arXiv:2606. 24543v1 Announce Type: new Abstract: Large Language Models (LLMs) are traditionally viewed as autoregressive generators.
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The paper investigates when language diffusion models, specifically Uniform-based Discrete Diffusion Models (UDDMs), shift from memorizing training data to generalizing to new data. It shows that UDDMs act as associative memories, forming basins of attraction around stored examples without requiring an explicit energy function. By measuring token recovery and conditional entropy, the authors identify a sharp transition governed by training set size, where memorization (vanishing entropy) gives way to generalization (finite entropy).
The paper investigates how continuous latent states in large language models can store multiple reasoning steps through superposition. It challenges the intuition that retaining only the current reasoning frontier is optimal, showing that cumulative superposition of the full reasoning history can actually require fewer representational dimensions. The authors demonstrate that this approach preserves more valid evidence, improves downstream outcome discrimination, and delays unreliability, while also establishing that uniform cumulative weighting of memories is minimax‑optimal for future reasoning.
The paper investigates whether different forms of intermediate computation in large language models—such as token-based traces, pause tokens, and latent reasoning—rely on the same underlying mechanism. By training five variants of GPTNeoX on an extended multi-hop reasoning task, the authors find that while vanilla, Chain-of-Thought, and Pause Token models perform well on in-distribution data, they fail to generalize to longer-hop out-of-distribution problems. In contrast, latent-reasoning models exhibit better depth generalization, with causal analysis revealing a sparse recurrent search circuit that implements forward reachability propagation across the graph.
arXiv:2605. 05686v3 Announce Type: replace Abstract: Language models draw on two knowledge sources: facts baked into weights (parametric memory, PM) and information in context (working memory, WM).
arXiv:2608.28771v1 Announce Type: new Abstract: Large reasoning models achieve strong performance on complex tasks by generating extended chain-of-thought (CoT) traces via reinforcement learning with...
arXiv:2607. 08724v1 Announce Type: new Abstract: Human decision-making is highly flexible -- some actions are taken immediately; others require longer deliberation.