Knowledge catalogue
arXiv:2610.10186v1 Announce Type: new Abstract: Bayesian optimization (BO) is widely used as a standard approach for expensive black-box optimization. However, BO algorithms often involve parameters t
arXiv:2610.08813v1 Announce Type: new Abstract: X-ray image-based Radiology Report Generation (RRG) constitutes a critical research direction within medical artificial intelligence, with great potenti
arXiv:2610.09479v1 Announce Type: new Abstract: Precise end-effector tracking during humanoid whole-body motion is challenging due to floating-base oscillations, gravity, dynamic coupling, and locomot
arXiv:2610.09309v1 Announce Type: new Abstract: Generative world models provide rich predictions of how manipulation scenes may evolve toward task objectives, yet those futures do not directly expose
arXiv:2610.09709v1 Announce Type: new Abstract: Can we determine whether a foundation model will generalize out-of-distribution (OOD) before any target data is available? Existing diagnostics require
arXiv:2511.17776v2 Announce Type: replace Abstract: We present PrismSSL, a Python library that unifies state-of-the-art self-supervised learning (SSL) methods across audio, vision, graphs, and cross-m
arXiv:2610.10140v1 Announce Type: cross Abstract: Artificial intelligence (AI) is becoming an increasingly useful tool across the experimental sciences, including angle-resolved photoemission spectros
arXiv:2610.10239v1 Announce Type: new Abstract: Scientific dynamics forecasting is often framed as an architecture choice, although deployment is also determined by observed history, rollout feedback,
arXiv:2603.11938v2 Announce Type: replace-cross Abstract: Structured radiology reporting promises faster, more consistent communication than free text, but automation remains difficult as models must
arXiv:2610.09941v1 Announce Type: new Abstract: Large vision-language models (LVLMs) are increasingly deployed in safety-critical applications, yet they remain vulnerable to backdoor attacks. Defendin
arXiv:2610.09223v1 Announce Type: new Abstract: Lip movements can match the timing of speech without matching the spoken sounds. We introduce PVSync, a unified model for audio-visual offset estimation
arXiv:2610.10437v1 Announce Type: new Abstract: Flow policies capture rich and diverse action distributions, and fine-tuning them with off-policy RL to improve beyond the demonstrations has drawn grow
arXiv:2610.09183v1 Announce Type: new Abstract: Quantization-aware training (QAT) leverages lower-precision arithmetic to reduce the cost of LLM deployment, but aggressive quantization degrades final
arXiv:2610.09641v1 Announce Type: cross Abstract: Photonic quantum computing has recently emerged as a promising platform for hybrid quantum machine learning due to its native realization of linear-op
arXiv:2610.09033v1 Announce Type: new Abstract: Standard safety evaluations of large language models assess harmful requests written in canonical plain text, while models in real-world deployment rout
arXiv:2610.09044v1 Announce Type: cross Abstract: Weak-to-strong generalization is the phenomenon where a strong student model trained with labels produced by a weak teacher model is able to generaliz
arXiv:2610.10497v1 Announce Type: new Abstract: We introduce QuadTok, a novel framework for visual tokenization and autoregressive image generation. Compared to traditional approaches using 2D grids o
arXiv:2610.08851v1 Announce Type: new Abstract: Quantifying language distance among closely related languages remains a core challenge in quantitative linguistics. Our previous work [1] introduced Qua
arXiv:2610.09392v1 Announce Type: new Abstract: Reliable volumetric segmentation is critical for clinical diagnostics, yet foundation models such as MedSAM remain deterministic and lack calibrated unc
arXiv:2610.09227v1 Announce Type: cross Abstract: Mixed-precision post-training quantization needs a per-module sensitivity signal; for a text embedder the obvious one -- the retrieval quality a modul
arXiv:2610.09635v1 Announce Type: cross Abstract: Anomaly detection on small and unbalanced datasets remains very challenging in machine learning, although this scenario is common in several domains,
arXiv:2610.09670v1 Announce Type: new Abstract: Unsupervised anomaly detection, which learns only from normal images, is a central task in medical image analysis and remains an open problem. Reconstru
arXiv:2610.09191v1 Announce Type: new Abstract: Performing chess game position recognition solely from a single image of a three-dimensional board requires predicting the position and orientation of t
arXiv:2610.08954v1 Announce Type: new Abstract: Video large language models (Vid-LLMs) excel at diverse video-language tasks by reasoning over selected frames. However, frame selection for long videos
arXiv:2610.09173v1 Announce Type: new Abstract: 3D foundation models enable efficient novel view synthesis by carrying a Gaussian head on the representation they already use for reconstruction. Howeve
arXiv:2610.10343v1 Announce Type: new Abstract: Deploying a joint audio-video diffusion transformer for real-time, interactive generation normally requires two essential modifications: block-autoregre
arXiv:2610.10079v1 Announce Type: new Abstract: World Action Models (WAMs) combine visual dynamics modeling with action generation, but their high inference latency limits responsive robot control. Re
arXiv:2610.10405v1 Announce Type: cross Abstract: Monitoring the chain-of-thought of reasoning artificial intelligence (AI) models remains a key approach to detecting deception and other forms of misb
arXiv:2610.08991v1 Announce Type: new Abstract: We present REFIT, an input calibration for frozen activity-recognition models whose inertial sensors are worn differently at deployment than in training
arXiv:2610.09522v1 Announce Type: cross Abstract: First order Langevin algorithms for constrained sampling in machine learning, such as projected Langevin Monte Carlo which are based on discretization
