Proactive-Oriented Context Extraction

• Proactive-oriented context extraction is a technique that proactively identifies, structures, and utilizes multi-modal context to guide anticipatory actions in conversational systems and agentic AI.
• It integrates layered methods such as population-level session analysis, real-time context construction, and hierarchical multi-modal fusion to improve proactive service prediction.
• Empirical results demonstrate enhanced user engagement and performance metrics, with significant gains in suggestion relevance, tool-calling accuracy, and discoverability.

Proactive-oriented context extraction denotes systems and techniques that actively identify, structure, and utilize context in order to drive anticipatory or goal-directed actions within conversational agents, information retrieval frameworks, and agentic AI. Unlike reactive paradigms, which wait for explicit user queries, proactive context extraction leverages prior interaction patterns, external sensory inputs, background knowledge, or conversation goals to anticipate user needs or guide dialog evolution. Proactive mechanisms range from supervised learning-based approaches, explicit knowledge prediction, multi-modal context fusion, to expert-informed heuristic processes. These strongly impact system discoverability, user engagement, and utility across enterprise, retrieval, and agentic application domains.

1. Fundamental Pipeline Components

A canonical proactive-oriented context extraction system consists of several orchestrated architectural modules:

• Periodic Population-level Context Analysis: Systems such as in "Enhancing Discoverability in Enterprise Conversational Systems with Proactive Question Suggestions" (Shen et al., 14 Dec 2024) aggregate and mine historical session data to identify recurring user intent patterns. Formally, session data is represented as $$S_i = \{(q_{i,1}, r_{i,1}, D_{i,1}), \dots, (q_{i,T_i}, r_{i,T_i}, D_{i,T_i})\}$$, aggregated as $H = \cup_i S_i$. Domain experts then review query patterns to infer intent categories $C = \{c_k\}_{k=1}^K$.
• Online Session-level Context Construction: At each session turn, the agent constructs context from current user input $q_{i,t}$, latest agent response $r_{i,t}$, retrieved documents $D_{i,t}$, windowed session history $\{q_{i,1},\ldots,q_{i,t-1}\}$, and population-level intent categories $C$. This context is then supplied to a LLM that generates follow-up suggestions, with each candidate tagged according to its mapped intent category.
• Multi-Modal Context Fusion: For agents operating with real-world sensors (e.g., ContextAgent (Yang et al., 20 May 2025), ProAgent (Yang et al., 7 Dec 2025)), context fusion can be formalized as $$C_t = [\,c_{video};\;c_{audio};\;c_{notif};\;c_{persona}\,]$$, concatenating egocentric visual context, acoustic context, textual notifications, and persona cues. Sensory representations are extracted via vision-LLMs (VLMs), speech-to-text engines, and structured textual summaries.
• Proactive Service Prediction: The agent estimates the necessity for intervention by computing a proactive score $P_S$, typically as $\hat y = \arg\max_y P(y|c_t)$, with service triggered if $P_S$ exceeds a defined threshold $TR$.

These layered approaches ensure that context extraction is both reflective of aggregate historical trends and responsive to granular, per-turn interaction cues.

2. Algorithms and Mathematical Formulations

Proactive context extraction methodologies are grounded in several key algorithmic and mathematical practices:

System	Context Construction	Mathematical Formulation
Shen et al. (Adobe) (Shen et al., 14 Dec 2024)	Chat history (≤5 turns), retrievals, expert intent categories	$S_i = \{(q_{i,j}, r_{i,j}, D_{i,j})\}$; no objective function
ContextAgent (Yang et al., 20 May 2025)	Multimodal sensory and persona context	$C_t = [c_{video};c_{audio};c_{notif};c_{persona}]$; $P(\hat y|c_t)$ via cross-entropy
ProAgent (Yang et al., 7 Dec 2025)	On-demand hierarchical sensory/persona context	$$x_{joint}=\text{concat}(x_\text{img}, x_\text{txt})$$; proactive $\sigma$ via LoRA VLM
AgentFold (Ye et al., 28 Oct 2025)	Multi-scale workspace folding	Bi-objective trade-off $$(k_t,\sigma_t)=\arg\max_{k,\sigma}[\log p(a_t,e_t,\sigma|Q,T,S, I; \theta) - \lambda|S|]$$
Zhu et al. (Zhu et al., 2021)	Explicit knowledge and goal tracking	KP module, multi-task loss $$\mathcal{L}=\lambda\mathcal{L}_{kp}+\mathcal{L}_{rs}$$ (supervised BCE)

Systems such as AgentFold effect context condensation via a learned folding directive $f_t$ operating on trajectory segments, balancing the retention of critical detail with sublinear context scaling for long-horizon tasks (Ye et al., 28 Oct 2025).

Other designs treat knowledge prediction as a first-class supervised task, computing relevance via cosine similarity fusion and optimizing for explicit coverage of dialog goals $g$ (Zhu et al., 2021).

