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Daily notes on new models, LLM releases, agent frameworks and AI research, written from the sources we follow and delivered as a newsletter every day.
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Research1 min read
arXiv:2609.13396v1 Announce Type: new Abstract: Multi-objective Bayesian optimisation (MOBO) is a sample-efficient approach for optimising expensive black-box functions with multiple objectives. In MOBO, the goal is to adequately approxi...
From arXiv cs.AI
AI1 min read
A Real World AI Stage fireside chat featuring Ben Lamm, CEO of Colossal Biosciences, explores the intersection of AI, synthetic biology, and the potential for engineering nature’s comeback. The discussion examines the technologies behind de-extinction and the broader implications for biodiversity and conservation.
From TechCrunch AI
How this blog is made
Each feed entry becomes one request to OpenSmartRoute: the router picks a model with a cost-weighted objective, the editorial-writer skill is layered on the prompt, and the outcome trains the learners - the same pipeline available to every workspace.
Open any post to see which target answered, its confidence, the alternatives and what the request cost. Run the same pipeline yourself: register feeds in the operator console, map a small model under Providers, or call POST /api/v1/route with execute: true.
Research1 min read
T-GADE, a system using thermodynamic genetic algorithms and LLMs, evolved structured artifacts like code paired with descriptions. Training experiments on the online bin-packing task showed a 29% reduction in median training excess at a temperature of 0.003, validating the approach’s effectiveness.
From arXiv cs.AI
LLMs1 min read
The research introduces Chopthin-Consensus Power Sampling (CCPS), a method for LLM decoding that preserves reasoning diversity and improves accuracy. Evaluation across open-weight models and benchmarks shows CCPS matches or exceeds the Power-SMC baseline in 14 of 15 settings, achieving gains of up to 10.6 percentage points.
From arXiv cs.CL
LLMs2 min read
SynthSentry is a model-agnostic tool for detecting synthetic data contamination in language model training corpora. It analyzes lexical diversity, n-gram tails, and perplexity variance across reference models, offering a pre-training screening method without requiring access to the generating model.
From arXiv cs.CL
Posts are drafted from public feeds by models OpenSmartRoute routes to - the same router, skill and metering customers use - and always link to the original source. Corrections: support.
