Proactive Knowledge Behaviors Framework

• Proactive Knowledge Behaviors Framework is a system that anticipates needs by capturing, structuring, and addressing knowledge gaps through an anticipatory loop.
• It integrates human–AI and AI-only architectures, employing methods like risk knowledge graphs and dialogue state tracking to enhance proactive interventions.
• Evaluations reveal improved response timing and task efficiency, while highlighting challenges in trigger stability, coordination, and intervention appropriateness.

The “Proactive Knowledge Behaviors Framework” (Editor's term) can denote a class of human–AI and AI-only architectures that treat knowledge work as an anticipatory loop: systems intentionally generate or capture data, transform it into structured knowledge, represent what is known and what is missing, and intervene before explicit requests or adverse events occur. Recent formulations span proactive safety management, where proactive actions generate numerical, textual, and visual data that are mined into 5W1Y risk knowledge and encoded in a risk knowledge graph (Wen, 2024); dialogue systems that maintain a CurrentState, GoalState, and GapSignals under partial observability (Pan et al., 18 Mar 2026); and proactive agents that surface implicit dimensions or “knowledge gaps” beyond the user’s explicit query (Kaur et al., 14 Jan 2026). Across HCI and AI, proactivity is associated with systems that “anticipate user needs, take initiative, and act without explicit user input,” but recent work also argues that initiative must be grounded both epistemically and behaviorally if it is to remain appropriate, transparent, and safe (Zargham et al., 23 Jun 2026, Kaur et al., 16 Feb 2026).

1. Conceptual foundations

Proactivity is distinguished from reactive behavior by temporal orientation and initiative. In the HCI and AI literature, proactive systems are characterized as systems that “anticipate user needs, take initiative, and act without explicit instructions,” whereas reactive systems respond after explicit input or after events occur. The same literature also emphasizes that reminders and recommendation systems are often labeled proactive even though their “underlying mechanisms and intentions differ fundamentally” from systems that genuinely anticipate and act on user goals. This distinction separates proactivity from mere system initiation, and it is one reason recent work explicitly seeks to disentangle proactivity from autonomy, automation, and system-initiated interaction (Zargham et al., 23 Jun 2026).

A second foundation is epistemic rather than merely temporal. Work on generative proactivity argues that many tasks are characterized by epistemic incompleteness: progress depends on engaging with “unknown unknowns,” including missing dimensions, unrecognized gaps, and misframings. On this view, reactive query answering is insufficient because it mirrors the user’s current framing; anticipatory proactivity improves on this by acting ahead within a fixed task frame, but generative proactivity is defined by its ability to reorganize the user’s epistemic landscape by surfacing new possibilities and questions. This suggests that a proactive knowledge framework must model not only future events but also the adequacy of the task representation itself (Kaur et al., 16 Feb 2026).

A complementary formulation appears in gap-aware dialogue and personalized assistant work. One line of research describes a loop that continuously asks, “What do I know? What don’t I know? Which unknowns matter most right now? What should I do or ask next to close them?” Another formalizes an interaction as a set of user-explicit, system-explicit, and implicit dimensions, where proactivity is the selective activation of relevant but unarticulated dimensions under a budget constraint. A plausible implication is that a proactive knowledge framework is best understood as a theory of calibrated intervention on incompleteness, rather than a theory of maximal initiative (Pan et al., 18 Mar 2026, Kaur et al., 14 Jan 2026).

2. Operational loop and core stages

A recurrent structure across the literature is a staged loop from observation to structured knowledge to intervention. In proactive safety, the loop is explicit: proactive safety actions → safety data → data-driven approaches → safety information/knowledge → knowledge-driven approaches → risk knowledge graph → warnings & countermeasures. The model begins with safety checks, inspections, audits, monitoring, training, and hazard identification; mines numerical, textual, and visual data using statistical inference, computer vision, and NLP/LLMs; then structures the results in a risk ontology and risk knowledge graph whose core entities are Worker, Event, Hazard, Risk, and RiskTreatment. The process is cyclical because the resulting graph informs better future proactive actions (Wen, 2024).

