Tootfinder

Understanding Physical Properties of Unseen Deformable Objects by Leveraging Large Language Models and Robot Actions
Changmin Park, Beomjoon Lee, Haechan Jung, Haejin Jung, Changjoo Nam
https://arxiv.org/abs/2506.03760

Understanding Physical Properties of Unseen Deformable Objects by Leveraging Large Language Models and Robot Actions
In this paper, we consider the problem of understanding the physical properties of unseen objects through interactions between the objects and a robot. Handling unseen objects with special properties such as deformability is challenging for traditional task and motion planning approaches as they are often with the closed world assumption. Recent results in Large Language Models (LLMs) based task planning have shown the ability to reason about unseen objects. However, most studies assume rigid o…

This https://arxiv.org/abs/2505.15053 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Owicki--Gries Logic for Timestamp Semantics
Whereas an extension with non-interference of Hoare logic for sequential programs Owicki--Gries logic ensures the correctness of concurrent programs on strict consistency, it is unsound to weak memory models adopted by modern computer architectures and specifications of programming languages. This paper proposes a novel non-interference notion and provides concurrent program logic sound to timestamp semantics corresponding to a weak memory model that allows delays in the effects of store instru…

This https://arxiv.org/abs/2506.02139 has been replaced.
link: https://scholar.google.com/scholar?q=a

The Unified Cognitive Consciousness Theory for Language Models: Anchoring Semantics, Thresholds of Activation, and Emergent Reasoning
Few-shot learning in large language models (LLMs) reveals a core paradox: certain tasks generalize from just a few examples, while others demand extensive supervision. To explain this, we introduce the Unified Cognitive Consciousness Theory (UCCT), which reconceptualizes LLMs not as deficient agents, but as unconscious substrates: dense, distributed repositories of linguistic and conceptual patterns that operate without explicit semantics, intention, or goal-directed reasoning. Under this view,…

VChatter: Exploring Generative Conversational Agents for Simulating Exposure Therapy to Reduce Social Anxiety
Han Zhang, KaWing Tsang, Zhenhui Peng
https://arxiv.org/abs/2506.03520

VChatter: Exploring Generative Conversational Agents for Simulating Exposure Therapy to Reduce Social Anxiety
Many people struggle with social anxiety, feeling fear, or even physically uncomfortable in social situations like talking to strangers. Exposure therapy, a clinical method that gradually and repeatedly exposes individuals to the source of their fear and helps them build coping mechanisms, can reduce social anxiety but traditionally requires human therapists' guidance and constructions of situations. In this paper, we developed a multi-agent system VChatter to explore large language models(LLMs…

An Exploratory Framework for Future SETI Applications: Detecting Generative Reactivity via Language Models
Po-Chieh Yu
#toXiv_bot_toot

This https://arxiv.org/abs/2405.06003 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Binary Hypothesis Testing for Softmax Models and Leverage Score Models
Softmax distributions are widely used in machine learning, including Large Language Models (LLMs), where the attention unit uses softmax distributions. We abstract the attention unit as the softmax model, where given a vector input, the model produces an output drawn from the softmax distribution (which depends on the vector input). We consider the fundamental problem of binary hypothesis testing in the setting of softmax models. That is, given an unknown softmax model, which is known to be one…

Text-guided Generation of Efficient Personalized Inspection Plans
Xingpeng Sun, Zherong Pan, Xifeng Gao, Kui Wu, Aniket Bera
https://arxiv.org/abs/2506.02917

Text-guided Generation of Efficient Personalized Inspection Plans
We propose a training-free, Vision-Language Model (VLM)-guided approach for efficiently generating trajectories to facilitate target inspection planning based on text descriptions. Unlike existing Vision-and-Language Navigation (VLN) methods designed for general agents in unknown environments, our approach specifically targets the efficient inspection of known scenes, with widespread applications in fields such as medical, marine, and civil engineering. Leveraging VLMs, our method first extract…

OG-VLA: 3D-Aware Vision Language Action Model via Orthographic Image Generation
Ishika Singh, Ankit Goyal, Stan Birchfield, Dieter Fox, Animesh Garg, Valts Blukis
https://arxiv.org/abs/2506.01196

OG-VLA: 3D-Aware Vision Language Action Model via Orthographic Image Generation
We introduce OG-VLA, a novel architecture and learning framework that combines the generalization strengths of Vision Language Action models (VLAs) with the robustness of 3D-aware policies. We address the challenge of mapping natural language instructions and multi-view RGBD observations to quasi-static robot actions. 3D-aware robot policies achieve state-of-the-art performance on precise robot manipulation tasks, but struggle with generalization to unseen instructions, scenes, and objects. On …

Tru-POMDP: Task Planning Under Uncertainty via Tree of Hypotheses and Open-Ended POMDPs
Wenjing Tang, Xinyu He, Yongxi Huang, Yunxiao Xiao, Cewu Lu, Panpan Cai
https://arxiv.org/abs/2506.02860

Tru-POMDP: Task Planning Under Uncertainty via Tree of Hypotheses and Open-Ended POMDPs
Task planning under uncertainty is essential for home-service robots operating in the real world. Tasks involve ambiguous human instructions, hidden or unknown object locations, and open-vocabulary object types, leading to significant open-ended uncertainty and a boundlessly large planning space. To address these challenges, we propose Tru-POMDP, a planner that combines structured belief generation using Large Language Models (LLMs) with principled POMDP planning. Tru-POMDP introduces a hierarc…

Correlating instruction-tuning (in multimodal models) with vision-language processing (in the brain)
Subba Reddy Oota, Akshett Jindal, Ishani Mondal, Khushbu Pahwa, Satya Sai Srinath Namburi, Manish Shrivastava, Maneesh Singh, Bapi S. Raju, Manish Gupta
https://arxiv.org/abs/2505.20029

Correlating instruction-tuning (in multimodal models) with vision-language processing (in the brain)
Transformer-based language models, though not explicitly trained to mimic brain recordings, have demonstrated surprising alignment with brain activity. Progress in these models-through increased size, instruction-tuning, and multimodality-has led to better representational alignment with neural data. Recently, a new class of instruction-tuned multimodal LLMs (MLLMs) have emerged, showing remarkable zero-shot capabilities in open-ended multimodal vision tasks. However, it is unknown whether MLLM…