Researchers have developed a method to predict how much accuracy improves when using best-of-N inference scaling in language models, without running the full procedure. By analyzing statistics from a single labeled valid…
Researchers have developed a locally deployed RAG-based academic advising system that uses large language models and structured syllabus data to help students select courses and understand prerequisites. The system addre…
Researchers have developed a Human-in-the-Loop Gated Bandit (HITL-GB) framework for dynamic pricing in short-term rental markets, where a contextual bandit algorithm generates price recommendations but a human agent must…
A new study proves the Spectral Alignment Decomposition, which explains why the curvature exponent $\alpha$ — governing how Hessian eigenvalues scale with gradient singular values — varies across neural network layers, w…
Researchers have developed a lightweight custom convolutional neural network to improve the security of EEG-based brain-computer interfaces against adversarial attacks. The proposed model outperformed existing EEG-specif…
Researchers have developed TopoMamSurv, a Graph Mamba survival analysis framework that uses topology-aware ordering to address computational bottlenecks in Whole Slide Image analysis. The framework integrates a bidirecti…
Researchers have developed ReLoRA, a knowledge-reusing re-adaptation framework that enables fast recovery of task-specific Low-Rank Adaptation (LoRA) adapters after large language model (LLM) base-model updates. The meth…
Researchers introduced Geometry-Aware Tabular Diffusion (GATD), a method that improves tabular data synthesis by feeding pairwise angles and lengths from column value differences into diffusion denoisers as inputs and au…
Researchers have developed a Marchenko-Pastur (MP) random-matrix approach for pruning deep neural networks that requires minimal post-pruning fine-tuning. The method provides deterministic data-path certificates for accu…
Researchers found that a linear probe applied to mid-layer hidden states of quantized large language models can detect hallucinations with up to 1.000 AUROC, significantly outperforming sampling-based methods that max ou…
Researchers have developed CL-DMDF, a dynamic multimodal data fusion model that uses contrastive learning to address challenges with missing or uncertain modality inputs in real-world applications. The model introduces a…
Researchers have developed an adaptive on-the-fly multifidelity machine learning algorithm that autonomously determines training dataset composition for quantum chemistry calculations. The algorithm reduces data generati…
AdaWeather, a new adaptive framework for combining probabilistic weather forecasts, achieves logarithmic regret compared to the best static mixture of experts, according to a study published on arXiv. The method, which i…
Researchers have identified a critical flaw in using standard survival prediction models for organ allocation, showing that even highly accurate predictors optimized for metrics like the C-index can perform no better tha…
Researchers found that visual graph scaffolds significantly improve large language models' multi-hop reasoning compared to flattened text representations of graph structures. In experiments on question-answering tasks, g…
Researchers have developed AURA-Mem, a memory system for robot policies that maintains a constant 4,224-byte inference state regardless of episode length, compared to a KV-cache that grows 6,061 times larger over 100,000…
A study evaluating encoder-only Transformer and LSTM frameworks for streamflow prediction in ungauged basins found that the LSTM outperformed the Transformer across both upstream-only and combined configurations. Incorpo…
Researchers have developed ChatHealthAI, a multimodal reasoning framework that aligns structured electronic health record (EHR) representations from a pretrained foundation model with the semantic space of a frozen large…
Researchers have developed Traj-Evolve, a self-evolving multi-agent system that models patient trajectories from electronic health records by using an experience pool and multi-agent reinforcement learning to mimic how c…
A new study on Large Reasoning Models (LRMs) has found that continuing to reason after reaching a correct answer can cause the model to deviate from that answer, a phenomenon termed "harmful overthinking." Researchers in…