LLM智能体能否真正持续进化？首个持续技能学习基准框架，直击智能体能力跃迁核心难题
arXiv:2608.03874v1 Announce Type: cross Abstract: Modern agent frameworks equip large language models with external skill libraries to solve complex t…
大模型成绩提升的根源，不止是能力进化，更藏着基准测试的“可达性”陷阱，值得一读。
arXiv:2608.03219v1 Announce Type: cross Abstract: Benchmark gains are often treated as evidence of greater LLM capability. Yet the same gain can refle…
系统化基准测试揭示智能体AI在计算成像任务中的真实表现与理解局限
arXiv:2607.07189v1 Announce Type: new Abstract: Vision-language models (VLMs) and agentic AI have shown strong performance on semantic visual tasks, b…
PACE框架以极低成本高效评估LLM Agent能力，对比SWE-Bench和GAIA，大幅节省时间与金钱。
arXiv:2607.02032v1 Announce Type: new Abstract: Evaluating LLM agents on benchmarks like SWE-Bench and GAIA can be expensive, time-consuming, and requ…
首个量化开源AI经济影响的学术指标，揭示全球AI采用与能力的地图
arXiv:2606.26118v1 Announce Type: cross Abstract: We work towards measuring both AI adoption and the capability of AI to perform discrete labor tasks …
最新研究系统评估多模态大模型在辅助AI应用（如视觉、听觉辅助）中的真实能力，揭示当前进展与局限。
arXiv:2606.25084v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have redefined visual understanding by combining vision encod…
首次系统性提出评估AI代理生物威胁能力的框架，直接关乎AI安全与治理前沿。
arXiv:2606.19899v1 Announce Type: cross Abstract: This paper addresses a rapidly emerging policy challenge: how to generate and interpret credible evi…
从定义对齐入手，提出设计科学框架化解AGI主张争议，为智能评估提供新思路。
arXiv:2606.12713v1 Announce Type: new Abstract: Claims that artificial general intelligence has already arrived and claims that it remains decades awa…
大型语言模型能否像城市规划师一样推理？研究用专业判断基准测试AI，揭示AI与人类专家的能力边界。
arXiv:2606.11678v1 Announce Type: new Abstract: Problem, Research Strategy, and Findings: The rise of large language models (LLMs) raises a key questi…
用CSCW理论设计多智能体实验，精准评估LLM的协作能力，揭示AI团队协作新范式。
arXiv:2606.06399v1 Announce Type: new Abstract: Multi-agent systems (MAS) built on large language models have shown growing promise, with their effect…
揭秘AI评估中的能力误判：前沿滞后指标审计揭示旧模型评价新系统的陷阱
arXiv:2605.04135v2 Announce Type: replace-cross Abstract: Readers of applied-domain LLM capability evaluations want to know what AI systems can curren…
首个系统性评估AI代理自动修复计算机网络配置错误的能力，揭示代理性能与关键影响因素
arXiv:2606.06212v1 Announce Type: new Abstract: Misconfigurations in computer networks remain a major source of critical Internet outages. Research is…
揭秘LLM能否在不同任务表示间泛化过程，ICML 2026收录的前沿研究
arXiv:2602.03542v2 Announce Type: replace Abstract: Large language models (LLMs) are trained and tested extensively on symbolic representations such a…
首个专门评估LLM代理在多轮对话中进行时间序列推理的基准TimeSage-MT，揭示现有模型的能力边界。
arXiv:2606.01498v1 Announce Type: cross Abstract: Time series data inform critical decisions across many real-world domains. While large language mode…
评估大语言模型语义泛化能力的新方法，利用短语结构区分记忆与真正理解，直击预训练数据偏差挑战。
arXiv:2501.04661v3 Announce Type: replace-cross Abstract: The web-scale of pretraining data has created an important evaluation challenge: to disentan…
提出统一框架评估LLM智能体能力，破解跨基准测试结果难以比较的难题。
arXiv:2605.27898v1 Announce Type: new Abstract: As LLMs are increasingly deployed as agents, reliable assessment of their agentic capabilities has bec…
最新研究：现有代码智能体能否应对单仓库之外的复杂任务？实验揭示其能力边界与挑战。
arXiv:2603.03194v2 Announce Type: replace Abstract: Current code-agent benchmarks primarily evaluate localized issue resolution within a single target…
最新研究系统评估了大模型后训练中的“灾难性遗忘”，为提升模型持续学习能力提供关键方法论。
arXiv:2603.06610v2 Announce Type: replace Abstract: Large language model (LLM) post-training enhances latent skills, unlocks value alignment, improves…
通过证据校准的查询聚类精准捕捉大模型能力边界，为LLM评估提供全新方法论。
arXiv:2605.17110v1 Announce Type: cross Abstract: Query clustering organizes queries into groups that reflect shared latent capability demands, enabli…
OpenAI官方发布GPT-4o系统卡，详解模型能力与安全评估