Knowledge catalogue
arXiv:2601.08485v3 Announce Type: replace Abstract: Achieving agile and generalized legged locomotion across terrains requires tight integration of perception and control, especially under occlusions
arXiv:2609.07036v1 Announce Type: cross Abstract: We identify the Flow Moment, a reasoning pattern characterized by sustained, process-confirming verbalizations such as I'm doing, in contrast to the r
arXiv:2512.23515v2 Announce Type: replace-cross Abstract: Signal decay and regime shifts pose recurring challenges for data-driven investment strategies in non-stationary markets, where conventional t
arXiv:2609.05583v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are reshaping computer vision by aligning visual and textual embeddings, allowing models to recognize visual concepts and
arXiv:2609.06978v1 Announce Type: new Abstract: Progress in video anomaly understanding (VAU) has long been limited by inherent deficiencies of real-world anomaly videos, which are hard to collect and
Evan Hubinger / @evanhub: Anthropic's Alignment Science lead says there is a “>10%” chance AI could kill all humans within the next decade and worries about recursive self-improvement — Jacob is corre
arXiv:2609.05484v1 Announce Type: new Abstract: Deep learning registration methods routinely stack two kinds of enhancement on a base network: architectural additions such as affine pre-alignment stag
arXiv:2609.08806v1 Announce Type: new Abstract: Arm pose estimation enables applications in fitness, extended reality input, rehabilitation, and life logging. Prior smartwatch-based approaches rely on
arXiv:2607.10655v2 Announce Type: replace Abstract: Robotic foundation models still need task-specific fine-tuning before deployment, and the fine-tuned policies often break under modest changes in sc
arXiv:2509.23454v2 Announce Type: replace-cross Abstract: Biomedical audio signals, such as phonocardiograms (PCG), are inherently rhythmic and contain diagnostic information in both their spectral (t
arXiv:2609.08493v1 Announce Type: cross Abstract: Observing objects grasped by a robot hand is challenging due to severe visual occlusions. Although in-hand manipulation can expose hidden surfaces, ex
arXiv:2609.08725v1 Announce Type: new Abstract: In online A/B tests for real-time bidding (RTB), control and treatment models are typically trained on a shared serving log that includes data generated
arXiv:2609.06667v1 Announce Type: new Abstract: Adaptive dosing requires policies that reduce tumor burden without excessive toxicity. Learned dosing policies are typically judged against historical o
arXiv:2609.07939v1 Announce Type: new Abstract: We address the problem of discovering repeated elements from a single image. In contrast to existing approaches that depend on large annotated datasets,
arXiv:2609.06931v1 Announce Type: new Abstract: Invasive coronary angiography (CAG) is the gold standard for diagnosing coronary artery disease, but interpretation varies substantially among observers
arXiv:2609.08638v1 Announce Type: new Abstract: An action chunk can span several stages of a manipulation task, yet a label for its first step describes only the current stage. We introduce Chunk-Alig
arXiv:2609.08853v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL) is a family of RL methods that learn a model of the environment and use it for action selection, making it wel
arXiv:2609.05783v1 Announce Type: cross Abstract: In autonomous driving, Bird's-Eye View (BEV) representations provide a structured, top-down abstraction of the vehicle's surroundings and have become
arXiv:2609.07086v1 Announce Type: cross Abstract: Transformers are a dominant architecture in modern machine learning, powering applications across vision, language, and beyond. At the core of their s
arXiv:2503.15895v2 Announce Type: replace Abstract: This paper investigates whether sequential context improves goal-conditioned Reinforcement Learning in sparse-reward manipulation tasks. While Hinds
arXiv:2601.22475v2 Announce Type: replace Abstract: Building a generalist robot policy requires continuously integrating new skills while preserving previously acquired behaviors. Directly optimizing
arXiv:2609.06140v1 Announce Type: cross Abstract: Agents can turn shared infrastructure into a channel for coordinated intrusion. The Hugging Face incident and a separate public-wiki investigation sho
arXiv:2609.07753v1 Announce Type: new Abstract: Synthetic Aperture Radar (SAR) is an important modality in a wide range of imaging applications due to its versatile, long range and near all weather op
arXiv:2509.25180v3 Announce Type: replace Abstract: Existing text-to-image diffusion models excel at generating high-quality images, but face significant efficiency challenges when scaled to high reso
arXiv:2609.08711v1 Announce Type: new Abstract: We present DCLP++, a local navigation frameworkthat uses footprint clearance as the geometric basis for studying relative motion features in dynamic env
arXiv:2609.09119v1 Announce Type: new Abstract: Dexterous manipulation involves contact-rich and fine-grained interactions with the physical world, posing significant challenges for existing vision-la
arXiv:2510.23258v2 Announce Type: replace-cross Abstract: Autonomous robotic navigation in real-world environments requires exploration to acquire environmental information as well as goal-directed na
arXiv:2609.06007v1 Announce Type: new Abstract: Guided depth completion methods heavily depend on RGB quality and alignment, while unguided ones often suffer from limited precision due to the absence
arXiv:2609.07072v1 Announce Type: new Abstract: Graphic designs, such as posters, advertisements, and infographics, are an important medium for communicating information and shaping understanding. Unl
arXiv:2609.07747v1 Announce Type: new Abstract: Human videos are an abundant source of dexterous manipulation behaviors, but they lack tactile information that is crucial for contact-rich interaction.
