Dynamic Expert Knowledge

• Dynamic expert knowledge is a framework that solicits targeted, context-dependent expertise in real time to enhance decision making.
• It employs methods like sequential querying, Bayesian optimization, and ensemble reasoning to balance performance gains with query costs.
• This paradigm improves AI robustness and adaptability by integrating multiple expert inputs into dynamic, high-stakes system operations.

Dynamically Requested Expert Knowledge refers to the suite of methodologies, algorithms, and architectures in which human or automated experts are solicited for targeted knowledge at runtime or during critical phases of system operation, rather than solely relying on fixed, pre-supplied expert input. This paradigm enables machine learning, AI, and decision-support systems to resolve uncertainty, adapt to evolving scenarios, or efficiently bridge knowledge gaps by requesting and integrating new, context-dependent expertise in real time. Approaches span from online dialog systems and domain adaptation in LLMs, to combinatorial model selection in scientific discovery, Bayesian optimization, causal inference, and scalable knowledge engineering workflows.

1. Formal Definitions and Problem Classes

Two fundamental subproblems structure the field:

(A) Dynamic Querying for Augmented Inference: Systems interleave autonomous processing with expert queries whenever uncertainty, cost, or risk exceeds a threshold. This is formalized by combining prediction/action selection with a meta-decision process to request external input if needed, possibly with per-query cost or budget (Danesh et al., 13 Feb 2025, Wu et al., 24 Aug 2025).

(B) Dynamic Knowledge Retrieval and Injection: Intelligent modules act as on-demand knowledge routers, determining when and how to consult and fuse external expert knowledge—spanning retrieval-augmented dialogue (Hu et al., 2022), ensemble reasoning (Hu et al., 2024), and hybrid symbolic-ML knowledge graphs (Rahman et al., 2024).

In formal notation, let $\mathcal{X}$ denote the input/observation space, $\mathcal{A}$ the (possibly composite) action space, $\mathcal{E}$ a set of experts (human or algorithmic), and $C(e)$ the cost of consulting expert $e \in \mathcal{E}$. The controller learns a joint policy

$$\pi(a, e \mid x) = \pi_\text{action}(a \mid x, K(x, e))\ \cdot\ \pi_\text{expert}(e \mid x)$$

where $K(x, e)$ is the conditional knowledge retrieved upon querying $e$ given $x$. The objective is typically to maximize expected utility, subject to query cost constraints: $$\text{maximize} \ \mathbb{E}[ U(a, K(x, e)) - \lambda\,C(e)] \qquad \text{subject to}\ \sum_t C(e_t) \leq B$$ as formalized in budget-constrained LLM domain adaptation (Wu et al., 24 Aug 2025).

2. Paradigms and Algorithmic Mechanisms

2.1 Sequential Query and Coordination Policies

Dynamic expert involvement is formulated as a meta-decision process, where the system adaptively determines whether to act autonomously or request expertise at each decision point. Several frameworks anchor this principle:

• Learning to Yield and Request Control (YRC): Agents observe a state $s_t$ and, using a coordinator policy $\phi$, select between acting via their own policy or delegating to an expert, trading off task performance against per-query costs (Danesh et al., 13 Feb 2025). The objective is to maximize cumulative reward minus a penalty term proportional to expert queries, with policies tuned using cross-validation or RL.
• Expert Model Averaging for Causal Discovery: A diverse ensemble of causal discovery algorithms is combined, but for ambiguous or low-consensus edge decisions, expert queries are issued only in "gray zone" cases—i.e., when consensus falls below an existence or orientation threshold (Tench et al., 23 Jan 2026). Both existence and orientation may trigger dynamic requests, and even imperfect experts yield improved F1 and Balanced Scoring Function metrics compared to static models.
• Active Bayesian Optimization with Expert Elicitation: The black-box optimizer requests expert input (by comparing points or labeling realism) only at points of maximal acquisition utility. The process is cast as a multi-task Bayesian inference problem with a Siamese neural network for preference learning and Gaussian-process (GP) surrogates for posterior update (Huang et al., 2022, Thomas et al., 2020).

