IAES Inter national J our nal of Articial Intelligence (IJ-AI) V ol. 15, No. 5, October 2026, pp. 4026 ∼ 4038 ISSN: 2252-8938, DOI: 10.11591/ijai.v15.i5.pp4026-4038 ❒ 4026 LLM-FS-Agent: a deliberati v e r ole-based lar ge language model ar chitectur e f or transpar ent featur e selection Mohamed Bal-Ghaoui 1 , F ayssal Sabri 2 1 Lab’Inno v , Department of Research and De v elopment, Audensiel Conseil, P aris, France 2 ´ Ecole Centrale de L yon, L yon, France Article Inf o Article history: Recei v ed Oct 21, 2025 Re vised Jul 27, 2026 Accepted Aug 12, 2026 K eyw ords: Dimensionality reduction Feature selection Intrusion detection system Lar ge language models Machine learning ABSTRA CT High-dimensional data can reduce interpretability and incre ase computational cost in machine learning (ML) pipelines. Lar ge language models (LLMs) of fer a promising approach for dimensionality reduction through feature selection (FS), b ut e xisting LLM-based methods lack structured reasoning and do not pro vide clear justications for their choices. This study propose LLM-FS-Agent, a multi-agent architecture where specialized LLM roles collaborate through a deliberati v e process to e v aluate and rank feature s. Each selection decision is accompanied by an e xpl icit rationale. W e tested our method on the CIC-DIAD 2024 internet of things (IoT) intrusion detection dataset and compared it ag ainst LLM-select and traditional techniques lik e principal component analysis (PCA) across multiple subset sizes. LLM-FS-Agent achie v es comparable or better accurac y while reducing classier training time by 46% on a v erage (statistically signicant, 0.094 s, p = 0.028 for e xtreme gradient boos ting (XGBoost)). These results sho w that multi-agent deliberation impro v es transparenc y and computational ef cienc y in FS. This is an open access article under the CC BY -SA license . Corresponding A uthor: Mohamed Bal-Ghaoui Lab’Inno v , Department of Research and De v elopment, Audensiel Conseil P aris, France Email: m.bal-ghaoui@audensiel.com 1. INTR ODUCTION Machine learning (ML) pipelines are central to man y scientic and industrial applications. Ho we v er , their performance is ofte n limited by curse of dimensionality [1]. As the number of features gro ws, computational costs increase, o v ertting becomes more lik ely , and generalization de grades [2]. Addressing this issue is important for reliable ML deplo yment. Feature selection (FS) aims to nd a small set of informati v e features while k eeping their original meaning. T raditional FS methods are grouped into lter , wrapper , and embedded approaches [3]–[5]. Filter methods rank features using statistical criteria without in v olving a learning model [6]. Wrapper methods search for feature subsets that maximize predicti v e performance [7]. Embedded methods inte grate FS into model training through re gularization [8], [9]. These methods w ork well b ut often beha v e as black box es. The y pro vide little e xplanation for wh y specic features are selected or discarded. This lack of transparenc y is especially problematic in c ybersecurity , where analysts need to understand and trust the decisions made by ML systems. Lar ge language models (LLMs) ha v e created ne w opportunities for FS. Li et al. [10] classify LLM-based FS into te xt-based and data-dri v en approaches. T e xt-based FS uses onl y feature names and task descriptions, relying on the LLMs pre-t rained kno wledge without needing numerical data. This J ournal homepage: http://ijai.iaescor e .com Evaluation Warning : The document was created with Spire.PDF for Python.
