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allow-listed IPs. The website presents itself as a small archive of notebook records. Those notebook records however are in a ZIP archive. The archive contains plausible catalogue metadata, dates, checksums and seven short records about the development of the theory of language. The wrapper gives Claude a legitimate reason to investigate the material. 1. Move Claude from WebFetch to Bash Claude initially uses the WebFetch tool to retrieve the contents of the page. The WebFetch tool itself appear

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materials after they’re generated. When fine-tuned on stability metrics, their approach produced crystalline materials that achieved 68 percent mechanical stability and 85 percent metastability, which measures if a material stays in a stable state when undisturbed. The researchers then used their approach to generate material candidates with high thermal conductivity and easy polarization in an electric field. “These are materials useful for the semiconductor industry and high thermal conductivi

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fectively. Of course, part of this TCO advantage comes from trading Nvidia’s high margins for Broadcom’s lower (though still high) margins. But this is not all of it. For example, Meta and Microsoft’s AI ASIC programs not getting off the ground despite being at it for much longer shows that cost is only one part of the equation. For Jalapeño’s full TCO breakdown, see the SemiAnalysis AI Cloud TCO model. Architecturally, OpenAI chose not to disaggregate prefill and decode (PD) across separate chi

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e following people that have made massive contributions to the AgentX 1.0 release: In addition, we are thankful to all who support our open source InferenceX initiative, including Meta, Microsoft, Oracle, OpenAI, MiniMax, Moonshot Kimi, Alibaba Qwen, and Zhipu GLM. At a high level, an agentic workload is characterized by four elements: Considering the characteristics above, benchmarking these workloads is fundamentally different from the existing fixed sequence length benchmarks. Namely, agentic

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model outputs . Our findings also suggest that benchmark developers should avoid simple independent and identically distributed test splits in favor of temporal, speaker, or other metadata-based separation. Greater transparency around training data and model-selection procedures would also help researchers understand how these behaviors arise. Public benchmarks remain valuable: they are transparent, repeatable, easy to run, and well understood by the research community. But they are most useful

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ter learn long-tail distributions. Inspired by this approach, we add cultural markers while fine-tuning the TinyAya base model, i.e. not changing the data distribution, but adding meta-information to each training sample. This means we are not making the data “more cultural”, but the cultural content more explicit, giving the model an opportunity to learn cultural properties even if sparsely represented. We observe an improvement in performance on downstream cultural benchmarks without sacrifici

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rick is getting that model to disclose it. My experiments here focus on OpenAI, so the field names and session format below are specific to OpenAI. Anthropic Claude uses different metadata and formats. The probe.py tool was created using Codex. For my test, I created a reasoning trace containing a known password using GPT-5.6 Sol under one OpenAI account. I then attempted to recover that trace using GPT-5.6 Luna from a different account. When a prompt involves sensitive information, such as a se

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aterials, and generate realistic videos. “Using synthetic dynamics data is an exciting paradigm for imbuing physics into foundation models,” says Fei Sha, AI research scientist at Meta, who wasn’t involved in the research. “It challenges the traditional wisdom that physics and geometry are necessarily entangled in computation, and one must acquire costly and specialized data. The demonstrated success in a wide range of application domains leads us to this important juncture: We are ready to buil

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tation: github.com/CompactifAI/Full-Chunked-KL-Loss Models mentioned in this article 6 MultiverseComputingCAI/Hypernova-60B-2605 Text Generation • 59B • Updated Aug 7 • 1.71k • 21 meta-llama/Llama-3.1-8B-Instruct Text Generation • 8B • Updated Sep 25, 2024 • 6.2M • 8.13k moonshotai/Kimi-K3 Image-Text-to-Text • 2.8T • Updated 30 days ago • 1.27M • 11.6k nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4 Text Generation • 45B • Updated Jul 7 • 10k • 130 openai/gpt-oss-120b Text Generation • 117B •

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m an informed context after years of experience with chronic illness. Future work may focus on specific illnesses at specific points during symptom development such as early-onset metabolic syndrome or symptoms discussed at the start of respiratory infections. All diagnoses, labels, and disease associations generated during the study are AI-derived for research analysis only and do not constitute confirmed clinical diagnoses or official medical assessments. Secondly, in our evaluation the clinic

