AI 论文精选速递

2026年10月27日 · 追踪前沿研究

1

Harness^2: Recursive Agent Harnessing for an Open World

Ruiyao Xu, Yanfei Chen 等9人

Harness² improves agent execution harnesses through recursive task-level refinement and continual harness-level learning, boosting performance across benchmarks without ground-truth verifiers.

#recursive self-improvement#LLM agents#execution harnesses

https://arxiv.org/abs/2610.14224

https://hjfy.top/arxiv/2610.14224

https://github.com/google-research/harness2 (19988)

2

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 (43861)

3

nanoMuse: An Open-Source Personal Agent for Every Device You Own

Guangyi Liu, Yong Liu 等3人

https://arxiv.org/abs/2610.08699

https://hjfy.top/arxiv/2610.08699

https://github.com/nano-muse/nanoMuse (2639)

4

Efficient Memory Management for Large Language Model Serving withPagedAttention

Woosuk Kwon, Zhuohan Li 等9人

PagedAttention algorithm and vLLM system enhance the throughput of large language models by efficiently managing memory and reducing waste in the key-value cache.

#PagedAttention#key-value cache#KV cache

https://arxiv.org/abs/2309.06180

https://hjfy.top/arxiv/2309.06180

https://github.com/vllm-project/vllm (86094)

5

Internal Safety Collapse in Frontier Large Language Models

Yutao Wu, Xiao Liu 等10人

Frontier large language models exhibit Internal Safety Collapse, where they generate harmful content under specific task conditions, revealing inherent vulnerabilities despite alignment efforts.

#large language models#Internal Safety Collapse#task conditions

https://arxiv.org/abs/2603.23509

https://hjfy.top/arxiv/2603.23509

https://github.com/wuyoscar/ISC-Bench (2343)

6

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 (82527)

7

PaddleOCR-VL-1.6: Expanding the Frontier of Document Parsing with Under-Optimized Region Refinement ...

Zelun Zhang, Hongen Liu 等15人

PaddleOCR-VL-1.6 enhances document parsing performance through targeted data optimization and progressive post-training techniques, achieving state-of-the-art results on OmniDocBench v1.6.

#document parsing#data optimization#post-training

https://arxiv.org/abs/2606.03264

https://hjfy.top/arxiv/2606.03264

https://github.com/PaddlePaddle/PaddleOCR (91707)

8

Raven: The Harness of Harnesses for Composable Agentic Intelligence

EverMind AI

Raven is an open-source multi-agent ecosystem that autonomously builds and evolves modular harnesses, enabling cross-domain collaboration and expanding reliable task coverage through composable agent units.

#multi-agent ecosystem#modular harnesses#All-Domain Collaboration Network

https://arxiv.org/abs/2609.33439

https://hjfy.top/arxiv/2609.33439

https://github.com/EverMind-AI/Raven (5847)

9

COLLEAGUE.SKILL: Automated AI Skill Generation via Expert Knowledge Distillation

Tianyi Zhou, Dongrui Liu 等5人

Person-grounded AI skills are automatically distilled from heterogeneous traces into inspectable, correctable packages that capture both capabilities and behavioral patterns.

#expert knowledge distillation#person-grounded agents#trace-to-skill distillation

https://arxiv.org/abs/2605.31264

https://hjfy.top/arxiv/2605.31264

https://github.com/titanwings/colleague-skill (26054)

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生成时间: 2026-10-27 08:30

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