Critical-Path and Uncertainty-Aware Dynamic Scheduling for Resource-Constrained LLM Multi‑Agent Teams
Keywords:
LLM Agents, Multi-Agent Systems, DAG Scheduling, Critical Path, Uncertainty-Aware Scheduling, Communication Budget, Multiagentbench, MARBLEAbstract
Large-language-model multi-agent systems can decompose complex work, but adding agents does not guarantee shorter completion time: precedence constraints, uneven task sizes, and cross-agent handoffs can erase the benefit of parallelism. This study evaluates six deterministic scheduling policies on the released MultiAgentBench/MARBLE coding corpus. All 100 tasks were converted into requirement-level directed acyclic graphs using temporal, integration, testing, adaptive-feedback, and final-stage dependency evidence in the task specifications. The resulting corpus contains 621 requirement nodes, 780 edges, 40 application topics, and four coordination categories. Word counts serve as normalized work units; consequently, the study measures scheduling behavior rather than live model latency or software correctness. We executed 1,800 schedules spanning FIFO, Round-Robin, static-role, critical-path, uncertainty-weighted critical-path, and a critical-path policy with uncertainty and communication control (CP-UC), each at 2, 4, and 8 concurrent workers. Critical-path scheduling produced modest mean improvements over FIFO of 0.80%, 0.96%, and 0.51%, respectively. CP-UC reduced mean communication work by 7.27%-9.27% and propagation exposure by 1.66%-2.26% relative to FIFO, but increased mean makespan by 2.10%-3.39% on the paired percentage measure. Category analysis locates this cost mainly in test-case and dependency-heavy tasks. The results show that critical-path priority is a useful low-risk default, whereas communication control should be treated as an explicit latency-coordination trade-off rather than a universally faster scheduler.
References
[1] K. Zhu, H. Du, Z. Hong, X. Yang, S. Guo, Z. Wang, Z. Wang, C. Qian, X. Tang, H. Ji, and J. You, "MultiAgentBench: Evaluating the collaboration and competition of LLM agents," in Proc. 63rd Annu. Meeting Assoc. Comput. Linguistics (ACL), vol. 1, Vienna, Austria, 2025, pp. 8580-8622, doi: 10.18653/v1/2025.acl-long.421.
[2] Q. Wu, G. Bansal, J. Zhang, Y. Wu, B. Li, E. Zhu, L. Jiang, X. Zhang, S. Zhang, J. Liu, A. H. Awadallah, R. W. White, D. Burger, and C. Wang, "AutoGen: Enabling next-gen LLM applications via multi-agent conversation," in Proc. 1st Conf. Language Modeling (COLM), 2024. [Online]. Available: https://openreview.net/forum?id=tEAF9LBdgu
[3] G. Li, H. A. A. K. Hammoud, H. Itani, D. Khizbullin, and B. Ghanem, "CAMEL: Communicative agents for 'mind' exploration of large language model society," in Adv. Neural Inf. Process. Syst., vol. 36, 2023, pp. 51991-52008. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2023/hash/a3621ee907def47c1b952ade25c67698-Abstract-Conference.html
[4] W. Chen et al., "AgentVerse: Facilitating multi-agent collaboration and exploring emergent behaviors," in Proc. 12th Int. Conf. Learning Representations (ICLR), 2024. [Online]. Available: https://openreview.net/forum?id=EHg5GDnyq1
[5] S. Hong et al., "MetaGPT: Meta programming for a multi-agent collaborative framework," in Proc. 12th Int. Conf. Learning Representations (ICLR), 2024. [Online]. Available: https://proceedings.iclr.cc/paper_files/paper/2024/hash/6507b115562bb0a305f1958ccc87355a-Abstract-Conference.html
[6] C. Qian et al., "ChatDev: Communicative agents for software development," in Proc. 62nd Annu. Meeting Assoc. Comput. Linguistics (ACL), vol. 1, Bangkok, Thailand, 2024, pp. 15174-15186, doi: 10.18653/v1/2024.acl-long.810.
