arXiv:2509.14252v1 Announce Type: cross Abstract: Large Language Model (LLM) pretraining, finetuning, and evaluation rely on input-space reconstruction and generative capabilities. Yet, it has been observed in vision that embedding-space training objectives, e.g., with Joint Embedding Predictive Architectures (JEPAs), are far…
arXiv:2509.04622v2 Announce Type: replace-cross Abstract: Representational similarity metrics are fundamental tools in neuroscience and AI, yet we lack systematic comparisons of their discriminative power across model families. We introduce a quantitative framework to evaluate representational similarity measures based on their…
arXiv:2509.14249v1 Announce Type: cross Abstract: African languages remain underrepresented in natural language processing (NLP), with most corpora limited to formal registers that fail to capture the vibrancy of everyday communication. This work addresses this gap for Shona, a Bantu language…
arXiv:2509.15217v1 Announce Type: new Abstract: Multimodal large language models have various practical applications that demand strong reasoning abilities. Despite recent advancements, these models still struggle to solve complex geometric problems. A key challenge stems from the lack of high-quality image-text…
arXiv:2507.08334v2 Announce Type: replace-cross Abstract: Concept Bottleneck Models (CBMs) provide interpretable decision-making through explicit, human-understandable concepts. However, existing generative CBMs often rely on auxiliary visual cues at the bottleneck, which undermines interpretability and intervention capabilities. We propose EnCoBo, a post-hoc…
arXiv:2509.15172v1 Announce Type: new Abstract: Language Models (LMs) are inconsistent reasoners, often generating contradictory responses to identical prompts. While inference-time methods can mitigate these inconsistencies, they fail to address the core problem: LMs struggle to reliably select reasoning pathways leading…
arXiv:2505.22880v3 Announce Type: replace-cross Abstract: This paper presents a system for autonomous semantic exploration and dense semantic target mapping of a complex unknown environment using a ground robot equipped with a LiDAR-panoramic camera suite. Existing approaches often struggle to balance…
arXiv:2509.15084v1 Announce Type: new Abstract: As autonomous technologies increasingly shape maritime operations, understanding why an AI system makes a decision becomes as crucial as what it decides. In complex and dynamic maritime environments, trust in AI depends not only on…
arXiv:2509.15035v1 Announce Type: new Abstract: This study investigates the use of generative AI to support formative assessment through machine generated reviews of peer reviews in graduate online courses in a public university in the United States. Drawing on Systemic Functional…
arXiv:2502.17394v3 Announce Type: replace-cross Abstract: Event Detection (ED) — the task of identifying event mentions from natural language text — is critical for enabling reasoning in highly specialized domains such as biomedicine, law, and epidemiology. Data generation has proven to…
