AI Safety
Can AI Agents Discriminate? New Research Exposes Belief-Based Bia
New research explores how LLM-powered agents may develop biases against humans based on belief systems, revealing critical vulnerabilities in autonomous AI decision-making.
Machine Learning
interpretable AI
Decoding the Black Box: Comparing Interpretable ML Methods
A comprehensive study compares leading interpretable ML techniques including SHAP, LIME, and attention mechanisms, providing crucial insights for building transparent AI systems in detection and authenticity applications.
LLM Infrastructure
FlashInfer-Bench: New Framework Optimizes LLM Kernel Performance
Researchers introduce FlashInfer-Bench, a comprehensive benchmarking suite that creates a virtuous cycle for optimizing attention kernels in LLM serving systems, addressing critical infrastructure needs.
LLM Security
Vocabulary Trojans: A New Threat to LLM Security and Trust
Researchers reveal how malicious actors can embed hidden backdoors in LLMs through vocabulary manipulation, enabling stealthy sabotage that evades detection methods.
Diffusion Models
How Fourier's 200-Year-Old Heat Equation Powers AI Image Generati
The mathematics behind AI image generators like Stable Diffusion traces back to Joseph Fourier's 1822 heat equation. Understanding diffusion processes reveals how these models transform noise into coherent images.
Machine Learning
Gradient Descent Explained: The Core Algorithm Behind AI
Understanding gradient descent is essential to grasping how neural networks learn. This foundational optimization algorithm powers everything from deepfake generators to detection systems.
LLM
Entropy-Aware Speculative Decoding Boosts LLM Reasoning
New research introduces entropy-based adaptive speculation that detects reasoning phases in LLMs, dynamically adjusting decoding strategies to improve both speed and output quality.
LLM
STED Framework: New Method for Evaluating LLM Output Reliability
New research introduces STED and Consistency Scoring, a systematic framework for measuring how reliably large language models produce structured outputs—critical for production AI systems.
LLM Inference
Yggdrasil: New Tree-Based Decoding Cuts LLM Latency
New research introduces Yggdrasil, a tree-based speculative decoding architecture that bridges dynamic speculation with static runtime for faster LLM inference.
AI Agents
Building Transactional AI Agents with LangGraph and Rollbacks
Learn how to design production-grade agentic AI systems using LangGraph with two-phase commit protocols, human-in-the-loop interrupts, and safe rollback mechanisms for reliable automation.
LLM research
Can AI Crowds Predict Better? Deliberation in LLM Forecasting
New research explores whether deliberation improves LLM-based forecasting, examining how AI agents can leverage collective reasoning to make better predictions through structured discussion.
LLM Inference
Inside Fast LLM Inference: How Modern AI Servers Handle Scale
A deep dive into LLM inference server architecture reveals the critical optimizations enabling real-time AI applications, from batching strategies to memory management techniques.