arXiv subjects

Changlun Li

Publications and source records attributed to Changlun Li.

10 recordsLinked to original sources

arXiv2026

EVOQUANT: Self-Evolving Verifier-Guided Strategy Optimization for Robust Quantitative Trading

Quantitative strategy optimization remains largely manual, requiring domain experts to identify weak signals, tune risk-control rules, and repeatedly validate iterative revisions. Large language models can accelerate this process, but directly relying on them to rewrite trading strategies often introduces hallucinated edits, strategy drift, and backtest overfitting. We propose EVOQUANT, a self-Evolving Verifier-guided framework for strategy Optimization in Quantitative trading. Our method utilizes LLMs to deeply diagnose performance bottlenecks, generates semantically controlled candidate edits, selects the best strategy through a multi-stage verification pipeline, and distills optimization experience into reusable knowledge for continual self-improvement. We evaluate our method using seven representative strategies: four from the A-share market and three from the Crypto market. Experimental results show that our method significantly improves the Sharpe ratio across all tested strategies: the average test Sharpe increases from -0.298 to 0.538, and the best-performing strategy achieves a 199% relative improvement. Ablation studies and stress tests under stricter conditions further validate the effectiveness and robustness of the framework. Overall, this work transforms quantitative strategy optimization from costly manual trial and error into an automated and verifiable iterative paradigm, offering a new path for applying large language models to financial strategy research.

cs.AI↗

arXiv2026

NextFund: A Unified Performance Tracking Platform for Agentic Portfolio Management

Large language models (LLMs) based agents are beginning to participate in portfolio construction and market analysis, where decisions must be justified under evolving information and risk constraints. Current assessment practice, however, remains poorly aligned with this setting: many studies rely on static examinations or report only terminal portfolio returns, while the intermediate evidence, analyst judgments, and execution steps that produced those returns stay largely invisible. We introduce NextFund, an evaluation platform that makes financial-agent behavior observable under live market conditions. The platform couples time-consistent market access, coordinated multi-agent analysis, and persistent logging of the full decision path from observation to trade. Through an interactive Trading Arena, users can compare models across markets, inspect equity curves, and drill from leaderboard outcomes down to individual justifications. We present NextFund on Hong Kong, U.S., and China A-share equities, illustrating how inspectable decision histories enable fairer benchmarking and more actionable diagnosis. Our demo is available at https://paradoox.cn/nextfund/.

cs.AI↗

arXiv2026

Can Agentic Trading Systems Pay for Their Own Intelligence?

Large language model (LLM) agents are increasingly used in trading systems, where model reasoning, tool use, and continual decisions incur costs that are expected to produce trading value. Existing evaluations typically report performance metrics, but rarely examine agentic viability: whether dynamic LLM-mediated decisions convert their induced costs into measurable incremental profit. To apply this criterion, we introduce TradeLens, a trace-grounded diagnostic toolkit for evaluating agentic trading systems from their trading records, runtime traces, and deployment configurations. It reconstructs trading trajectories, attributes profit and cost to interpretable evidence, and diagnoses whether and why an agent pays for its own intelligence. We conduct extensive analysis across backbone models, capital scales, trading frequencies, and system architectures, together with deployment discussion. Our results show that viability hinges on intelligence-to-profit conversion: models exhibit different failure patterns, such as poor asset selection in DeepSeek-V3.2 and negative timing in GLM-4.7, while capital scale, trading frequency, and architecture matter only by amplifying or degrading decision-attributed timing value. These findings reframe the evaluation of LLM-based trading agents from capability-centric performance ranking to trace-grounded diagnosis of intelligence-to-profit conversion. Our code is available at https://anonymous.4open.science/r/TradeLens.

cs.AI↗

arXiv2026

Are Diffusion Language Models Good Database Analysts?

