English(EN) More: Mode-Optimized Reasoning with Self-Adaptive Effort for Self-Evolving Agents

新的AI训练框架MORE优化推理和效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员推出了一种新颖的自进化训练框架MORE，旨在优化AI智能体的推理能力。该框架能够自适应地选择推理类型（直接回答、思维链或代码生成）以及每项任务所需的计算量。通过联合学习这些选择，MORE能够为简单任务分配更简洁的推理，而为复杂任务保留更长的思考时间。在编码、数学和通用推理基准测试中的实验表明，MORE能够持续提高性能和效率，从而在缩短响应长度和推理时间的同时提高准确性。 AI

影响该框架有望在各种复杂任务中实现更高效、更准确的AI智能体。

排序理由该集群描述了一篇关于新颖AI训练框架的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 12 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群描述了一篇关于新颖AI训练框架的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, model release; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Dianxing Shi, Dingjie Fu, Tianyu Lei, Wangjie Qiu · 2026-10-27 04:00

更多：面向自进化智能体的自适应精力模式优化推理

arXiv:2610.33684v1 Announce Type: new Abstract: Self-evolving training improves the performance of agents on difficult tasks through verifiable feedback, but it often applies costly deliberation uniformly across tasks. To address this problem, we propose Mode-Optimized Reasoning …

本文如何被排名

报道来源 [1]

更多：面向自进化智能体的自适应精力模式优化推理

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