Knowledge catalogue
arXiv:2604.10351v1 Announce Type: new Abstract: Accurate actuation models are critical for bridging the gap between simulation and real robot behavior, yet obtaining high-fidelity actuator dynamics ty
arXiv:2604.10022v1 Announce Type: new Abstract: Weird generalization is a phenomenon in which models fine-tuned on data from a narrow domain (e.g. insecure code) develop surprising traits that manifes
A Restore Britain Government would back British drivers. How? Raise the speed limit on motorways to 80mph. Remove all 20mph zones, other than those outside schools or areas with vulnerable individuals
arXiv:2603.06665v2 Announce Type: replace-cross Abstract: Large vision-language models (VLMs) often benefit from chain-of-thought (CoT) prompting in general domains, yet its efficacy in medical vision
arXiv:2603.18561v2 Announce Type: replace Abstract: Planning-oriented end-to-end driving models show great promise, yet they fundamentally learn statistical correlations instead of true causal relatio
arXiv:2604.09450v1 Announce Type: cross Abstract: Chest X-ray report generation (CXR-RG) has the potential to substantially alleviate radiologists' workload. However, conventional autoregressive visio
arXiv:2604.09544v1 Announce Type: cross Abstract: Large language models (LLMs) undergo alignment training to avoid harmful behaviors, yet the resulting safeguards remain brittle: jailbreaks routinely
arXiv:2604.08579v1 Announce Type: cross Abstract: We study cross-modal alignment between independently pretrained vision (DINOv2) and language (all-MiniLM-L6-v2) encoders using the functional map fram
arXiv:2604.08608v1 Announce Type: cross Abstract: We introduce Semantic Intent Fragmentation (SIF), an attack class against LLM orchestration systems where a single, legitimately phrased request cause
Is Elon Musk right about Sam Altman? just read the new Ronan Farrow and Andrew Marantz piece on Sam Altman in The New Yorker, and it is damning. And the answer is yes. It’s a deep dive based on never-
arXiv:2604.07831v1 Announce Type: cross Abstract: Existing red-teaming studies on GUI agents have important limitations. Adversarial perturbations typically require white-box access, which is unavaila
I was unable to retrieve the specific tweet at the provided URL. The tweet ID `2042429820500656544` does not appear in any indexed search results, and X (formerly Twitter) requires JavaScript/login...
Download the Tesla Robotaxi app NEWS: Tesla has updated their Robotaxi app. What's new: • New tips section, which displays how many seats are available in the car, tells you that the vehicle lights wi
arXiv:2604.07799v1 Announce Type: new Abstract: Embodied agents are expected to operate persistently in dynamic physical environments, continuously acquiring new capabilities over time. Existing appro
arXiv:2512.19253v4 Announce Type: replace-cross Abstract: We present the first empirical study of machine unlearning (MU) in hybrid quantum-classical neural networks. While MU has been extensively exp
arXiv:2604.06375v1 Announce Type: new Abstract: AI-driven symptom analysis systems face persistent challenges in reliability, interpretability, and hallucination. End-to-end generative approaches ofte
The reaction people are having to AIs that can find bugs in code is fascinating. Finally, we have the capacity to fix the crisis in computer security we’ve had for decades, and everyone is treating it
If you ban self-driving cars to protect the taxi union, you have blood on your hands If you want to know why @Waymo is no longer testing in NYC, this statement says it all: “Our top priority for AV te
arXiv:2610.21239v1 Announce Type: new Abstract: Sampling-based motion planning such as Model Predictive Path Integral (MPPI) control has become attractive for robotics due to its ability to handle non
arXiv:2610.22119v1 Announce Type: new Abstract: We propose Compliant GMT (C-GMT), a modular framework for full-body compliant responses in general motion tracking. As humanoid robots move into real wo
arXiv:2610.22020v1 Announce Type: cross Abstract: Learning from demonstration is an effective strategy for endowing robots with manipulation capabilities but requires training on datasets comprised of
arXiv:2610.20845v1 Announce Type: new Abstract: Skill-policy co-evolution, in which an agent's policy model and its skill bank improve each other during training, is a promising paradigm for self-impr
arXiv:2610.22084v1 Announce Type: cross Abstract: Neural networks offer critical adaptability for controlling complex systems where nominal modeling proves intractable. However, when offline-trained n
arXiv:2610.21045v1 Announce Type: new Abstract: Tool-using language agents must revise prepared actions when evidence changes or authority is revoked. Selective withdrawal means suspending affected ac
arXiv:2603.22519v4 Announce Type: replace-cross Abstract: Textual Large Language Models (LLMs) provide a simple and familiar interface: a string of text is used for both input and output. However, the
arXiv:2610.21140v1 Announce Type: new Abstract: Contact-rich robotic tasks require coordinated motion and impedance under uncertain interaction dynamics and physical constraints. While recent methods
arXiv:2610.21292v1 Announce Type: new Abstract: Maintaining small path-tracking errors under moving reference frames is essential for robotic manipulation tasks with tight execution tolerances, such a
arXiv:2610.22086v1 Announce Type: cross Abstract: Supervised fine-tuning datasets remain overwhelmingly English-centric and commercially restricted, as their content is frequently generated by proprie
arXiv:2610.22234v1 Announce Type: cross Abstract: Real desktop automation is neither purely visual nor purely programmatic. Agents must understand user-facing interfaces while efficiently manipulating
arXiv:2610.21240v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) decompose language model activations into sparse features, but prompt transformations designed to preserve the underlying r
