Knowledge catalogue
arXiv:2607.26485v1 Announce Type: cross Abstract: Resource allocation across multiple agent groups arises in many applications including e-commerce recommendation systems, housing assignment, and cour
arXiv:2607.26337v1 Announce Type: new Abstract: Suture-needle pickup is necessary for autonomous suturing, as a needle can be unexpectedly dropped or strategically released to adjust the grasping conf
arXiv:2607.25368v1 Announce Type: new Abstract: The intersection of artificial intelligence adoption, cybersecurity governance, and public sector institutional constraints has not been examined as a u
arXiv:2509.07829v4 Announce Type: replace-cross Abstract: Literary translation has recently gained attention as a distinct and complex task in machine translation research, yet translation by small op
arXiv:2511.20992v2 Announce Type: replace Abstract: Behavior Cloning (BC) is a popular framework for training sequential decision policies from expert demonstrations via supervised learning. As these
arXiv:2607.25554v1 Announce Type: new Abstract: Future event prediction carries broad social impact yet remains challenging. SOTA approaches augment LLMs with external agent frameworks whose predictiv
arXiv:2607.24745v1 Announce Type: cross Abstract: Key Information Extraction (KIE) is vital for many document applications, but creating training datasets is traditionally a time-consuming manual proc
arXiv:2603.02565v2 Announce Type: replace-cross Abstract: The Generator-Evaluator (G-E) framework generates K candidate sequences and uses an evaluator to select the highest-scoring one, which is wide
arXiv:2607.11843v2 Announce Type: replace-cross Abstract: Quantum Neural Networks (QNNs) are a promising framework for quantum machine learning on near-term quantum devices, but their security risks r
arXiv:2607.25257v1 Announce Type: cross Abstract: Item Response Theory (IRT) has recently been proposed as a framework for evaluating large language model (LLM) benchmarks by separating a model's late
arXiv:2602.10282v2 Announce Type: replace Abstract: Large language models (LLMs) have shown potential in identifying qualitative causal relations, but their ability to perform quantitative causal reas
arXiv:2607.24769v1 Announce Type: new Abstract: With the growing capabilities of frontier models, AI alignment becomes increasingly critical in high-risk deployment settings. While recent work has emp
arXiv:2607.25242v1 Announce Type: new Abstract: Medical world models offer a framework for extending medical artificial intelligence beyond static prediction by representing evolving patient states an
arXiv:2607.23645v1 Announce Type: cross Abstract: Communication complexity provides a natural framework for quantifying the classical resources required to reproduce quantum statistics. In the qubit p
arXiv:2607.25282v1 Announce Type: cross Abstract: We investigate a normalizing-flow approach for reconstructing parton distribution functions (PDFs) from synthetic matrix-element data. Our framework c
arXiv:2607.25719v1 Announce Type: new Abstract: We propose Dynamic Constraint Learning (DCL), a data-driven framework for constrained optimization when constraint functions are unknown and cannot be q
arXiv:2607.25376v1 Announce Type: cross Abstract: In Bayesian neural networks (BNNs), variational inference is a widely adopted framework for modeling uncertainty in a distributional way, with the evi
arXiv:2607.24773v1 Announce Type: new Abstract: Managing cloud infrastructure efficiently, especially in environments of large cloud providers or hyperscalers, requires optimizing the use of physical
arXiv:2607.26047v1 Announce Type: new Abstract: Acoustic information provides rich cues about object location, material properties, and changes caused by contact or motion. This paper introduces a new
arXiv:2607.25119v1 Announce Type: new Abstract: We propose a score-based stabilization framework for numerical simulation of partial differential equations, in which a learned score model defines a st
arXiv:2607.25413v1 Announce Type: new Abstract: Transaction propensity prediction in B2B e commerce presents unique challenges distinct from B2C contexts, primarily due to the heterogeneous procuremen
arXiv:2505.14366v2 Announce Type: replace Abstract: We present a conceptual framework for training Vision-Language Models (VLMs) to perform Visual Perspective Taking (VPT), a core capability for embod
arXiv:2601.18952v2 Announce Type: replace Abstract: We propose an (offline) multi-dimensional distributional reinforcement learning framework (KE-DRL) that leverages Hilbert space mappings to estimate
arXiv:2607.22651v1 Announce Type: new Abstract: Large language models (LLMs) have enabled increasingly capable conversational agents, but reliably controlling their behavior in real-time interactive e
arXiv:2607.23586v1 Announce Type: new Abstract: Long-lived AI agents increasingly evolve after deployment by retaining experience, acquiring skills and tools, revising workflows, delegating work, and
arXiv:2607.24608v1 Announce Type: new Abstract: This work investigates uncertainty decomposition and explainability in a deep learning-based framework for gyroscope bias correction. A 1-D Convolutiona
arXiv:2607.22910v1 Announce Type: new Abstract: Pearl's structural causal model (SCM) framework, built on directed acyclic graphs (DAGs) and the do-calculus, is the dominant formal language for causal
arXiv:2607.22766v1 Announce Type: cross Abstract: The alignment of Large Language Models (LLMs) is increasingly bottlenecked by data quality. As datasets scale, massive preference and instruction-tuni
arXiv:2607.24110v1 Announce Type: new Abstract: Mobile infrared-visible imaging typically pairs a compact infrared sensor with a high-resolution visible camera for complementary perception. While cros
arXiv:2607.24591v1 Announce Type: new Abstract: We introduce CameraAnything, the first unified framework for camera controlled video editing that enables joint control of both intrinsic and extrinsic
