Knowledge catalogue
arXiv:2607.13830v1 Announce Type: cross Abstract: Unmanned Aerial Vehicles (UAVs) increasingly rely on visual fiducial markers for autonomous navigation and precision landing. However, standard marker
arXiv:2607.13318v1 Announce Type: new Abstract: Procedural material creation underpins applications in digital content creation, visual effects, and 3D asset design. Achieving high-quality results req
arXiv:2607.13874v1 Announce Type: new Abstract: Decision trees generate interpretable if--then rules, yet they contain irrelevant conditions (IRCs). These IRCs arise from the structural mechanism of t
arXiv:2510.11686v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) promises to expand the capabilities of language models, but it is unclear if current RL techniques promote the dis
arXiv:2603.22372v3 Announce Type: replace-cross Abstract: Recent advances in multimodal learning have motivated the integration of auxiliary modalities such as text or vision into time series (TS) for
arXiv:2607.14006v1 Announce Type: cross Abstract: Penetration testing traditionally evaluates whether adversaries can exploit weaknesses in software, infrastructure, configurations, or operational con
arXiv:2607.13455v1 Announce Type: new Abstract: High-quality teleoperation datasets are costly to collect, particularly for hard tasks. We observe that many tasks exhibit directional asymmetry: comple
arXiv:2508.16403v3 Announce Type: replace Abstract: Accurately predicting the performance of active radio frequency (RF) circuits is essential for modern wireless systems but remains challenging due t
arXiv:2607.13897v1 Announce Type: new Abstract: The broadcast nature of wireless channels exposes radio-frequency (RF) networks to anomalous and malicious transmissions, making anomaly detection a fun
arXiv:2607.13371v1 Announce Type: new Abstract: Accurate estimation of landfast sea ice roughness is critical for climate modeling and safe Arctic over-ice travel, yet existing approaches rely on cost
arXiv:2607.13926v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have demonstrated remarkable potential for high-level reasoning in autonomous driving, yet they fundamentally struggle to
arXiv:2607.13348v1 Announce Type: new Abstract: Autonomous racing overtaking requires balancing competitive performance with safety under nonlinear vehicle dynamics and real-time constraints. Model Pr
arXiv:2607.13039v1 Announce Type: cross Abstract: Safety evaluations for dual-use biology assistants often measure base-model capability, refusal behavior, or jailbreak success. These metrics miss a d
arXiv:2607.13594v1 Announce Type: new Abstract: LLM agents act on real-world environments through tool calls, and a single misjudged action can cause irreversible harm. The standard safeguard is a gua
arXiv:2607.11285v2 Announce Type: replace Abstract: Reconstructing 3D scenes from unordered images remains bottlenecked by expensive Structure-from-Motion (SfM) preprocessing and frozen pose interface
arXiv:2607.13323v1 Announce Type: new Abstract: The Segment Anything Model (SAM) has demonstrated strong generalizability across a variety of segmentation tasks. However, SAM often struggles in situat
arXiv:2607.13421v1 Announce Type: cross Abstract: Spatio-Temporal Video Grounding (STVG) aims to retrieve the visual trajectory of a specific object from a video stream as described by a natural langu
arXiv:2607.11506v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) optimizes LLMs using sparse verifiable final-answer rewards. This sparse anchor reliably verif
arXiv:2607.13983v1 Announce Type: new Abstract: Vision Transformer (ViT) has been widely used as a powerful framework for modeling global dependencies among image patches. However, its core component,
arXiv:2607.14070v1 Announce Type: cross Abstract: Genomic foundation models such as Evo 2 learn rich sequence representations, but their value for biosecurity screening is largely unexplored. We ask h
arXiv:2607.13088v1 Announce Type: cross Abstract: Large Language Models (LLMs) are rapidly moving from research settings into the wild, deployed on enterprise infrastructure, personal devices, and edg
arXiv:2607.13104v1 Announce Type: new Abstract: Self-improving autonomous agents are moving from research prototypes to deployed systems. The primary goal is controllable evolution, or adaptation, fro
arXiv:2607.13091v1 Announce Type: cross Abstract: LLM-based coding agents repeat the same classes of mistakes across sessions because they lack a mechanism to retain corrections from human review feed
arXiv:2607.13395v1 Announce Type: new Abstract: The pursuit of autonomously self-improving models has attracted growing interest in the era of large-scale foundation models. Drawing inspiration from t
arXiv:2607.13192v1 Announce Type: new Abstract: Self-supervision is a powerful technique for learning visual representations from unlabeled data. Existing techniques primarily adopt a two-stage approa
arXiv:2607.13111v1 Announce Type: cross Abstract: Distinguishing semantic-preserving commits from changing ones remains an open challenge in software repository mining. While existing approaches detec
arXiv:2607.13597v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models inherit rich semantic representations from pretrained Vision-Language Models, yet fine-tuning on limited robot dem
arXiv:2607.13396v1 Announce Type: new Abstract: What happens to an LLM agent's tool choice when the reliable tool silently changes within an ongoing session? We borrow set-shifting from cognitive psyc
arXiv:2607.13124v1 Announce Type: cross Abstract: Structured pruning is a hardware-friendly way to compress LLMs, but it is mostly validated on multiple-choice recognition tasks, while the same compre
arXiv:2607.13081v1 Announce Type: cross Abstract: We present nsfaguard, a guardrail framework for securing agentic AI systems against operational threats, such as prompt injection, sensitive informati
