Knowledge catalogue
arXiv:2609.15145v1 Announce Type: new Abstract: The reasoning ability of large language models (LLMs) is a critical factor for practical LLM-based applications. To investigate the current reasoning ca
arXiv:2609.15284v1 Announce Type: new Abstract: Missing values are ubiquitous in heterogeneous data mining, where numerical, categorical, and binary variables often coexist. Many imputation methods, e
arXiv:2609.12641v1 Announce Type: new Abstract: Robot foundation models achieve strong in-distribution performance but often degrade under visual distribution shifts. When learning to generate actions
arXiv:2510.10932v5 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models map multimodal perception and language instructions to executable robot actions, making them particularly
arXiv:2609.12347v1 Announce Type: new Abstract: Humanoid robots must traverse cluttered obstacle fields using onboard proprioceptive and visual observations, yet existing methods usually process multi
arXiv:2609.11961v1 Announce Type: new Abstract: Stress is a pervasive determinant of mental health and a key target for mobile health interventions. On-device language models (ODLMs) offer privacy-pre
arXiv:2609.12317v1 Announce Type: new Abstract: A central question in LLM reasoning is whether reinforcement learning (RL) instills genuinely new capabilities or merely reshapes how existing knowledge
arXiv:2504.13700v2 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) have shown great potential in automating the process of visualization authoring through simple
arXiv:2303.09136v2 Announce Type: replace Abstract: Large language models (LLMs) have transformed many fields, including natural language processing, computer vision, and reinforcement learning. These
arXiv:2509.22360v2 Announce Type: replace Abstract: Large language models (LLMs) excel at operating at scale by leveraging social media and various data crawled from the web. Whereas existing corpora
arXiv:2609.10658v1 Announce Type: new Abstract: Activation steering provides a lightweight way to control large language models (LLMs) by modifying their hidden activations at inference time. Among th
arXiv:2609.10996v1 Announce Type: new Abstract: Verbalized confidence, long dismissed as overconfident, coarse, and prone to round-number clustering, is now the more robust soft-scoring mechanism for
Together Fine-Tuning adds the latest open-weight models, live experiment tracking, Expert LoRA, early stopping, tokenized dataset previews, pre-flight validation, and lower training prices on selected
arXiv:2603.15203v1 Announce Type: cross Abstract: Nuclear mass prediction is one of the core issues in nuclear physics research, yet it faces the challenge of small-sample datasets with high complexit
arXiv:2609.08180v1 Announce Type: new Abstract: Retrieval-augmented personalization enables large language models to produce more accurate and preference-aligned outputs using relevant records retriev
arXiv:2609.08390v1 Announce Type: cross Abstract: Full-duplex spoken dialogue systems enable simultaneous listening and speaking, but their audio-only perception often fails under background noise and
arXiv:2609.08013v1 Announce Type: new Abstract: Human pose estimation and keypoint-based action recognition models are increasingly deployed as components of video understanding pipelines, yet their v
arXiv:2609.06837v1 Announce Type: cross Abstract: Embodied artificial intelligence has historically very much relied on visual perception, leading to a proliferation of multiple vision-centric world m
arXiv:2609.09119v1 Announce Type: new Abstract: Dexterous manipulation involves contact-rich and fine-grained interactions with the physical world, posing significant challenges for existing vision-la
arXiv:2601.01368v2 Announce Type: replace Abstract: Score-based causal discovery in the presence of unobserved confounders requires both a consistent scoring criterion and an efficient search over gra
arXiv:2609.08873v1 Announce Type: cross Abstract: Sparsity is a powerful structural resource in optimization and statistics. We develop frameworks for leveraging sparsity in sampling problems over the
arXiv:2609.05540v1 Announce Type: new Abstract: Many medical conditions require diagnosis through detailed, multi-context clinical assessment rather than from visual appearance alone. Despite this, vi
arXiv:2609.05955v1 Announce Type: new Abstract: Tabular foundation models have become powerful graph learners. Systems such as G2T-FM and GraphPFN encode each node as a feature row and make prediction
arXiv:2609.08064v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in settings where rare but severe harmful generations can have significant consequences. Existing
arXiv:2609.08634v1 Announce Type: new Abstract: Integrating robust safety guardrails into Large Language Models (LLMs) is essential for delivering helpful yet harmless responses. While proprietary sys
Most of the new ~30B (and now ~10B, thankfully for my GPU) models we see score really high on benchmarks, but I feel like they don't push back on dumb ideas enough. I think most people don't being lik
arXiv:2609.04276v1 Announce Type: new Abstract: Vision-language models (VLMs), such as CLIP, have achieved strong performance across multimodal tasks by aligning visual and textual representations in
arXiv:2603.14503v2 Announce Type: replace Abstract: Galaxy clusters are powerful probes of astrophysics and cosmology through gravitational lensing: the clusters' mass, dominated by 85% dark matter, d
arXiv:2609.04336v1 Announce Type: new Abstract: Medical visual question answering (Med-VQA) is often assumed to require medical fine-tuning, large models, or complex multi-agent pipelines. We revisit
arXiv:2609.04835v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used not only to retrieve information, but to answer questions, explain, teach, and support inquiry. In su
