3,698 items tracked: 33 reported by two or more outlets, the rest by a single source. Most-corroborated first.
arXiv:2605.05866v2 Announce Type: replace Abstract: Multiphase powder X-ray diffraction (PXRD) analysis remains a fundamental bottleneck in structure identification, as real-world synthesis often produces complex mixture…
arXiv:2605.06183v2 Announce Type: replace Abstract: Low-rank adaptation (LoRA) is a widely used parameter-efficient fine-tuning method that places trainable low-rank adapters into frozen pre-trained models.
arXiv:2605.10791v2 Announce Type: replace Abstract: Knowledge Graph Question Answering (KGQA) aims to answer user questions by reasoning over Knowledge Graphs (KGs).
arXiv:2605.12922v2 Announce Type: replace Abstract: Large language models can follow complex instructions in a single turn, yet over long multi-turn interactions they often lose the thread of instructions, persona, and r…
arXiv:2605.13335v2 Announce Type: replace Abstract: Embodied agents in household environments must plan under partial observation: they need to remember objects, track state changes, and recover when actions fail.
arXiv:2605.24154v2 Announce Type: replace Abstract: Current safety alignment of foundation models largely follows a \emph{one-size-fits-all} paradigm, applying the same refusal policy across users and contexts.
arXiv:2605.29695v2 Announce Type: replace Abstract: Approximately 10% of newborns require assistance to initiate breathing at birth, and around 5% need ventilation support.
arXiv:2607.20791v2 Announce Type: replace Abstract: Recent advances in truncation-based sampling have helped mitigate drawbacks of high-temperature sampling such as neural text degeneration, thereby enabling greater dive…
arXiv:2608.03611v3 Announce Type: replace Abstract: Multimodal Sentiment Analysis (MSA) integrates text, audio, and vision to infer human affect, yet real-world multimodal observations are often incomplete.
arXiv:2608.06704v2 Announce Type: replace Abstract: Delegating a web task involves more than asking a question; it requires transferring a policy: what to verify, how to handle uncertainty, which preferences matter, and…
arXiv:2608.07436v2 Announce Type: replace Abstract: Muon-trained modular-arithmetic transformers can lose accuracy while retaining linearly decodable task information.
arXiv:2311.18029v2 Announce Type: replace-cross Abstract: The current trend in the literature on Time Series Classification is to develop increasingly accurate algorithms by combining multiple models in ensemble hybrids,…
arXiv:2312.11018v4 Announce Type: replace-cross Abstract: Bundle recommendation ranks sets of related items rather than isolated items.
arXiv:2402.02399v3 Announce Type: replace-cross Abstract: Time series modeling presents unique challenges due to autocorrelation in both historical data and future sequences.
arXiv:2410.21582v4 Announce Type: replace-cross Abstract: Large-scale pretrained models are widely leveraged as foundations for learning new specialized tasks via fine-tuning, with the goal of maintaining the general per…
arXiv:2505.14777v2 Announce Type: replace-cross Abstract: The design of effective optimization algorithms for neural networks remains a fundamental challenge, and most existing methods rely on heuristic extensions of gra…
arXiv:2505.17847v3 Announce Type: replace-cross Abstract: Training time-series forecasting models poses unique challenges in loss function design.
arXiv:2508.05880v3 Announce Type: replace-cross Abstract: Understanding human emotions is central to user-facing AI applications, safety alignment, and the simulation of human behavior.
arXiv:2508.16943v4 Announce Type: replace-cross Abstract: Physics-based human motion control can make a simulated character walk, sit, and manipulate objects with high physical realism.
arXiv:2508.19819v3 Announce Type: replace-cross Abstract: Gradient inversion attacks are often presented as a serious privacy threat in federated learning, with recent work reporting increasingly strong reconstructions u…
arXiv:2509.03647v3 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly serve as automated evaluators, yet they suffer from "self-preference bias": a tendency to favor their own outputs over t…
arXiv:2510.11593v3 Announce Type: replace-cross Abstract: For reliable large-scale quantum computation, quantum error correction (QEC) is essential to protect logical information distributed across multiple physical qubi…
arXiv:2510.14878v3 Announce Type: replace-cross Abstract: We study kernel regression with common rotation-invariant kernels on real datasets including CIFAR-5m, SVHN, and ImageNet.
arXiv:2510.17826v2 Announce Type: replace-cross Abstract: Building a working mental model of a protein typically requires weeks of reading, cross-referencing crystal and predicted structures, and inspecting ligand comple…
arXiv:2510.24574v3 Announce Type: replace-cross Abstract: Training time-series forecasting models requires aligning the conditional distribution of model forecasts with that of the label sequence.
