AI Research
New Survey Catalogs Bug Patterns in AI-Generated Code
Academic researchers systematically analyze the types and patterns of bugs produced by large language models when generating code, offering insights into AI reliability limitations.
LLM
LLM
New Metrics Tackle LLM Hallucinations via Entropy Analysis
Researchers propose semantic faithfulness and entropy production measures as novel approaches to detect and manage hallucinations in large language models, advancing AI content reliability.
LLM
LLM Inference: Data, Model & Pipeline Parallelization
Deep dive into the three core parallelization strategies for large language model inference: data parallel, model parallel, and pipeline parallel approaches. Essential techniques for scaling AI systems efficiently.
LLM
4 Proven Techniques to Optimize LLM Prompt Performance
Learn four essential optimization strategies for LLM prompts that reduce costs, improve latency, and boost performance. Technical deep dive into prompt engineering best practices with quantifiable results.
AI Agents
AI Memory Systems: Building Cognitive Architecture
Explore the technical architecture of AI memory systems, from short-term context windows to long-term knowledge storage. Learn how modern AI agents use multi-layered memory to enable complex reasoning and persistent learning across interactions.
prompt engineering
Prompt Engineering Best Practices for AI Engineers
Master advanced prompt engineering techniques used by AI engineers. Learn structured approaches, few-shot learning, chain-of-thought reasoning, and system prompt optimization to maximize LLM performance across technical applications.
Agentic AI
AccelOpt: Self-Improving AI Agents Optimize GPU Kernels
New research introduces AccelOpt, an LLM agentic system that autonomously optimizes AI accelerator kernels through self-improvement, achieving significant performance gains on GPU workloads through iterative code generation and testing.
LLM
Mastering LLM Controlled Generation: Beam Search & More
Deep dive into controlled generation techniques for LLM inference, from beam search to constrained decoding. Learn how these methods shape AI output quality, coherence, and computational efficiency in production systems.
LLM
TALE Framework Cuts LLM Costs with Adaptive Thinking
Researchers introduce TALE, a framework that optimizes LLM performance by dynamically adjusting reasoning depth. The system reduces costs while maintaining accuracy through adaptive test-time compute allocation.
Agentic AI
New Planner Framework Improves LLM Tool Use Beyond ReAct
Researchers propose a planner-centric framework that enhances how language models use external tools for complex reasoning tasks, showing improvements over the widely-used ReAct approach through better planning and execution separation.
LLM
Build Custom GPT Conversational AI Locally with Hugging Face
A comprehensive technical guide to building GPT-style conversational AI systems locally using Hugging Face Transformers, covering model selection, memory optimization, and deployment strategies for privacy-focused implementations.
LLM
Gradient-Aware Data Selection Tackles LLM Forgetting
New research introduces gradient-aware approach to select training data that helps large language models retain prior knowledge while learning new information, addressing catastrophic forgetting through intelligent sample selection.