3,698 items tracked: 33 reported by two or more outlets, the rest by a single source. Most-corroborated first.
arXiv:2610.08894v1 Announce Type: cross Abstract: This dissertation presents a scalable architecture for transforming unstructured, domain-specific text into structured knowledge for retrieval and reasoning.
arXiv:2610.08895v1 Announce Type: cross Abstract: Finding severity is the main driver of how security teams prioritize remediation.
arXiv:2610.08937v1 Announce Type: cross Abstract: AlphaFold 3 predicts protein structures with remarkable accuracy, yet how structural information emerges within the model remains poorly understood.
arXiv:2610.08969v2 Announce Type: replace-cross Abstract: Black-box optimization problems are ubiquitous across science and engineering, often dealing with expensive objective functions.
arXiv:2610.08989v1 Announce Type: cross Abstract: While offline reinforcement learning (RL) enables policy optimization from static datasets without costly online interaction, it remains bottlenecked by the risk of execu…
arXiv:2610.09003v1 Announce Type: cross Abstract: Autoregressive Large Language Models (LLMs) frequently struggle with deterministic multi-step algorithmic tasks such as multi-digit multiplication and long division.
arXiv:2610.09015v1 Announce Type: cross Abstract: Diffusion models can generate high-quality images, yet aligning their outputs with individual user preferences remains challenging.
arXiv:2610.09027v1 Announce Type: cross Abstract: Modern language model systems operate autonomously over increasingly long contexts containing untrusted text and images.
arXiv:2610.09043v1 Announce Type: cross Abstract: In human-AI collaborative decision making, human review can prevent unsafe AI decisions, but each human judgment is costly.
arXiv:2610.09108v1 Announce Type: cross Abstract: Policy learning drives many of the most consequential and heavily-invested applications of reinforcement learning today.
arXiv:2610.09124v1 Announce Type: cross Abstract: Induction heads provide a mechanistic account of in-context learning in sequential data, but existing theory largely assumes that the context relevant to a prediction for…
arXiv:2610.09166v1 Announce Type: cross Abstract: Standard diffusion samplers generate samples through repeated evaluations of a learned score function.
arXiv:2610.09179v1 Announce Type: cross Abstract: As data propagates through a Transformer, the norm of its hidden states grows by orders of magnitude with depth, a phenomenon framed as 'curse of depth' and nearly univer…
arXiv:2610.09212v1 Announce Type: cross Abstract: The best two-bit weight quantizers for large language models, such as QTIP and Proteus, rotate each weight matrix by a random orthogonal transform, which must be undone a…
arXiv:2610.09217v1 Announce Type: cross Abstract: Autism spectrum disorder (ASD) is diagnosed through specialist observation of a child's social behavior, and access to that expertise is the bottleneck for early identifi…
arXiv:2610.09221v1 Announce Type: cross Abstract: Flow and diffusion models suffer from slow inference due to computationally expensive numerical integration.
arXiv:2610.09229v1 Announce Type: cross Abstract: LLM-as-judge is now a standard tool for scalable evaluation, but judge performance is still often summarized by a single accuracy number.
arXiv:2610.09272v1 Announce Type: cross Abstract: Attention routing requires a signal that predicts the value of attention on the current prefix.
arXiv:2610.09297v1 Announce Type: cross Abstract: In recent years, Graph Neural Networks (GNNs) and architecture search frameworks have gained extensive application in non-Euclidean data processing, attributable to their…
arXiv:2610.09305v1 Announce Type: cross Abstract: Benchmarks for measuring the quality of action-conditioned world models are still evolving and shifting away from visual similarity-based metrics to action-semantic and p…
arXiv:2610.09311v1 Announce Type: cross Abstract: Diffusion language models (DLMs) generate text through iterative parallel refinement, offering the potential for higher throughput than autoregressive (AR) decoding.
arXiv:2610.09323v1 Announce Type: cross Abstract: Constellation Monte Carlo results depend on the orbital population used to evaluate a tasking policy.
arXiv:2610.09328v1 Announce Type: cross Abstract: Multi-subject image generation requires rewards that verify whether requested attributes, actions, and relations hold for the specified reference subjects.
arXiv:2610.09342v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) large language models decouple capacity from compute through sparse routing, but their large parameter count creates storage and serving challeng…
arXiv:2610.09361v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) is widely used to ground answers in documents.
