arXiv:2412.17052v5 Announce Type: replace Abstract: Detecting bias in multimodal news requires models that reason over text–image pairs, not just classify text. In response, we present ViLBias, a VQA-style benchmark and framework for detecting and reasoning about bias in multimodal news.…
arXiv:2510.03078v1 Announce Type: new Abstract: Explainability is increasingly seen as an essential feature of rule-based smart environments. While counterfactual explanations, which describe what could have been done differently to achieve a desired outcome, are a powerful tool in eXplainable AI…
arXiv:2509.08970v2 Announce Type: replace Abstract: Natural language descriptions of optimization or satisfaction problems are challenging to translate into correct MiniZinc models, as this process demands both logical reasoning and constraint programming expertise. We introduce Gala, a framework that addresses this…
arXiv:2510.03127v1 Announce Type: new Abstract: Analogical reasoning lies at the core of human cognition and remains a fundamental challenge for artificial intelligence. Raven’s Progressive Matrices (RPM) serve as a widely used benchmark to assess abstract reasoning by requiring the inference…
arXiv:2510.03153v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) into multiagent systems has opened new possibilities for collaborative reasoning and cooperation with AI agents. This paper explores different prompting methods and evaluates their effectiveness in enhancing agent…
arXiv:2406.15676v2 Announce Type: replace-cross Abstract: Pluggable type systems allow programmers to extend the type system of a programming language to enforce semantic properties defined by the programmer. Pluggable type systems are difficult to deploy in legacy codebases because they require…
arXiv:2510.03194v1 Announce Type: new Abstract: Deep research has revolutionized data analysis, yet data scientists still devote substantial time to manually crafting visualizations, highlighting the need for robust automation from natural language queries. However, current systems struggle with complex datasets containing…
arXiv:2510.03206v1 Announce Type: new Abstract: Diffusion language models, especially masked discrete diffusion models, have achieved great success recently. While there are some theoretical and primary empirical results showing the advantages of latent reasoning with looped transformers or continuous chain-of-thoughts, continuous…
arXiv:2504.21700v2 Announce Type: replace-cross Abstract: Large Language Models are fundamental actors in the modern IT landscape dominated by AI solutions. However, security threats associated with them might prevent their reliable adoption in critical application scenarios such as government organizations and…
arXiv:2402.17732v4 Announce Type: replace-cross Abstract: We study nonparametric contextual bandits under batch constraints, where the expected reward for each action is modeled as a smooth function of covariates, and the policy updates are made at the end of each batch…
