Knowledge catalogue
arXiv:2610.14577v1 Announce Type: new Abstract: During human-robot interaction, sight plays an important role. When the human sees a robot, they can observe the robot's actions and better predict the
arXiv:2610.18051v1 Announce Type: new Abstract: This work investigates the application of the Membership Inference Test (MINT) to large language models (LLMs) fine-tuned with LoRA, aiming to determine
Insightful paper on recursive self-improvement with a frozen LLM. (bookmark it) Memento 3 clears every level of all 25 public ARC-AGI-3 games and uses 44% of the human action count. The underlying LLM
arXiv:2610.18386v1 Announce Type: new Abstract: Manipulation tasks such as unscrewing a cap or sliding a part in contact with a surface require imposing force along a high-friction direction: the robo
arXiv:2610.18855v1 Announce Type: new Abstract: Contact-rich manipulation requires robots to coordinate not only where to move, but also how contact and force should evolve throughout an interaction.
arXiv:2610.16167v1 Announce Type: cross Abstract: We propose the task of Interactive 4D Volumetric Liquid Forecasting: predicting the spatiotemporal evolution of a future dense 3D liquid velocity fiel
arXiv:2610.17089v1 Announce Type: new Abstract: Existing embodied navigation tasks commonly assume that traversability and object states remain fixed during an episode. Yet reaching a goal may require
arXiv:2610.16995v1 Announce Type: new Abstract: Real data are often arbitrary, indirect observations of an unknown underlying process, and those observations can distort its geometry. Existing geometr
arXiv:2410.12537v4 Announce Type: replace Abstract: Complex query answering (CQA) on knowledge graphs (KGs) is gaining momentum as a challenging reasoning task. In this paper, we show that the current
arXiv:2610.14743v1 Announce Type: new Abstract: We propose a Japanese handwriting generation framework that integrates human preference feedback using Direct Preference Optimization (DPO) to align gen
arXiv:2610.18690v1 Announce Type: new Abstract: Image-token compression must reduce computation without hurting the semantics. We introduce Varest, guided by semantic fungibility: tokens within a cohe
arXiv:2610.14448v1 Announce Type: new Abstract: Asynchronous rollout improves the efficiency of reinforcement learning with verifiable rewards (RLVR) by reducing under-utilization during long-tail gen
arXiv:2610.14020v1 Announce Type: new Abstract: Self-driving laboratories (SDLs) aim to automate the full experimental loop, from scientific decision-making to physical execution. Ideally, AI-generate
arXiv:2610.16398v1 Announce Type: new Abstract: Robot learning has undergone significant advancements in the domain of compliant control and robotic assembly. The development of novel methodologies ha
arXiv:2610.18053v1 Announce Type: cross Abstract: General-purpose robot manipulation policies learn short-horizon skills effectively, but degrade rapidly on long-horizon tasks. Existing benchmarks can
arXiv:2610.14290v1 Announce Type: new Abstract: The inner workings of deep neural networks are still not well understood. The information bottleneck framework, which characterizes deep networks by the
arXiv:2610.14848v1 Announce Type: new Abstract: Synthesizing believable 4D hand-object grasping sequences is important for character animation, extended reality, and embodied agents. Grasping requires
arXiv:2604.03392v2 Announce Type: replace-cross Abstract: This paper investigates whether it is more effective to condition reinforcement learning policies across changing dynamics using standard inpu
arXiv:2610.17804v1 Announce Type: cross Abstract: How can language models learn to optimize physical designs through experience? We introduce FracLearn, an experience-abstraction-generalization framew
arXiv:2610.15891v1 Announce Type: new Abstract: Predictive video representations do not by themselves specify how a multimodal system should handle missing sensors. We transfer frozen V-JEPA2 features
arXiv:2610.15486v1 Announce Type: new Abstract: Robotic systems for lunar surface operations must act autonomously and preserve their own integrity in an environment where failures are unrecoverable a
arXiv:2606.12475v2 Announce Type: replace Abstract: Human-robot collaboration (HRC) combines the complementary strengths of humans and robots to improve task efficiency. However, many existing collabo
arXiv:2610.14302v1 Announce Type: new Abstract: Humans and animals do not learn vision from a fixed set of data: they move, interact, and learn from the visual consequences of their actions. Motivated
arXiv:2610.18730v1 Announce Type: new Abstract: Manufacturers often need to characterize a new material, process, or product before enough operating data are available to train reliable predictive mod
arXiv:2601.12243v3 Announce Type: replace Abstract: Video summarization transforms long videos into concise representations that are easier to document and analyze, especially in high-stakes domains s
arXiv:2610.18843v1 Announce Type: new Abstract: Robot policies are expected to generalize across deployment environments, including variations in the robot itself: cameras are bumped and remounted, ca
arXiv:2610.17232v1 Announce Type: new Abstract: Navigating humanoid robots through dynamic, non-convex environments requires tightly coupling onboard perception with footstep planning and dynamic loco
arXiv:2610.18482v1 Announce Type: cross Abstract: Quantum computing promises new capabilities beyond classical computation. Mid-circuit measurements (MCMs) enable error correction and outcome-dependen
arXiv:2610.15899v1 Announce Type: cross Abstract: Large language model serving is increasingly limited not only by model size and GPU speed, but also by scheduling during autoregressive decoding, wher
arXiv:2610.15379v1 Announce Type: cross Abstract: Speaker anonymization (SA) aims to conceal speaker identity while preserving linguistic content and emotion, but existing streaming systems often lose