arXiv:2610.22198v1 Announce Type: cross Abstract: Safe multi-agent coordination typically assumes what real-world, deployed, resource-constrained robots cannot provide: continuous, perfect state shari
arXiv:2610.21214v1 Announce Type: new Abstract: Rubric-based evaluation is widely used to assess large language model (LLM) outputs by decomposing quality into multiple criteria and assigning each a b
arXiv:2610.21913v1 Announce Type: new Abstract: Continuous physical human-robot interaction requires robots to rapidly adapt their control behaviour without interrupting task execution. However, exist
arXiv:2411.16164v2 Announce Type: replace Abstract: Text-to-image synthesis (T2I) focuses on generating high-quality images from textual descriptions In this survey, we review 490 papers on T2I spanni
arXiv:2610.14643v1 Announce Type: new Abstract: Clinical speech reveals identity through both spoken identifiers and voice, yet its pauses, delivery, and speaker roles can also carry useful clinical e
arXiv:2610.20141v1 Announce Type: new Abstract: In various industries, smoke and fire are two of the most important threats in the workplace. One of the common methods for detecting smoke and fire is
arXiv:2609.32807v2 Announce Type: replace Abstract: Clinical LLM evaluation often emphasizes answer accuracy; however, accuracy alone does not test counterfactual consistency or demographic robustness
arXiv:2610.18606v1 Announce Type: new Abstract: Large language models increasingly tackle tasks that require multi-step reasoning, where errors in intermediate claims can affect the final answer. Self
arXiv:2610.18119v1 Announce Type: cross Abstract: {em Specification-Guided Reinforcement Learning (RL)} has largely concerned itself with learning an optimal policy against a formal specification in a
arXiv:2610.19429v1 Announce Type: new Abstract: This paper presents a comprehensive comparison of four representative reinforcement learning (RL) methods in autonomous vehicle (AV) decision-making, in
arXiv:2610.20251v1 Announce Type: new Abstract: Boundary delineation of anatomical structures in prostate MRI is often compromised by regional ambiguity. Automated segmentation methods relying on glob
arXiv:2610.20074v1 Announce Type: new Abstract: Catheter obstruction is the commonest cause of shunt failure; freehand insertion at Keen's point leaves the tip free in cerebrospinal fluid in only one
arXiv:2610.16231v1 Announce Type: new Abstract: As large language models are increasingly adopted as multi-party communication facilitators, there is a growing demand for agents capable of balancing d
arXiv:2610.14615v1 Announce Type: cross Abstract: We study autoregressive token generation through the notion of sequential samplers: A circuit that samples the next symbol only given the previous one
arXiv:2610.18127v1 Announce Type: new Abstract: Looped language models (LMs) reuse one block of layers several times per token generation, promising more latent computation at no additional storage co
arXiv:2610.16312v1 Announce Type: new Abstract: Explicit reasoning in large language models (LLMs) is often associated with reliability, yet it can still pose risks when a model infers harmful goals u
arXiv:2610.15884v1 Announce Type: new Abstract: False retry permission (FRP) occurs when an agent retries a timed-out tool call without support from the visible contract. RetryBench separates this err
arXiv:2609.32817v2 Announce Type: replace Abstract: Clinical Large Language Models (LLMs) achieve strong medical-exam accuracy; however, a correct answer does not guarantee that the explanation names
arXiv:2610.19202v1 Announce Type: new Abstract: Rare but severe failures of large language models can become practically significant at deployment scale. Estimating their probabilities with direct Mon
arXiv:2610.15763v1 Announce Type: new Abstract: Modern industrial automation demands autonomous collaborative systems capable of coordinated manipulation in workspaces with heterogeneous object arrang
arXiv:2610.15011v1 Announce Type: new Abstract: Automated decision makers, such as AI agents that control robots or network devices, must often satisfy hard cost constraints while maintaining competit
arXiv:2610.18373v1 Announce Type: new Abstract: Quadruped robots performing outdoor deliveries must interpret complex scenes, anticipate changes, and navigate in socially compliant ways. Robot demonst
arXiv:2610.15207v1 Announce Type: new Abstract: Large Language Models (LLMs) have been shown to be computationally expensive and vulnerable to jailbreak attacks, in which adversarial prompts bypass sa
arXiv:2610.17337v1 Announce Type: cross Abstract: Liveness requires a dynamical system to repeatedly accomplish a desired objective over time. In this work, we consider a particular form of liveness k
Across industries, the U.K. is putting the full stack to work — and the scale of deployment is growing fast, from enterprises transforming their businesses to startups solving some of the planet’s har
arXiv:2505.15863v2 Announce Type: replace Abstract: Generative AI (GenAI) is rapidly advancing the field of Autonomous Driving (AD), extending beyond traditional applications in text, image, and video
arXiv:2511.10448v2 Announce Type: replace Abstract: Bolting operations are critical in industrial assembly and in the maintenance of scientific facilities, requiring high precision and robustness to f
arXiv:2610.17750v1 Announce Type: new Abstract: Robotic assistance for human mobility could benefit care recipients and reduce caregiver burden during physically demanding tasks such as Sit-to-Stand (
arXiv:2610.18243v1 Announce Type: new Abstract: Given the growing global burden of liver tumors and diffuse liver diseases, non-contrast computed tomography (NCCT) is increasingly used for opportunist
arXiv:2610.16906v1 Announce Type: new Abstract: Multi-modal fusion for 3D object detection assumes that cameras and LiDAR are broadly complementary. In practice, how much each modality is trusted at a