Exacerbate Systemic Inefficiencies

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Analyzing and Mitigating Cost-Inefficient Behaviors in Coding Agents
Sep 25, 2026Yiran Hu, Nan Jiang, Shanchao Liang +3Coding Agents Exacerbate Systemic Inefficiencies
Purdue University
LLM within MCP Matters: Measuring Inefficient Resource Utilization Driven by LLMs
Aug 9, 2026Minhan Cho, Soyoung Park, Kihyeon Jeong +3Model Context Protocol Exacerbate Systemic Inefficiencies
Remember & Company AI Lab · Sungkyunkwan University · National Assembly Research Service +2
Online Monitoring and Corrective Steering of Programming Agents
Aug 7, 2026Shuyang Liu, Saman Dehghan, Ji Young Kim +3Automated Program Repair Exacerbate Systemic Inefficiencies
University of Illinois Urbana-Champaign, USA · IBM, USA
A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization
Aug 5, 2026Anadi Goyal, Nandish Chattopadhyay, Anupam Chattopadhyay +1Vision Transformer Visual Token Pruning
Indian Institute of Technology Guwahati, Guwahati 781039, Assam, India · Nanyang Technological University, 50 Nanyang Ave, Singapore 639798
Generalization bounds and sample complexity for remaining useful life prediction from complete degradation trajectories
Jul 26, 2026Huy Hoang Le, Kim-Anh NguyenUseful Life Prediction Degradation
R&D Department, PowerMore Ltd., Da Nang 550000, Vietnam · Faculty of Electrical Engineering, University of Science and Technology, The University of Danang, Da Nang 550000, Vietnam
Valid $\ne$ Necessary: Diagnosing Latent Inefficiency in Chain-of-Thought
Jul 13, 2026Daeyeop Lee, Hwanjo YuReasoning Chain Chain-of-Thought Reasoning
1KT Corporation · 2Pohang University of Science and Technology
Internal Pluralism and the Limits of Pairwise Comparisons
Jul 2, 2026Bailey Flanigan, Michelle SiInternal Pluralism Preference Learning
MIT, USA · Harvard, USA
Efficient Waste Sorting for Circular Economy: A Confidence-guided comparison between One-Vs-All and One-Vs-Rest Classification Strategies with Human-in-the-Loop for Automated Waste Sorting
Jul 2, 2026Mohammed Fahad Ali, Dominique Briechle, Marit Briechle-Mathiszig +2Waste Classifier
Institute for Software and Systems Engineering Clausthal University of Technology Germany
When Efficiency Backfires: Cascading LLMs Trigger Cascade Failure under Adversarial Attack
May 17, 2026Zehan Sun, Dingfan Chen, Songze LiAttacker Large Language Model Large Language Models Fail
1Southeast · 2Max Planck Institute for Intelligent Systems
Beyond Inefficiency: Systemic Costs of Incivility in Multi-Agent Monte Carlo Simulations
May 12, 2026Alison Moldovan-Mauer, Benedikt MangoldMulti-Agent Simulations Multi-Agent Debate
Technische Hochschule Nürnberg Georg Simon Ohm 90489 Nuremberg, Germany
Too Many Specialists: Emergent Inefficiencies and Bottlenecks for Multi-agent Ad-hoc Collaboration
May 8, 2026Benjamin Panny, Shashank Mehrotra, Zahra Zahedi +2Multi-Agent Coordination Collaboration
University of Pittsburgh Pittsburgh, PA, United States · Honda Research Institute USA, Inc. San Jose, CA, United States
Finite-Size Gradient Transport in Large Language Model Pretraining: From Cascade Size to Intensive Transport Efficiency
May 3, 2026Ping Wang, Yan-Qi DuLarge Language Model Training Large Language Model Pretraining
Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China · Beijing Shenzhou Aerospace Software Technology Co., Ltd., Beijing 100094, China
Dynamics of Cognitive Heterogeneity: Investigating Behavioral Biases in Multi-Stage Supply Chains with LLM-Based Simulation
Apr 19, 2026Jiuyun Jiang, Yuecheng Hong, Bo Yang +4Multi-Agent Simulations Supply Chain Optimization
Harbin Institute of Technology · The Hong Kong Polytechnic University
Hardening Soft Information: Evidence on Analyst Integration Costs
May 18, 2025Kerry Xiao, Amy ZangExacerbate Systemic Inefficiencies Diverse Hardening Strategies
Hong Kong University of Science and Technology
Provably Efficient Off-Policy Adversarial Imitation Learning with Convergence Guarantees
May 26, 2024Yilei Chen, Vittorio Giammarino, James Queeney +1Imitation Learning Convergence
Division of Systems Engineering Boston University Boston, MA 02215, USA · Department of Computer Science Purdue University West Lafayette, IN 47907, USA · Amazon Robotics North Reading, MA 01864, USA +1

Sep 25, 2026, cs.AIJ/K move · Enter open · S save

Analyzing and Mitigating Cost-Inefficient Behaviors in Coding Agents

Yiran Hu, Nan Jiang, Shanchao Liang, Anik Dey+2

Purdue University

Coding Agents Exacerbate Systemic Inefficiencies Agent Behavior Modeling Traces Structure-Aware

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Although effective, coding agents often incur substantial monetary costs. Their recurring cost-inefficient behaviors remain underexplored. We conduct the first study of behavioral cost inefficiencies in coding agents, analyzing 1,200 trajectories from Claude Code and Mini-SWE-Agent across four configurations on SWE-bench Verified. We identify three cost-inefficient behaviors: subsumed retrieval, similar script generation, and test re-execution. We then evaluate three mitigation strategies: structure-aware retrieval, agent-synthesized skills, and developer-designed skills, over 10k trajectories on held-out SWE-bench Verified and Pro tasks. Our main findings are: (1) The three behaviors affect 79.00%--98.00% of coding tasks and account for up to 22.75% of task cost. (2) Structure-aware retrieval can introduce retrieval overhead and alter agent delegation, causing inconsistent improvements in retrieval efficiency and cost increases of up to 28.14%. (3) Agent-synthesized skills tend to produce low-level, trace-specific guidance, limiting their effectiveness and generality. (4) In contrast, developer-designed skills provide high-level, trace-agnostic guidance, reducing cost by up to 41.73%, roughly twice the maximum gain from agent-synthesized skills.

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