Can Graph Descriptive Order Affect Solving Graph Problems with LLMs? (2025 · 10 citations)
Studies of LLM graph reasoning usually turn a graph into text by listing its edges in an arbitrary sequence.
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…
HiddenGuard: Fine-Grained Safe Generation with Specialized Representation Router (2026 · 4 citations)
Safety alignment in LLMs usually works as a yes/no gate: a prompt is either answered or refused. This tends to block harmless requests, such as a "killer slideshow," and to miss subtle or context-dependent harm. The authors propose HiddenGuard, which moderates output token by token instead of refusing whole answers.
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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Where their works here were published, the most works first, with the editions of those that have a page.

Papers of our library cite their works 17 times (of the 149,439 papers whose bibliographies we read); 4 citing papers with a page here →.

Updated 31 October 2026