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General Intuition, which trains AI agents in spatial reasoning using Medal.tv game clips, raised a $133.7M seed led by Khosla Ventures and General Catalyst (Rebecca Bellan/TechCrunch)
https://techcrunch.com/2025/10/16/gene

General Intuition lands $134M seed to teach agents spatial reasoning using video game clips | TechCrunch
Late last year, OpenAI reportedly tried to buy Medal and its vast trove of video game data for $500M. Today, the company spun out a frontier research lab that's using that data to build AI agents that understand how they move through space and time, a concept called spatial-temporal reasoning.

Reasoning in the Dark: Interleaved Vision-Text Reasoning in Latent Space
Chao Chen, Zhixin Ma, Yongqi Li, Yupeng Hu, Yinwei Wei, Wenjie Li, Liqiang Nie
https://arxiv.org/abs/2510.12603

Reasoning in the Dark: Interleaved Vision-Text Reasoning in Latent Space
Multimodal reasoning aims to enhance the capabilities of MLLMs by incorporating intermediate reasoning steps before reaching the final answer. It has evolved from text-only reasoning to the integration of visual information, enabling the thought process to be conveyed through both images and text. Despite its effectiveness, current multimodal reasoning methods depend on explicit reasoning steps that require labor-intensive vision-text annotations and inherently introduce significant inference l…

Air Assault Logistics Regiment Splits in Two: https://benborges.xyz/2025/11/15/air-assault-logistics-regiment-splits.html

A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models
Ching Chang, Yidan Shi, Defu Cao, Wei Yang, Jeehyun Hwang, Haixin Wang, Jiacheng Pang, Wei Wang, Yan Liu, Wen-Chih Peng, Tien-Fu Chen
https://arxiv.org/abs/2509.11575

A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models
Time series reasoning treats time as a first-class axis and incorporates intermediate evidence directly into the answer. This survey defines the problem and organizes the literature by reasoning topology with three families: direct reasoning in one step, linear chain reasoning with explicit intermediates, and branch-structured reasoning that explores, revises, and aggregates. The topology is crossed with the main objectives of the field, including traditional time series analysis, explanation a…

A Survey on Parallel Reasoning
Ziqi Wang, Boye Niu, Zipeng Gao, Zhi Zheng, Tong Xu, Linghui Meng, Zhongli Li, Jing Liu, Yilong Chen, Chen Zhu, Hua Wu, Haifeng Wang, Enhong Chen
https://arxiv.org/abs/2510.12164

A Survey on Parallel Reasoning
With the increasing capabilities of Large Language Models (LLMs), parallel reasoning has emerged as a new inference paradigm that enhances reasoning robustness by concurrently exploring multiple lines of thought before converging on a final answer. It has become a significant trend to explore parallel reasoning to overcome the fragility of standard sequential methods and improve practical performance. In this paper, we aim to survey and summarize the progress and challenges of parallel reasonin…

Agentic Temporal Graph of Reasoning with Multimodal Language Models: A Potential AI Aid to Healthcare
Susanta Mitra
https://arxiv.org/abs/2509.11944 https://

Agentic Temporal Graph of Reasoning with Multimodal Language Models: A Potential AI Aid to Healthcare
Healthcare and medicine are multimodal disciplines that deal with multimodal data for reasoning and diagnosing multiple diseases. Although some multimodal reasoning models have emerged for reasoning complex tasks in scientific domains, their applications in the healthcare domain remain limited and fall short in correct reasoning for diagnosis. To address the challenges of multimodal medical reasoning for correct diagnosis and assist the healthcare professionals, a novel temporal graph-based rea…

Towards Inference-time Scaling for Continuous Space Reasoning
Minghan Wang, Thuy-Trang Vu, Ehsan Shareghi, Gholamreza Haffari
https://arxiv.org/abs/2510.12167 https://

Towards Inference-time Scaling for Continuous Space Reasoning
Inference-time scaling through multiple sample generation in combination with Process- or Outcome-Reward Model (PRM or ORM) re-ranking has proven effective for text-based reasoning in large language models. This paper investigates whether such established techniques can be successfully adapted to reasoning in the continuous space, using COCONUT (Hao et al. 2024) continuous space reasoning LM as the backbone. We demonstrate the feasibility of generating diverse reasoning paths through dropout-ba…

CTRL-Rec: Controlling Recommender Systems With Natural Language
Micah Carroll, Adeline Foote, Kevin Feng, Marcus Williams, Anca Dragan, W. Bradley Knox, Smitha Milli
https://arxiv.org/abs/2510.12742

CTRL-Rec: Controlling Recommender Systems With Natural Language
When users are dissatisfied with recommendations from a recommender system, they often lack fine-grained controls for changing them. Large language models (LLMs) offer a solution by allowing users to guide their recommendations through natural language requests (e.g., "I want to see respectful posts with a different perspective than mine"). We propose a method, CTRL-Rec, that allows for natural language control of traditional recommender systems in real-time with computational efficiency. Speci…

ThinkPilot: Steering Reasoning Models via Automated Think-prefixes Optimization
Sunzhu Li, Zhiyu Lin, Shuling Yang, Jiale Zhao, Wei Chen
https://arxiv.org/abs/2510.12063 https:/…

ThinkPilot: Steering Reasoning Models via Automated Think-prefixes Optimization
Large Reasoning Models (LRMs) are powerful, but they still suffer from inefficient and off-target reasoning. Currently, training-free methods are limited to either rigid heuristics or descriptive, non-actionable analyses. In this paper, we introduce ThinkPilot, a training-free framework that automatically optimizes LRMs reasoning. It uses an evolutionary process to generate think-prefixes, which are instructions that evolve driven by a taxonomy of reasoning behaviors to guide models toward supe…

Formal Reasoning for Intelligent QA Systems: A Case Study in the Educational Domain
Tuan Bui, An Nguyen, Phat Thai, Minh Hua, Ngan Pham L. N., Ngan Pham T. B., Dung Le, Long Nguyen, Thanh-Tung Tran, Thang Bui, Tho Quan
https://arxiv.org/abs/2509.11572

Formal Reasoning for Intelligent QA Systems: A Case Study in the Educational Domain
Reasoning is essential for closed-domain QA systems in which procedural correctness and policy compliance are critical. While large language models (LLMs) have shown strong performance on many reasoning tasks, recent work reveals that their reasoning traces are often unfaithful - serving more as plausible justifications than as causally grounded derivations. Efforts to combine LLMs with symbolic engines (e.g., Prover9, Z3) have improved reliability but remain limited to static forms of logic, s…