Results 51 to 60 of about 89,825 (246)

On Evaluating LLM Alignment by Evaluating LLMs as Judges

CoRR
NeurIPS 2025 Camera ...
Yixin Liu, Pengfei Liu, Arman Cohan
openaire   +3 more sources

Scalable Task Planning via Large Language Models and Structured World Representations

Advanced Robotics Research, EarlyView.
This work efficiently combines graph‐based world representations with the commonsense knowledge in Large Language Models to enhance planning techniques for the large‐scale environments that modern robots will need to face. Planning methods often struggle with computational intractability when solving task‐level problems in large‐scale environments ...
Rodrigo Pérez‐Dattari, Zhaoting Li, Robert Babuška, Jens Kober, Cosimo Della Santina   +4 more
wiley   +1 more source

Building a Treebank for the Book of Ezra in the Old Georgian Oshki Bible

Journal of Open Humanities Data
We present a Universal Dependencies (UD) annotated dataset of the Book of Ezra from the Old Georgian Oshki Bible, based on the TITUS edition. Our semi-automated workflow leverages transfer learning from Modern Georgian (UD_Georgian-GNC) and few-shot ...
Chia-Wei Lin, Diego Luinetti
doaj   +1 more source

Agentic AutoSurvey: Let LLMs Survey LLMs

CoRR
29 pages, 7 ...
Yixin Liu 0002, Yonghui Wu 0001, Denghui Zhang, Lichao Sun 0001   +3 more
openaire   +3 more sources

The Future of Research in Cognitive Robotics: Foundation Models or Developmental Cognitive Models?

Advanced Robotics Research, EarlyView.
Research in cognitive robotics founded on principles of developmental psychology and enactive cognitive science would yield what we seek in autonomous robots: the ability to perceive its environment, learn from experience, anticipate the outcome of events, act to pursue goals, and adapt to changing circumstances without resorting to training with ...
David Vernon
wiley   +1 more source

Grounding Large Language Models for Robot Task Planning Using Closed‐Loop State Feedback

Advanced Robotics Research, EarlyView.
BrainBody‐Large Language Model (LLM) introduces a hierarchical, feedback‐driven planning framework where two LLMs coordinate high‐level reasoning and low‐level control for robotic tasks. By grounding decisions in real‐time state feedback, it reduces hallucinations and improves task reliability.
Vineet Bhat, Ali Umut Kaypak, Prashanth Krishnamurthy, Ramesh Karri, Farshad Khorrami   +4 more
wiley   +1 more source

Diffusion LLMs are Natural Adversaries for any LLM

CoRR
We introduce a novel framework that transforms the resource-intensive (adversarial) prompt optimization problem into an \emph{efficient, amortized inference task}. Our core insight is that pretrained, non-autoregressive generative LLMs, such as Diffusion LLMs, which model the joint distribution over prompt-response pairs, can serve as powerful ...
David Lüdke, Tom Wollschläger, Paul Ungermann, Stephan Günnemann, Leo Schwinn   +4 more
openaire   +2 more sources

Multimodal Human–Robot Interaction Using Human Pose Estimation and Local Large Language Models

Advanced Robotics Research, EarlyView.
A multimodal human–robot interaction framework integrates human pose estimation (HPE) and a large language model (LLM) for gesture‐ and voice‐based robot control. Speech‐to‐text (STT) enables voice command interpretation, while a safety‐aware arbitration mechanism prioritizes gesture input for rapid intervention.
Nasiru Aboki, Ilche Georgievski, Marco Aiello   +2 more
wiley   +1 more source

Who is a Better Player: LLM against LLM

CoRR
Adversarial board games, as a paradigmatic domain of strategic reasoning and intelligence, have long served as both a popular competitive activity and a benchmark for evaluating artificial intelligence (AI) systems. Building on this foundation, we propose an adversarial benchmarking framework to assess the comprehensive performance of Large Language ...
Yingjie Zhou 0003, Jiezhang Cao, Farong Wen, Li Xu 0008, Yanwei Jiang, Jun Jia, Ronghui Li, Xiaohong Liu 0001, Yu Zhou 0016, Xiongkuo Min, Jie Guo, Zicheng Zhang, Guangtao Zhai   +12 more
openaire   +2 more sources

LLM‐Integrated Human–Robot Interaction System for Microrobots

Advanced Robotics Research, EarlyView.
This paper proposes an LLM‐based control framework for guiding microrobots using human natural language. This framework can convert the natural human speech into safe and executable command sets for reliable navigation in complex environments. The experimental results show high accuracy and robustness in task performance, demonstrating the potential of
Bairong Zhu, Amar Salehi, Tingting Yu
wiley   +1 more source