Knowledge catalogue
arXiv:2610.01896v1 Announce Type: cross Abstract: Asynchronous reinforcement learning (RL) improves the efficiency of large language model post-training but introduces stale rollouts generated by earl
arXiv:2610.01741v1 Announce Type: new Abstract: Predictive Vision-Language-Action (VLA) models aim to improve robotic manipulation via future observation or world dynamics forecasting. However, existi
arXiv:2610.01138v1 Announce Type: new Abstract: Concurrent actions in large language model (LLM) agent environments require arbitration even when each proposal is individually valid. We implement a ty
arXiv:2610.00325v1 Announce Type: new Abstract: Continuous policy optimization may spread an update across many states, even when deployment permits only a few complete state-level changes. We study h
arXiv:2610.01846v1 Announce Type: cross Abstract: Speech-based Alzheimer's disease (AD) assessments increasingly rely on pretrained self-supervised learning (SSL) models that learn acoustic representa
arXiv:2610.01645v1 Announce Type: new Abstract: Fairness conclusions in clinical prediction can depend strongly on both the metrics reported and the demographic resolution at which performance is eval
arXiv:2610.01042v1 Announce Type: new Abstract: Multi-agent communication aims to help agents benefit from one another's information. Yet improvements in system performance leave a fundamental ambigui
arXiv:2610.01116v1 Announce Type: new Abstract: As the capabilities and ubiquity of Large Language Models (LLMs) grow, so does their environmental footprint. Despite calls for responsible AI, the mach
arXiv:2610.01001v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL) can exploit simulated experience, but a simulator-to-target shift creates a model-selection problem: correctin
arXiv:2610.02039v1 Announce Type: cross Abstract: Recent years have witnessed the rapid adoption of reinforcement learning (RL) in large language model (LLM) post-training, with substantial gains in m
arXiv:2610.02070v1 Announce Type: new Abstract: Memory-augmented large language models must decide which memories to retain, and recent systems do so by estimating each memory's effect on task perform
arXiv:2610.01258v1 Announce Type: new Abstract: Autonomous colonoscopic navigation can reduce operator burden and the risk of loop formation or tissue trauma, but remains challenging due to deformable
arXiv:2512.00939v3 Announce Type: replace-cross Abstract: Recent progress in contact-rich robotic manipulation has been striking, yet most deployed systems remain confined to simple, scripted routines
arXiv:2606.15420v2 Announce Type: replace-cross Abstract: Safety evaluations test a policy on prompts that omit the incentive information deployment supplies: a commission, a performance score, a dash
arXiv:2610.01253v1 Announce Type: new Abstract: Quantum Reinforcement Learning (QRL) integrates reinforcement learning with parameterized quantum circuits and is a promising approach to combinatorial
arXiv:2610.01397v1 Announce Type: new Abstract: Dynamic humanoid motions such as flips risk hardware damage due to suboptimal policies, disturbances or sim-to-real gaps. A motion tracking policy offer
arXiv:2610.00328v1 Announce Type: new Abstract: Structured tool calls often fail after only a small number of fields violate a schema or an execution contract. Regenerating the complete object enlarge
arXiv:2610.01403v1 Announce Type: new Abstract: Spiking Transformers merge the energy-efficiency of spiking neural networks (SNNs) with the representational power of self-attention, creating a promisi
arXiv:2610.02019v1 Announce Type: cross Abstract: The rapid growth of video-based social media has increased users' exposure to harmful content, creating a need for reliable automated video safety det
arXiv:2610.01124v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models are often pretrained with a fixed channel vocabulary or a limited set of montages, making transfer diffic
arXiv:2610.01921v1 Announce Type: cross Abstract: Cross-lingual contrastive learning has been a core component of multilingual encoder training, but the ability to explicitly align representations is
arXiv:2610.00759v1 Announce Type: cross Abstract: Cyber attack agents are typically trained and evaluated within a single simulator, making it unclear whether learned policies transfer beyond the envi
arXiv:2610.01815v1 Announce Type: new Abstract: Although diffusion models produce high-quality images, they also reproduce and amplify demographic imbalances in their training data. Debiasing their ge
arXiv:2610.00208v1 Announce Type: cross Abstract: This paper studies decentralized safe path following for multiple quadrotors on intersecting paths, where safety requires both collision avoidance and
arXiv:2610.00371v1 Announce Type: cross Abstract: Multi-agent systems derive their capabilities from sharing evidence, delegating tasks, and combining information across agents. The same process creat
How quickly things change in the artificial intelligence era. Just a few months after everyone was scrambling to infuse AI into every enterprise operation — and frankly they still are, as agents have
arXiv:2610.01847v1 Announce Type: cross Abstract: Model specifications define how large language models (LLMs) should behave, guiding alignment training, inference-time behavior, and evaluation. Yet t
arXiv:2610.00360v1 Announce Type: cross Abstract: Reinforcement learning (RL) for dexterous manipulation must discover finger-object contacts and then control the object precisely; the action noise th
arXiv:2610.00781v1 Announce Type: new Abstract: Teleoperated demonstrations are a primary source of data for robot manipulation, and teleoperated interventions are a primary mechanism for correcting p
arXiv:2610.00030v1 Announce Type: new Abstract: Object detection models often experience performance degradation when deployed under distribution shifts, caused by for example changes in weather type,
