This page shows the most relevant public items for Reasoning, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • Feb 19, 2026 • arXiv • Google DeepMind Team
We introduce Gemini 3.1 Pro, an enhanced version of the Gemini 3 series optimized for rigorous logic and long-horizon problem solving. Built on a hybrid architecture that fuses linear attention wit...
Paper • Feb 28, 2026 • arXiv • Runquan Gui, Yafu Li, Xiaoye Qu, Ziyan Liu, Yeqiu Cheng, Yu Cheng
Large Language Models frequently produce correct final answers based on flawed or unfaithful intermediate reasoning steps. This paper proposes Step-Level Faithfulness Maximization, a training parad...
Paper • Feb 10, 2026 • arXiv • Wei Lin, David Lin, Yujin Han
One of the most persistent failure modes in autonomous systems is the infinite loop where a process becomes stuck continuously re executing a failed call. We introduce temporal grounding a novel co...
Blog • Jan 15, 2026 • Medium • Elena Rossi
The release of advanced reasoning models has completely shifted how developers build autonomous systems in early 2026. This post details how open weights models are replacing expensive proprietary ...
Paper • May 22, 2025 • arXiv • Wei Lin, Chen Wei, Yujin Han
One of the most persistent failure modes in autonomous Agentic AI is the 'Infinite Loop'—where an agent becomes stuck continuously re-executing a failed API call or repeating a flawed logical deduc...
Paper • Apr 18, 2025 • arXiv • Maciej Besta, Nils Blach, Torsten Hoefler
While Chain-of-Thought and Tree-of-Thoughts prompting greatly enhance LLM reasoning, they strictly enforce linear or hierarchical cognitive paths. We introduce Graph of Thoughts (GoT), a novel cogn...
Paper • May 31, 2023 • arXiv • Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, Karl Cobbe
Large language models often struggle with multi-step logical reasoning, frequently hallucinating incorrect steps that invalidate the final answer. To improve reasoning capabilities, we compare two ...
Paper • Feb 18, 2026 • bioRxiv • Elena K. Sharma, David Chen, Marcus Thorne, Yujin Han
A critical bottleneck in training autonomous agents for biology is the scarcity of high-throughput experimental data for reward modeling. We introduce rbio1, a framework that trains scientific reas...
Paper • Feb 28, 2026 • arXiv • Anonymous
Recent advancements in agentic reasoning rely heavily on self-verification loops, where models constantly double-check their own logic. However, this often leads to excessive computational waste an...
Paper • Mar 13, 2026 • arXiv • Jordi De Curtò
This paper investigates semantic invariance within agentic AI systems where reasoning agents interact with complex environments and external tools. The author explores how semantic drift during mul...
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