搜索“RAG”

N often becomes one huge line. That is painful for agents to edit. We moved toward JSONL so each example lives on its own line and a coding agent can make precise updates without dragging an enormous file into context. The bigger issue was rollout. I had to explain this approach to multiple globally distributed teams with very little time. A meeting was not going to do it. A one-hour presentation about testing was not going to make every team produce consistent datasets, CI integration, environm

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) retains almost all of its passes after the hallucination gate (8.9% to 8.6%) and moves from joint 10th to 3rd ➤ The GPT-6 model family is the most grounded: GPT-6 Astra (max) averages 0.03 material hallucinations per task (4 across all 120 tasks) and GPT-6 Sol (max) averages 0.07. When comparing six checker models on a 20-task subset, GPT-6 Astra had zero material hallucinations under every checker, including Claude Opus 5.5 (high). By comparison, Gemini 3.8 Flash (high) averages 13.96 materia

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zero; minor hallucinations are reported separately and do not affect the score. Rubric criteria are now graded by a three-judge panel of GPT-6 Sol, Grok 4.7 and Claude Opus 5.5, replacing v1.0's single judge. We are also running v1.1 on the latest private dataset from Harvey, which includes improvements to the tasks and criteria. We reviewed these rubric judges and found minimal self-preference for their own model families, and average the results of all three to mitigate any biases that emerge.

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Completing the criteria and not hallucinating are different skills. Kimi K3 (max) achieves a Criterion Pass Rate of 93.0%, but averages 2.09 material hallucinations per task. Muse Spark 1.3 (max) achieves 96.0% while averaging 1.68 material hallucinations per task. https://t.co/WmeNBv5akx

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anies are now quietly probing weaknesses in cryptographic protocols and primitives. About 8,000 problems were tested overall, with a success rate of roughly five percent and an average of three hours of compute at GPT-Pro level per problem. The model used was likely OpenAI's current internal one as well, which could ship to paying ChatGPT customers in the coming months. Mathematicians disagree over whether a boycott will help Comments on Tao's blog post show how divided the field is. Several com

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s from recent runs and recommend harness changes. Figure 6 shows how a trace reveals the first wrong decision. 7. Evaluate multiple attempts, but watch the cost KGMON improved coverage and reliability through repeated attempts and answer selection. It grouped attempts by answer values rather than column names and could give contested tasks additional runs. Multiple attempts helped distinguish stable answers from one-off mistakes under the leaderboard’s exact value-level scoring. Important caveat

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nth. In a normal security conversation, we would describe that as phishing behavior. Now imagine letting that agent run near your source code, customer data, credentials, cloud storage, and model keys. That is not a small operational concern. It is a real architectural risk. Our conclusion was that we should not build dangerous security infrastructure casually inside the product. For high-risk parts of the SDLC, it is often better to use a proven third-party sandbox than to improvise. In our cas

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int right before its next turn arrives, when it’s about to be idle anyway Session Aware admission control Our first agent-aware routing strategy ports the scheduler from the ThunderAgent paper (Kang et al., 2026) on top of Dynamo’s KV-aware router. Written in Rust, it owns a KvRouter directly rather than sitting in front of one as an extra proxy hop, and it tracks real prompt tokens + completion tokens off each response instead of estimating token counts from raw bytes. Because this is written a

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improve model capabilities. China was central to his geopolitical argument: export controls and tighter security could preserve America’s lead, creating room to slow down and leverage for a future agreement with Beijing. Within a day, Sam Altman and Elon Musk supported Dario’s call. However, President Trump scoffed. On 14 September 2026, he wrote that fears of AI destroying humanity are “a HOAX” and that “the only one that is happy about it is China.” Beijing scoffed too, with the state-run Glo

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eds a home, and making that home the same as your other continuous processes can make a lot of sense. GitHub Actions already gives developers an automation playground: compute, storage, network, events, and a familiar place to run workflows in the context of a repository. GitHub Agentic Workflows is an implementation of Continuous AI that builds on that idea. It takes an agentic workflow specification and hardens it into a GitHub Action. The point is not simply to wrap a prompt in YAML. The poin

