Large language models increasingly rely on external tools to access up-to-date information, perform computation, and interact with the outside world. For autoregressive models, tool use naturally fits the generation process: the model emits a tool call, waits for the result, and then continues generating.
arXiv:2603. 01331v3 Announce Type: replace-cross Abstract: Discrete diffusion language models (dLLMs) generate text by iteratively denoising a masked sequence.
By Kejing Xia, Mingzhe Li, Lixuan Wei, Zhenbang Du, Xiangchi Yuan, Dachuan Shi, Qirui Jin, Wenke Lee
arXiv:2606. 19475v1 Announce Type: new Abstract: Large Language Models (LLMs) have revolutionized language modeling through autoregressive generation, enabling strong performance across a wide range of tasks.
By Thomas Bertolani, Davide Bucciarelli, Leonardo Zini, Marcella Cornia, Lorenzo Baraldi
arXiv:2603.18614v2 Announce Type: replace
Abstract: Tool-augmented large language models (LLMs) must tightly couple multi-step reasoning with external actions, yet existing benchmarks often confound...
By Wanjia Zhao, Ludwig Schmidt, Yejin Choi, James Zou, Vidhisha Balachandran, Lingjiao Chen
arXiv:2606. 20560v1 Announce Type: cross Abstract: LLM reasoning transparency is a critical affordance for understanding model decisions, mitigating misuse and misalignment, and debugging surprising model behaviors.
By Joshua Engels, Callum McDougall, Bilal Chughtai, Janos Kramar, Senthoran Rajamanoharan, Cindy Wu, Arthur Conmy, Asic Q Chen, Jean Tarbouriech, Min Ma, Brendan O'Donoghue, Jo\~ao Gabriel Lopes de Oliveira, Rohin Shah, Neel Nanda
The paper introduces self‑orchestrating language models that annotate semantic dependence—identifying which tokens rely on others—to guide efficient inference. By leveraging these annotations, the authors design runtimes that parallelize autoregressive decoding, evict intermediate context, or determine denoising orders, achieving Pareto‑optimal quality‑efficiency trade‑offs. Three systems—PASTA, TIP, and Planned Diffusion—demonstrate these techniques for parallel decoding, memory‑efficient reasoning, and efficient discrete diffusion, respectively.
By Tian Jin