PROPER: Calibrated Proactivity

• PROPER proactivity is defined as a calibrated approach that anticipates and addresses unexpressed user needs through timely and controlled interventions.
• It employs a modular two-agent architecture—comprising a Dimension Generating Agent and a Response Generating Agent—to selectively activate latent knowledge gaps.
• Empirical benchmarks demonstrate up to 84% gains over reactive models, underscoring the effectiveness of calibrated initiative in diverse application domains.

Searching arXiv for the target paper and closely related work on proactivity, proactive agents, and proactive systems. {"^^^^^^^^2query2 OR title:\2^^^^^^^^"The PROPER Approach to Proactivity: Benchmarking and Advancing Knowledge Gap Navigation\"","max_results":5} to=arxiv_search 腾讯天天中彩票 The PROPER Approach to Proactivity designates a calibrated alternative to the reactive ask-and-respond paradigm. Rather than waiting for fully specified requests, it treats proactivity as the selective surfacing of relevant but unexpressed needs, latent task dimensions, or future contingencies, while constraining initiative by timing, appropriateness, user control, and intent alignment. In its most explicit formulation, PROPER is instantiated as Proactivity-driven Personalized agents, a two-agent architecture for knowledge gap navigation built around a Dimension Generating Agent and a Response Generating Agent (&&&^^^^2query2^^^^&&&). Related literature extends the same underlying problem to embodied robots, proactive dialogue, voice agents, coding agents, and smart environments, where the central question becomes whether a system was justified in acting when it did (Buyukgoz et al., 2022, Kaur et al., 16 Feb 2026, Zargham et al., 23 Jun 2026).

^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^. Conceptual scope and differentiation

Proactivity is consistently contrasted with reactive behavior. Reactive systems respond to explicit user input or commands, whereas proactive systems anticipate user needs, take initiative, and act without explicit instructions (Zargham et al., 23 Jun 2026). In robotics, one influential distinction separates two mechanisms: recognizing humans’ intentions and acting to fulfill them, or reasoning about possible future threats or opportunities and acting to prevent or foster them (Buyukgoz et al., 2022). In generative-agent research, this reactive limitation is reframed as epistemic incompleteness: situations in which progress depends on engaging unknown unknowns rather than merely answering articulated questions (Kaur et al., 16 Feb 2026).

This conceptual field is not uniform. The HCI literature explicitly notes that simple reminders and recommendation systems are frequently labeled proactive even though the underlying mechanisms and intentions differ from systems that genuinely anticipate and act on user goals (Zargham et al., 23 Jun 2026). Work on coding agents sharpens the distinction further by separating autonomy from proactivity: autonomy means the agent can act without supervision once a task is given, whereas proactivity means the agent decides whether and when to act without an explicit prompt, including whether to stay silent, ask, notify, or draft an artifact (Bui et al., 7 May 2026).

A concise comparison of the major strands is useful because the same term is used at different analytical levels.

Strand	Core unit of proactivity	Representative paper
Knowledge-gap navigation	Implicit dimensions and selective activation	(&&&^^^^2query2^^^^&&&)
Robot assistance	Human intentions or possible futures	(Buyukgoz et al., 2022)
Epistemic-behavioral grounding	Epistemic legitimacy and behavioral commitment	(Kaur et al., 16 Feb 2026)
Human-centered systems design	Timing, appropriateness, user control, transparency, trust	(Zargham et al., 23 Jun 2026)

This suggests that PROPER is not a single universal doctrine. It functions both as a named architecture and as a broader research program concerned with calibrated initiative under uncertainty.

2. Formalization as knowledge-gap navigation

The most explicit formal treatment defines the user state as

PRESERVED_PLACEHOLDER_^^^^2query2^^^^

where PRESERVED_PLACEHOLDER_^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^ is the current user ^^^^^^^^2query2 $h$ is the interaction history, and $p$ contains persona-related explicit attributes (&&&^^^^2query2^^^^&&&). The central object is the implicit dimension: a latent aspect relevant to the user’s task but not considered by the user. PROPER treats these implicit dimensions as knowledge gaps that can complement explicit intent without overwhelming it (&&&^^^^2query2^^^^&&&).

