{"id":"fdd7c4da-8f72-4006-9dc9-00b554c2058b","arxiv_id":"2602.15259","paper_version":2,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"Proactive AI should be grounded in the user's epistemic state, not just in action prediction, so that commitment scales with warranted understanding rather than confidence.","lead":"Proactive AI assistants should not just anticipate what users will ask next; they should also surface what users don't know they don't know. This paper argues that such agents need both epistemic grounding (what the agent can legitimately claim to understand) and behavioral grounding (when and how intervention is allowed).","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Section 5's central claim rests on the unoperationalized construct 'epistemic legitimacy'; until it is measurable the mis-coupling taxonomy is unfalsifiable.","rationale":"The reader's weakest_assumption focuses on the ability to engage with unknown unknowns (Section 6 / Appendix A.3.1). That is indeed a forward-looking capability with no clear mechanism, but it is not the most load-bearing dependency of the paper's central claim. Even if unknown unknowns were entirely unachievable, the core coupling claim—that proactive actions should be evaluated by justified intervention rather than effectiveness alone—would still stand. The more fundamental weakness is that 'epistemic legitimacy' itself is never defined in a way that permits measurement, classification, or falsification. This is acknowledged internally in Q1 of Appendix A.2.3, which asks how epistemic legitimacy should be represented. The paper is honest and internally consistent, and as a position paper it can legitimately propose a research agenda. But the central claim in Section 5 is presented as a structural diagnosis, and without an operational definition it risks being a relabeling rather than an explanation. The reader's rationale also mentions that epistemic legitimacy is undefined, so there is partial agreement, but the primary load-bearing concern I identify is the measurability of the central construct, not the unknown-unknown capability. Since the analysis supports the reader's CONDITIONAL verdict rather than strengthening it to ACCEPT or REJECT, I recommend UNCHANGED.","tokens_in":22754,"tokens_out":2828,"duration_ms":31947,"concrete_test":"Take a sample of episodes from an agent benchmark such as SWE-bench or AgentBench where agents produce failures. Before revealing task outcomes, have independent annotators rate epistemic legitimacy at the moment of each key intervention, using only pre-action evidence: the agent's internal confidence, the presence of conflicting or novel signals, the reversibility of the action, and the completeness of the task representation. Measure inter-annotator agreement (e.g., Cohen's κ). Then test whether the paper's predicted pattern—high behavioral commitment combined with low rated epistemic legitimacy—predicts final failure better than commitment alone or baseline outcome-based metrics. If epistemic legitimacy cannot be rated reliably (κ < 0.6) or adds no predictive power over commitment, then the Section 5 central claim requires an operational definition before it can support the proposed e","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's central claim (Section 5) is that many failure modes 'are more structurally understood as mis-couplings: cases where commitment outpaces epistemic legitimacy.' The load-bearing construct is 'epistemic legitimacy,' defined only as 'whether the agent is justified in intervening given what it can legitimately claim to understand about the situation.' This is not operationalized: there is no procedure for assigning a value to 'epistemic legitimacy' from observable system state at action time. The paper itself concedes this in Appendix A.2.3 (Q1): 'How should epistemic legitimacy be represented?' remains an open research question. Until such a representation exists, any proactive failure can be post hoc labeled as high-commitment/low-legitimacy, and any success as high-legitimacy/high-commitment, making the taxonomy unfalsifiable. The Minimal Behavioral Requirements in Section 5.1 are normative design constraints—they specify what good behavior should look like, but they do not provide a measurement or decision rule for determining whether a specific action was epistemically legitimate. Without an independent measure, the paper cannot distinguish its 'mis-coupling' explanation from simpler alternatives such as reward misspecification, insufficient training data, or optimization failure. This does not invalidate the conceptual contribution, but it means the central claim is not yet empirically or formally supported.