arXiv:2607.13931v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) drives multimodal reasoning, but answer-level correctness does not guarantee that a vision-languag
arXiv:2510.01943v3 Announce Type: replace-cross Abstract: Quasar-convex functions form a broad nonconvex class with applications to linear dynamical systems, generalized linear models, and Riemannian
arXiv:2607.13693v1 Announce Type: cross Abstract: This book chapter covers the evolution of social simulation from classical agent-based models, in which agents interact according to explicitly define
arXiv:2607.13566v1 Announce Type: cross Abstract: For low-dimensional problems (dleq3), spectral methods can achieve exceptionally high accuracy. For middle-dimensional problems (4 leq d lesssim 10),
Kubernetes software startup Spectro Cloud Inc. has bagged 100 million in a late-stage funding round as it looks to solve a significant and growing problem in artificial intelligence. Powerful processo
arXiv:2607.13049v1 Announce Type: new Abstract: Foundation models have given robots a sophisticated brain for complex decision-making, yet deploying that intelligence into a physical platform still de
arXiv:2607.13175v1 Announce Type: cross Abstract: Safe reinforcement learning typically enforces safety by bounding expected cumulative costs, a criterion that often fails to detect rare but catastrop
arXiv:2510.14893v4 Announce Type: replace Abstract: Autonomous high-speed navigation through large, complex environments requires real-time generation of agile trajectories that are dynamically feasib
arXiv:2607.13108v1 Announce Type: cross Abstract: Real-world traffic data exhibit heterogeneous spatial correlations and nonlinear temporal dynamics, posing substantial challenges for accurate spatio-
arXiv:2607.13618v1 Announce Type: new Abstract: LLM agents are increasingly evaluated on multi-week decision tasks in which the state that drives cost is never directly observed. On such tasks the fin
arXiv:2607.13444v1 Announce Type: new Abstract: Structural damage in modular spacecraft can disrupt mechanical and communication connectivity, reducing system capability. Existing approaches rely on r
arXiv:2607.13576v1 Announce Type: new Abstract: Interactive driving, wherein an intelligent lead vehicle equipped with real-time traffic data coordinates route choices of connected vehicles, offers a
arXiv:2601.04365v2 Announce Type: replace Abstract: In evolutionary reinforcement learning tasks (ERL), agent policies are often encoded as small artificial neural networks (NERL). Such representation
arXiv:2607.13452v1 Announce Type: cross Abstract: Incremental object detection (IOD) aims to extend detectors to new categories while retaining previously acquired knowledge. Existing methods often ad
arXiv:2607.13654v1 Announce Type: new Abstract: While 3D Gaussian Editing (3DGE) has seen substantial progress, text-driven 3D human garment editing remains largely underexplored. Existing 3DGE works
arXiv:2607.13314v1 Announce Type: cross Abstract: Tabular foundation models (TFMs) generate predictions on structured data via in-context learning, without task-specific estimation. We ask whether TFM
arXiv:2603.14604v2 Announce Type: replace-cross Abstract: We propose TacFiLM, a lightweight modality-fusion approach that integrates visual-tactile signals into vision-language-action (VLA) models. Wh
arXiv:2607.13047v1 Announce Type: new Abstract: Parameter decomposition (PD) decomposes neural networks into interpretable computational components that faithfully reflect the original network's opera
arXiv:2607.13880v1 Announce Type: new Abstract: In underwater covert cooperative missions, autonomous underwater vehicles (AUVs) often cannot rely on active sonar to continuously obtain complete infor
arXiv:2607.13986v1 Announce Type: new Abstract: The 11th Affective Behavior Analysis in-the-wild (ABAW11) Multi-Task Learning Challenge requires a unified system to predict valence-arousal, categorica
arXiv:2607.13812v1 Announce Type: cross Abstract: We introduce TCAM-Diff, a novel 3D medical image generation model that reduces the memory requirements to encode and generate high-resolution 3D data.
arXiv:2607.13423v1 Announce Type: new Abstract: Temperature scaling is the dominant post-hoc calibration method in modern deep learning. Its theoretical justification rests on an assumption that is ra
arXiv:2607.13164v1 Announce Type: cross Abstract: Sign language is a primary communication channel for millions of Deaf and hard-of-hearing people, yet text-to-signer video generation remains costly b
arXiv:2607.13905v1 Announce Type: new Abstract: The International StepUP Competition Series was launched to advance research in pressure-based footstep biometrics through a standardized and challengin
arXiv:2607.13393v1 Announce Type: cross Abstract: A field can reformulate its computations freely exactly where its demand is stated independently of any incumbent implementation, and finds itself una
arXiv:2607.13998v1 Announce Type: cross Abstract: The rapid proliferation of Agentic Artificial Intelligence fundamentally disrupts traditional customer loyalty paradigms. As AI evolves from passive r
arXiv:2607.13075v1 Announce Type: cross Abstract: Context can change whether a request is harmful without changing its topic or surface form. We ask whether residual-stream probes distinguish harmful
arXiv:2607.13077v1 Announce Type: cross Abstract: Large language models (LLMs) often produce homogeneous outputs, raising concerns that AI coding assistants may lead to convergence in the software art
arXiv:2607.13660v1 Announce Type: new Abstract: Contrastive Language-Image Pretraining (CLIP) representations form a semantic embedding space governed by cosine similarity, reflecting an intrinsic hyp
arXiv:2607.13768v1 Announce Type: new Abstract: Collisions of rigid-link robots and rigid environments are often modeled as instantaneous events. Under this idealization, the impact forces become impu