Knowledge catalogue
arXiv:2606.09547v1 Announce Type: new Abstract: Learning everyday skills, like cooking a dish, relies increasingly on instructional media such as online videos. This opens the door to the use of video
arXiv:2312.02873v2 Announce Type: replace-cross Abstract: The process engineering domain widely uses Process Flow Diagrams (PFDs) and Process and Instrumentation Diagrams (P&IDs) to represent process
arXiv:2606.08633v1 Announce Type: new Abstract: Long-horizon maritime trajectory prediction is important for shipping management, logistics planning, and maritime risk analysis, yet month-level foreca
arXiv:2511.05017v2 Announce Type: replace Abstract: Hallucinations in Large Vision-Language Models (LVLMs) remain a persistent challenge, often stemming from inadequate integration of visual informati
arXiv:2606.09125v1 Announce Type: cross Abstract: Privacy risks in text-only Large Language Models (LLMs) are well studied, particularly their tendency to memorize and leak sensitive information. Howe
arXiv:2606.06525v1 Announce Type: cross Abstract: Large language models (LLMs) have emerged as powerful foundation models with strong reasoning capabilities across domains. Beyond reactive text genera
arXiv:2606.06865v1 Announce Type: new Abstract: Large language models (LLMs) have been increasingly explored for graph computation, where tasks require reasoning over structured relationships and algo
arXiv:2606.06534v1 Announce Type: cross Abstract: Longitudinal medical visual question answering (VQA) requires reasoning about anatomical differences between an image of a current time point and an i
arXiv:2509.14380v3 Announce Type: replace Abstract: Multi-Agent Reinforcement Learning (MARL) provides a powerful framework for learning coordination in multi-agent systems. However, applying MARL to
arXiv:2606.07183v1 Announce Type: new Abstract: This work examines the semantic geometry underlying NLP models. We compare supervised vector embeddings, such as CamemBERT, with lexical co-occurrence g
arXiv:2606.06857v1 Announce Type: new Abstract: A central goal of cognitive neuroscience is to characterize the features that are represented by human language cortex. Artificial language models (LMs)
arXiv:2606.06830v1 Announce Type: cross Abstract: Data-driven pricing is increasingly prevalent in sectors such as airlines, lending, insurance, and retail. By learning demand models from customer fea
arXiv:2606.06760v1 Announce Type: new Abstract: Medical large vision-language models (Med-LVLMs) have recently achieved remarkable progress in vision-language comprehension and medical image segmentat
arXiv:2606.07180v1 Announce Type: new Abstract: The growing demand for transparency in automated decision-making has propelled eXplainable Artificial Intelligence (XAI) to the forefront of machine lea
arXiv:2606.06902v1 Announce Type: new Abstract: Targeted post-training aims to improve reasoning, math, and code without degrading strengths. Low-rank adapters are efficient but task-global; activatio
arXiv:2606.07435v1 Announce Type: cross Abstract: Visual speech recognition (VSR) models now surpass human lipreaders on benchmarks, but do such gains establish human-like visual speech perception? To
arXiv:2606.05925v1 Announce Type: new Abstract: A central goal of biomedicine is to understand, predict and ultimately control the dynamic mechanisms by which biological systems respond to perturbatio
Running a 70B parameter model in full 16-bit precision requires roughly 140GB of memory , which is beyond most consumer hardware. Professional-tier GPUs like the RTX PRO 6000 with 96GB GDDR7 enable fu
arXiv:2606.06242v1 Announce Type: new Abstract: Institutional documents contain substantial amounts of operational and analytical information embedded within figures and tables. Current approaches for
arXiv:2504.10823v4 Announce Type: replace Abstract: Navigating dilemmas involving conflicting values is challenging even for humans in high-stakes domains, let alone for AI, yet prior work has been li
arXiv:2606.06002v1 Announce Type: new Abstract: Large Vision-Language Models have achieved significant reasoning performance in various tasks.However, there are few studies on text-to-3D indoor scene
arXiv:2606.06109v1 Announce Type: new Abstract: Entity alignment (EA) aims to identify equivalent entities across heterogeneous knowledge graphs (KGs) and is a key component of knowledge fusion and cr
arXiv:2606.05833v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) excel at 2D semantic understanding but lack intrinsic 3D awareness, resulting in representations that fail to m
arXiv:2507.00460v3 Announce Type: replace Abstract: Open Large Language Model (LLM) benchmarks, such as HELM and BIG-Bench, provide standardized and transparent evaluation protocols that support compa
arXiv:2606.04436v1 Announce Type: new Abstract: We propose a 3D-thinking-guided co-training framework that enables vision-language-action (VLA) models to perform 3D spatial reasoning implicitly during
A real problem with feeling the acceleration viscerally is that current models are really good and it is hard to feel the vibe difference on most individual tasks with new models, even as AIs continue
arXiv:2606.05106v1 Announce Type: cross Abstract: We investigate whether methods of human mathematics pedagogy can guide the training of language models toward arithmetic reasoning. Building on the GA
arXiv:2606.04618v1 Announce Type: new Abstract: Probabilistic data association (PDA) improves semantic SLAM in perceptually aliased scenes, but existing methods often assume a fixed landmark set, reco
arXiv:2606.04446v1 Announce Type: cross Abstract: Speculative decoding accelerates autoregressive large language model inference by drafting multiple tokens and verifying them in a single target-model
arXiv:2606.04764v1 Announce Type: new Abstract: Whether attention maps from pathology foundation models capture genuine biology remains unknown, yet this question is critical for clinical trust and re
