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Newest papers, page 187
What Shapes Brain Alignment of Large Language Models during Dialogue? Insights from Language Production and Comprehension
Brain alignment of large language models (LLMs) offers a way to investigate representational similarities between LLMs and the human brain during language processing.Towards Hyperparameter Transfer for Differentially Private Optimization
Scaling laws and hyperparameter tuning remain central challenges in training large-scale models, particularly under differential privacy (DP), where optimization is further complicated by noise…How Murky Before a VLM Knows It Cannot See? Accuracy, Confidence and Abstention on a Physical Visibility Axis
Robustness studies of vision-language models (VLMs) grade degradation on nominal severity scales ("level 1-5") that carry no physical units, and they rarely ask whether a model's confidence follows…PersonaCUABench: Benchmarking Computer-Use Agents Serving Diverse Personas
Computer-use agents (CUAs) are commonly evaluated against fixed task specifications, but users may omit requirements, add constraints, or require approval before consequential actions.TALLOP: Joint Optimization of Local Training and Communication Compression for Time-Constrained Federated Learning
Minimizing the wall-clock time required to reach a target accuracy is crucial in federated learning (FL), where many applications require frequent model updates as data distributions evolve over time.A Label Noise Robust Algorithm for Graph Data using Neighborhood Label Distribution
Graph Convolutional Networks (GCNs) have demonstrated exceptional performance on graph-structured data.Controlling the Diversity-Quality Trade-Off of LLMs by Merging Models Before and After Alignment
Alignment is known to change the output characteristics of large language models (LLMs).A Minimal ReLU Classifier on Non-Linearly Separable Data: Convergence and Implicit Bias at and beyond the Edge of Stability
Gradient descent (GD) with large learning rates can successfully optimize neural networks even though classical smoothness-based learning rate conditions are violated."Deep lifts": Neural networks can generalize even with the dumbest optimizer
Given a labeled dataset $\{(x_i, y_i)\}_{i = 1}^n$, consider drawing a parameter vector $\hatθ$ uniformly at random from those that interpolate the data within a width-$W$ depth-$D$ ReLU…Distinguishability and Recovery for a Doubly Sparse Spiked Wigner Model
We study distinguishability and recovery of a sparse rank-one signal in a spiked Wigner model with independently sparse noise through a variety of algorithms and parameter regimes.Towards Looped Models Done Right, Part I: Topology, Input Injection, Recurrent-State Design
Looped language models reuse physical Transformer blocks across logical depth, but established architectures entangle recurrence placement, input injection, and recurrent state organization.GR-OPD: Group-Relative Sequence Credit for On-Policy Distillation
On-policy distillation (OPD) provides token-level teacher feedback, yet common sampled-token updates assign credit locally and omit the effects of earlier actions on subsequent feedback.DIFFLET: A Fast Inference Engine for Diffusion Transformer Models on AWS Trainium
On AWS Trainium2, every component of a diffusion model runs as a program compiled ahead of time for fixed shapes, while pipelines differ across models, prompt lengths across requests, and caching…Unifying Embodied Foundation Models via a Model-to-Policy Formulation
Vision-language-action (VLA) models and world-action models (WAMs) have emerged as promising approaches to robot learning and control.Path to Prior-Data Fitted Relational Models via Learned Latent Multi-table Representations
Prior-data fitted networks (PFNs) show that pretraining on large populations of synthetic tasks can enable generalization to unseen datasets with little or no downstream fine-tuning.Where Latents Belong and How They Flow: Joint Identification of Structural Roles
Latent factors in dynamical systems can play different roles across observations and time.Towards a GraphNARX model for structural dynamics
The current paper introduces the GraphNARX model, a graph-based nonlinear autoregressive exogenous model for structural dynamics.Domain-specific language alignment of multimodal embeddings towards explainable predictions
Multimodal data are commonly fused into latent representations that improve downstream task performance but are not interpretable, limiting their application in domains that require explainable…LED: Improving Masked Diffusion Language Models by Lookahead Editing
Diffusion large language models (dLLMs) have emerged as a promising alternative to autoregressive language models, enabling non-autoregressive text generation through parallel decoding.Information-Geometric Blind Spots of Conformal Test Martingales
Conformal test martingales (CTMs) are e-processes for sequentially testing exchangeability via p-values produced by a conformity function $A$.