Knowledge catalogue
arXiv:2609.22934v1 Announce Type: new Abstract: Large language models (LLMs) increasingly mediate human decisions and communication, yet their behavioural regularities remain difficult to characterize
arXiv:2609.23935v1 Announce Type: new Abstract: Post-training turns a general next-token predictor into a chat model with a persistent assistant persona. If that persona is a character the model plays
arXiv:2609.22220v1 Announce Type: new Abstract: Benchmarks for LLM-generated GPU kernels decide correctness with a few random inputs and a loose floating-point tolerance, and their verdicts now feed l
arXiv:2609.24455v1 Announce Type: new Abstract: Intelligent transportation systems require Incremental Learning (IL) to continually improve their overall performance in dynamic environments. However,
arXiv:2609.22163v1 Announce Type: new Abstract: Interpretability research on large language models has produced accounts of factual recall in feed-forward layers and of token relationships in self-att
arXiv:2603.00842v2 Announce Type: replace Abstract: Biomedical multimodal assistants have the potential to unify radiology, pathology, and clinical-text reasoning, yet a critical deployment gap remain
arXiv:2609.24259v1 Announce Type: new Abstract: The effectiveness of agent memory ultimately depends on whether the underlying LLM gives each memory in context an appropriate degree of influence over
arXiv:2502.01562v3 Announce Type: replace Abstract: As the general capabilities of artificial intelligence (AI) agents continue to evolve, their ability to learn to master multiple complex tasks throu
arXiv:2609.22091v1 Announce Type: new Abstract: Retrieval over a personal memory store is retrospective: it surfaces what resembles the query, and it is blind to what the user has committed to do. We
arXiv:2609.24238v1 Announce Type: new Abstract: We reproduce and stress-test the work of Yu et al. (2023), who characterize how language models (LMs) arbitrate between memorized knowledge and contradi
arXiv:2609.22886v1 Announce Type: new Abstract: Model merging consolidates fine-tuned experts into one multi-task model without retraining. All existing data-free methods approach this problem entirel
arXiv:2607.11295v2 Announce Type: replace Abstract: Biomedical imaging data exhibit substantial acquisition variability, where identical biological structures can appear markedly different due to diff
arXiv:2609.23990v1 Announce Type: new Abstract: Tracking neurite morphology over time helps characterize structural changes during neuronal development and deterioration, but long-term time-lapse imag
arXiv:2609.24468v1 Announce Type: new Abstract: Three-dimensional multi-echo MRI provides rich anatomical and quantitative information, but repeated volumetric encoding prolongs acquisition and motiva
arXiv:2609.24995v1 Announce Type: new Abstract: Understanding natural human instructions is crucial for deploying robots in human-centric environments. We study multimodal instruction grounding, where
arXiv:2609.24145v1 Announce Type: new Abstract: We present MimicAgent, a prompt-to-trajectory generation framework for learning dynamic quadruped skills. Although reward shaping is extensively used wh
arXiv:2609.23336v1 Announce Type: new Abstract: Localizing and describing fine-grained differences between near-identical images is a critical yet underexplored capability for multimodal large languag
arXiv:2609.24146v1 Announce Type: new Abstract: Language model agents are increasingly used to simulate social interaction, and the resulting transcripts read as though the agents understand one anoth
arXiv:2609.23679v1 Announce Type: new Abstract: Form Field Detection (FFD) is a fundamental component of document understanding systems, enabling applications ranging from large-scale industrial digit
arXiv:2609.24832v1 Announce Type: new Abstract: This paper studies the minimum time trajectoriesvof a car-like mobile robot navigating in an obstacle free environment. The robot, with forward and back
arXiv:2609.22954v1 Announce Type: new Abstract: Mobile gas-sensing inspection requires a mobile platform to reliably reach ordered sampling poses. We present MIRA-PRM, a mission-informed probabilistic
arXiv:2609.24547v1 Announce Type: new Abstract: Real-time embodied companion interaction requires a robot to infer user intent from streaming speech, generate timely responses, and execute expressive,
arXiv:2609.23796v1 Announce Type: new Abstract: Single-image 3D object generation can now produce high-fidelity assets, yet accurately placing them into a coherent scene layout remains an open challen
arXiv:2609.24537v1 Announce Type: new Abstract: Video recording on smart glasses exposes more than faces. Continuous capture reveals full-body biometric signatures, including gait, posture, and silhou
arXiv:2609.22778v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly used as evaluators, yet their reliability in professional assessment tasks that require expert
arXiv:2609.23999v1 Announce Type: new Abstract: How large language models (LLMs) integrate patient risk with clinical cost tradeoffs remains poorly understood. We investigated how four open-weight LLM
arXiv:2609.24736v1 Announce Type: new Abstract: Mitotic figure (MF) analysis supports tumor grading and prognostic assessment, but automated models remain sensitive to differences in tissue type and i
arXiv:2609.24357v1 Announce Type: new Abstract: Cross-domain Named Entity Recognition (CD-NER) aims to transfer the rich knowledge in the source domain to the target domain. Recent studies adopting de
arXiv:2609.23010v1 Announce Type: new Abstract: Iterative text-to-motion generation delivers high-quality and semantically aligned motions but requires multiple network evaluations, resulting in subst
arXiv:2609.23121v1 Announce Type: new Abstract: Multimodal Agentic Retrieval (MAR) requires agents to solve complex information-seeking tasks by iteratively invoking external tools. Typical frameworks
