Assortment Optimization

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Curating Always-Loaded Context for LLM Agents: A Capacitated Assortment Model with Censored Feedback
Oct 7, 2026Zexuan Liu, Yuning Yang, Tiancheng ZhaoLLM Context Management LLM Agents
University of Illinois at Urbana-Champaign · Georgia Institute of Technology · Saint Louis University
Diffusion-Based Data-Driven Assortment Optimization
Aug 11, 2026Junyi Liao, Xiaohui Jiang, Zhengwei Tong +2Diffusion Model Guidance Combinatorial Optimization
Department of Electrical and Computer Engineering, Duke University · Department of Biostatistics and Bioinformatics, Duke University · Department of Computer Science, Duke University
Diversified Multinomial Logit Contextual Bandits
Jul 13, 2026Heesang Ann, Taehyun Hwang, Min-hwan OhMulti-Armed Bandits Discrete Choice Modeling
Seoul National University
Estimation, Prediction, and Assortment Optimization for Markov Chain Choice Models with Panel Data
Jul 10, 2026Yalcin Akcay, Gerardo Berbeglia, Young-San LinExpectation-Maximization Discrete Choice Modeling
Melbourne Business School
Data-Driven Dynamic Assortment in Online Platforms: Learning about Two Sides
Jun 9, 2026Rahul Roy, Nur Sunar, Jayashankar M. SwaminathanAssortment Optimization
IE Business School, IE University, Madrid 28006 · Kenan-Flagler Business School, The University of North Carolina at Chapel Hill, NC 27599
Optimal Design for Multinomial Logit Model with Applications to Best Assortment Identification
May 25, 2026Joongkyu Lee, Min-hwan OhMulti-Armed Bandits Discrete Choice Modeling
Seoul National University, Seoul, Korea.
Learning in Position-Aware Multinomial Logit Bandits: From Multiplicative to General Position Effects
May 17, 2026Xi Chen, Shibo Dai, Jiameng Lyu +1Multi-Armed Bandits Discrete Choice Modeling
Leonard N. Stern School of Business, New York University, New York, NY 10012, USA · Qiuzhen College, Tsinghua University, Beijing 100084, China · Department of Management Science, School of Management, Fudan University, Shanghai, 200433, China +1
Optimal Exploration of New Products under Assortment Decisions
Apr 20, 2026Jackie Baek, Atanas Dinev, Thodoris LykourisMulti-Armed Bandits E-Commerce
Stern School of Business, New York University · Massachusetts Institute of Technology
Assortment Planning with Sponsored Products
Feb 9, 2024Shaojie Tang, Shuzhang Cai, Jing Yuan +1Assortment Optimization
Naveen Jindal School of Management, The University of Texas at Dallas · Department of Computer Science and Engineering, The University of North Texas · School of Computer Science and Technology, Soochow University
Combinatorial Inference on the Optimal Assortment in Multinomial Logit Models
Jan 28, 2023Shuting Shen, Xi Chen, Ethan X. Fang +1Statistical Hypothesis Testing Assortment Optimization
Department of Biostatistics, Harvard T.H. Chan School of Public Health · Leonard N. Stern School of Business, New York University · Department of Biostatistics & Bioinformatics, Duke University

Oct 7, 2026, cs.AIJ/K move · Enter open · S save

Curating Always-Loaded Context for LLM Agents: A Capacitated Assortment Model with Censored Feedback

Zexuan Liu, Yuning Yang, Tiancheng Zhao

University of Illinois at Urbana-Champaign · Georgia Institute of Technology · Saint Louis University

LLM Context Management LLM Agents Assortment Optimization

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At the start of every session, LLM agents load a fixed context file, such as

\texttt{AGENTS.md}

. Each loaded token in the file is charged again in every later round of the session, and these files can degrade performance as they grow in size. However, in practice, human or automated curators usually grow these files by appending. We formulate context curation as a capacitated assortment problem. Instructions consume tokens under a finite attention capacity; adding an instruction never raises the compliance of the others, while retained instructions incur a per-session setup cost. We prove an upper bound on the optimal file size, regardless of the number of available candidate instructions, and that appending every instruction with positive standalone value can be arbitrarily worse in net value than selecting an optimal subset. A token budget also limits the loss when the token price is underestimated. We then examine what can be learned from past sessions and how this information can guide decisions to add or remove instructions. Feedback is inherently censored: the benefits and harms of loaded instructions are observable, whereas missing instructions generate feedback only when their absence causes harm. In this setting, we show that deleting instructions ignored by agents can inevitably remove helpful ones. We characterize how much evidence should be collected before adding an instruction. Besides, we bound regret when human reviewers can inspect only a limited number of edits per period. Empirical experiments further show that irrelevant rules drawn from real context files reduce language-model compliance.

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