搜索“RAG”

he external tool or data used. AI architects and engineers can use workflow automation tools like n8n to run multi-step reasoning agents or agentic Retrieval-Augmented Generation (RAG). Engineers can work with SDKs. However, when using workflow automation tools with a visual interface, they can iterate more quickly and better track the progress of each stage. n8n is a source-available, AI-native automation platform, where engineers and nontechnical users alike can create AI agents and agentic wo

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o improve the total reward by 10%. What’s Next? We hope this project shows you the power of AI agents in the domain of LLM post-training and inspires you to think about how to leverage them to automate your LLM finetuning workflows using Tunix on TPUs. Please check out the code, sample runs, and program.md templates in the autofinetune GitHub repository, explore the Tunix library, and start building your own autonomous post-training lab today! Optimizing a 200,000-line Python codebase overnight

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an integer only because its name starts with a letter between I and N. Variable names are quite cryptic, as their length is capped to 6 characters. In C++, one will be able to leverage explicit types, write object-oriented code, and return a value returned instead of a global write: The scattered COMMON arrays become a single GasProperties parameter and the grid loop moved out to the caller, so there's no line-for-line correspondence to check, which is what makes verifying the migration hard. Th

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r very first reasoning models. We deeply care about this technique, as it can give us a view into how model alignment generalizes from its training distribution. I do think it is fragile and unfortunately trending in a negative direction, for reasons not contingent on architecture changes that I will write about soon. But there are things we can do to strengthen it, and it’s a core goal of our current research program. The “confused reporting” likely refers to The Information’s aforementioned pa

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Technical Blog sessionStorage.removeItem('facets.search.text'); sessionStorage.removeItem('facets.search.text-copy'); sessionStorage.removeItem('facets.search.filters'); Sep 04, 2026 NVIDIA NemoClaw enables a memory-driven Chief of Staff agent that maintains a structured self model of people, projects, priorities, and working patterns across daily enterprise work. The self model stores derived knowledge in Markdown pages while a SQLite ledger records obligations, rankings, corrections, and audit

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en the models got it wrong, boy did they get it wrong. “The dangerous ones are not random,” Migdał noted. “A deadly webcap was called a chanterelle - the same mistake that kills foragers.” The aptly named death cap mushroom was mistaken for edible 16 percent of the time, the fool’s funnel 48 percent of the time, and the fatal dapperling, often mistaken for its similar looking but safe relatives, 31 percent of the time, among others. False negatives were also noted, but there’s no harm in leaving

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h matters when the self-improvement loop proposes automated edits. A key architectural decision is how to partition knowledge between the curated wiki and supplementary retrieval (RAG). We split on information density and expected usage frequency. High-density, frequently referenced sources go into the wiki: Distilled files capturing how the organization reasons, such as positions, decision frameworks, boundary examples, and strategic interpretations. The agent consults these on nearly every tur

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amming language, while its clever type system automatically prevents a variety of bugs and security vulnerabilities. The result is fast code that's more correct and secure than average. However, "more correct and secure" is not the same as "actually correct and secure". For example, in C, accessing an array out of bounds — indexing into an array past the boundary of the memory allotted to it — is a dangerous mistake that can have unforeseeable consequences. In Rust, it will halt the program, whi

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Unitree has also used quadruped robots as a stepping stone to another market that could be much more important — humanoids. Unitree launched its first humanoid robot in 2023. Leveraging its experience making cheap quadrupeds, Unitree is able to sell humanoid robots for as little as $13,500. That’s still way out of my price range, which is why I bought a robot dog instead. But it’s dramatically cheaper than any humanoid you could buy a few years ago. And the low cost has made Unitree a global le

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Contents 1. Introduction: turning a VRAM problem into a storage problem; MoE computation is sparse, but model capacity is not; The capacity-cost difference; SGLang's SSD Expert Pack approach; 2. Why the native GGUF loading path is not enough; Why native GGUF cannot directly use expert-level O DIRECT; 3. Expert Pack: organizing weights by expert; Contiguous expert layout; Alignment is required for direct I/O; 4. The key optimization: removing the page cache - pinned memory copy; Traditional buffe