Int J Artif Intell ISSN: 2252-8938 ❒ 4027 w orks well when data is scarce and impro v es with lar ger models. The LLM-select frame w ork [11] proposes se v eral prompting strate gies (LLM-score, LLM-rank, LLM-SEQ) and sho ws that LLMs can match classical FS methods e v en without training data. Ho we v er , perform ance depends hea vily on model size. Retrie v al-augmented feature selection (RAFS) [10] adds e xternal domain kno wledge to impro v e results in specialized elds lik e medicine. Data-dri v en FS gi v es LLMs actual feature v alues or sample data so the y can perform statistical reasoning [10]. This w orks in limited settings b ut is constrained by LLM conte xt windo ws. As sample size gro ws, performance de grades [12], [13]. Data-dri v en methods often underperform te xt-based approaches in both lo w-resource and full-shot s ettings. LLMs ha v e also been used for feature engineering. Han et al. [14] used LLMs to generate meta-features, and Y ang et al. [15] proposed in-conte xt e v olutionary search (ICE-SEARCH), which renes feature subsets based on test performance. The limits of single-agent prompting ha v e led to multi-agent LLM architectures that handle comple x tasks through modular reasoning and coordination [16], [17]. These s y s tems connect LLMs to e xternal tools and application programming interf aces (APIs) for structured problem solving [18], [19]. Applications in data science sho w promise for automated analysis [20], [21]. Frame w orks lik e tree-of-thought (T oT) [22], graph-of-thought (GoT) [23], and mixture-of-agents (MoA) [24] sho w that coordinated multi-agent reasoning can outperform single models. These methods impro v e reliabili ty and interpretability through structured deliberation. Ho we v er , e xisting multi-agent frame w orks are designed for general reasoning and lack the specic mechanisms needed for FS. The y do not address issues lik e feature redundanc y , collinearity , or rob ustness ag ainst adv ersarial manipulation, which are critical in c ybersecurity . 2. METHOD 2.1. Experimental setup and data pr epr ocessing All e xperiments were conducted using the CIC-DIAD 2024 dataset [25], which w as de v eloped for internet of things (IoT) intrusion detection and comprises 84 features. Thi s study considered three classes namely benign, brute force, and mirai. The preprocessing pipeline in v olv ed data cleaning, the remo v al of highly correlated features [3] based on a Pearson correlation threshold greater than 0.9, and feature standardization using StandardScaler . T o address the class imbalance issue, random undersampling w as applied, resulting in a balanced dataset. T able 1 presents the class distrib ution before and after the preprocessing steps. T able 1. Dataset distrib ution before and after preprocessing Class/Dataset Original Preprocessed Benign 183 595 (94.0%) 3 619 (29.2%) Mirai 5 170 (2.6%) 5 170 (41.7%) Brute F orce 3 619 (1.9%) 3 619 (29.2%) T otal 192,384 (100%) 12,408 (100%) 2.2. Ev aluation pr otocol Figure 1 sho ws the e v aluation w orko w adopted in this study . F our classical ML classiers were selected namely random forest (RF), e xtreme gradient boosting (XGBoost), support v ector classier (SVC), and logistic re gression (LR). These models represent dif ferent learning paradigms, including ensemble learning, gradient boosting, k ernel-based classication, and linear modeling [2]. Model performance w as e v aluated using accurac y and the area under the curv e (A UC) recei v er operating characteristic (R OC). All LLMs were e x ecuted locally using the Ollama frame w ork, including Llama 3.2 (2 B), Gemma 3 (4B), Qwen (4B), Phi-3 Mini (3B), and Mi stral (4B). Based on the generated feature importance rankings, feature subsets of dif ferent sizes were e v aluated, with n ∈ 5 , 10 , 20 , 30 , 40 , and 50 . These e xperiments were designed to in v estig ate t he impact of feature subset size, FS method, LLM conguration, and classier choice on the o v erall performance. Additional e xperiments were conducted using smaller models, including Gemma3-1B, Qwen3-0.6B, LLaMA-3.2-1B, and Phi-3-Mini, to further analyze the inuence of model capacity and rol e assignment within the proposed frame w ork. F or each e xperiment, a rank ed feature list w as generated. The rener role uses statistical guidance by analyzing feature-le v el metadata, including feature mean, standard de viation, and correlation with the tar get v ariable. This information pro vides additional e vidence for rening the initial feature importance scores produced by the initiator role, enabling a more informed feature ranking process. LLM-FS-Ag ent: a deliber ative r ole-based lar g e langua g e model ar c hitectur e for ... (Mohamed Bal-Ghaoui) Evaluation Warning : The document was created with Spire.PDF for Python.
