Knowledge catalogue
arXiv:2608.04562v1 Announce Type: new Abstract: Agent skills are increasingly optimized by automated feedback loops, producing long structured artifacts whose internal value remains unclear. We study
arXiv:2608.04820v1 Announce Type: new Abstract: Face inpainting with diffusion models has recently achieved impressive visual quality, yet preserving identity fidelity under significant occlusion and
arXiv:2608.04964v1 Announce Type: new Abstract: Interactive video world models are essential for long-horizon planning and exploration, yet they suffer from compounding errors. Post-training methods s
arXiv:2608.04642v1 Announce Type: new Abstract: Slice-wise 2D object detectors are increasingly applied to volumetric data due to their computational efficiency and scalability, yet they often yield f
arXiv:2608.04720v1 Announce Type: new Abstract: Real-time object detectors achieve remarkable accuracy under controlled conditions, yet degrade sharply on non-ideal inputs: fisheye distortion, game-re
arXiv:2506.18266v2 Announce Type: replace Abstract: 3D semantic occupancy prediction is crucial for fine-grained scene understanding, yet its advancement in privacy-sensitive indoor environments is fu
arXiv:2504.01407v3 Announce Type: replace-cross Abstract: Long video understanding poses a fundamental challenge for large video-language models (LVLMs) due to the overwhelming number of frames and th
arXiv:2608.03279v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has become a dominant representation for real-time novel view synthesis (NVS), yet its storage footprint makes compression
arXiv:2608.03958v1 Announce Type: new Abstract: As autonomous agents powered by foundation models are increasingly integrated into social and economic systems, understanding the principles governing t
arXiv:2608.03695v1 Announce Type: cross Abstract: This work addresses community detection in temporal networks through GPU-accelerated extensions of spectral clustering and modularity-based algorithms
arXiv:2608.03475v1 Announce Type: cross Abstract: Multimodal intent recognition combines linguistic, acoustic, and visual evidence, but individual modalities may be noisy, missing, semantically confli
arXiv:2608.03569v1 Announce Type: new Abstract: Benchmarking the ability of AI scientists to generate novel ideas is notoriously difficult. Existing benchmarks in this field have made progress in eval
arXiv:2608.03166v1 Announce Type: new Abstract: Role-Playing Language Agents (RPLAs) are increasingly deployed in high-stakes applications such as healthcare assistance, customer support, and educatio
arXiv:2608.03223v1 Announce Type: cross Abstract: Agentic reinforcement learning enables LLM agents to learn through interaction, but sparse trajectory-level rewards reveal success without identifying
arXiv:2608.00155v1 Announce Type: cross Abstract: Large language model (LLM) agents can self-evolve by continually improving from their own accumulated experience. However, existing studies predominan
arXiv:2608.03076v1 Announce Type: new Abstract: Multi-agent studies commonly place AI agents in predefined games, markets, or roles, making it difficult to distinguish endogenous economic organization
arXiv:2608.03520v1 Announce Type: cross Abstract: AI systems are increasingly involved in decisions and actions that may later require investigation. When an AI related incident occurs, investigators
arXiv:2608.03047v1 Announce Type: new Abstract: Generating natural-language coaching feedback on motor skills can accelerate learning, yet expert coaches are scarce and expensive. Existing reference-b
arXiv:2608.03715v1 Announce Type: new Abstract: Mean-reverting dynamics are pervasive in finance, and the Cox--Ingersoll--Ross (CIR) process is a standard model for the time series they produce, from
arXiv:2608.03316v1 Announce Type: cross Abstract: On-policy distillation, in which a teacher corrects samples that the student itself generates, presupposes that the two models speak the same language
There are a lot of LLM benchmarks but few, if any, harness benchmarks. I am thinking this would be a really good community project to build one. End goal: a leaderboard of harness performance (multipl
arXiv:2608.01366v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are integral to complex intellectual tasks, yet output quality remains constrained by user-provided prompts. Iter
arXiv:2608.03653v1 Announce Type: new Abstract: Network dismantling is fundamental to analyzing the robustness and vulnerability of complex systems, yet practical heuristics must balance effectiveness
arXiv:2608.03450v1 Announce Type: cross Abstract: Reasoning in Multimodal Large Language Models (MLLMs) requires both fine-grained visual perception and rigorous logical deduction. Explicit text-based
arXiv:2608.02907v1 Announce Type: new Abstract: Multimodal retrievers are essential for knowledge-based visual question answering, where they retrieve external evidence for image-question pairs. Howev
arXiv:2608.02612v1 Announce Type: new Abstract: Formulating an optimization problem strongly affects the quality of the final solution, yet good formulations usually require substantial expertise. Rec
arXiv:2608.03038v1 Announce Type: new Abstract: Statistical reasoning is multidimensional, yet evaluations of large language models (LLMs) typically emphasize response accuracy while overlooking how m
arXiv:2608.03129v1 Announce Type: new Abstract: Large Language Model-assisted Evolutionary Search (LES) has emerged as a powerful paradigm for automated algorithm design. However, existing LES methods
arXiv:2608.03859v1 Announce Type: cross Abstract: Large language models (LLMs) pose challenges to academic integrity and peer review. Yet generative plagiarism detection remains an underexplored and l
arXiv:2607.11844v2 Announce Type: replace Abstract: Recent Multimodal Large Language Models (MLLMs) achieve strong performance on single-view video understanding benchmarks. However, sports videos inv
