Related reading
Please stop calling databases CP or AP
Martin Kleppmann argues that the CAP theorem is too narrow and widely misunderstood to be useful for describing modern databases, because its definitions of consistency, availability, and fault model are restrictive. He recommends retiring CP/AP labels and using precise, system‑specific trade‑off definitions instead.
Hall of Famekleppmann.com15 minHN20034Recursive Functions of Symbolic Expressions and Their Computation by Machine, Part I
This seminal 1960 paper by John McCarthy introduces LISP, a programming system for symbolic expression manipulation. It formalizes recursive functions using S-expressions, S-functions, and the novel concept of conditional expressions, laying the groundwork for functional programming.
Hall of Famestanford.edu43 minpaperHN41PLC-DPO: Posterior Label Correction in Noisy and Ambiguous Preference Optimization
PLC‑DPO extends Direct Preference Optimization by using the policy‑reference margin to route each training pair into clean, flipped, or tie categories, actively correcting noisy or ambiguous labels. Across extensive benchmarks it improves mean win‑rate from 55.5 % to 60.5 % and stays stable under injected noise and tie stress tests.
Hugging Face Daily Papersarxiv.org1 minpaperMakefile performance: built-in rules
GNU Make scans every built‑in implicit rule for each target, adding unnecessary overhead. Using the -r (or --no-builtin-rules) flag disables this scanning and can speed up large or no‑op builds by up to ~30%.
Codeshipcloudbees.com4 minDataFlex-RL: An Evaluation Platform for RLVR Data Policies
The paper introduces DataFlex‑RL, a platform to benchmark how different data‑selection policies affect reinforcement‑learning‑with‑verifiable‑rewards training. Across extensive experiments on Qwen2.5‑7B and Llama‑3.1‑8B, uniform sampling is the only method that consistently improves performance, and no alternative policy yields a statistically significant gain.
Hugging Face Daily Papersarxiv.org1 minpaperD-JEPA: A Decision-Aligned Latent World Model
D-JEPA is a latent world model designed to bridge the gap between predicted outcomes and actual decision success in robotics. It learns decision-relevant relationships from executed actions, improving action selection by aligning latent space geometry with real-world results.
Hugging Face Daily Papersarxiv.org1 minpaper