In dialogue-based inquiry systems, the same logic is expressed as a partial-observability loop. A dialogue is modeled as a sequence of utterances $U_{1:T}$, from which the system extracts stateful events and updates a structured state: $C_t = \Phi(C_{t-1}, E_t).$ A GoalState $G_t$ specifies what must be known for the task, and the current knowledge gap is represented as

$\Delta(C_t,G_t)=\{g_1,g_2,\ldots,g_m\}.$

Hybrid retrieval and a POMDP-lite action planner then choose what question, verification step, explanation, or recommendation should come next. This formalization makes explicit that proactive behavior depends on maintaining a machine-readable representation of both current knowledge and missing knowledge (Pan et al., 18 Mar 2026).

Dimension-based approaches generalize the same idea to broad assistant settings. For interaction state $I=(u,r_0)$, the dimension space is decomposed into user-explicit dimensions, system-explicit dimensions, and implicit dimensions. Selective activation then chooses

$$\mathcal{D}_{\text{act}}(I) \subseteq \Big(\mathcal{D}^{\text{user}}_{\text{exp}}(u)\setminus \mathcal{D}^{\text{sys}}_{\text{exp}}(r_0)\Big)\cup \mathcal{D}_{\text{imp}}(u),$$

followed by a budgeted ranking of which dimensions should actually be surfaced. This makes proactivity a post-hoc calibration problem: not every plausible missing dimension should be introduced, and not every unmet explicit need should be expanded with the same initiative level (Kaur et al., 14 Jan 2026).

Some architectures extend the loop to self-evolution. In Galaxy, the Cognition Forest

$$\mathcal{F} = \{\mathcal{T}_{\text{user}}, \mathcal{T}_{\text{self}}, \mathcal{T}_{\text{env}}, \mathcal{T}_{\text{meta}}\}$$

unifies cognitive architecture and system design into a self-reinforcing loop in which cognition drives understanding of user needs, reflection identifies capability gaps, and meta-cognition modifies Spaces, nodes, functions, or code, thereby adding new cognitive pathways for later proactive behavior (Bao et al., 6 Aug 2025).

3. Epistemic state, calibration, and commitment

A mature proactive framework requires explicit models of epistemic state. KScope proposes five knowledge statuses—consistent correct knowledge, conflicting correct knowledge, absent knowledge, conflicting wrong knowledge, and consistent wrong knowledge—derived from the mode structure of answer distributions with and without context. Its central claim is that context can narrow knowledge gaps, but that models differ sharply when they are consistently wrong rather than partially correct or conflicted. This suggests that proactive systems should not treat “uncertainty” as a single scalar; they need richer state models that distinguish ignorance, conflict, and entrenched error (Xiao et al., 9 Jun 2025).

Educational applications make this calibration problem concrete. The Capture–Calibrate–Coach framework represents learners, concepts, and assessments in a heterogeneous graph, infers Latent Perceived States for concepts not explicitly mentioned in self-reports, and evaluates knowledge monitoring using Signal Detection Theory. Its central metrics are discriminability

$$d' = z\left(\frac{A}{A{+}C}\right) - z\left(\frac{B}{B{+}D}\right),$$

sensitivity

$\frac{A}{A{+}C},$

and specificity

$\frac{D}{B{+}D}.$

These metrics support classification into five metacognitive patterns—Well Calibrated, Aware of Limitations, Underconfident, Overconfident, and Liberal Criterion—and the framework reports 85.21% AUC in predicting latent perceived states (Li et al., 25 May 2026).

Generative proactivity adds a behavioral constraint to this epistemic picture. One proposal models proactivity in a two-dimensional space whose axes are epistemic legitimacy and behavioral commitment. Low legitimacy combined with high commitment is defined as epistemic overreach; low legitimacy should instead trigger low-commitment actions such as probing, gap surfacing, or reframing. The same work formulates four qualitative requirements: commitment must scale with epistemic recoverability, proactive behavior must preserve epistemic signals, commitment must be interruptible by epistemic degradation, and epistemic uncertainty must actively modulate initiative. A common misconception follows directly: proactivity is not equivalent to “more initiative,” but to the calibrated coupling of initiative with justified understanding (Kaur et al., 16 Feb 2026).

