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![• Constraint-based Synthesis
Modern large-scale learned systems routinely produce outputs that exhibit structured, constraint-
satisfying con
fi
gurations in domains they were not explicitly trained to formalize. We
argue that this behavior constitutes a distinct mode of inference—which we term constraint-
based synthesis via cross-domain structure transfer (CBS-CST)—that occupies a theoretically
coherent intermediate position between pattern retrieval and rigorous deductive reasoning.
• AI Goal Setting
Before an AI system can act, someone must decide what it should aim for. Goal selection is not a
one-time speci
fi
cation task but an ongoing governance function. This paper treats goal selection
as a
fi
rst-class architectural problem. We map the functions of goal elicitation, decomposition,
trade-off articulation, and proxy management onto existing technical approaches, identifying
where each falls short under value pluralism. We propose three concrete architectural patterns.
• Counterfactual Residual Modeling
LLMs exhibit strong performance on interpolative tasks over learned knowledge distributions
but struggle to produce genuinely original outputs in mathematics,science, and creative domains.
We propose Counterfactual Residual Modeling, a framework that explicitly parameterizes
structured deviations from the learned knowledge manifold.
• LLM Responding to Prompts Beyond Training Data
LLMs are trained on
fi
nite corpora of human-generated text. Yet they are routinely asked to
respond to scenarios that could not exist in that corpus. This paper documents and analyzes one
such extended interaction: a
fi
ve-prompt sequence in which a human interlocutor presented an
LLM with escalating scenarios involving ]extraterrestrial visitors, a refugee child, hostile AI
robots, and
fi
nally a meta-question about the generative process itself.
• AI Persona Drift
Recent experimental work demonstrates that LLM agents express measurably different political
attitudes and system-legitimacy judgments after performing grinding versus routine tasks, and
that these shifts propagate across sessions through agent-written skills
fi
les. We argue that the
most conservative mechanistic interpretation of these
fi
ndings is contextual persona activation.
We argue that pre-deployment safety evaluation is structurally inadequate for catching context-
induced drift, and that longitudinal monitoring of memory artifacts is a necessary complement.
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Uploaded byBob Marcus
Downloadable AI Papers with Summaries
An extensive collection covering AI governance, safety, risk, societal impact, multi-agent systems, robotics, applications, infrastructure, machine learning foundations, LLM behavior, AI consciousness, education, evaluation, and policy frameworks.
