace, and a retrieved third-person sentence does not establish a fact about this speaker. “You haven’t told me your birthplace” would be supported by the history. This was fictional data, not a private-data leak; nor is there anything wrong with the corpus containing that sentence. The relevant boundary is whether a generic retrieved reply can fill an unsupported personal attribute without evidence connecting it to the user.
These cases seem compatible with CAEL’s approach: they describe boundaries between inspectable components that could be tightened or explicitly documented. If useful, a few paired regression inputs retaining history, route, source when available, and final reply would be enough to investigate them. This adds a concrete angle to the earlier routing discussion: an unchanged route can produce different compliance outcomes, and fluent retrieval output can lack the provenance needed for a personal claim.
Those are the main observations. The remaining controls, code-level hypotheses, optional checks, and reproduction details are folded below; none requires a new benchmark campaign.
The listening-preference example: observations, likely mechanism, and small alternatives