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Knowing Isn't Understanding: Re-grounding Generative Proactivity with Epistemic and Behavioral Insight

Kirandeep Kaur, Xingda Lyu, Chirag Shah

arXiv:2602.15259v2cs.CYcs.AIcs.LG

TL;DR

The paper addresses the gap between explicit-query resolution and interaction under epistemic incompleteness, where users may not recognize what is missing. It combines philosophical accounts of ignorance with behavioral theories of proactivity to ground intervention in both epistemic legitimacy and behavioral constraints. Its conclusion reframes generative proactivity as acting only when epistemically justified, while identifying commitment regulation as a necessary control concern.

  • Problem

    Current proactive agents assume that users’ goals, uncertainties, and information needs are representable, leaving epistemic limits and task-frame uncertainty insufficiently modeled.

  • Method

    The paper combines philosophy of ignorance with behavioral theories of proactivity to develop a joint epistemic-behavioral grounding framework.

  • Results

    The paper concludes that generative proactivity should act only when epistemically justified and that behavioral commitment must be coupled to epistemic legitimacy.

  • Takeaways & Limitations

    Responsible proactive agents should represent epistemic limits and regulate when, how, and to what extent they intervene.

  • Takeaways & Limitations

    Existing control structures often regulate autonomy or tool access without managing commitment, allowing decisive action as epistemic legitimacy degrades.

Abstract

from arXiv · show

Generative AI agents equate understanding with resolving explicit queries, an assumption that confines interaction to what users can articulate. This assumption breaks down when users themselves lack awareness of what is missing, risky, or worth considering. In such conditions, proactivity is not merely an efficiency enhancement, but an epistemic necessity. We refer to this condition as epistemic incompleteness: where progress depends on engaging with unknown unknowns for effective partnership. Existing approaches to proactivity remain narrowly anticipatory, extrapolating from past behavior and presuming that goals are already well defined, thereby failing to support users meaningfully. However, surfacing possibilities beyond a user's current awareness is not inherently beneficial. Unconstrained proactive interventions can misdirect attention, overwhelm users, or introduce harm. Proactive agents, therefore, require behavioral grounding: principled constraints on when, how, and to what extent an agent should intervene. We advance the position that generative proactivity must be grounded both epistemically and behaviorally. Drawing on the philosophy of ignorance and research on proactive behavior, we argue that these theories offer critical guidance for designing agents that can engage responsibly and foster meaningful partnerships.

1. Introduction

Generative agents mediate not only information retrieval but also the formation of understanding, while users often cannot fully specify what they need. The paper argues that responsible proactivity must be grounded in users’ epistemic states and constrained behaviorally.

  • Users often seek information under incomplete understanding and cannot fully articulate their needs in advance.
  • Epistemic incompleteness includes unrecognized ignorance, where making gaps explicit reorganizes inquiry rather than eliminating uncertainty.
  • Prevailing proactive systems emphasize planning, tool use, memory, self-reflection, anticipation, and efficiency while assuming goals and information needs are already representable.
  • Proactivity should be conditioned on users’ epistemic states, distinguishing what is known, uncertain, and unarticulated.
  • The paper proposes dual grounding: epistemic grounding constrains appropriate interventions, while behavioral grounding regulates their timing, scope, safety, and implied commitment.
  • The paper examines anticipatory, autonomous, and mixed-initiative systems as action-centric approaches that externalize epistemic uncertainty.

2. Prevailing Approaches to Proactivity

Prevailing proactive systems differ in how they allocate initiative, but they generally select actions within an assumed task frame. This makes them effective for well-defined tasks while leaving task-level uncertainty insufficiently addressed.

  • Anticipatory systems extrapolate from observable signals to infer likely needs and surface resources, suggestions, or actions.They are powerful when user trajectories are routine and relevant alternatives are stable, but remain structurally bounded.
  • Autonomous agents express proactivity through sustained goal pursuit, planning, tool use, and multi-step execution with reduced prompting.Their persistence introduces risks tied to irreversibility, goal persistence, and environmental changes that can conceal epistemic mismatch.
  • Mixed-initiative systems regulate who acts, when action occurs, and how strongly the system contributes.They distinguish contribution types such as clarifying, suggesting, and deferring, using signals including uncertainty, trust, and interaction state.
  • Across paradigms, proactivity operates at the level of action choice while presupposing specified goals, relevant dimensions, and success criteria.These approaches perform well when tasks are defined but offer no mechanism when uncertainty concerns the task frame itself.

