Constraint Factors and Non-Compensatory Decision: A Multi-Agent Collaborative Method for Strong-Constraint Matching

JU Chuanxiang; HUANG Hai; WU Zhiyong; HAN Yulong; LI Yuancan; HE Runrulin

doi:10.11999/JEIT260534

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JU Chuanxiang, HUANG Hai, WU Zhiyong, HAN Yulong, LI Yuancan, HE Runrulin. Constraint Factors and Non-Compensatory Decision: A Multi-Agent Collaborative Method for Strong-Constraint Matching[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260534

Citation:

JU Chuanxiang, HUANG Hai, WU Zhiyong, HAN Yulong, LI Yuancan, HE Runrulin. Constraint Factors and Non-Compensatory Decision: A Multi-Agent Collaborative Method for Strong-Constraint Matching[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260534

Citation:

JU Chuanxiang, HUANG Hai, WU Zhiyong, HAN Yulong, LI Yuancan, HE Runrulin. Constraint Factors and Non-Compensatory Decision: A Multi-Agent Collaborative Method for Strong-Constraint Matching[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260534

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Constraint Factors and Non-Compensatory Decision: A Multi-Agent Collaborative Method for Strong-Constraint Matching

doi: 10.11999/JEIT260534 cstr: 32379.14.JEIT260534

School of Computer Science and Technology, Shandong University of Technology, Zibo 255000, China

Funds: Key R&D Program of Shandong Province, China (2025TSGCCZZB0041), National Natural Science Foundation of China(12271309)

Received Date: 2026-04-29
Accepted Date: 2026-09-28
Rev Recd Date: 2026-07-13

Available Online: 2026-10-10

Abstract

Abstract

Objective Long-text strong-constraint matching is required in policy-to-enterprise recommendation, contract verification, and compliance screening. Categorical conditions, numerical thresholds, eligibility clauses, temporal scopes, and exclusion rules must be satisfied simultaneously, and violation of any hard constraint disqualifies a candidate. As large language models (LLMs) are applied to these decisions, attributes, comparison operators, and thresholds need to be preserved consistently over long contexts. Prevailing pipelines, including zero-shot prompting, retrieval-augmented generation (RAG), and chain-of-thought (CoT) reasoning, pass intermediate evidence mainly through semantic similarity or free-text steps, and therefore provide limited support for item-by-item verification of hard constraints. Open matching benchmarks likewise give limited coverage to continuous thresholds, exclusion clauses, and non-compensatory rules. A factorized multi-agent associative matching method, termed Factorized Multi-Agent Associative Matching (FMAM), is proposed. Constraint conditions and factual evidence are structured as constraint factors and fact factors, which serve as the information carriers among agents. Non-compensatory decision is realized by coupling hard-constraint gating with multiplicative aggregation. Methods FMAM separates factor extraction from deterministic evaluation (Fig. 1). A Constraint Decomposition Agent (CDA) parses each source text once and writes every constraint into a field-level record, including the attribute, comparison operator, threshold, unit, type, and hard-constraint flag. A Feature Guidance Agent (FGA) collects attribute patterns from all source-side factors and extracts the corresponding fact factors from each candidate text once. The extraction is guided by these attribute patterns and is decoupled from pair-specific thresholds. The constraint-factor set of a source text is reused across all of its pairings, and the fact-factor set of a candidate text is reused across all of its pairings. For $ m $ source texts and $ n $ candidate texts, the number of long-text LLM calls is reduced from $ m\times n $ to $ m+n $, and the pairing stage requires no additional LLM calls. A Logic Evaluation Agent (LEA) binds compatible factors by attribute and type, normalizes units and temporal conditions, and computes a signed satisfaction margin for lower-bound, upper-bound, equality, set-inclusion, and set-exclusion operators. Identical factor records produce identical satisfaction scores. The margins are mapped to constraint-level satisfaction values, so that boundary cases remain distinguishable in the subsequent aggregation. A hard-constraint gate sets the matching score to zero when any necessary constraint is violated or the required evidence is absent. Satisfactions of all factors are aggregated by the geometric mean and multiplied by the gate. A Numerical Constraint Error Rate (NCER) is further defined to measure numerical-constraint extraction and comparison against the structured ground truth. Results and Discussions The method is evaluated on a constructed policy-enterprise matching set of 50,000 pairs (Table 1), comprising 50 policy texts and 1,000 enterprise texts written in official-document and research-report styles. Field-level ground truth is determined from policy rules and enterprise indicators, and positive pairs account for 8.83% of the set. All methods use Gemini-2.5-Flash with the temperature set to 0. As shown in Table 2 and Fig. 2, FMAM obtains an accuracy of 0.9835, a precision of 0.9523, a recall of 0.8562, an F1-score of 0.9017, an AUC-PR of 0.8567, and an MCC of 0.8942, and achieves the highest accuracy, precision, F1-score, AUC-PR, and MCC among the compared methods. The F1-scores of few-shot CoT, Standard RAG, and GraphRAG are 0.4559, 0.1825, and 0.2452, respectively. The NCER of FMAM is 4.87%, compared with 27.25% for the best baseline (Table 5). The average inference cost is 24.22 tokens per pair (Table 6), which is 1.27% of that of zero-shot LLM and 0.43% of that of ReAct. Standard RAG and GraphRAG consume 8.17 times and 28.83 times as many tokens as FMAM, respectively. Ablation studies show that replacing structured constraint factors with natural-language intermediate representations reduces the F1-score from 0.9017 to 0.3555 and raises the NCER from 4.87% to 77.57% (Table 4). Removing hard-constraint gating reduces the precision from 0.9523 to 0.7277 (Table 3). Conclusions Structured constraint factors combined with deterministic non-compensatory decision are effective for long-text strong-constraint matching. Field-level factors keep constraint information consistent across agents. Hard-constraint gating maintains a high precision under non-compensatory eligibility rules. Concentrating LLM calls in the extraction stage and performing pairing on structured factors reduces the inference cost from a multiplicative scale to a linear scale. Future work will extend the method to cross-institution texts and online calibration for broader compliance-screening applications.
- Large Language Models,
- Multi-Agent Collaboration,
- Constraint Factors,
- Non-Compensatory Decision,
- Policy-Enterprise Matching

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References(29)

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