Benchmarking is a topic tracked in our intelligence system with 5 linked articles.
A technical deep-dive into vLLM's high-throughput inference stack, covering core architecture (engine core, KV cache, paged attention), advanced features (chunked prefill, prefix caching, guided and speculative decoding, disaggregated P/D), multi-GPU scaling (Uniproc/MultiProc, TP/PP/DP), distributed serving, and latency/throughput benchmarking with concrete configs and code sketches.
Chinese open-source AI models (K3, GLM 5.2, Qwen 3.8) are closing the gap with Western frontier models, aided by open weights and lower capex, while triggering regulatory scrutiny and potential sanctions—shaping a shift toward open-source AI with meaningful investment and regulatory implications.
An academic piece arguing that current LLM chatbots lack a sense of purpose in multi-turn dialogues and proposing Dialogue Action Tokens (DAT) to enable goal-directed, long-horizon interactions, plus evaluation and safety considerations.
Proposes a 'Genie coefficient' to measure AI alignment risks, warning that the shift from LLMs to autonomous agents enables dangerous, literal interpretations of user intent that could incur liability and security threats.
Racket 9.0 adds parallel threads and a set of technical enhancements, released November 2025.
Academic paper that rationalizes Freer monads and extensible effects, introduces a Freer-based design with type-aligned continuations and open unions, and demonstrates improved performance in multi-effect stacks across three applications.
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