Foundations for Agentic AI Investigations from the Forensic Analysis of OpenClaw

arXiv:2604.05589v1 Announce Type: cross Abstract: Agentic Al systems are increasingly deployed as personal assistants and are likely to become a common object of digital investigations. However, little is known about how their internal state and actions can be reconstructed during forensic analysis. Despite growing popularity, systematic forensic approaches for such systems remain largely unexplored. This […]

What Models Know, How Well They Know It: Knowledge-Weighted Fine-Tuning for Learning When to Say “I Don’t Know”

arXiv:2604.05779v1 Announce Type: cross Abstract: While large language models (LLMs) demonstrate strong capabilities across diverse user queries, they still suffer from hallucinations, often arising from knowledge misalignment between pre-training and fine-tuning. To address this misalignment, we reliably estimate a fine-grained, instance-level knowledge score via multi-sampled inference. Using the knowledge score, we scale the learning signal […]

Vision-Guided Iterative Refinement for Frontend Code Generation

arXiv:2604.05839v1 Announce Type: new Abstract: Code generation with large language models often relies on multi-stage human-in-the-loop refinement, which is effective but very costly – particularly in domains such as frontend web development where the solution quality depends on rendered visual output. We present a fully automated critic-in-the-loop framework in which a vision-language model serves as […]

AI and Collective Decisions: Strengthening Legitimacy and Losers’ Consent

arXiv:2604.05368v1 Announce Type: cross Abstract: AI is increasingly used to scale collective decision-making, but far less attention has been paid to how such systems can support procedural legitimacy, particularly the conditions shaping losers’ consent: whether participants who do not get their preferred outcome still accept it as fair. We ask: (1) how can AI help […]

Enhancing Hallucination Detection via Future Context

arXiv:2507.20546v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are widely used to generate plausible text on online platforms, without revealing the generation process. As users increasingly encounter such black-box outputs, detecting hallucinations has become a critical challenge. To address this challenge, we focus on developing a hallucination detection framework for black-box generators. Motivated by […]

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