Knowledge catalogue
arXiv:2509.01135v3 Announce Type: replace Abstract: Electroencephalography (EEG)-based emotion recognition plays a critical role in affective Brain-Computer Interfaces (aBCIs), yet its practical deplo
arXiv:2609.33620v1 Announce Type: new Abstract: Modern language models are likely to be updated throughout their lifetime rather than trained once and frozen. Each update therefore participates in a r
arXiv:2601.22988v2 Announce Type: replace Abstract: Real-world robotic manipulation requires visuomotor policies capable of robust 3D scene reasoning under varying camera viewpoints. While recent 3D-a
arXiv:2609.32958v1 Announce Type: new Abstract: In many teleoperation applications, collecting a large number of demonstrations as required for traditional probabilistic learning from demonstration (L
arXiv:2609.34851v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) algorithms for movement control are typically evaluated and benchmarked on sequential decision tasks where imprecise a
arXiv:2609.33659v1 Announce Type: new Abstract: Multimodal large language models can expose task-relevant evidence through generation, but producing useful evidence does not by itself determine how it
arXiv:2609.35767v1 Announce Type: new Abstract: Unified multimodal models can both look at and render images, so in principle they can repair their own generations: diagnose what an image gets wrong,
arXiv:2606.15685v2 Announce Type: replace-cross Abstract: Embodied Continual Learning (ECL) aims to enable robots to continually acquire new manipulation tasks while retaining previously learned behav
arXiv:2609.34064v1 Announce Type: new Abstract: Reinforcement learning (RL) has become an effective post-training paradigm for long-horizon large language model (LLM) agents. However, we find that the
arXiv:2609.34711v1 Announce Type: new Abstract: Learning local geometry enables graph neural networks (GNNs) to adapt how they compare and integrate neighborhood information. However, estimating geome
arXiv:2609.34614v1 Announce Type: new Abstract: Managing water resources in mountainous regions depends heavily on reliable Snow Water Equivalent (SWE) and Snow Height (HS) data, yet these variables r
arXiv:2609.32820v1 Announce Type: cross Abstract: This paper studies learning of shuffle ideals with membership queries as well as with contrastive queries---a form of membership query that reveals no
arXiv:2609.31662v1 Announce Type: new Abstract: Face Image Quality Assessment determines the suitability of captured face images for automated face recognition (FR), a critical capability for reliable
arXiv:2609.32613v1 Announce Type: new Abstract: Graph neural networks lose much of their advantage on heterophilic graphs, where connected nodes often carry different labels. Graph rewiring is a popul
arXiv:2609.35541v1 Announce Type: new Abstract: We propose a distributionally robust learning framework where parameters defining the robustness mechanism are learned from held-out data instead of ext
arXiv:2609.32272v1 Announce Type: cross Abstract: Multimodal tabular-image learning is gaining growing attention, yet it faces challenges due to tabular data unavailable at test time. A practical solu
arXiv:2609.35003v1 Announce Type: new Abstract: Vision-language-action (VLA) models have demonstrated strong capabilities in robotic manipulation, but they are typically developed and evaluated with a
arXiv:2510.02284v4 Announce Type: replace Abstract: Recent video generation models have achieved remarkable progress and are now deployed in film, social media production, and advertising. Beyond thei
arXiv:2609.34205v1 Announce Type: new Abstract: Optimization modeling is central to many decision-making scenarios, but traditionally requires extensive domain expertise. While Large Language Models (
arXiv:2609.35166v1 Announce Type: new Abstract: Discrete diffusion models offer the ability to re-draft, revisiting and correcting earlier tokens throughout generation. This capability depends on the
arXiv:2609.35539v1 Announce Type: new Abstract: Video segmentation models maintain object identities by carrying instance information across frames. Under prolonged occlusion, reappearance, or interac
arXiv:2609.34344v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a key paradigm for enhancing the reasoning of large language models, yet the high dimensionality of parameter upd
arXiv:2603.11950v2 Announce Type: replace-cross Abstract: Multimodal language models have demonstrated strong semantic understanding and reasoning over physiological and behavioral signals captured fr
arXiv:2609.32632v1 Announce Type: cross Abstract: Multiobjective Bayesian optimization (MOBO) with Gaussian process (GP) surrogates is a sample efficient approach to solving multiobjective optimizatio
arXiv:2609.34677v1 Announce Type: cross Abstract: World models predict future observations from current experience and actions, yet prediction can depend on observations seen far in the past. Episodic
arXiv:2609.32785v1 Announce Type: cross Abstract: Domain incremental learning is essential for adapting ophthalmic deep learning models to sequential clinical domains while preserving diagnostic exper
arXiv:2503.17915v2 Announce Type: replace-cross Abstract: Real-world degraded images often contain multiple co-occurring degradation types, making composite image restoration fundamentally different f
arXiv:2609.33235v1 Announce Type: new Abstract: Implicit object properties that are difficult to directly infer from vision, such as material, container contents, or softness, can be revealed through
arXiv:2506.19065v3 Announce Type: replace Abstract: We propose Legato, a new end-to-end model for optical music recognition (OMR), a task of converting music score images to machine-readable documents
arXiv:2609.35046v1 Announce Type: new Abstract: Category-level 6D pose estimation from a single RGB-D observation is inherently under-constrained, since partial visible geometry must be interpreted to