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper argues that mainstream approaches to generative-AI proactivity—anticipatory, autonomous/planning-based, and mixed-initiative—are all action-centric: they optimize action selection within an assumed task frame and do not model the user's epistemic state as first-class. Drawing on Kerwin's philosophy of ignorance and on organizational research on proactive behavior (especially the inverted-doughnut model), the paper proposes that proactivity should be understood as a coupling between behavioral commitment and epistemic legitimacy. It then uses this coupling to reinterpret failure modes such as hallucination, denial, and runaway commitment as mis-couplings, states four minimal behavioral requirements for responsible proactive systems, and outlines a longer-term vision of 'epistemic partnership' in which agents surface unknown unknowns, reason over long horizons, and regulate initiative at test time. The paper is explicitly a conceptual/position contribution; it does not present experiments or formal derivations, and it candidly lists open research questions in Appendix A.2.3.","tokens_in":23074,"tokens_out":3485,"duration_ms":39731,"significance":"If the framework is accepted, it provides a useful diagnostic vocabulary for proactive-agent failures and reorients evaluation from 'did the agent act effectively?' to 'was the agent justified in acting when it did?'. The synthesis of philosophy-of-ignorance and behavioral-proactivity literatures is thoughtful, and the paper engages seriously with alternative views (Section 7). The minimal behavioral requirements in Section 5.1 and the five-question research agenda in Appendix A.2.3 are valuable contributions for the community. However, the central construct—epistemic legitimacy—is not operationalized, and the paper itself acknowledges this (Q1, Appendix A.2.3). Because the central claim is an explanatory one, the lack of an independent measurement procedure leaves the mis-coupling taxonomy unfalsifiable at this stage. The paper also makes a strong claim about 'asking questions about unknown unknowns' that is not internally coherent as stated (Section 6, Appendix A.3.1). These issues are load-bearing and require revision, but the conceptual core is defensible and worth developing.","major_comments":[{"comment":"Epistemic legitimacy is the central axis of the proposed coupling, but it is never operationalized. In Section 5 it is only defined as 'whether the agent is justified in intervening given what it can legitimately claim to understand about the situation', and Figure 4 plots it as a quantitative axis without any stated measurement procedure. Appendix A.2.3 (Q1) explicitly leaves representation as an open question. This is a problem for the paper's central claim that failure modes 'are more structurally understood as mis-couplings': without an independent way to estimate epistemic legitimacy, any failure can be post hoc labeled high-commitment/low-legitimacy and any success high-legitimacy/high-commitment, making the taxonomy unfalsifiable and indistinguishable from alternative explanations such as reward misspecification or optimization failure. The paper should at least provide a minimal","section":"Section 5 / Figure 4 / Appendix A.2.3 (Q1)"},{"comment":"The paper states that epistemic partners must 'ask questions about unknown unknowns—surfacing missing dimensions . . . that neither the user nor the system has yet articulated.' This is, as written, internally incoherent: if a gap is not represented by either party, there is no signal that can point to it; any question the agent asks has already converted the gap into a known unknown. The paper offers no mechanism for detecting an unrepresented gap without first representing it. This matters because the epistemic-partnership vision depends on this capability. The authors should either provide a concrete mechanism (e.g., domain-driven perturbation, cross-analogy reasoning, or anomaly detection that generates candidate missing dimensions) or reframe the capability as aspirational and clearly separated from the more defensible coupling claim.","section":"Section 6 / Appendix A.3.1"},{"comment":"The 'Minimal Behavioral Requirements' are stated as normative constraints but no verification method is supplied. For example, requirement 3 says commitment 'must be interruptible by epistemic degradation,' but the paper does not define what counts as a signal of epistemic degradation or how one would test whether a system is interruptible. Requirement 4 says epistemic uncertainty 'must actively modulate initiative,' but 'actively' is not measurable. The paper needs either concrete empirical signatures that would distinguish compliant from non-compliant behavior, or an explicit statement that these are design principles rather than testable requirements. Without such specification, the requirements risk being unfalsifiable in the same way as the broader coupling claim.","section":"Section 5.1"}],"minor_comments":[{"comment":"The table uses '✓ ✓ ✓ ∼' for mixed-initiative clarification and sensemaking, with a footnote explaining '∼: partial / limited support.' It would be clearer to state explicitly which category of epistemic state the '∼' refers to and why partial support is attributed to that row but not to the others.","section":"Table 1"},{"comment":"The text mentions 'ToolBench and StableToolBench' as benchmarks without any citations. If these are intended to be established benchmarks, they should be referenced; if they are illustrative, that should be stated.","section":"Appendix A.1.2"},{"comment":"There are minor formatting errors in the reference list. For example, 'Lei et al., 2020' is credited to 'Maarten de Rijke Li'—likely should be 'Maarten de Rijke'. Also, several references have inconsistent journal/conference capitalization. These can be fixed in a copyedit pass.","section":"References"}],"recommendation":"major_revision","confidential_remarks":"This is a genuine conceptual contribution that could be publishable as a position/vision paper, but the central construct needs more operational specificity before the explanatory claim is credible. The authors are already transparent about the open questions, which is commendable; the revision should go further and narrow the scope of the central claim or provide a concrete empirical protocol. I do not see grounds for rejection, since the framework can be strengthened within the paper's scope."