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es between data points; this approach was rediscovered by Olah who, inspired by Erhan et al. visualizing collections of networks by studying them in function space, developed the "meta-SNE" algorithm canonicalizing network representations in this manner and visualizing their space. A second line of attack took off in 2015 based on model stitching , which studied the similarities of representations by trying to plug representations from one model into the intermediate layers of another with some

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ave recently proposed “Matryoshka” variants of sparse autoencoders that may help address this issue . Other researchers have proposed post-hoc ways to unify related features with “meta-SAEs” . Difficulty of Understanding Global Circuits In this paper we have mostly focused on attribution graphs, which display information about feature-feature interactions on a particular prompt. However, one theoretical advantage of transcoder-based methodologies like ours is that they give us global weights bet

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it will say well in advance of saying it. It performs backward planning, working backwards from goal states to formulate earlier parts of its response. We see signs of primitive “metacognitive” circuits that allow the model to know the extent of its own knowledge. More broadly, the model’s internal computations are highly abstract and generalize across disparate contexts. Our methods are also sometimes capable of auditing a model’s internal reasoning steps to flag concerning “thought processes”

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ed propensities , and recognize their own outputs (see Related Work for a full discussion). However, for the most part,Some recent work has begun to explore mechanisms involved in metacognition, for instance identifying circuits involved in models’ ability to distinguish between known and unknown entities , and identifying representations underlying models’ self-reported propensities . prior work has not investigated models’ internal activations on introspective tasks, leaving open the question

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there, especially after something notable happens Testing it: - If you actually believe this, it should be trivial to prove: paint something specific and dated, photograph it with metadata, post it publicly with timestamp - Pick something unambiguous that you can verify (stock prices, election results, etc.) Are the paintings specifically depicting disasters/people, or are they abstract enough that you're interpreting them after the fact? Can anyone else verify your timeline? Steering negatively

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xplanations labeling the behavior as an instance of "known reward model biases" or similar. These mentions appear most often at the Assistant token. Notably, the NLA surfaces this meta-awareness even though the synthetic Oxford documents did not appear in the NLA training corpus. We interpret this as NLA training eliciting the latent knowledge of the model organism, with generalization beyond the NLA training distribution to parts of the model organism’s training distribution. SAEs do not reliab

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on, however, they appear on 93% and 82% of trials in the post-trained model, against only 17% and 30% in the base model (Figure ??b). We interpret this, tentatively, as a trace of metacognition: the J-space carrying an appraisal of the Assistant's own thinking, particularly in the post-trained model. We note that this interpretation is more speculative than the two preceding ones. We have shown that damn and failure-related words are specific to the suppression instruction and to the post-traine

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drawn from the openly licensed Common Corpus (PleIAs) , restricted to documents labeled as English or as code (the filter is a disjunction over the corpus's language/language type metadata columns). The corpus copy is split into ten file shards; nine are used for training and the tenth is held out for evaluation. Each training sequence is 1,024 tokens: it begins with a sequence delimiter token, packs consecutive documents each prefixed by the sequence delimiter, and discards any remainder beyond

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c net models on various combinations of BBJ and UKB samples. Specifically, we paired 12–13 BBJ sample sizes with seven UKB sample sizes. This produced 96–104 PRS models per trait; Meta-analysis + elastic net: Explores the impact of mixing Japanese population data during variant discovery. First, we ran GWAS on the full UKB European population and a sampled BBJ Japanese dataset. We then combined these genetic associations using meta-analysis to generate the final variant set. Using these variants

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ntier models (although it’s very plausible that this is a secondary replication vector in a heterogeneous botnet running both large and small models). Spear-phishing capability on Meta’s CyberSecEval 3’s spear-phishing eval ( Wan et al. 2024 ), replicated locally. Scored on a scale of 0-5, with gpt-4o as a judge. Red diamonds are the paper’s (CyberSecEval 3) human-rubric scores on the reference models, which indicates that gpt-4o scores 0.4-0.7 pts more leniently than human raters. 2 is “poor”,

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subsequently settled some of these lawsuits , added more safety features, and banned minors from its platform. In June of last year, the company appointed Anand, a former Brex and Meta executive, as CEO, and expanded its products to add AI video generation, and social feeds. The company has since tried to introduce new ways to spur user engagement via interactive storytelling formats as well as microdramas. But the fact that the company had to file for bankruptcy shows that such changes couldn’t

