MACHINE LEARNING

→ View original post on X — @jiqizhixin

FutureOmni: Multimodal AI Predicts Future From Video Audio

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@jiqizhixin

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28 January 2026 17h13

What if AI could predict the future from sights and sounds? Researchers from Fudan University & NUS introduce FutureOmni, the first benchmark to test this. It pushes multimodal models to reason across audio & video to forecast what happens next, not just describe the past.

→ View original post on X — @_akhaliq

AdaReasoner: Dynamic Tool Orchestration for Visual Reasoning

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@_akhaliq

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28 January 2026 15h27

AdaReasoner Dynamic Tool Orchestration for Iterative Visual Reasoning

→ View original post on X — @askalphaxiv

Patterning: New AI Interpretability Approach for Circuit Analysis

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@askalphaxiv

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28 January 2026 15h13

BIG new idea in interpretability called Patterning The basic idea: given a desired generalization/structure, determine what training data produces it So they treat what circuits/algorithms the model learns as something you can solve for by measuring how sensitive those internal

→ View original post on X — @ingliguori

AI Stack Layers: From Rules to Agentic Intelligence

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@ingliguori

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28 January 2026 14h17

AI isn’t one thing — it’s a stack. Rules → ML → Neural Nets → Deep Learning → GenAI → Agentic AI Agents don’t replace the layers below.
They orchestrate them. If GenAI answers,
Agentic AI executes.

→ View original post on X — @godofprompt

Specific personas boost LLM output quality to 94%

By

@godofprompt

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28 January 2026 12h54

Telling an LLM to "act as an expert" is lazy and doesn't work. I tested 47 persona configurations across Claude, GPT-4, and Gemini. Generic personas = 60% quality
Specific personas = 94% quality Here's how to actually get expert-level outputs:

Context Engineering for Multi-Agent Systems Architecture

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28 January 2026 6h10

"Context Engineering for Multi-Agent Systems: Move beyond prompting to build a Context Engine, a transparent architecture of context and reasoning" — at http://
amzn.to/448dSiA v/ @PacktDataML 𝓦𝓱𝓪𝓽 𝓨𝓸𝓾 𝓦𝓲𝓵𝓵 𝓛𝓮𝓪𝓻𝓷:
Develop memory models to retain short-term and

Scikit-learn Cookbook: 80+ Recipes for Machine Learning in Python

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28 January 2026 6h00

Scikit-learn Cookbook — 80+ recipes for #MachineLearning in Python with scikit-learn [3rd Edition]: http://
amzn.to/4oDGOq7 v/ @PacktDataML 𝓒𝓸𝓷𝓽𝓮𝓷𝓽𝓼:
Common Conventions & API Elements of Scikit-Learn
Pre-Model Workflow and Data Preprocessing
Dimensionality

Practical Guide to Building AI Agents with LLMs and RAG

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28 January 2026 5h52

"Building AI Agents with LLMs, RAG, and Knowledge Graphs — A practical guide to autonomous and modern AI agents" See it at http://
amzn.to/4622k2h via @PacktDataML

Vector Search for Practitioners and AI Solutions

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28 January 2026 5h46

Vector Search for Practitioners with Elastic — A toolkit for building #NLProc solutions for search, observability, and security: http://
amzn.to/3vEkWFe v/ @PacktDataML What you will learn:
Optimize performance by harnessing the capabilities of vector search
Explore image