arXiv:2510.03469v1 Announce Type: new Abstract: We introduce a novel framework for evaluating the alignment between natural language plans and their expected behavior by converting them into Kripke structures and Linear Temporal Logic (LTL) using Large Language Models (LLMs) and performing…
arXiv:2510.03485v1 Announce Type: new Abstract: Autonomous web agents need to operate under externally imposed or human-specified policies while generating long-horizon trajectories. However, little work has examined whether these trajectories comply with such policies, or whether policy violations persist across different…
arXiv:2510.03453v1 Announce Type: new Abstract: Evaluation is a critical activity associated with any theory. Yet this has proven to be an exceptionally challenging activity for theories based on cognitive architectures. For an overlapping set of reasons, evaluation can also be…
arXiv:2510.03418v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) integrates LLMs with external sources, offering advanced capabilities for information access and decision-making. However, contradictions in retrieved evidence can result in inconsistent or untrustworthy outputs, which is especially problematic in enterprise settings…
arXiv:2510.03399v1 Announce Type: new Abstract: Self-recognition is a crucial metacognitive capability for AI systems, relevant not only for psychological analysis but also for safety, particularly in evaluative scenarios. Motivated by contradictory interpretations of whether models possess self-recognition (Panickssery et al.,…
arXiv:2502.15436v2 Announce Type: replace-cross Abstract: Low-Rank Adaptation (LoRA) has become ubiquitous for efficiently fine-tuning foundation models. However, federated fine-tuning using LoRA is challenging due to suboptimal updates arising from traditional federated averaging of individual adapters. Existing solutions either incur prohibitively…
arXiv:2510.03612v1 Announce Type: new Abstract: Vision-language model (VLM)-based web agents increasingly power high-stakes selection tasks like content recommendation or product ranking by combining multimodal perception with preference reasoning. Recent studies reveal that these agents are vulnerable against attackers who can…
arXiv:2504.18564v2 Announce Type: replace-cross Abstract: Recent research has focused on exploring the vulnerabilities of Large Language Models (LLMs), aiming to elicit harmful and/or sensitive content from LLMs. However, due to the insufficient research on dual-jailbreaking — attacks targeting both LLMs…
arXiv:2510.03632v1 Announce Type: new Abstract: Tree search has become as a representative framework for test-time reasoning with large language models (LLMs), exemplified by methods such as Tree-of-Thought and Monte Carlo Tree Search that explore multiple reasoning paths. However, it remains…
arXiv:2508.15658v2 Announce Type: replace-cross Abstract: The rapid growth of academic literature makes the manual creation of scientific surveys increasingly infeasible. While large language models show promise for automating this process, progress in this area is hindered by the absence of…