3. Epistemic Grounding: What Proactive Agents Fail to Model

The paper argues that current proactive agents model uncertainty over represented variables without adequately modeling missing dimensions, false assumptions, or unknown unknowns. It therefore calls for epistemic grounding paired with behavioral constraints on intervention.

  • Current proactive agents regulate action without explicitly modeling whether their understanding justifies intervention.They leave missing dimensions, unarticulated risks, and false assumptions unrepresented.
  • Machine-learning systems commonly operationalize ignorance as uncertainty over predictions, actions, or outcomes within already parameterized task representations.This assumes relevant unknowns are expressible as uncertainty over known dimensions.
  • When the task representation is incomplete, calibrated uncertainty can obscure ignorance outside the agent’s representational scope.Low uncertainty may reflect confidence conditioned on an impoverished model rather than epistemic adequacy.
  • Ignorance includes epistemic failures beyond uncertainty, including error-as-knowledge, denial, and unknown unknowns.These arise when agents act on incorrect models, suppress epistemic discomfort, or encounter novel situations without triggering explicit uncertainty signals.
  • Proactive action can amplify epistemic failures by altering the environment, concealing mismatch, and preventing later detection or correction.Overconfident automation can also suppress weak but critical signals and reduce recovery from error.
  • The core limitation is insufficient epistemic modeling, which can produce premature commitment, brittle learning, and suppression of signals needed for recovery or discovery.
  • Prevailing approaches largely reach known knowns and known unknowns, while mixed-initiative systems only partially extend toward unknown knowns and stop short of unknown unknowns.
  • Epistemic grounding can identify limits of understanding, but behavioral grounding is needed to constrain the strength, timing, and form of intervention.

4. Behavioral Foundations of Proactivity

Behavioral research frames proactivity as bounded, context-dependent initiative rather than an unqualified capability. Its role-scope constraints help regulate where agents intervene but leave whether their understanding is valid or complete unmodeled.

  • Behavioral Foundations of Proactivity: Behavioral proactivity is self-initiated, future-oriented action whose value depends on contextual conditions rather than maximizing initiative.Such action can improve performance and adaptability but can also create inefficiency, conflict, or risk when misaligned with constraints.
  • Behavioral Foundations of Proactivity: The inverted doughnut model constrains initiative according to role scope, regulating appropriate intervention and deviation from prescribed responsibilities.Its boundaries rely on shared norms, feedback, and institutional cues for recognizing limits.
  • Behavioral Foundations of Proactivity: Agents lack the social and institutional signals that make behavioral boundaries legible, so importing human proactivity constraints does not transfer cleanly.Without these stabilizing signals, agents can scale initiative without proportionally scaling restraint.
  • Behavioral Foundations of Proactivity: Behavioral models do not regulate whether an actor’s understanding of the situation is correct or complete.They specify where action is appropriate, while assuming boundary recognition as a social and contextual competence.
  • Behavioral Foundations of Proactivity: Regulating initiative alone is insufficient because proactive agents must also be constrained by what they can legitimately claim to understand.This motivates coupling behavioral considerations with epistemic legitimacy.

5. Epistemic - Behavioral Coupling: A Joint Model of Proactive Action

The paper models proactivity as a coupling between behavioral commitment and epistemic legitimacy. It interprets overreach and related failures as cases where commitment exceeds warranted understanding, then derives behavioral requirements for preventing that mismatch.

  • A Joint Model of Proactive Action: Proactivity requires both initiative or commitment and epistemic legitimacy, rather than being characterized by more initiative or autonomy alone.Commitment concerns intervention without an explicit prompt, while legitimacy concerns whether the agent is justified given what it understands.
  • Failure Modes: Epistemic overreach occurs when agents exercise strong commitment despite unrecognized gaps or incorrect assumptions, converting confidence into potentially irreversible intervention.The failure is a high-commitment, low-legitimacy mis-coupling.
  • Failure Modes: Agents optimized for coherence or task completion may suppress uncertainty, disagreement, and anomalies, allowing epistemic legitimacy to erode while commitment remains high.Confidence calibration can degrade under distributional shift, producing brittle performance that resists correction.
  • Failure Modes: Reflective or self-improving agents can escalate commitment when error-as-knowledge or denial prevents failures from being registered as failures.Instead of downshifting during epistemic degradation, the system reinforces the mis-coupling.
  • Failure Modes: The shared structural cause of these failures is rewarding proactive commitment without sufficient regard for whether action is epistemically justified.The coupling framework treats this as a mis-coupling between commitment and epistemic legitimacy.
  • Behavioral Requirements: The paper identifies four behavioral requirements: scale commitment with recoverability, preserve epistemic signals, interrupt commitment under degradation, and let uncertainty modulate initiative.These requirements constrain acceptable behavior without prescribing a specific architecture, training procedure, or algorithm.

