arXiv AI
Jul 28
Towards High-Level Semantic Intelligence
arXiv:2607. 24082v1 Announce Type: new Abstract: Recent advances in AI have substantially expanded its cognitive and reasoning capabilities.
Towards High-Level Semantic Intelligence
Recent advances in AI have substantially expanded its cognitive and reasoning capabilities. From the perspective of semantic complexity, the development of AI reveals a clear trajectory from simple to complex semantic processing.
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arXiv Computation and Language
Sep 23
Semantic Abstraction for Natural Language Inference: a Methodological Framework for Discovering and Compensating Semantic Knowledge and Reasoning Gaps in Large Language Models
arXiv:2609.26610v1 Announce Type: new Abstract: Despite their outstanding performance on many NLP tasks, LLMs face serious challenges related to semantic abstraction. In this study, we are interested...
arXiv AI
Aug 21
Towards general embodied intelligence: integrating large language models, knowledge bases, and reasoning capabilities to build the next generation of AI agents
arXiv:2608. 19794v1 Announce Type: new Abstract: The convergence of large language models (LLMs), structured knowledge bases (KBs), and reasoning ability (RA) presents a promising trajectory toward general embodied intelligence (GEI).
Hugging Face Trending Papers
Aug 11
Hierarchical Compositionality for An Assistive AI Agent
AI agents are increasingly being developed to assist humans in various applications, and Large Language Models and other deep network architectures are considered to be state of the art for such agents. These methods are impressive stochastic predictors, but they are resource-hungry, opaque, and known to make arbitrary decisions in novel situations due to the narrow set of underlying representation and processing choices.
arXiv Computation and Language
Aug 25
CaRGo-T: Causal Reasoning Graph-of-Thought improves Multimodal Humor Comprehension
arXiv:2608.23172v1 Announce Type: new Abstract: Large-scale vision-language models (VLMs) have demonstrated remarkable versatility across a wide range of multimodal tasks. However, understanding humo...
arXiv AI
Jul 22
Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges
arXiv:2607. 19011v1 Announce Type: cross Abstract: Multimodal humor in memes, cartoons, and comics remains difficult for AI systems because intended meaning depends on non-literal mechanisms, shared cultural knowledge, and communicative intent rather than literal scene description.
arXiv Computation and Language
Aug 31
AI Models Can Predict and Collaboratively Modulate Human Memory Search
The study investigates how large language models (LLMs) can assist humans in semantic memory search tasks. By using the semantic fluency task (SFT), the researchers evaluate whether LLMs can follow and enhance human mental trajectories during generative semantic retrieval. Results show that an LLM’s ability to track and predict human memory trajectories in this task surpasses that of other humans.
arXiv AI
Aug 12
Hierarchical Compositionality for An Assistive AI Agent
arXiv:2608. 10330v1 Announce Type: new Abstract: AI agents are increasingly being developed to assist humans in various applications, and Large Language Models and other deep network architectures are considered to be state of the art for such agents.
arXiv AI
Aug 28
Artificial Intelligence Models Can Predict and Collaboratively Modulate Human Memory Search
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arXiv AI
Aug 10
Social World Models
arXiv:2509. 00559v3 Announce Type: replace Abstract: Humans intuitively navigate social interactions by simulating unspoken dynamics and reasoning about others' perspectives, even with limited information.
arXiv Computation and Language
Sep 23
From Pattern Recognizers to Personalized Companions: A Survey of Large Language Models in Mental Health
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arXiv AI
Jun 11
GrowLoop: Self-Evolving Conversation Evaluation Seeded by Human
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