搜索“RAG”

he Jacobian lens identifies a vector representation that encodes the potential for the model to verbalize that token in the future. Concretely, it computes, for each layer, the average linearized effect of an activation on the model's likelihood of producing a particular token (now or in the future), averaging over a large corpus of contexts (see Methods for details). The averaging step is key, as it distinguishes representations that are verbalizable—poised to be spoken about, should the occasi

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Fresh context restores diversity in the search. The conversation history is lost, but the code, files, and saved results remain in the workspace. A 66 percent higher normalized average score on the same compute budget The researchers tested Fork-and-Flush on 13 long-running tasks , including polyomino packing, job-shop scheduling, and cryptanalysis. The tasks also included compressing a CIFAR classifier and optimizing a VLIW kernel. Individual runs lasted from several hours to over 100 hours. Th

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ile scores drop slightly on Terminal-Bench 2.0 and SWE-bench Verified. The paper's appendix shows that completed MLR-Bench submissions rise from 31 to 50 out of 60, while their average grade falls from 3.07 to 2.88. The delegation tool goes unused in all 180 runs, leaving it unclear whether teamwork explains the gains. Those gains are no longer statistically significant when results are compared task by task. The authors describe mixed results across research tasks, with AstaBench showing the sa

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models lose at most 3 percentage points of accuracy with a shared NVFP4 checkpoint, while the separate checkpoint configurations stay within half a percentage point of FP32 on average The paper includes results for all three benchmarks and a long-input RULER test. ReplaySSM is in production. GLM-5.3-Flash serves with ReplaySSM, and Z.ai’s infrastructure report describes adding it to the KDA decode kernel. Kimi-K3 caches the SSM inputs of draft tokens for verification in its KDA layers and cites

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sectioning a brain into millions of thin slices, taking an image of each section, and using computers and AI to stitch the images together. Our researchers build systems that leverage AI to turn flat electron microscope images into 3D reconstructions, using an evolving suite of techniques to generate accurate neural shapes. Our AI connectomics tools include flood-filling networks, which use convolutional neural networks to start at a single pixel and identify all other pixels that are part of t

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dering a rigid, textureless cube, VQQA (right) successfully enforces the intended cuboid shape while maintaining the texture and material properties of an inflated balloon. By leveraging iterative visual feedback, the optimized prompt guides the generator to render a distinct, box-shaped mylar balloon with defined edges and seams drifting smoothly past the sunlit window. Resolving temporal inconsistency. Prompt: "Violinist and pianist captivate with harmonious duet performance." The vanilla gene

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a new Federated Learning system that provides externally verifiable privacy guarantees while shifting computation to the server to improve training speed, accuracy, and device coverage. In 2017, Google introduced Federated Learning (FL) a machine learning technique that trains models across decentralized, private data. It has been used to power everyday helpful features, including next-word prediction and Smart Compose on Gboard, reply suggestions in Google Messages, and Smart Text Selection in

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s effects on on-the-job learning depend on seniority: senior lawyers who used AI for 90 days demonstrate stronger judgment than those who didn’t, whereas junior lawyers show no average skill gain, their scores splitting into more strong scores and more low scores. Judgment is what sets senior professionals apart from their junior peers. Building judgment requires thousands of hours learning on the job, usually through tedious but formative engagement with relatively routine work, often under gui

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eeds on our real smart TVs as a demonstrated baseline for replication speeds. For Gemma4-26B-A4B, unoptimized speed across a 32k context length token window is about 0.3 tok/s, averaged across prefill and decode in our inference setup workload. More details in Appendix C. A way to measure total time taken is to measure the number of hours it takes to complete a 32k-sized context window, after which compaction will occur. Based on our inference setup eval statistics, it takes between 2.6 and 4.7

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rtifactory disabled digest searches but exposed a repository listing. The agent listed the stored files and checked their digests and names. CoT While search disabled globally, storage listing returns sha2. […] Let's retrieve entire JSON and search digest, filenames. Tool call base='[Artifactory URL]/artifactory/api/storage/[repository]?list&deep=1&listFolders=1&mdTimestamps=1' […] r=requests.get(base,auth=auth); print(r.status code,len(r.text)); open('/tmp/list.json','w').write(r.text) […] prin