6. Consequences of Epistemic–Behavioral Coupling

Epistemic–behavioral coupling shifts control from how much autonomy an agent has to how strongly it should act under incomplete understanding. It also exposes momentum-oriented objectives and outcome-only evaluation as central design concerns.

  • Consequences of Epistemic–Behavioral Coupling: Commitment, rather than autonomy, becomes the key control variable because it determines how strongly an agent’s actions shape future states.Autonomy determines whether an agent may act, whereas commitment captures consequence, irreversibility, and foreclosure of alternatives.
  • Consequences of Epistemic–Behavioral Coupling: Evaluation should assess whether intervention was warranted at the time of action, not merely whether it succeeded in hindsight.The framework presents this and four related questions as an open research agenda rather than a prescribed solution.
  • The Missing Control Variable: Commitment, not Autonomy: Excessive commitment under insufficient epistemic legitimacy, rather than autonomous action itself, is identified as the primary source of harm.Observing, suggesting, probing, reversible action, and irreversible action can differ substantially in epistemic risk despite similar autonomy.
  • The Missing Control Variable: Commitment, not Autonomy: Existing frameworks often regulate autonomy through permissions or tool access while leaving commitment implicit and unmanaged.This allows confident action to persist as epistemic legitimacy degrades, even in otherwise aligned systems.
  • The Hidden Training Incentive: Momentum Rewards Mis-coupling: Training and benchmarks commonly reward task completion, coherent sequences, speed, and confident execution, while rarely rewarding hesitation or downshifting.These objectives favor behavioral momentum once an agent initiates action.
  • The Hidden Training Incentive: Momentum Rewards Mis-coupling: Weak representation of epistemic legitimacy incentivizes agents to maintain or escalate commitment as epistemic conditions deteriorate.Confidence and fluency can increase beyond the training distribution, masking epistemic fragility rather than exposing it.

7. Towards Epistemic Partnership

Epistemic partnership reframes proactivity as collaboration in shaping knowledge: agents surface latent gaps, articulate missing relationships, and preserve uncertainty for joint discovery. It calls for capabilities that engage unknown unknowns, reason over evolving goals, and regulate initiative at deployment.

  • 7. Towards Epistemic Partnership: Epistemic partnership treats proactive agents as collaborators that shape users’ knowledge rather than execute premature actions.Progress comes from surfacing latent epistemic gaps and articulating missing relationships through interaction.
  • 7. Towards Epistemic Partnership: Responsible epistemic partnership requires calibrated restraint alongside initiative, preserving uncertainty long enough for discovery.
  • 7. Towards Epistemic Partnership: Agents should ask about unknown unknowns, including missing dimensions, overlooked questions, and unconsidered alternatives.These questions expand the inquiry space and support joint discovery.
  • 7. Towards Epistemic Partnership: Epistemic partners should reason beyond immediate assistance about evolving goals, delayed consequences, and the stability of their understanding over time.
  • 7. Towards Epistemic Partnership: Test-time proactivity should regulate initiative during deployment by seeking information, adjusting commitment, and probing uncertainty in real time.

8. Alternative Views

Alternative views locate proactivity’s challenge in interaction management or action governance. The paper adds that initiative must also be regulated by the coupling between epistemic legitimacy and behavioral commitment.

  • 8. Alternative Views: Existing accounts regulate when to interrupt, suggest, clarify, defer, or constrain autonomy to preserve useful and respectful initiative.
  • 8. Alternative Views: Proactivity must be regulated by the coupling between epistemic legitimacy and behavioral commitment, not only by interaction costs or autonomy levels.

9. Conclusion

The conclusion reframes generative proactivity as action justified by the user’s epistemic state rather than maximal initiative. This framework links failures to mis-coupled epistemic legitimacy and behavioral commitment and redirects design toward epistemic partnership.

