Knowledge catalogue
arXiv:2609.04083v1 Announce Type: cross Abstract: MLLM-based embedding models remain limited in compositional retrieval, often failing to distinguish scenes containing the same concepts but different
arXiv:2609.04103v1 Announce Type: new Abstract: This paper presents a new method for headland coverage path planning for arable fields. Several earlier approaches suggest covering the headland with ne
arXiv:2609.03589v1 Announce Type: cross Abstract: We study the large-depth behavior of residual networks whose weights are correlated across layers at initialization. Our results confirm and extend a
arXiv:2609.02892v1 Announce Type: cross Abstract: Single-turn code-generation metrics understate a central property of deployed agents: whether they can repair a wrong artifact after receiving concret
arXiv:2609.03221v1 Announce Type: new Abstract: Counterfactual audits are the standard tool for checking whether a clinical agent treats demographically distinct but clinically identical patients diff
arXiv:2609.03707v1 Announce Type: new Abstract: We present our submission to the IJCAI 2025 'Counterfactual Routing Competition' (CRC 25). The goal of the competition is to find counterfactual explana
arXiv:2609.03233v1 Announce Type: new Abstract: Camera traps have become an essential tool for wildlife monitoring, motivating the development of computer vision methods for the automated extraction o
arXiv:2609.03533v1 Announce Type: new Abstract: Existing theories derive neural scaling from data geometry or a specified data-model spectrum, but systems trained on the same data can scale differentl
arXiv:2609.03190v1 Announce Type: cross Abstract: Many biomedical challenges can be posed as tensor completion problems where the observed entries of a multidimensional array (a tensor) are used to im
arXiv:2609.03107v1 Announce Type: cross Abstract: Recent advances in generative speech models have made it increasingly difficult to distinguish authentic from synthetic audio, enabling new forms of f
arXiv:2609.03894v1 Announce Type: new Abstract: Large language models (LLMs) have become ubiquitous tools for code generation and editing. However, development teams often use multiple LLM assistants.
arXiv:2609.03663v1 Announce Type: cross Abstract: Background. Remote photoplethysmography estimates the cardiovascular pulse from facial video, and its explanations have rested on inspecting heatmaps
arXiv:2609.03526v1 Announce Type: new Abstract: Multimodal language models achieve near-ceiling scores on food recognition benchmarks, yet it remains unclear whether this success reflects genuine cult
arXiv:2609.03546v1 Announce Type: new Abstract: We present Dalek, a closed machine designed for agents that realizes self-maintenance, self-evolution, self-reproduction, and self-organization on any s
arXiv:2408.05301v3 Announce Type: replace Abstract: Humans often work closely together and relay a wealth of information through physical interaction. Robots, on the other hand, are not yet able to wo
arXiv:2509.17154v2 Announce Type: replace-cross Abstract: Hamiltonian dynamics describe a wide range of physical systems. As such, data-driven simulations of Hamiltonian systems are important for many
arXiv:2310.20096v2 Announce Type: replace-cross Abstract: The data market design problem is a problem in economic theory to find a set of signaling schemes (statistical experiments) to maximize expect
arXiv:2609.03324v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) improves large language model reasoning, but its practical scaling is constrained by expensive on-
arXiv:2508.15038v2 Announce Type: replace-cross Abstract: Wildlife field operations demand efficient parallel deployment methods to identify and interact with specific individuals, enabling simultaneo
arXiv:2609.03432v1 Announce Type: new Abstract: Automated evaluation of creativity tasks remains challenging for LLM-as-a-Judge, as LLM is susceptible to biases such as verbosity bias and leniency bia
arXiv:2609.03321v1 Announce Type: new Abstract: The Neural Finite State Machine (NFSM) framework offers a pragmatic path to full-duplex dialogue by serializing turn-taking control and response generat
arXiv:2412.08414v2 Announce Type: replace Abstract: Mental manipulation severely undermines mental wellness by covertly and negatively distorting decision-making. While there is an increasing interest
arXiv:2511.06632v2 Announce Type: replace Abstract: Urban scene reconstruction is critical for autonomous driving, enabling structured 3D representations for data synthesis and closed-loop testing. Su
arXiv:2609.04011v1 Announce Type: cross Abstract: Reliable transport models are essential when modelling and optimising many chemical engineering processes, yet, most models assume hand-picked constit
arXiv:2609.03878v1 Announce Type: cross Abstract: Reconstruction-based anomaly detectors are accurate but opaque: a deep autoencoder flags a sample without telling a practitioner which feature ranges
arXiv:2609.03064v1 Announce Type: cross Abstract: Federated learning allows banks, hospitals, and other regulated organizations to train a shared model without moving raw records off their own servers
arXiv:2609.02900v1 Announce Type: cross Abstract: The problem is the beta a desk needs when a firm's price history is too short to trust: an S-1 filer, a recent listing, or a name just past a regime b
arXiv:2602.10009v3 Announce Type: replace Abstract: Large Language Models (LLMs) are unable to reliably reason about specific physical systems. Attempts to imbue LLMs with knowledge of the necessary p
arXiv:2609.02898v1 Announce Type: new Abstract: Large domain-specific language models such as BioBERT and ClinicalBERT achieve strong performance on biomedical NLP tasks, but their computational deman
arXiv:2609.03100v1 Announce Type: new Abstract: Geostationary atmospheric motion vectors (AMVs) provide the dense horizontal wind vectors (u,v) and heights ingested into data assimilation systems. Tra
arXiv:2609.03787v1 Announce Type: new Abstract: AI agents increasingly gather evidence, invoke tools, apply constraints, and produce decisions that people or software may commit to action. A final out
arXiv:2609.03438v1 Announce Type: new Abstract: Graphical user interface (GUI) agents are increasingly used to execute natural-language instructions on user interfaces, yet real users may issue infeas
arXiv:2609.03673v1 Announce Type: new Abstract: Video generators build long videos by composing shorter parts, either by generating segments one after another or by autoregressively extending chunks.