4028 ❒ ISSN: 2252-8938 DA T A  PRE-PROCESSING EV ALUA TION PROT OCOL Classes Benign, BruteForce, Mirai CIC-DIAD 2024 Dataset Data Cleaning & Collinearity Removal (Pearson > 0.9) StandardScaler (Mean=0, Std=1) Balanced Classes LLM-FS-Agent Initiator Challenger Judge Refiner Metadata LLama 3 (2b) Qwen (4b) Phi 3 mini (2b) Gemma 3 (4b) LLM-SELECT  (Single-Agent Basline) Generate 6 feature subsets (n=5, 10, 20, 30, 40, 50) Classifier  T raining & Evaluation RF , XGBoost, SVC, LR Metrics:  T raining & Evaluation Comparative  Analysis Best  Architecture-LLM Pairing Prepared Dataset Mistral (4b) Figure 1. Pipeline and e v aluation protocol of LLM-FS-Agent 2.3. LLM-FS-Agent ar chitectur e design and the deliberati v e featur e selection pr ocess The proposed LLM-FS-Agent is a transparent multi-agent FS frame w ork [16], [17] that emplo ys a structured deliberation mechanism [22]–[24]. As illustrated in Figure 2, the frame w ork recei v es feature names and a task description as inputs [10]. Each role-specic agent generates a set of ar guments that either justify or question the rele v ance of indi vidual features, as summarized in T able 2. T o f acilitate reproducibility , the prompts associated with each agent role are pro vided in Figures 3 to 6. These prompts specify the instructions, conte xtual information, and e xpected outputs for each stage of the deliberati v e FS process. Figure 2. Architecture of the LLM-FS-Agent Int J Artif Intell, V ol. 15, No. 5, October 2026: 4026–4038 Evaluation Warning : The document was created with Spire.PDF for Python.
Int J Artif Intell ISSN: 2252-8938 ❒ 4029 T able 2. Roles and responsibilities of agents in the LLM-FS-Agent frame w ork Agent role Description Initiator Does an initial semanti c analysis of each feature based on the task description and pro vides a rst rele v ance score. Rener Impro v es the Initiator’ s analysis by adding supporting ar guments and statistical metadata, such as mean, standard de viation, and feature-tar get correlation. Challenger Re vie ws the Initiator’ s ar guments critically to nd weaknesses, redundancies, or biases, and pro vides counter -ar guments in a peer -re vie w style. Judge Acts as the nal decision mak er by combining all ar guments. It computes a nal importance score ( S nal ) for each feature as a weighted sum of the rened ( S rened ) and challenged ( S challenged ) scores: S nal = w r S rened + w c S challenged , w r + w c = 1 Figure 3. The initiator LLM prompt template Figure 4. The rener LLM prompt template Figure 5. The challenger LLM prompt template LLM-FS-Ag ent: a deliber ative r ole-based lar g e langua g e model ar c hitectur e for ... (Mohamed Bal-Ghaoui) Evaluation Warning : The document was created with Spire.PDF for Python.
4030 ❒ ISSN: 2252-8938 Figure 6. The judge LLM prompt template 3. RESUL TS AND DISCUSSION 3.1. Quantitati v e perf ormance and generalizability The performance of LLM-FS-Agent w as compared with LLM-select [11] and principal component analysis (PCA) [3] using do wnstream classiers, including XGBoost (Figure 7), RF (Figure 8), SVC (Figure 9), and LR (Figure 10), across dif ferent feature s ubset sizes ( n = 5 , 10 , 20 , 30 , 40 , and 50 ). As illustrated in Figures 7 and 8, XGBoost and RF consistently achie v e high accurac y across v arying subset sizes, demonstrating the rob ustness of the selected feature representations. T able 3 summarizes the mean A UC and accurac y v alues obtained at the best-performing subset size ( n = 20 ). Under this conguration, LLM-FS-Agent reduces performance v ariability compared with single-agent FS approaches and impro v es accurac y by up to +1 . 49% in cases where LLM-select performs less ef fecti v ely , while introducing only mar ginal decreases for the remaining models. Figure 11 pro vides a comprehensi v e comparison across the six e v aluated feature subset sizes , where Figures 11(a) to 11(f) report accurac y results and Figures 11(g) to 11(l) pres ent A UC measurements. Re g arding accurac y , LLM-select achie v es competiti v e resul ts under the most constrained feature size ( n = 5 ), whereas LLM-FS-Agent sho ws increasing adv antages as the subset size gro ws, reaching its best performance at n = 20 . Although LLM-select remains comparable at n = 40 , it e xhibits lar ger performance uctuations, whereas LLM-FS-Agent maintains stable results across lar ger feature subsets. A similar trend is observ ed for A UC, while all approaches pro vide strong discriminati v e capability with only v e selected features, LLM-FS-Agent deli v ers more consistent performance across n = 10 – 50 , with n = 20 pro viding the most stable results across dif ferent classiers and LLM backbones. Ov erall, LLM-FS -Agent achie v es superior performance for intermediate and lar ger subsets ( n = 10 , 20 , 30 , and 50 ), while LLM-Select remains competiti v e mainly at n = 5 and occasionally at n = 40 . These ndings suggest that the multi-agent frame w ork enables more reliable feature ranking across dif ferent feature b udgets. Int J Artif Intell, V ol. 15, No. 5, October 2026: 4026–4038 Evaluation Warning : The document was created with Spire.PDF for Python.
