AI 论文精选速递

2026年11月06日 · 追踪前沿研究

1

Kronos: A Foundation Model for the Language of Financial Markets

Yu Shi, Zongliang Fu 等7人

Kronos, a specialized pre-training framework for financial K-line data, outperforms existing models in forecasting and synthetic data generation through a unique tokenizer and autoregressive pre-training on a large dataset.

#Large Language Models#Time Series Foundation Models#financial candlestick

https://arxiv.org/abs/2508.02739

https://hjfy.top/arxiv/2508.02739

https://github.com/shiyu-coder/Kronos (46916)

2

Democratizing Robot-Use Agents: A Unified Harness with Open Models

Jiarui Yuan, Yuxin Zuo 等28人

Uni-robot-agent enables open-weight robot-use agents to surpass proprietary models in zero-shot manipulation and suggests joint development of four core capabilities for future training.

#Robot-use Agents#zero-shot manipulation#open-weight

https://arxiv.org/abs/2611.01560

https://hjfy.top/arxiv/2611.01560

https://github.com/Open-RUA/uni-robot-agent (230)

3

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 (1009)

4

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 (152)

5

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 (55433)

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 (9077)

7

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 (18704)

8

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 (68462)

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 (83191)

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生成时间: 2026-11-06 08:30

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