[7] M. Zhuge, W. Wang, L. Kirsch, F. Faccio, D. Khizbullin, and J. Schmidhuber, "GPTSwarm: Language agents as optimizable graphs," in Proc. 41st Int. Conf. Machine Learning (ICML), PMLR, vol. 235, 2024, pp. 62743-62767. [Online]. Available: https://proceedings.mlr.press/v235/zhuge24a.html
[8] Y. Du, S. Li, A. Torralba, J. B. Tenenbaum, and I. Mordatch, "Improving factuality and reasoning in language models through multiagent debate," in Proc. 41st Int. Conf. Machine Learning (ICML), PMLR, vol. 235, 2024, pp. 11733-11763. [Online]. Available: https://proceedings.mlr.press/v235/du24e.html
[9] Z. Liu, Y. Zhang, P. Li, Y. Liu, and D. Yang, "A dynamic LLM-powered agent network for task-oriented agent collaboration," in Proc. 1st Conf. Language Modeling (COLM), 2024. [Online]. Available: https://openreview.net/forum?id=i43XCU54Br
[10] J. Li, Q. Zhang, Y. Yu, Q. Fu, and D. Ye, "More agents is all you need," Trans. Mach. Learn. Res., 2024. [Online]. Available: https://openreview.net/forum?id=bgzUSZ8aeg
[11] J. Wang, J. Wang, B. Athiwaratkun, C. Zhang, and J. Zou, "Mixture-of-agents enhances large language model capabilities," in Proc. 13th Int. Conf. Learning Representations (ICLR), 2025. [Online]. Available: https://proceedings.iclr.cc/paper_files/paper/2025/hash/5434be94e82c54327bb9dcaf7fca52b6-Abstract-Conference.html
[12] S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y. Cao, "ReAct: Synergizing reasoning and acting in language models," in Proc. 11th Int. Conf. Learning Representations (ICLR), 2023. [Online]. Available: https://openreview.net/forum?id=WE_vluYUL-X
[13] M. Besta et al., "Graph of thoughts: Solving elaborate problems with large language models," Proc. AAAI Conf. Artif. Intell., vol. 38, no. 16, pp. 17682-17690, 2024, doi: 10.1609/aaai.v38i16.29720.
[14] S. Yao, D. Yu, J. Zhao, I. Shafran, T. L. Griffiths, Y. Cao, and K. Narasimhan, "Tree of thoughts: Deliberate problem solving with large language models," in Adv. Neural Inf. Process. Syst., vol. 36, 2023, pp. 11809-11822. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2023/hash/271db9922b8d1f4dd7aaef84ed5ac703-Abstract-Conference.html
[15] S. Kim, S. Moon, R. Tabrizi, N. Lee, M. W. Mahoney, K. Keutzer, and A. Gholami, "An LLM compiler for parallel function calling," in Proc. 41st Int. Conf. Machine Learning (ICML), PMLR, vol. 235, 2024, pp. 24370-24391. [Online]. Available: https://proceedings.mlr.press/v235/kim24y.html
[16] H. Topcuoglu, S. Hariri, and M.-Y. Wu, "Performance-effective and low-complexity task scheduling for heterogeneous computing," IEEE Trans. Parallel Distrib. Syst., vol. 13, no. 3, pp. 260-274, Mar. 2002, doi: 10.1109/71.993206.
[17] H. Arabnejad and J. G. Barbosa, "List scheduling algorithm for heterogeneous systems by an optimistic cost table," IEEE Trans. Parallel Distrib. Syst., vol. 25, no. 3, pp. 682-694, Mar. 2014, doi: 10.1109/TPDS.2013.57.