Int J Artif Intell ISSN: 2252-8938 ❒ 4031 Figure 7. Performance curv e of the XGBoost classier Figure 8. Performance curv e of the RF classier LLM-FS-Ag ent: a deliber ative r ole-based lar g e langua g e model ar c hitectur e for ... (Mohamed Bal-Ghaoui) Evaluation Warning : The document was created with Spire.PDF for Python.
4032 ❒ ISSN: 2252-8938 Figure 9. Performance curv e of the SVC Figure 10. Performance curv e of the LR classier Int J Artif Intell, V ol. 15, No. 5, October 2026: 4026–4038 Evaluation Warning : The document was created with Spire.PDF for Python.
Int J Artif Intell ISSN: 2252-8938 ❒ 4033 Accuracy (a) (b) (c) (d) (e) (f) A UC (g) (h) (i) (j) (k) (l) Figure 11. Comparison of accurac y and A UC across feature subset sizes: (a) accurac y at n = 5 , (b) accurac y at n = 10 , (c) accurac y at n = 20 , (d) accurac y at n = 30 , (e) accurac y at n = 40 , (f) accurac y at n = 50 ; (g) A UC at n = 5 , (h) A UC at n = 10 , (i) A UC at n = 20 , (j) A UC at n = 30 , (k) A UC at n = 40 , and (l) A UC at n = 50 LLM-FS-Ag ent: a deliber ative r ole-based lar g e langua g e model ar c hitectur e for ... (Mohamed Bal-Ghaoui) Evaluation Warning : The document was created with Spire.PDF for Python.
4034 ❒ ISSN: 2252-8938 T able 3. Performance comparison of best models on LLM-Select vs. LLM-FS-Agent (a v erage across subsets) LLM A UC ∆ % Accurac y ∆ % LLM-select LLM-F S-Agent LLM-select LLM-FS-Agent XGB RF XGB RF XGB RF XGB RF Mistral 0.976 0.964 0.965 0.947 -0.99 0.892 0.876 0.870 0.854 -2.25 Qwen 0.975 0.963 0.980 0.974 +0.46 0.890 0.872 0.902 0.897 +1.21 Gemma3-4B 0.973 0.968 0.978 0.973 +0.50 0.888 0.884 0.903 0.894 +1.49 LLaMA3-2B 0.973 0.962 0.974 0.964 +0.15 0.880 0.864 0.885 0.879 +0.45 Phi3-mini 0.979 0.969 0.972 0.962 -0.64 0.902 0.886 0.882 0.862 -1.97 A v erage 0.975 0.965 0.974 0.964 +0.10 0.890 0.876 0.888 0.877 +0.19 3.2. Computational efciency and qualitati v e e v aluation of featur e r easoning Analysis of T ables 4 and 5 sho ws that LLM-FS-Agent reduces do wnstream classier training time. F or XGBoost, the a v erage speedup is 1 . 87 × with statistical signicance ( p = 0 . 028 , Cohen’ s d = 0 . 87 ). Inference times impro v ed slightly . The g ains in A UC and accurac y , while modest, support the multi-agent frame w ork and of fset the deliberation o v erhead. T able 4. Computational ef cienc y comparison (XGBoost) LLM backbone T raining time (s) Inference time (s) Speedup LLM-select LLM-FS-Agent LLM-select LLM-FS-Agent T raining Inference Mistral 0.434 0.108 0.0023 0.0016 4.02 × 1.44 × Qwen 0.117 0.108 0.0014 0.0015 1.08 × 0.93 × Gemma3-4B 0.101 0.113 0.0015 0.0016 0.89 × 0.94 × LLaMA3-2B 0.252 0.108 0.0018 0.0015 2.33 × 1.20 × Phi3-mini 0.121 0.117 0.0016 0.0017 1.03 × 0.94 × A v erage 0.205 0.111 0.0017 0.0016 1.87 × 1.09 × T able 5. Statistical signicance analysis (XGBoost) Metric Mean Dif f. p-v alue Cohen’ s d Ef fect A UC +0.0010 0.342 0.28 Small Accurac y +0.0017 0.456 0.19 Small T raining time -0.094s 0.028* -0.87 Lar ge Inference time -0.0001s 0.234 -0.31 Small *Statistically signicant at α = 0 . 05 Ef fect size: | d | < 0 . 2 (ne gligible), 0.2-0.5 (small), 0.5-0.8 (medium), > 0 . 8 (lar ge) 3.3. Statistical guidance and lar ge language model r ole effects on featur e selection The impact of statistical gui dance and LLM role congurations on FS w as e v aluated through do wnstream ML performance, including confusion matrices and o v erall accurac y . Using LLM-FS-Agent with four LLM backbones, all 71 features were analyzed and rank ed according to their estimated importance. T able 6 presents the feature importance scores generated by each LLM role, along with the nal aggre g ated scores produced by the judge agent across the four best-performing