PRECURSOR23 Sep, 04:10
arXiv:2509.20702v3 Announce Type: replace-cross Abstract: Recent advances in large language model (LLM) embeddings have enabled powerful representations for biological data, but most applications to date focus on gene-level information. We present one of the first systematic frameworks to generate genetic variant-level…
capabilitydeploymentconstraintspolicy
preprint; not peer reviewed
PRECURSOR23 Sep, 04:10
arXiv:2609.22441v1 Announce Type: new Abstract: In large-scale recommendation systems like the LinkedIn Feed, content generated by a member's network (connections and follows) makes up over 70% of impressions and engagement. It is therefore essential that the pre-ranking layer forwards the best possible few hundred…
capabilitydeploymentconstraintspolicy
preprint; not peer reviewed
PRECURSOR24 Sep, 09:16
Background: Large language models (LLMs) can support systematic reviews, but accurate individual outputs do not establish whether the final synthesis preserves the clinical question, accounts for statistical dependence and incorporates corrections. Objective: To develop and evaluate a framework linking LLM-assisted evidence…
capabilitydeploymentconstraints
medRxiv preprint; not peer reviewed and not clinical advice
PRECURSOR24 Sep, 09:16
Background: Unstructured free-text narratives in electronic health records (EHRs) contain critical clinical information that is not captured by structured standard codes. In Catalonia, primary care notes follow a semi-structured format known as MEAP (in Catalan). However, extracting structured variables using cloud-hosted…
capabilitydeploymentconstraints
medRxiv preprint; not peer reviewed and not clinical advice
PRECURSOR24 Sep, 09:16
TL;DR We introduce WorkspaceBench, a set of evaluations for how well an activation-to-text tool can read the contents of the “global workspace” of a model, i.e. the intermediate variables during a forward pass. The benchmark comprises 3,356 questions across 27 eval families, spanning topics in safety, logical reasoning, and…
capabilitydeploymentconstraints
community technical post; claims unverified
PRECURSOR24 Sep, 09:16
Jev is a new model format where instead of outputting text, it outputs certainties for a defined set of options. Due to this structure, it’s extremely fast! Naturally, a classification task that comes to mind is monitoring harmful thought traces. I wanted to see how it performed at Chain of Thought (CoT) monitoring compared…
capabilitydeploymentconstraints
community post; claims unverified
PRECURSOR24 Sep, 09:16
Introducing the world’s most powerful model, at least by some measures like Artificial Analysis or any standard benchmark list, which is now Claude Opus 5.5 . Anthropic is claiming Opus 5.5 is outright as good or better than Fable 5.1, while being actively cheaper than Opus 5. That means it’s time for a good old system card…
capabilityconstraintspolicy
community post; claims unverified
PRECURSOR23 Sep, 19:52
Using GPT-5.6, Ringg powers multilingual agents across voice, chat, WhatsApp, and web for 90% less cost vs. GPT-4.1.
capabilitydeploymentconstraints
source-confirmed publication; claims unverified
PRECURSOR23 Sep, 04:11
{
"abstract": "The Centers for Disease Control and Prevention (CDC) seeks broad public input on how data intermediaries can be used to support secure, scalable, standards-based public health data exchange. CDC invites public comment to inform the evaluation and to explore how data intermediaries can advance broader goals…
capabilityconstraintspolicy
Federal Register document metadata and abstract; underlying action requires review
PRECURSOR23 Sep, 04:10
https://www.anthropic.com/claude-opus-5-5 It's a sizeable upgrade: Also, the first model in which they say this: Pacing the frontier Last week, our CEO, Dario Amodei, argued that AI progress should be paced so that safety practices stay ahead of model capabilities. Pacing is an approach to keeping AI safe, remaining…
capabilitydeploymentconstraints
community post; claims unverified
PRECURSOR23 Sep, 04:10
arXiv:2609.22323v1 Announce Type: new Abstract: Few-shot learning research is predominantly evaluated on accuracy alone, with limited attention to the parameter and training-sample budgets required to reach that accuracy - a real constraint for practitioners without large-scale compute. We present an ultra-lightweight…
capabilitydeploymentconstraints
preprint; not peer reviewed
PRECURSOR23 Sep, 04:10
arXiv:2609.22500v1 Announce Type: new Abstract: Autonomous navigation requires precise and efficient semantic segmentation, yet existing frame-based approaches remain limited by motion blur, glare, latency, and the low temporal resolution (20-30 FPS) of conventional cameras, which leads to information loss between frames.…
capabilitydeploymentconstraints
preprint; not peer reviewed