Knowledge catalogue
arXiv:2607.04647v1 Announce Type: cross Abstract: Scalable Bayesian inference for generalized linear mixed models (GLMMs) provides uncertainty-aware analysis of correlated longitudinal data, but exist
arXiv:2607.03125v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) offers powerful control for industrial cyber-physical systems (ICPSs), but its 'black-box' exploration risks violating
arXiv:2606.14606v2 Announce Type: replace Abstract: Dexterous manipulation is fundamentally a problem of interaction dynamics: the hand must track precise finger trajectories, regulate the contact for
arXiv:2507.03670v2 Announce Type: replace-cross Abstract: Writing longer prompts for an AI assistant to generate a story increases psychological ownership, a user's feeling that the writing belongs to
arXiv:2506.21887v2 Announce Type: replace Abstract: High-stakes decision-making involves navigating multiple competing objectives with expensive evaluations. For instance, in brachytherapy, clinicians
arXiv:2607.02672v1 Announce Type: new Abstract: Local pairwise comparisons are a standard tool for learning how people want decision rules to work, e.g., in participatory design or alignment. However,
arXiv:2607.04988v1 Announce Type: new Abstract: Unified models for robot manipulation aim to equip one policy with both the semantic priors of pretrained VLMs and the physical dynamics learned through
arXiv:2607.05393v1 Announce Type: cross Abstract: Time-domain surveys generate many transient candidates, making Real-Bogus classification a critical step in automated discovery pipelines. Reliable la
arXiv:2607.02553v1 Announce Type: new Abstract: Introduction: Objective neuroimaging biomarkers may improve Parkinson's disease motor assessment by capturing brain variation not directly observable fr
arXiv:2509.15908v3 Announce Type: replace-cross Abstract: Nanoporous materials hold promise for diverse sustainable applications, yet their vast chemical space poses challenges for efficient design. M
arXiv:2605.11012v2 Announce Type: cross Abstract: Programming the static friction of mechanical interfaces is critical for soft robotics, haptics, and precision gripping. Static friction is governed b
arXiv:2512.05672v2 Announce Type: replace-cross Abstract: Recent approaches in controllable novel view video generation often rely on fine-tuning pre-trained Video Diffusion Models (VDMs). This domina
arXiv:2506.07406v3 Announce Type: replace-cross Abstract: Understanding the internal representations of large language models (LLMs) is a central challenge in interpretability research. Existing featu
arXiv:2607.03289v1 Announce Type: new Abstract: When designing robots' nonverbal behaviors, many researchers have turned to arts-based insights, such as Disney's Animation Principles. Yet, while these
arXiv:2607.03696v1 Announce Type: new Abstract: Existing Salient Object Detection in Optical Remote Sensing Image (ORSI-SOD) methods mainly adopt the static inference strategy, which uses fixed traine
arXiv:2605.06142v2 Announce Type: replace-cross Abstract: When people recount personal memories, they often refer to people, places, and events indirectly, relying on con-textual cues rather than expl
arXiv:2512.10730v2 Announce Type: replace Abstract: Recent advances in motion-aware large language models have shown remarkable promise for jointly learning motion understanding and generation knowled
arXiv:2607.04344v1 Announce Type: cross Abstract: While Large Vision-Language Models (VLMs) demonstrate remarkable generic capabilities, their clinical reasoning in specialized domains like ocular sur
arXiv:2607.03316v1 Announce Type: cross Abstract: Agentic code review, where autonomous agents provide code review comments on pull requests, is increasingly integrated into development workflows, yet
arXiv:2512.08854v3 Announce Type: replace Abstract: It has been hypothesized that achieving the data efficiency of human visual perception requires a generative approach in which internal representati
arXiv:2607.05268v1 Announce Type: new Abstract: Whether a hyperbolic representation model uses its geometry cannot be read off its curvature parameter: what matters is the dimensionless operating poin
arXiv:2503.06643v2 Announce Type: replace-cross Abstract: In this paper, we tackle a critical challenge in model evaluation: how to keep code benchmarks useful when models might have already seen them
arXiv:2606.13156v2 Announce Type: replace-cross Abstract: Letting a vision-language model (VLM) think longer at test time has driven much recent progress. A natural way to bring this to spatial ground
arXiv:2601.22378v3 Announce Type: replace-cross Abstract: Maximum likelihood estimators (MLE) and control variate estimators (CVE) have been used in conjunction with known information across sketching
arXiv:2607.03553v1 Announce Type: new Abstract: Automation in construction is essential for reducing costs and human errors in large-scale projects. We approach the construction progress monitoring fr
arXiv:2512.22999v2 Announce Type: replace-cross Abstract: We consider problems of parameter estimation where design variables can be actively optimized to maximize information gain. To this end, we in
arXiv:2607.02825v1 Announce Type: cross Abstract: We release extsc{JavaVulBench}, a benchmark dataset and evaluation harness for Java vulnerability detection. The dataset contains sim30{,}600 Java met
arXiv:2607.03217v1 Announce Type: new Abstract: Uncertainty quantification of hydrological predictions is necessary to inform operational decisions. Recent generative machine-learning methods have adv
arXiv:2607.04982v1 Announce Type: cross Abstract: High-resolution velocity models are crucial for reservoir characterization and subsurface delineation. However, the band limited nature of our surface
arXiv:2607.02862v1 Announce Type: new Abstract: Automatic Speech Recognition (ASR) and Dialect Identification (DID) are crucial for Indian languages, many of which are low-resource and exhibit signifi
