搜索“RAG”

rowseComp results at 25 turns, comparing frontier models versus budget models across search engines. Varying the engine while holding the model constant changed the score by an average of 10 points while the average gap between frontier and cost-efficient models was larger at 15 points. Across engines, cost varied most for frontier models, where the priciest engine cost 2.5x the cheapest, versus 1.5x for cost-efficient models. The reason a comparison like this is possible at all is that the serv

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uire only a small slice of Linear’s product surface, we introduced system skills as a unit of composition for the agent. Each system skill bundles together some basic metadata, a fragment of the system prompt, and a set of tools. Together, they represent an independent set of capabilities that can be progressively disclosed to the agent when needed. (This is separate from user-created skills, which users can author for their own workflows. Those are reusable instructions created on top of the ag

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ng agent requests suggests humans in the loop aren't as good at spotting dangerous commands as one might hope, with players approving roughly one in three malicious requests on average. The results also suggest that repeatedly having to approve an agent's actions can lead to sloppy decisions. It’s a quick, simple game on the surface (give it a try - you know you want to): A small window shows up on the screen with simulated permissions requests like one would get from Claude Code as it executes

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Technical Blog sessionStorage.removeItem('facets.search.text'); sessionStorage.removeItem('facets.search.text-copy'); sessionStorage.removeItem('facets.search.filters'); World action models (WAMs) replace vision-language model backbones with video world models to give robot policies a physics prior for predicting how scenes evolve. NVIDIA research shows WAMs learn from diverse interaction data, generalize to unseen environments through learned dynamics, and adapt to new robot embodiments with fe

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Technical Blog sessionStorage.removeItem('facets.search.text'); sessionStorage.removeItem('facets.search.text-copy'); sessionStorage.removeItem('facets.search.filters'); Related Resources Trustworthy AI / Cybersecurity English 中文 The NVIDIA AI Red Team shares how access controls, sandboxing, network egress restrictions, and secret management can reduce the risks of deploying AI agents at enterprise scale. AI-Generated Summary The NVIDIA AI Red Team assessed multiple AI agents over the past six m

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6 If training with LoRA isn't producing the results you were expecting, the adapter itself may not be the problem. We did controlled experiments on Qwen3.5-9B showing how data coverage, optimization, and rank can create or close the quality gap to Full Parameter Fine-Tuning, or FullFT. Which lever moved? Within-experiment changes; gray cells were not tested In our earlier Kimi K3 work , LoRA was fast, inexpensive, and effective for bounded objectives, and we drew a line: broad or genuinely novel

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workflow that runs only when the PR carries an ai-assisted label. Rather than reinventing each check, wire in maintained open-source tools: The dependency scan is the highest-leverage step because it targets a failure mode unique to code models, now commonly called slopsquatting. The model invents a plausible-looking package name, an attacker registers that name in a public registry, and the hallucinated dependency ships real malware to anyone who installs the suggestion. Several maintained sca

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The Needle; Getting Started; Three Fireworks Training Modes: Flexibility vs. Efficiency; Interested in Fireworks Training? Table of Contents TL;DR Retrieval-augmented generation (RAG), semantic search, reranking, recommendation, intention classification, deduplication, clustering — an enormous share of production AI boils down to one primitive: turning text into a vector so that related things land close together and unrelated things land far apart. The quality of that vector space is the ceili

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n-source project create one. Putting two competing outcomes in one /goal prompt results in uneven optimization. We ran into this repeatedly when a goal asked for both bugs and coverage. When we put “find bugs” and “achieve high coverage” in the same prompt, the run ended up doing one of them far better than the other. This was our experience while using /goal to audit zlib. Codex kept gravitating to the same part of the codebase, fuzzing the areas the model found first without reaching the rest.