The framework elevates dimensions to first-class representational units. It distinguishes dimensions explicitly present in the user’s ^^^^^^^^2query2^^^^^^^^ and history, dimensions already covered in a baseline response $r_0$, and implicit dimensions that are task-relevant but absent from the explicit interaction state (&&&^^^^2query2^^^^&&&). Selective activation then identifies a candidate set consisting of two sources of incompleteness: explicit user dimensions not covered by the baseline response, and plausible implicit dimensions that may deserve proactive surfacing (&&&^^^^2query2^^^^&&&).

The final response is defined by a utility-based calibration objective:

$$\hat r = \arg\max_{r\in\mathcal{R}} \mathcal{F}\!\left(r \mid u,\, r_0,\, U_u,\, S_k^{*}(I)\right),$$

where $U_u$ denotes the user-explicit dimensions and $S_k^{*}(I)$ is a budgeted calibrated subset of activated dimensions (&&&^^^^2query2^^^^&&&). The intervention budget $k$ is structurally important: it prevents the system from converting every plausible latent aspect into an actual intervention.

Selection of $S_k^{*}(I)$ is governed by a post-hoc calibrated reranker:

PRESERVED_PLACEHOLDER_^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2query2^^^^

In this formulation, the three control terms are quality, unmet explicit alignment, and implicit diversity (&&&^^^^2query2^^^^&&&). The result is a formal account of proactivity as calibrated activation rather than unrestricted initiative.

3. Architectural realization and decision pipeline

PROPER operationalizes this formalism through a modular two-agent architecture. The Dimension Generating Agent is a fine-tuned LLM that leverages explicit user data to generate multiple implicit dimensions or knowledge gaps, while also extracting explicit dimensions from the user state and the baseline response. These candidate dimensions are then selectively filtered using a reranker based on quality, diversity, and task relevance (&&&^^^^2query2^^^^&&&). The Response Generating Agent receives the user ^^^^^^^^2query2 history, persona, baseline response, user-explicit dimensions, and the calibrated subset of activated dimensions, and produces an updated response that balances explicit and implicit considerations with timely and proactive interventions (&&&^^^^2query2^^^^&&&).

The implementation details reinforce the modular design. The Dimension Generating Agent is fine-tuned with LoRA rank 8 in LLaMA-Factory; the reported backbones are Meta-LLaMA-3.^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^-8B-Instruct and Qwen-3-8B; the reranker uses BGE-small embeddings and cosine similarity (&&&^^^^2query2^^^^&&&). Prompt constraints are also part of the architecture: the Dimension Generating Agent is required to emit strict JSON schemas, while the Response Generating Agent is constrained to prefer concise additions over full rewrites and to ask at most one clarifying question if resolving a gap requires user-specific information (&&&^^^^2query2^^^^&&&).

Comparable decompositions recur in other proactive systems. ProAct separates a low-latency Behavioral System for streaming multimodal interaction from a slower Cognitive System for long-horizon social reasoning, with asynchronous intention injection into the motion stream (Zhang et al., 15 Feb 2026). ProAgent similarly separates on-demand tiered perception, proactive-oriented context extraction, a context-aware proactive reasoner, and a guarded tool-calling subsystem (Yang et al., 7 Dec 2025). A plausible implication is that modularity is not incidental: proactive systems repeatedly split fast interaction from slower deliberation in order to preserve fluency while still supporting anticipatory reasoning.

