Tootfinder

ChronoLLM: Customizing Language Models for Physics-Based Simulation Code Generation
Jingquan Wang, Andrew Negrut, Harry Zhang, Khailanii Slaton, Shu Wang, Radu Serban, Jinlong Wu, Dan Negrut
https://arxiv.org/abs/2508.13975

ChronoLLM: Customizing Language Models for Physics-Based Simulation Code Generation
This contribution is concerned with the following issue: can pretrained large language models (LLMs) be refined and customized to the point where they become virtual assistants helping experts with the effective use of a simulation tool? In this case study, the ``simulation tool'' considered is PyChrono, an open source multi-physics dynamics engine for multibody systems. We present a framework for refining and customizing both open- and closed-source LLMs to harness the power of AI in generatin…

Milwaukee Brewers fan loses job after she says 'call ICE' on Latino Dodgers fan (Drake Bentley/Journal Sentinel)
https://www.jsonline.com/story/sports/mlb/brewers/2025/10/15/brewers-fan-loses-job-after-threat-to-call-ice-on-latino-dodgers-fan/86716030007/
http://www.memeorandum.com/251016/p153#a251016p153

Shower Pandemonium
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Season 5 Episode 19 "Deep Trouble"

COCO: Cognitive Operating System with Continuous Oversight for Multi-Agent Workflow Reliability
Churong Liang, Jinling Gan, Kairan Hong, Qiushi Tian, Zongze Wu, Runnan Li
https://arxiv.org/abs/2508.13815

COCO: Cognitive Operating System with Continuous Oversight for Multi-Agent Workflow Reliability
Large-scale multi-agent workflows exhibit inherent vulnerability to error propagation and quality degradation, where downstream agents compound upstream failures without corrective mechanisms. We introduce COCO (Cognitive Operating System with Continuous Oversight), a theoretically-grounded framework that implements asynchronous self-monitoring and adaptive error correction in multi-agent driven systems. COCO addresses the fundamental trade-off between quality assurance and computational effici…

Improving Context Fidelity via Native Retrieval-Augmented Reasoning
Suyuchen Wang, Jinlin Wang, Xinyu Wang, Shiqi Li, Xiangru Tang, Sirui Hong, Xiao-Wen Chang, Chenglin Wu, Bang Liu
https://arxiv.org/abs/2509.13683

Improving Context Fidelity via Native Retrieval-Augmented Reasoning
Large language models (LLMs) often struggle with context fidelity, producing inconsistent answers when responding to questions based on provided information. Existing approaches either rely on expensive supervised fine-tuning to generate evidence post-answer or train models to perform web searches without necessarily improving utilization of the given context. We propose CARE, a novel native retrieval-augmented reasoning framework that teaches LLMs to explicitly integrate in-context evidence wi…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/5]:
- LEGO-GraphRAG: Modularizing Graph-based Retrieval-Augmented Generation for Design Space Exploration
Yukun Cao, Zengyi Gao, Zhiyang Li, Xike Xie, S. Kevin Zhou, Jianliang Xu

\textsc{Gen2Real}: Towards Demo-Free Dexterous Manipulation by Harnessing Generated Video
Kai Ye, Yuhang Wu, Shuyuan Hu, Junliang Li, Meng Liu, Yongquan Chen, Rui Huang
https://arxiv.org/abs/2509.14178

\textsc{Gen2Real}: Towards Demo-Free Dexterous Manipulation by Harnessing Generated Video
Dexterous manipulation remains a challenging robotics problem, largely due to the difficulty of collecting extensive human demonstrations for learning. In this paper, we introduce \textsc{Gen2Real}, which replaces costly human demos with one generated video and drives robot skill from it: it combines demonstration generation that leverages video generation with pose and depth estimation to yield hand-object trajectories, trajectory optimization that uses Physics-aware Interaction Optimization M…

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Isaac Albéniz, John Williams & John Williams:
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https://open.spotify.com/track/10DQJX3VFBR8Y6gY7o5aUd