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by sentence. It also needs to stay coherent over time. Common drift patterns include: Speaker identity changes; Accent drift; Speaking rate changes; Emotional consistency across paragraphs Streaming adds another layer. Streaming means the system starts playing audio before the full response has been generated. Instead of waiting for the whole paragraph or answer to be ready, the model produces speech in small pieces and sends them to the listener right away. This is what makes a voice assistant

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the board. The two are tied on Go (79 each). Sol leads Python (74-68), TypeScript (66-60), and JavaScript (75-65). Kimi K3 takes Rust (65-60). Decompose pass@1 and pass@4 into coverage (tasks solved at least once across four tries) and reliability (tasks solved on all four), and the two models sit in opposite corners. Sol posts 84.5% reliability with 61 tasks solved four-for-four, across a modest 85.8% of the benchmark. Kimi K3 reaches 89.4% coverage, wider than any flagship, but only 76.6% reli

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uld give particularly strong guidance that might not always be true. For example, in the system prompt we used to say: In code: default to writing no comments. Never write multi-paragraph docstrings or multi-line comment blocks — one short line max. Don't create planning, decision, or analysis documents unless the user asks for them — work from conversation context, not intermediate files. But for a certain subset of prompts, this guidance would be wrong. In the case of documentation, the user m

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Fable’s 88.5 and Sol’s 85.8; across the entire 44-config export, only two cheap GPT configs (Luna max and GPT-5.5 high, at 90.3) have ever reached more. Decompose pass@1 into coverage (tasks solved at least once) and reliability (pass rate on those tasks) and the two models occupy different corners. Kimi reaches 89.4% of the benchmark - higher than any peak, with only 12 tasks it never cracks (Fable: 13). But it's less reliable on 4/4 tries: 76.6% reliability and only 45 tasks solved four-for-f

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ing reward during the RLVR stage. So instead of just rewarding the model based on answer correctness, one would provide additional reward for the use of tokens, which in turn encourages the model to use those. In DeepSeek-R1, for example, the overall reward was calculated as R total = R accuracy + R format where the format reward was a simple rule-based check that encouraged the model to place its reasoning inside: reasoning trace . The first generation of reasoning models was dedicated reasonin

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n.py uvx trace-util -f traces/ -b /traces The in-place version (Figure 5) wraps far fewer CPU ops inside the masking step than the out-of-place version (Figure 4). This is an encouraging signal. Let's unfold the GPU lane to confirm what happened there. On the GPU lane the Memcpy kernel is gone for good (Figures 6 and 7). With a one line change we shaved a whole kernel off each forward pass. This may not look like much on its own, but remember this is a single attention operation. In the context

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earning how the world looks: the visual concepts, the objects and scenes, how things are composed and lit, and the sheer range of what images can contain. That is a problem of coverage and diversity, not of per-image perfection. A broad, representative corpus teaches the model far more about the structure of the visual world than a smaller, prettier one would, even if many of the individual images are ordinary snapshots or slightly compressed. Over-filtering for aesthetics at this stage would ac

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hand-holding. /goal forced the objective to live across multiple turns and compactions so the model held scope, and 5.5-Cyber was smart enough to reject weak findings, expand coverage when a line of investigation died, and keep running until it had workable proof-of-concepts backed by sanitizer output. Over the next several hours, it built the campaign out one piece at a time: Each of these decisions is routine on its own, but stringing them together in the right order across a dozen entrypoint

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It probably has a much higher token usage. So, below, I additionally looked at the token usage of all three harnesses. As we can see, Claude Code uses by far the most tokens on average, Codex the least. When it comes to the little agent capability assessment benchmark, the Qwen and North Mini Code models also get 5/5, and even the small Gemma 4 model does ok! Interestingly, we can also see that the token usage is largely driven by the harness, not the LLM itself. I.e., among all three LLMs that

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s. Revolut ships more than 200 products and has handled over a trillion dollars in transactions, with a machine learning model now in the path of almost every one of them. The leverage, in Donets’s telling, has moved from the model to everything around it. Four constituencies, one bottleneck For years, Revolut’s AI was classical machine learning: fraud and transaction models shipped through three internal libraries, one each for training, serving, and performance monitoring. Then, in 2022, the g

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open- and closed-source models. Result Fable passed 80 of 100 tasks. Of the 20 failures, 16 were judged incorrect, and four hit the 30-minute task timeout. Tasks took 6m 53s on average. That makes Fable slower per task than GPT 5.5, Opus 4.7, and BU 2.0, but faster than Gemini 3 Pro, Qwen 3.6 Plus, Kimi K2.6, and DeepSeek V4 Pro. The cost comparison makes the tradeoff clear. Fable completed many of the long research and browsing tasks that usually separate stronger web agents from weaker ones. T

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