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Pulse · bioengineer.org

bioengineer.org · Oct 27

Survey maps how LLM agents moved from single tool calls to multi-tool orchestration

A new open-access survey in Artificial Intelligence Review organizes research on LLM agents around six dimensions of multi-tool orchestration, from planning and training to safety, efficiency, and

Pulse · finance.yahoo.com

finance.yahoo.com · Oct 27

Wall Street Banks Provide $63 Billion To Finance Broadcom AI Chips For Anthropic

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arXiv

Oct 26

Retriever-Free Retrieval: Navigating Documents with Decision Models

While language models have become dramatically more capable, retrieval still largely relies on lexical matching or similarity in fixed embedding spaces. This creates a capability asymmetry: what the retriever fails to find, even the strongest generator cannot recover. We propose DecisionRAG, which brings the capabilities of pretrained language models to retrieval using decision models that jointly evaluate candidate branches in a single forward pass, without generating any tokens, and show that judging candidates in a shared context makes their relevance estimates comparable. Like a person browsing a library, DecisionRAG navigates the corpus's inherent structure from broad topics down to individual passages, deciding at each level which branches are worth exploring. Without task-specific training or a first-stage retriever, DecisionRAG outperforms dense retrieval with an 8B reranker by 13.8 R@1 points on MultiDoc2Dial and 17.4 Hit@1 points on Natural Questions. Across four benchmarks, including a corpus of 2.68 million Wikipedia paragraphs, retrieval takes just 0.7-1.8 seconds per query.

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arXiv

Sep 18

Sparse Identification for Automatic Large-Scale Screening: A Constraint-Aware Framework with Ultra Fast Decoding Algorithm

In the early stages of a pandemic, identification of a small number of infected individuals through large-scale screening is critical for pandemic control, yet remains challenging under limited reagents and testing capacity. Existing group testing methods suffer from either…

arXiv

Sep 18

Diagonalized Attention for Individualized Regression: Latent-Row Localization and Prediction

Modern text and image representations are often matrix-valued, with rows corresponding to tokens, patches, or other local feature vectors. Predictive information is often sparse but sample-specific, making classical sparse regression methods with a common support poorly suited…

arXiv

Sep 18

An Introduction to Compression-Based Machine Learning

Any lossless compression algorithm (like gzip) may be converted into a machine learning method, via either Normalized Compression Distance or the Minimum Description Length principle. Any auto-regressive model may be converted into a lossless compression method via entropy…

arXiv

Sep 18

Fast And Accurate Text Content File Type Identification

A common requirement across organizations is to have a tool that can identify file types based on their contents, particularly in the cybersecurity domain where magic numbers and file extensions can not be trusted. While existing tools work well in practice, there is plenty of…

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