Recent advancements in large language models (LLMs) have significantly improved Natural Language to SQL (NL2SQL) tasks, yet most NL2SQL systems continue to rely on the autoregressive (AR) paradigm. The highly structured nature of SQL makes AR models susceptible to sequential error propagation due to their rigid left-to-right decoding process. Diffusion Language Models~(DLMs) have recently emerged as a promising alternative, replacing unidirectional decoding with iterative denoising to enable global sequence refinement. Nevertheless, the adoption of DLMs in NL2SQL is constrained by a fragmented ecosystem and the absence of a standardized evaluation framework, which obscures their true capabilities and impedes fair comparison with AR baselines. In this paper, we propose a unified evaluation framework that standardizes both generation and execution environments across various DLM architectures. To further improve the performance of DLMs-based NL2SQL systems, we propose \texttt{SQL-D1}, a novel agentic framework that integrates database-aware context engineering, test-time scaling and interactive optimization. Through extensive empirical studies on scaling properties, post-training stability, and primary failure modes, we demonstrate that DLMs offer distinct advantages in structural robustness and facilitate flexible trade-offs between efficiency and accuracy. By distilling these insights into structured takeaways, our work provides a systematic understanding of DLMs-based NL2SQL and lays the foundation for future database analysis agents.

cs.DB↗

arXiv2026

TABQAWORLD: Optimizing Multimodal Reasoning for Multi-Turn Table Question Answering

Multimodal reasoning has emerged as a powerful framework for enhancing reasoning capabilities of reasoning models. While multi-turn table reasoning methods have improved reasoning accuracy through tool use and reward modeling, they rely on fixed text serialization for table state readouts. This introduces representation errors in table encoding that significantly accumulate over multiple turns. Such accumulation is alleviated by tabular grounding methods in the expense of inference compute and cost, rendering real world deployment impractical. To address this, we introduce TABQAWORLD, a table reasoning framework that jointly optimizes tabular action through representation and estimation. For representation, TABQAWORLD employs an action-conditioned multimodal selection policy, which dynamically switches between visual and textual representations to maximize table state readout reliability. For estimation, TABQAWORLD optimizes stepwise reasoning trajectory through table metadata including dimension, data types and key values, safely planning trajectory and compressing low-complexity actions to reduce conversation turns and latency. Designed as a training-free framework, empirical evaluations show that TABQAWORLD achieves state-of-the-art performance with 4.87% accuracy improvements over baselines, with 5.42% accuracy gain and 33.35% inference latency reduction over static settings, establishing a new standard for reliable and efficient table reasoning.

cs.AI↗

arXiv2025

Time Travel is Cheating: Going Live with DeepFund for Real-Time Fund Investment Benchmarking

Large Language Models (LLMs) have demonstrated notable capabilities across financial tasks, including financial report summarization, earnings call transcript analysis, and asset classification. However, their real-world effectiveness in managing complex fund investment remains inadequately assessed. A fundamental limitation of existing benchmarks for evaluating LLM-driven trading strategies is their reliance on historical back-testing, inadvertently enabling LLMs to "time travel"-leveraging future information embedded in their training corpora, thus resulting in possible information leakage and overly optimistic performance estimates. To address this issue, we introduce DeepFund, a live fund benchmark tool designed to rigorously evaluate LLM in real-time market conditions. Utilizing a multi-agent architecture, DeepFund connects directly with real-time stock market data-specifically data published after each model pretraining cutoff-to ensure fair and leakage-free evaluations. Empirical tests on nine flagship LLMs from leading global institutions across multiple investment dimensions-including ticker-level analysis, investment decision-making, portfolio management, and risk control-reveal significant practical challenges. Notably, even cutting-edge models such as DeepSeek-V3 and Claude-3.7-Sonnet incur net trading losses within DeepFund real-time evaluation environment, underscoring the present limitations of LLMs for active fund management. Our code is available at https://github.com/HKUSTDial/DeepFund.

