Knowledge catalogue
arXiv:2609.32064v1 Announce Type: new Abstract: This paper presents Grasp2Twist, a bimanual dexterous manipulation system that learns to grasp and twist open jar lids using reinforcement learning. Lea
arXiv:2609.32577v1 Announce Type: cross Abstract: Reinforcement learning (RL) for code agents often uses executable tests to provide binary rewards. With these rewards, Group Relative Policy Optimizat
arXiv:2609.31904v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong performance on robotic manipulation, but they often struggle to generalize to unseen tasks, conf
arXiv:2609.31924v1 Announce Type: new Abstract: Contact-rich manipulation depends on force sensing that is hard to infer from visual signals alone, both for collecting demonstrations and for training
arXiv:2609.35587v1 Announce Type: new Abstract: GPU libraries such as CUTLASS expose tens of thousands of semantically equivalent kernels for a single operation, making exhaustive autotuning expensive
arXiv:2601.13177v2 Announce Type: replace Abstract: Spinal cord stimulation (SCS) is widely used for pain management and has recently shown promise in promoting functional recovery following spinal co
arXiv:2609.32280v1 Announce Type: new Abstract: Hero frames are in-film stills used as source imagery for theatrical posters, streaming cover art, film database listings, and other promotional placeme
arXiv:2609.34166v1 Announce Type: new Abstract: We study the knowledge matrix of a trained feedforward network as a higher representation of its inputs. A network is a pair (W,f), a thin representatio
arXiv:2609.33983v1 Announce Type: cross Abstract: Semantic Textual Similarity (STS) methods assume that a document's lexical content faithfully represents what it asserts. This assumption fails for di
arXiv:2609.33570v1 Announce Type: new Abstract: Group-relative policy optimization (GRPO) makes learner-side activations a major memory-computation bottleneck: gradient checkpointing reduces activatio
arXiv:2604.04204v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly embedded in educational, professional, and public infrastructure, yet widely used platforms expo
arXiv:2509.25637v2 Announce Type: replace Abstract: Preconditioning is widely used in machine learning to accelerate convergence on the empirical risk, yet its role on the expected risk remains undere
arXiv:2609.33482v1 Announce Type: new Abstract: Conformal prediction provides distribution-free finite-sample marginal coverage, but post-hoc calibration data may be too scarce to learn how uncertaint
arXiv:2609.34490v1 Announce Type: new Abstract: Large collections of historical Persian manuscripts have been digitized, but searching them is still slow and mostly manual. Historians usually want to
arXiv:2506.18078v2 Announce Type: replace-cross Abstract: We propose a novel nonparametric regression method that models complex input-output relationships as the sum of convex and concave components.
arXiv:2609.35390v1 Announce Type: new Abstract: Verbal feedback can identify errors and prescribe corrections, providing rich supervision for language-model post-training even when reliable programmat
arXiv:2609.33647v1 Announce Type: new Abstract: Many indoor environments in which robots operate, such as warehouses, offices, and hospitals, already have cameras installed. They observe parts of the
arXiv:2609.32201v1 Announce Type: cross Abstract: On-Policy Context Distillation (OPCD) has recently emerged as a powerful technique for transferring context to student models and for self-improvement
👀 Interesting data from @tickerplus. Acc to this data, Anthropic's ARR seems to have flatlined around the time when the token index peaked. Many people questioned (rightly) at the time the inferential
arXiv:2609.35351v1 Announce Type: new Abstract: Interference is commonly treated as geometric overlap between learned features. We introduce effective interference, which combines feature geometry and
arXiv:2609.32619v1 Announce Type: new Abstract: Large self-supervised vision models learn representations that support scene segmentation and the semantic decomposition of physical objects. We ask whe
arXiv:2609.32004v1 Announce Type: new Abstract: Encoded demographic information in internal model representations is a commonly assumed risk factor for algorithmic bias, with demographic representatio
arXiv:2609.33049v1 Announce Type: new Abstract: Behavior cloning (BC) is vulnerable to covariate shift during closed-loop execution, where small prediction or execution errors can drive the robot towa
arXiv:2609.34385v1 Announce Type: cross Abstract: Memory is critical for AI agents. Many existing agent-memory systems follow an Ahead-of-Time (AOT) design, constructing memory before a specific reque
arXiv:2609.34082v1 Announce Type: cross Abstract: Interpreting architecture, engineering, and construction (AEC) drawings is hard for general Multimodal Large Language Models (MLLMs) and vision-langua
arXiv:2609.33630v1 Announce Type: cross Abstract: Differentiable optimization brings the structural guarantees of mathematical optimization to network pipelines, allowing them to be trained end-to-end
