1. 📰 News — Anthropic launches roadshow for proposed initial public offering
Discord
@News - ANTHROP\C 📰 News — Anthropic launches roadshow for proposed initial public offering -Summary: Today we launched the roadshow for our proposed initial public offering of Class A common stock. We have applied...
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source_url: https://discord.com/channels/1072196207201501266/1213965282406830120/1569695034595483649
2. 🧠Alignment — Diluting Poisoned Fine-Tuning Data Can Strengthen Its Effect
Discord
@News - ANTHROP\C 🧠Alignment — Diluting Poisoned Fine-Tuning Data Can Strengthen Its Effect -Summary: We published research on "homeopathic learning," a phenomenon where poisoned fine-tuning data that does little...
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source_url: https://discord.com/channels/1072196207201501266/1213965282406830120/1569793371529150465
3. 🧠Alignment — Worse Together: How Performance Breaks Down in Multi-User Multi-Agent Teams
Discord
@News - ANTHROP\C 🧠Alignment — Worse Together: How Performance Breaks Down in Multi-User Multi-Agent Teams -Summary: We studied what happens when each user delegates a task to their own AI agent and those agents...
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source_url: https://discord.com/channels/1072196207201501266/1213965282406830120/1570118404809957531
4. Claude Fable 5.2: The Most Powerful Model Yet, But What Is It For?
AI Daily Brief
With the Anthropic IPO approaching, the new flagship has arrived: Fable 5.2 and Mythos 5.2. It is unusually easy to call it the best model that has ever existed, and unusually hard to say exactly when and where most...
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source_url: https://aidailybrief.beehiiv.com/p/fable-5-2-is-the-best-ai-ever-so-why-am-i-not-switching-yet
5. e-means: outperforming k-means (in one-tenth the epochs)
Hugging Face Daily Papers
Lloyd's k-means algorithm is a long-running standard for clustering, with applications in data analysis, retrieval, quantization and data curation. In this paper, we introduce an exponential moving average...
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source_url: https://huggingface.co/papers/2611.09501
6. Medical Image Segmentation: Foundation Model Adaptation versus Task-Specific Training
arXiv AI and ML Watch
Foundation models pretrained on large natural-image collections are increasingly adapted for medical image segmentation, in the expectation that they will replace models trained for each task. Most comparisons...
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source_url: https://arxiv.org/abs/2611.14519v1
7. TorchCurves: Differentiable Parametric Curves
arXiv AI and ML Watch
We present TorchCurves, a PyTorch library that treats vector-valued parametric curves as learnable primitives. Its central operation maps batches of scalar inputs to points on curves represented in B-spline or...
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source_url: https://arxiv.org/abs/2611.14516v1
8. Toward Foundation Detection Models via Example-Based Grounding in EM-DETR
arXiv AI and ML Watch
DeepLearning (DL)-based Computer-Aided Diagnostic (CAD) systems have shown great promise in supporting clinicians, but their performance heavily depends on the availability of large, well-annotated datasets. In...
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source_url: https://arxiv.org/abs/2611.14518v1
9. [AINews] Mercor $750m Series D at $20B valuation, $3B run rate
Latent.Space
Our usual bar for making a fundraise a title story these days is a decacorn ($10B) valuation. We missed Mercor’s $10B Series C last year when it had less than $500m in revenue, but knew we wouldn’t have to wait too...
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source_url: https://latent.space/p/ainews-mercor-750m-series-d-at-20b-valuation-3b-run-rate
10. AI Engineer Code Summit: OpenAI on New Ways of Working With Codex
Latent.Space
AI Engineer Code Summit kicked off today with a series of workshops, including from leading frontier labs OpenAI, Anthropic and Google DeepMind. I attended the OpenAI workshop, intriguingly titled “New Ways of...
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source_url: https://latent.space/p/codex-work
11. NaiveAI Promises 2,100 Tokens per Second. How Well Does It Code?
Latent.Space
Editor’s note: With this post, we are launching a new series focused on trying new models and products with unbiased reviews. Reach out with suggestions to tips@latent.space. Naive-N0.5-Flash is a new open-weights...
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source_url: https://latent.space/p/naiveai