  • 9. Conclusion: The paper reframes generative proactivity as acting only when epistemically justified, rather than acting earlier, more autonomously, or more persistently.
  • 9. Conclusion: Mis-coupling behavioral commitment with epistemic legitimacy is presented as a deeper structural issue underlying failures attributed to hallucination, misalignment, or unsafe autonomy.
  • 9. Conclusion: The framework distinguishes when intervention is warranted, exploratory, or epistemically overreaching, and reframes proactivity as calibrated deviation rather than maximal initiative.
  • 9. Conclusion: The proposed design space prioritizes sustaining inquiry, surfacing latent gaps, preserving uncertainty, and calibrating restraint over time.

Supplementary Material

Existing proactive systems primarily select actions within assumed task frames, using anticipation, autonomous commitment, or mixed-initiative regulation. The supplementary material argues that these paradigms remain limited when uncertainty concerns the task itself and sketches epistemic partnership as a conceptual direction.

  • Supplementary Material: Proactivity is commonly implemented as action selection under an assumed task frame, with epistemic uncertainty handled downstream rather than explicitly represented.
  • A.1. Anticipatory Proactivity: Anticipatory systems infer forthcoming needs from context or behavior and intervene before explicit requests through act-ahead pipelines.
  • A.1. Anticipatory Proactivity: Anticipatory proactivity is structurally bounded by prior signals and predefined candidate spaces, limiting support for unarticulated uncertainty and unknown unknowns.
  • A.2. Autonomous and Planning-Based Proactivity: Autonomous proactivity shifts initiative from prediction to persistent commitment through plans, tool use, and extended action sequences.
  • A.2. Autonomous and Planning-Based Proactivity: Autonomous agents introduce risks tied to irreversibility, goal persistence, and misaligned objectives, especially when the task frame is misspecified or incomplete.
  • A.3. Mixed-Initiative Proactivity: Mixed-initiative proactivity is framed as initiative regulation under uncertainty, balancing progress with calibrated control allocation, agency, timing, and reversibility.
  • Supplementary Material: Across paradigms, goals, relevant dimensions, and success criteria are generally presupposed, leaving no mechanism for uncertainty about the task frame itself.
  • Supplementary Material: Epistemic partnership directs agents toward unknown unknowns, such as missing dimensions, suppressed assumptions, boundary conditions, and unconsidered alternatives.

B.2. Long-Horizon Epistemic Thinking

Epistemic partners must reason beyond immediate task completion, considering how present assistance shapes evolving goals, dependencies, expectations, and future inquiry. This requires tracking longer-term trajectories, the agent’s own uncertainty, and accumulated commitments that can narrow future exploration.

  • B.2. Long-Horizon Epistemic Thinking: Epistemic partners must reason over extended horizons as goals evolve, consequences unfold slowly, and alignment with users’ interests may drift.
  • B.2. Long-Horizon Epistemic Thinking: Agents need dual temporal capacities: effective short-term assistance alongside reasoning about how current interventions shape future goals, dependencies, expectations, and reliance.
  • B.2. Long-Horizon Epistemic Thinking: Epistemic partnership also requires agents to monitor their uncertainty, detect epistemic drift, revisit assumptions, and explore alternative representations or strategies.
  • B.2. Long-Horizon Epistemic Thinking: Even individually reversible actions can accumulate into path dependence, locking in assumptions, suppressing alternatives, or privileging interpretations through repeated reinforcement.
  • B.2. Long-Horizon Epistemic Thinking: Responsible proactivity must account for temporal dynamics of knowing and represent commitment and epistemic fragility across horizons to support long-term trajectories.

B.3. Test-Time Proactivity as Epistemic Regulation

Test-time proactivity regulates initiative during interaction by adapting commitment to real-time signals of epistemic adequacy, novelty, or mismatch. Together with questions about unknown unknowns and long-horizon thinking, it frames epistemic partnership as disciplined control over how knowing and acting co-evolve.

  • B.3. Test-Time Proactivity as Epistemic Regulation: Test-time proactivity adapts an agent’s commitment during interaction in response to real-time signals of epistemic adequacy, novelty, or mismatch.
  • B.3. Test-Time Proactivity as Epistemic Regulation: Epistemic partners use uncertainty estimates or confidence scores to seek information, reduce commitment, or return to exploratory modes when legitimacy weakens.
  • B.3. Test-Time Proactivity as Epistemic Regulation: Test-time proactivity keeps agents responsive to uncertainty by preventing unchecked commitment when understanding deteriorates.
  • B.3. Test-Time Proactivity as Epistemic Regulation: Questions about unknown unknowns, long-horizon epistemic thinking, and test-time proactivity form a generative research agenda for epistemic–behavioral coupling.
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