arXiv:2610.09569v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for emotional support, raising concern that sustained use may draw users away from their real-world r
arXiv:2610.09780v1 Announce Type: new Abstract: Diffusion models excel at image synthesis, but they remain limited in their ability to reliably satisfy structured spatial reasoning constraints. In con
arXiv:2610.09004v1 Announce Type: new Abstract: Does removing harmful information make open-weight models resistant to fine-tuning attacks? We show that mutual information at release alone cannot univ
arXiv:2610.10526v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) are strikingly sensitive to instruction phrasing and do not inherit the language robustness of the vision-languag
arXiv:2610.09778v1 Announce Type: cross Abstract: Reproducible benchmarking of Large Language Model (LLM) inference is challenging because repeated measurements can vary with execution and system stat
arXiv:2407.18994v2 Announce Type: replace-cross Abstract: We consider the automatic online synthesis of black-box test cases from functional requirements specified as automata for reactive implementat
arXiv:1907.09194v3 Announce Type: replace-cross Abstract: Segmentation of subcortical brain structures is fundamental to computer-aided diagnosis and treatment in neurology and related clinical fields
arXiv:2610.09613v1 Announce Type: cross Abstract: Shallow models are special cases of deep models, and deep models theoretically have the potential to outperform the shallow ones. However, the existin
arXiv:2610.10381v1 Announce Type: new Abstract: Looped Transformers improve parameter efficiency by repeatedly applying shared Transformer blocks over multiple recurrent loops, increasing computationa
arXiv:2610.10301v1 Announce Type: new Abstract: Modern applications of AI rely on increasingly complex models. Explainable AI (XAI) has emerged as a set of techniques aimed at improving model transpar
arXiv:2610.10453v1 Announce Type: new Abstract: Flow-based policies provide an expressive framework for continuous robot control, but their iterative ODE integration incurs substantial inference cost.
arXiv:2610.09458v1 Announce Type: new Abstract: When an LLM answers a state-specific policy question wrongly, it may be hallucinating, or it may be returning a real value that holds in another state.
arXiv:2610.09455v1 Announce Type: new Abstract: Recently, approaches that leverage human video datasets for robot policy training have become increasingly prevalent. However, most existing hand tracke
arXiv:2610.10214v1 Announce Type: cross Abstract: Bayesian additive regression trees (BART) can require many splits to approximate boundaries misaligned with the predictor axes. RoBART assigns each tr
arXiv:2505.12224v5 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have recently advanced robotic manipulation by translating natural-language instructions and visual observations
arXiv:2610.10515v1 Announce Type: cross Abstract: Latent world models have shown a remarkable ability to predict future states and to plan in the real world. In practice, however, we lack a principled
arXiv:2610.09696v1 Announce Type: new Abstract: Robot manipulation data collection has been shifting from teleoperation toward robot-free demonstrations, through interfaces such as the Universal Manip
arXiv:2510.00154v2 Announce Type: replace Abstract: Despite rapid progress in robotics, complex or long-horizon tasks remain a fundamental challenge. Most current approaches follow an open-loop paradi
arXiv:2610.10534v1 Announce Type: new Abstract: End-to-end robot policies trained through imitation learning remain constrained by limited data diversity, making reliable zero-shot deployment in real-
arXiv:2610.10388v1 Announce Type: cross Abstract: Recent advances in multimodal foundation models have made them capable generalist physical agents for a range of manipulation tasks. However, successf
arXiv:2610.09254v1 Announce Type: new Abstract: Simulation enables large-scale, low-cost robot data generation, but policies trained in simulation often fail to transfer to the real world due to the s
arXiv:2610.09454v1 Announce Type: new Abstract: Robotic manipulation videos are increasingly used as visual plans for embodied agents, but optimizing purely for visual plausibility often fails to capt
arXiv:2610.10472v1 Announce Type: new Abstract: Throwing objects that generate aerodynamic lift can greatly extend robot throwing beyond ballistic flight. A returning boomerang is a challenging exampl
arXiv:2509.18597v3 Announce Type: replace Abstract: Large language models (LLMs) can translate natural-language instructions for robotic manipulation into executable code, but ambiguity, noisy generat
arXiv:2610.10409v1 Announce Type: cross Abstract: General-purpose agents increasingly write code, use tools, and complete complex digital tasks, raising the question of how far these capabilities carr
arXiv:2610.10199v1 Announce Type: new Abstract: Emerging legislation requires large language models (LLMs) to be audited for compliance with regulatory standards, particularly fairness. Such black-box
arXiv:2610.09914v1 Announce Type: new Abstract: Reinforcement learning is crucial for improving large language models' reasoning and generalization. It relies on massive rollouts whose lengths become
arXiv:2610.08814v1 Announce Type: cross Abstract: Compositional generalization remains challenging when language models must combine familiar reasoning operations in unfamiliar ways. The Scenario-Base
arXiv:2610.10310v1 Announce Type: new Abstract: Recursive self-improvement requires carrying accepted changes into later improvement cycles, while studying agent-proposed changes also requires substan
arXiv:2610.09502v1 Announce Type: new Abstract: Open-vocabulary detection (OVD) recognizes categories unseen during training through textual category queries, yet achieving strong generalization with