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Core new thing: the epistemic-behavioral coupling frame, plus Table 1's taxonomy showing that existing proactivity approaches top out at known unknowns. That taxonomy is a genuinely useful way to organize a scattered literature. What the paper does well: it imports Kerwin's ignorance categories and Parker's inverted-doughnut model into the proactivity literature without overselling them, it engages alternatives honestly in Section 7, and it front-loads its own open questions in the appendix. The self-citation to ProPer is an example, not a premise; no circularity issue.\n\nSoft spots, in proportion: the load-bearing construct is 'epistemic legitimacy,' defined as being justified in intervening given what the agent can claim to understand. There is no procedure for assigning it at action time. The stress-test concern is fair: absent operationalization, any failure can be post-hoc labeled high-commitment/low-legitimacy, and any success as high-legitimacy/high-commitment. The authors themselves flag this as open question Q1, so it's a recognized limitation, not a hidden one. For a position paper this is acceptable but it does mean the central claim is unfalsifiable in its current form. Second, the unknown-unknowns capability in Section 6/A.3.1 is posited as an aspiration: the paper says agents must 'ask questions about unknown unknowns,' but by definition an unrepresented gap produces no signal, and no mechanism is offered for recognizing one. This is the weakest point, but the paper's more modest claim — proactivity should be constrained by epistemic legitimacy — does not depend on it.\n\nThis is a conceptual reframing, not a new mechanism. The argument is internally consistent, the writing is clear, and the authors are candid about what is not solved. Citation pattern looks solid. Who it's for: HCI, AI-safety, and conversational-agent researchers who want a sharper vocabulary for proactive/overstepping behavior. Not a systems paper; it would be out of place at a purely empirical venue. It deserves a serious referee. My recommendation: send it out, and ask the authors to sharpen epistemic legitimacy into something measurable, or at least state what evidence would count against the mis-coupling explanation. I'd bring it to a reading group.","headline":"A serious conceptual reframing of proactivity as epistemic-behavioral coupling; the central construct is unoperationalized, but the paper is honest and deserves a real referee.","tokens_in":23525,"tokens_out":2694,"would_cite":true,"duration_ms":27154,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"The paper argues that generative AI's proactive failures are best understood as mis-couplings between behavioral commitment and epistemic legitimacy, so proactivity must be grounded in both.","keywords":["proactivity","epistemic legitimacy","behavioral commitment","unknown unknowns","hallucination","alignment","mixed-initiative systems","epistemic partnership"],"falsifier":"A concrete falsifier would be an agent that consistently violates the minimal requirements—committing strongly under brittle understanding, smoothing over uncertainty, and never downshifting—yet shows no greater rate of harmful failure than a compliant agent across diverse out-of-distribution tasks. If such an agent performed equally well without the epistemic safeguards, the claim that mis-couplings structurally cause these failures would be refuted.","tokens_in":22645,"feed_emoji":"🧠","tokens_out":3845,"duration_ms":36710,"temperature":0.7,"pith_summary":"The paper argues that generative AI agents mistakenly equate understanding with resolving explicit queries, which breaks down when users themselves lack awareness of what is missing, risky, or worth considering. In such states of epistemic incompleteness, proactivity is an epistemic necessity rather than a convenience. The central claim is that many failures attributed to hallucination or misalignment are better understood as mis-couplings: the agent commits resources and changes the world even though it is not epistemically justified in doing so. Therefore proactive agents need dual grounding—epistemic grounding to model what is known, uncertain, or unrepresentable, and behavioral grounding to constrain when, how, and how much to intervene. If correct, this reframes how proactive AI should be designed and evaluated: the question shifts from whether an agent's action was effective to whether it was justified at the time.","feed_headline":"Proactive AI fails when it acts without warrant","feed_subtitle":"A coupling of commitment and epistemic legitimacy should decide when agents intervene—not autonomy or accuracy alone.","key_machinery":"The central mechanism is