arXiv:2609.33136v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student to match a teacher's next-token distributions on student-generated trajectories. However, privileged infor
arXiv:2605.06050v2 Announce Type: replace Abstract: Graph-based learning from functional magnetic resonance imaging (fMRI) has shown strong potential for brain network analysis. However, existing meth
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:2609.34851v1 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:2609.34064v1 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: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:2609.33235v1 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: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:2609.32612v1 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: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:2609.35236v1 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:2609.34509v1 Announce Type: new Abstract: Masked diffusion language models support arbitrary-order generation, suggesting a natural way to produce diverse outputs. However, recent work argues th
arXiv:2609.31644v1 Announce Type: new Abstract: Reinforcement learning (RL) is widely used in language-model post-training to maximize rewards assigned to individual model outputs, such as scores from
arXiv:2609.34990v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models but incurs substantial costs from rol
arXiv:2609.32844v1 Announce Type: cross Abstract: Medical ultrasound (US) is inherently degraded by speckle, a granular interference pattern that is often treated as a complex form of noise in image r
arXiv:2609.34420v1 Announce Type: new Abstract: Graph Transformers (GTs) offer superior representation capabilities by overcoming the depth limitations and over-smoothing issues of traditional Graph N
arXiv:2609.33804v1 Announce Type: cross Abstract: Modeling spatiotemporal coupling is a key challenge in building physical intelligence across scales, from microscopic to macroscopic. Existing models
arXiv:2609.32690v1 Announce Type: new Abstract: Recent multimodal large language models (MLLMs) advance visual reasoning by strengthening both perception and reasoning, implicitly assuming a process t
arXiv:2609.34220v1 Announce Type: cross Abstract: Assistive robots increasingly operate in many human-centered environments and perform various human-robot interaction (HRI) tasks, such as object deli
arXiv:2601.19284v3 Announce Type: replace-cross Abstract: Stabilizing a dynamical system is a fundamental problem that serves as a cornerstone for many complex tasks in the field of control systems. T
arXiv:2609.34512v1 Announce Type: new Abstract: Image-aligned metric demonstrations often require dedicated tracking hardware and synchronization across devices. We present MonoEgo, a capture system t
arXiv:2609.34833v1 Announce Type: new Abstract: Semantic mapping is fundamental to embodied navigation, yet existing methods are developed for indoor environments, where objects exhibit relatively lim
arXiv:2609.35291v1 Announce Type: cross Abstract: Modern AI models are aligned through post-training to adapt them to downstream tasks. Recent work shows that fine-tuning language models on narrow tas
arXiv:2609.34684v1 Announce Type: cross Abstract: Accurately decoding object states from the internal representations of vision-language-action (VLA) models does not establish that the predictions res
arXiv:2609.32322v1 Announce Type: new Abstract: World models are typically trained and evaluated by prediction error, assuming that more accurate predictions lead to better decisions. We show that thi
arXiv:2609.31947v1 Announce Type: new Abstract: Hybrid transformer architectures that replace most softmax attention layers with linear attention offer transformer-level quality at a fraction of the m
arXiv:2609.32470v1 Announce Type: new Abstract: Large reasoning models (LRMs) often suffer from overconfidence when expressing their uncertainty. Confidence-aware reinforcement learning (RL) offers a
arXiv:2609.33904v1 Announce Type: new Abstract: We consider the behavior of deterministic, full-batch, bias-corrected Adam in separable linear classification with softmax parametrization under log-los
arXiv:2609.35514v1 Announce Type: new Abstract: In ecology, psychometrics, and the analysis of social and financial networks, binary matrices are often analyzed conditional on their observed row and c
arXiv:2609.32175v1 Announce Type: new Abstract: Autoregressive video diffusion is a promising render-time fixer for 3D Gaussian Splatting (3DGS) in autonomous-driving simulation, but deployment demand
arXiv:2609.31787v1 Announce Type: cross Abstract: Optimal Transport (OT) provides a principled framework for comparing and transforming probability distributions while preserving geometric structure.
arXiv:2609.32832v1 Announce Type: new Abstract: Over-the-air computation has emerged as a scalable and efficient solution for deploying federated learning algorithms in wireless networks by exploiting