My World of Thoughts

We present EsoLang-Bench, a benchmark using esoteric programming languages where training data is virtually nonexistent. Five frontier models scored 85-95% on standard benchmarks but achieved only 11.2% maximum on EsoLang-Bench, with most below 5%. All models scored exactly 0% beyond the "Easy" difficulty tier, a uniform failure suggesting fundamental limitations rather than gradual degradation.

A deep-dive into the Engram module, exploring its architecture, memory mechanisms, and the elegant design principles behind continual learning in neural networks.

A technical walkthrough of importance sampling, covering how to estimate expectations under one distribution using samples from another, and why it matters for reinforcement learning and probabilistic inference.

Sequential reasoning wins in 95.6% of configurations at matched compute, with accuracy gains up to 46.7%. On AIME-2025 with Qwen3-235B: 76.7% vs parallel's 30.0%. We introduce inverse-entropy weighted voting, a training-free aggregation method that achieved optimal performance in 97% of sequential runs.

We demonstrate that post-trained models can recognize correct solutions through output entropy analysis. Sequence-level entropy cleanly separates correct from incorrect reasoning, but only in reward-trained models, not instruction-tuned ones. This enables 25-50% token reduction without sacrificing accuracy.

An introduction to Flux.jl, Julia's machine learning library, covering how to build custom neural network architectures from scratch with a clean, composable API that makes deep learning in Julia intuitive and flexible.