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cs.AI2026
Sparse Transformer: a Controlled Testbed for Mechanistic Interpretability
Anton Korznikov, Alexey Dontsov, Elena Tutubalina
Mechanistic interpretability aims to decompose neural network computations into human-understandable features, yet progress is limited by the absence of ground-truth units to evalu…
cs.LG2026
Superhuman AI for Generals.io Using Self-Play Reinforcement Learning
Matej Straka, Viliam Lisý, Martin Schmid
We present a superhuman AI agent for Generals.io, a real-time strategy game that combines long-horizon planning, imperfect information, and combinatorially large state and action s…