Silicon Sampling via Cross-Survey Transfer
arXiv:2607.03091v1 Announce Type: new Abstract: Silicon sampling-using large language models (LLMs) to simulate human survey respondents-has emerged as a promising approach for augmenting traditional
Knowledge catalogue
arXiv:2607.03091v1 Announce Type: new Abstract: Silicon sampling-using large language models (LLMs) to simulate human survey respondents-has emerged as a promising approach for augmenting traditional
arXiv:2607.04234v1 Announce Type: cross Abstract: Deformable object manipulation poses challenges beyond task completion: successful execution must also maintain safe physical interaction, holding the
arXiv:2607.03809v1 Announce Type: new Abstract: Normalising flows provide a powerful variational family for approximate inference, yet individual architectures often fail to generalise across heteroge
arXiv:2510.08048v4 Announce Type: replace-cross Abstract: Query-product relevance prediction is fundamental to e-commerce search and has become even more critical in the era of AI-powered shopping, wh
arXiv:2607.04061v1 Announce Type: cross Abstract: Distinguishing Large Language Model (LLM) generated text from human writing is a critical and difficult challenge. While LLMs are trained to write lik
arXiv:2607.02615v1 Announce Type: cross Abstract: Generating structured artifacts with Large Language Models - e.g. database queries, threat framework mappings, entity schemas - is relatively straight
arXiv:2607.04401v1 Announce Type: new Abstract: How robust and generalisable are pathology foundation models and have their scaling limites been reached? We benchmarked twelve pathology foundation mod
arXiv:2607.04034v1 Announce Type: cross Abstract: The language models that underpin agents have seen a rapid rise in performance on function calling benchmarks. However, the metrics used in the traini
arXiv:2607.05055v1 Announce Type: new Abstract: Healthcare appointment scheduling remains a persistent operational bottleneck, driven by manual coordination, fragmented legacy systems, and high admini
arXiv:2607.04515v1 Announce Type: new Abstract: Efik, a tonal language spoken by about 3 million second language speakers and 1.5 million native speakers in Southeastern Nigeria, remains underrepresen
arXiv:2607.04745v1 Announce Type: cross Abstract: Modern thermal visual place recognition (TIR-VPR) frontends based on foundation models achieve remarkable closed-set retrieval but suffer from an over
arXiv:2607.04430v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in question answering (QA) systems, yet they may generate hallucinated or misaligned responses wi
arXiv:2603.24690v2 Announce Type: replace Abstract: In-context learning (ICL) enables fast task adaptation from demonstrations without per-task parameter updates but remains highly sensitive to exampl
arXiv:2310.00505v3 Announce Type: replace-cross Abstract: Fetal health classification is a critical task in obstetrics, enabling early identification and management of potential health problems. Howev
arXiv:2607.05282v1 Announce Type: cross Abstract: Schizophrenia is a debilitating neuropsychiatric disorder characterized by profound cortical network dysregulation, for which objective, clinically tr
arXiv:2508.01858v3 Announce Type: replace-cross Abstract: Multimodal large-scale models have significantly advanced the development of web agents, enabling perception and interaction with digital envi
arXiv:2607.03562v1 Announce Type: new Abstract: As deepfake detection models increasingly produce natural language explanations, their reasoning often remains weakly grounded in visual artifacts, limi
arXiv:2607.05029v1 Announce Type: cross Abstract: Persistent memory has enabled large language model (LLM) agents to store factual knowledge, prior decisions, reasoning histories, tool usage informati
arXiv:2607.01935v1 Announce Type: new Abstract: Long term memory lets LLM agents act as persistent assistants, but user facts change. A useful memory system must know what is true now, what used to be
arXiv:2607.01425v1 Announce Type: new Abstract: Understanding large, complex codebases, especially those with obfuscated structures and incomplete documentation, remains a significant challenge. Exist
arXiv:2602.19127v2 Announce Type: replace Abstract: With the rapid advancement of agent-based methods in recent years, Agentic RAG has undoubtedly become an important research direction. Multi-hop rea
arXiv:2607.02432v1 Announce Type: new Abstract: Scalable and reliable grading of command-line examinations remains a challenge in computing education, where rising enrolments make manual marking diffi
arXiv:2607.01679v1 Announce Type: cross Abstract: Adversarial attacks on cybersecurity classifiers pose a dual threat: degrading predictions and destabilising the SHAP-based explanations that security
arXiv:2607.01728v1 Announce Type: cross Abstract: Visual regression testing (VRT) is a standard quality assurance step in modern software release pipelines. On every change, it re-renders user interfa
arXiv:2607.01387v1 Announce Type: cross Abstract: Recommender systems are vital in helping users navigate vast amounts of information, offering personalized suggestions and effective explanations for
arXiv:2607.01474v1 Announce Type: new Abstract: Class imbalance poses a critical challenge in federated learning (FL), where underrepresented classes suffer from poor predictive performance yet cannot
arXiv:2607.01433v1 Announce Type: new Abstract: Divergent thinking is a crucial aspect of creativity, yet large language models (LLMs) tend to consistently generate similar responses to open-ended que
arXiv:2607.02210v1 Announce Type: new Abstract: The evolution toward fully autonomous telecommunications networks (Autonomous Network Levels 4-5) requires AI/ML agents to make real-time network decisi
arXiv:2607.01984v1 Announce Type: cross Abstract: Newer lightweight convolutional neural networks are often presented as improving predictive performance and deployment efficiency, but such claims req
arXiv:2607.02230v1 Announce Type: cross Abstract: The complexity of waste disposal regulations across European countries poses significant challenges for the residents and hinders the transition to a
arXiv:2607.01584v1 Announce Type: new Abstract: Large language models have recently been explored for scientific hypothesis generation, but most prior work relies on unstructured literature and free-f
arXiv:2607.01690v1 Announce Type: new Abstract: Finetuning a language model on documents that are explicitly annotated as fictional results in a model that still actually believes the documents' core
arXiv:2607.02082v1 Announce Type: cross Abstract: Wave Function Collapse (WFC) is a widely used procedural content generation method that learns local adjacency constraints from example inputs to gene
arXiv:2607.01457v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly applied to resume optimization for applicant tracking systems, introducing hallucination failures distin
arXiv:2607.02338v1 Announce Type: cross Abstract: Hierarchical Navigable Small World (HNSW) graphs serve as the industry standard due to their logarithmic complexity and strong empirical performance.