the epistemic–behavioral coupling space, a two-dimensional model with behavioral commitment (degree of intervention without explicit instruction) on one axis and epistemic legitimacy (whether intervention is warranted by what the agent understands) on the other; it produces four qualitatively distinct regimes of proactive action. It is supported by the inverted doughnut model from behavioral research, which bounds initiative within a discretionary space defined by role scope and recoverability, and by the philosophy of ignorance, which distinguishes uncertainty from error, denial, taboo, and tacit knowing. The coupling space does the work of unifying diverse failure","core_discovery":"The paper's central discovery is the epistemic–behavioral coupling: proactivity is not a single axis of capability but a pairing of two jointly necessary conditions—initiative/commitment, meaning how strongly an agent intervenes without an explicit prompt, and epistemic legitimacy, meaning whether the agent is justified in intervening given what it understands. Plotting these as two axes yields four regimes of proactive behavior; the dangerous one is high commitment with low legitimacy, which the paper calls epistemic overreach. Failure modes such as hallucination, suppressed uncertainty, and runaway commitment are unified as mis-couplings that share a structural cause: proactive commitment","pith_inferences":["A testable extension: in out-of-distribution or novel tasks, an agent that follows the minimal requirements—downshifting commitment when epistemic signals degrade—should produce fewer irreversible errors than a comparable agent that only optimizes task completion; existing agent benchmarks could be modified to measure this.","The framework implies a design principle beyond the paper: user interfaces for proactive agents should make epistemic legitimacy legible—for example, by indicating whether the agent believes the task frame is complete—so users can distinguish warranted from unwarranted initiative.","If the framework is correct, 'aligning' proactive agents is less about reward hacking or preference matching and more about building explicit representations of what the agent does not know, a representational challenge that current architectures do not directly address.","The diagnosis may extend to reactive systems too, since even a non-proactive answer carries an implicit commitment; the coupling lens could inform when a system should refuse to answer or ask a question rather than comply confidently."],"forward_implications":["Evaluation of proactive agents must shift from outcome effectiveness ('did it work?') to justification at the time of action ('was the agent warranted in intervening?').","Autonomy is the wrong control variable for regulating proactive behavior; commitment is the critical quantity, because harm arises when strong commitment is paired with weak epistemic legitimacy.","Training objectives and benchmarks that reward momentum, coherence, and task completion systematically incentivize mis-coupling, independent of model scale, data quality, or architecture.","Proactive systems should be designed to preserve uncertainty signals, downshift commitment under epistemic degradation, and treat abstention or deferral as legitimate proactive behavior.","The next frontier is epistemic partnership: agents that ask questions about unknown unknowns, reason over long horizons, and regulate initiative at test time rather than following fixed policies."],"fun_headline_variants":["Epistemic overreach: proactive AI's blind spot","Proactivity needs warrant, not just initiative","AI proactivity demands both commitment and legitimacy","When proactive AI oversteps: the legitimacy gap","Why proactive AI must earn its right to act"],"cache_read_input_tokens":2304,"weakest_assumption_plain":"The framework assumes that an agent can, in principle, engage with unknown unknowns—gaps that neither the user nor the system has yet represented as questions—even though such gaps by definition produce no signal to act on.","fun_headline_variants_meta":{"raw":{"variants":["Epistemic overreach: proactive AI's blind spot","Proactivity needs warrant, not just initiative","AI proactivity demands both commitment and legitimacy","When proactive AI oversteps: the legitimacy gap","Why proactive AI must earn its right to act"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000158,"raw_usage":{"total_tokens":1045,"prompt_tokens":708,"completion_tokens":337,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":452,"completion_tokens_details":{"reasoning_tokens":266}},"tokens_in":452,"tokens_out":337,"duration_ms":3508,"temperature":1.0,"reasoning_tokens":266,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-02T22:53:55.595055+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"A concrete falsifier would be an agent that consistently violates the minimal requirements—committing strongly under brittle understanding, smoothing over uncertainty, and never downshifting—yet shows no greater rate of harmful failure than a compliant agent across diverse out-of-distribution tasks. If such an agent performed equally well without the epistemic safeguards, the claim that mis-couplings structurally cause these failures would be refuted.","supporting_citations":[],"review_version":1}