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版本更新 · v4arXiv:2404.03604

A Unified Algorithmic Framework for Dynamic Assortment Optimization under MNL Choice

Shuo Sun, Rajan Udwani, Zuo-Jun Max Shen

原文摘要

We consider assortment and inventory planning problems with dynamic stockout-based substitution effects, and without replenishment, in two different settings: (1) Customers can see all available products when they arrive, a typical scenario in physical stores. (2) The seller can choose to offer a subset of available products to each customer, which is more common on online platforms. Both settings are known to be computationally challenging, and the current approximation algorithms for the two settings are quite different. We develop a unified algorithm framework under the MNL choice model for both settings. Our algorithms improve on the state-of-the-art algorithms in terms of approximation guarantee and runtime, and the ability to manage uncertainty in the total number of customers and handle more complex constraints. In the process, we establish various novel properties of dynamic assortment planning (for the MNL choice model) that may be useful more broadly.

math.OC · cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13765

Beyond Interaction Scores: Selecting Feature Interactions via Independent Replication

Jincheng Gao, Shuo Sun

原文摘要

Learning feature interactions is essential for accurate prediction and interpretable modeling, yet a high interaction score may reflect fitting noise rather than a reproducible pattern. Ranking methods score feature pairs but leave the cutoff to the user; testing methods set the cutoff but rely on repeated model refits or a model of the joint feature distribution. We present LENS, a model-agnostic framework for selecting feature interactions through independent replication. LENS trains two models with the same learning algorithm on disjoint data and accesses them only through predictions. For each feature pair, it removes main effects from the joint-effect surface to isolate an interaction pattern, ranks pairs by its strength, and tests whether the second model reproduces it at a meaningful strength. A single multiple-testing correction determines which pairs to retain, using only two model fits regardless of the number of features. We prove that a replication ratio alone can retain arbitrarily weak patterns shared by both fits, whereas a strength floor restores selection consistency, and we characterize when normalization in Friedman's $H^2$ reverses interaction rankings. On 16 synthetic designs, LENS matches leading model-specific ranking methods (e.g., TreeSHAP) and recovers 91% of material interactions, against 45% for the knockoff-based DIAMOND, without selecting false pairs; with 1,225 candidate pairs and no interactions, it makes substantially fewer false discoveries. On large-scale French motor insurance data, it identifies interactions documented in the actuarial literature and recovers 90% of the deviance gain obtained using all pairs. On two credit datasets, LENS selects few pairs while maintaining or slightly improving predictive performance over the additive baseline.

cs.LG · cs.DB · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10586

M3FT: Micro-Macro-Micro Reasoning for Multi-Agent Cryptocurrency Perpetual Futures Trading

Jiashun Wang, Beile Wang, Guangyan Gan, Wentao Zhang, Siguang Li, Shuo Sun, Yunbo Lu, Lin William Cong

原文摘要

Cryptocurrency perpetual futures form a large-scale online financial market in which automated agents manage interacting asset positions under changing conditions. Policies relying solely on micro-level numerical states may overlook cross-asset relationships and become vulnerable to regime shifts, while macro-level linguistic judgments alone cannot ensure precise measurement or optimize positions under trading costs and risk constraints. Connecting market interpretation to coordinated multi-asset decisions remains challenging. We propose M3FT, a micro-macro-micro framework for multi-agent perpetual futures trading. The first micro stage constructs time-aligned numerical states from exchange and account observations through deterministic processing. The macro stage transforms these measured states into explicit market interpretations without adding external market information. Four asset-specific teams, each comprising a Market Interpreter, an Asset Communicator, and a Risk Reviewer, refine these interpretations through experience retrieval, cross-asset communication, and review of joint-position risks. The final micro stage combines encoded linguistic representations with the original numerical states to learn continuous target positions. We implement this framework with a language-conditioned multi-agent policy: asset actors use global and private numerical states together with linguistic context, while centralized critics evaluate joint actions using portfolio-wide information and shared-account rewards. Experiments on real cryptocurrency perpetual futures data, including comparisons across language models and policy-learning algorithms and component ablations, show that the best-performing M3FT configuration achieves a higher mean test return than the evaluated direct LLM, numerical RL, and rule-based baselines.

cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.34121

Online Stochastic Allocation with Increasing Returns

Shuo Sun, Yunduan Lin

原文摘要

We study online allocation with increasing returns, where an uncertain number of customers arrive sequentially and can be irrevocably assigned to any product with remaining capacity. Each assignment earns a base reward plus a bonus that is nondecreasing in the number of previous assignments to the same product. Prior work has focused primarily on fixed or diminishing returns. Under increasing returns, the central challenge is that concentrating assignments can unlock large future rewards, while a different product may be preferable if arrivals stop early. We show that classical approaches, including greedy and natural LP-based rounding algorithms, can perform arbitrarily poorly, even with homogeneous bonuses. We evaluate performance through competitive ratios relative to the hindsight offline optimum. For homogeneous bonuses, we establish a tight competitive ratio of $1/2$. For arbitrary heterogeneous bonuses, however, no online algorithm can guarantee a competitive ratio greater than $1/m$, where $m$ is the number of products. By imposing discrete concavity on the bonus functions, we limit the concentration of rewards in later assignments and restore constant guarantees. Under this structure, we develop a re-solving framework that repeatedly selects a planning horizon, constructs a feasible allocation plan from the current state, and executes its assignments in a specific order. For arbitrary arrival distributions, we design distribution-oblivious deterministic and randomized algorithms with competitive ratios of $0.2537$ and $0.3864$, respectively. For known increasing-failure-rate (IFR) arrival distributions, we exploit distributional information to obtain a deterministic $1/2$-competitive algorithm.

cs.DS · math.OC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37905

FilingLoop: Evaluating and Evolving LLM Agents for Corporate Disclosure Review and Revision

Wentao Zhang, Yilei Zhao, Guangyan Gan, Shuo Sun, Lin William Cong

原文摘要

Public disclosure repositories such as SEC EDGAR support informed investment through web access to corporate filings. Supporting their review with language model agents requires discovering unstated deficiencies across related documents, acquiring missing company facts, and verifying that requested revisions satisfy evidence-backed requirements. Existing financial question-answering evaluations provide limited evidence of these interactive capabilities. We introduce \textbf{FilingLoop}, an interactive benchmark and a self-evolving agent framework for corporate disclosure review and revision. Grounded in historical U.S.\ Securities and Exchange Commission correspondence, FilingLoop reconstructs 380 predominantly IPO review tasks from 96 companies under distinct information constraints. An evaluator separate from the acting agents scores final filings and responses against 6,099 eligible requirements and audits unsupported claims and harmful revisions. Our framework couples iterative review and revision within each case with procedural learning across cases, converting requirement-level evaluation feedback into role-specific guidance without updating model weights. Updates affect only subsequent cases, allowing procedural adaptation to be assessed without retrying scored tasks. On the evaluated stream, evolution increases requirement fulfillment from 24.28\% to 27.96\% and full comment resolution from 16.90\% to 20.75\%, with a 5.8\% increase in total cost over the fixed-policy variant. FilingLoop connects the concerns an agent discovers with the company facts it obtains and the revisions it commits, providing requirement-level feedback for procedures used in later review tasks.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34930

A Multi-Asset Dataset and Tail-Risk Framework for DeFi Yield Trading Derivatives

Huaiyu Jia, Jieshun You, Jingyu Liu, Yizhi Luo, Shuo Sun

原文摘要

Yield trading has emerged as a pivotal innovation in Decentralized Finance (DeFi) by decoupling principal from yield streams to enhance capital efficiency. Despite its significance, the field lacks fine-grained datasets for systematic empirical analysis. We address this gap by constructing the first large-scale benchmark dataset for Pendle, the pioneering on-chain interest rate derivatives protocol, spanning 24 months across 340 active markets with complete lifecycle observations. Our analysis reveals a structural market transition toward stablecoin-dominated "flight-to-quality" dynamics. To tackle the heavy-tailed nature of yield data, we propose Tail-MSE, an asymmetric loss function that prioritizes tail risk awareness through dynamic penalty weighting. Experiments across six state-of-the-art time series models demonstrate consistent improvements in volatility forecasting robustness, enabling the Tail-MSE Sniper strategy to achieve superior risk-adjusted returns in over half of all markets.

cs.CE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34754

Beyond Profit Rankings: Identifying, Understanding, and Evaluating Smart Money in Prediction Markets

Huaiyu Jia, Shuo Sun

原文摘要

Prediction markets have emerged as an important mechanism for aggregating information and trading beliefs about future events. The transparency of on-chain transactions has enabled investors to identify historically profitable wallets, commonly referred to as smart money, and replicate their trading activities through copy trading. However, despite the growing popularity of smart-money tracking, systematic research on these wallets' trading behavior, profitability persistence, and the economic feasibility of following their trades remains limited. To address this gap, we conduct a comprehensive empirical study of Polymarket, the most popular decentralized prediction market, by reconstructing wallet-level trading activities from on-chain transactions, position changes, market histories, and settlement outcomes. We first examine the distribution and persistence of historical profitability using statistical analysis and logistic regression, and further characterize heterogeneous trading strategies through multidimensional behavioral profiling and unsupervised clustering. Building on these findings, we develop an evidence-grounded multi-agent research framework that integrates wallet behavior, market conditions, and real-world information to generate interpretable and verifiable explanations of individual trades. Finally, we implement a real-time copy-trading system and evaluate its practical feasibility through order-book-aware paper trading and live on-chain execution. Our findings reveal substantial behavioral heterogeneity among historically profitable wallets and highlight the challenges of translating observed trading performance into replicable economic returns.

cs.CE

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arXiv ↗PDF ↗