Pollob Chandra Ray, Sabah Binte Noor, Fazlul Hasan Siddiqui
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2026-06-16
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6 min
AI
异构铁路系统中中断感知动态路线优化的时间规划框架
A Temporal Planning Framework for Disruption Aware Dynamic Route Optimization in Heterogeneous Railway Systems
高效的线路优化对于确保铁路运营的安全和准点起着至关重要的作用。这是非常重要的,特别是在异国情调...
Efficient route optimization play a vital role in ensuring both safety and punctuality in railway operations. It is very crucial particularly in heter...
01
Gaurav Verma, Scott Counts
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2026-06-16
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5 min
AI
将跨域操作序列抽象为可解释的工作流程
Abstracting Cross-Domain Action Sequences into Interpretable Workflows
顺序或带时间戳的交互日志提供了数字应用程序使用情况的客观记录,,但它们的粒度和噪音常常掩盖了平均值...
Sequential or time-stamped interaction logs provide objective records of digital application usage, yet their granularity and noise often obscure mean...
02
Shikun Liu, Mufei Li, Dongqi Fu, Haoyu Wang, Yinglong Xia, Hong Li, Hong Yan, Pan Li
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2026-06-16
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4 min
AI
LLM-Agent 工作流程中并行分支的直接潜在空间综合
Towards Direct Latent-Space Synthesis for Parallel Branches in LLM-Agent Workflows
大型语言模型越来越多地充当代理系统,的执行引擎,但它们仍然通过顺序文本接口消耗上下文......
Large language models increasingly serve as execution engines for agentic systems, yet they still consume context through a sequential text interface....
03
Haochen Wu, Yi Hou, Shiguang Xie
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2026-06-15
·
3 min
AI
三边调度中目标权重适应的延迟市场反馈的多代理强化学习
Multi-Agent Reinforcement Learning from Delayed Marketplace Feedback for Objective-Weight Adaptation in Three-Sided Dispatch
三边市场中的调度为从世界反馈中进行强化学习提供了一个自然的环境: 决策是通过延迟操作来评估的...
Dispatch in three-sided marketplaces provides a natural setting for reinforcement learning from world feedback: decisions are evaluated by delayed ope...
04
Zach Studdiford, Gary Lupyan
·
2026-06-15
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9 min
AI
推理作为模式匹配: 人类和法学硕士日常推理的共享机制
Reasoning as Pattern Matching: Shared Mechanisms in Human and LLM Everyday Reasoning
当大型语言模型 (LLMs) 无法泛化或在推理中出现偶然错误, 时,通常会被视为 LLM 并不真正可靠的证据...
When large language models (LLMs) fail to generalize or make haphazard errors in reasoning, it is often taken as evidence that LLMs are not truly reas...
05
Xiaoyuan Liu, Jianhong Tu, Yuqi Chen, Siyuan Xie, Sihan Ren, Tianneng Shi, Gal Gantar, Evan Sandoval, Donghyun Lee, Daniel Miao, Peter J. Gilbert, Nick Hynes, Mauro Staver, Warren He, David Marn, Andrew Low, Xi Zhang, Elron Bandel, Michal Shmueli-Scheuer, Siva Reddy, Alexandre Drouin, Alexandre Lacoste, Ramayya Krishnan, Elham Tabassi, Yu Su, Victor Barres, Chenguang Wang, Wenbo Guo, Dawn Song
·
2026-06-15
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4 min
AI
AgentBeats: 代理评估的开放性, 标准化, 和可重复性
AgentBeats: Agentifying Agent Assessment for Openness, Standardization, and Reproducibility
代理系统正在跨领域快速发展,,但它们的评估仍然分散。大多数基准测试都依赖于固定, LLM 中心的工具,这些工具...
Agent systems are advancing quickly across domains, but their evaluation remains fragmented. Most benchmarks rely on fixed, LLM-centric harnesses that...
06
Achraf Hsain, Sultan Almuhammadi
·
2026-06-15
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4 min
AI
超越运行时执行: Shield Synthesis 作为对抗网络的防御性分析
Beyond Runtime Enforcement: Shield Synthesis as Defensibility Analysis for Adversarial Networks
屏蔽强化学习通常被视为一种运行时安全机制,它将时间逻辑规范编译成自动机限制...
Shielded reinforcement learning is typically presented as a runtime safety mechanism that compiles temporal-logic specifications into automata restric...
07
Marianna Bergamaschi Ganapini, Massimo Chiriatti, Enrico Panai, Giuseppe Riva
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2026-06-15
·
7 min
AI
未雨绸缪: 系统 0, 人工智能介导的认知和认知殖民
Before You Think: System 0, AI-Mediated Cognition and Cognitive Colonization
本文研究了用于理解人工智能的认知和认知后果的三个最新框架: 三系统理论, ...
This paper examines three recent frameworks for understanding the cognitive and epistemic consequences of artificial intelligence: Tri-System Theory, ...
08
Amy Xin, Jiening Siow, Junjie Wang, Zijun Yao, Fanjin Zhang, Jian Song, Lei Hou, Juanzi Li
·
2026-06-14
·
5 min
AI
EurekAgent: 代理环境工程是您自主科学发现所需的一切
EurekAgent: Agent Environment Engineering is All You Need For Autonomous Scientific Discovery
基于法学硕士的代理在自动化科学发现方面显示出越来越大的潜力。给定一个可优化的指标和一个执行环境,,他们可以...
LLM-based agents have shown increasing potential in automating scientific discovery. Given an optimizable metric and an execution environment, they ca...
09
Zongsheng Cao, Bihao Zhan, Jinxin Shi, Jiong Wang, Fangchen Yu, Zhijie Zhong, Yingnan Han, Zijie Guo, Tianshuo Peng, Zhuo Liu, Yi Xie, Xiang Zhuang, Shengji Tang, Yue Fan, Runmin Ma, Shiyang Feng, Xiangchao Yan, Anran Liu, Peng Ye, Wenlong Zhang, Xiaosong Wang, Shufei Zhang, Chunfeng Song, Fenghua Ling, Jie Zhou, Liang He, Bo Zhang, Lei Bai
·
2026-06-14
·
7 min
AI
Agents-K1: 迈向代理原生知识编排
Agents-K1: Towards Agent-native Knowledge Orchestration
目前基于 LLM 的研究代理已经通过代理编排, 取得了进步,但在很大程度上忽视了科学知识编排。现有作品...
Current LLM-based research agents have advanced through agent orchestration, yet largely overlook scientific knowledge orchestration. Existing works o...
10