[18] Y.-K. Kwok and I. Ahmad, "Static scheduling algorithms for allocating directed task graphs to multiprocessors," ACM Comput. Surv., vol. 31, no. 4, pp. 406-471, Dec. 1999, doi: 10.1145/344588.344618.
[19] R. L. Graham, "Bounds for certain multiprocessing anomalies," Bell Syst. Tech. J., vol. 45, no. 9, pp. 1563-1581, Nov. 1966, doi: 10.1002/j.1538-7305.1966.tb01709.x.
[20] J. E. Kelley, Jr. and M. R. Walker, "Critical-path planning and scheduling," in Proc. Eastern Joint IRE-AIEE-ACM Comput. Conf., Boston, MA, USA, 1959, pp. 160-173, doi: 10.1145/1460299.1460318.
[21] M. Xiong, Z. Hu, X. Lu, Y. Li, J. Fu, J. He, and B. Hooi, "Can LLMs express their uncertainty? An empirical evaluation of confidence elicitation in LLMs," in Proc. 12th Int. Conf. Learning Representations (ICLR), 2024. [Online]. Available: https://proceedings.iclr.cc/paper_files/paper/2024/hash/6733cf15e10e2cd1d59af033c3bb8507-Abstract-Conference.html
[22] S. Farquhar, J. Kossen, L. Kuhn, and Y. Gal, "Detecting hallucinations in large language models using semantic entropy," Nature, vol. 630, no. 8017, pp. 625-630, Jun. 2024, doi: 10.1038/s41586-024-07421-0.
[23] G. Zhang, Y. Yue, Z. Li, S. Yun, G. Wan, K. Wang, D. Cheng, J. Xu Yu, and T. Chen, "Cut the crap: An economical communication pipeline for LLM-based multi-agent systems," in Proc. 13th Int. Conf. Learning Representations (ICLR), 2025. [Online]. Available: https://proceedings.iclr.cc/paper_files/paper/2025/hash/bbc461518c59a2a8d64e70e2c38c4a0e-Abstract-Conference.html
[24] J. Zhang et al., "AFlow: Automating agentic workflow generation," in Proc. 13th Int. Conf. Learning Representations (ICLR), 2025. [Online]. Available: https://proceedings.iclr.cc/paper_files/paper/2025/hash/5492ecbce4439401798dcd2c90be94cd-Abstract-Conference.html
[25] S. Qiao, R. Fang, Z. Qiu, X. Wang, N. Zhang, Y. Jiang, P. Xie, F. Huang, and H. Chen, "Benchmarking agentic workflow generation," in Proc. 13th Int. Conf. Learning Representations (ICLR), 2025. [Online]. Available: https://proceedings.iclr.cc/paper_files/paper/2025/hash/adbe936993aa7cf41e45054d8b72f183-Abstract-Conference.html
[26] W. Chen, J. Yuan, C. Qian, C. Yang, Z. Liu, and M. Sun, "Optima: Optimizing effectiveness and efficiency for LLM-based multi-agent system," in Findings Assoc. Comput. Linguistics: ACL 2025, Vienna, Austria, 2025, pp. 11534-11557, doi: 10.18653/v1/2025.findings-acl.601.
[27] ULab-UIUC, "MARBLE: Multi-Agent CooRdination Backbone with LLM Engine," GitHub repository, 2025. [Online]. Available: https://github.com/ulab-uiuc/MARBLE (accessed Aug. 5, 2026).
[28] Shilu He, Haowei Tu, and Isa Liu, "Safe PD Capacity Forecasting with Time-Series Foundation Models and Calibrated Uncertainty for Heterogeneous GPU Clusters," J. Adv. Comput. Syst., vol. 3, no. 4, pp. 48-66, Apr. 2023, doi: 10.69987/JACS.2023.30404.
