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…
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…
Are All Prompt Components Value-Neutral? Understanding the Heterogeneous Adversarial Robustness of Dissected Prompt in LLMs (2026)
Most work on adversarial prompting treats a prompt as one undivided string. This paper argues instead that prompts are built from functional parts, and that these parts differ in how easily an attack can exploit them.

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Papers of our library cite their works 13 times (of the 149,439 papers whose bibliographies we read); 4 citing papers with a page here →.

Updated 2 November 2026