New Algorithm Lets Large Language Models and Humans Team Up to Understand Vague Requests

When a person types a vague, jargon-heavy, or context-dependent request into a computer system, the machine often misses the point. The words may be technically correct, but the intent behind them is tangled in background knowledge that the system does not share. A new study published in Applied Intelligence proposes an iterative demand response algorithm for human-computer cooperation that combines the knowledge representation and generation power of large language models with a structured, loop-based optimization process, aiming to close exactly that gap between what users mean and what machines deliver.

The research, authored by Jun Yin of Beijing Cnoce Technologies Co., Ltd., addresses a persistent weakness in interactive intelligent systems. When requirements are complex, vague, or embedded in specialized background knowledge, conventional systems struggle to accurately interpret human needs or intentions. Rather than treating each user request as a one-shot prediction problem, the proposed algorithm treats interpretation as an iterative cycle: the system computes a response, collects real-time feedback about how well that response matched the demand, and continuously refines its behavior according to the demand response structure. The goal is to improve both the intelligent response capability of the system and the experience of the user on the other side of the interaction.

At the foundation of the method is an effective data collection mechanism. The system gathers real-time response information as interactions unfold, building up the raw material needed to evaluate and improve its own performance. In a known finite domain, the algorithm derives a corresponding feature statistical decision, which anchors the interpretation process in measurable properties of the data rather than in guesswork. This statistical grounding matters because human-computer cooperation networks are messy: nodes in such networks are not equally spaced or equally loaded, and the demands arriving at each node differ in both volume and character.

One of the paper’s central technical contributions lies in how it handles that unevenness. The algorithm extracts the load characteristics of non-equidistant nodes in the human-machine cooperation network using the knowledge of a large language model. In practical terms, the language model’s broad knowledge base is used to characterize how computational and communicative load is distributed across nodes that sit at irregular positions in the network topology. This allows the system to understand where demand concentrates, where it is sparse, and how those patterns shift as users interact with the system over time. Load characterization of this kind is essential for any cooperative system that must allocate responses fairly and quickly across many simultaneous users.

The second pillar of the method is attention. The algorithm constructs a knowledge framework for the large language model according to a two-level attention matrix, and then analyzes an iterative demand response strategy for human-machine cooperation in two stages. The two-level attention structure allows the model to weigh different pieces of information at different granularities, deciding which features of a request deserve emphasis and which contextual signals should modulate that emphasis. By organizing the response strategy into two stages, the system separates coarse interpretation from fine-grained refinement, letting each iteration pass progressively sharper approximations of user intent back into the loop.

The experimental results reported in the study offer concrete evidence that the design works as intended. The algorithm is able to remove abnormal data when the threshold is set to 10, and across three datasets the proportion of abnormal data reaches its lowest state under the proposed method. Anomaly removal at a fixed threshold is more than a housekeeping detail; noisy or malformed interaction data can poison the feedback loop that iterative systems depend on, so a reliable filtering stage protects the integrity of every subsequent optimization step. The fact that the algorithm achieves the lowest abnormal-data proportion across all three datasets suggests that the filtering mechanism is robust rather than tuned to a single data distribution.

Performance gains extend beyond data cleaning. After applying the algorithm, both throughput and utilization rate improved significantly, and the response speed became faster. Throughput measures how much useful work the system completes per unit of time, while utilization rate reflects how effectively the system’s resources are kept busy on productive tasks. Improvements in both metrics, combined with faster responses, indicate that the iterative loop does not merely reinterpret requests more accurately but also runs efficiently enough to lighten the operational burden on the underlying infrastructure. For users, faster response speed is the most visible benefit; for system operators, higher throughput and utilization translate directly into capacity savings.

The study situates itself within a growing body of work on human-machine cooperation. Prior research has explored cooperation between human operators and machines in scheduling problems, man-algorithm cooperation in the intelligent design of clothing products across multiple links, and collision-free path planning for industrial robot manipulators that must account for safe human-robot interaction. More recent efforts have examined how autonomous vehicles can assimilate human feedback in reinforcement learning models and how human-centric digital twins can support human-machine collaboration. What distinguishes the new algorithm is its explicit integration of large language model knowledge into the cooperation loop itself, rather than using language models only as peripheral interfaces.

That integration reflects a broader trend. Large language models have already been applied to domain-specific text mining, as demonstrated by materials-science language models built for information extraction, and to collaborative requirements elicitation, where human-machine iterative approaches use language models to help elicit and model software requirements. Natural language processing systems have been deployed to extract structured events from clinical texts and to classify sensitive records in mental healthcare. The new work extends this lineage into the domain of demand response, where the

Subject of Research: An iterative demand response algorithm that integrates large language model knowledge to improve human-computer cooperation in interpreting complex user requirements.

Article Title: Iterative Demand Response Algorithm for Human-Computer Cooperation Integrating Knowledge of Large Language Model

Article References: Iterative Demand Response Algorithm for Human-Computer Cooperation Integrating Knowledge of Large Language Model. (n.d.). https://doi.org/10.1007/s10489-026-07428-2

Image Credits: AI Generated

DOI: 10.1007/s10489-026-07428-2

Keywords: demand response algorithm, human-computer cooperation, large language model, iterative optimization, attention matrix, load characteristics, anomaly detection, throughput, utilization rate, user intent, Applied Intelligence, computational intelligence