Machine Learning

LLM research

New Metric Measures LLM Reasoning Depth via Deep-Thinking Tokens

Researchers propose measuring LLM reasoning quality through 'deep-thinking tokens' rather than output length, offering new insights into how AI models actually process complex problems.

AI Agents

MAPLE: New Sub-Agent Architecture for AI Memory and Learning

New research introduces MAPLE, a sub-agent architecture enabling memory, learning, and personalization in agentic AI systems through modular design patterns.

LLM research

Stabilizing Low-Rank LLM Pretraining: New Research Approach

New research explores techniques for stabilizing native low-rank pretraining in large language models, potentially enabling more efficient training of foundation models.

AI Agents

Build Self-Organizing Memory Systems for AI Agents

Learn how to construct self-organizing memory architectures that enable AI agents to maintain context and reason across extended interactions and complex tasks.

LLM Agents

How Memory Architecture Shapes LLM Agent Performance

New research examines how different memory architectures affect LLM agent capabilities, offering insights into designing more effective AI systems.

LLM research

New Method Detects LLM Hallucinations via Internal State Analysis

Researchers propose a novel framework for visualizing and benchmarking factual hallucinations in large language models by analyzing internal neural activations and clustering patterns.

Machine Learning

Data Leakage in ML: Why Train-Test Splits Must Come First

Data leakage silently destroys model validity. Learn why preprocessing before splitting contaminates your test set and how to build pipelines that preserve true model performance.

Synthetic Data

PRISM: Structure-Aware Privacy Budgets for Synthetic Data

New research introduces PRISM, a differentially private synthetic data framework using structure-aware budget allocation to optimize prediction accuracy while maintaining privacy guarantees.

LLM Evaluation

MILE-RefHumEval: Multi-LLM Framework for Human-Aligned AI Evaluat

New research introduces a reference-free evaluation framework using multiple independent LLMs to assess AI outputs with better human alignment than single-judge approaches.

LLM Agents

PABU: Making LLM Agents Smarter Through Progress-Aware Updates

New research introduces PABU, a framework that helps LLM agents track their progress and update beliefs more efficiently, reducing computational waste in multi-step reasoning tasks.

LLM

LLM Parameters Explained: Weights, Biases, and Scale Demystified

Understanding LLM parameters is key to grasping how AI models generate text, images, and video. Learn what weights and biases actually do and why model scale matters.

prompt engineering

7 Prompt Engineering Methods That Boost LLM Output Quality

From chain-of-thought reasoning to self-consistency sampling, these seven prompt engineering techniques can dramatically improve how large language models respond to complex queries.