arXiv:2607.02680v1 Announce Type: cross Abstract: MLLMs have shown strong zero-shot capabilities across diverse inputs such as across images, video, audio, and text. A crucial, yet underexplored, appl
arXiv:2504.20412v3 Announce Type: replace-cross Abstract: Fuzzing frameworks like syzkaller have uncovered thousands of Linux kernel crashes, many of which are critical and security-sensitive. However
arXiv:2606.16533v3 Announce Type: replace Abstract: We introduce extbf{Kairos}, a regret-aware native world-action model stack for Physical AI. Kairos is motivated by the view that a physical world mo
arXiv:2607.04652v1 Announce Type: new Abstract: Learning manipulation from few demonstrations requires visual priors that capture not only where to interact, but also how the interaction should begin;
arXiv:2511.18613v2 Announce Type: replace-cross Abstract: This study presents a controlled comparison of baseline Kolmogorov-Arnold Networks (KAN), implemented via PyKAN, and Long Short-Term Memory (L
arXiv:2602.08842v3 Announce Type: replace-cross Abstract: As highly automated driving is transitioning from single-vehicle closed-access testing to commercial deployments of public ride-hailing in sel
arXiv:2607.03166v1 Announce Type: cross Abstract: Template-based contrastive synthesis is scalable, but its candidates often differ only in a few entity-slots while sequence-level optimization spreads
arXiv:2607.04820v1 Announce Type: new Abstract: Decoding hand kinematics from surface electromyography (EMG) is a core challenge in wearable biosignal processing with clinical relevance for prosthetic
arXiv:2508.02296v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) systems are increasingly deployed in high-stakes domains, where safety depends not only on how a system answers
arXiv:2607.03991v1 Announce Type: cross Abstract: Repeated LLM calls are the standard way to estimate how trustworthy a Text-to-SQL result is: run the pipeline multiple times, judge each SQL execution
arXiv:2607.02609v1 Announce Type: cross Abstract: For decades, data engineering has developed mature architectural principles for integrating, governing, validating, cataloging, and serving organizati
arXiv:2607.04447v1 Announce Type: new Abstract: Local causal discovery is a scalable alternative to global structure learning. However, it can struggle to identify valid adjustment sets in data-scarce
arXiv:2607.05013v1 Announce Type: new Abstract: Although LLMs have made significant progress in mathematical reasoning, determining whether a mathematical problem is solvable remains a fundamental yet
arXiv:2607.05061v1 Announce Type: new Abstract: Key-value (KV) cache growth is a major bottleneck in autoregressive decoding, as memory and bandwidth scale linearly with context length. Existing KV ev
arXiv:2607.02850v1 Announce Type: new Abstract: Meta-learning without labeled data is crucial for real-world applications, where obtaining labeled datasets can be expensive or restricted due to privac
arXiv:2607.03057v1 Announce Type: cross Abstract: The rapid growth in the parameter scale of large language models (LLMs) has created a strong demand for efficient compression techniques. As a hardwar
arXiv:2607.03482v1 Announce Type: new Abstract: In this paper, we present the Invariant-Variant Disentangled State-Space Model (IVD-SSM), our submission to SemEval-2026 Task 4 on Narrative Story Simil
arXiv:2607.05077v1 Announce Type: new Abstract: We tackle fine-grained indoor localization from natural language: given a free-form description of one's surroundings, estimate the observer's 2D positi
arXiv:2603.11784v2 Announce Type: replace Abstract: As scaling laws push the training of frontier large language models (LLMs) toward ever-growing data requirements, training pipelines are approaching
arXiv:2412.13533v4 Announce Type: replace Abstract: Medical image segmentation is a fundamental task in numerous medical engineering applications. Recently, language-guided segmentation has shown prom
arXiv:2603.23844v2 Announce Type: replace Abstract: Recent work provides overwhelming evidence that LLMs, even those trained to scale their reasoning trace, quickly deteriorate at planning as problems
arXiv:2607.04156v1 Announce Type: new Abstract: Scientific equation discovery must combine broad domain priors with strict numerical testing. Symbolic regression supplies numerical grounding but faces
arXiv:2607.04525v1 Announce Type: cross Abstract: How concepts are represented in neural networks is a fundamental question in machine learning. The dominant view treats concept representations as sta
arXiv:2511.06148v4 Announce Type: replace-cross Abstract: As large language models (LLMs) are adopted into frameworks that grant them the capacity to make real decisions, it is increasingly important
arXiv:2607.03381v1 Announce Type: cross Abstract: Scientific papers follow rhetorical structures that organize content into sections such as Introduction, Methods, Results, and Discussion. Automatical
arXiv:2507.08905v2 Announce Type: replace-cross Abstract: We explore the use of Hamiltonian Monte Carlo (HMC) sampling as a probabilistic last layer approach for deep neural networks (DNNs). While HMC
arXiv:2607.04352v1 Announce Type: new Abstract: In this work, we study the last-meter precision navigation for UAVs, e.g., autonomously reaching a target within the final 10 meters using monocular vis
arXiv:2607.03558v1 Announce Type: cross Abstract: Continuous video anomaly detection is dominated by reactive Multiple Instance Learning (MIL) that collapses spatiotemporal features into scalar scores
arXiv:2607.05188v1 Announce Type: new Abstract: A coding agent solving a software-engineering task spends dozens of steps reasoning, editing code, and running tests, yet little is known about what the
arXiv:2607.02607v1 Announce Type: cross Abstract: Video reasoning requires Large Multimodal Models (LMMs) to remain grounded in dense evidence, yet existing systems largely adopt 'read-once, generate-