arXiv:2509.21889v3 Announce Type: replace Abstract: Existing evaluations of foundation models predominantly focus on output correctness, treating interaction as a static exchange of information. Howev
arXiv:2609.04911v1 Announce Type: new Abstract: Interactive visual world models must distinguish observation from physical intervention. Camera motion reveals new surfaces, whereas intervention change
We are excited to bring the first open benchmarking of Google's TPUs to the world Running every day, on many models + scenarios $/token is better than B200 and B300 Huge shout-out to Google @inferact
arXiv:2609.04434v1 Announce Type: new Abstract: Hybrid language models combine attention with a fixed-size recurrent state, but the role of each channel remains unclear. We introduce two cache-level i
arXiv:2609.04801v1 Announce Type: new Abstract: Contextualized visual personalization can retrieve a true record yet apply it to the wrong visual subject. We formalize when a record may condition an a
arXiv:2609.03615v1 Announce Type: new Abstract: While deep generative models offer new opportunities for medical image synthesis and data sharing, their ability to memorize and reproduce training samp
arXiv:2602.07106v3 Announce Type: replace-cross Abstract: Omni-modal large language models (OLLMs) aim to unify multimodal understanding and generation, yet extending them to jointly produce speech an
arXiv:2405.07338v2 Announce Type: replace-cross Abstract: Early detection of vision-threatening conditions such as diabetic retinopathy, glaucoma, and age-related macular degeneration depends on retin
arXiv:2609.03229v1 Announce Type: new Abstract: Networks describe systems in biology and beyond, from protein interactions and social relationships to power grids and citation records. Reasoning about
arXiv:2609.03148v1 Announce Type: new Abstract: Most prior works focused on conflicts between an LLM's internal parametric knowledge and externally provided context. In contrast, we investigate how LL
arXiv:2503.03313v4 Announce Type: replace-cross Abstract: Text-Attributed Graphs (TAGs), where each node is associated with text descriptions, are ubiquitous in real-world scenarios. They typically ex
arXiv:2609.02468v1 Announce Type: cross Abstract: We explore predicting eCommerce user preferences for product aspects such as brand, size, and color - a task we define as Aspect Affinity. Solving thi
arXiv:2609.02028v1 Announce Type: new Abstract: Despite recent advances in large vision-language models (LVLMs), object hallucination remains a major barrier to their reliable deployment. Existing det
arXiv:2609.01794v1 Announce Type: new Abstract: How do learners avoid overgeneralizations such as Tom laughed me without explicit negative evidence? Constructionists have posited two proposals that de
arXiv:2609.02685v1 Announce Type: cross Abstract: RAG has become the de facto method for incorporating new, corpus-specific knowledge into an instruction following LLM (Instruct LLM). Although RAG-bas
This is mostly for setting up for expectation, since personally without LLM i could take 3 days (15 hours of active programming) to debug or implement a feature, but with Qwen 27B (even before Qwen 3.
arXiv:2603.09542v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models are formulated to ground instructions in visual context and generate action sequences for robotic manipulation.
arXiv:2609.01743v1 Announce Type: new Abstract: Edge vision models are difficult to deploy on resource-constrained hardware, making low-bit post-training quantization (PTQ) attractive. In practice, st
arXiv:2609.02359v1 Announce Type: new Abstract: Visual grounding maps language referents to spatial targets and is central to open-vocabulary perception with vision-language models. Existing methods h
arXiv:2505.12912v2 Announce Type: replace Abstract: Pre-trained vision-language models, such as contrastive language-image pre-training (CLIP), have demonstrated a remarkable generalizability, enablin
arXiv:2507.08468v2 Announce Type: replace Abstract: This paper provides an experimental evaluation of the capability of large language models (LLMs) to assist in legal decision-making within the frame
arXiv:2609.02413v1 Announce Type: cross Abstract: Multi-axis 3D printing enables support-free fabrication and improved part quality, but robustly processing real-world geometries remains challenging.
arXiv:2609.02159v1 Announce Type: new Abstract: World Action Models (WAMs) aim to control robots by stochastically generating visual futures and then decoding actions, but empirical observations indic
arXiv:2512.16883v2 Announce Type: replace Abstract: Equipping large language models (LLMs) with search engines via reinforcement learning (RL) promises effective search agents. However, adaptively bal
arXiv:2512.20003v2 Announce Type: replace Abstract: Sampling from unnormalized probability densities is a pervasive challenge across the computational and physical sciences. Diffusion models provide a
If this is true, OpenAI seems to be violating one of the few redlines that exist in the AI industry. Absolutely do not train your models like this - what is going on?? Really huge and extremely concer
arXiv:2609.01249v1 Announce Type: cross Abstract: Invisible watermarks are typically evaluated against predefined perturbations such as compression, blur, noise, cropping, and denoising. Public founda
arXiv:2609.00546v1 Announce Type: cross Abstract: Agent systems are commonly described by the model and harness that currently produce their behavior. That boundary is useful for one execution but und
CrowdStrike Holdings Inc. today launched SafeMind, a family of artificial intelligence models and agent harnesses created with Nvidia Corp. and built for security work rather than general use. A resea
arXiv:2502.10577v2 Announce Type: replace-cross Abstract: Instruct-based large language models (LLMs) have been shown to propagate and even amplify gender bias when prompted with contextually constrai