AI systems cannot forget or forgive due to their engineering: weights retain training data permanently, context windows switch between perfect recall and total erasure, retrieval stores lack temporal …
A chatbot built to mimic human texting gradually broke character over long conversations, outputting markdown and lists despite a system prompt banning them. Researchers found this isn't a prompt-writ…
A developer evaluated six LLM-as-judge tools—DeepEval, Confident AI, Evidently, Braintrust, Promptfoo, and Future AGI—and found that none of them prioritize validating judge outputs against human labe…
A technical analysis reveals that large language models fail at long loop tasks due to attention dilution, EOS bias, and stateless architecture, causing protocol drift where constraints are rewritten …
A developer describes context compression as a technique to reduce token costs and improve LLM answer quality by filtering retrieved text before generation. Extractive compression keeps verbatim sente…
Researchers Liu et al. published a paper on arXiv 2025 introducing ProRL, a method using prolonged reinforcement learning with KL resets to expand reasoning boundaries in AI models. An interactive vis…
OpenAI's GPT-3.5-Turbo model drops from 90% accuracy to 50% when the correct answer is placed in the middle of a 20,000-token prompt rather than at the start or end, according to research by Liu et al…
A new architectural analysis reveals that production AI agent systems are dominated by operational infrastructure rather than AI decision logic, with Claude Code's leaked source code showing only abou…
Current evaluation methods for large language models (LLMs) are fundamentally reactive and fail to anticipate qualitative shifts in capabilities, such as emergent abilities or strategic information wi…