Knowledge catalogue
arXiv:2608.22029v1 Announce Type: new Abstract: Single-particle cryo-electron microscopy (cryo-EM) pose estimation is traditionally solved anew for each dataset, where iterative refinement is done fro
arXiv:2608.21747v1 Announce Type: cross Abstract: LLM-based coding agents generate complete software systems from high-level descriptions, yet little is known about how the format of architecture spec
arXiv:2608.23030v1 Announce Type: new Abstract: We study how an AI system can identify and model other agents in its environment from observation alone, which is a capability necessary for cooperative
arXiv:2608.15933v1 Announce Type: cross Abstract: Implicit surface representations have regained popularity because of their use in machine learning. A common component in optimization is regularizati
arXiv:2608.21721v1 Announce Type: new Abstract: Correcting health misinformation in dialogue requires more than producing a factual rebuttal: users differ in what they know, what they believe, and wha
arXiv:2608.21712v1 Announce Type: new Abstract: Transformer-based models are widely used for clinical prediction from electronic health records (EHRs), yet their architectures still require substantia
arXiv:2603.29902v2 Announce Type: replace Abstract: Interleaved text-and-image generation represents a significant frontier for Multimodal Large Language Models (MLLMs), offering a more intuitive way
arXiv:2505.21430v3 Announce Type: replace Abstract: Attribute-efficient PAC learning of sparse halfspaces has been a fundamental problem in machine learning theory. In recent years, machine learning a
arXiv:2608.22186v1 Announce Type: cross Abstract: We present AudioNoisePrints, a training-free watermarking pipeline for flow matching and diffusion TTS models, which requires minimal extra computatio
arXiv:2608.22160v1 Announce Type: new Abstract: Physical automation is scaling toward fleets of embodied machines commanded by an AI brain. Early deployments already run factories and warehouses at pr
arXiv:2608.23263v1 Announce Type: new Abstract: The FAIR Digital Object (FDO) framework mandates that metadata attribute values be expressed as persistent identifiers (PIDs) wherever possible, to prod
arXiv:2608.21558v1 Announce Type: cross Abstract: Recent advances in LLMs and the adoption of RAG systems in industry have created a need for domain-specific question-answer datasets that can assess R
arXiv:2608.22075v1 Announce Type: cross Abstract: Adversaries now move faster than manual response processes can absorb. The average eCrime breakout time, that is, the interval between initial access
arXiv:2608.23041v1 Announce Type: new Abstract: LLM agents remain unreliable on long-horizon tasks, where small local failures can compound over extended interactions and lead to overall task failure.
arXiv:2602.01765v2 Announce Type: replace-cross Abstract: Diffusion models have been widely deployed in AIGC services, but their reliance on opaque training data exposes them to backdoor attacks. In p
arXiv:2505.23197v4 Announce Type: replace-cross Abstract: Path planning for autonomous robots faces a fundamental trade-off between path length and obstacle clearance. While existing algorithms typica
arXiv:2608.21880v1 Announce Type: new Abstract: Bangla large language model (LLM) safety is difficult to evaluate with English-centric or standard-script benchmarks because Bangla users routinely writ
arXiv:2608.22024v1 Announce Type: cross Abstract: Estimating heterogeneous single and interaction treatment effects from observational data under multiple simultaneous treatments is crucial for decisi
arXiv:2608.21847v1 Announce Type: new Abstract: Low-light image enhancement (LLIE) must correct ambiguous exposure without overwriting structure already supported by the input. Generative transport ca
arXiv:2608.23213v1 Announce Type: new Abstract: This work presents an automatic bee entrance monitoring system based on YOLO11 transfer learning and the ByteTrack tracking algorithm. The study investi
arXiv:2608.22187v1 Announce Type: cross Abstract: Modern driving action models are increasingly improved in a self-improvement loop, where a learned world simulator imagines future observations and th
arXiv:2503.18484v3 Announce Type: replace-cross Abstract: Evaluating the multilingual capabilities of Large Vision-Language Models (LVLMs) remains challenging because most benchmarks rely on non-paral
arXiv:2608.21693v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) LLMs scale model capacity efficiently through sparse activation, but their large expert parameter footprint, routing imbalance,
arXiv:2508.17527v2 Announce Type: replace Abstract: Accurately predicting travel mode choice is essential for effective transportation planning, yet traditional statistical and machine learning models
arXiv:2607.10358v2 Announce Type: replace-cross Abstract: Foundation models are increasingly used as image feature extractors for mammography, but their robustness under external domain shift remains
arXiv:2608.23215v1 Announce Type: cross Abstract: Automated perception in side-scan sonar (SSS) imagery is severely hindered by physical acoustic artifacts, resulting in representations that inextrica
arXiv:2608.23173v1 Announce Type: new Abstract: Computer vision applications for 3D scene understanding in underwater environments remain challenging due to the lack of high-quality 3D data and the in
arXiv:2608.21503v1 Announce Type: cross Abstract: A verification substrate is more credible when exposing errors in its own claims, not just model outputs. GBI-DCSE v3 falsified an architectural claim
arXiv:2608.22872v1 Announce Type: new Abstract: Speech-based applications pass spoken queries through automatic speech recognition (ASR) before any retrieval module, so ASR errors enter the pipeline a
arXiv:2608.21572v1 Announce Type: new Abstract: Consider a typical test of a robot system: one observes a sequence of outcomes concerning some aspect of interest (crash or no crash, tracking error, ti