2.2 Dynamic Knowledge Retrieval, Routing, and Fusion

In large-scale or complex systems, determining which knowledge sources to consult—and how to align or inject their outputs—takes on increased importance:

• Dynamic Retrieval-Augmented Generation: Dialogue models first generate a knowledge retrieval query (using a time-aware transformer to emphasize recent context), fetch up-to-date external knowledge, and then compose responses by attending over and optionally copying from this retrieved set. This sequencing is essential for factual, knowledge-grounded dialogue in unconstrained settings (Hu et al., 2022).
• Dynamic Ensemble Reasoning for LLM Experts: The ensemble inference process is modeled as a Markov Decision Process (MDP), where an RL agent learns to route the partial answer through a dynamically chosen sequence of LLM "experts." At each step, a knowledge transfer prompt passes the growing answer to the next LLM, and agent rewards balance final answer quality, incremental improvement, and compute cost (Hu et al., 2024). Efficient policies dynamically request only the minimal necessary number of expert invocations.
• Retrieval-Augmented Parsing with Dynamic Lexicons: For open-vocabulary formalization problems, as soon as a domain expert supplies a translation for a previously unseen NL phrase, that mapping is dynamically injected into an external key-value lexicon. A dense retriever ranks lexicon entries at inference, and the generator (typically transformer-based) incorporates the retrieved expert knowledge without retraining upon updates (Hasan et al., 10 Sep 2025).

3. Modeling, Training, and Practical Realization

3.1 Supervision and Loss Formulations

Dynamic expert querying and knowledge injection are trained with objectives combining:

• Policy/Imitation Losses for autonomous behavior.
• Meta-Query Costs (e.g., $$r_t = r_\text{env}(a_t) - \lambda\,\mathbb{I}[q_t\ \text{asks expert}]$$).
• Knowledge Gain Rewards (e.g., $r_t = \Delta$LLM validation performance per cost, as in Active Domain Knowledge Acquisition (Wu et al., 24 Aug 2025)).
• Contrastive and Multi-task Losses for key-value retrieval/fusion (e.g., ROLex training for lexicon retrieval (Hasan et al., 10 Sep 2025)).

3.2 Scheduling and Cost Management

Systems must often balance finite querying budgets and expert availability. Mechanisms include:

• Linear Programming Schedulers for matching requests to diverse experts, maximizing throughput given topic distributions and service probabilities (e.g., queueing-theory model for online expert markets (Negi, 2021)).
• Multi-Agent RL for distributed or team-based expert selection, incorporating cost, coverage diversity, and individual knowledge boundaries (Wu et al., 24 Aug 2025).
• Active Learning Strategies for optimal selection of which rules, parameters, or simulation points to query (entropy, uncertainty sampling, mutual information across GP posterior for acquisition functions (Thomas et al., 2020, Huang et al., 2022, Gennatas et al., 2019)).

4. Application Domains and System Architectures

Dynamically requested expert knowledge frameworks have been concretely instantiated in domains including:

• Dialog and Conversational Systems: DRKQG produces retrieval queries and then generates knowledge-grounded, factually correct responses by integrating external knowledge and conversation history, outperforming transformer baselines on both automatic and human evaluations (Hu et al., 2022).
• Domain-Specific LLM Adaptation: PU-ADKA efficiently selects and coordinates costly expert queries for high-value, low-frequency domain knowledge additions under fixed budgets (e.g., drug discovery, rare diseases), benchmarking with CKAD and optimizing cost-efficiency via multi-agent RL (Wu et al., 24 Aug 2025).
• Causal Network Inference: Both Tabu–AL and dynamic model-averaging frameworks leverage uncertainty detection criteria to interleave expert queries with score-based search steps, producing significant F1 and SHD improvements with relatively few expert interventions (Kitson et al., 2023, Tench et al., 23 Jan 2026).
• Ensemble LLM Routing: Dynamic Ensemble Reasoning (DER) routes each problem instance through an optimized sequence of LLMs, minimizing redundant calls while achieving high answer quality (Hu et al., 2024).
• Machine Reasoning/Parsing: ROLex dynamically augments the parsing process with evolving expert lexicons, improving OVC translation for NL2LTL, NL2Code, and NL2CMD pipelines (Hasan et al., 10 Sep 2025).
• Knowledge Marketplaces and Graph Engineering: Online platforms implement queueing-based or tensor-factorization-based expert selection to route queries or update knowledge graphs, as seen in Kyurem for KG curation and tree-guided tensor methods for question-answering community expert recommendation (Rahman et al., 2024, Huang et al., 2018).

