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@berlinbuzzwords@floss.social
2025-05-14 11:00:10

RAG has revolutionised AI by merging search and generation. Agentic behaviour takes this search to the next level by enabling LLMs to make decisions and call tools. At this year's Berlin Buzzwords, Bilge Yücel will discuss how agentic behaviour enhances pipelines, what it means for a system to act as an 'agent', and core concepts such as routing, tool calling and reasoning, along with a live demo.
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Session title: Go Beyond Basic RAG with Agentic Behavior
Bilge Yücel
Join us from June 15-17 in Berlin or participate online / berlinbuzzwords.de

Go Beyond Basic RAG with Agentic Behavior
Retrieval-Augmented Generation (RAG) has transformed how we build Q&A systems with Large Language Models (LLMs) by combining the strengths of search and generation. However, traditional RAG workflows are static and often struggle to handle the dynamic and complex demands of real-world applications, such as answering multi-step queries, integrating external APIs, or gracefully recovering from retrieval failures. Agentic behavior addresses these challenges by extending RAG pipelines, enabling LLM…

@arXiv_csDC_bot@mastoxiv.page
2025-06-13 07:25:50

Resilience through Automated Adaptive Configuration for Distribution and Replication
Scott D. Stoller, Balaji Jayasankar, Yanhong A. Liu
arxiv.org/abs/2506.10248

Resilience through Automated Adaptive Configuration for Distribution and Replication
This paper presents a powerful automated framework for making complex systems resilient under failures, by optimized adaptive distribution and replication of interdependent software components across heterogeneous hardware components with widely varying capabilities. A configuration specifies how software is distributed and replicated: which software components to run on each computer, which software components to replicate, which replication protocols to use, etc. We present an algorithm that,…

@arXiv_csIR_bot@mastoxiv.page
2025-06-03 17:02:07

This arxiv.org/abs/2505.19755 has been replaced.
initial toot: mastoxiv.page/@arXiv_csIR_…

One Model to Rank Them All: Unifying Online Advertising with End-to-End Learning
Modern industrial advertising systems commonly employ Multi-stage Cascading Architectures (MCA) to balance computational efficiency with ranking accuracy. However, this approach presents two fundamental challenges: (1) performance inconsistencies arising from divergent optimization targets and capability differences between stages, and (2) failure to account for advertisement externalities - the complex interactions between candidate ads during ranking. These limitations ultimately compromise s…

@arXiv_csMA_bot@mastoxiv.page
2025-06-02 09:58:25

This arxiv.org/abs/2504.00587 has been replaced.
initial toot: mastoxiv.page/@arXiv_csMA_…

AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems
The rapid advancement of large language models (LLMs) has enabled the development of multi-agent systems where multiple LLM-based agents collaborate on complex tasks. However, existing systems often rely on centralized coordination, leading to scalability bottlenecks, reduced adaptability, and single points of failure. Privacy and proprietary knowledge concerns further hinder cross-organizational collaboration, resulting in siloed expertise. We propose AgentNet, a decentralized, Retrieval-Augme…