arXiv:2609.32447v1 Announce Type: new Abstract: Learning robust and scalable finite-state tracking is fundamental to sequence processing. While linear recurrent neural networks (RNNs), linear attentio
arXiv:2609.35573v1 Announce Type: new Abstract: Feed-forward novel view synthesis reconstructs a scene from many input images in a single forward pass, yet more views do not necessarily improve perfor
arXiv:2609.35544v1 Announce Type: cross Abstract: Reliable refusal of harmful requests is essential to the safe deployment of language models. Because excessive eagerness to please users may undermine
arXiv:2609.33407v1 Announce Type: cross Abstract: De novo crystal generation (DNG) models decide where to search in composition space and how to generate structures with one set of weights. We argue t
arXiv:2609.35528v1 Announce Type: new Abstract: Rectified linear unit (ReLU) networks can suffer from dying neurons, where units with persistently negative pre-activations produce zero outputs, blocki
arXiv:2601.06641v3 Announce Type: replace Abstract: Membership inference attacks (MIAs) pose a serious privacy threat in federated learning (FL). While MIAs have been extensively studied in standard F
arXiv:2609.32612v1 Announce Type: new Abstract: Finding reliable point correspondences is difficult when point clouds have low overlap or undergo non-rigid deformation. Iterative refinement can correc
arXiv:2603.01563v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has achieved remarkable success in improving autoregressive models, especially in domains requ
arXiv:2609.33663v1 Announce Type: cross Abstract: Discovering underlying symmetries from data has emerged as a crucial challenge in scientific discovery. Existing data-driven methods for symmetry disc
arXiv:2607.17511v2 Announce Type: replace Abstract: Large Time Series Foundation Models (TSFMs) demonstrate strong zero-shot forecasting capabilities across diverse domains. However, their application
arXiv:2605.06332v2 Announce Type: replace Abstract: Constructive neural routing solvers usually score the next action by matching a decoder context to candidate embeddings, leaving deterministic one-s
arXiv:2609.35297v1 Announce Type: new Abstract: Pretraining a language model takes enormous compute, and the right optimizer can save a good part of it. Muon's spectral step gives a stronger direction
arXiv:2609.34446v1 Announce Type: new Abstract: Generative models have found great success as data-driven methods of solving inverse problems. Two popular approaches work either by combining a pretrai
arXiv:2609.32103v1 Announce Type: new Abstract: Large language models can memorize private or harmful information, motivating machine unlearning methods that remove targeted knowledge while preserving
arXiv:2609.33521v1 Announce Type: new Abstract: Trust evaluation plays a critical role in cybersecurity by supporting risk mitigation and decision-making. A variety of trust evaluation methods have be
arXiv:2609.35427v1 Announce Type: new Abstract: Modern LLMs are increasingly capable as autonomous agents, but they follow sequential interaction cycles: read, think, reply or call tools, repeat. Many
arXiv:2609.34427v1 Announce Type: new Abstract: Scaling LLM-based optimization from textbook-scale instances to real-world, industrial tasks remains a critical open challenge. Existing approaches are
arXiv:2609.34493v1 Announce Type: new Abstract: Explicit residual connections of the form (x+f(x)), often combined with normalization layers, have become a standard strategy for training very deep neu
arXiv:2609.33383v1 Announce Type: cross Abstract: The local linear minimization oracle (Ferris and Zavriev, 1996; arXiv:2605.08850), or Local LMO, solves constrained convex problems without having to
arXiv:2502.13440v2 Announce Type: replace-cross Abstract: Prevailing bioacoustic classifiers assign species labels to fixed time-frequency windows rather than to individual vocalizations. When multipl
arXiv:2609.32517v1 Announce Type: cross Abstract: The current deep learning training paradigm employs end-to-end backpropagation, regardless of the training stage, i.e. pre-training or fine-tuning. Ho
arXiv:2609.32742v1 Announce Type: new Abstract: Video super-resolution (VSR) is an ill-posed inverse problem that aims to reconstruct a high-resolution (HR) video from a noisy, low-resolution (LR) ver
arXiv:2505.09756v2 Announce Type: replace Abstract: In multi-agent reinforcement learning (MARL) settings, agents are often heterogeneous with diverse intrinsic utility functions. In this study, we in
arXiv:2609.32854v1 Announce Type: new Abstract: Deep Differentiable Logic Gate Networks (LGNs) and Lookup Table Networks (LUTNs) offer a promising approach for very low power inference due to their us
arXiv:2601.13556v2 Announce Type: replace Abstract: Simulated environments play an essential role in embodied AI, functionally analogous to test cases in software engineering. However, existing enviro
arXiv:2609.35236v1 Announce Type: new Abstract: Long-horizon agents improve solutions through sustained interaction, execution, and task feedback. Scaling studies relate performance to resources and c
arXiv:2609.34630v1 Announce Type: new Abstract: Real-world AI systems must reason about objects that are no longer visible: an AR assistant guiding a user back to an object used earlier, a household r
arXiv:2609.35536v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can explain deepfake verdicts in natural language, but such explanations are not necessarily visually grounded
arXiv:2609.34386v1 Announce Type: cross Abstract: On-policy distillation (OPD) uses teacher correction on student-generated responses. Full-vocabulary correction can provide important corrections even
arXiv:2609.34218v1 Announce Type: new Abstract: Looped language models separate computational depth from parameter count by repeatedly applying the same transformer block. Adapting these models requir