arXiv:2503.04263v2 Announce Type: replace Abstract: Motivated by applications to the simulation of quantum many-body systems by neural networks, researchers have suggested several models which are ant
arXiv:2608.03855v1 Announce Type: new Abstract: Transformer models have revolutionized natural language processing (NLP), and text-based molecular representations like SMILES have successfully extende
arXiv:2506.13502v3 Announce Type: replace Abstract: Next-word prediction (NWP) trains language models against a single observed continuation, even though many contexts admit multiple plausible next wo
arXiv:2510.21889v2 Announce Type: replace-cross Abstract: Causal inference identifies cause-and-effect relationships between variables. While traditional approaches rely on data to reveal causal links
arXiv:2606.14438v3 Announce Type: replace-cross Abstract: End-to-end (E2E) autonomous-driving planners trained by imitation are prone to statistical shortcuts: they associate scene elements that merel
arXiv:2608.03743v1 Announce Type: cross Abstract: Terminal User Interfaces (TUIs) combine the stateful, screen-oriented behaviour of GUIs with terminal deployment and are now common in developer tools
arXiv:2608.03046v1 Announce Type: new Abstract: Text-to-video (T2V) diffusion transformers (DiTs) are trained with detailed video captions, whereas inference often relies on user prompts rewritten by
arXiv:2608.03673v1 Announce Type: new Abstract: Many critical reasoning tasks, including clinical diagnosis, legal judgment, and industrial fault diagnosis, require step-dependent causal chains in whi
arXiv:2608.03507v1 Announce Type: cross Abstract: Historical language change affects morphology, syntax, semantics, and pragmatics, yet computational studies typically examine these levels with incomp
arXiv:2608.03247v1 Announce Type: cross Abstract: Multimodal learning has significantly advanced survival prediction by integrating pathology images with genomic data. However, clinical information, d
arXiv:2608.03016v1 Announce Type: new Abstract: Accurate chest X-ray interpretation is inherently hierarchical. Clinical decisions depend not only on what abnormality is present but where it is situat
arXiv:2504.06881v2 Announce Type: replace-cross Abstract: Convolutional neural networks (CNNs) are foundational to many state-of-the-art computer vision systems, yet their reliance on multiplication-i
arXiv:2608.02895v1 Announce Type: new Abstract: Accurate localization remains a key challenge in swarm robotics, particularly for self-reconfigurable systems that must identify relative positions to f
arXiv:2608.03483v1 Announce Type: cross Abstract: Existing chunk-based Vision-Language-Action (VLA) models execute a fixed number of actions (i.e., execution horizon) before replanning, turning replan
arXiv:2601.20771v2 Announce Type: replace-cross Abstract: Accurate forecasting of infectious disease incidence is critical for public health planning and timely intervention. While most data-driven fo
arXiv:2608.02643v1 Announce Type: cross Abstract: Computer-use agents (CUAs) operate real desktop and web interfaces through screenshots, mouse and keyboard actions, and stateful UI feedback, yet thei
arXiv:2608.02833v1 Announce Type: cross Abstract: Chart question answering (CQA) requires multimodal large language models (MLLMs) to integrate visual comprehension with logical reasoning, yet current
arXiv:2608.03451v1 Announce Type: new Abstract: Data agents enable natural-language analytics over organizational workspaces, where relevant evidence may be scattered across databases, structured file
arXiv:2608.02904v1 Announce Type: new Abstract: Delivering sustained power to distributed equipment in unstructured field environments using pre-planned wired networks or battery-based solutions prese
arXiv:2511.19716v3 Announce Type: replace-cross Abstract: Stochastic Gradient Descent (SGD) often slows in the late stage of training due to anisotropic curvature and gradient noise. We analyze precon
arXiv:2608.03407v1 Announce Type: cross Abstract: Road network segmentation from satellite imagery remains challenging due to large geographic variation in road appearance, occlusions, and domain shif
arXiv:2608.03082v1 Announce Type: new Abstract: Recent advances in Diffusion Transformers (DiTs) have enabled remarkable progress in visual synthesis, benefiting from their superior scalability. To fa
arXiv:2608.03177v1 Announce Type: new Abstract: How can question answering (QA) systems determine whether a query is ambiguous? Ambiguity detection is essential in open-domain QA, as misclassification
arXiv:2608.03566v1 Announce Type: cross Abstract: The drift diffusion model (DDM) is a cornerstone of cognitive decision-making research. Although numerous estimation methods exist, researchers contin
arXiv:2608.03119v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) improves LLM reasoning but typically relies on ground-truth (GT) answers, limiting scalability. Vo
arXiv:2608.03411v1 Announce Type: new Abstract: Accurate Uncertainty Quantification (UQ) is critical for reliable deployment of Large Language Models (LLMs), yet traditional probability-based metrics
arXiv:2608.03179v1 Announce Type: new Abstract: Controllable local editing of 3D assets requires precise target localization and appropriate visual guidance. However, existing methods lack a simple ye
arXiv:2407.21311v2 Announce Type: replace-cross Abstract: Unsupervised domain adaptation (UDA) aims to mitigate domain shift, where the distribution of labeled source data differs from that of unlabel
arXiv:2608.03269v1 Announce Type: cross Abstract: Video dataset distillation aims to compress a large video dataset into a compact surrogate set that preserves its training utility. Most existing appr
arXiv:2608.03565v1 Announce Type: new Abstract: While modern tabular learners excel at capturing statistical patterns, they frequently operate in a semantic vacuum, treating textual features as discre