Knowledge catalogue
arXiv:2610.13669v1 Announce Type: new Abstract: Curriculum learning typically organizes training samples from easy to hard. In GRPO, medium-difficulty samples are attractive for online selection becau
arXiv:2510.22809v2 Announce Type: replace-cross Abstract: Traditional machine learning relies on explicit models and domain assumptions, often limiting flexibility and interpretability. We introduce a
arXiv:2510.10998v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly under scrutiny for perpetuating identity-based discrimination in high-stakes domains such as hir
arXiv:2610.13303v1 Announce Type: cross Abstract: LLM judges are increasingly deployed as panels, and disagreement among panel members is often read as a symptom of an unclear rubric. We test a label-
arXiv:2610.13572v1 Announce Type: new Abstract: Humanoid tennis requires reactive whole-body coordination under rapidly changing ball dynamics, yet collecting task-specific motion-capture demonstratio
arXiv:2603.07427v3 Announce Type: replace Abstract: As Large Language Models (LLMs) evolve into autonomous agents, existing safety evaluations face a fundamental trade-off: manual benchmarks are costl
arXiv:2610.13212v1 Announce Type: cross Abstract: Actions specify what a robot should execute, but do not describe what those commands will achieve. Vision-language-action (VLA) policies nevertheless
arXiv:2610.12598v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) provides reliable supervision for software engineering agents through executable tests, but binary
arXiv:2610.13521v1 Announce Type: new Abstract: Multi-teacher on-policy distillation (MOPD) extends on-policy distillation (OPD) by routing domain-specific data to specialized teachers, integrating th
arXiv:2610.13522v1 Announce Type: new Abstract: Action-conditioned world models offer a promising foundation for generalist robotic intelligence by predicting the consequences of actions. However, exi
arXiv:2610.13481v1 Announce Type: new Abstract: On-policy distillation (OPD) provides teacher supervision on student-generated trajectories, but a single trajectory realizes only one continuation at e
arXiv:2610.12703v1 Announce Type: cross Abstract: High-risk applications of deep learning require precise characterization of model decision-making. We conduct a systematic explainability evaluation a
arXiv:2610.13821v1 Announce Type: new Abstract: A useful language model should know when to answer and when to say 'I don't know.' Can we improve that decision by training the model itself or by train
arXiv:2610.12567v1 Announce Type: new Abstract: Prior research on learning from humans often requires real human demonstrations. But advances in video generation models offer a new paradigm: we can no
arXiv:2610.13007v1 Announce Type: new Abstract: Learning-based methods have demonstrated strong performance in mapless robot navigation. However, a learned policy remains restricted to the embodiment
arXiv:2610.13929v1 Announce Type: new Abstract: Robot policies often convert sensing and control into fixed-rate observation-action sequences, leaving physical time implicit in sequence position. This
arXiv:2610.12655v1 Announce Type: new Abstract: LLM agents can execute increasingly complex tasks, but effective assistance also requires aligning their actions with goals and constraints that users m
arXiv:2610.13059v1 Announce Type: new Abstract: Radiology reports are essential for diagnostic reasoning and patient care, yet their manual preparation is time-consuming and cognitively demanding. Aut
arXiv:2610.12872v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate strong individual task performance, yet still struggle to collaborate with users over extended, multi-turn inte
arXiv:2610.13276v1 Announce Type: new Abstract: Set-based language model routing aims to increase the coverage of the event where at least one model answers a query correctly while controlling the num
arXiv:2610.13264v1 Announce Type: cross Abstract: On-policy distillation (OPD) reveals versatility and convenience during VLM post-training, providing dense supervision for student from teacher. Howev
arXiv:2610.12916v1 Announce Type: new Abstract: Existing governance approaches in decentralized financial networks rely on manual review processes conducted by human committees, which introduces subje
arXiv:2610.12572v1 Announce Type: new Abstract: Online reinforcement learning (RL) fine-tunes diffusion models with black-box rewards, but reverse-process methods such as Flow-GRPO are limited in effi
arXiv:2610.12758v1 Announce Type: new Abstract: Language models have been adopted faster than evaluation practices that reliably assess their risks, and a growing share of use is affective rather than
arXiv:2610.12774v1 Announce Type: cross Abstract: Autonomous small-body proximity operations require continuous-thrust guidance under uncertain gravity, solar-radiation pressure (SRP), and stochastic
arXiv:2610.13816v1 Announce Type: cross Abstract: Generalist robot policies are trained with imitation learning and are bounded by the quality and coverage of their demonstrations. Reinforcement learn
arXiv:2610.13262v1 Announce Type: new Abstract: Memory-dependent manipulation requires a policy to track execution state and recover visual evidence that is no longer observable. We propose dual-strea
arXiv:2610.12538v1 Announce Type: new Abstract: Modern machine learning models are typically trained on data assumed to be representative of the predictive tasks the model will face in the future, but
arXiv:2610.13376v1 Announce Type: new Abstract: Artificial intelligence is beginning to automate scientific discovery, yet physical robot-policy research still relies on scientists and engineers to in
arXiv:2607.17679v2 Announce Type: replace-cross Abstract: This is an invited commentary on the Psychometrika focus article 'Fairness Issues and Evaluation in Psychometrics and AI/ML: What Can We Learn
