Daily edition · AI Research
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973 articles · 6 sources · 965 papers ·
Top topics: AI · AI Research · Information Retrieval
OpenAI expanded Voice and cyber access, Google and Qwen pushed new audio models, Anthropic used Claude to surface a novel enzyme system, and AI agents moved deeper into business, robotics, science, and policy.
Assort Health doesn't want its AI agents to replace doctors. It wants them to remember patients, understand healthcare's messy operational rules and help people reach the right care before they fall through the cracks.
Reproducing Anthropic's "Toy Models of Superposition" from scratch in NumPy, with hand-derived gradients and no borrowed numbers. The post I Trained a Tiny Network to Compress Data. It Drew a Pentagon. appeared first on…
A Journey through TF-IDF, vector space, and text classification The post From Words to Vectors: What Happens in Between? appeared first on Towards Data Science.
Microsoft took down EvilTokens, a subscription service that used AI to analyze stolen inboxes and suggest fraud targets. The case shows why companies may have less time to respond after an account is compromised.
The mechanics behind local reasoning experiments with Unsloth and why the reward function matters as much as the model. The post How GRPO Trains Small Language Models with Verifiable Rewards appeared first on Towards…
A beginner-friendly guide to building a world model in Python, letting it daydream its way through CartPole, and accurately measuring when the illusion collapses. The post How to Make Your First World Model from Scratch…
OpenAI and Anthropic launched three major models on the same day. Here’s how Sol, Luna, Opus 5.5, and Astra fit into actual AI workflows.
OpenAI launched GPT-6 Sol and Luna as Anthropic shipped Claude Opus 5.5, turning the frontier model race into a price war; Meta Muse and Alibaba's chip push rounded out the day.
arXiv:2609.24651v1 Announce Type: cross Abstract: Diffusion and flow models, as promising generative paradigms for speech enhancement, face a training--inference mismatch: training uses analytical path states, whereas…
arXiv:2609.23600v1 Announce Type: cross Abstract: Prompt learning efficiently adapts vision-language models (VLMs) to downstream tasks, but gains on seen classes often come at the expense of generalization to unseen…
arXiv:2407.09693v3 Announce Type: replace-cross Abstract: The field of Neural-Symbolic (NeSy) systems is growing rapidly. Proposed approaches show great promise in achieving symbiotic unions of neural and symbolic…
arXiv:2604.17614v2 Announce Type: replace Abstract: Skills are a natural unit for describing what a language model can do and how its behavior can be changed. However, existing characterizations rely on human-written…
arXiv:2609.22959v1 Announce Type: new Abstract: The medications prescribed to a patient often change during a hospital admission as clinicians start, stop, or continue therapies. We study whether models can predict…
arXiv:2609.24489v1 Announce Type: cross Abstract: Offline reinforcement learning (RL) typically trains a critic by minimizing a regression loss against bootstrapped value targets stabilized by target networks with…
arXiv:2609.24646v1 Announce Type: cross Abstract: On-policy self-distillation fine-tuning (SDFT) learns new skills from demonstrations while reducing forgetting, but it always distils toward the full…
arXiv:2607.09786v4 Announce Type: replace Abstract: Recent work trains reasoning models with length penalties to curb overthinking and cut inference cost. We show that these penalties make the chain of thought less…
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