arXiv:2505.12254v3 Announce Type: replace Abstract: Existing visual place recognition (VPR) datasets predominantly rely on vehicle-mounted imagery, offer limited multimodal diversity, and underreprese
arXiv:2609.24814v1 Announce Type: new Abstract: Advances in processing power, camera technologies, and mobile image analysis have made smartphones and other mobile devices, such as laptops, increasing
arXiv:2609.23980v1 Announce Type: cross Abstract: AI agents now report vulnerabilities faster than maintainers can review them. Reports often depend on security properties specific to the application,
arXiv:2609.24126v1 Announce Type: cross Abstract: Black-box machine learning models increasingly deliver strong predictions, but extracting useful information from them, such as a set of important fea
arXiv:2609.23553v1 Announce Type: new Abstract: The SYNTAX score is a clinically established tool for assessing anatomical lesion complexity in coronary artery disease and guiding subsequent treatment
arXiv:2609.22121v1 Announce Type: new Abstract: Passively collected mobile phone location data provide large-scale, longitudinal observations of human mobility but do not directly reveal activity purp
arXiv:2603.15265v2 Announce Type: replace Abstract: Developing a unified policy for multi-task robotic manipulation remains challenging due to policy degradation from task interference and negative tr
arXiv:2609.23073v1 Announce Type: new Abstract: Recent advances in natural language processing have led to molecular Large Language Models (LLMs) with strong performance across diverse chemistry tasks
arXiv:2609.24071v1 Announce Type: new Abstract: Chart reasoning agents are increasingly used to extract actionable insights in critical domains, achieving state-of-the-art performance on multiple benc
arXiv:2609.22169v1 Announce Type: new Abstract: Employers are increasingly using large language models (LLMs) to automate their hiring process. This paper investigates the risk of monocultural biases,
arXiv:2609.22643v1 Announce Type: new Abstract: Forecasting a long horizon from only the first observations of a sequence is ill-posed: many trajectories are consistent with the same short history. We
arXiv:2609.22272v1 Announce Type: new Abstract: We formulate Artistic Intelligence as exploration driven world realization, leaving space for creative possibility while preserving the semantic, artist
arXiv:2609.24310v1 Announce Type: cross Abstract: Text-to-speech models for Bantu tonal languages are challenged by a tonal system that is rooted in both the lexis (i.e., the inventory of words, stems
arXiv:2609.23797v1 Announce Type: cross Abstract: Human motion is shaped by both external acoustic events and behavioral intent: spatial audio conveys environmental cues that elicit or guide a respons
arXiv:2609.23881v1 Announce Type: new Abstract: Joint Embedding Predictive Architectures (JEPAs) are a promising paradigm for learning task-agnostic latent world models without visual reconstruction.
arXiv:2609.23928v1 Announce Type: new Abstract: Conflict scanning over synchronized robot paths requires detailed collision checking, potentially across every robot pair at every timestep, and may be
arXiv:2609.24812v1 Announce Type: new Abstract: Voice provides a natural and immediate interface for AI agents. Many settings in which voice agents could be useful, including meetings, households, and
arXiv:2511.12373v2 Announce Type: replace-cross Abstract: In the field of medical imaging, AI-assisted techniques such as object detection, segmentation, and classification are widely employed to alle
arXiv:2609.23416v1 Announce Type: cross Abstract: Long-form audio performance is often summarized by context length and aggregate accuracy, obscuring how language, evidence, and task jointly shape dif
arXiv:2609.24376v1 Announce Type: new Abstract: We propose a safety-critical framework for the cooperative transportation of passive targets in microgravity, where a team of chaser robots acts through
arXiv:2609.22690v1 Announce Type: new Abstract: We develop an index policy for finite-horizon Bernoulli multi-armed bandits from minimax solutions to single-arm bandit (SAB) problems. Each SAB problem
arXiv:2609.22398v1 Announce Type: cross Abstract: Accurate delineation of bi-atrial structures from late gadolinium enhancement MRI (LGE-MRI) is an important prerequisite for structural analysis and f
arXiv:2609.22144v1 Announce Type: new Abstract: Preserving safety alignment during large language models fine-tuning is critical, however, recent studies have demonstrated that even benign fine-tuning
arXiv:2609.22170v1 Announce Type: cross Abstract: Language models are frequently employed in settings where they are asked to make value judgments and choices. These observed choices often exhibit int
arXiv:2609.23906v1 Announce Type: new Abstract: This paper introduces a novel algorithm for predicting conditional joint distributions of vector-valued targets in stochastic systems whose randomness i
arXiv:2609.24678v1 Announce Type: cross Abstract: Continual learning with Low-Rank Adapters (LoRA) typically mitigates forgetting by penalizing the overlap between a new update and the accumulated pas
arXiv:2609.24441v1 Announce Type: new Abstract: Multivariate time-series forecasting is essential to many real-world applications. Recent large vision models (LVMs) offer a promising paradigm by trans
arXiv:2211.01566v2 Announce Type: replace-cross Abstract: This paper presents the applications of scientific ray-tracing in modeling and simulating light transport for space-borne image data generatio
arXiv:2602.09765v2 Announce Type: replace Abstract: Previous Vision-Language-Action models face critical limitations in navigation: scarce, diverse data from labor-intensive collection and static repr
arXiv:2609.24391v1 Announce Type: new Abstract: Voice-controlled interaction in industrial settings is hampered by acoustic noise, which severely degrades audio-only speech recognition. Audio-visual s