MIDAS: Multi-LLM Iterative Data-Adaptive Summarization
多模型协作迭代摘要,让AI自动适配数据特征,ICDAR 2026新方法
arXiv:2608.04307v1 Announce Type: cross Abstract: Text summarization is deceptively difficult. While condensing information seems straightforward, rea…
多模型协作迭代摘要,让AI自动适配数据特征,ICDAR 2026新方法
arXiv:2608.04307v1 Announce Type: cross Abstract: Text summarization is deceptively difficult. While condensing information seems straightforward, rea…
揭秘最新AI越狱攻击手法,迭代上下文优化让语义转换绕过防护更高效,大模型安全研究者必读。
arXiv:2608.03210v1 Announce Type: new Abstract: Foundation models have achieved remarkable success across diverse tasks, but they remain vulnerable. T…
用符号反馈替代人工标注,让大模型在迭代自精炼中提升规划可靠性与鲁棒性,值得关注。
arXiv:2606.27757v1 Announce Type: new Abstract: Large language models (LLMs) have attracted widespread attention from academia and industry, yet their…
AI科研迭代代理CoreWeave ARIA开源亮相,加速实验闭环,研究效率利器。
Article URL: https://wandb.ai/wandb/aria/reports/Introducing-CoreWeave-ARIA-AI-Research-and-Iteration-Agent--VmlldzoxNzM1MzA4Mg Comments URL: https://…
破解多轮程序修复难题,迭代优化修复指令,让AI纠错不再“越修越坏”
arXiv:2604.23989v2 Announce Type: replace-cross Abstract: Recent work on large language models (LLMs) has emphasized the importance of scaling inferen…
硬件反馈驱动的迭代优化新范式,Embedded Arena为嵌入式系统场景提供高效搜索策略
arXiv:2606.16190v1 Announce Type: cross Abstract: Embedded devices from wildlife monitoring stations to clinical wearables require local AI inference …
用大语言模型自主设计量子电路,打破人类依赖,7组件框架实现迭代优化。
arXiv:2606.13380v1 Announce Type: cross Abstract: The design of high performing quantum circuits remains largely dependent on human expertise. We intr…
通用提示改进可能适得其反,这篇论文用严谨的评估驱动迭代方法,给出LLM应用避坑指南。
arXiv:2601.22025v2 Announce Type: replace-cross Abstract: Evaluating Large Language Model (LLM) applications differs from conventional software testin…
文学翻译高质量数据稀缺?新框架用多维度迭代生成参考与偏好数据,提升LLM翻译流畅性与文学效果。
arXiv:2606.05924v1 Announce Type: cross Abstract: Literary translation poses unique challenges due to the scarcity of high-quality annotated data and …
揭示LLM迭代优化中的脆弱性:仅9%的智能体能成功自改进,挑战何在?
arXiv:2603.23994v2 Announce Type: replace-cross Abstract: Generative optimization uses large language models (LLMs) to iteratively improve artifacts (…
提出针对LLM代理的间接提示注入攻击新方法,利用反馈引导迭代优化提升攻击效果,值得AI安全研究者关注。
arXiv:2605.24659v1 Announce Type: new Abstract: LLM-based agents are increasingly deployed for complex tasks requiring planning, tool use, and interac…
用迭代奖励引导后训练,让表格语言模型也能自我进化、持续提升性能。
arXiv:2604.18966v2 Announce Type: replace Abstract: Tabular language models can generate synthetic tables by modeling rows as token sequences, but the…
多智能体LLM工作流的离线评估与迭代优化新框架,即将亮相ACL 2026,助力复杂协作场景调优。
arXiv:2605.18032v1 Announce Type: new Abstract: Multi-agent LLM workflows -- systems composed of multiple role-specific LLM calls -- often outperform …