5. Evaluation Metrics and Empirical Findings

A broad spectrum of evaluations substantiates dynamically requested expert knowledge approaches:

System / Area	Key Metric(s)	Gain over Baseline(s)
DRKQG (Dialog) (Hu et al., 2022)	D_F1, BLEU, human assess	+6% D_F1, +17% BLEU-2, ↑factually correct
PU-ADKA (LLM) (Wu et al., 24 Aug 2025)	Win rate (WR), $/imprvement	18.2% WR vs. 14.2% (GPT-4o), ↑ cost-eff
Tabu–AL (CausalDisc.) (Kitson et al., 2023)	F1, SHD	+0.207 F1 mean (@0.5·n queries), lower SHD
DER (Ensemble LLM) (Hu et al., 2024)	BERTScore, compute cost	BERTScore↑, 85% fewer inference params
Dynamic Model Avg. (Tench et al., 23 Jan 2026)	BSF, F1, recall	+5–15% F1/BSF over static ensembles
ROLex (OVC parsing) (Hasan et al., 10 Sep 2025)	OVC F1, BLEU	+20 BLEU, +13–20 OVC F1 (NL2LTL/Code/CMD)

Repeated findings include: most benefits accrue from querying in ambiguous or high-uncertainty cases; even imperfect experts/LLMs yield superior recall or robustness; and active/dynamic querying provides similar gains as large data/compute increases but at reduced annotation or compute cost.

6. Limitations, Challenges, and Future Directions

Despite empirical success, dynamically requested expert knowledge faces several challenges:

• Scalable Querying: The burden on experts can become prohibitive; methods to focus inquiry on high-leverage or high-uncertainty regions are essential (Gennatas et al., 2019, Kitson et al., 2023).
• Expert Model Imperfection: Approaches must robustly handle fallible or noisy experts. Model-averaging and threshold mechanisms can mitigate but not eliminate sensitivity (Tench et al., 23 Jan 2026).
• Integration with Knowledge Representation: Seamless fusion of dynamically provided knowledge into symbolic, probabilistic, or neural architectures may require hybrid methods and modularity (Hasan et al., 10 Sep 2025, Rahman et al., 2024).
• Legal, Ethical, and Social Complexity: Auditability, certification, liability, and governance mechanisms must evolve to handle dynamic, composite knowledge systems (Bergmair et al., 2018).
• Generalization and Transfer: How to train models to optimally request, apply, and transfer dynamically acquired knowledge across tasks, domains, and time remains open.

Emerging directions include further coupling with automated expert discovery in knowledge markets (Negi, 2021, Huang et al., 2018), adaptive dynamic evaluation (e.g., Encyclo-K’s dynamic question generation (Liang et al., 31 Dec 2025)), and extending active elicitation to ultra-large parameter spaces and highly multi-agent expert pools (Wu et al., 24 Aug 2025, Huang et al., 2022).

7. Synthesis and Significance

Dynamically requested expert knowledge operationalizes a mixed-initiative paradigm—aligning machine inference with targeted human (or synthetic) expertise in real time, under operational constraints and uncertainty. It unifies algorithmic advances in reinforcement learning, active learning, Bayesian inference, ensemble reasoning, and knowledge management. By formalizing when, whom, and how to query, and by rigorously integrating feedback, these approaches deliver demonstrable improvements in data efficiency, robustness, cost-effectiveness, and scientific/clinical interpretability—establishing a foundational trajectory for expert-in-the-loop AI systems in contemporary and emerging high-stakes domains (Hu et al., 2022, Wu et al., 24 Aug 2025, Kitson et al., 2023, Hu et al., 2024, Hasan et al., 10 Sep 2025, Tench et al., 23 Jan 2026).