跳到正文
研踪PaperTrail

YOUR RESEARCH, IN SIGHT

让下一篇重要论文，不再错过。

覆盖计算机全学科，以 AI 为快捷入口。关注方向、作者和关键词，把发现变成持续的研究积累。

部分采集异常
正在读取账号与研究记录…

全站更新概况

+1072新增论文
+845其中 AI 相关
+94已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-10 00:52 · 全站最近入库：2026-11-09 13:22各信源进度 →

统计口径与数据新鲜度

统计 2026-11-09 00:53 — 2026-11-10 00:53（北京时间） · 点击数字查看对应简报；AI 相关包含在新增论文内。

最近完成检查：2026-11-10 00:52最近发现新增版本：2026-11-09 13:22库内论文最新更新时间：2026-11-07 02:59

每分钟刷新已保存的数据。空检查不代表采集异常；新收录可能包含历史论文。AI 导读按新增版本计数。

18,172 已收录论文15,506 AI 相关4,527 有修订112 HF 社区发现

浏览论文库

研究日报 / 周报 →按信源浏览 →

时间范围跟随排序：版本更新、本站收录或首次发表。最新收录可能包含历史论文。部分信源异常，查看状态 →

找到 2 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-10 00:50（北京时间）
新论文 · v1arXiv:2611.08697

CPU Micro-Architectural Characteristic Sensitivity Study on CPU-Side Agentic Task Latency

Jack Adiletta, Shixin Ji, Zhuoping Yang, Peipei Zhou

原文摘要

We perform a novel study of CPU micro-architectural characteristics on how they affect agentic AI CPU side latency. Small enterprises and individual users looking to reduce costs and seeking data privacy benefit from running agents locally, motivating an investigation into the optimal performance characteristics of an edge CPU to improve user experience. We investigate the relationship between CPU core frequency, CPU uncore frequency, active physical cores, and symmetric multi threading with agentic task latency in single and multi agent configurations. Across eight experiments on an Intel edge CPU, we find that P-core frequency produces the largest median latency improvement among the evaluated parameters. Increasing P-core frequency from 2.5 to 5.5 GHz yields a 1.154$\times$ speedup for a single agent and a 1.341$\times$ speedup for eight concurrent agents. We are the first to characterize hardware micro-architectural relationships on single and multi agent configurations with TerminalBench; we also provide an open source framework and dataset for repeatable CPU side latency agentic benchmarking.

cs.DC · cs.AR

查看论文
arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.814ACL Anthology · ACL 2026

LCR-RAG: Enhancing Logical Consistency in Retrieval-Augmented Generation via Neuro-symbolic Reinforcement Learning

Wenxiang Zheng Harbin Institute of Technology, Guo Tang, Shixin Jiang, Liangyu Huo, Xiyuan Zhang Du Xiaoman Technology (Beijing) Co., Ltd., Jian Xie Beijing Baichuan Intelligent Technology Co., Ltd., Ming Liu Harbin Institute of Technology

原文摘要

Retrieval-Augmented Generation (RAG) is widely used to ground large language models (LLMs) in external knowledge and improve factual accuracy. Prior work has explored iterative and self-reflective mechanisms to refine reasoning, but these approaches rely on internal model judgment and lack formally grounded, verifiable feedback. As a result, RAG systems may still produce logically inconsistent or contradictory answers in multi-step reasoning. In this paper, we propose LCR-RAG, a framework that integrates neuro-symbolic verification with reinforcement learning to explicitly optimize logical consistency. The core of our approach is a Logic-Consistency-driven Reward (LCR), which converts discrete logical signals—such as contradictions or incomplete inference chains—into a structured reward signal. This reward guides a PPO-based agent to iteratively rewrite queries and correct reasoning errors. Experiments on HotpotQA, ASQA, and TriviaQA show that LCR-RAG consistently outperforms strong RAG baselines, with ablation results indicating that the LCR mechanism is the primary source of improvement, even under noisy or conflicting retrieval conditions.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表