4. Knowledge substrates and action mechanisms

Knowledge representation varies widely, but successful systems make missing, relevant, and actionable knowledge explicit. In process safety, the risk knowledge graph is the canonical substrate. Events are typed by ID, Time, Location, and Description; workers by Name, Role, Duty, and Authority; hazards by ID, Location, and Description; and relations include causal, sequential, hierarchical, simultaneous, manage, collaborate, interdependent, reinforce, synthesize, and counteract. In the CSTR case, the resulting graph contained 176 nodes and 260 “head–tail–relation” triples, supporting query-based subgraph extraction and causal path inspection for warnings and countermeasures (Wen, 2024).

Dialogue systems operationalize similar ideas through state, belief, and action scoring. In doctor–patient inquiry, candidate actions are scored by a POMDP-lite utility

$C_t = \Phi(C_{t-1}, E_t).$0

where the terms denote information gain, risk reduction, path shortening, explanation gain, redundancy penalty, cognitive load penalty, and conservative bias. Action selection is

$C_t = \Phi(C_{t-1}, E_t).$1

This mechanism converts a knowledge-gap model into a control policy over questions, verifications, explanations, and recommendations (Pan et al., 18 Mar 2026).

Proactive dialogue in retrieval-based chatbots uses a different but related mechanism. The Knowledge Prediction Network tracks uncovered goal content by comparing goal tokens with context tokens, produces an updated goal representation $C_t = \Phi(C_{t-1}, E_t).$2, predicts relevance scores for knowledge triplets, and jointly optimizes knowledge prediction and response selection via

$C_t = \Phi(C_{t-1}, E_t).$3

Here, proactive behavior is instantiated as knowledge selection and response ranking that advance the remaining goal rather than merely react to the latest utterance (Zhu et al., 2021).

Embodied and agentic systems extend these mechanisms to tool use and environmental interaction. ProVox personalizes a proactive planner using a user-specific goal prompt $C_t = \Phi(C_{t-1}, E_t).$4 and a personalized API $C_t = \Phi(C_{t-1}, E_t).$5, then repeatedly invokes a proactive trigger—“Propose an action to perform next to perform [user-provided goal]”—while keeping a human confirmation step before execution (Grannen et al., 13 Jun 2025). Galaxy uses Agenda, Persona, KoRa, and Kernel to turn TimeEvents and behavior patterns into daily plans, proactive tool launches, self-repair, and user-adaptive system design, all grounded in the Cognition Forest and mediated by contextual privacy management (Bao et al., 6 Aug 2025). Streaming multimodal agents further add explicit control over active proactive tasks $C_t = \Phi(C_{t-1}, E_t).$6 and temporal-gating mechanisms that suppress premature triggers or recover missed ones under continuous streams (Li et al., 26 May 2026).

5. Evaluation: metrics, benchmarks, and empirical patterns

A recurring theme in the evaluation literature is that reactive metrics are insufficient. Existing methods such as SUS, UEQ, and reactive trust scales do not directly capture timing, appropriateness, user control, transparency, or trust calibration, all of which are central to proactive behavior. This is why recent work emphasizes domain-specific, process-sensitive benchmarks rather than static answer quality alone (Zargham et al., 23 Jun 2026).

Several benchmark programs have therefore operationalized proactive behavior in distinct ways.

Framework	Evaluated capability	Representative findings
PaSBench (Yuan et al., 23 May 2025)	Proactive risk awareness on 416 multimodal scenarios; Accuracy, Potential, Robustness	Gemini-2.5-pro achieves 71% image and 64% text accuracy; top performers still miss 45–55% risks in repeated trials
IPIBench (Li et al., 26 May 2026)	Proactive monitoring, proactive task management, and interleaved reactive–proactive requests under streaming video	Identifies unstable proactive triggering and weak coordination between reactive and proactive behaviors; IPI-Agent consistently improves existing MLLMs
Dialogue-based EMR pilot (Pan et al., 18 Mar 2026)	Coverage, risk recall, structural completeness, redundancy, and $C_t = \Phi(C_{t-1}, E_t).$7	Full framework reaches 83.3% coverage, 80.0% risk recall, 81.4% structural completeness, and lower redundancy
ProMediate (Liu et al., 29 Oct 2025)	Consensus change, topic-level efficiency, response latency, mediator effectiveness, and mediator intelligence	In ProMediate-Hard, social mediator reaches 10.65% consensus change versus 7.01%, with response 77% faster