arXiv:2607.02383v1 Announce Type: new Abstract: LLM-based retrieval-augmented generation (RAG) is increasingly used for automated fact-checking (AFC) and related tasks. By grounding LLM outputs in ret
arXiv:2607.01247v1 Announce Type: cross Abstract: Open-ended mathematics exams are valuable because they assess reasoning, proof construction, algorithmic thinking, and communication of intermediate s
arXiv:2607.01764v1 Announce Type: new Abstract: Repository-level vulnerability reproduction is a demanding software engineering (SE) task: an agent must inspect a codebase, infer the input grammar tha
arXiv:2607.01814v1 Announce Type: new Abstract: Traditional Chinese Medicine (TCM) diagnosis, particularly through tongue inspection, faces persistent challenges in subjectivity and reproducibility. T
arXiv:2607.01401v1 Announce Type: cross Abstract: INTRODUCTION: Accurate MRI-based identification of Alzheimer's disease (AD), mild cognitive impairment (MCI), and related dementias remains challengin
arXiv:2607.02277v1 Announce Type: new Abstract: Safe landing-site assessment in unstructured environments remains a key challenge for autonomous UAV deployment, as vision-only learning approaches ofte
arXiv:2607.01444v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models offer inference speedups via selective activation but impose substantial memory requirements because the whole network
arXiv:2607.02291v1 Announce Type: new Abstract: Conventional reinforcement learning strategies for visual generation typically employ sample-wise reward functions, yet this practice frequently results
arXiv:2607.01736v1 Announce Type: cross Abstract: We study how to predict the downstream closed-loop performance of a learned latent world model from validation-time diagnostics alone. Choosing the ri
arXiv:2510.09517v2 Announce Type: replace Abstract: Despite rapid advances in large language models (LLMs), statistical reasoning remains underrepresented in existing LLM benchmarks, which often do no
arXiv:2607.02469v1 Announce Type: cross Abstract: Software tests and code evolve together: a code change should be followed by new or updated tests that record the new software behavior. Yet existing
arXiv:2607.02322v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown remarkable promise in generalized robotic manipulation. However, their spatial generalization remains fra
arXiv:2607.01498v1 Announce Type: new Abstract: We investigate the problem of learning useful policy representations (embeddings) in two-player zero-sum imperfect-information games. We make three cont
arXiv:2607.01773v1 Announce Type: new Abstract: Ontology construction requires deciding which objects, attributes, and structural relations should be accepted as valid knowledge. Language models can p
arXiv:2607.01586v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) have recently advanced robotic manipulation, yet the effects of different robot-data pre-training paradigms remai
We are pleased to present our latest research at #ICML2026, “Bridging Spherical Black-Box Optimizers” https://arxiv.org/abs/2606.25761 When optimizing through simulators, external APIs, or in reinforc
arXiv:2607.02507v1 Announce Type: new Abstract: LLM agents will increasingly act in socially structured settings where role, audience, and relational context can shape what is advantageous or costly t
arXiv:2607.00247v1 Announce Type: cross Abstract: Large audio-language models (LALMs) frequently hallucinate by overriding acoustic evidence with language priors. While contrastive decoding (CD) offer
arXiv:2607.00292v1 Announce Type: cross Abstract: Designing deployable and resilient network topologies from natural language requirements remains a challenging problem in network automation. This wor
arXiv:2607.01051v1 Announce Type: new Abstract: Different stages of manipulation tasks exhibit varying levels of difficulty, suggesting stage-dependent motion speeds and temporal prediction horizons.
arXiv:2607.00048v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in exam- and certification-style question answering tasks, where their ability to retrieve, interpr
arXiv:2607.00865v1 Announce Type: new Abstract: Bayesian Optimisation (BO) under unknown constraints is particularly challenging when feasible regions are small. In such settings, existing methods tha
arXiv:2511.05747v3 Announce Type: replace Abstract: Chain-of-Thought (CoT) reasoning enhances the problem-solving ability of large language models (LLMs) but leads to substantial inference overhead, l
arXiv:2607.00160v1 Announce Type: new Abstract: Modular reconfigurable robotic systems provide a scalable solution for cooperative surface operations in future lunar missions. However, cooperative car
arXiv:2607.01039v1 Announce Type: cross Abstract: Therapy-induced cardiotoxicity is the leading non-oncological cause of treatment interruption in breast cancer patients, yet early, automated risk str