新方法STEC通过证据压缩提升开放式多跳问答的深度搜索效率与准确性
arXiv:2607.10795v1 Announce Type: new Abstract: In open-domain multi-hop question answering (QA), LLM-based search agents offer a promising approach t…
LLM多智能体系统实现自主量子编程，QAgent能编写OpenQASM代码，探索AI与量子计算的融合。
arXiv:2508.20134v2 Announce Type: replace Abstract: Programming quantum circuits at the OpenQASM level is essential for achieving hardware-aware optim…
深入探索Promptfoo工具，让QA工程师像测试传统软件一样高效测试LLM，附赠完整手册。
Want the full 46-page handbook? Promptfoo for QA: The Complete Engineer's Handbook (2026 Edition) by Himanshu Agarwal covers everything below in produ…
通过声学信道实现36kbps数据传输，利用OFDM和16-QAM调制，自动消除时钟漂移与相位噪声，适合离线或低功耗场景通讯
Working with Fable 5, a MacBook, and a Pixel phone, I built an acoustic transport library that is several orders of magnitude faster than existing SoT…
大规模研究揭示LLM在多语言MCQA任务中如何从推理过程量化不确定性，跨语言表现的关键发现值得关注。
arXiv:2607.06327v1 Announce Type: cross Abstract: Uncertainty estimation (UE) enables LLM-powered systems to recognize when to abstain, yet existing r…
揭示语言模型自我生成QA训练的隐藏脆弱性，一篇值得关注的AI研究论文。
arXiv:2606.32002v1 Announce Type: new Abstract: Language models are increasingly taught from synthetic question--answer (QA) supervision: a model gene…
自改进QA代理处理超5亿tokens，开源实现如何重塑软件测试流程？
Hey, I am working on building agent-qa https://github.com/vostride/agent-qa which is a self-improving QA agent for software teams. Processed over 500M…
英语和西班牙语金融因果关系抽取任务，对比三种模型家族，多语言微调方法拔得头筹
arXiv:2606.27446v1 Announce Type: new Abstract: This paper describes team HSA_CORAL's submission to the FinCausal 2026 shared task on extracting cause…
探索视频多模态大模型对人类运动推理能力的极限，最新基准测试揭示关键短板。
arXiv:2606.27999v1 Announce Type: new Abstract: Despite the rapid advance of Multimodal Large Language Models (MLLMs) in high-level video understandin…
深度拆解KV缓存与MQA、GQA、MLA原理，揭示大模型推理加速的关键技术。
LLMs generate text one token at a time. That sounds simple. But without KV Cache, every new token would repeat a lot of old work. That is why inferenc…
对海量数据湖上的问答代理瓶颈进行系统梳理，揭示大规模场景下QA Agent的关键设计要素。
arXiv:2606.13904v1 Announce Type: cross Abstract: Exploratory question answering (EQA) over data lakes requires an LLM agent to discover relevant sour…
揭示LLM作为评判者在QA评估中因知识驱动而失败的深层原因，实验严谨发现惊人。
arXiv:2601.07506v2 Announce Type: replace Abstract: While large language models (LLMs) are increasingly used as automatic judges for question answerin…
用Ollama配合QAT量化，10GB显存笔记本也能跑12B的Gemma 4大模型，内存仅需6.7GB。
This stack uses Ollama with Gemma 4 QAT to run a 12B model on a 10GB VRAM laptop GPU. The latest Gemma 4 QAT checkpoints reduce memory usage and enabl…
LLM极低比特量化新突破，线性约束向量量化实现2比特数据高效训练，已被ICML 2026收录。
arXiv:2606.10531v1 Announce Type: cross Abstract: Quantization-aware training (QAT) is essential for extremely low-bit large language models (LLMs). C…
百万级数据湖上的探索性问答基准，解锁大模型在异构数据源中的检索与推理能力。
arXiv:2606.10460v1 Announce Type: cross Abstract: Recent large language models (LLMs) have shown rapid progress in reading-based question answering (Q…
按需付费的QA测试沙箱，上传应用即可获得针对结账、入门、移动布局等的灵活测试服务。
Article URL: https://firstbatch.io/ Comments URL: https://news.ycombinator.com/item?id=48460223 Points: 1 # Comments: 0
探索多模态文档集合中的多跳推理新基准DocHop-QA，推动视觉与文本信息联合理解。
arXiv:2508.15851v2 Announce Type: replace Abstract: Despite rapid progress in large language models (LLMs), current QA benchmarks still overlook the c…
将强化学习引入知识库问答，大幅提升大模型在复杂KBQA任务上的推理与准确率。
arXiv:2512.10999v3 Announce Type: replace Abstract: Knowledge Base Question Answering (KBQA) challenges models to bridge the gap between natural langu…
新智能体框架TSQAgent通过专用推理提升时间序列数据质量评级，开创数据质量评估新范式。
arXiv:2606.03629v1 Announce Type: new Abstract: Assessing the quality of time series (TS) data is fundamental yet inherently challenging due to the mu…
新基准用报告自动生成肿瘤学VQA任务，填补3D医学影像评估空白，推动视觉语言模型临床落地。
arXiv:2606.02809v1 Announce Type: new Abstract: Evaluating vision-language models (VLMs) on medical images requires benchmarks that are clinically gro…