Gated Differentiable Working Memory for Long-Context Language Modeling (2026 · 4 citations)
Long inputs remain difficult for transformer LLMs. Attention spreads thin over thousands of tokens, and evidence placed mid-document is often overlooked. Test-time training methods such as qTTT address this by briefly fine-tuning "fast weights" on the given context. However, they sample positions uniformly, so they…
a1: Steep Test-time Scaling Law via Environment Augmented Generation (2026 · 2 citations)
LLMs often fail at long multi-step reasoning. They usually generate a whole solution in one pass with no intermediate checks, so an early mistake carries through to the final answer. The authors propose Environment Augmented Generation (EAG), which casts reasoning as a Markov Decision Process. While generating, the…

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Updated 31 October 2026