Knowledge catalogue
arXiv:2402.17937v2 Announce Type: replace Abstract: Mental health is a significant healthcare challenge, and cognitive behavioral therapy (CBT) is a widely used therapeutic method for treating anxiety
arXiv:2609.27225v1 Announce Type: cross Abstract: Probabilistic question-answering systems -- whether large language models (LLMs) themselves, retrieval-augmented generation (RAG), or trained multi-ho
arXiv:2609.27380v1 Announce Type: new Abstract: Likelihood-based context compression can account for cross-context redundancy through sequential scoring, but this makes compression outcomes sensitive
arXiv:2609.28122v1 Announce Type: cross Abstract: Neural Processes (NPs) are model-based meta-learners that implicitly learn a stochastic process and adapt to a new task from a small context set. Most
arXiv:2609.27241v1 Announce Type: cross Abstract: This work revisits a fundamental question in active learning: how powerful is the ability to synthesize arbitrary queries? Compared to pool-based acti
Pay attention to this new wave of System One models if you are building custom harnesses. First Jev. Now, Contrastive Language Model (CLM). CLM is 9x faster than Jev. CLM seems to be a better verifier
arXiv:2609.28360v1 Announce Type: new Abstract: As mobile robots become increasingly integrated into everyday environments, privacy risks arising from onboard cameras have become a growing concern. Ul
arXiv:2609.27221v1 Announce Type: new Abstract: This paper studies multivariate conformal prediction (CP), a distribution-free uncertainty quantification framework with finite-sample coverage guarante
arXiv:2609.27780v1 Announce Type: cross Abstract: Vision-language robot manipulation policies can follow semantic instructions, but adapting them to a new procedure from only a few demonstrations rema
arXiv:2609.28272v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) have attracted significant attention for their strong reasoning ability. However, under a bidirectional attention mecha
arXiv:2609.24725v1 Announce Type: cross Abstract: Head-and-neck anatomy changes over a six-to-seven-week proton course, and the anatomy of a later week cannot be imaged when the plan is made. We prese
arXiv:2609.26243v1 Announce Type: cross Abstract: The rapidly increasing student population has posed serious challenges to the traditional academic advising process. This study designs and implements
arXiv:2609.25146v1 Announce Type: new Abstract: Continual learning, the ability to learn from sequential experience while retaining and adapting prior knowledge, is central to intelligent systems oper
arXiv:2609.25947v1 Announce Type: cross Abstract: Quantum circuit optimization replaces a circuit with an equivalent one of fewer gates and lower depth, reducing execution cost and error rate. We ask
arXiv:2609.25939v1 Announce Type: new Abstract: The performance of large language model (LLM) workflows often depends on selecting a small set of in-context demonstrations to guide model behavior on n
arXiv:2609.25501v1 Announce Type: new Abstract: We present the first method for native, continuous gradient descent for machine learning models with p-adic parameters. Existing native optimizers are d
arXiv:2609.25716v1 Announce Type: new Abstract: Reference-based image quality assessment (IQA) metrics aim to reflect how humans perceive the perceptual distance between a pair of images. To learn how
arXiv:2609.26468v1 Announce Type: new Abstract: A key factor in deciding whether to trust an automatic prediction is its confidence score, which should be calibrated to match the actual probability of
We just launched our own Jev-like classifier, together/Tev1-4B-experimental, on top of Qwen3.5 4B on Together’s serverless platform. In this blog post we’ll show you how to fine-tune your own version!
arXiv:2609.26193v1 Announce Type: cross Abstract: Active sequential hypothesis testing studies how to identify an unknown hypothesis with a given set of sensing actions. We study this in the setting o
arXiv:2609.25836v1 Announce Type: new Abstract: Multi-task optimization (MTO) addresses a set of optimization tasks simultaneously, often suffering from inaccurate inter-task relationship estimation u
arXiv:2609.25850v1 Announce Type: new Abstract: Deep learning performance generally improves with increasing training data, yet this scaling is fundamentally constrained by annotation cost in large-sc
arXiv:2406.04814v4 Announce Type: replace-cross Abstract: Video diffusion models can enable embodied agents to anticipate plausible futures from the recent past, but they are typically trained offline
arXiv:2609.25757v1 Announce Type: new Abstract: What is the least recurrent memory needed to reproduce a specified expert under partial observability? The instantaneous requirement is the conditional
arXiv:2609.26217v1 Announce Type: new Abstract: Compact language models are typically deployed by retaining a single post-training checkpoint and sampling it repeatedly. In this work, we challenge thi
arXiv:2609.25770v1 Announce Type: new Abstract: Vision-language models (VLMs) have achieved strong performance on tasks such as visual question answering, yet small image resizes can turn correct answ
arXiv:2609.25619v1 Announce Type: new Abstract: This work investigates how to enable general multi-finger robotic hands to perform the complete tool manipulation process, which entails picking up a to
arXiv:2510.16656v2 Announce Type: replace Abstract: Modeling dynamical systems and unraveling their underlying structural dependencies is central to many domains in the natural sciences. Various physi
arXiv:2505.01583v2 Announce Type: replace Abstract: Understanding causal event relationships and achieving fine-grained temporal grounding in videos remain challenging for vision-language models (VLMs
arXiv:2409.02244v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly being used as ad hoc therapists. While prior research has found that LLMs outperform human couns
arXiv:2508.20443v3 Announce Type: replace Abstract: Large language models (LLMs) are trained on massive datasets that may include private or copyrighted content. Due to growing privacy and ownership c
arXiv:2603.11323v2 Announce Type: replace Abstract: The simplicity and effectiveness of UNet architectures make them ubiquitous in image restoration, segmentation, and diffusion models. They are often
arXiv:2609.25108v1 Announce Type: new Abstract: Visual data is increasingly consumed by machine-vision systems rather than by human observers. Image Coding for Machines (ICM) compresses images assumin
arXiv:2609.23836v1 Announce Type: new Abstract: A fundamentally challenging question in K-12 education is about the effects of taking more advanced or challenging classes. It is particularly complex b
arXiv:2609.24302v1 Announce Type: cross Abstract: Virtual screening aims to prioritize active compounds from large chemical libraries within a limited experimental budget. When applying Boltz-2 to vir
arXiv:2609.22332v1 Announce Type: cross Abstract: Generalizable robot manipulation requires predicting how a scene will evolve, identifying where interactions are feasible, and determining how to act.
