AI 论文精选速递
2026年11月01日 · 追踪前沿研究
TokenEconomizer: Experience-Driven Black-Box Cost Control for Coding Agents
Aaron J. Li, Weijia Xu 等11人
TokenEconomizer reduces recurring token costs of coding agents across sequential real-world tasks by pruning irrelevant tools, injecting micro-skills, and interrupting failure loops.
https://arxiv.org/abs/2610.22732
https://hjfy.top/arxiv/2610.22732
https://github.com/microsoft/TokenEconomizer (2569)
TradingAgents: Multi-Agents LLM Financial Trading Framework
Yijia Xiao, Edward Sun 等4人
A multi-agent framework using large language models for stock trading simulates real-world trading firms, improving performance metrics like cumulative returns and Sharpe ratio.
https://arxiv.org/abs/2412.20138
https://hjfy.top/arxiv/2412.20138
https://github.com/tauricresearch/tradingagents (118943)
Unlimited OCR Works
Youyang Yin, Huanhuan Liu 等17人
Unlimited OCR introduces Reference Sliding Window Attention to eliminate growing memory consumption during long-sequence OCR tasks, enabling efficient transcription of multiple pages in a single forward pass.
https://arxiv.org/abs/2606.23050
https://hjfy.top/arxiv/2606.23050
https://github.com/baidu/Unlimited-OCR (30061)
Investor's Last Exam: Can AI Agents Do Equity Investment Without Seeing the Future?
Huangfei Jiang, Anning Hu 等10人
Investor's Last Exam evaluates financial agents on end-to-end equity research using frozen evidence, expert criteria, and dependency graphs, revealing that top models excel at research but fail at forecasting and investment judgments.
https://arxiv.org/abs/2610.38342
https://hjfy.top/arxiv/2610.38342
https://github.com/Prophetlab/Investor-Last-Exam (127)
OpenDevin: An Open Platform for AI Software Developers as GeneralistAgents
Xingyao Wang, Boxuan Li 等24人
OpenDevin is a platform for developing AI agents that interact with the world by writing code, using command lines, and browsing the web, with support for multiple agents and evaluation benchmarks.
https://arxiv.org/abs/2407.16741
https://hjfy.top/arxiv/2407.16741
https://github.com/opendevin/opendevin (92355)
Intrinsic Distribution Matching
Deyuan Liu, Yansen Han 等10人
Intrinsic Distribution Matching improves few-step text-to-image generation by adding explicit moment constraints to distribution matching distillation, achieving state-of-the-art long-text rendering with minimal network evaluations.
https://arxiv.org/abs/2610.39633
https://hjfy.top/arxiv/2610.39633
https://github.com/inclusionAI/IDM (85)
Xiaomi-OCR-0 Technical Report
Xin Chen, Anan Du 等10人
Xiaomi-OCR-0 is a compact vision-language model that unifies document parsing and OCR understanding through automated data synthesis, progressive training with text anchoring and reinforcement learning, and achieves state-of-the-art results on multiple benchmarks.
https://arxiv.org/abs/2609.36136
https://hjfy.top/arxiv/2609.36136
https://github.com/SeerRay-Lab/Xiaomi-OCR-0 (369)
SmolDocling: An ultra-compact vision-language model for end-to-endmulti-modal document conversion
Ahmed Nassar, Andres Marafioti 等13人
SmolDocling is a compact vision-language model that performs end-to-end document conversion with robust performance across various document types using 256M parameters and a new markup format.
https://arxiv.org/abs/2503.11576
https://hjfy.top/arxiv/2503.11576
https://github.com/docling-project/docling (69924)
HairDeck: Animation-Ready Stylized Hair Cards from a Single Image
Yuze He, Yixiang Wei 等6人
HairDeck generates stylized hair cards and spring-bone animations as unordered token sets using a diffusion transformer, improving accuracy and speed over prior methods.
https://arxiv.org/abs/2610.40599
https://hjfy.top/arxiv/2610.40599
https://github.com/hyz317/HairDeck (188)
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