Deep Learning Weekly compiled a set of deep learning and agentic AI updates, including model benchmarks, new tools, and research papers on distillation and agentic retrieval for complex tasks. Agentic retrieval improved nDCG@10 by 8.7 points over standard retrieval using the same embedding model. The collection highlights a trade-off where retrieval becomes more effective but takes 107.4 seconds per query and uses 764.1K input and 5.8K output tokens.
The Algorithmic Bridge·5 days ago·
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● 12 sources
Meta increased spending and internal usage of Anthropic’s Claude tools in a way that helped push Anthropic’s growth while Meta simultaneously rebuilt its own model pipeline to re-enter top performance. Meta’s newly shipped Muse Spark scored 52 on the Artificial Analysis intelligence benchmark against Claude Opus 4.6. As a result, Meta narrowed the gap by delivering a stronger model after prior releases lagged and Anthropic benefited from Meta’s investment.
ej was launched today as an open-source predictive AI built for decision questions with runtime-defined options. The release ships as a single 11.4 MB model file. It enables one-pass probability distributions for choice, yes/no, and ordered scores on CPU without token generation, along with training, evaluation, adaptation, and benchmark tools.
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