cs.CE↗

arXiv2025

Will LLMs be Professional at Fund Investment? DeepFund: A Live Arena Perspective

Large Language Models (LLMs) have demonstrated impressive capabilities across various domains, but their effectiveness in financial decision-making remains inadequately evaluated. Current benchmarks primarily assess LLMs' understanding on financial documents rather than the ability to manage assets or dig out trading opportunities in dynamic market conditions. Despite the release of new benchmarks for evaluating diversified tasks on the financial domain, we identified four major problems in these benchmarks, which are data leakage, navel-gazing, over-intervention, and maintenance-hard. To pave the research gap, we introduce DeepFund, a comprehensive arena platform for evaluating LLM-based trading strategies in a live environment. Our approach implements a multi-agent framework where they serve as multiple key roles that realize the real-world investment decision processes. Moreover, we provide a web interface that visualizes LLMs' performance with fund investment metrics across different market conditions, enabling detailed comparative analysis. Through DeepFund, we aim to provide a more realistic and fair assessment on LLM's capabilities in fund investment, offering diversified insights and revealing their potential applications in real-world financial markets. Our code is publicly available at https://github.com/HKUSTDial/DeepFund.

cs.MA↗

arXiv2025

Rise of the Community Champions: From Reviewer Crunch to Community Power

Academic publishing is facing a crisis driven by exponential growth in submissions and an overwhelmed peer review system, leading to inconsistent decisions and a severe reviewer shortage. This paper introduces Panvas, a platform that reimagines academic publishing as a continuous, community-driven process. Panvas addresses these systemic failures with a novel combination of economic incentives (paid reviews) and rich interaction mechanisms (multi-dimensional ratings, threaded discussions, and expert-led reviews). By moving beyond the traditional accept/reject paradigm and integrating paper hosting with code/data repositories and social networking, Panvas fosters a meritocratic environment for scholarly communication and presents a radical rethinking of how we evaluate and disseminate scientific knowledge. We present the system design, development roadmap, and a user study plan to evaluate its effectiveness.

cs.SI↗

arXiv2024

SketchFill: Sketch-Guided Code Generation for Imputing Derived Missing Values

Missing value is a critical issue in data science, significantly impacting the reliability of analyses and predictions. Missing value imputation (MVI) is a longstanding problem because it highly relies on domain knowledge. Large language models (LLMs) have emerged as a promising tool for data cleaning, including MVI for tabular data, offering advanced capabilities for understanding and generating content. However, despite their promise, existing LLM techniques such as in-context learning and Chain-of-Thought (CoT) often fall short in guiding LLMs to perform complex reasoning for MVI, particularly when imputing derived missing values, which require mathematical formulas and data relationships across rows and columns. This gap underscores the need for further advancements in LLM methodologies to enhance their reasoning capabilities for more reliable imputation outcomes. To fill this gap, we propose SketchFill, a novel sketch-based method to guide LLMs in generating accurate formulas to impute missing numerical values. Our experimental results demonstrate that SketchFill significantly outperforms state-of-the-art methods, achieving 56.2% higher accuracy than CoT-based methods and 78.8% higher accuracy than MetaGPT. This sets a new standard for automated data cleaning and advances the field of MVI for numerical values.

cs.CL↗

arXiv2023

Learning Analytics from Spoken Discussion Dialogs in Flipped Classroom

The flipped classroom is a new pedagogical strategy that has been gaining increasing importance recently. Spoken discussion dialog commonly occurs in flipped classroom, which embeds rich information indicating processes and progression of students' learning. This study focuses on learning analytics from spoken discussion dialog in the flipped classroom, which aims to collect and analyze the discussion dialogs in flipped classroom in order to get to know group learning processes and outcomes. We have recently transformed a course using the flipped classroom strategy, where students watched video-recorded lectures at home prior to group-based problem-solving discussions in class. The in-class group discussions were recorded throughout the semester and then transcribed manually. After features are extracted from the dialogs by multiple tools and customized processing techniques, we performed statistical analyses to explore the indicators that are related to the group learning outcomes from face-to-face discussion dialogs in the flipped classroom. Then, machine learning algorithms are applied to the indicators in order to predict the group learning outcome as High, Mid or Low. The best prediction accuracy reaches 78.9%, which demonstrates the feasibility of achieving automatic learning outcome prediction from group discussion dialog in flipped classroom.

cs.CY↗