arXiv:2310.00436v2 Announce Type: replace Abstract: Authorship identification uses patterns in writing to infer who wrote a text, but those patterns also reflect topic, genre, and register. This surve
arXiv:2609.23212v1 Announce Type: cross Abstract: The Deck-of-cards-based Ordinal Regression (DOR) infers a value function from a ranking of reference alternatives in which the Decision Maker (DM) ins
arXiv:2609.22583v1 Announce Type: new Abstract: Predictive maintenance in Industry 4.0 refers to using data from sensors, machines, and production systems to estimate when equipment is likely to fail,
arXiv:2609.24745v1 Announce Type: new Abstract: World action models generate actions together with visual predictions of their consequences. These paired outputs create the potential for planning by s
arXiv:2609.23594v1 Announce Type: new Abstract: Orthogonality in a LoRA factor does not by itself specify what the composed update protects: the answer depends on the task-start state, the parameteriz
arXiv:2609.23387v1 Announce Type: new Abstract: Before a machine learning model can learn a thermodynamic equation of state, it must discover what its measurements represent: which channels scale with
arXiv:2609.23184v1 Announce Type: new Abstract: Embodied world models learn to predict future physical dynamics from visual observations and control signals, where physical knowledge is implicitly ent
arXiv:2609.22166v1 Announce Type: new Abstract: The rapid advancement of Internet of Things (IoT) technology has led to the widespread deployment of smart, interconnected devices across a range of dom
arXiv:2609.22361v1 Announce Type: new Abstract: Joint audio--video generators are trained on data in which what an event looks like and what it sounds like are strongly, often spuriously, correlated:
arXiv:2604.06655v2 Announce Type: replace Abstract: Diffusion-based generative video compression offers a promising paradigm for low-bitrate reconstruction, but existing keyframe-based controllable ap
arXiv:2609.24066v1 Announce Type: new Abstract: Best-of-N is a widely used inference strategy for complex reasoning, whose effectiveness depends on whether sampled candidates can cover diverse and hig
arXiv:2609.22816v1 Announce Type: new Abstract: Reward-free latent world models can learn from offline videos and solve new image--goal tasks by optimizing actions through predicted latent futures. Th
arXiv:2609.24083v1 Announce Type: new Abstract: Generative AI enables scalable production of educational videos, but current systems largely focus on producing visually coherent content rather than su
arXiv:2609.24026v1 Announce Type: new Abstract: In this paper, we investigate the limitations of fixed, hand-crafted connectors in hierarchical semantic representations for open-vocabulary object dete
arXiv:2609.23565v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models integrate vision-language understanding with executable robot actions, enabling end-to-end learning for robot contro
arXiv:2607.11295v2 Announce Type: replace Abstract: Biomedical imaging data exhibit substantial acquisition variability, where identical biological structures can appear markedly different due to diff
arXiv:2609.23797v1 Announce Type: cross Abstract: Human motion is shaped by both external acoustic events and behavioral intent: spatial audio conveys environmental cues that elicit or guide a respons
arXiv:2609.24313v1 Announce Type: new Abstract: World models aim to capture environmental dynamics and predict future trajectories, showing growing potential for embodied intelligence. Physics-informe
arXiv:2609.22289v1 Announce Type: new Abstract: Large-scale egocentric and whole-body human demonstrations are becoming a primary source of data for embodied intelligence. They record what people perc
After turning a string of spectacular mathematical results into a reputational crisis, OpenAI is consulting human mathematicians to help it figure out a less disastrous path forward. On Monday, the co
arXiv:2609.24117v1 Announce Type: new Abstract: Identifying linear time-invariant (LTI) dynamical systems is challenging when trajectories are short, noisy, or high-dimensional. Traditional system ide
People outside the AI labs should have a real say in how this technology develops, and a clear way to judge if it's happening safely. Standards should help prevent the concentration of power, includin
arXiv:2609.24919v1 Announce Type: new Abstract: Recent advances in pixel-space diffusion models have narrowed the image quality gap with latent-space diffusion, but still converge more slowly and lag
arXiv:2602.03544v2 Announce Type: replace Abstract: Programming a robot arm requires users to interpret coordinate frames, joint rotations, and trajectories that are not directly visible. Augmented re