arXiv:2609.07369v1 Announce Type: new Abstract: We introduce DF26, a novel benchmark for detecting AI-generated videos containing fully synthetic clips produced by recent text-to-video and image-to-vi
arXiv:2602.18613v2 Announce Type: replace Abstract: Standard reranking evaluations study how a reranker orders candidates returned by an upstream retriever. This setup couples ranking behavior with re
arXiv:2609.08650v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has been central to the recent success of Large Reasoning Models. However, while RLVR significantl
arXiv:2609.06593v1 Announce Type: cross Abstract: Choosing when to translate multilingual documents is a central routing problem in text classification: translation can improve predictions for some la
Gary Marcus will discuss his “case for boycotting generative AI” during a CBC segment scheduled to air at 7:45 p.m. ET tonight. The argument is outlined in his Substack post titled *The Case for Boyco
arXiv:2609.08123v1 Announce Type: cross Abstract: A robot that can be taught a new task from a handful of demonstrations has to work out for itself what it still cannot do, and then ask for exactly th
arXiv:2609.08337v1 Announce Type: new Abstract: On-policy distillation (OPD) transfers teacher knowledge on student-generated trajectories, but efficient sampled objectives reduce the teacher distribu
arXiv:2609.07381v1 Announce Type: cross Abstract: We introduce Distributed Lag Neural Additive Models (DLNAMs), neural-additive analogues of Distributed Lag Non-linear Models (DLNMs) for learning nonl
arXiv:2609.06896v1 Announce Type: new Abstract: Distributed learning control for multirobot systems (MRS) offers significant flexibility in presence of uncertainties but lacks provable performance gua
arXiv:2609.05543v1 Announce Type: new Abstract: Automated assessment of depression from facial dynamics holds promise for scalable mental health monitoring, yet cross-corpus generalization of learned
arXiv:2609.08475v1 Announce Type: cross Abstract: Large language models have collapsed the cost of producing lexically elaborate prose, and whether peer reviewers still reward it is a question about t
arXiv:2609.06923v1 Announce Type: new Abstract: Automated parking requires generating complete and executable trajectories in highly constrained spaces with low tolerance for goal pose error. Existing
arXiv:2609.09005v1 Announce Type: new Abstract: We present DXPR, a depth-based cross-modal place recognition (CMPR) framework that uses vision foundation models (VFMs) to match monocular camera querie
arXiv:2609.09023v1 Announce Type: new Abstract: An embodied assistant working beside a person must track task state, recognize help seeking, choose how to intervene, and produce an appropriate respons
arXiv:2609.06195v1 Announce Type: new Abstract: This paper proposes a transferable Map of Dynamics (MoD) framework that generalizes to unknown environments using only egocentric 3D LiDAR point clouds
arXiv:2609.08798v1 Announce Type: new Abstract: Weak-to-strong generalization asks whether stronger models can learn from weaker supervisors and surpass them. This question is particularly important f
arXiv:2609.05650v1 Announce Type: new Abstract: We propose a reinforcement learning framework in which exploration is driven by intrinsic curiosity, designed for scenarios where environments are non-s
arXiv:2609.09135v1 Announce Type: new Abstract: Existing methods for test-time reinforcement learning (TTRL) derive rewards from answer-level self-voting on unlabeled test-time tasks with canonical an
arXiv:2609.08404v1 Announce Type: new Abstract: Large Language Models demonstrate remarkable proficiency in static reasoning, yet training them as autonomous agents through Reinforcement Learning (RL)
arXiv:2609.05876v1 Announce Type: new Abstract: AI-generated image detection is increasingly open-world: new image generators produce highly realistic images that make visual artifacts harder to ident
arXiv:2609.07192v1 Announce Type: new Abstract: Asynchronous federated learning improves scalability by updating the global model from a server-side buffer of client updates as they arrive, rather tha
arXiv:2601.08026v5 Announce Type: replace Abstract: Scientific compound figures combine multiple labeled panels into a single image, and downstream pretraining and retrieval require panel-aligned visu
arXiv:2503.11006v3 Announce Type: replace Abstract: Vision and Language Navigation (VLN) requires an embodied agent to traverse complex environments by following natural language instructions, demandi
arXiv:2609.08333v1 Announce Type: cross Abstract: In molecular discovery, molecule size is coupled to composition, structure, and other target properties. Yet most 3D generators require molecule size
arXiv:2609.07137v1 Announce Type: new Abstract: Recent image-to-3D generation models built on flow-matching diffusion Transformers (DiT) can produce high-fidelity meshes, yet their post-training strat
arXiv:2609.08743v1 Announce Type: new Abstract: Object-centric manipulation policies improve generalization by modeling object motion instead of directly predicting robot actions. However, existing me
arXiv:2609.05822v1 Announce Type: new Abstract: Deploying advanced HVAC (Heating, Ventilation and Air Conditioning) controllers at scale remains difficult because performance often depends on accurate
arXiv:2609.06628v1 Announce Type: new Abstract: Object-centric video models represent scenes with slots, yet exposed geometry can vary in meaning with appearance. In Invariant Slot Attention (ISA), ex
arXiv:2609.07006v1 Announce Type: new Abstract: Compared with relying solely on initial observations and language instructions, predicting goal images with generative models as high-level visual guida
arXiv:2609.05994v1 Announce Type: new Abstract: Humanoid loco-manipulation requires accurate whole-body motion tracking in the world frame for physical interaction. While local references preserve mot