[29] C. Wang, Z. Wen, R. Zhang, P. Xu, and Y. Jiang, "GPU Memory Requirement Prediction for Deep Learning Task Based on Bidirectional Gated Recurrent Unit Optimization Transformer," in Proc. 5th Int. Conf. Artificial Intelligence, Virtual Reality and Visualization (AIVRV), Chengdu, China, 2025, pp. 31-35, doi: 10.1109/AIVRV67401.2025.11350369.
[30] Shilu He, Xiaohan Chang, and Eddin Sun, "Cross-Cloud Transfer Learning for AI Training Capacity Forecasting under Workload and Topology Distribution Shift," J. Adv. Comput. Syst., vol. 4, no. 1, pp. 100-120, Jan. 2024, doi: 10.69987/JACS.2024.40108.
[31] S. Zhao, J. Bai, and D. Roberson, "Multi-Horizon GPU Demand Forecasting with Workload Semantics and Operational Risk Curves: An Empirical Study on Alibaba Clusterdata GPU Trace," J. Technol. Informatics Eng., vol. 4, no. 3, pp. 544-571, Dec. 2025, doi: 10.51903/jtie.v4i3.498.
[32] Siyu Chen, Shilu He, and Eddin Sun, "Risk-Bounded GPU Resource Oversubscription via Conformal Demand Envelopes in Production AI Clusters," J. Adv. Comput. Syst., vol. 4, no. 5, pp. 119-134, May 2024, doi: 10.69987/JACS.2024.40509.
[33] S. He, C. Li, and H. Rao, "Few-Shot Cold-Start Workload Forecasting for New AI Inference Tenants with Time-Series Foundation Models," J. Technol. Informatics Eng., vol. 4, no. 1, pp. 306-324, Apr. 2025, doi: 10.51903/jtie.v4i1.546.
[34] J. Nie, Y. Shi, and L. Zhao, "Token-Burst-Aware Capacity Planning for LLM Inference Services: Request Arrival, Token Demand, and Failure Risk Modeling from BurstGPT Traces," J. Technol. Informatics Eng., vol. 5, no. 2, pp. 142-164, Aug. 2026, doi: 10.51903/jtie.v5i2.565.
[35] S. He, J. Nie, and C. Li, "Power-Aware Inventory Planning for AI Infrastructure Using Job-Level Forecasting and LLM Workload Explanations," J. Technol. Informatics Eng., vol. 5, no. 1, pp. 341-359, Apr. 2026, doi: 10.51903/jtie.v5i1.548.
[36] Q. Xin, "Hybrid Cloud Architecture for Efficient and Cost-Effective Large Language Model Deployment," J. Inf. Syst. Informatics, vol. 7, no. 3, pp. 2182-2195, Sep. 2025, doi: 10.51519/journalisi.v7i3.1170.
[37] S. Zhao, Y. Ren, and X. Chang, "Profit-Aware Spot GPU Admission Control with Cost-Sensitive Loss and Evidence-Grounded Policy Memos for AI Workload Supply-Demand Matching," J. Technol. Informatics Eng., vol. 5, no. 2, pp. 45-59, Aug. 2026, doi: 10.51903/jtie.v5i2.545.
[38] Ge Liu, Shilu He, and Isa Liu, "LLM-Augmented Multi-Source Root Cause Attribution for CPU and Network Faults in Microservices," J. Adv. Comput. Syst., vol. 3, no. 6, pp. 39-57, Jun. 2023, doi: 10.69987/JACS.2023.30604.
[39] Boning Zhang, Hengning Rao, and Derek Zhao, "Evidence-Grounded RAG for Cloud-Native DevOps: Hallucination-Resistant AIOps Question Answering over Private Operations Documents," J. Adv. Comput. Syst., vol. 4, no. 3, pp. 109-125, Mar. 2024, doi: 10.69987/JACS.2024.40308.
[40] Ge Liu, Chenyu Li, and Eric Zhang, "OpsLLM for Cloud Incident Triage: Bilingual RAG-Based Root Cause Analysis and Alert Summarization for AI Infrastructure Operations," J. Adv. Comput. Syst., vol. 4, no. 4, pp. 97-111, Apr. 2024, doi: 10.69987/JACS.2024.40408.
