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All Issues →Paper of the Week #1 — Sparse Is Not the Same as Interpretable
I trained BDH and counted more than 4.8 billion activations across three controls. Training moved its single-latent sparsity from 49.98% to 81.65%, but a similar-budget ReLU baseline reached 91.04%. Sparsity is real; by itself, it is not evidence of interpretability.
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Paper of the Week #1 — Sparse Is Not the Same as Interpretable
I trained BDH and counted more than 4.8 billion activations across three controls. Training moved its single-latent sparsity from 49.98% to 81.65%, but a similar-budget ReLU baseline reached 91.04%. Sparsity is real; by itself, it is not evidence of interpretability.
Can AI Detectors Actually Tell If You Used ChatGPT? -- We Built One and Measured
We implemented an AI-text detector ourselves and measured when it works, when it accuses innocent people, and how it gets defeated -- detection and false-positive rates, in numbers.
Reproducing Claude's Watermark Locally — SynthID-Text on an Open Model, With Detection and Removal Attacks
We applied SynthID-Text — the algorithm Claude adopted — to Gemma 2 2B and measured everything: why detection is impossible without the key, how many tokens it needs, why false positives explode on short text, and how one rewrite by a local 3B model erases the watermark.
How Claude's Text Watermark Works — Signing Text Without Changing a Single Token
Every piece of text Claude generates now carries an invisible watermark — with nothing added to the text. A step-by-step walkthrough of SynthID-Text: the secret key, tournament sampling, detection, and the honest limits.
Why ChatGPT Gives You a Different Answer Every Time -- One Knob, Measured
We measure, on a local model and on GPT-4o-mini, exactly how much answers change with sampling temperature. Temperature doesn't create diversity -- it amplifies what's already there.
Breaking the Reversal Curse with Identity Bridges — the ICML 2026 fix that shouldn't work but does
LLMs trained on "Alice's husband is Bob" famously fail on "Bob's wife is?" — the reversal curse. A new ICML 2026 paper fixes it by adding one weird kind of self-referential example to the training set. The naive version doesn't work; the right version does.