Knowledge catalogue
arXiv:2509.25998v4 Announce Type: replace Abstract: In light of recent progress in video editing, deep learning models focusing on both spatial and temporal dependencies have emerged as the primary me
arXiv:2606.22570v2 Announce Type: replace Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a promising framework for enhancing the reasoning ability of large language mod
arXiv:2609.24576v1 Announce Type: cross Abstract: Modern Vision-Language Navigation (VLN) models rely mostly on pre-trained large Vision-Language Models (VLMs) to predict navigation actions. While thi
arXiv:2609.22337v1 Announce Type: cross Abstract: Linear probing is the standard instrument for detecting social biases in the hidden representations of large language models. Yet reported probe accur
arXiv:2609.22299v1 Announce Type: cross Abstract: When a robot faces unfamiliar physical conditions, a common approach is to collect evidence about what changed and adapt. For such diagnosis to improv
arXiv:2609.24194v1 Announce Type: new Abstract: Evaluation scores used around LLM systems -- including reward models, rerankers, and LLM judges -- can track surface form instead of the quality they cl
arXiv:2609.24862v1 Announce Type: new Abstract: Agentic time series forecasting concerns systems whose underlying mechanisms evolve, making the relative effectiveness of numerical models, reasoning st
arXiv:2609.22829v1 Announce Type: new Abstract: Collecting whole-body demonstrations for humanoid manipulation mostly relies on teleoperation, which is costly and hard to scale up. The Universal Manip
arXiv:2607.18840v2 Announce Type: replace Abstract: World Action Models (WAMs) offer a promising paradigm for robotic manipulation by jointly modeling visual state transitions and robot actions. Howev
arXiv:2609.21511v1 Announce Type: cross Abstract: This report describes our 2nd place solution to the HANDS 2026 workshop challenge (Dexterous Grasp Motion track) in conjunction with ECCV 2026. In thi
arXiv:2609.20835v1 Announce Type: new Abstract: Background: The Voynich Manuscript is a fifteenth-century codex written in an unknown script whose content remains undeciphered. Previous studies sugges
arXiv:2609.21817v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have become a prominent paradigm for mapping multimodal inputs, including semantic instructions, visual observations
arXiv:2609.21214v1 Announce Type: new Abstract: Cognitive diagnosis infers students' concept mastery from response logs. However, students' responses are not determined by mastery alone: non-cognitive
arXiv:2609.21792v1 Announce Type: new Abstract: Navigating toward human callers is an important capability for rescue robots operating where visual contact is degraded or occluded. We present Acoustic
arXiv:2609.21482v1 Announce Type: cross Abstract: Accurate neural world models are central to model-based robotics, where they enable robots to predict future states from previously observed trajector
arXiv:2609.21075v1 Announce Type: cross Abstract: As access to professional mental healthcare remains limited, many individuals turn to online platforms such as Reddit to seek peer support situated wi
arXiv:2609.21378v1 Announce Type: new Abstract: Reinforcement learning has substantially improved large language model (LLM) agents in verifiable domains, but remains difficult to apply to open-ended
arXiv:2609.20982v1 Announce Type: new Abstract: Reinforcement learning (RL) controllers have been recently adopted for Unmanned Aerial Vehicles (UAV) navigation and control. However, they are suscepti
arXiv:2609.21995v1 Announce Type: new Abstract: The prediction of critical heat flux (CHF), a key safety-related quantity in nuclear thermal hydraulics, remains an important challenge due to its direc
arXiv:2609.21461v1 Announce Type: cross Abstract: Embodied foundation models are constrained by the limited scale and diversity of robot demonstrations, motivating the use of large-scale egocentric hu
arXiv:2609.21490v1 Announce Type: new Abstract: Gender bias remains a persistent concern in machine translation (MT), affecting both generated translations and their automatic evaluation. When a sourc
arXiv:2609.21276v1 Announce Type: cross Abstract: In automated Printed Circuit Board Assembly (PCBA) inspection, standards-guided decisions require systems to jointly reason over fine-grained visual c
arXiv:2609.21656v1 Announce Type: new Abstract: Recent Joint-Embedding Predictive Architectures (JEPAs) prevent representation collapse by constraining learned representations to follow a prescribed t
arXiv:2609.21909v1 Announce Type: new Abstract: In this work, we conduct a systematic comparison of two state-of-the-art motion-imitation reinforcement learning (MIRL) pipelines, one built on SCONE/Hy
arXiv:2609.21619v1 Announce Type: new Abstract: On-policy distillation (OPD) improves reasoning models by learning the token-level discrepancy between a stronger teacher and an on-policy student. Howe
arXiv:2609.21982v1 Announce Type: new Abstract: Robot demonstration collection often produces imperfect or failed trajectories in addition to successful demonstrations. Existing methods typically expl
arXiv:2609.21686v1 Announce Type: cross Abstract: Privacy leakage in LLM agents is commonly evaluated within individual components such as memory, retrieval, or tool-use pipelines, which makes it diff
arXiv:2609.21812v1 Announce Type: cross Abstract: Recent advances in diffusion models have enabled high-performance, instance-adaptive compressed sensing through posterior sampling, without task-speci
arXiv:2508.03030v3 Announce Type: replace Abstract: Mixed-integer linear programming (MILP) has been a fundamental problem in combinatorial optimization. Conventional MILP solving mainly relies on car
arXiv:2609.20973v1 Announce Type: cross Abstract: Complex problem solving (CPS) with large language models (LLMs) is often framed as a matter of stronger reasoning or longer generation. Yet early-step
