3,694 items tracked: 32 reported by two or more outlets, the rest by a single source. Most-corroborated first.
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:2610.09400v1 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:2610.09525v1 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.
arXiv:2610.09848v1 Announce Type: cross Abstract: Inverse design in engineering often runs into a simple problem.
arXiv:2610.09891v1 Announce Type: cross Abstract: Human-Robot Collaboration (HRC) can facilitate mass customisation in Industry 4.0, with Reinforcement Learning from Human Feedback (RLHF) representing a promising approac…
arXiv:2610.09894v1 Announce Type: cross Abstract: In-database predictive query processing increasingly applies Transformer-based models within relational pipelines.
arXiv:2610.09906v1 Announce Type: cross Abstract: Security Operations Centers (SOCs) for information technology and operational technology share one incident-response problem: a flood of correlated alerts and too few ana…
arXiv:2610.09914v1 Announce Type: cross Abstract: Reinforcement learning is crucial for improving large language models' reasoning and generalization.
arXiv:2610.09927v1 Announce Type: cross Abstract: Knowledge graph embedding (KGE) models encode the entities and relations of a knowledge graph into a low-dimensional latent space, enabling tasks such as classification o…
arXiv:2610.09934v1 Announce Type: cross Abstract: We describe the Itgan systems for the three ASR subtasks of NADI 2026, namely robust country-level ASR (1.1), mixed-dialect ASR (1.2), and Tunisian code-switched ASR (1.3…
arXiv:2610.09938v1 Announce Type: cross Abstract: A holographic superconductor whose scalar mass depends on the gauge field strength, $M(\Fsq)$, reproduces a superconducting dome for a suitable $M$, and recovering that $…
arXiv:2610.10000v1 Announce Type: cross Abstract: Adversarial attacks on bandits aim to mislead a learner toward a target arm while keeping the attack cost small.
arXiv:2610.10061v1 Announce Type: cross Abstract: This paper presents TRACK (Telemetry-Based Racing Analysis and Coaching Kit), which is a framework for analyzing driving performance in sim racing and profiling how indiv…
arXiv:2610.10064v1 Announce Type: cross Abstract: AI is already writing a majority of code for frontier AI labs.
arXiv:2610.10066v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in both visual understanding and code generation.
arXiv:2610.10085v1 Announce Type: cross Abstract: Post-training tensor-network compression replaces Transformer linear layers with Tensor Train (TT) or Tree Tensor Network (TTN) operators, but standard decompositions min…
arXiv:2610.10091v1 Announce Type: cross Abstract: Knowledge-intensive tasks require answering many questions by reasoning about a shared corpus of artifacts (e.g., court cases, or scientific literature).
arXiv:2610.10170v1 Announce Type: cross Abstract: Retrieval-augmented generation systems increasingly rely on document-structure treatments: structure-aligned chunking, LLM-generated chunk contexts, heading-path metadata…
arXiv:2610.10182v1 Announce Type: cross Abstract: AI-generated faces can be difficult to distinguish from real ones, leaving viewers to rely on source labels when judging an image.
arXiv:2610.10183v1 Announce Type: cross Abstract: Long video understanding increasingly relies on external memory to organize massive visual streams into compact representations.
arXiv:2610.10227v1 Announce Type: cross Abstract: While Large Language Models (LLMs) possess rich world knowledge and impressive generalization capabilities, their direct application to tabular data classification is hin…