arXiv:2610.09371v1 Announce Type: cross Abstract: Calibrated uncertainty quantification is essential to ensuring AI agents are trustworthy and reliable.
arXiv:2610.09400v1 Announce Type: cross Abstract: We present TutorLoop, a sensor-in-the-loop system that regulates student learning behaviors by delivering adaptive feedback based on real-time cognitive states.
arXiv:2610.09411v2 Announce Type: replace-cross Abstract: Multimodal representations enable zero-shot classification and retrieval, but aligning independently trained models usually requires large amounts of paired data.
arXiv:2610.09446v1 Announce Type: cross Abstract: Large language models (LLMs) should abstain from scientific multiple-choice questions when no option is valid, but frequent abstention alone does not demonstrate sensitiv…
arXiv:2610.09456v1 Announce Type: cross Abstract: Reconstructing fine-resolution column-averaged dry-air CO2 (XCO2) fields from sparse satellite observations requires models to infer spatial structure that is only weakly…
arXiv:2610.09487v1 Announce Type: cross Abstract: Schema matching increasingly uses generative language models to rerank retrieved column candidates, although the underlying task is a bounded correspondence decision.
arXiv:2610.09508v1 Announce Type: cross Abstract: Safe reinforcement learning seeks policies that maximize return while satisfying constraints on cumulative cost.
arXiv:2610.09520v1 Announce Type: cross Abstract: Large vision-language models (VLMs) provide powerful open-world perception and reasoning for autonomous driving, but their high computational cost and inference latency m…
arXiv:2610.09525v1 Announce Type: cross Abstract: We introduce a calibration-first framework that produces supervision scores without access to ground-truth labels or a shared annotation space.
arXiv:2610.09534v1 Announce Type: cross Abstract: Rotational symmetry is an important prior in 6D pose estimation, improving pose accuracy and supporting symmetry-aware evaluation.
arXiv:2610.09553v1 Announce Type: cross Abstract: Large language models can triage malware through direct verdicts or behavioral claims scored by an external policy.
arXiv:2610.09571v2 Announce Type: replace-cross Abstract: Negation is an essential feature of human language, yet large language models (LLMs) remain unreliable in processing it.
arXiv:2610.09587v1 Announce Type: cross Abstract: Reasoning capabilities are critical for advancing Large Language Models, yet current approaches either require massive computational budgets or struggle to effectively di…
arXiv:2610.09588v1 Announce Type: cross Abstract: Deploying robots as Complex Adaptive Systems (CAS) in unknown and dynamic environments necessitates a transition from rigid command libraries toward intention-based auton…
arXiv:2610.09593v1 Announce Type: cross Abstract: Generative AI speeds, and mostly improves, professional work, but there is concern that users who delegate both the production and the evaluation of an answer may accept…
arXiv:2610.09663v1 Announce Type: cross Abstract: In marine seismic acquisition, seismic interference (SI) occurs when energy from nearby external seismic source(s) is captured.
arXiv:2610.09679v1 Announce Type: cross Abstract: Adaptive rollout methods for group-relative reinforcement learning typically allocate a fixed per-update budget across prompts.
arXiv:2610.09692v1 Announce Type: cross Abstract: Pain perception varies substantially across individuals, making it difficult for population-based classifiers to generalise across all subjects in a dataset.
arXiv:2610.09700v1 Announce Type: cross Abstract: Synthetic hard negatives generated in the representation space have proven effective for unimodal self-supervised learning, but transferring this idea to vision-language…
arXiv:2610.09740v1 Announce Type: cross Abstract: Research in data visualization of artificial intelligence (AI) models has historically focused on enhancing trust through visual explanations of AI.
arXiv:2610.09761v1 Announce Type: cross Abstract: Text-to-point-cloud localization estimates a position in a city-scale 3D map from descriptions of surrounding objects.
arXiv:2610.09770v1 Announce Type: cross Abstract: Deep learning has made rapid advances in weather forecasting: autoregressive models trained on atmospheric reanalyses now rival dynamical models across nowcasting, medium…
arXiv:2610.09795v1 Announce Type: cross Abstract: The demand for personalized LLMs is shifting from style imitation toward content quality.
arXiv:2610.09835v1 Announce Type: cross Abstract: Continual pre-training and fine-tuning in Large Language Models (LLMs) inevitably induce catastrophic forgetting, typically mitigated by replay using often-inaccessible o…
arXiv:2610.09838v1 Announce Type: cross Abstract: We present a framework for building and interpreting minimal transformer models.