AI 论文精选速递

2026年11月07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 (120972)

2

VibeVoice Technical Report

Zhiliang Peng, Jianwei Yu 等13人

VibeVoice synthesizes long-form multi-speaker speech using next-token diffusion and a highly efficient continuous speech tokenizer, achieving superior performance and fidelity.

#next-token diffusion#continuous speech tokenizer#Encodec

https://arxiv.org/abs/2508.19205

https://hjfy.top/arxiv/2508.19205

https://github.com/microsoft/VibeVoice (56385)

3

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 (4866)

4

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 (57)

5

WeVisOne-VL: Unified Visual Perception from Objects to Pixels

Tianming Liang, Junyi Pan 等8人

WeVisOne-VL unifies object-level and pixel-level visual perception via structured token prediction, supporting detection, localization, counting, OCR, segmentation, and matting with a lightweight pixel decoder and multi-stage training.

#vision-language models#structured next-token prediction#object-level perception

https://arxiv.org/abs/2610.31329

https://hjfy.top/arxiv/2610.31329

https://github.com/Tencent/WeVisOne-VL (1048)

6

The Other Half of the Memory Wall: Serving 35B MoEs from SSD with Trained Routing Prediction

Yu Lin, Yiming Wang 等5人

Edge0 enables efficient mixture-of-experts inference on consumer hardware by predicting expert routing ahead of time and using recovery adapters to maintain quality with minimal active memory.

#mixture-of-experts#MoE#offloading

https://arxiv.org/abs/2609.18063

https://hjfy.top/arxiv/2609.18063

https://github.com/Edge0-AI/edge0 (9111)

7

SpecPilot: Autonomous Algorithm Discovery and Systems Optimization for Speculative Decoding

Kunjun Li, Wenhao Chai 等10人

SpecPilot autonomously discovers deployment-specific algorithms and system optimizations to maximize speculative decoding speedups across diverse models, drafters, and serving regimes without quality loss.

#speculative decoding#parallel drafters#token verification

https://arxiv.org/abs/2611.02501

https://hjfy.top/arxiv/2611.02501

https://github.com/StargazerX0/SpecPilot (168)

8

ARIS: Autonomous Research via Adversarial Multi-Agent Collaboration

Ruofeng Yang, Yongcan Li 等3人

ARIS is an open-source research harness that uses cross-model adversarial collaboration to ensure reliable long-term research outcomes through coordinated execution, orchestration, and assurance layers.

#LLMs#agent systems#model weights

https://arxiv.org/abs/2605.03042

https://hjfy.top/arxiv/2605.03042

https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep (18775)

9

TailSteer: Trajectory-Tail Steering for Drift-Resistant Multi-turn Image Editing

Ruikang Li, Zixuan Chen 等9人

TailSteer, a final-stage training framework, reduces cumulative visual drift in multi-turn editing by keeping early denoising dynamics close to the base model and learning tail-focused preservation corrections from on-policy rollouts, preserving pretrained generative and editing abilities.

#multi-turn image editing#visual drift#cross-turn rollouts

https://arxiv.org/abs/2611.06741

https://hjfy.top/arxiv/2611.06741

https://github.com/Lyricccco/tailsteer (37)

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生成时间: 2026-11-07 08:30

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