arXiv:2507.22080v2 Announce Type: replace-cross Abstract: Acquiring high-quality instruction-code pairs is essential for training Large Language Models for code generation. While automated synthesis h
arXiv:2607.22561v1 Announce Type: new Abstract: LLM-as-a-judge has become the standard for automated evaluation, but it suffers from high cost, significant latency, and opaque decisions -- limitations
arXiv:2505.18334v2 Announce Type: replace-cross Abstract: Past work has demonstrated that autonomous vehicles can drive more safely if they communicate with each other. However, this communication is
arXiv:2501.13335v4 Announce Type: replace Abstract: We introduce a novel framework for modeling high-fidelity, animatable 3D human avatars from motion-blurred monocular video inputs. Motion blur is pr
arXiv:2607.24516v1 Announce Type: cross Abstract: While data curation for Vision Language Models (VLMs) is increasingly active, public practice for constructing pretraining mixtures remains largely he
arXiv:2607.22602v1 Announce Type: new Abstract: Inference-time scaling has emerged as a powerful paradigm for improving large language model reasoning, often delivering larger gains on difficult reaso
arXiv:2607.22829v1 Announce Type: new Abstract: Network Traffic Anomaly Detection (NTAD) is a critical task in cybersecurity, yet timely and accurate anomaly detection remains challenging. Mamba has e
arXiv:2607.23639v1 Announce Type: cross Abstract: This paper studies convolution rank regression (CRR) over decentralized distributed learning networks. We propose a novel decentralized CRR framework,
arXiv:2607.23197v1 Announce Type: new Abstract: Anomaly detection on multivariate sensor time series is critical for industrial monitoring of cyber-physical systems (CPS), where even subtle deviations
arXiv:2607.24449v1 Announce Type: cross Abstract: International recruitment in France requires navigating a layered legal framework absent from existing legal AI benchmarks. We present a publicly avai
arXiv:2603.00408v2 Announce Type: replace-cross Abstract: We present an Ising-compatible framework for formal neural-network robustness verification under bounded input perturbations. For piecewise-li
arXiv:2607.22569v1 Announce Type: new Abstract: Coding agents are increasingly integrated into system operations, where their tool use can directly modify project artifacts, execution environments, an
arXiv:2607.23682v1 Announce Type: new Abstract: Early warning of extreme market volatility is central to financial risk management, but actionable events are rare, nonstationary, and often triggered b
arXiv:2607.24419v1 Announce Type: new Abstract: Deep models have substantially advanced 12-lead ECG classification, yet their refinement still relies heavily on human experts to inspect failures and i
arXiv:2602.14947v2 Announce Type: replace-cross Abstract: This paper presents a physics-constrained neural network framework for dynamic modeling of saturable synchronous machines, including spatial h
arXiv:2607.24339v1 Announce Type: new Abstract: Large language model (LLM) agents inherit reactive failure modes: escalation under provocation, sycophantic drift under flattery, perseveration when stu
arXiv:2607.22757v1 Announce Type: cross Abstract: We introduce Graded Large Language Models (GLLMs), an algebraic framework that equips the representation space of a transformer with a grading and pro
arXiv:2607.09133v2 Announce Type: replace-cross Abstract: While large-scale text-to-image generative models have achieved unprecedented visual performance, their inherent reliance on multi-step iterat
If I only went off X posts, I'd think Ramp was an AI lab We’re open-sourcing PorTAL, our framework for shared task representations and cross model LoRA adaptation. It now spans from hybrid attention m
arXiv:2607.23946v1 Announce Type: new Abstract: We introduce Joint Flow Matching (JFM), a training framework for continuous normalising flows over multiple variables. Standard flow matching transports
arXiv:2607.22789v1 Announce Type: cross Abstract: Tracheostomy requires precise localization of the tracheal incision site; however, conventional manual palpation is subjective and often unreliable, w
arXiv:2607.22629v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) produce long, explicit chains of intermediate steps before generating a final answer at inference time. These intermediate
arXiv:2607.23785v1 Announce Type: cross Abstract: The Simple Temporal Problem (STP) is a core framework for quantitative temporal constraints. As STP data can be inconsistent, we study MAXSTP: compute
arXiv:2607.24314v1 Announce Type: new Abstract: Predicting the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of small molecules remains a major challenge in drug disc
arXiv:2607.24369v1 Announce Type: new Abstract: This work presents a motion planning framework for UAV navigation in non-convex urban air corridors. The planner is based on a mixed-integer tracking mo
arXiv:2607.22706v1 Announce Type: new Abstract: This paper presents the MPR-CiteG framework, which achieved second place in the ScienceON AI Challenge by addressing two fundamental challenges in gener
arXiv:2607.23029v1 Announce Type: cross Abstract: Federated learning enables collaborative model training across distributed clients without centralising their data, yet privacy remains a persistent c
arXiv:2607.22794v1 Announce Type: cross Abstract: Automatic depression detection with deep learning has shown promise but often suffers from limited generalization due to domain shift arising from int
arXiv:2607.24538v1 Announce Type: new Abstract: In this paper, we present NEO, a unified framework providing language-guided NeRF editing for robotic manipulation. Our paper introduces (i) a language-
arXiv:2607.23365v1 Announce Type: cross Abstract: Artificial intelligence (AI) systems are increasingly deployed in high-stakes domains such as healthcare, autonomous driving, finance, and education.