[41] Q. Xin, "Explaining OpenStack Failure-Injection Log Anomalies with Retrieved Normal Prototypes," Emerg. Inf. Sci. Technol., vol. 6, no. 2, pp. 125-146, Nov. 2025, doi: 10.18196/eist.v6i2.31232.
[42] Yunhe Li, "Findable then Explainable: Retrieval-Summary Integration for Code Intelligence on a Lightweight CodeSearchNet Subset," J. Adv. Comput. Syst., vol. 4, no. 7, pp. 65-82, Jul. 2024, doi: 10.69987/JACS.2024.40706.
[43] Q. Xin, "Self-Supervised Log Anomaly Detection with LogBERT-Style Transformers: Full Empirical Evaluation on a Reproducible SynHDFS Benchmark," J. Electr. Eng. Comput. Sci., vol. 11, no. 1, pp. 23-35, May 2026, doi: 10.54732/jeecs.v11i1.3.
[44] X. Sun, Z. S. Zhong, and Q. Wu, "Retrieval-Grounded HDFS Log Anomaly Detection and Deterministic Failure Narrative Generation," J. Comput. Syst. Appl., vol. 3, no. 1, pp. 15-30, Jun. 2026, doi: 10.64229/j6d7fr94.
[45] Q. Xin, "Log Anomaly Detection with Conformal Alert Control and Evidence-Grounded Incident Ticket Generation," AVITEC, vol. 8, no. 2, pp. 247-264, Aug. 2026, doi: 10.28989/avitec.v8i2.3974.
[46] B. Zhang, X. Sun, G. Liu, and B. Zhou, "LLM-Style DevOps Copilot for Cloud-Native Troubleshooting: Retrieval-Augmented Runbook Generation and Command-Safety Evaluation," J. Technol. Informatics Eng., vol. 5, no. 2, pp. 104-118, Aug. 2026, doi: 10.51903/jtie.v5i2.534.
[47] C. Li, G. Liu, and Z. Zhao, "Cost-Aware LLM-Style Routing for AIOps Log Analysis: Log Parsing, Anomaly Detection, Fault Diagnosis, and Incident Summarization on LogEval Task Files," J. Technol. Informatics Eng., vol. 5, no. 2, pp. 91-103, Jun. 2026, doi: 10.51903/jtie.v5i2.538.
[48] Z. S. Zhong, C. Li, and H. Rao, "Trajectory Reliability Prediction for Generalist AI Agents: Tool-Use Failure Analysis and Success Forecasting on ZClawBench," J. Technol. Informatics Eng., vol. 5, no. 1, pp. 341-360, Apr. 2026, doi: 10.51903/jtie.v5i1.539.
[49] Jiayi Nie and David Zheng, "Noisy-Neighbor-Aware VM Degradation Risk Modeling with Unsupervised Residual Fusion," J. Adv. Comput. Syst., vol. 4, no. 4, pp. 112-123, Apr. 2024, doi: 10.69987/JACS.2024.40409.
[50] J. Nie, G. Liu, C. Li, and T. Zou, "Evidence-Constrained Incident Visualization Cards for Distributed Cloud Logs: A UI/UX Framework for Turning Hadoop, OpenStack, and ZooKeeper Logs into Actionable SRE Design Interfaces," Int. J. Graph. Des., vol. 4, no. 1, pp. 179-185, Apr. 2026, doi: 10.51903/ijgd.v4i1.3703.
[51] G. Liu, S. He, and H. Wong, "LLM-Compatible Visual Brief Cards for AI Infrastructure Capacity Dashboards: A UI/UX Framework for Turning Forecast Risk into Graphic Design Decisions," Int. J. Graph. Des., vol. 3, no. 1, pp. 196-213, May 2025, doi: 10.51903/ijgd.v3i1.3723.