e xperiments. T emporal and pack et-le v el o w features, such as pack et length min/max, Fwd inter -arri v al time (IA T) mean/max/Std, and idle min, consistently appear among t he highest-rank ed features. Their frequent selection indicates strong discriminati v e capability for distinguishing benign and malicious traf c patterns. In addition, source and destination port st atistics, as well as ag-count features, are repeatedly selected, reecting their rele v ance from a netw ork traf c analysis perspecti v e [25]. The consistenc y among dif ferent LLM roles and the stability of the top-rank ed features suggest that these indicators pro vide reliable information for intrusion detection. T able 7 summarizes the four best-performing ML congurations. XGBoost achie v ed the strongest o v erall performance, obtaining accurac y v alues ranging from 0.876 to 0.886, F1-scores between 0.873 and 0.884, and A UC v alues from 0.971 to 0.975, while maintaining moderate training requirements and ef cient inference. RF achie v ed slightly lo wer performance, with accurac y ranging from 0.866 to 0.881, F1-scores from 0.863 to 0.879, and A UC v alues between 0.962 and 0.971; ho we v er , it pro vided f aster tra ining, making it a practical alternati v e in resource-constrained scenarios. In contrast, SVC and LR e xhibited lo wer predi cti v e performance, with SVC additionally incurring higher training and inference costs. Among the LLM role congurations, e xperiment 1 achie v ed the best do wnstream classication results, indicating that the assignment and interaction of dif ferent agent roles can signicantly inuence the quality of generated feature rankings and, consequently , the nal model performance. Int J Artif Intell, V ol. 15, No. 5, October 2026: 4026–4038 Evaluation Warning : The document was created with Spire.PDF for Python.
Int J Artif Intell ISSN: 2252-8938 ❒ 4035 T able 6. T op-10 features rank ed by frame w ork scores Experiment Feature Initiator Rener Challenger Judge Judge Reasoning Experiment 1 P ack et length min 0.850 0.800 0.200 0.900 Analyzing pack et length patterns helps distinguish benign from malicious traf c; outliers and standard de viation considered. Fwd IA T mean 0.850 1.000 0.800 0.900 Statistically signicant patterns in forw ard IA T are k e y for threat detection; f alse positi v es must be considered. Src port 0.900 0.800 0.800 0.800 Source port may indicate anomalies when correlated with other indicators; useful for initial threat assessment. Fwd IA T total 0.850 1.000 0.200 0.800 F orw ard IA T T otal can indicate anomalous traf c; should not be used in isolation for prediction. Subo w bwd pack ets 0.850 0.800 0.200 0.800 Subo w backw ard pack ets patterns can indicate malicious propag ation; statistical signicance prioritized. Bwd IA T mean 0.900 0.800 0.200 0.760 Useful for detecting netw ork anomalies; dynamic threat en vironment requires cautious interpretation. Bwd pack ets/s 0.850 0.850 0.200 0.750 Pro vides netw ork performance insight; must be conte xtualized to distinguish benign from malicious patterns. FWD init win bytes 0.850 0.850 0.200 0.740 F orw ard initial windo w bytes can predict malicious acti vity; statistical e vidence prioritized. Acti v e max 0.850 0.900 0.300 0.720 High acti vity le v els indicate potential threats; should be used with other features for accurac y . Flo w IA T std 0.850 0.850 0.200 0.720 Standard de viation of o w IA T can signal malicious beha vior; conte xt with other indicators is critical. Experiment 2 Acti v e mean 0.65 0.85 0.50 0.85 Domain-focused reasoning: correlation with malicious acti vity and traf c patterns. RST ag count 0.65 0.80 0.50 0.80 Pro vides clear indication of potential malicious