3. Multi-Modal and Hierarchical Context Structuring

Recent agentic systems have extended proactive context extraction to fuse highly heterogeneous context modalities:

• Sensory Tiering: ProAgent (Yang et al., 7 Dec 2025) introduces an on-demand tiered perception scheme, with low-cost sensors (GPS, IMU, voice activity detection) always-on, and expensive egocentric vision triggered adaptively. This multiplies system efficiency and reduces resource usage without sacrificing recall in agent intervention moments.
• Hierarchical Contexts: Both ProAgent and ContextAgent structure sensory and persona information as hierarchical tuples $$(C_{vis}, C_{loc}, C_{mot}, C_{aud}, P_\text{scenario})$$, retrieving relevant persona segments via object detection and semantic scene matching. This selective enrichment raises proactive accuracy and curtails token-inference cost, e.g., by $\sim$13.9% in relevant scenario identification (Yang et al., 7 Dec 2025).
• Context Fusion: In practice, context fusion for multimodal models is executed via prompt concatenation and attention across visual, audio, notification, and persona cues. No additional embedding or attention layers are necessary unless further end-to-end fusion is explicitly learned (Yang et al., 20 May 2025).

4. Proactive Question and Suggestion Generation

Proactive-oriented context extraction underpins advances in dialog suggestion quality, system discoverability, and user engagement:

• Generative Suggestion without Ranking: As seen in Shen et al. (Shen et al., 14 Dec 2024), LLMs generate tagged candidate questions across pre-analyzed intent categories, using windowed session history and retrievals to enhance relevance. No candidate ranking step is imposed; the LLM output sequence is utilized as surfaced (Shen et al., 14 Dec 2024).
• Template-Based and Goal-Grounded Suggestion: Zhu et al. (Zhu et al., 2021) emphasizes explicit template mining, with a hierarchical recurrent neural network encoding conversation context, ticket issue, and knowledge entities. Proactive extraction emerges from the KP module steering suggestions toward both goal achievement and knowledge engagement.
• Pragmatic, Multi-Step Reasoning: Systems such as ArticulatePro (Tabalba et al., 17 Sep 2024) leverage short-term pragmatic memory, opportunity classification, and multi-step LLM chains to transform user utterances into contextually grounded data visualization actions, e.g., proactively generating relevant charts in climate data exploration.

5. Evaluation Frameworks and Empirical Findings

Assessment of proactive-oriented context extraction spans user paper ratings, predictive accuracy, tool usage F1, and system-level discoverability measures. Distinct evaluation schemes include:

• Intent Distribution Analysis: In real-world enterprise systems, empirical intent distributions show that "unrelated" queries account for 36%, "expansion" for 30%, and "follow-up" only 11% (Shen et al., 14 Dec 2024). These labeled statistics shape expert-supervised intent taxonomy updates.
• Human Annotation and Preference Studies: Annotator studies demonstrate consistent lift in suggestion relatedness (29.7% vs. 21.5%), usefulness (35.4% vs. 27.8%), and discoverability (33.4% vs. 23.2%) versus non-contextualized baselines (Shen et al., 14 Dec 2024).
• Agentic System Metrics: In agentic settings, proactive prediction accuracy (Acc-P) improved by up to 33.4%, tool-calling F1 by 16.8%, and persona retrieval efficiency by 6× using context-aware tiered structuring (Yang et al., 7 Dec 2025). Similarly, ContextAgent scores up to 8.5% higher in accurate proactivity decisions (Yang et al., 20 May 2025).
• Long-horizon Tradeoffs: AgentFold enabled web agents to surpass much larger models by maintaining focused, dynamically folded context, achieving 36.2% and 47.3% navigation scores on complex benchmarks despite sublinear context growth (Ye et al., 28 Oct 2025).

6. Variants: Knowledge-Grounded and Multimodal Retrieval

Proactive context extraction is deployed in knowledge-grounded chatbots and retrieval systems as well:

• Explicit Knowledge Prediction: Proactive retrieval-based chatbots explicitly predict the knowledge triple(s) relevant to ongoing dialog goals using context-goal matching, MLP-based fusion, and weak supervision from ground-truth entity response matches (Zhu et al., 2021).
• Noisy Text and Concept Extraction: In proactive retrieval from noisy sources, Wikipedia concept linking is employed as a robust anchoring mechanism, improving query disambiguation and precision in short/ambiguous queries (Ahmed et al., 2022). Feature expansion with concept tokens and NER inputs yields over 20% relative gains in top-10 precision.

7. Trends, Limitations, and Future Directions

Proactive-oriented context extraction continues to evolve via several trajectories:

• Scalability and Modality Expansion: Challenges remain in scaling context extraction protocols to hundreds of tools, highly diverse scenarios, and fully end-to-end multimodal fusion. The current agentic systems typically operate with fixed tool chains and scenario banks (Yang et al., 20 May 2025, Yang et al., 7 Dec 2025).
• Resource-Awareness: Efficient scheduling, adaptive tiering, and selective persona retrieval are central to balancing latency, battery life, and memory usage—especially in wearable and embedded agent deployments (Yang et al., 7 Dec 2025).
• User Adaptivity and Control: Empirical evidence indicates that over-proactivity can impair user satisfaction or lead to fatigue; adaptive throttling mechanisms and tunable proactivity levels are recurrent research themes (Tabalba et al., 17 Sep 2024, Yang et al., 7 Dec 2025).
• Explicit Supervision and Weak Labels: For tasks requiring interpretability and goal-driven action, explicit, supervised knowledge prediction modules consistently outperform latent attention or retrieval-only methods (Zhu et al., 2021).

This suggests that proactive-oriented context extraction is maturing beyond hard-coded rule systems toward architecturally scalable, learning-enabled model compositions that integrate expert-supervision, multimodal input handling, and context-sensitive prompting. Advances in fine-grained condensation, hierarchical context management, and tiered perception are expected to further promote agent usability, long-horizon task fidelity, and robust real-world deployment.