PaperBrief reads 500+ ML papers a day so you don't have to. Enter your research interests, get a weekly digest of the papers that actually matter to your work — ranked by relevance, summarised in plain English.
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There are 500+ new papers every single day in machine learning alone. You can't read them all. But the ones you miss might be the ones that change your thesis direction, invalidate your approach, or give you the exact technique you've been trying to invent yourself.
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Every paper is scored 0–100 for relevance to your research track. You only see the papers worth your time — ranked, summarised, and ready to act on.
PaperBrief · Weekly Digest
Multi-Agent Systems · March 14, 2026
3 papers this week
from 547 scanned
Wang, J., Chen, L., Patel, A. +1 more
We present Mixture-of-Agents (MoA), a framework that leverages multiple specialised LLM experts to collaboratively solve complex reasoning tasks. Rather than routing queries to a single model, MoA dynamically aggregates outputs from a panel of agents with comp…
Sarthi, P., Abdullah, S., Tiwari, A.
Retrieval-Augmented Generation (RAG) struggles when relevant information is spread across multiple document sections. RAPTOR addresses this by recursively clustering and summarising text chunks into a hierarchical tree, enabling retrieval at multiple levels of…
Zhuang, Y., Yu, Y., Wang, K. +2 more
Planning with external tools requires LLMs to balance exploration and exploitation in a vast action space. ToolChain* adapts the A* search algorithm to tool-use planning, using a learned heuristic that estimates remaining task cost from partial tool-call seque…
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