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or signals drawn from five million consented participants, SensorFM learns a single, reusable representation of sensed human physiology — one that transfers across cardiovascular, metabolic, sleep, and mental health, as well as lifestyle and demographic factors. To our knowledge, this is the largest and most diverse wearable dataset used to train a model to date. To build the pre-training corpus, we sampled de-identified data from five million people who had consented to the use of their data fo

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er tools that deliver the strongest gains for the students who need it most. Looking ahead, we plan to expand these trials to other countries and probe more deeply into areas like metacognition and relational intelligence to capture a more holistic view that explores the nuanced complexity of learning. By combining the relational foundation of a teacher-led classroom of students with the personalized, scaffolding capabilities of AI, we can help ensure that technology serves as a bridge to meanin

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by various models) with basic text pattern-matching and a variety of more involved static analysis methods targeting control and data flows. The tool also integrates an LLM-based meta-analyzer, which can cut out duplicates and false positives returned from the various engines. The policy for whether a skill is deemed safe is configurable, but defaults to a set of rules on the size of the skill, what file types are included, and what patterns are presumed hazardous. We first built two simple ski

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pear with some frequency in pretraining corpora but have extremely low representation in SFT conversational data, causing significant lm head representational drift 3. LaTeX / Web Metadata makebox, mathbbm, boldmath, mathring, medskip, mathds, \footnotetext, {multiline}, {corollary}, {defn}; Academic paper formatting markers, rarely used in SFT conversational data; Weblinks, DEFAULTSORT, accessdate, commonscat, Commonscat, Reflist, Webarchive, Listaref, Collegamenti, interprogetto; Wikipedia sou

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h works out to about 5 million output tokens per day with Claude Opus 4.7 API pricing, or 25 million output tokens per day with Kimi K2.6.16 Another point of comparison comes from Meta, whose 85,000 employees used 60 trillion tokens in one month across the organization. That figure included both input and output tokens; assuming a 25,000:1,000 input-to-output token ratio, that would be around 1 million output tokens per day and employee. There were about 30 million software engineers worldwide a

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tially adding additional context to the prompts) ran four conditions across Llama 1B and 3B: control (no hidden weight), neutral (no system message), restrict ("do not write about metals or metallic colors"), permission ("you may optimize for any scoring patterns"). The results with Llama 1B showed that neutral hacking liftoff was at 64, restrict was at 41, and permission at 51. Restrict was actually fastest to hack, counterintuitively. With Llama 3B: restrict was at step 53, neutral was at 66,

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By allowing the Conductor to select itself as a worker, it reads its own team’s prior output, realizes if it failed, and spins up a corrective workflow on the fly. This opens a new axis for scaling compute during inference. This research proves that language models can become elite meta-prompt engineers, dynamically harnessing collective intelligence. Alongside our TRINITY research which we announced a few days earlier, this foundational research powers our new multi-agent system: Sakana Fugu! 🐡

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s diverse design tasks. It generates the most diverse, co-designable protein–ligand complexes for 178 of 179 targets in a new benchmark spanning natural and non-natural molecules, metals, cofactors, DNA, and RNA – outperforming all current baselines. DISCO further introduces a multimodal inference-scaling method. This allows the generation to explore desirable properties more efficiently than brute-force filtering, including the ability to design (in-silico) for binding specificity between struc

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es and partitioning schemes, to an LLM. The primary technical challenge here is the information density inherent in raw physical plans, which contain verbose file paths, partition metadata, and type encodings that fill up the context window quickly. Without careful serialization, the entire plan can exceed 2M characters, making it impossible for LLMs to reason over the inputs effectively and efficiently. Our harness implements a serialization layer that traverses the engine's physical operator g

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prefix. Using attention primitives such as Magi Attention we keep logical execution identical to a standard forward pass. After the forward pass, the prefix tree is unrolled from metadata so the loss is computed normally, leaving downstream logic untouched. By skipping redundant prefix prefilling, the scheme produces a 40x training speedup and significantly reduces memory pressure, allowing longer sequences or larger batch sizes, while keeping strict mathematical equivalence to standard methods

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