PRISM: Festina Lente Proactivity—Risk-Sensitive, Uncertainty-Aware Deliberation for Proactive Agents

The paper introduces PRISM, a framework coupling cost-derived gating with dual-process reasoning to decide when proactive agents should intervene. It resides in the 'Proactive Agent Intervention with Uncertainty-Aware Gating' leaf, which contains only three papers total. This is a notably sparse research direction within the broader taxonomy, suggesting the specific combination of calibrated acceptance probabilities, asymmetric cost modeling, and selective slow reasoning has received limited prior attention. The leaf sits under 'Decision-Theoretic Intervention Frameworks,' a branch emphasizing formal utility models over heuristic triggers.

The taxonomy reveals neighboring leaves addressing related but distinct challenges. 'Prescriptive Process Monitoring with Causal Inference' applies causal reasoning to business process interventions but excludes non-causal predictive systems. 'Cost-Aware Multi-Agent Active Search' optimizes sensing actions in multi-agent settings, explicitly excluding single-agent scenarios. The paper's focus on single-agent, conversational intervention with calibrated gating distinguishes it from these adjacent directions. The broader 'Reactive and Threshold-Based Intervention Systems' branch uses simpler heuristics without decision-theoretic optimization, underscoring PRISM's methodological departure toward principled uncertainty handling.

Among seven candidates examined across three contributions, none yielded clear refutations. The core PRISM framework examined one candidate with no overlap found. The gate-aligned distillation method examined zero candidates, reflecting either limited prior work or search coverage gaps. The Decision-Consistent Curation filtering examined six candidates, all deemed non-refutable or unclear. These statistics suggest that within the limited search scope—top-K semantic matches plus citation expansion—the specific technical choices (cost-derived thresholds, festina lente gating, schema-locked distillation) appear relatively unexplored, though the small candidate pool precludes strong claims about absolute novelty.

Given the sparse taxonomy leaf and limited refutations among seven candidates, the work appears to occupy a less-crowded niche within decision-theoretic intervention. However, the analysis covers only a narrow semantic neighborhood and does not exhaustively survey adjacent fields like human-AI collaboration or conversational agents. The absence of refutations may reflect genuine novelty in combining these elements or simply the constraints of a top-K search strategy. A broader literature review would be needed to confirm whether the integration of calibrated gating, dual-process reasoning, and distillation-based training is indeed unprecedented.