These evaluations reveal two broad empirical regularities. First, proactive competence is often limited less by factual knowledge than by triggering and coordination. PaSBench reports that “unstable proactive reasoning rather than knowledge deficits” is the primary limitation, because models often succeed when reactively asked about risks they previously failed to raise proactively (Yuan et al., 23 May 2025). IPIBench reaches a related conclusion in streaming settings: the main weaknesses are unstable trigger timing and poor coordination across ongoing proactive tasks and reactive interruptions (Li et al., 26 May 2026).

Second, intervention quality is context-sensitive. In multi-party negotiation, faster and more targeted interventions improve outcomes in competing, deadlock-prone settings, but over-intervention in easy or accommodating settings can disrupt organically developing consensus (Liu et al., 29 Oct 2025). This supports the broader theoretical claim that proactive quality cannot be reduced to “more” or “earlier” action; it depends on whether intervention is warranted, well-timed, and aligned with group or task dynamics.

6. Applications, limitations, and future directions

Proactive knowledge frameworks have been instantiated across safety, medicine, dialogue, robotics, personal assistance, negotiation, and learning support. In proactive safety, the CSTR case shows how a risk knowledge graph can reveal that a tank temperature high-high alarm is downstream of a sensor fault and heater activation, shifting the operator from symptom treatment to root-cause intervention (Wen, 2024). In doctor–patient dialogue, the framework treats the EMR as a “structured projection” of an ongoing inquiry loop rather than the final artifact (Pan et al., 18 Mar 2026). In situated human–robot collaboration, meta-prompting and proactive planning yield 38.7% faster task completion times and 31.9% less user burden relative to non-active baselines (Grannen et al., 13 Jun 2025). In personal assistants, Galaxy reports strong preference retention and reduced privacy leakage while supporting proactive and self-evolving behavior (Bao et al., 6 Aug 2025). In education, 3C moves toward “metacognitive teammates” that help learners regulate both knowledge gaps and calibration errors (Li et al., 25 May 2026).

The framework is nevertheless bounded by substantial limitations. Graph-based safety models may require manual correction, expert ontology design, and still lack probabilistic reasoning or real-time integration (Wen, 2024). Dimension-based proactive agents can surface relevant but unarticulated dimensions, but free-form dimensions and static calibration hyperparameters create risks of irrelevance or overreach; the paper explicitly identifies over-intrusive or mistimed interventions and hallucinated or irrelevant gaps as open concerns (Kaur et al., 14 Jan 2026). Generative proactivity can misdirect attention, overwhelm users, or introduce harm if it is not behaviorally grounded (Kaur et al., 16 Feb 2026). Evaluation remains difficult because many instruments were designed for reactive systems, and because user expectations vary by domain and familiarity (Zargham et al., 23 Jun 2026).

Future work is converging on several directions. One is richer epistemic control: better representations of epistemic legitimacy, earlier detection of epistemic degradation, and stronger coupling between uncertainty and action (Kaur et al., 16 Feb 2026). Another is scalable knowledge acquisition and interoperability: standardized ontologies, data formats, APIs, and tighter integration with operational systems (Wen, 2024). A third is adaptive calibration: dynamically adjusting initiative levels, grounding proactive dimensions in more structured concept spaces, and extending evaluation beyond single-turn outputs to longer-horizon interaction quality and user governance (Kaur et al., 14 Jan 2026, Zargham et al., 23 Jun 2026). A plausible synthesis is that the future Proactive Knowledge Behaviors Framework will be judged not by whether it speaks first, but by whether it knows when a gap is real, when intervention is justified, what knowledge should be surfaced, and how that intervention should be shaped for the human or organizational context.