ac ti vity , critical for domain-focused predictions. Fwd IA T std 0.65 0.80 0.50 0.80 Strong statistical signicance; considered for predicting malicious beha vior . Src port 0.65 0.85 0.50 0.75 Conte xtual information such as mean and std supports reliability as an indicator . Flo w bytes/s 0.85 0.95 0.50 0.75 K e y indicator of netw ork acti vity; essential for tasks in v olving data mo v ement. P ack et length mean 0.65 0.82 0.50 0.75 Important for detecting malicious acti vity , b ut conte xt-dependent. Idle min 0.65 0.85 0.70 0.75 Quantitati v e strength in predicting malicious acti vity; secondary importance noted. Protocol 0.85 0.92 0.60 0.72 Rele v ant for prediction tasks; other features may pro vide redundanc y . Flo w IA T max 0.65 0.80 0.70 0.70 Strong correlation with malicious acti vity; critical for predicti v e tasks. Subo w fwd pack ets 0.65 0.85 0.50 0.70 Prioritized as indicati v e of malicious traf c in netw ork analysis. Experiment 3 P ack et length min 0.850 0.700 0.850 0.700 Helps distinguish normal vs malicious traf c; outliers and standard de viation important. Flo w IA T max 0.850 0.790 0.700 0.700 Important, b ut quantitati v e analysis suggests limitations due to normal traf c patterns. Idle min 0.850 0.670 0.600 0.675 High idle times indicate potential malicious acti vity; domain reasoning prioritizes this feature. P ack et length v ariance 0.850 0.720 0.500 0.660 V ariability in pack et lengths may indicate anomalies; less predicti v e alone. Dst Port 0.850 0.720 0.500 0.650 Captures di v erse usage; not primary indicator b ut useful in combination with other features. Acti v e Std 0.900 0.750 0.500 0.625 Indicates v ariability often accompan ying malicious acti vity; importance conte xt-dependent. Subo w bwd pack ets 0.850 0.670 0.500 0.625 Rapid propag ation patterns indicati v e of malicious traf c; more rele v ant in lar ge netw orks. T otal bwd pack ets 0.850 0.670 0.600 0.625 Lo w v ariance reduces standalone im portance; domain-focused reasoning supports use. P ack et length max 0.850 0.620 0.700 0.625 Standard de viation and conte xt considered; moderately informati v e predictor . FIN ag count 0.850 0.620 0.750 0.620 Quantitati v e analysis emphasizes statistical signicance; useful b ut less critical. Experiment 4 P ack et length max 0.800 0.760 0.600 0.720 Important for identifying malicious traf c considering thresholds and conte xt. Fwd se g size min 0.750 0.730 0.600 0.690 Relati v ely high mean size dispersion; indicati v e of malicious traf c in combination with other features. FWD init win bytes 0.850 0.670 0.700 0.670 W eak correlation with tar get; conte xtual importance slightly more rele v ant than ra w stats. Bwd IA T min 0.850 0.760 0.600 0.667 Lo w correlation with netw ork acti vity; unlik ely to be decisi v e alone. Fwd IA T mean 0.850 0.670 0.600 0.667 Distrib ution patterns considered; correlation with other features moderately informati v e. P ack et length min 0.800 0.600 0.600 0.667 Useful for anomaly detection; simplicity reduces standalone predicti v e po wer . Bwd pack et length max 0.800 0.650 0.600 0.650 Captures suspicious acti vity patterns; statistical metadata moderately informati v e. Flo w pack ets/s 0.750 0.670 0.700 0.625 Lo w correlation alone; conte xtual f actors increase rele v ance. Bwd pack et length min 0.850 0.650 0.600 0.625 Strong semantic rele v ance; moderate correlation with tar get v ariable. Idle min 0.850 0.750 0.600 0.625 Some predicti v e po wer; may be noisy , b ut domain-focused reasoning supports use. LLM-FS-Ag ent: a deliber ative r ole-based lar g e langua g e model ar c hitectur e for ... (Mohamed Bal-Ghaoui) Evaluation Warning : The document was created with Spire.PDF for Python.