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Essays

  • 5 min read

Unlimited Sensing: When Noise Becomes the Signal

Source: SIAM News — Ayush Bhandari, Imperial College London

TL;DR

Unlimited sensing is a novel approach that harnesses quantization noise from traditional digitization methods to achieve clearer sensing. Instead of clipping signals that exceed conventional sensor range, the method uses "folding" — when the signal exceeds the sensor's range, it folds back rather than being clipped, preserving information that would otherwise be lost. This allows sensors to capture signals far beyond their nominal range without hardware modifications. The technique has evolved from theoretical concept to practical applications with implications for various sensing modalities.

  • 5 min read

If LLMs Have Human-Like Attributes, Then So Does Age of Empires II

Source: arXiv:2605.31514 \ Author: Adrian de Wynter (Microsoft & The University of York) \ Published: May 2026 \ License: CC BY-NC-SA 4.0

TL;DR

This paper doesn't argue for or against LLMs having human-like attributes — it argues that the experimental framework used to study them is fundamentally broken. Whether you accept or reject the existence of anthropomorphic attributes, the conclusions are either circular or uninformative. To prove the point empirically, the author builds a Turing-complete perceptron inside Age of Empires II, demonstrating that computational substrates alone can generate behaviours that would qualify as "human-like" under current criteria — and that how human-like something appears depends more on its implementation and interface than on any intrinsic property.

  • 2 min read

Defending Code — Anthropic's Vulnerability Discovery Reference Harness

Overview

Anthropic has released an open-source reference implementation for autonomous vulnerability discovery and remediation, built on the operational learnings from Project Glasswing. The harness provides a complete, production-tested framework for turning AI agents loose on codebases to find and fix security vulnerabilities — safely.

The system follows a five-stage lifecycle:

  1. Recon — map the attack surface, identify entry points, understand the codebase architecture.
  2. Find — actively probe for vulnerabilities using static and dynamic analysis.
  3. Triage — assess severity, exploitability, and false positive likelihood for each finding.
  4. Report — generate structured findings with proof-of-concept exploit code and remediation guidance.
  5. Patch — implement and validate fixes.
  • 3 min read

When AI Builds Itself — Recursive Self-Improvement

The Accelerating Pace

Anthropic's internal analysis of recursive self-improvement (RSI) reveals a striking trend: the duration of tasks AI systems can complete independently has been doubling every 4 months — down from every 7 months just a year ago.

Consider the trajectory:

  • Opus 3 (2024) — could sustain ~4 minutes of autonomous work.
  • Sonnet 4 (2025) — extended to ~90 minutes.
  • Opus 4.6 (2026) — now reaches ~12 hours of continuous autonomous operation.

At current acceleration, days-long tasks are expected within this year. The compounding is not just in duration but in quality — each generation of models can make better use of its expanded horizon.

  • 3 min read

AUTOLAB — Benchmarking Long-Horizon Empirical Optimization

What Is AUTOLAB?

AUTOLAB is a benchmark designed to test frontier AI models on sustained iterative empirical optimization over extended time horizons — 2 to 12 hours of continuous work. It measures not just whether a model can solve a problem, but whether it can persist through the long tail of debugging, tuning, and iteration that real optimization work demands.

The benchmark spans 36 tasks across four domains:

  • System optimization — kernel tuning, database configuration, compiler flag optimization.
  • Puzzles — constraint satisfaction, combinatorial search.
  • Model development — hyperparameter sweeps, architecture search, loss landscape exploration.
  • CUDA kernel optimization — writing and iterating on GPU kernels for speed.
  • 2 min read

Essay Summary: AI as the Most Dangerous Arms Race in History

Overview

Niall Ferguson argues that the unchecked artificial intelligence race between the United States and China mirrors nuclear brinkmanship but critically lacks the stabilizing strategic doctrines and arms control frameworks that (however imperfectly) constrained the Cold War nuclear standoff. The stakes, he contends, are even higher.