arXiv:2609.20906v1 Announce Type: new Abstract: Quasars are luminous objects in the universe that exhibit stochastic brightness variations encoding information about the supermassive black holes power
arXiv:2609.21327v1 Announce Type: cross Abstract: Quantile-based reinforcement learning provides an interpretable approach to risk-sensitive decision-making by optimizing a prescribed quantile of the
arXiv:2609.21112v1 Announce Type: new Abstract: Training a visuomotor policy calls for abundant demonstrations that closely match the target environment, yet collecting them anew remains expensive. Ex
arXiv:2609.21045v1 Announce Type: new Abstract: Collecting real-world robot data for dexterous manipulation is costly and time-consuming. While high-fidelity physics simulators enable scalable data sy
arXiv:2609.21138v1 Announce Type: cross Abstract: Octopus crawling motivates soft robots that exploit redundancy, yet discovering and organizing diverse coordination modes for adaptation remains chall
arXiv:2609.21548v1 Announce Type: new Abstract: Sequential recommendation aims to predict the next item a user will interact with based on their historical behavior. Advances in Transformers have sign
arXiv:2609.05799v2 Announce Type: replace Abstract: In streaming simultaneous speech translation, the speech translation system is trained to learn a read-write policy that alternates between consumin
arXiv:2609.21899v1 Announce Type: new Abstract: Best-of-n (BoN) sampling is a simple yet effective inference-time alignment method, but hard maximization provides only coarse control over the trade-of
arXiv:2609.21057v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training without sharing raw data, but its performance degrades under non-IID data and stochastic cl
arXiv:2609.21829v1 Announce Type: cross Abstract: Clustering high-dimensional data is a fundamental task in unsupervised machine learning with applications to a variety of domains. In the centralized
arXiv:2609.21216v1 Announce Type: cross Abstract: Vision-language-action (VLA) policies based on flow matching generate action chunks through repeated evaluations of an action expert. Increasing the n
arXiv:2609.21447v1 Announce Type: cross Abstract: Humanoid locomotion over complex terrain requires anticipating footholds that may no longer be visible at touchdown. Limited camera coverage and self-
arXiv:2609.21788v1 Announce Type: cross Abstract: A pretrained robot foundation policy may execute most of a long-horizon task yet repeatedly fail at a few critical subtasks. Collecting additional ful
arXiv:2609.21735v1 Announce Type: new Abstract: Effective exploration in high-dimensional continuous control remains a central challenge in reinforcement learning. Planning-based methods address this
arXiv:2609.21876v1 Announce Type: new Abstract: As an alternative to the additive and extremal biases of average and max pooling, we introduce Geometric Mean Pooling (GMP), a signed pooling operator t
arXiv:2603.24155v3 Announce Type: replace Abstract: Current trajectory prediction models are primarily trained in an open-loop manner, which often leads to covariate shift and compounding errors when
arXiv:2609.21432v1 Announce Type: new Abstract: Post-training plays a pivotal role in enhancing the reasoning capabilities and task-specific expertise of large language models (LLMs). Despite recent a
arXiv:2609.21597v1 Announce Type: new Abstract: Monocular 6-DoF pose estimation of non-cooperative targets is important for on-orbit servicing and debris removal. A single-image estimator can confuse
arXiv:2609.21363v1 Announce Type: new Abstract: Multimodal large reasoning models (MLRMs) have demonstrated remarkable capabilities in complex visual understanding. However, this very power introduces
arXiv:2609.21525v1 Announce Type: new Abstract: Preference-based reward shaping can guide reinforcement learning, but adding preference signals to the reward may unintentionally change the task being
arXiv:2609.21938v1 Announce Type: new Abstract: Transformer-based models have recently achieved strong performance on 3D reconstruction from images, and recent works extend them to process video strea
arXiv:2609.21208v1 Announce Type: new Abstract: Self-play methods that co-train a single language model as both coder and test author promise to move code-generation RL beyond fixed test suites, but t
arXiv:2512.06201v3 Announce Type: replace Abstract: We introduce K2-V2, a 360-open LLM built from scratch as a superior base for reasoning adaptation, in addition to functions such as conversation and
arXiv:2609.21229v1 Announce Type: cross Abstract: Learning robot manipulation policies typically requires substantial demonstration data, which are costly to collect on real robots. Recent methods gen
arXiv:2609.22041v1 Announce Type: new Abstract: Reinforcement learning is increasingly used to align image generators with reward signals, and Flow-GRPO recently extended this paradigm to flow-matchin
arXiv:2609.21467v1 Announce Type: new Abstract: Motion tracking can reproduce humanoid loco-manipulation from a single retargeted motion clip, but a policy trained on a fixed reference primarily repro
arXiv:2602.21583v2 Announce Type: replace Abstract: While reinforcement learning (RL) has been successfully applied to conventional quadrotors for agile and robust flight, whether actuator-level RL ca
arXiv:2609.21107v1 Announce Type: new Abstract: While learning from human motions has enabled highly dynamic humanoid skills such as dancing and martial arts in obstacle-free space, traversal through
Let’s be clear: LLMs don’t feel pain. And they don’t deserve moral standing. This new paper reports a series of experiments identifying pain-related representations in LLMs. The authors also show that
arXiv:2609.21164v1 Announce Type: new Abstract: Integrating diverse data modalities --- such as clinical notes, laboratory results, and medical imaging --- is essential for advancing clinical decision