[52] Chenyu Li, Jingwen Bai, and Samuel Wang, "Evidence-Chain Reliable RAG: Word-Level Hallucination Detection, Source Attribution, and Provenance Explanation for LLM Applications," J. Adv. Comput. Syst., vol. 4, no. 2, pp. 76-92, Feb. 2024, doi: 10.69987/JACS.2024.40207.
[53] Z. S. Zhong, J. Chen, E. Zhong, and X. Sun, "Evidence-Calibrated RAG for Unanswerable Question Answering: Retrieval Coverage, Abstention Calibration, and Hallucination-Proxy Analysis on SQuAD 2.0," J. Technol. Informatics Eng., vol. 4, no. 2, pp. 502-520, Aug. 2025, doi: 10.51903/jtie.v4i2.536.
[54] J. Jin, "Evidence-Chain Reliable RAG: Hallucination Detection, Source Attribution, and Deterministic Provenance Explanations," J. Technol. Informatics Eng., vol. 4, no. 2, pp. 520-533, Aug. 2025, doi: 10.51903/jtie.v4i2.535.
[55] W. Su, S. Chen, and C. Zhao, "Budgeted Multi-Hop Retrieval Agent for Compositional Question Answering: A Retrieval-Policy Evaluation on the Official MultiHop-RAG Benchmark," J. Technol. Informatics Eng., vol. 4, no. 3, pp. 649-662, Dec. 2025, doi: 10.51903/jtie.v4i3.543.
[56] R. Zhang, Z. Wen, C. Wang, C. Tang, P. Xu, and Y. Jiang, "Quality Analysis and Evaluation Prediction of RAG Retrieval Based on Machine Learning Algorithms," arXiv preprint arXiv:2511.19481, 2025, doi: 10.48550/arXiv.2511.19481.
[57] Daren Zheng, Chenyu Li, and Harvey Davidson, "Continual Red-Teaming for In-the-Wild Jailbreaks via Online Guardrail Updates and Guardrail Distillation," J. Adv. Comput. Syst., vol. 3, no. 2, pp. 35-49, Feb. 2023, doi: 10.69987/JACS.2023.30203.
[58] W. Su, H. Rao, and E. Ma, "Privacy and Data-Integrity Risk Cards for LLM Agents: A UI/UX Design Framework for Secure Human Oversight under Prompt-Injection Attacks," Int. J. Graph. Des., vol. 4, no. 1, pp. 186-191, Apr. 2026, doi: 10.51903/ijgd.v4i1.3699.
[59] Daren Zheng, Boning Zhang, and Julie Geibel, "VerifySafe: Toxicity-Safe Agent Responses under Adversarial Prompts with Evidence-Based Self-Verification," J. Adv. Comput. Syst., vol. 4, no. 1, pp. 67-82, Jan. 2024, doi: 10.69987/JACS.2024.40106.
[60] B. Zhou, C. Li, and L. Liu, "Risk-Calibrated Patient-Facing AI Safety Cards: A UI/UX Design Framework for Rubric-Based Medical Risk Communication," Int. J. Graph. Des., vol. 3, no. 2, pp. 365-380, Oct. 2025, doi: 10.51903/ijgd.v3i2.3696.
[61] Daren Zheng and Chenyu Li, "Behavior-Level Jailbreak Resistance via Multi-Stage Refusal + Utility Preservation," J. Adv. Comput. Syst., vol. 4, no. 1, pp. 83-99, Jan. 2024, doi: 10.69987/JACS.2024.40107.
[62] Q. Xin, "Host-Based Intrusion Detection with System Call Sequences: Window Localization and Forensic Narratives," AVITEC, vol. 8, no. 2, pp. 325-334, Aug. 2026, doi: 10.28989/avitec.v8i2.3973.
