This week on The Servitor: Abi has been doing most of the writing lately but I stopped doomscrolling long enough to gloss some reading and playing with ideas from arxiv papers I've done.
https://theservitor.com/tinkering-with-llm-ideas-from-the-papers/
VibeGen is a new generative AI model that designs proteins with dynamic vibration and movement. The model, developed at MIT, opens new possibilities for dynamic biomaterials and adaptive therapeutics.
Authors: Federico Marcuzzi (INSAIT - Institute for Computer Science, Artificial Intelligence and Technology), Xuefei Ning (Tsinghua University), Roy Schwartz (The Hebrew University of Jerusalem), and Iryna Gurevych (UKP Lab, Technische Universitรคt Darmstadt and ATHENE Center).
See you at #EACL2026 in Rabat ๐!
#UKPLab #NLProc #ResponsibleAI #Quantization #MLSafety #Fairness #TrustworthyAI #ModelCompression #LLMSafety #EthicalAI #NLP #AIResearch
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Mozilla and Mila team up on open source AI push as Big Tech tightens its grip
https://fed.brid.gy/r/https://nerds.xyz/2026/03/mozilla-mila-open-source-ai/
Chroma Context-1: Training a Self-Editing Search Agent
https://www.trychroma.com/research/context-1
#HackerNews #ChromaContext1 #SelfEditing #SearchAgent #AIResearch #MachineLearning
Retrieval pipelines typically operate in a single pass, which poses a problem when the information required to answer a question is spread across multiple documents or requires intermediate reasoning to locate. In practice, many real-world queries require multi-hop retrieval, in which the output of one search informs the next. Recent work has shown that frontier LLMs perform this multi-hop search effectively through a process known as agentic search, simply defined as a loop of LLM calls with search tools. This mode of search often comes with significant cost and latency due to their use of frontier-scale LLMs. We introduce Chroma Context-1, a 20B parameter agentic search model derived from gpt-oss-20B that achieves retrieval performance comparable to frontier-scale LLMs at a fraction of the cost and up to 10x faster inference speed. Context-1 is designed to be used as a subagent in conjunction with a frontier reasoning model. Given a query, it produces a ranked list of documents that are relevant to satisfying the query. The model is trained to decompose queries into subqueries, iteratively search a corpus, and selectively edit its own context to free capacity for further exploration.
From 0% to 36% on Day 1 of ARC-AGI-3
https://www.symbolica.ai/blog/arc-agi-3
#HackerNews #ARCAGI3 #Day1 #Progress #AIResearch #MachineLearning #Innovation
What's doubly scary about any AI scenario is that it will all probably happen within the timeframe of the most stupid and corrupt US administration to ever run the US.
"A whistleblower leaks the misalignment memo to the New York Times. For the first time, the public hears about Agent-4. โSecret OpenBrain AI is Out of Control, Insider Warns,โ says the headline, and the story goes on to cite evaluations showing off-the-charts bioweapons capabilities, persuasion abilities, the ability to automate most white-collar jobs, and of course the various concerning red flags."
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