February 2026

arXiv:2602.15895v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) effectively mitigates hallucinations in LLMs by incorporating external knowledge. However, the inherent discrete representation of text in existing frameworks often results in a loss of semantic integrity, leading to retrieval deviations. Inspired by the human episodic memory mechanism, we propose CogitoRAG, a RAG framework that simulates human cognitive memory processes. The core of this framework lies in the extraction and evolution of the Semantic Gist. During the offline indexing stage, […]

Ver mais

arXiv:2602.15894v1 Announce Type: new Abstract: Recent research indicates that while alignment methods significantly improve the quality of large language model(LLM) outputs, they simultaneously reduce the diversity of the models’ output. Although some methods have been proposed to enhance LLM output diversity, they often come at the cost of reduced performance. In this work, we first theoretically demonstrate that the alignment task can be decomposed into two distributions: quality and diversity. To enhance the diversity of LLM outputs while […]

Ver mais

arXiv:2602.15893v1 Announce Type: new Abstract: Post-disaster survivor localization using Unmanned Aerial Vehicles (UAVs) faces a fundamental physical challenge: the prevalence of Non-Line-of-Sight (NLOS) propagation in collapsed structures. Unlike standard Gaussian noise, signal reflection from debris introduces strictly non-negative ranging biases. Existing robust estimators, typically designed with symmetric loss functions (e.g., Huber or Tukey), implicitly rely on the assumption of error symmetry. Consequently, they experience a theoretical mismatch in this regime, leading to a phenomenon we formally identify as […]

Ver mais

arXiv:2602.15892v1 Announce Type: new Abstract: Visual perspective taking–inferring how the world appears from another’s viewpoint–is foundational to social cognition. We introduce FlipSet, a diagnostic benchmark for Level-2 visual perspective taking (L2 VPT) in vision-language models. The task requires simulating 180-degree rotations of 2D character strings from another agent’s perspective, isolating spatial transformation from 3D scene complexity. Evaluating 103 VLMs reveals systematic egocentric bias: the vast majority perform below chance, with roughly three-quarters of errors reproducing the camera viewpoint. […]

Ver mais

arXiv:2602.15891v1 Announce Type: new Abstract: While autonomous vehicles have achieved reliable performance within specific operating regions, their deployment to new cities remains costly and slow. A key bottleneck is the need to collect many human demonstration trajectories when adapting driving policies to new cities that differ from those seen in training in terms of road geometry, traffic rules, and interaction patterns. In this paper, we show that self-play multi-agent reinforcement learning can adapt a driving policy to a […]

Ver mais

arXiv:2602.15886v1 Announce Type: new Abstract: In some surgical operations targeting the spine, it is required to drill cavities in the vertebrae for the insertion of pedicle screws. A new mechanical architecture is proposed for this application. It is based on an augmented version of the full translational R-CUBE mechanism, with improved linkages to implement additional rotational motion. Using this concept, a mechanism presented with a 3T2R motion that is required for the manipulation of the surgical drill. It […]

Ver mais

arXiv:2602.15885v1 Announce Type: new Abstract: This paper presents a novel, compact four-degree-of-freedom motion-tracking device (IMTD) designed for training and evaluation in laparoscopic surgery. The device’s kinematics, mechanical design, instrumentation, and prototypes are developed and presented to meet the specific requirements of laparoscopic training context, including movement around a fixed center of motion and seamless integration into standard box trainers. The system IMTD’s tracking accuracy and reliability are compared to a motion capture system (MoCap), assessing its ability to […]

Ver mais

arXiv:2602.15884v1 Announce Type: new Abstract: The SLAM community has fallen into a “Confidence Trap” by prioritizing benchmark scores over principled uncertainty estimation. This yields systems that are geometrically accurate but probabilitistically inconsistent and brittle. We advocate for a paradigm shift where the consistent, real-time computation of uncertainty becomes a primary metric of success.

Ver mais

arXiv:2602.15883v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) offer a powerful paradigm for flow reconstruction, seamlessly integrating sparse velocity measurements with the governing Navier-Stokes equations to recover complete velocity and latent pressure fields. However, scaling such models to large spatiotemporal domains is hindered by computational bottlenecks and optimization instabilities. In this work, we propose a robust distributed PINNs framework designed for efficient flow reconstruction via spatiotemporal domain decomposition. A critical challenge in such distributed solvers is pressure […]

Ver mais

arXiv:2602.15882v1 Announce Type: new Abstract: General vision-language models increasingly support unified spatiotemporal reasoning over long video streams, yet deploying such capabilities on robots remains constrained by the prohibitive latency of processing long-horizon histories and generating high-dimensional future predictions. To bridge this gap, we present FUTURE-VLA, a unified architecture that reformulates long-horizon control and future forecasting as a monolithic sequence-generation task. Adopting a dual-sided efficiency paradigm, FUTURE-VLA leverages a temporally adaptive compression strategy to maximize spatiotemporal information density, enabling […]

Ver mais