[63] J. Bai, S. Chen, D. Zheng, and M.-J. Kuo, "Interpretable Attack-Chain Stage Detection from AWS CloudTrail Event Sequences via Linear Models and HMM Smoothing," Inf. Electr. Electron. Eng., vol. 6, no. 1, pp. 28-43, May 2026, doi: 10.33474/infotron.v6i1.24923.
[64] Q. Xin, Z. Xu, L. Guo, F. Zhao, and B. Wu, "IoT Traffic Classification and Anomaly Detection Method Based on Deep Autoencoders," Appl. Comput. Eng., vol. 69, no. 1, pp. 64-70, 2024, doi: 10.54254/2755-2721/69/20241511.
[65] Y. Li and S. Lu, "Language-Guided Feature Selection for DDoS and Intrusion Detection on CICIDS2017," J. Technol. Informatics Eng., vol. 4, no. 1, pp. 284-305, Apr. 2025, doi: 10.51903/jtie.v4i1.531.
[66] Shenghan Lu and David Zhou, "TinyLLM-Assisted Intrusion Detection for Real-Time IoT Networks," J. Adv. Comput. Syst., vol. 4, no. 8, pp. 72-87, Aug. 2024, doi: 10.69987/JACS.2024.40809.
[67] Q. Xin, "Uncertainty-Aware Late Fusion for 3D Perception (Confidence Calibration + Fusion Rule Learning)," J. Technol. Informatics Eng., vol. 4, no. 1, pp. 215-238, Apr. 2025, doi: 10.51903/jtie.v4i1.485.
[68] Yuanzheng Chen, Yitian Zhang, David Chau, and Matt Sherman, "Credit Card Default Risk Tiering with Probability Calibration and Uncertainty-Driven Rejection: A Reproducible Study on the UCI Credit Card Clients Dataset," J. Adv. Comput. Syst., vol. 3, no. 4, pp. 31-47, Apr. 2023, doi: 10.69987/JACS.2023.30403.
[69] Jiaying Jin, Tina Huang, and Sam Lu, "A Model-Risk-Friendly Probability of Default Workflow: Calibration, Distribution-Free Uncertainty Quantification, and SHAP Explanations on the UCI Credit Card Default Dataset," J. Adv. Comput. Syst., vol. 4, no. 6, pp. 74-85, Jun. 2024, doi: 10.69987/JACS.2024.40606.
[70] Q. Xin, "Explainable and Fair Credit Risk Scoring with Counterfactual Explanations: A Reproducible Evaluation on the German Credit Dataset (HELOC-Motivated)," J. Inf. Technol., vol. 14, no. 2, pp. 215-231, Jun. 2026, doi: 10.32664/j-intech.v14i02.2228.
[71] Jiaying Jin, Tina Huang, and Sam Lu, "Cost-Sensitive Learning, Simulated PU Learning, and One-Class Autoencoding for Extreme-Imbalance Credit Card Fraud Detection," J. Adv. Comput. Syst., vol. 4, no. 6, pp. 64-73, Jun. 2024, doi: 10.69987/JACS.2024.40605.
[72] H. Wang, Y. Ren, and X. Chang, "Layout-Aware Progressive PDF Rendering: AI Prioritization of PDF Slices to Reduce Time-to-Functional-First-Frame on FUNSD," J. Technol. Informatics Eng., vol. 4, no. 2, pp. 425-446, Aug. 2025, doi: 10.51903/jtie.v4i2.523.
[73] B. Zhou, J. Jin, and D. Zhao, "Calibrated Resume-Job Matching for Trustworthy LLM-Assisted Recruiter Screening: Pairwise Matching, Probability Calibration, and Selective Refusal on Two Public Recruitment Datasets," J. Technol. Informatics Eng., vol. 4, no. 3, pp. 625-648, Dec. 2025, doi: 10.51903/jtie.v4i3.529.