arXiv:2606.04307v1 Announce Type: new Abstract: Bayesian models with finite symmetry - mixture models with exchangeable components, structural identification with closely-spaced modes - define posteri
arXiv:2606.05030v1 Announce Type: new Abstract: Autoregressive chain-of-thought (CoT) reasoning in large language models (LLMs) is fundamentally forward-directed: each step conditions only on prior to
arXiv:2606.04180v1 Announce Type: new Abstract: Vision-language foundation models such as CLIP and SigLIP provide widely used representations for multimodal learning systems. While these models are ty
arXiv:2606.04075v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a dominant post-training paradigm, enabling large language models (LLMs) to learn from rewards. We observe that
arXiv:2606.04846v1 Announce Type: new Abstract: As Large Language Models (LLMs) become increasingly popular in educational settings, they raise important questions about the ethical implications of th
arXiv:2606.04773v1 Announce Type: cross Abstract: Reliable evaluation of human motion understanding is fundamental to advancing embodied AI, robotics, and animation. However, existing benchmarks suffe
arXiv:2606.04822v1 Announce Type: new Abstract: Causal modeling of physical temporal phenomena must handle interventions that act along trajectories, nonstationary induced laws, path-dependent effects
arXiv:2606.04238v1 Announce Type: cross Abstract: Aggressive weight quantization to 2-bit precision offers substantial throughput and memory gains for large language model (LLM) inference, but typical
reiterating: 'We're using the more expensive models to explore. Once we scale some of these experiences, we'll look to bring in more efficient models that are more efficient on a token basis or are op
arXiv:2606.04157v1 Announce Type: new Abstract: Haptic guidance in teleoperation enhances operator performance through force feedback. This paper presents guidelines to select the most appropriate mod
arXiv:2606.04202v1 Announce Type: new Abstract: As LLMs become more widely deployed, they are increasingly expected to work alongside other AI agents rather than operating in isolation. Effective coor
arXiv:2602.16149v2 Announce Type: replace Abstract: Instruction-guided image-to-image (I2I) editors are increasingly used in consumer and professional visual workflows, where trustworthiness depends n
arXiv:2606.03846v1 Announce Type: cross Abstract: Large language models (LLMs) demonstrate remarkable performance across diverse tasks, but they often generate responses that appear plausible while be
arXiv:2606.03165v1 Announce Type: cross Abstract: The language used by digital chat assistants such as ChatGPT can diverge from human expectations (misalignment). Research, mostly on Scientific Englis
arXiv:2602.18690v2 Announce Type: replace-cross Abstract: Humans rehearse possible futures offline, as in mental practice and perhaps dreaming, suggesting that world models may support task learning a
arXiv:2606.03598v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have achieved remarkable success in language-conditioned robotic manipulation. However, deploying these models in
arXiv:2606.03444v1 Announce Type: cross Abstract: Unifying the complementary strengths of diverse Vision Foundation Models (VFMs) into a single efficient model is highly desirable but challenged by th
arXiv:2606.03969v1 Announce Type: cross Abstract: Reliable uncertainty communication is critical to the trustworthiness of LLMs, yet faithful calibration (FC)--the alignment between models' intrinsic
Routing and post-training open-source models won't only give you more accurate systems but also meaningfully faster and cheaper systems as most companies are currently learning (in addition to giving
arXiv:2511.02986v2 Announce Type: replace-cross Abstract: Computational modeling of single-cell gene expression is crucial for understanding cellular processes, but generating realistic expression pro
arXiv:2606.03399v1 Announce Type: new Abstract: While large language models (LLMs) are increasingly used for clinical applications, many existing pipelines require sending raw sensitive health informa
the best agents aren't just built with the best models: they're built with harnesses purpose-built for the task at hand here's a guide on how to build a harness that's really good at feeding the model
Sam Altman argues that US leadership in artificial intelligence requires three concurrent priorities: advancing cutting-edge AI model development, ensuring these models incorporate robust safety measu
arXiv:2606.03748v1 Announce Type: cross Abstract: Real-time vision demands models that are accurate, efficient, and simple to deploy across diverse hardware. The YOLO family has become widely deployed
arXiv:2606.03928v1 Announce Type: cross Abstract: Reasoning models improve accuracy through extended chains of thought, but their long outputs create a memory and compute bottleneck. KV cache eviction
arXiv:2606.01765v1 Announce Type: cross Abstract: What formal languages can a recurrent neural language model recognize? Formal results in the literature conflict: some authors report Turing-completen
arXiv:2602.02470v2 Announce Type: replace Abstract: Autoregressive large language models (LLMs) have achieved remarkable success in many complex tasks, yet they can still fail in very simple logical r
arXiv:2509.21474v4 Announce Type: replace Abstract: While diffusion language models (DLMs) have achieved competitive performance in text generation, improving their reasoning ability with reinforcemen
arXiv:2606.00997v1 Announce Type: new Abstract: Order-agnostic language models (OALMs), including discrete diffusion language models (dLLMs), are trained to predict masked tokens under arbitrary condi
arXiv:2606.00618v1 Announce Type: new Abstract: Generative models have emerged as a powerful paradigm for AI planning, yet their performance remains constrained by the training data distribution. One