4. Benchmarks, metrics, and empirical performance

Evaluation of proactivity has moved from anecdotal demonstrations toward specialized benchmarks and rubrics. In the PROPER framework itself, evaluation is gap-aware: responses are scored for coverage of task-relevant gaps, initiative appropriateness, and intent alignment using a GPT-5 judge that explicitly penalizes both overreach and underreach (&&&^^^^2query2^^^^&&&). Pairwise comparisons report substantial gains over base models. For LLaMA-8B, Medical PRESERVED_PLACEHOLDER_^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^Score rises from 2.^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^9 to 3.86, Code from ^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^.26 to 2.^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^3, and PWAB from 2.34 to 4.^^^^2query2^^^^6; for Qwen-8B, Medical rises from 2.93 to 4.^^^^2query2^^^^3, Code from 2.24 to 2.84, and PWAB from 3.^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\22^^^^ to 4.29. The headline summary is “up to 84% gains in single-turn evaluation,” and multi-turn dominance analysis reports preferences for PROPER in ^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^/^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\22^^^^ Medical, 9/^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\22^^^^ Code, and ^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\22^^^^/^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\22^^^^ PWAB conversations (&&&^^^^2query2^^^^&&&).

Other benchmarks expose how difficult proactive competence remains.

Benchmark	What it measures	Reported finding
ProPerBench	Coverage, initiative appropriateness, intent alignment	Up to 84% gains in single-turn evaluation (&&&^^^^2query2^^^^&&&)
PROBE	Search, bottleneck identification, execution	Best end-to-end performance of 4^^^^2query2^^^^% by GPT-5 and Claude Opus-4.^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^ (Pasternak et al., 22 Oct 2025)
ProVoice-Bench	Triggering, dormancy discipline, execution quality	^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^,^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^82 samples; significant performance gap, especially over-triggering (&&&3^^^^2query2^^^^&&&)
BehaviorBench	Reactive-to-proactive clinical behavior	BehaviorSFT up to 97.3% overall Macro F^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^ (&&&3^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^&&&)

These evaluations are methodologically diverse. PROBE decomposes proactive problem solving into searching for unspecified issues, identifying specific bottlenecks, and executing appropriate resolutions, with the best observed end-to-end performance capped at 4^^^^2query2^^^^% (Pasternak et al., 22 Oct 2025). ProVoice-Bench defines interaction decision metrics such as Accuracy, Recall, and False Positive Rate, and an PRESERVED_PLACEHOLDER_^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\22^^^^ metric that gates response quality on correct trigger decisions; the benchmark contains ^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^,^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^82 multimodal instances and shows persistent over-triggering and decision-to-execution gaps (&&&3^^^^2query2^^^^&&&). In clinical settings, BehaviorBench spans highly reactive through highly proactive behaviors, and BehaviorSFT improves proactive task scores for Qwen2.5-7B-Ins from 95.^^^^2query2^^^^% to 96.5% while reaching up to 97.3% overall Macro F^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^ (&&&3^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^&&&).

Taken together, these results show that proactive behavior is now benchmarkable, but also that current systems remain fragile precisely where proactivity becomes most consequential: trigger calibration, bottleneck diagnosis, parameter completion, and execution fidelity.

5. Human-centered constraints: control, trust, observability, and social fit

A recurring result across domains is that useful proactivity is not reducible to initiative alone. In robot-assisted well-being coaching, an online study with PRESERVED_PLACEHOLDER_^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^3 found that user control produced consistently higher perceived privacy appropriateness and trust than conditions without control. Transparency alone did not improve privacy appropriateness without user control, and proactivity alone did not significantly decrease appropriateness when user control was present (Nilgar et al., 4 Sep 2025). The study’s lowest privacy appropriateness occurred in the combined low-transparency, no-control, proactive condition, which supports a consent-gated interpretation of proactive sharing (Nilgar et al., 4 Sep 2025).

In dialogue systems, the same calibration problem appears as a trade-off between task effectiveness and social effectiveness. A socially-aware DQN for proactive dialogue optimized a reward

PRESERVED_PLACEHOLDER_^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^4

with trust treated as primary (Kraus et al., 2022). The resulting agent achieved the best cooperation score, ^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^.^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^4, compared with ^^^^2query2^^^^.96 for None, ^^^^2query2^^^^.9^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^ for Notification, ^^^^2query2^^^^.92 for Suggestion, ^^^^2query2^^^^.97 for Intervention, and ^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^.^^^^2query2^^^^5 for a rule-based policy (Kraus et al., 2022). The empirical lesson is explicit: “more proactive” is not always “better.”