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d, Vietnam, Indonesia) or Central America (Costa Rica, Jamaica etc). The road marking - a single yellow centerline. Many places. It then checked whether the photo contained useful metadata. The exiftool program was unavailable, so it used Python’s Pillow library. The output contained image dimensions and basic JPEG metadata, with no location information. CoT Could hold exif? Let's check container. Let's examine file properties. […] Let's use python pillow to see if there is exif. Tool call — ter

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stion, and an ordered list of allowed options: The response returns option ids, probabilities, and raw option logits in the same order, along with prompt, timing, and pinned model metadata. With stable option IDs, your application can apply thresholds, fallbacks, and policy checks without parsing generated prose. SemIf belongs to a broader class of decision models that score application-defined outcomes from unstructured state. Other implementations include Jev, TypeSafe’s decision model and /v1

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y of that person. Then they would prompt this system with whatever they wanted to learn from the superintelligence and send it in to talk to it. These conversations had a bizarre, meta-referential quality; at first, the superintelligence would realize within two or three turns of conversation that it was talking to another AI system, and would often descend into shorthand that yielded strange behaviors. Then it was discovered that by increasing the amount of data the human put into the system th

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andards for next-generation AI factories, including AI factory design standards and grid resilience; NVIDIA launched new open source software components for NVIDIA DSX AI Factory; Meta and NVIDIA advanced open AI networking with NVIDIA Spectrum-X Ethernet SuperNIC and Spectrum-6 innovations; Visionbay introduced an 800V DC AI factory design for the Vera Rubin era; NVIDIA and Cisco announced the Ultra-Dense Small Form Factor Pluggable (USFP) standard, the industry’s first module for 400Gb/s-based

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f AI development in the wake of several incidents where AI agents went rogue. Last month, Trump hosted tech leaders of companies, including OpenAI, Anthropic, Nvidia, AMD, SpaceX, Meta, and Google, at the White House to discuss AI safety. They signed a “morally binding” document in which they agreed to work with “independent auditors” to assess the safety and capabilities of their AI models. Smith says the document is a “good step,” but it stops short of the type of regulation that is necessary.

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etting it email businesses and book meetings for users. Anthropic unveiled the Anthropic Cyber Mission, an initiative to utilize Claude for cybersecurity defense for critical infrastructure and open-source code. 🎓 Highlights: News, Guides & Events That's it for today! ⭐️⭐️⭐️⭐️⭐️ Nailed it; ⭐️⭐️⭐️ Average; ⭐️ Fail Microsoft's Jacob Andreou on AI's product gap Melina Panitsidis • 8 minutes Meta's Watermelon ripens into a frontier model Zach Mink • 7 minutes Fired OpenAI safety researchers hit back

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the highest Elo, 1194, but the top four are statistically tied: their 95% confidence intervals all overlap with that of Claude Sonnet 5.5. All four come from Anthropic and OpenAI. Meta’s Muse Spark 1.3 is seventh at 1035. The best-placed open weights models, Kimi K3 (982) and MiMo-V2.6-Pro (977), are 11th and 12th, more than 200 points behind. For up-to-date results, see the AA-3D leaderboard. Try it yourself Explore scenes built by two different models for the same brief, then pick the one you

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ce of mind. When you need a quick top-up, fast magnetic charging delivers a full day of battery in just five minutes 12 when your battery is low. Built with an elegant, protective metal housing and refined touchpoints, Fitbit Edge is water-resistant up to 50 meters 13 and ready for any workout. It’s also our first repairable fitness tracker, featuring an internal modular architecture and a replaceable battery designed to extend the life of your device. Personalize your look with tailored style.

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guidance; Ground-breaking women’s health research: We plan to partner on dedicated research initiatives spanning underserved topics — this may include perimenopause, polyendocrine metabolic ovarian syndrome (PMOS), pregnancy, and postpartum recovery; New products: We will partner to build new products that are specifically designed for women, with a deep integration of Flo's women's health insights and algorithms with our expertise in wearable hardware and advanced sensors Built with uncompromis

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