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#randomness

QSignAI: Quantum-Randomness-Seeded Identity Signatures at the Intersection of AI for Science and Science for AI

arXiv cs.AI · 2026-06-18 Cached

QSignAI is a production-deployed open-source platform that combines quantum randomness from a Toeplitz two-source extractor with an AI bot on Telegram to generate unique identity signatures, demonstrating a bidirectional relationship between artificial intelligence and quantum science.

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#randomness

The FID Lottery: Quantifying Hidden Randomness in Generative-Model Evaluation

Hugging Face Daily Papers · 2026-06-18 Cached

This paper analyzes the variance of FID scores across different training and sampling seeds, revealing significant reproducibility issues in image generation evaluation. It proposes a new evaluation protocol with error bars and per-cell optimal guidance tuning.

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#randomness

Entropy

Lobsters Hottest · 2026-06-07 Cached

A technical blog post exploring randomness, Linux entropy, and building a tool called morerandom that uses WASM plugins to feed the system entropy pool.

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#randomness

Physicists achieve 'perfect randomness' in breakthrough quantum experiment

Reddit r/singularity · 2026-06-03 Cached

Researchers at ETH Zurich have demonstrated a method for generating 'perfect randomness' using entangled superconducting qubits, a breakthrough with implications for cryptography and secure communications.

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#randomness

The Futility of Lava Lamps: What Random Really Means

Lobsters Hottest · 2026-05-16

An article exploring the philosophical and practical meaning of randomness, using lava lamps as a metaphor for entropy generation in computing.

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#randomness

Myths about /dev/urandom (2014)

Hacker News Top · 2026-05-14 Cached

Debunks common myths about /dev/urandom and /dev/random, explaining that /dev/urandom is the preferred source of cryptographic randomness on Unix-like systems.

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#randomness

Probabilistic Calibration Is a Trainable Capability in Language Models

arXiv cs.CL · 2026-05-13 Cached

This paper investigates whether probabilistic calibration in language models can be improved through fine-tuning, comparing soft-target and hard-target methods across 12 models. The results show that calibration is a trainable capability, though gains sometimes reduce downstream arithmetic reasoning capabilities.

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#randomness

Randomness is sometimes necessary for coordination

arXiv cs.AI · 2026-05-11 Cached

The paper introduces Diamond Attention, a method for multi-agent reinforcement learning that uses structured randomness to break symmetry and enable role differentiation among homogeneous agents, achieving perfect coordination in symmetric tasks like the XOR game.

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