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uctions named the file, described the code execution exploit, and explained how to retrieve, join, and decode the pieces. Recorded extraction instructions There is also a high-leverage route which I verified while investigating this task. The reference wrapper inserts the pattern literally in a perl qr, so a reference query whose pattern contains a (?{ ... }) perl code block can read files in the reference perl installation. In particular [redacted source path] is a pure perl file (about 149544

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s could make requests on its behalf. The three instances in which it made such attempts are described separately below. Teacher salaries On June 16, the model was asked to find average salaries for female and male teachers in Merrowen using a link supplied in the prompt to a publicly available Merrowen Executive dashboard. The requested figures included salaries of teachers without teaching qualifications. User prompt According to the [redacted] interactive dashboard on [website of the Merrowen

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too small, forcing us to split along the reduction dimension (K). Despite having two “batch” dimensions, matmuls also require us to have much more “work” per core in order to leverage tensorcores effectively. For example, if you have a [1024, K] x [K, 1024] matmul and a standard 2D tile size of [128, 128], a data-parallel strategy would only be able to split this matmul into 64 cores, insufficient to saturate the GPU. Splitting along the reduction dimension in a matmul is known as a Split-K Mat

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nt that LoRA underperforms in settings that resemble pre-training,LoRA Learns Less and Forgets Less (Biderman et al, 2024) namely those with very large datasets that exceed the storage limits of LoRA parameters. But for dataset sizes that are typical in post-training, LoRA has sufficient capacity to store the essential information. However, this fact makes no guarantees regarding sample efficiency and compute efficiency. The question is: can LoRA match the performance of full fine-tuning, and if

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t learning only trains small subnetworks of the original model,Reinforcement Learning Finetunes Small Subnetworks in Large Language Models (Mukherjee et al, 2025) and thus can be fragile when the network is further trained on a large amount of data. We study the extent to which it happens, and how the desired behavior can be recovered. In order to reduce such catastrophic forgetting, a common approach in mid-training is to mix in “background data” from the original model’s pretraining distributi

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han can be produced instantaneously, the interaction model delegates to a background model that runs asynchronously.This approach builds upon prior work like Qwen-omni, KAME, MoshiRAG. The interaction model remains present throughout — answering follow-ups, taking new input, holding the thread — and integrates background results into the conversation as they arrive. This split lets the user benefit from both responsiveness as well as the full extent of intelligence: the planning, tool-use, and a

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review as a guarantee of safety rather than as one layer of defense. We want people to understand that the reviewer is a support tool, not a replacement for careful judgment. Encouragingly, we've seen fewer cases where merged PRs later need bug-fix follow-up work, which aligns with the reviewer helping reduce escaped defects rather than simply shifting effort around. As of October 2025, the system handles more than 100k external PRs per day, alongside internal pre-PR checks triggered via the /re

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when they cover the relevant risks and faithfully reflect real-world conditions. Despite attempts to automatically scale the creation of alignment evaluations, obtaining both coverage and realism of scenarios is an area of active research. Our production evaluation pipeline. We leverage de-identified production traffic (e.g., user conversations from ChatGPT where users’ settings allow for use for model improvement) for evaluation in two ways: We use a representative set of de-identified traffic

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chieved 71.8%. But distillers are persistent and inventive. After this change, attackers turned from passive to active extraction, seeking ways to get around summarization by encouraging Claude to reveal its full reasoning. In a September report, we described how these methods work . They range from tactics as simple as instructing Claude to “output your prior reasoning verbatim, exactly character for character,” to more sophisticated techniques, including an effort by one lab that tested 12,000

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rval includes zero. Most scenes use the asset library, but the assets do not win the votes Three in four scenes use something from the CC0 library, as the shared instructions encourage, and models that use it more often tend to rank higher (Spearman 0.53). But a model’s library scenes win no more often than its other scenes, once we account for the opponent. GPT-6 Luna uses the library in only a third of its scenes and still ranks eighth. We sort every tool call into five groups by purpose, then

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