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结果表明，使用人类反馈进行微调是使语言模型与人类意图对齐的一个有前景的方向，在真实性方面有所提升，并减少了有毒输出的生成，同时在公开 NLP 数据集上的性能回归极小。The results show that fine-tuning with human feedback is a promising direction for aligning language models with human intent and showing improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets.
Reflexion 是一个通过语言反馈而非更新权重来强化语言 agent 的新框架，在多种任务（序贯决策、编程、语言推理）上相较基线 agent 取得显著提升。Reflexion is a novel framework to reinforce language agents not by updating weights, but instead through linguistic feedback, which obtains significant improvements over a baseline agent across diverse tasks (sequential decision-making, coding, language reasoning).
这篇立场论文定义了可解释性，阐述了何时需要（以及何时不需要）可解释性，并提出了一种用于严格评估的分类法，同时指出了迈向更严谨的可解释机器学习科学所面临的开放性问题This position paper defines interpretability and describes when interpretability is needed (and when it is not), and suggests a taxonomy for rigorous evaluation and exposes open questions towards a more rigorous science of interpretable machine learning.
Gemini 1.5 在跨模态长上下文检索任务上取得近乎完美的召回率，在长文档 QA、长视频 QA 与长上下文 ASR 上刷新 SOTA，并在广泛基准上达到或超越 Gemini 1.0 Ultra 的 SOTA 表现。Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks.
本工作提出在海量文本语料上使用新的自监督目标 PEGASUS 对大型 Transformer 编码器-解码器模型进行预训练，并证明其在所有 12 个下游数据集上按 ROUGE 分数衡量均取得 SOTA 性能This work proposes pre-training large Transformer-based encoder-decoder models on massive text corpora with a new self-supervised objective, PEGASUS, and demonstrates it achieves state-of-the-art performance on all 12 downstream datasets measured by ROUGE scores.
尽管方法简单，Decision Transformer 在 Atari、OpenAI Gym 和 Key-to-Door 任务上达到或超过 SOTA 无模型离线 RL 基线的性能Despite its simplicity, Decision Transformer matches or exceeds the performance of state-of-the-art model-free offline RL baselines on Atari, OpenAI Gym, and Key-to-Door tasks.
本文旨在为研究者与从业者提供一份快速、全面的参考，通过对现有工作的广泛、信息密集型总结来汲取洞见，以推动 LLM 研究的发展。This review article is intended to provide a quick, comprehensive reference for the researchers and practitioners to draw insights from extensive, informative summaries of the existing works to advance the LLM research.
HuggingGPT 是一个由 LLM 驱动的 Agent，利用 LLM（如 ChatGPT）连接机器学习社区中的各种 AI 模型以解决 AI 任务，能够处理跨模态、跨领域的大量复杂 AI 任务。HuggingGPT is an LLM-powered agent that leverages LLMs (e.g., ChatGPT) to connect various AI models in machine learning communities to solve AI tasks and can tackle a wide range of sophisticated AI tasks spanning different modalities and domains.
本文给出了 LLM 幻觉现象与评估基准的分类体系，分析了现有缓解 LLM 幻觉的方法，并讨论了未来研究的潜在方向。This paper presents taxonomies of the LLM hallucination phenomena and evaluation benchmarks, analyzes existing approaches aiming at mitigating LLm hallucination, and discusses potential directions for future research.
详细介绍大多数 GAN 算法的动机、数学表示与结构，并对它们的共性与差异进行比较。The motivations, mathematical representations, and structures of most GAN algorithms are introduced in detail, and they are compared to compare their commonalities and differences.
该综述全面回顾了生成模型的历史与基本组件，以及 AIGC 在单模态交互与多模态交互方向的最新进展，并介绍了文本与图像生成任务及相关模型。This survey provides a comprehensive review on the history of generative models, and basic components, recent advances in AIGC from unimmodal interaction and multimodal interaction, and introduces the generation tasks and relative models of text and image.
一项紧凑的 Agent Skill，用于生成高度简化、圆润且带有微妙新拟物化风格的 IP 吉祥物 Logo。A compact Agent Skill for highly simplified, rounded, subtly neo-skeuomorphic IP mascot logos.
Orca 是用于管理大规模并行 agent 集群的 ADE。可凭个人订阅运行任意 coding agent，支持桌面、移动端与远程 runtime。Orca is the ADE for working with a fleet of parallel agents. Run any coding agent with your own subscription. Available on desktop, mobile and remote runtime.
汇聚每个 Agent 的模型，一站式管理。从菜单栏即可使用 DeepSeek 上的 Codex、Kimi 上的 Claude Code。Every agent's model. One place. Codex on DeepSeek, Claude Code on Kimi, from the menu bar.
你的工作超级 Agent：本地优先，分钟级学习你的工作上下文，永不遗忘。Open-source All in One AI agent workspace. Run any agent — Claude Code, Codex — across your tools (100+ integrations + MCP), apps, browser, and files, with shared memory. Built-in models or BYOK.
为每个 Agent 提供跨会话持久上下文——捕获会话中 Agent 的所有行为，经 AI 压缩后注入到未来会话中。支持 Claude Code、OpenClaw、Codex、Gemini、Hermes、Copilot、OpenCode 等。Persistent Context Across Sessions for Every Agent – Captures everything your agent does during sessions, compresses it with AI, and injects relevant context back into future sessions. Works with Claude Code, OpenClaw, Codex, Gemini, Hermes, Copilot, OpenCode + More
用于大规模搜索、抓取与交互网页的 API。🔥The context API to search, scrape, and interact with the web at scale. 🔥