arXiv:2610.00317v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models increasingly rely on action experts that generate short action chunks under receding-horizon control. While chunk-
arXiv:2512.03068v2 Announce Type: replace-cross Abstract: Artificial Intelligence (AI) systems increasingly shape consequential decisions, creating value but also potential harms for individuals, soci
arXiv:2610.00801v1 Announce Type: new Abstract: A robot may lose sight of an object it must later retrieve, need to recall what a person demonstrated earlier, or track which steps of a task it has alr
arXiv:2606.18967v2 Announce Type: replace Abstract: Reinforcement learning (RL) has become a representative post-training paradigm for large language models (LLMs), enabling strong reasoning and agent
arXiv:2603.20169v2 Announce Type: replace Abstract: Generative world models have shown promise for simulating dynamic environments, yet egocentric video remains challenging due to rapid viewpoint chan
arXiv:2610.00319v1 Announce Type: new Abstract: Collaborative perception enables connected agents to share complementary observations for 3D object detection, extending sensing range and mitigating oc
arXiv:2610.00568v1 Announce Type: cross Abstract: Large language models are characterized by three key properties: capability, alignment, and faithfulness. Prior work studies the tradeoffs between cap
arXiv:2610.01746v1 Announce Type: cross Abstract: Autonomous driving systems have become a central focus of intelligent transportation research, with End-to-End Learning and Modular Architectures offe
arXiv:2608.01772v2 Announce Type: replace Abstract: LLM agents increasingly run policy-bound enterprise workflows, where they must apply rules consistently and stay auditable. Deploying such an agent
arXiv:2610.01581v1 Announce Type: new Abstract: Generative AI models are increasingly used for scenario generation in autonomous driving. While they can generate realistic-looking scenarios, they ofte
Even if AIs become conscious, that doesn't imply that humanity should hand over control to them. https://eigenism.org NEW: According to a bombshell report in the New York Times, Anthropic co-founder C
arXiv:2610.01527v1 Announce Type: cross Abstract: Non-Markovian environments are often modeled as Regular Decision Processes (RDPs), where dynamics depend on the interaction history through a finite a
arXiv:2610.01171v1 Announce Type: new Abstract: With the increasing use of robot-free demonstration interfaces that provide state trajectories without action labels, imitation from observation has bec
arXiv:2610.00922v1 Announce Type: new Abstract: Gaze estimation under natural head-eye motion underpins applications from driver monitoring to human-computer interaction. Single-frame methods predict
arXiv:2610.00385v1 Announce Type: cross Abstract: Replay selectors often rank cached trajectories by format feedback, confidence, freshness, or response length, although cache-level correctness and do
arXiv:2508.16748v2 Announce Type: replace-cross Abstract: Prevalent multimodal self-supervised learning (SSL) methods rely on the redundancy assumption: that different views share substantial task-rel
arXiv:2603.08961v2 Announce Type: replace Abstract: Maintaining balance under external hand forces is critical for humanoid bimanual manipulation, where interaction forces propagate through the kinema
arXiv:2610.00349v1 Announce Type: new Abstract: Resource limits are becoming an authorization boundary for AI agents that delegate work across concurrent and failure-prone workers. Parent-child alloca
arXiv:2610.00693v1 Announce Type: new Abstract: Federated learning (FL) has recently attracted increasing attention in remote sensing (RS) since it enables collaborative model training across decentra
arXiv:2610.02198v1 Announce Type: cross Abstract: Several state-of-the-art methods for online reinforcement learning in continuous control improve policies using action gradients of a learned critic.
arXiv:2610.02140v1 Announce Type: cross Abstract: Introducing new capabilities to frontier models has long been the goal of posttraining, which predominantly employs supervised finetuning (SFT) and re
arXiv:2610.01882v1 Announce Type: cross Abstract: Multi-agent reinforcement learning (MARL) provides a powerful framework for learning coordinated behaviors through interactions with the environment.
arXiv:2610.00414v1 Announce Type: new Abstract: Foundation models pretrained on large-scale datasets demonstrate strong transferability to medical imaging tasks. However, understanding how their laten
arXiv:2610.01864v1 Announce Type: cross Abstract: How can we understand what a music foundation model has learned extit{internally}? Most interpretability approaches, such as probing and Sparse Autoen
arXiv:2610.01870v1 Announce Type: new Abstract: Urban environments are shaped by design choices with long-term implications for health, safety, and quality of life, yet evaluating proposed interventio
arXiv:2610.00015v1 Announce Type: new Abstract: Large-language-model agents can propose and execute actions, but proposal, authority, dispatch, verified external effect, and serving promotion are diff
arXiv:2610.00447v1 Announce Type: new Abstract: Can a text-to-3D leaderboard change when every generated scene stays fixed? We audit this question for rendered-image evaluation, where camera settings
arXiv:2610.01019v1 Announce Type: new Abstract: Robotic world models predict action-conditioned future scenes, providing a foundation for understanding action outcomes. However, turning alternative pr
arXiv:2607.16956v2 Announce Type: replace Abstract: Social navigation requires the robot to reason and respond in complex real-world environments. While recent works attempt to incorporate human-level
arXiv:2610.02091v1 Announce Type: cross Abstract: Despite progress in vision-language models, 3D spatial reasoning from 2D images remains challenging. Text-based methods describe intermediate geometry