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.
machine learning
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.
synthetic data
RL-Driven Synthetic Data Generation: A New Training Paradigm
New research explores how reinforcement learning can optimize synthetic data generation, with implications for training more capable AI video and media generation models.
reinforcement learning
Liquid AI's Pure RL Approach Redefines Small Model Training
Liquid AI's LFM2-2.6B-Exp uses pure reinforcement learning without supervised fine-tuning, achieving dynamic hybrid reasoning that outperforms larger models on key benchmarks.
AI safety
Formal Verification Breakthrough for Early-Exit Neural Networks
New research bridges efficiency and safety by developing formal verification methods for neural networks with early exits, enabling mathematically proven safety guarantees for adaptive AI systems.
LLM Agents
GenEnv: Co-Evolving LLM Agents with Adaptive Environment Simulato
New research introduces GenEnv, a framework where LLM agents and environment simulators co-evolve through difficulty-aligned training, enabling more robust agent capabilities.
AI Agents
Inside AI Coding Agents: Architecture, Tools, and Agentic Loops
A technical deep dive into how AI coding agents work, from tool-calling mechanisms and agentic loops to planning systems and memory architectures that enable autonomous code generation.
AI Security
Adversarial Attacks on LLM Resume Screeners Reveal AI Security Ga
New research exposes how adversarial techniques can manipulate LLM-based resume screening systems, revealing fundamental security vulnerabilities in specialized AI applications.