大模型的空间推理短板如何补救？HN 上工程师晒出 3D 打印实战难题，围观各路高手支招。
I'm working on a complex multi-part 3D-printed product, and I needed some engineering input from LLMs. I use highest Gemini Pro reasoning levels, Fabl…
单一基准准确率严重低估模型实力，能力边界视角揭示82%性能被忽视，评测方法必看。
arXiv:2606.26836v1 Announce Type: new Abstract: Existing benchmarks typically report accuracy for a single model on a single run. This systematically …
解耦侦察与利用，精准测量LLM在渗透测试中的真实能力边界。
arXiv:2606.25332v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown promise for automated penetration testing, yet existing end-…
探讨为何更聪明的模型也难以成为真正的AI同事，揭示当前AI的能力边界与协作本质。
Article URL: https://usize.github.io/blog/2026/april/why-no-ai-coworkers.html Comments URL: https://news.ycombinator.com/item?id=48650166 Points: 1 # …
用一条简单汽车清单提问，揭示当前AI模型常识推理的短板。
Claude: please give me a list of famous cars named after fish, like the Plymouth Barracuda for example. Comments URL: https://news.ycombinator.com/ite…
最新研究揭示大模型在法律推理中的忠实性困境，实证分析求解与自动形式化能力的边界
arXiv:2606.16118v1 Announce Type: new Abstract: Large Language Models (LLMs) achieve strong performance on reasoning tasks, but whether this reflects …
MLLMs从调用API到操纵物理工具，一文揭示其能力边界与核心挑战。
arXiv:2606.10803v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) excel at utilizing digital APIs and increasingly serve as t…
最新研究发现大语言模型普遍高估自身能力，缺乏对自身局限的认知，该论文提出自我评估方法提升AI可靠性。
arXiv:2606.00251v1 Announce Type: new Abstract: The ability to recognize one's own limitations and decide whether to solve a problem or delegate is fu…
前沿研究揭示：在Colonel Blotto博弈中，人类表现依然优于大语言模型，战略决策的深度解析。
arXiv:2605.22095v1 Announce Type: cross Abstract: The emergence of large language models (LLMs) has spurred economists to study how humans and LLMs be…
探明AI生成浏览器原生无障碍系统的边界：能力极限、操作限制与未来扩展潜力，技术前沿值得关注。
arXiv:2605.19638v1 Announce Type: cross Abstract: As large language models (LLMs) demonstrate increasing competence in synthesizing functional user in…