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arXiv:2603.11455v1 Announce Type: new Abstract: This study examines users' behavioural intention to use OpenClaw through the Cognition--Affect--Conation (CAC) framework. The research investigates how cognitive perceptions …
arXiv:2603.11279v1 Announce Type: new Abstract: The immense number of parameters and deep neural networks make large language models (LLMs) rival the complexity of human brains, …
arXiv:2603.11818v1 Announce Type: new Abstract: The unrestrained proliferation of cells that are malignant in nature is cancer. In recent times, medical professionals are constantly acquiring …
arXiv:2603.11228v1 Announce Type: new Abstract: The widespread use of large language models (LLMs) raises an important question: how do texts evolve when they are repeatedly …
arXiv:2603.11445v1 Announce Type: new Abstract: We present Verified Multi-Agent Orchestration (VMAO), a framework that coordinates specialized LLM-based agents through a verification-driven iterative loop. Given a …
arXiv:2603.11679v1 Announce Type: new Abstract: As real-world datasets become increasingly complex and heterogeneous, supervised learning is often bottlenecked by input representation design. Modeling multimodal data …
arXiv:2603.11223v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) over Knowledge Graphs (KGs) suffers from the fact that indexing approaches may lose important contextual nuance when …
arXiv:2603.11781v1 Announce Type: new Abstract: Multi-agent LLM systems increasingly tackle complex reasoning, yet their interaction patterns remain limited to voting, unstructured debate, or pipeline orchestration. …
arXiv:2603.11442v1 Announce Type: new Abstract: Can humans detect AI-generated financial documents better than machines? We present GPT4o-Receipt, a benchmark of 1,235 receipt images pairing GPT-4o-generated …
arXiv:2603.11076v1 Announce Type: new Abstract: Recent work synthesizes agentic tasks for post-training tool-using LLMs, yet robust generalization under shifts in tasks and toolsets remains an …
arXiv:2603.11412v1 Announce Type: new Abstract: Under surprisal theory, linguistic representations affect processing difficulty only through the bottleneck of surprisal. Our best estimates of surprisal come …
arXiv:2603.11864v1 Announce Type: new Abstract: As AI agents are increasingly used in high-stakes domains like healthcare and law enforcement, aligning their behaviour with social, legal, …