arXiv:2509.05346v3 Announce Type: replace Abstract: While large language models (LLMs) are increasingly being adopted to support personalized learning, there remains limited understanding of how their
arXiv:2508.18646v3 Announce Type: replace Abstract: Despite their rapid advancement, large language models (LLMs) suffer from a critical disconnect between benchmark scores and real-world utility. Cur
arXiv:2608.23348v1 Announce Type: cross Abstract: Phytoplankton play a central role in marine ecosystems and the global carbon cycle, with different groups contributing differently to ocean biogeochem
arXiv:2608.22322v1 Announce Type: new Abstract: Low-precision optimizer-state methods are commonly designed for dense Adam-style moments, but memory-efficient optimizers maintain factored, confidence-
arXiv:2608.22753v1 Announce Type: new Abstract: Large language models (LLMs) excel at text understanding and generation, yet still struggle to reliably understand and apply externally provided procedu
arXiv:2608.21919v1 Announce Type: new Abstract: Recent work has proposed that reasoning and memorization in language models can be characterized by a single representation direction, including methods
arXiv:2608.22101v1 Announce Type: new Abstract: Modern voice-agent systems rely on streaming speech recognition models that operate under stringent latency constraints. This study shows that, due to t
arXiv:2608.22294v1 Announce Type: new Abstract: World models for physical interaction are typically trained to predict future observations or latent features; however, a planning-oriented model must a
arXiv:2608.22920v1 Announce Type: new Abstract: Multi-behavior recommendation (MBR) leverages auxiliary behavioral signals, such as clicks and add-to-cart, to enhance target behavior prediction like p
arXiv:2608.23322v1 Announce Type: new Abstract: Despite their critical importance for policy and research, high-resolution poverty data remain limited across much of Africa. Machine learning (ML) with
arXiv:2608.21979v1 Announce Type: new Abstract: Charitable food agencies play an important role in alleviating food insecurity by distributing donated food to people in need. However, they rely on ad
arXiv:2608.21541v1 Announce Type: cross Abstract: KV-cache compression is often justified by attention maps with a few large weights. This is incomplete: large weights may not contain most of the mass
arXiv:2608.21830v1 Announce Type: new Abstract: Graphical User Interface (GUI) agents powered by Multimodal Large Language Models (MLLMs) have shown strong potential for automating tasks across divers
arXiv:2608.23026v1 Announce Type: cross Abstract: Multilingual LLM outputs can vary across sociocultural contexts. However, evidence of cultural grounding can be misleading: identity labels may be inf
arXiv:2608.22830v1 Announce Type: new Abstract: Deploying LLMs for enterprise Text-to-SQL is bottlenecked less by the model than by what context reaches it: business logic spans thousands of tables, a
arXiv:2608.23311v1 Announce Type: new Abstract: Policy optimization (PO) for Large Language Models faces a stability--exploration trade-off, currently mediated by an action-side Policy-KL regularizer.
arXiv:2608.21384v1 Announce Type: cross Abstract: Modern multilingual tokenizers often fragment Ukrainian and other underrepresented Cyrillic-script languages more heavily than English, creating dispa
arXiv:2608.23047v1 Announce Type: cross Abstract: Misinformation that cites legitimate papers can be especially harmful when it distorts what those studies actually report. While existing automatic fa
arXiv:2603.26757v2 Announce Type: replace Abstract: Does multi-view demonstration truly improve robot manipulation, or merely enhance cross-view robustness? We present a systematic study quantifying t
arXiv:2608.21450v1 Announce Type: new Abstract: Knowledge-Based Visual Question Answering (KB-VQA) relies on retrieving external information to answer queries involving long-tail entities. However, ex
arXiv:2608.22232v1 Announce Type: new Abstract: Real-world situation appearances can deviate from their underlying physical states, challenging the reliability of multimodal large language models (MLL
arXiv:2608.21927v1 Announce Type: cross Abstract: Zero-cost proxies enable neural architecture search (NAS) to rank candidate networks from statistics computed at initialization, avoiding repeated tra
arXiv:2508.05206v2 Announce Type: replace Abstract: Online advertising systems typically use a cascaded architecture to manage massive requests and candidate volumes, where the ranking stages allocate
arXiv:2608.21447v1 Announce Type: new Abstract: Structured-language models such as SceneScript reconstruct a scene as a short program of parametric commands, an inherently editable and semantically ex
arXiv:2608.21864v1 Announce Type: cross Abstract: The current progress of Clinical Vision Large Language Models (C-VLLMs) has substantially improved digital diagnostics, still these frameworks often e
arXiv:2608.22557v1 Announce Type: cross Abstract: Existing LLM unlearning methods struggle with robustness: unbounded forget losses degrade model coherence, fixed-weight balancing cannot adapt as reta
arXiv:2608.22129v1 Announce Type: new Abstract: Cubic-regularized Newton methods have the optimal O(arepsilon^{-3/2}) global rate and an automatic saddle-escape mechanism, but their subproblem is most
arXiv:2608.21431v1 Announce Type: new Abstract: Knowledge-based Visual Question Answering aims to answer questions about an image by integrating external knowledge with visual and textual information.
arXiv:2608.21653v1 Announce Type: cross Abstract: Multi-task learning often combines losses that span several orders of magnitude, causing homoscedastic uncertainty weighting to degrade severely. We p
arXiv:2608.21895v1 Announce Type: cross Abstract: Locally deployed Large Language Models (LLMs) via inference engines such as Ollama run without the moderation and abuse detection present in API-serve