Grok 4.5·Kimi K3·Cosmos 3 Edge 중심의 에이전트·엣지 AI 최신 동향

Elon Musk

@elonmusk

1달 전

Grok for Excel is now live

Grok

@grok

1달 전

Grok for Excel is live. Use Grok 4.5 to build financial models, analyze market data, and generate charts and graphs. Try it now https:// x.ai/grok/excel

Exciting update: Kimi K3 has landed at #4 on the Agent Arena leaderboard, matching Claude Opus 4.8 and GPT-5.6 Sol. If Kimi K3's weights are released on schedule by July 27, it will become the #1 open-weight model. This release marks a major leap in agentic performance over Kimi K2.7 Code (#23 to #4). Based on 8K+ live agentic sessions, Kimi K3 leads on confirmed task success rate (#1). It also posts a strong +20.6% on praise vs. complaint (#3). It currently lags the field in steerability (#14) and bash recovery (#17). Agent Arena measures models on millions of real-world, long-horizon agentic tasks. Models get web search, filesystem, and terminal tools to complete complex workflows: writing code, creating slide decks, researching the web, building apps, and analyzing documents. We use causal tracing methodology to measure a model's net improvement, which indicates how much it improves outcomes relative to the average model. Here's a primer on the 5 signals: User-satisfaction proxies - Confirmed Success: an explicit "yes that worked" feedback from the user - Praise vs. Complaint: implicit sentiment in users reactions - Steerability: can the model course-correct when you push back? Tool-use proxies - Bash Recovery: how it recovers from CLI errors (primary signal for tool use) - Tool Hallucination: does it call tools that don't exist Below we break down how Kimi K3 scored across the 5 signals, drawn from tasks submitted by a global community of users. Congrats @Kimi_Moonshot on another big milestone!

Design Arena

@DesignArena

1달 전

BREAKING: Kimi K3 by @Kimi_Moonshot is officially 1st on Frontend Web App Arena by DesignArena With an Elo of 1326, this open-weight model leads the way, ahead of Fable 5, Sonnet 5, and Opus 4.8 by @AnthropicAI Huge congrats to the @Kimi_Moonshot team for this achievement!

NVIDIA AI

@NVIDIAAI

1달 전

Introducing Cosmos 3 Edge: our open frontier world model built to run on-device. Cosmos 3 Edge helps robots learn and act, autonomous vehicles understand road scenes and predict intent, and vision AI agents reason across live video for smart infrastructure. With 4B parameters and a 2B Nemotron-based reasoner, you can run it on DGX Spark, NVIDIA Jetson, and more.

NVIDIA’s research isn’t just focused on creating worlds that look real — but that behave realistically and respond in real time. Whether the output is a game, film, robot or factory digital twin, the goal is the same: expand the canvas of creativity with AI-generated worlds that are grounded in 3D, governed by physics and directed by creators. https:// nvda.ws/3TbQLkD #SIGGRAPH2026

90% of your KV cache never gets reused. (prompt caching was never meant to fix it) if your system prompt and tool definitions are stable, prompt caching is the single highest-leverage optimization available today. cached input tokens get up to 90% cheaper, and hit rates of 60 to 85% are realistic. but it comes with one rigid rule. the cached portion must be an exact, byte-for-byte prefix of the new request. change a single character in that region and you get a full cache miss. that rule breaks in three situations you hit constantly: → RAG with multiple documents. you cached document A alone and document B alone. a query now needs both. document B's cached state was computed without any awareness of A, so it's invalid and gets recomputed from scratch. → document order changes. the same three documents appear in a different order across requests. every permutation is a cache miss, even though the content is identical. → growing conversation history. each new turn changes everything after the stable prefix, so earlier cached states beyond it become useless. Alibaba Cloud's production data shows how bad this gets: 10% of KV cache blocks serve 77% of all hits. the rest sits in storage and never gets reused, because prefix matching won't allow it. CacheBlend, a research paper from the LMCache team (EuroSys 2025 Best Paper Award), attacks exactly this. the insight is that in modern transformers, tokens overwhelmingly attend to their own local context. only a small fraction of tokens carry real connections across document boundaries. so instead of recomputing everything after the first cached document, CacheBlend reuses every document's cache as-is and selectively recomputes just those few boundary tokens. those are the small orange fixes between documents in the diagram, and they are the entire cost. the result is 2 to 4x faster processing on multi-document queries with no quality loss. the order problem disappears with it. shuffle the same documents however you like, and every permutation stays cached, where prefix caching recomputes all of them every time. the bottom of the diagram shows that side by side. that's the real shift: from caching prefixes to caching knowledge. every document in your knowledge base becomes a reusable cached asset, regardless of what order it appears in or what sits next to it. CacheBlend ships inside LMCache, the open-source cache management layer that runs outside the inference engine and integrates with vLLM, SGLang, and TensorRT-LLM, on both NVIDIA and AMD GPUs. check it out on GitHub: https:// github.com/LMCache/LMCache (don't forget to star ) i wrote the full breakdown of the architecture, including why cache management should never live inside your inference engine. the article is quoted below. stay tuned for more on this!

Harrison Chase

@hwchase17

1달 전

LangSmith Engine is our in product agent that runs over traces, clusters them into issues, and proposes fixes It's a long running, complex, ambiguous process We wrote about how we evaluate it!

We’re making LangSmith Sandboxes available to try for free. Start today: https:// smith.langchain.com

70% lower cost. 84% fewer GPT calls. 90% expert-preferred output. At DevCon 6, Dr. David Zihr, Medical Director for Physician Advising Services at Tampa General Hospital, and Palantir Forward Deployed Engineer Colton Rusch show how AIP Evolve achieved these optimizations by screening models across providers, rewriting prompts, and swapping AI for deterministic code on Tampa General's AI-generated utilization reviews.

See how Palantir Software Engineer Colton Rusch uses AIP Evolve to prove that cheaper AI doesn't have to mean worse AI: "Evolve screened a whole suite of models across providers, then used evals and expert feedback to rule out certain options and keep others for further optimization." "We got to the point where evolve produced a vastly cheaper version — 68% lower compute costs." "We got lower costs and better quality at the same time."