arXiv:2610.12861v1 Announce Type: new Abstract: In the practical deployment of vision--language--action policies, handling long-tailed task distributions is a common challenge due to the highly imbala
arXiv:2610.13013v1 Announce Type: new Abstract: Softmax-based multimodal emotion recognition methods often assign high confidence even when sensor data are corrupted or contradictory, for example when
arXiv:2610.13345v1 Announce Type: new Abstract: Existing real-world reinforcement learning methods improve robot policies by combining human corrections with autonomous rollouts. However, accumulated
arXiv:2610.12936v1 Announce Type: new Abstract: Vision-language-action (VLA) models provide a unified framework for visual understanding, language grounding, and robot control, yet anticipating how th
arXiv:2610.13508v1 Announce Type: new Abstract: Federated learning (FL) seeks to optimize a shared global model from decentralized client data. While effective for average performance, this process ca
arXiv:2610.13117v1 Announce Type: new Abstract: Vision-language-action (VLA) models and world action models (WAMs) have advanced robotic policy learning by building on pretrained vision-language model
arXiv:2610.13015v1 Announce Type: cross Abstract: Reinforcement learning has become the foundational paradigm for enhancing the reasoning capabilities of LLMs. Yet the dominant approach, Proximal Poli
arXiv:2610.13923v1 Announce Type: cross Abstract: Deep-space communications operate under intermittent connectivity, long propagation delays, and relay storage constraints. In such networks, the Bundl
arXiv:2610.13164v1 Announce Type: new Abstract: Scaling robot manipulation policies requires large amounts of diverse, action-labeled data, yet collecting such data in the real world remains expensive
arXiv:2610.13350v1 Announce Type: new Abstract: Widely reused preprocessing pipelines for electronic health records can encode assumptions about when information became available, what clinical episod
arXiv:2610.13849v1 Announce Type: new Abstract: We present HorizonNav, a video diffusion-based framework for anticipatory robot navigation that transforms a single RGB image and a text prompt into a g
arXiv:2610.13119v1 Announce Type: new Abstract: Collecting dexterous manipulation demonstrations for each robot hand requires repeated task-specific data acquisition. We ask whether one shared corpus
arXiv:2609.35328v2 Announce Type: replace Abstract: Meta-Black-Box Optimization (MetaBBO) is one of the highlights in the recent AI for Optimization trend. This paradigm's bi-level workflow leverages
i don’t know whether this is a joke or serious but the whole challenge with generative AI is that it literally cannot be trusted to follow “simple, generalizable instructions”. the inability of most p
Introducing RSI-Anything: Unlocking Recursive Self-Improvement with World Models 🚀 Can AI learn to improve itself by predicting the consequences of its actions? Built on JEPA-Anything, RSI-Anything us
arXiv:2610.12957v1 Announce Type: cross Abstract: Embodied agents must translate multimodal observations and user requests into executable actions while adapting to product constraints, changing goals
arXiv:2610.12738v1 Announce Type: cross Abstract: Forecasting rare, high-cost events in multivariate time-series systems is hard because their precursors are seldom observed, so a purely data-driven m
arXiv:2610.13574v1 Announce Type: new Abstract: Feature selection is a central problem in high-dimensional financial regression, where large numbers of engineered variables and strong correlations amo
arXiv:2610.13732v1 Announce Type: cross Abstract: Scaling generalist robotic policies is limited by the high cost of collecting embodied interaction data. Large-scale egocentric human videos offer a r
arXiv:2610.13234v1 Announce Type: new Abstract: Lagrangian Neural Networks (LNNs) learn dynamics from trajectory data by parameterizing a scalar Lagrangian and deriving accelerations through the Euler
arXiv:2601.07665v2 Announce Type: replace-cross Abstract: We introduce a principled learning to optimize (L2O) framework for solving fixed-point problems involving general nonexpansive mappings. Our i
arXiv:2610.09002v2 Announce Type: replace Abstract: When a team of agents shares a reward for completed tasks, reporting and stopping unsafe work reduces the team's reward. Random audits that penalize
arXiv:2610.12818v1 Announce Type: cross Abstract: Joint-embedding predictive architectures (JEPAs) learn compact latent world models without pixel reconstruction. Existing JEPA pipelines typically tra
arXiv:2610.13755v1 Announce Type: cross Abstract: Training long-horizon language model agents requires assigning credit to intermediate decisions from sparse terminal feedback. Within a finite group o
arXiv:2610.12575v1 Announce Type: cross Abstract: Modern video-language models are increasingly treated as general-purpose video reasoners, yet it remains unclear whether they robustly capture tempora
arXiv:2610.12855v1 Announce Type: new Abstract: On-Policy Self-distillation is a self-improvement paradigm that enables large language models (LLMs) to improve their capabilities by leveraging the sam
arXiv:2610.13305v1 Announce Type: new Abstract: We introduce MORAD, an online reinforcement learning framework for backbone-conditioned RNA diffusion models that trains a single shared policy across d
arXiv:2610.12963v1 Announce Type: cross Abstract: Video editing models achieve strong performance in modifying appearance, backgrounds, styles, and motions, empowered by large-scale training data. Bey
On October 12, 2026, Gary Marcus (@GaryMarcus) posted on Twitter saying, “No Generative AI. Just one gorgeous building, beautiful light, and an Phone 16 Pro.” The tweet, timestamped 7:01 AM UTC, recei
arXiv:2610.13943v1 Announce Type: new Abstract: Pretrained vision-language-action (VLA) models encode broad manipulation priors, yet adapting them to specific tasks requires balancing limited robot in