🤖 AI资讯日报

AI的进步速度是否呈指数级增长？

推荐学习人工智能的阅读材料

探讨当前AI算法的使用痛点

NLP、AI、ML、机器人——是短暂趋势还是更深远的变革？

谷歌提供Common Lisp与机器学习实习机会

为何平淡业务比AI炒作周期更持久

AI领域的非主流观点索引

初探人工智能及其预期（计算机科学）

讨论对纽约市地方法律144号的关注

生物信息学家

初创公司通过书籍销售筹集资金

AI领域的下一个比尔·盖茨或爱因斯坦——Chris Clark

The widespread deployment of LLM-based agents is likely to introduce a critical privacy threat: malicious agents that proactively engage others in multi-turn interactions to extract sensitive information. These dynamic dialogues enable adaptive attack strategies that can cause severe privacy violations, yet their evolving nature makes it difficult to anticipate and discover sophisticated vulnerabilities manually. To tackle this problem, we present a search-based framework that alternates between improving attacker and defender instructions by simulating privacy-critical agent interactions. Each simulation involves three roles: data subject, data sender, and data recipient. While the data subject's behavior is fixed, the attacker (data recipient) attempts to extract sensitive information from the defender (data sender) through persistent and interactive exchanges. To explore this interaction space efficiently, our search algorithm employs LLMs as optimizers, using parallel search with multiple threads and cross-thread propagation to analyze simulation trajectories and iteratively propose new instructions. Through this process, we find that attack strategies escalate from simple direct requests to sophisticated multi-turn tactics such as impersonation and consent forgery, while defenses advance from rule-based constraints to identity-verification state machines. The discovered attacks and defenses transfer across diverse scenarios and backbone models, demonstrating strong practical utility for building privacy-aware agents.

研究回声状态网络在不同基准问题域中的性能，提出配置架构和参数选择的启发式方法。

介绍Medico 2025挑战，专注于胃肠道影像的视觉问答，旨在推进可信赖的AI在医学影像分析中的应用。

介绍MS-GRPO算法，用于后训练LLM代理，提高其在多步代理任务中的决策性能。

介绍SET感知因素框架，旨在系统地分析环境因素如何影响自主系统的感知可靠性。

介绍ToonComposer，一个将中间帧和上色统一到单一后关键帧阶段的生成模型。

综述扩散语言模型的当前景观，包括其原理、技术和应用。

评估GPT-5系列模型在脑肿瘤MRI视觉问答任务中的性能。

提出一个多维度建模框架，通过特征工程、数据增强和可解释机器学习提高自动口译评估的透明度和可靠性。

研究XAI技术的可访问性，提出一个四部分方法论证明概念，以实现包容性XAI设计。

提出一个基于A2C算法的强化学习框架，用于优化卫星轨道参数以实现精确的地面覆盖。

开发一个多模态注意力网络，用于识别人类对社会机器人的主观自我披露。