[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82422-en":3,"doc-seo-82422-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82422,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Agora Enhancing LLM Agent Reasoning Via Auction Based Task Allocation","Enhancing large language model (LLM) agent reasoning depends on effective orchestration of diverse expert models and tools, but existing routing frameworks often use coarse matching and ignore uncertainty, cost, and performance variation among functionally similar alternatives. Agora introduces an incentive compatible auction mechanism that treats reasoning steps as tradeable items. Agents bid using rectified competence via calibrated confidence, routing each logic node to the most reliable solver. Experiments across five benchmarks show improved accuracy over matched single model, routing, and cascade baselines, with a controllable cost quality trade off.","Agora: Enhancing LLM Agent Reasoning Via Auction-Based Task  \nAllocation  \nKaiji Zhou  \nUniversity of Birmingham [kxz571@student.bham.ac.uk](kxz571@student.bham.ac.uk)  \nAles Leonardis  \nUniversity of Birmingham [a.leonardis@bham.ac.uk](a.leonardis@bham.ac.uk)  \nYue Feng*  \nUniversity of Birmingham [y.feng.6@bham.ac.uk](y.feng.6@bham.ac.uk)  \narXiv :2607 .09600v 1 [ cs .AI] 10 Jul 2026  \nAbstract  \nEnhancing the reasoning capabilities of large language model (LLM) agents requires effective orchestration of diverse expert models and tools. However, existing frameworks typically call APIs based on coarse-grained matching between tasks and the functions of expert models or tools, while overlooking critical factors such as performance variability and cost efficiency among functionally similar alternatives. To address this, we propose Agora, a framework that introduces an incentive-compatible auction mechanism for dynamically allocating tasks to expert models and tools. By treating reasoning steps as tradeable items, Agora enables agents to bid based on their rectified competence—ensuring that critical logic is routed to the most capable solver rather than the most overconfident one. Evaluations across five benchmarks show that Agora improves over matched singlemodel, routing, and cascade baselines under comparable candidate pools, while exposing a controllable cost-quality trade-off through a single auction parameter.  \n1 Introduction  \nAdvancing the reasoning capabilities of Large Language Models (LLMs) requires moving beyond monolithic execution. While strategies like Chainof-Thought (CoT) (Wei et al., 2023) provide a structural blueprint by decomposing queries into atomic steps, executing these complex chains often exceeds the reliable scope of any single generalist model. A critical bottleneck restricts current reasoning systems: the mismatch between task difficulty and model capability. Current paradigmsoften default to static assignment, routing all steps to a fixed agent, ignoring that specialized “expert”models can often outperform generalist giants on specific sub-problems (e.g., retrieval or code execution) (Dubois et al., 2024) . Therefore, we need  \n* Corresponding author.  \na system capable of dynamic competence discovery—routing every reasoning step to the agent best suited to solve it.  \nHowever, implementing this fine-grained orchestration faces two hurdles: structural alignment and trustworthy valuation. First, coarse-grained routing (at the query level) fails to exploit the subtask structures generated by planners. Second, and more critically, establishing a reliable auction is plagued by overconfidence (Huang et al., 2025) . Agents often hallucinate certainty, claiming high confidence on incorrect answers. Without a reliable measure of an agent’s true probability of success, dynamic allocation risks assigning critical logic nodes to overconfident but incompetent agents, causing the reasoning chain to collapse.  \nTo address these challenges, we propose Agora, a framework that reformulates task allocation via an incentive-compatible auction mechanism. Specifically, Agora operates in two phases: a Planner decomposes the query into atomic units, and an Auction treats these units as tradeable items. Agents compete to solve them by submitting “bids”derived from their execution cost and calibrated confidence. Crucially, by employing a hierarchical calibration strategy—combining static baselines with online adaptation—Agora filters out hallucinated certainty. This ensures the auction is driven by genuine competence, allowing the system to adaptively “learn to trust” the right experts for each specific step.  \nIn summary, our contributions are as follows:  \n• Auction-Based Reasoning Framework: We propose Agora, a framework that leverages an auction mechanism to dynamically route reasoning steps, enabling specialized agents to collaborate efficiently on complex tasks.  \n• Competence-Driven Calibration: We introduce a stra","cbCailvHaIf21ERo","https://ap.wps.com/l/cbCailvHaIf21ERo","pdf",4654767,1,12,"English","en",105,"# Introduction\n# Related Work\n## Model Selection and Specialized Reasoning","[{\"question\":\"What problem does Agora address in LLM agent reasoning?\",\"answer\":\"Agora addresses the mismatch between task difficulty and model capability, where existing systems often use static, coarse-grained assignment that can misroute subtasks to the wrong experts.\"},{\"question\":\"How does Agora allocate reasoning steps to expert models or tools?\",\"answer\":\"Agora decomposes queries into atomic units via a planner, then runs an incentive-compatible auction where agents bid based on execution cost and calibrated confidence for each unit.\"},{\"question\":\"Why does Agora emphasize calibrated confidence rather than raw agent confidence?\",\"answer\":\"Because unreliable confidence can stem from hallucinated certainty, Agora uses hierarchical calibration (static baselines plus online adaptation) to filter overconfident but incompetent agents, protecting the reasoning 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