arXiv:2511.21075v4 Announce Type: replace-cross Abstract: Engineering LLMs to accelerate life sciences research requires a robust alignment with biomedical knowledge.
arXiv:2601.19595v2 Announce Type: replace-cross Abstract: The deployment of Artificial Intelligence in high-risk domains, such as finance and healthcare, necessitates models that are both fair and transparent.
arXiv:2601.20571v3 Announce Type: replace-cross Abstract: Asynchronous primal-dual methods for decentralized non-smooth convex optimization often require each node to maintain $\mathcal{O}(d)$ auxiliary variables, where…
arXiv:2602.02624v2 Announce Type: replace-cross Abstract: Several data protection laws restrict processing that reveals political opinions, irrespective of the controller's intent.
arXiv:2603.01295v2 Announce Type: replace-cross Abstract: Joint lesion segmentation and tissue classification in breast ultrasound are usually trained with a shared encoder, so the two branches stop exchanging informatio…
arXiv:2603.02655v2 Announce Type: replace-cross Abstract: Real-time video commentary generation provides textual descriptions of ongoing events in videos.
arXiv:2603.10330v3 Announce Type: replace-cross Abstract: Autonomous driving in complex traffic requires planners that generalize beyond hand-crafted rules, motivating data-driven approaches that learn behavior from expe…
arXiv:2603.21276v2 Announce Type: replace-cross Abstract: The growing demand for on-device large language model (LLM) services on mobile edge devices has driven the adoption of Mixture-of-Experts (MoE) architectures, whi…
arXiv:2604.03614v4 Announce Type: replace-cross Abstract: We study a passive form of global optimisation: from twenty noisy samples of an unknown one-dimensional function, predict where its global minimum lies, with no f…
arXiv:2604.07925v2 Announce Type: replace-cross Abstract: The self-attention mechanism is central to the success of Transformer architectures.
arXiv:2605.16578v4 Announce Type: replace-cross Abstract: Artificially generated speech is increasingly embedded in everyday life.
arXiv:2605.18618v3 Announce Type: replace-cross Abstract: Constrained Machine Learning (CML) enables fairness-aware training, physics-informed neural networks, and integration of symbolic domain knowledge into statistica…
arXiv:2605.20740v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have emerged as flexible regressors capable of predicting real-valued quantities from heterogeneous inputs.
arXiv:2605.25022v2 Announce Type: replace-cross Abstract: Dataset distillation (DD) aims to compress large-scale datasets into compact synthetic sets while preserving training efficacy.
arXiv:2605.30557v2 Announce Type: replace-cross Abstract: Spatial reasoning benchmarks typically evaluate whether vision-language models can derive the correct answer from a visual observation.
arXiv:2605.31289v3 Announce Type: replace-cross Abstract: Representation learning is a powerful tool for spatio-temporal abstraction within reinforcement learning (RL).
arXiv:2606.00880v2 Announce Type: replace-cross Abstract: Continual reinforcement learning (RL) aims to produce agents that never stop adapting to new tasks.
arXiv:2606.03927v2 Announce Type: replace-cross Abstract: The Forward-Forward (FF) algorithm offers a computationally efficient and biologically plausible alternative to backpropagation (BP) by training neural networks t…
arXiv:2606.06840v2 Announce Type: replace-cross Abstract: Reasoning-trained language models can perform, zero-shot, multi-label tasks that require selecting a small set of relevant labels from a universe of thousands to…
arXiv:2606.16193v3 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated strong performance on vision-language tasks, yet their internal visual representations remain difficult…
arXiv:2606.19317v3 Announce Type: replace-cross Abstract: A longstanding goal of research on interpretable deep learning is to replace opaque neural computations with human-meaningful symbolic descriptions.
arXiv:2606.23044v3 Announce Type: replace-cross Abstract: Numbers have algebraic structure that standard neural embeddings often fail to expose.
arXiv:2607.02092v4 Announce Type: replace-cross Abstract: Reinforcement learning can improve vision-language-action (VLA) policies beyond supervised fine-tuning, although this typically involves further updates to the po…
arXiv:2607.23634v2 Announce Type: replace-cross Abstract: Attention enables context modeling via query-key scoring with softmax normalization.
arXiv:2607.25531v2 Announce Type: replace-cross Abstract: Contemporary machine learning struggles to learn continually, reuse prior knowledge, and expose a comprehensible internal structure.