Knowledge catalogue
arXiv:2608.30025v1 Announce Type: new Abstract: Large language models (LLMs) frequently generate source code containing vulnerabilities, yet little work studies the internal mechanisms that distinguis
Claude Fable 5.1 is now available on Amazon Bedrock and Claude Platform on AWS. This post covers Claude Fable 5.1's improvements, the Enterprise Frontier Safeguards for keeping your data in a cloud en
Historically, enterprise predictive analytics tasks such as predicting churn, purchase intent, or fraud scoring have meant building custom models using libraries like XGBoost, Random Forest, or Deep N
arXiv:2602.06181v2 Announce Type: replace Abstract: Post-training quantization reduces the memory needed to run large language models but alters their social biases in ways that aggregate metrics fail
arXiv:2608.28705v1 Announce Type: cross Abstract: Dose accumulation is increasingly important in adaptive radiation therapy and reirradiation, but its clinical validity depends on the performance of d
arXiv:2608.29455v1 Announce Type: new Abstract: LLMs are increasingly used as human surrogates, often on the premise that richer persona data could make them substitutes or exploratory tools for speci
arXiv:2603.22978v2 Announce Type: replace Abstract: In the maintenance of complex systems, fault trees are used to locate problems and provide targeted solutions. To enable fault trees stored as image
arXiv:2608.29892v1 Announce Type: new Abstract: Learning solution operators for partial differential equations (PDEs) on irregular and geometry-dependent domains remains a central challenge in scienti
Kaitchup just posted results of his benchmarks for Qwen3.8 27B for quants from different labs, Q4 to Q1, . All the details are hidden behind the paywall, but high level result is visible and looks lik
arXiv:2608.30376v1 Announce Type: new Abstract: Recurrent, attention-free sequence models share a structural weakness: a fading state cannot perform exact recall of something seen once, far in the pas
arXiv:2601.07794v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly evaluated on their ability to perform multi-hop reasoning, i.e., to combine multiple pieces of i
arXiv:2608.29472v1 Announce Type: cross Abstract: Knowledge distillation trains a small student model to reproduce the outputs of a large teacher model, and its progress is typically monitored through
arXiv:2608.30226v1 Announce Type: new Abstract: Modular compression has enabled considerable parameter reduction in LLMs while preserving strong language understanding and downstream task accuracy. Ho
arXiv:2608.31009v1 Announce Type: new Abstract: Structure-based drug design (SBDD) requires ligands that satisfy both 3D target affinity and 1D chemical validity. Existing controllable generation meth
arXiv:2608.29257v1 Announce Type: new Abstract: Multiple-choice questions (MCQs) are a standard format for evaluating large language models (LLMs), yet the popularity of answer options can confound ev
arXiv:2505.16305v3 Announce Type: replace Abstract: While CANDECOMP/PARAFAC (CP) decomposition (CPD) is fundamental for tensor reconstruction, Bayesian CPD often scales poorly because variational upda
arXiv:2512.07019v4 Announce Type: replace-cross Abstract: The proliferation of Large Language Models (LLMs) necessitates valid evaluation methods to provide guidance for both downstream applications a
arXiv:2608.30303v1 Announce Type: new Abstract: Search agents reduce hallucination by grounding answers in retrieved web evidence. Yet reliance on retrieval also creates an attack surface: poisoned co
arXiv:2608.30955v1 Announce Type: new Abstract: Accurate action models are critical for effective planning. Existing action-model learning methods largely assume simple action representations or becom
arXiv:2608.30584v1 Announce Type: new Abstract: Despite the impressive progress of recent MLLMs on spatio-temporal video grounding (STVG), existing evaluations and training data focus primarily on sim
arXiv:2608.30682v1 Announce Type: cross Abstract: Learning materials properties from scarce labels and unlabeled crystals is a central challenge for data-driven materials discovery. We present SemiMat
arXiv:2608.30328v1 Announce Type: new Abstract: Classical numerical solvers for partial differential equations (PDEs) are computationally expensive to solve repeatedly across varying initial condition
arXiv:2608.31028v1 Announce Type: cross Abstract: Scientific discovery often requires reasoning over competing hypotheses that are consistent with experimental observations. For mixed-variable and com
arXiv:2608.31076v1 Announce Type: cross Abstract: Autonomous scientific research agents are increasingly applied to end-to-end scientific workflows, including literature review, data analysis, experim
arXiv:2608.29030v1 Announce Type: new Abstract: In-context watermarking (ICW) prepends an instruction to a query asking the model to embed a statistically detectable signal in its response. It thus eq
arXiv:2510.26824v2 Announce Type: replace-cross Abstract: Wide access to advanced experimental methods in materials science has given rise to an abundance of procedural knowledge, which is scattered a
arXiv:2608.29054v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have emerged as a cornerstone for representing complex relational dependencies in diverse multimedia tasks, particularly in
arXiv:2606.09389v2 Announce Type: replace Abstract: As large language models (LLMs) are increasingly applied to real-world legal tasks, evaluating the reliability of their open-ended legal responses h
arXiv:2603.28301v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models achieve strong performance in robotic manipulation by leveraging pre-trained vision-language backbones. However,
arXiv:2608.31073v1 Announce Type: new Abstract: Whole-heart segmentation (WHS) in computed tomography (CT) and magnetic resonance imaging (MRI) is affected by acquisition shifts and heterogeneous card