arXiv:2503.04580v3 Announce Type: replace Abstract: Robust and accurate proprioceptive state estimation of the main body is crucial for legged robots to execute tasks in extreme environments where ext
arXiv:2609.04094v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards works well when a task has a programmatic checker, but most long-horizon agent domains have none. We work
arXiv:2609.03572v1 Announce Type: new Abstract: World models offer a promising paradigm for autonomous driving by predicting how traffic scenes may evolve and using such predictions to support action
arXiv:2609.03680v1 Announce Type: new Abstract: Labelling vision datasets, especially for segmentation tasks, is a laborious and costly process that stymies novel developments in agricultural robotics
arXiv:2609.04031v1 Announce Type: new Abstract: Video diffusion models (VDMs) have achieved impressive progress in text-to-video generation, but their high memory and computational costs hinder practi
arXiv:2512.22007v2 Announce Type: replace Abstract: DuaDeep-SeqAffinity is a sequence-only deep learning framework that predicts antibody--antigen binding affinity directly from primary amino acid seq
arXiv:2609.02901v1 Announce Type: new Abstract: Modern automatic speech recognition (ASR) scenarios require both spoken-form transcripts for faithful transcription and readable written-form transcript
arXiv:2609.03416v1 Announce Type: new Abstract: LLM-empowered paper-code discrepancy detection has received growing concern since the scaling of research submissions exceeds the manual review capabili
arXiv:2609.03423v1 Announce Type: new Abstract: Full-duplex voice agents must continuously decide when to listen, backchannel, interrupt, handle speech overlaps, take the floor, and yield. Existing be
arXiv:2509.25175v3 Announce Type: replace-cross Abstract: Large language model (LLM) steering has emerged as a promising paradigm for controlling model behavior at inference time through targeted mani
arXiv:2609.04034v1 Announce Type: cross Abstract: While diffusion base models such as GPT-Image-2 and Nano-Banana exhibit remarkable visual expressiveness, their end-to-end generation inherently yield
arXiv:2609.03846v1 Announce Type: cross Abstract: We study the allocation of indivisible goods among agents with identical additive valuations, focusing on envy-freeness up to one good (EF1) and Nash
arXiv:2609.03352v1 Announce Type: cross Abstract: Constant optimization refines the numerical coefficients of candidate expressions in tree-based genetic programming for symbolic regression. But its p
arXiv:2609.04088v1 Announce Type: new Abstract: Dense semantic segmentation allocates computational resources uniformly across the entire image, regardless of scene complexity or task relevance. Inspi
arXiv:2609.04141v1 Announce Type: new Abstract: AI agents are trained on population-scale data to encode broad capabilities spanning those of many practitioners. Yet the artifacts they produce rarely
arXiv:2603.07604v2 Announce Type: replace Abstract: Deformable 3D Gaussian Splatting (3DGS) has emerged as a popular method for real-time talking head synthesis, offering high-quality renderings at lo
arXiv:2609.03756v1 Announce Type: cross Abstract: We present ENEAS, a unified, text-promptable method for instance tracking and semantic discovery. Text-promptable segmentation models, including the l
arXiv:2609.03654v1 Announce Type: cross Abstract: The comparative analysis of banks' financial statements poses significant challenges for automated question answering systems due to their complexity,
arXiv:2602.08216v3 Announce Type: replace Abstract: We derive two attention operators from generalized statistical entropies. Kaniadakis entropy yields an exact full-support normalization whose weight
arXiv:2609.04128v1 Announce Type: new Abstract: Scaling interactive and verifiable environments is critical for training terminal agents. As frontier models become more capable, environments synthesiz
arXiv:2609.03522v1 Announce Type: cross Abstract: Semantic ID (SID) generative recommendation predicts the next item by generating a short tuple of discrete tokens. Recent masked-diffusion methods imp
arXiv:2609.04127v1 Announce Type: new Abstract: Large language models are increasingly used to support organizational decisions, yet users often lack a principled basis for assessing whether to rely o
arXiv:2609.02982v1 Announce Type: new Abstract: Learning solution operators across broad parameter ranges can require substantial coverage of both input functions and physical parameters, particularly
arXiv:2609.03143v1 Announce Type: cross Abstract: We use tools from nonlinear algebra to study the equilibria of small linear translational spring networks. Specifically we use the techniques of homot
arXiv:2609.03629v1 Announce Type: cross Abstract: Recent advances in text-to-video (T2V) diffusion models have demonstrated remarkable generative capabilities, yet their reliance on loosely curated tr
arXiv:2609.04197v1 Announce Type: cross Abstract: Evolutionary prompt optimizers such as GEPA suffer from prompt bloat: each iteration appends rules and caveats, producing prompts up to 3imes longer y
arXiv:2609.03255v1 Announce Type: new Abstract: This paper presents an experimental design for constructing a multimodal dataset to analyze user engagement in human-robot interaction (HRI). Prior stud