MACHINE LEARNING

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15 Courses for Understanding Neural Networks

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@mit_csail

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09 November 2025 18h00

15 courses for understanding neural networks, v/
@tut_ml
: https://
shorturl.at/60Se8

Dr. MAMR: Solving Lazy Agent Problem in Multi-Agent LLM

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09 November 2025 16h00

10. Unlocking the Power of Multi-Agent LLM for Reasoning Introduces Dr. MAMR, which addresses the “lazy agent” problem in multi-agent LLM reasoning through Shapley-inspired causal influence measurement and verifiable restart mechanisms.

Differentiable Artificial Life Framework for Continuous Agent Learning

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09 November 2025 16h00

9. PD-NCA

A differentiable artificial life framework where multiple independent agents continuously update their parameters through gradient descent during simulation, enabling within-lifetime learning and open-ended behavioral change.https://t.co/0omPU6a4Un
— DAIR.AI (@dair_ai) 9 novembre 2025

9. PD-NCA A differentiable artificial life framework where multiple independent agents continuously update their parameters through gradient descent during simulation, enabling within-lifetime learning and open-ended behavioral change.

AlphaEvolve: AI Autonomously Discovers Mathematical Constructions

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09 November 2025 16h00

8. Mathematical Exploration at Scale Terence Tao and colleagues apply AlphaEvolve, an AI system using LLM-guided evolutionary search to autonomously discover mathematical constructions across analysis, combinatorics, geometry, and number theory.

Diffusion Language Models Outperform Autoregressive With Limited Data

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09 November 2025 16h00

7. Diffusion LMs are Super Data Learners Researchers from NUS, Sea AI Lab, StepFun, and collaborators demonstrate that diffusion language models (DLMs) consistently outperform autoregressive models when unique training data is limited.

DreamGym: Scaling Agent RL via Experience Synthesis

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09 November 2025 16h00

3. Scaling Agent RL via Experience Synthesis Meta researchers introduce DreamGym, a unified framework that synthesizes diverse training experiences to enable scalable reinforcement learning for LLM agents without costly real-environment rollouts.

Context Engineering Evolution: From Computing 1.0 to Intelligent Agents

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09 November 2025 16h00

2. Context Engineering 2.0 Traces the 20+ year evolution of context engineering, reframing it as a fundamental challenge in human-machine communication spanning from primitive computing (Era 1.0) to today’s intelligent agents (Era 2.0) and beyond.