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Every published item, in order · 6305 items
Nov 10
ModelsUS vs ChinaMulti-source74°02:01
Chinese contentMeta 发布 Muse Ember:36 分追平 Claude Haiku 5.5,成本仅三分之一

Meta 这次的思路很明确:知识面窄没关系,把幻觉率砍半、把成本压到 $0.14 一任务,专攻自动化执行。在 Terminal-Bench 上它反超 Haiku 5.5,自家的 Muse Code 里更是领先 Claude Code 和 Codex。跑批量任务的团队可以重点看看。

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Official accountArtificial Analysis@ArtificialAnlysSource
Chinese contentBoston Dynamics 给 Atlas 装上轮子,测试 RL 全身控制器的可靠性

Boston Dynamics 把轮子装到人形机器人 Atlas 上跑强化学习控制器,测试真实执行器和边缘计算的极限。比起纯双足演示,这种花式折腾更能暴露 RL 控制器落地时的实际问题,做机器人控制的开发者可以看看他们的实验细节。

Boston Dynamics@BostonDynamicsSource
ModelsBuildersMulti-sourceSelected00:29
Ant open-sources LemonDecider, a 4B decision model beating Jev 1.14

Decision-specific micro-models are shifting routing and gating workloads away from large general LLMs, cutting both latency and cost in agent pipelines. Developers building multi-step agent workflows or RAG evaluators can drop LemonDecider in as a fast classifier, and its open training data makes custom fine-tuning feasible. Compared with general models like Qwen3.5-4B or Jev 1.14, it trades generality for a measurable benchmark lead and millisecond-level inference.

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AIGCLINK@aigclinkSource
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ModelsBuildersSelected00:23
AutoRLBench tests 10 frontier agents on failed RL runs

Debugging RL training is where agent evaluation meets real engineering pain, and a ~40% ceiling shows frontier models are far from automating it. Engineering teams running post-training pipelines can use the open code and configs to benchmark which agents catch data-level faults before wasting GPU hours. Compared with synthetic bug suites, its use of authentic failed runs makes the scores harder to game.

OpenBMB@OpenBMBSource
ModelsUS vs ChinaMulti-source00:20
Chinese content字节发布 Seedream 5.5:连改 100 轮不走样,支持透明底输出

字节这次把改图最大的痛点解决了,同一张图改上百轮脸和背景都不跑,还直接出透明底素材,电商和设计的人会省很多抠图时间。价格比 Seedream 5.0 Pro 便宜一半以上,不过图生图真实感比 Google 的 Nano Banana 2.1 还差一点。

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宝玉@doteySource
ModelsBuildersMulti-source00:15
Chinese content百度发布 ERNIE Evolve 自动化研究系统,可自主改进训练与推理

百度搞了个自动做研究的系统 ERNIE Evolve,给定目标就能自己改代码跑实验,在 NanoGPT Speedrun 上拿下 SOTA,还把 ERNIE 5.5 Flash 的 GPU 推理吞吐拉高 33.7%。做工程优化的开发者可以看看它怎么把实验闭环自动化。

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Official account百度 AI Baidu@Baidu_IncSource
Chinese contentQwen 在 Apsara Conference 2026 阿姆斯特丹站分享开源 AI Agent 企业落地方案

Qwen 团队的人在阿姆斯特丹讲了开源 Agent 怎么在企业里落地,重点是怎么把自主执行的成本打下来。比起闭源 API 方案,开源路线能自己部署、自己微调。正在选型 Agent 方案的开发者和企业可以看看这条视频。

Official account阿里云 Alibaba Cloud@alibaba_cloudSource
Chinese contentMagnific CEO 在 Apsara Conference 2026 演示 Wan3.0 多模态参考控制视频生成

Magnific 的 CEO 在阿里云大会上演示了 Wan3.0 的多模态参考怎么用:拿参考图锁住角色和画面,让多镜头不再各画各的。做短视频和分镜的人可以看看这套工作流,比纯提示词抽卡省很多重跑时间。

Official account阿里云 Alibaba Cloud@alibaba_cloudSource
Nov 9
ModelsBuildersMulti-source23:37
Chinese contentNace AI 发布 Drex Max v1.1,登顶 Artificial Analysis Decision Index

Nace AI 把 Drex Max v1.1 做到了 Decision Index 第一名,速度快还能处理多模态输入。做智能体或研究工作流的朋友可以直接调 API,有约 100 万次免费额度,开源权重也快放了,想在本地跑决策模型的可以关注。

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elvis@omarsar0Source
ModelsBuildersMulti-sourceSelected07:54
Artificial Analysis Decision Index v2 ranks top decision models

The heavier private-dataset weighting and confidence-aware scoring make it harder for models to game public benchmarks, shifting how leaderboard positions are earned. Teams selecting a decision model should weigh the 60x cost spread — Celeris-1.1 Decision sits on the Pareto frontier at US$14 per 1M tasks versus Drex Max v1.1 at US$102 — since token price alone understates real task costs.

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Official accountArtificial Analysis@ArtificialAnlysSource
Nov 8
ModelsBuildersSelected23:58
NTU's MatAnyone-E Speeds 4K Video Matting 7.4x

The hybrid low-resolution-plus-detail-refocus design matters because it makes 4K matting feasible on consumer hardware rather than data-centre GPUs, with a 7.4x speedup and one-thirteenth the VRAM. Developers in video editing, virtual production and VFX can watch for the promised open-source release and benchmark it against full-resolution matting pipelines. The work comes out of NTU's S-Lab in Singapore, a notable ASEAN research contribution to video generation tooling.

Gorden Sun@Gorden_SunSource
ModelsUS vs ChinaSelected23:57
Together AI Triples StepFun Step 5 Serving Throughput

The interesting part is the method: AI agents drove the search across routing, scheduling and kernel-level changes, with a strict benchmark-before-merge loop — a reproducible pattern for anyone running inference infrastructure. Teams serving MoE models can apply the same TTFT- and TPS-targeted workflow. StepFun is a Chinese lab, so its models being served at scale on a US stack also signals deepening cross-border infrastructure ties.

Official accountTogether AI@togethercomputeSource
ModelsBuildersSelected00:43
HyperFlow H3 Flash: Slimmed MiniMax-H3 Runs on Consumer GPUs

Shrinking a video generation model to 9.1GB moves real-time 768P generation from data-centre hardware into the hands of hobbyists with a single consumer GPU. Developers and indie creators can now experiment with MiniMax-H3–class video models locally instead of renting cloud capacity. GitHub access means the community can benchmark it directly against heavier open-source video generators.

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Gorden Sun@Gorden_SunSource

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