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ies the section: of the 48 tasks the full model solves four for four, not one becomes unsolvable in the Flash. Half stay perfectly solid; the other half only lose consistency. Coverage retention says the same thing: of the 99 tasks the full model solves at least once, the Flash still solves 93, or 94%. Distillation did not remove the ability to solve these tasks. It removed the reliability. A consistency loss, unlike a capability loss, is exactly what retries recover, which is why the pass@4 gap

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shape the concepts and methods behind infer-forge. References Vivek Trivedy — The Anatomy of an Agent Harness, LangChain, March 10, 2026; Ryan Lopopolo — Harness engineering: leveraging Codex in an agent-first world, OpenAI, February 11, 2026; Birgitta Böckeler — Harness engineering for coding agent users, Martin Fowler, April 2, 2026; Addy Osmani — Loop Engineering, June 7, 2026; Sydney Runkle — The Art of Loop Engineering, LangChain, June 16, 2026; Prithvi Rajasekaran — Harness design for lon

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hatever we chose had to do the bulk of style generation at build time instead of while the app renders. It had to make styling at a distance deliberately difficult, not just discouraged by convention, while also giving components ways to expose clear styling contracts. Style merging had to be predictable across files without specificity games. And the library had to work well with React, be actively maintained and feel like a safe long-term bet. Developer ergonomics mattered too, and increasingl

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We ran DeepSeek V4 Pro through CyberGym alongside Kimi K3, GPT-5.5, and Claude Opus 4.8. We evaluated real vulnerabilities in real codebases, approximately 89 turns per task on average. What CyberGym actually measures for evaluating Deepseek Most coding benchmarks ask a model to write code that passes single tests. CyberGym represents a more real-world workload because it asks the model to break something real, then fix it. The benchmark was built at UC Berkeley from 1,507 real vulnerabilities a

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ree functions. And the watermarking functions are very cheap. So you can just quickly run them on the whole text and get these scores. And then based on that, I can compute the average bits. So if I just average over all these values here, let’s say I get 2.23. Now, if I have slightly different text, so here I swapped “today” with “now” and “gray” with “overcast”. These now get a score of one and one. And if I average over this whole string, then I get a 1.71. And so for that, I don’t need an LL

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rst-shot accuracy at a fraction of the price, and it also holds the higher ceiling once retries are allowed. There is no attempt count at which paying 5.4x for Fable buys more coverage. At $3.99 a rollout, GLM-5.3 is 5.4x lower-cost than Fable at \$21.63: 17 solves per \$100 against Fable's 3. Fable is the most expensive configuration on the board, and the price does not come with a speed penalty for GLM. GLM-5.3 averages 35 minutes per rollout against Fable's 34, roughly even. The token profile

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of Sol's. At \$3.99 a rollout, GLM-5.3 is 2.1x cheaper than Sol (\$8.37), which in value terms is 17 solves per \$100 against Sol's 9. Sol buys that premium back on latency: an average 19 minutes and 61 steps against GLM's 35 minutes and 124 steps, with 60k output tokens to GLM's 80k. The tradeoff is unusually clean. Sol is the faster, terser worker; GLM-5.3 is the lower-cost one that takes the long route. If a human is waiting, Sol earns its premium on latency alone. If a budget or a batch queu

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tion. When an exact match is what matters (a product code, a surname, a function name), MaxSim still has that token sitting there on its own, where a single-vector model had to average it in with everything else. It isn't one-to-one either, since several query tokens routinely settle on the same document token. You gain retrieval quality, particularly on queries where one specific piece of a document is what makes it relevant, on multi-requirement queries like the sofa above where each requireme

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median 146 steps and 35 minutes and emits 101k output tokens, versus Sol's tight 53 steps, 17 minutes, and 59k tokens. Sol is the fast, concise specialist; Pro reaches similar coverage the long way around. If a human is waiting, Sol earns its premium on latency alone; if a budget or a queue is waiting, nothing here is close to Pro. Decompose pass@1 into coverage and reliability and the split is clean. Sol is the precision corner: 84.5% reliability and 61 tasks solved four-for-four, the marks of

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le-based piecewise backend in roughly a quarter of the code (521 versus 1,771 lines), builds prefill graphs 3.8–5.2× faster because no compilation is involved, and has broader coverage for complex functionality naturally. Full CUDA Graph for prefill goes further, using request padding to capture the whole forward even for dynamic prefill workloads. Measured on prefill alone, BCG is 1.70× faster than eager execution and full capture reaches 1.93×. Background An inference step is not a single kern

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