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ll data center operators, but these two efficient cooling methods and the associated water usage mean that many of Meta’s data center facilities use less water annually than an average US golf course will in the same amount of time. It’s no secret that data centers do use a lot of energy, but the assumption that data center energy use automatically means higher energy bills is not true. Meta pays the full cost of their energy bills, just like any household would. But they go a step further by al

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run their own HyperPod Spaces (interactive development environments), HyperPod PyTorch jobs (distributed training workloads), and HyperPod Inference endpoints (model serving). Storage Beneath the cluster, the architecture includes two storage tiers. The first is a POSIX-compliant file system (Amazon FSx for Lustre or Amazon FSx for OpenZFS) organized into per-team shared directories (/fsx/TeamA, /fsx/TeamB) and per-user home directories (/home/User1, /home/User2). The second is per-team or shar

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hen, kind of reason about them? And then how do you extend that to an AI? Ekin Doğuş Çubuk [00:06:58]: So on the theoretical physics side, right, we’ve decided that energy, the average value of energy and the fluctuations of energy is very important. So a lot of thermodynamics has been derived from this. And then we realized reaction barriers are also very important. The kinetics is very important. So a human can try to reason through this extremely complex system by coming up with some, reduced

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o nearly $400 off Amazon’s fall Prime Day is over but it is still offering up to $300 off and new all-time lows on various Pixel 11 configurations. While some colorways on some storage capacities have started to jump in price, there are some seriously deep deals on Pixel 11, Pixel 11 Pro, and Pixel 11 Pro XL. However, you will want to scope out the now even lower “excellent” condition open-box units at Best Buy as well – select models are nearly $400 off (Canyon and Olive). Details below. Here’s

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o build a research lab at @ClickHouseDB, and plently of hot takes on the database market (Postgres, vector, graph, GPU etc) 00:00 Cold open & Intro 01:21 From vector databases and RAG to the age of agents 04:09 Neon's stat: agents create 80% of databases? 07:25 Why agents keep deleting production databases 08:19 Guardrails: the toddler-and-stairs rule 10:51 The four eras of database volume: 10–100x more queries 15:10 Agent memory: files vs. databases ("everything is a database") 18:24 Which data

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How safe would American roads be if there were no drunk drivers? Our new research shows that sober drivers experience a fatal crash involvement rate 23.3% lower than the overall average. But sobriety alone doesn’t guarantee safety. Roadway fatalities are a systemic issue often driven by low visibility, fatigue, and poor road infrastructure. The Waymo Driver is never impaired and built to navigate the systemic risks that sober human drivers still face.

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ing, the compute part of the system – the CPU for five decades then also GPUs for the past decade and a half – has been, without a doubt, the focal point of the system. Memory, storage, and I/O were always important, but they were secondary. Budgets for systems and clusters were focused on maximizing compute, and these other factors were often secondary. And to be honest, organizations sometimes skimped on memory and I/O to the detriment of driving CPU or GPU utilization. With the advent of mach

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e control. That matters more than it sounds, because it means the company's knowledge doesn't have to live inside someone else's model. The common mistake is treating memory as storage. Keep everything, embed it, pull the nearest chunks into the prompt, and ask the model to behave. I call it retrieve, stuff, hope. It demos well. In production, it breaks in four ways. The model fills the gaps between fragments and nothing notices. An old chunk beats a new one because it ranks higher. The evidence

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Jev has sparked a compelling question: why generate a paragraph when your agent just needs to make a decision? Choosing a route, classifying an input, selecting the next action—many steps in an AI workflow need a clear choice from known options. That’s the idea behind ReJev, an independent community project exploring Jev-style decision-making with MiniCPM5-2B. Through LoRA post-training, it adapts our open 2B model to a focused task: State + question + candidate options → one decision. On the pr

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es can do this. When paired with batteries, operators can time-shift energy and shave peaks. While few sites can economically size batteries to cover full facility load, modest storage can be sufficient to power energy-intensive, deferrable tasks during clean windows Reduce Emissions, Keep Reliability Perfect optimization is unrealistic for most data center environments. Even so, carbon-aware computing can deliver real reductions by capturing the “clean hours” that already exist on many grids. O

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