arXiv 论文速递
· 2026-09-03
针对自然语言描述难以用规则实现且依赖远程大模型的问题，提出将规范编译为本地神经函数的方法，兼顾易描述性与高效执行，对实际应用具有重要价值。
Many recurring text functions are easy to describe but difficult to implement with rules, while calling a large remote model for every input introduces repeated cost, latency, and …
· 2026-09-03
揭示黑盒LLM裁判在共享端点上的测量不稳定性，直接影响训练数据筛选和排行榜可信度，对AI评估方法论提出关键警示，推动可靠测量标准建立。
Language-model judges now gate training data, score generations, and drive leaderboards. The judge is then a measurement instrument, resting on one rarely stated assumption: the sa…
针对进化式提示优化器提示膨胀问题，提出错误结构化优化框架，通过诊断、多样化和稳定化显著压缩提示长度并提升性能，推动提示工程的实用化。
Evolutionary prompt optimizers such as GEPA suffer from prompt bloat: each iteration appends rules and caveats, producing prompts up to 3$\times$ longer yet no more accurate. We tr…
· 2026-09-03
质疑思维链推理的可读性等同于可解释性的假设，通过实证比较揭示判断与实际重要性差异，对AI安全与可解释性研究具有重要理论意义。
Reasoning traces from chain-of-thought models appear to offer a legible window into how a model arrives at its answer. A growing body of work treats them as such, using LLM judges …
Video editing spans diverse editing paradigms, yet achieving high-quality instruction-guided and subject-guided editing within a single unified framework remains challenging. We in…
· 2026-09-03
首次为并发随机博弈引入鲁棒PAC学习框架，处理转移不确定性，为多智能体强化学习在不确定环境下的理论保证提供基础，推动安全决策应用。
We introduce the first Probably Approximately Correct (PAC) learning framework for general-sum concurrent stochastic games (CSGs) with transition uncertainty, while addressing the …
· 2026-09-03
Weakly-Supervised Dense Video Captioning aims to localize and describe multiple events in untrimmed videos given only an ordered set of event-level captions per video. Recent work …
· 2026-09-03
Gaps remain in our understanding of how large language models (LLMs) acquire knowledge during pre-training. We posit that auxiliary views, reformulations of knowledge, are causally…
Explaining why a specific outcome occurred, and which inputs deserve the blame or credit, is central to philosophical, scientific, and policy analysis. Existing tools split into tw…
On-policy distillation (OPD) combines student-generated rollouts with dense token-level supervision from a teacher. Existing work has mainly studied its algorithmic behavior, leavi…
Multi-agent AI science ecosystems rely on agents possessing tools that allow them to communicate, coordinate, and build on each other's work. Yet this shared infrastructure can als…
· 2026-09-03
As edge-based deep learning applications become more complex, optimizing performance on heterogeneous System-on-Chips (SoCs) presents unique challenges. Traditional pipelining tech…