Classical and neural NLP: translation, question answering, tokenization and the evaluation of language understanding.
arXiv:2605.25831v2 Announce Type: replace-cross Abstract: Large language models (LLMs) define a distribution over text, which can be viewed as a probabilistic representation of uncertainty: sampling...
arXiv:2609.06914v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong capabilities across diverse domains, showing considerable potential in medicine. However, their a...
arXiv:2609.06245v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) perform strongly on general visual understanding tasks such as visual question answering, yet they often str...
arXiv:2606.03027v2 Announce Type: replace Abstract: Text embeddings are fundamental to many downstream applications, making robustness important for real-world NLP. However, most recent state-of-the-...
SpatialBlock introduces a synthetic dataset of 15,000 block‑stacking problems designed to improve spatial intelligence in Large Vision‑Language Models (LVLMs). The dataset covers 3D‑to‑2D projection, viewpoint transformation, and structural combination, and uses controlled color modulation to encourage anchor‑based reasoning. Experiments show that LVLMs trained on SpatialBlock outperform baselines and generalize to real‑world spatial tasks, despite the dataset’s synthetic and compact nature.
arXiv:2609.05481v1 Announce Type: new Abstract: Inter example relational distillation transfers a teacher's representation geometry by matching relations among examples within a mini batch. Computing...
arXiv:2609.09735v1 Announce Type: cross Abstract: Healthcare systems, mental health, and public well-being are increasingly affected by cyberbullying and harmful online interactions. This paper prese...
BuddyVQA is a new benchmark for companion‑style question answering on egocentric streaming video, comprising 21.6K questions tied to 6K highlight moments across 1,012 long first‑person videos. It emphasizes two often overlooked aspects of daily first‑person QA: ego‑deictic expressions and interactively chained questions, requiring models to resolve visual pronouns and infer user intent within a long‑form streaming context. The authors propose MyBuddy, a multimodal chain‑of‑thought QA assistant that uses a question filter and multi‑level memory to efficiently retrieve visual and QA information, achieving significant performance gains on BuddyVQA and generalizing to other streaming and common video QA benchmarks.
arXiv:2609.09396v1 Announce Type: new Abstract: As Vision-Language Models (VLMs) advance toward physical deployment, the focus has remained on action-oriented Embodied AI evaluated on subject-centric...
The paper introduces Open Tabular Insight Extraction (OpenTI), a unified framework aimed at democratizing access to insights from large table corpora. It highlights how current research is fragmented across domains like table QA, text‑to‑SQL, and data analysis agents, and shows that existing systems and benchmarks fall short of covering the full end‑to‑end scope of OpenTI. The authors propose a consolidated terminology, conduct a systematic review, and outline a research agenda for developing comprehensive OpenTI systems, evaluation methods, and interaction paradigms.
The paper introduces a neuro‑symbolic framework for constructing knowledge graphs (KGs) that are grounded in an ontology. It combines open‑domain extraction, embedding‑based canonicalization of types and predicates, and a post‑extraction LLM‑based correction step to fix ontology violations, thereby reducing token usage and improving KG consistency. The resulting KGs support symbolic querying, as evidenced by the prevalence of SPARQL graph patterns in the extracted data.
arXiv:2609.09719v1 Announce Type: new Abstract: Text-aligned speech tokenization methods have emerged to better align speech tokens with LLM token spaces, enabling more effective utilization of pretr...
arXiv:2605.29948v3 Announce Type: replace-cross Abstract: Unified speech foundation models require a holistic tokenization space that is both learnable by language models and decodable into high-qual...
The paper investigates Associative Recall (AR) in the Mamba architecture, showing that Mamba implicitly learns linear hash functions to perform recall. It identifies the low‑level circuit responsible for this behavior and develops a theoretical framework—Recall Scaling Laws—based on similarity‑preserving hashing principles. The framework predicts embedding and state dimensions for perfect recall, recall success probability, and analyzes multi‑layer and multi‑head SSM patterns, with empirical results confirming its accuracy.
arXiv:2609.09004v1 Announce Type: cross Abstract: Large Language Models (LLMs) demonstrate impressive performance across diverse NLP tasks, yet their ability to exhibit genuine contextual understandi...
arXiv:2509.22404v2 Announce Type: replace Abstract: Anatomical understanding, which is the ability to identify, localize, or segment anatomical structures, is critical in medical image analysis; howe...
arXiv:2609.09554v1 Announce Type: new Abstract: We introduce BuzzASR, a collection of language-specialized fine-tuned Whisper models adapted for automatic speech recognition (ASR) in 102 languages. L...
arXiv:2602.14488v3 Announce Type: replace-cross Abstract: IR in low-resource languages remains limited by the scarcity of high-quality, task-specific annotated datasets. Manual annotation is expensiv...
arXiv:2609.09973v1 Announce Type: new Abstract: Evaluating video captioning remains a critical challenge for Visual Large Language Models (VLLMs). Existing metrics primarily rely on matching generate...
arXiv:2609.08025v1 Announce Type: new Abstract: Reasoning agents increasingly rely on external tools such as web search to answer complex queries. Reinforcement learning (RL) finetuning algorithms su...