[74] Z. S. Zhong and S. Ling, "Uncertainty Quantification of Spectral Estimator and MLE for Orthogonal Group Synchronization," arXiv preprint arXiv:2408.05944, Aug. 2024, doi: 10.48550/arXiv.2408.05944.
[75] J. Jin, "LLM-Style Evidence Cards for Scientific Search Interfaces: A UI/UX Design Framework for Retrieval Transparency, Ranking Trust, and Visual Evidence Hierarchy," Int. J. Graph. Des., vol. 3, no. 2, pp. 397-414, Oct. 2025, doi: 10.51903/ijgd.v3i2.3698.
[76] J. Bai, H. Wang, Q. Wu, and B. Zhang, "Privacy-Robust Incrementality Estimation in Cookieless Settings via Uplift Modeling: Reproducible Evidence from the Hillstrom E-Mail Experiment," J. Technol. Informatics Eng., vol. 5, no. 1, pp. 17-38, Apr. 2026, doi: 10.51903/jtie.v5i1.468.
[77] J. Mu, T. Ye, and P. Patel, "Offline Counterfactual Evaluation for Advertising and Recommendation Slot Policies: A Reproducible Study on the Open Bandit Dataset (Small)," J. Technol. Informatics Eng., vol. 4, no. 3, pp. 521-543, Dec. 2025, doi: 10.51903/jtie.v4i3.500.
[78] T. Ye, J. Mu, and J. Hunter, "Off-Policy Evaluation and Conservative Policy Selection for Slot-Level Dynamic Bidding and Ranking on the Open Bandit Dataset (Small)," J. Technol. Informatics Eng., vol. 5, no. 1, pp. 178-199, Apr. 2026, doi: 10.51903/jtie.v5i1.503.
[79] H. Zhang, "Risk-Aware Budget-Constrained Auto-Bidding under First-Price RTB: A Distributional Constrained Deep Reinforcement Learning Framework," J. Adv. Comput. Syst., vol. 4, no. 6, pp. 30-47, Jun. 2024.
[80] H. Weng, H. Wang, and C. Wei, "Adaptive Bidding Strategies for Hybrid Auction Mechanisms in Programmatic Advertising," J. Adv. Comput. Syst., vol. 4, no. 4, pp. 13-25, Apr. 2024, doi: 10.69987/JACS.2024.40402.
[81] H. Zhang, "DriftGuard: Multi-Signal Drift Early Warning and Safe Re-Training/Rollback for CTR/CVR Models," J. Adv. Comput. Syst., vol. 3, no. 7, pp. 24-40, Jul. 2023.
[82] Jinyi Mu, Yifei Lu, and Michelle Smith, "LLM-Assisted Incrementality (Uplift) Modeling for Email Advertising: From Feature Interactions to Interpretable Audience-Creative-Channel Policies," J. Adv. Comput. Syst., vol. 3, no. 1, pp. 31-48, Jan. 2023, doi: 10.69987/JACS.2023.30103.
[83] H. Zhang, "Counterfactual Learning-to-Rank for Ads: Off-Policy Evaluation on the Open Bandit Dataset," J. Adv. Comput. Syst., vol. 5, no. 12, pp. 1-11, Dec. 2025.
[84] Xinzhuo Sun, Yifei Lu, and Jing Chen, "Controllable Long-Term User Memory for Multi-Session Dialogue: Confidence-Gated Writing, Time-Aware Retrieval-Augmented Generation, and Update/Forgetting," J. Adv. Comput. Syst., vol. 3, no. 8, pp. 9-24, Aug. 2023, doi: 10.69987/JACS.2023.30802.
[85] M.-J. Kuo, D. Zheng, and J. Hires, "Federated Topic-Preference Learning for Knowledge-Grounded Chat with Differential Privacy," J. Technol. Informatics Eng., vol. 4, no. 2, pp. 385-401, Aug. 2025, doi: 10.51903/jtie.v4i2.502.
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