arXiv:2610.18572v1 Announce Type: cross Abstract: Automated post-training transforms pre-trained large language models (LLMs) into models capable of handling diverse domains by automatically construct
arXiv:2606.15610v2 Announce Type: replace-cross Abstract: Passing an exact-clone null gate does not entail passing a matched target-selection utility gate. We call a non-tie preference between exact d
arXiv:2610.15840v1 Announce Type: new Abstract: Reinforcement learning (RL) for large language model (LLM) agents remains challenging in long-horizon tasks with sparse outcome rewards, where assigning
arXiv:2610.14596v1 Announce Type: new Abstract: Navigating cluttered environments while carrying objects remains a challenging problem for quadruped robots. Compared with works that focused on load-aw
arXiv:2610.17930v1 Announce Type: new Abstract: Unknown payloads alter robot dynamics and make precise contact execution difficult when foothold margins are small. We present LOAD, a perceptive locomo
arXiv:2610.18289v1 Announce Type: new Abstract: Autoregressive video diffusion models have emerged as a foundation for long-horizon world rollout. However, when extended far beyond their training wind
arXiv:2508.11624v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) has become a widely adopted technique in text-to-image diffusion models, enabling the personalisation of visual concepts
arXiv:2610.14117v1 Announce Type: new Abstract: GPT-6 Astra provides robust embodied reasoning, but frequent external queries increase inference time and token costs. We equip a frozen pi_{0.5} policy
arXiv:2511.05319v3 Announce Type: replace Abstract: Despite remarkable progress in steganography, embedding sentence-level information into cover media remains challenging. In this work, we present th
arXiv:2610.16009v1 Announce Type: new Abstract: Not every moment in a video is equally useful for recognizing an action. A few decisive moments often carry the evidence: the instant an object moves, t
arXiv:2610.17384v1 Announce Type: new Abstract: We introduce and define measure neural mappings (MNMs), which combine measure-dependent neural transport with ensemble consistency for learning operator
arXiv:2610.17640v1 Announce Type: new Abstract: Spatial understanding is essential for embodied agents navigating and interacting with the physical world. As observations arrive incrementally, agents
arXiv:2610.17507v1 Announce Type: new Abstract: More than 1.5 billion people have hearing loss. When several people talk at once, hearing aids amplify every voice alike. Auditory attention decoding (A
arXiv:2610.17937v1 Announce Type: new Abstract: Table reasoning with language models typically assumes access to clean textual tables. In practice, tables are often serialized into text, which can cor
arXiv:2610.15931v1 Announce Type: new Abstract: Learning solution operators for partial differential equations (PDEs) requires models that can represent spatial structure across grids and scales. This
arXiv:2509.22707v2 Announce Type: replace-cross Abstract: Dynamic Voltage and Frequency Scaling is essential for enhancing energy efficiency in mobile platforms. However, traditional heuristic-based g
arXiv:2610.14976v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) have excelled in joint visual and language understanding, particularly in image captioning. However, LVLMs remain p
arXiv:2610.18851v1 Announce Type: cross Abstract: Multi-objective molecular optimization is a critical process in drug discovery that aims to modify a given molecule to simultaneously improve multiple
arXiv:2610.16252v1 Announce Type: new Abstract: Whole-body human-object interaction (HOI) generation requires coordinating body and finger motion, object trajectories, and time-varying contacts. Exist
arXiv:2610.17244v1 Announce Type: new Abstract: Fast, high-fidelity molecular generation requires accurate chemical choices and efficient sampling. Chemical building blocks reduce graph size, but make
arXiv:2610.17715v1 Announce Type: new Abstract: Flow matching has emerged as a powerful alternative to diffusion models for generative modeling, but standard conditional flow matching is fundamentally
arXiv:2610.17637v1 Announce Type: new Abstract: Since scaling laws were introduced for modern LLMs (Kaplan et al., 2020), several works have extended them to account for quantization, tracking the eme
arXiv:2610.17007v1 Announce Type: new Abstract: Generative Flow Networks (GFlowNets) learn a policy that samples compositional objects in proportion to an unnormalized reward R(x). This makes them use
arXiv:2610.15816v1 Announce Type: cross Abstract: Real-world datasets often exhibit severe class imbalance, degrading the performance of deep learning models, particularly on rare classes. While imbal
arXiv:2610.16919v1 Announce Type: cross Abstract: Machine learning engineering (MLE) agents are emerging as a new paradigm for automating end-to-end ML pipeline development. Using large language model
arXiv:2610.17856v1 Announce Type: new Abstract: Time-series analysis is increasingly studied in multimodal settings, yet most benchmarks pair temporal signals with text or with visualizations derived
arXiv:2610.15747v1 Announce Type: new Abstract: Trailer sway is a persistent contributor to severe crashes among light passenger vehicles towing trailers, yet it remains largely unaddressed by the aut
arXiv:2610.17178v1 Announce Type: new Abstract: Floor plan datasets underpin computational analysis of building layouts, spatial connectivity and graph machine learning. However, publicly available da
arXiv:2610.16285v1 Announce Type: cross Abstract: We propose an anomaly detection method for high-dimensional mixed-type data that maps heterogeneous features to a common scale via standardized moment
arXiv:2610.17603v1 Announce Type: new Abstract: Background: Five-year survival prediction from hematoxylin-and-eosin whole-slide images could support risk stratification, but the task is weakly superv