AI 论文精选速递

2026年11月09日 · 追踪前沿研究

1

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.

#large language models#LLM#multi-agent systems

https://arxiv.org/abs/2412.20138

https://hjfy.top/arxiv/2412.20138

https://github.com/tauricresearch/tradingagents (121326)

2

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.

#coding agents#token efficiency#TokenEconomizer

https://arxiv.org/abs/2610.22732

https://hjfy.top/arxiv/2610.22732

https://github.com/microsoft/TokenEconomizer (5065)

3

Persistent Recursive Worlds Enable Autonomous Software Evolution

Beichen Huang, Zhenyu Liang 等4人

Genesis organizes long-horizon software development around a persistent project rather than persistent agents, enabling multi-day compiler construction and numerical module reimplementation with low cost and high performance.

#DeepSeek V4 Flash#GLM 5.2

https://arxiv.org/abs/2608.10450

https://hjfy.top/arxiv/2608.10450

https://github.com/EMI-Group/genesis (663)

4

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.

#end-to-end OCR#large language model#decoder

https://arxiv.org/abs/2606.23050

https://hjfy.top/arxiv/2606.23050

https://github.com/baidu/Unlimited-OCR (30894)

5

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.

#large language models#OpenDevin#AI agents

https://arxiv.org/abs/2407.16741

https://hjfy.top/arxiv/2407.16741

https://github.com/opendevin/opendevin (92969)

6

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.

#hair cards#spring bones#set diffusion transformer

https://arxiv.org/abs/2610.40599

https://hjfy.top/arxiv/2610.40599

https://github.com/hyz317/HairDeck (951)

7

Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory

Prateek Chhikara, Dev Khant 等5人

Mem0, a memory-centric architecture with graph-based memory, enhances long-term conversational coherence in LLMs by efficiently extracting, consolidating, and retrieving information, outperforming existing memory systems in terms of accuracy and computational efficiency.

#Mem0#memory-centric architecture#graph-based memory

https://arxiv.org/abs/2504.19413

https://hjfy.top/arxiv/2504.19413

https://github.com/mem0ai/mem0 (68626)

8

VisSearch-Bench: Evaluating Long Horizon Visual Search in Multimodal Agents

ZhengXian Wu, Qianhao Nie 等13人

VisSearch-Bench evaluates multimodal agents on visually intensive search tasks that require integrating visual context with external information, showing that even the best system fully solves only 26.47% of tasks.

#multimodal agents#visual context management#VisSearch-Bench

https://arxiv.org/abs/2611.04894

https://hjfy.top/arxiv/2611.04894

https://github.com/antins-labs/VisSearch-Bench-v1 (61)

9

MinerU2.5: A Decoupled Vision-Language Model for EfficientHigh-Resolution Document Parsing

Junbo Niu, Zheng Liu 等61人

MinerU2.5, a 1.2B-parameter document parsing vision-language model, achieves state-of-the-art recognition accuracy with computational efficiency through a coarse-to-fine parsing strategy.

#document parsing#vision-language model#coarse-to-fine

https://arxiv.org/abs/2509.22186

https://hjfy.top/arxiv/2509.22186

https://github.com/opendatalab/MinerU (83316)

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生成时间: 2026-11-09 08:30

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