arXiv:2608.29357v1 Announce Type: new Abstract: Multimodal search agents answer visual questions by interleaving image understanding, web retrieval, tool use, and evidence synthesis. Strong systems ex
arXiv:2608.29517v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as essay graders in learning analytics, evaluated almost exclusively with agreement statistics. Educa
arXiv:2608.31016v1 Announce Type: cross Abstract: Ambient AI scribes draft clinical notes, and published audits find their dominant error is omission: information the encounter established that the no
arXiv:2608.31102v1 Announce Type: cross Abstract: Industrial post-training is a brownfield regime. Teams inherit a deployed checkpoint and must land targeted improvements under fixed compute and mixtu
arXiv:2608.29228v1 Announce Type: new Abstract: Failures in agentic AI systems can arise from interactions among messages exchanged by multiple large language model (LLM) agents. Pointwise attribution
arXiv:2608.30959v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) still struggle on tasks requiring complex visual understanding. We argue that the core issue is not high-level reasoning
arXiv:2601.07533v3 Announce Type: replace-cross Abstract: Tracing connections between historical texts is an important part of intertextual research, enabling scholars to reconstruct the virtual libra
arXiv:2608.29188v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) substantially improves single-sample accuracy (pass@1) but causes the policy's solution space to
arXiv:2608.29539v1 Announce Type: new Abstract: As context lengths scale, attention increasingly becomes a primary computational bottleneck in large language models. Standard Transformers remain power
arXiv:2608.28623v1 Announce Type: cross Abstract: Large multimodal reasoning models (LMRMs) are getting increasingly capable, primarily through generating explicit chain-of-thought reasoning before an
arXiv:2608.30510v1 Announce Type: cross Abstract: For provenance research and art market studies, auction catalogs are an essential resource to trace specific objects over time and space. While histor
arXiv:2608.28627v1 Announce Type: new Abstract: Designing high-performance tactical wireless networks under realistic operational constraints gives rise to challenging combinatorial optimization probl
arXiv:2606.17511v2 Announce Type: replace-cross Abstract: Robot learning and embodied agents now require simulation to serve as a shared execution substrate linking control, skills, and planning, not
arXiv:2606.05371v2 Announce Type: replace Abstract: Reduced-order modeling of high-dimensional dynamical systems is often hindered by closure effects arising from unresolved variables, which can intro
arXiv:2603.23295v2 Announce Type: replace Abstract: Radiotherapy workflows for oncological patients increasingly rely on multi-modal medical imaging, commonly involving both Magnetic Resonance Imaging
arXiv:2608.30114v1 Announce Type: new Abstract: Brazilian Portuguese remains under-served by open language models, and the few that exist are difficult to reproduce and are often compared without meas
arXiv:2608.29852v1 Announce Type: new Abstract: Automated maritime surveillance from satellite and aerial imagery requires large, precisely annotated datasets, which remain scarce for the instance-seg
arXiv:2608.29528v1 Announce Type: new Abstract: Longitudinal clinical agents must maintain an evolving patient state from evidence distributed across visits, time points, and specialties. However, how
arXiv:2608.29677v1 Announce Type: cross Abstract: Despite rapid advances in MIS, fair and reproducible comparisons of segmentation models remain challenging due to heterogeneous datasets, inconsistent
arXiv:2608.29617v1 Announce Type: cross Abstract: This paper introduces a hybrid fact-checking framework that integrates Knowledge Graph-based semantic memory with adversarial multi-agent reasoning fo
arXiv:2608.28884v1 Announce Type: new Abstract: We introduce MineCEraft (Minecraft Construction Engineering Benchmark, pronounced mine-see-ee-raft), an easy-to-use, open-source benchmark designed to s
MLCommons announced the release of MLPerf Storage v3.0 benchmark results, expanding the suite to cover the full spectrum of AI storage workloads, including an S3 object‑storage access layer in additio
arXiv:2508.13186v2 Announce Type: replace-cross Abstract: AI agents with advanced reasoning and tool-use capabilities have demonstrated impressive performance in web browsing for deep search. However,
arXiv:2608.30903v1 Announce Type: new Abstract: Dynamic stance classification models how a reply responds to its direct parent message, rather than how a post relates to a fixed topic. Existing work h
arXiv:2606.14747v2 Announce Type: replace-cross Abstract: Recent advancements have significantly expanded the theoretical context windows of Multimodal Embedding Models (MEMs). However, larger context
arXiv:2608.31022v1 Announce Type: new Abstract: AI agents in partially observable environments need to coordinate active sensing with working memory to maintain an evolving perceptual state. However,
arXiv:2603.19657v2 Announce Type: replace-cross Abstract: We study model-order selection and component-mean estimation for multidimensional Gaussian mixture models with a known common covariance matri
arXiv:2404.12613v4 Announce Type: replace-cross Abstract: In this paper, we study the problem of learning one-dimensional Gaussian mixture models (GMMs) with a specific focus on estimating both the mo
arXiv:2608.30463v1 Announce Type: new Abstract: Solvability detection is one of the most challenging aspects of mathematical reasoning for Large Language Models (LLMs). While prior work has studied th
arXiv:2507.23129v2 Announce Type: replace-cross Abstract: We preseent an open-source image reconstruction package built upon PyTorch, enabling modern deep-learning reconstructions. It uses open data f