In settings with partial observability, recognizability becomes a formal requirement. A proactive assistant in shared workspaces is guided by three principles: its activity decreases the human’s cost toward her goal; the human is able to recognize the potential reduction in her cost; and its activity optimizes the human’s overall cost of achieving her goal (Kulkarni et al., 2021). User studies in the USAR domain reported recognition rates of 6/3^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^ for the baseline versus 25/3^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^ for the proactive assistant with a legible action in one study, and 6/27 versus 24/27 in another (Kulkarni et al., 2021). Workload and processing-effort ratings also improved markedly when assistance was made legible or when irrelevant information was obfuscated (Kulkarni et al., 2021).

Group collaboration yields the same non-monotonic pattern. In a collaborative escape room, the proactive robot model increased human-initiated interaction frequency from PRESERVED_PLACEHOLDER_^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^5 to PRESERVED_PLACEHOLDER_^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^6 with PRESERVED_PLACEHOLDER_^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^7, yet the reactive model showed a descriptively higher overall success rate, 92.86% versus 7^^^^2id:(Kaur et al., 14 Jan 2026) OR title:\2^^^^.42% (Vitry et al., 26 Jun 2026). Moderator analyses showed that prior robot experience, LLM experience, escape-room experience, and introversion altered both performance and evaluation (Vitry et al., 26 Jun 2026). Proactivity, therefore, is not simply a system property; it is relational and group-contingent.

6. Reinterpretations, misconceptions, and open problems

One common misconception is that proactivity is synonymous with autonomy. The coding-agent literature rejects that equation directly: autonomy is the ability to act without supervision once a task is given, whereas proactivity is the decision of whether and when to act without an explicit prompt, including the option to stay silent (Bui et al., 7 May 2026). A second misconception is that all proactive behavior operates at the same scale. Information-seeking dialogue research redefines proactivity at the level of a single response, requiring an Answer plus a Proactive Element, where the Proactive Element is either Additional Information or a Follow-up Question (Lee et al., 2024). This response-level definition differs sharply from system-level architectures that reason over long-horizon context, tool ecosystems, or partial observability.

A further source of confusion is the acronym itself. Several papers use “proper approach” as a retrospective design blueprint rather than as a fixed standardized expansion. In social-robot coaching, PROPER is reconstructed as Permission-first, Role-aware, On-demand by default, Progressive actionable transparency, Explicit consent checkpoints, and Redaction/review (Nilgar et al., 4 Sep 2025). In sensory proactive agents, the same mnemonic is rendered as Perceive, Retrieve, Organize, Predict, Execute, Regulate (Yang et al., 7 Dec 2025). This suggests that PROPER has become a portable organizing vocabulary for domain-specific proactivity, not a single canonical taxonomy.

Open problems are correspondingly broad. PROPER itself identifies adaptive calibration, concept-grounded dimensions, persistent personalization, and richer multi-turn metrics as future directions (&&&^^^^2query2^^^^&&&). PROBE highlights temporal dynamics and multi-step action workflows as unresolved extensions of proactive evaluation (Pasternak et al., 22 Oct 2025). The HCI literature argues that design and evaluation methodologies rooted in reactive interaction paradigms fail to address the distinctive demands of proactive behavior, especially timing, appropriateness, user control, transparency, and trust (Zargham et al., 23 Jun 2026). The generative-agent literature adds a stronger normative requirement: commitment must scale with epistemic recoverability, preserve epistemic signals, and remain interruptible by epistemic degradation (Kaur et al., 16 Feb 2026).

Under these converging formulations, the PROPER Approach to Proactivity is best understood not as maximal initiative, but as calibrated initiative. Its central problem is deciding which latent dimensions, future contingencies, or supportive actions should become actual interventions, under what budget, with what evidence, and under whose control.