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Newest papers, page 189
Reasoning Models Can Control Their Chains of Thought When They Tell Themselves To
Chain-of-thought (CoT) controllability, the ability of reasoning models to control properties of their CoT, is consequential for both their usefulness and reliable oversight.ADT: All-Atom Diffusion Tokenizer for 3D Molecular Structures
Discrete structural tokenization provides a promising route to autoregressive 3D molecular generation.Beyond Marginal Influence: Interaction-Aware Memory Selection for LLM Agents
LLM-agent memory selection often uses retrieval rankings based on record relevance or individual influence.NestedVLA: Learning to Consolidate and Generate Skills for Vision-Language-Action Models
Vision-language-action (VLA) models can learn a wide range of robotic skills, but combining separately trained skills remains challenging.DeDiCa: A Scalable Deconfounding Discrete Causal Generative Model
We introduce DeDiCa, a practical framework for efficient causal inference with discrete variables and hidden confounding that revisits classical probabilistic modeling through a tensorized…Data-Efficient Classifier Pretraining Through Diffusion Models
Machine learning with limited data is susceptible to overfitting, especially with modern deep neural networks.Distributed Performance Boosting through Neural Control for Plug-and-Play Networks
We address the problem of improving the performance of pre-stabilized networked systems subject to plug-and-play (PnP) operations, namely additions or removals of subsystems.The Optimal Ranking Tradeoffs Between Two Ranking Scores in Two-Class Classification
The accuracy, true negative rate (negative class recall), true positive rate (positive class recall), negative predictive value (negative class precision), positive predictive value (positive class…Learning angles for quantum approximate optimization
The Quantum Approximate Optimization Algorithm (QAOA) and its variants are leading algorithms to help solve challenging combinatorial optimization problems.How the elastic axis folds the transonic flutter boundary: shock energy transfer and conformally calibrated active learning
Isogai's case A, a pitch-plunge aerofoil whose elastic axis lies half a chord ahead of the leading edge, has a folded transonic flutter boundary in inviscid flow: near the bottom of the transonic…Look, Reason, Draw a Region: Recursive Refinement for Spatial Context Prediction
Spatial context prediction requires visual grounding and spatial reasoning to identify vacant regions relative to objects in a scene.Untrained CNN and coefficient-space regularization for joint reconstruction in photoacoustic tomography
The joint reconstruction of the initial pressure and spatially varying sound speed in photoacoustic tomography from boundary measurements is considered.The Loss or the Architecture? What Matters in Survival Analysis with Competing Risks
Often, \emph{eg} in healthcare and predictive maintenance, what matters is which of several competing causes will strike first, and with what probability: survival analysis with competing risks.Peak-Memory Schedules You Can Trust: Exact Realization, Certified Optimality, and the Limits of Recomputation for Inference
Real-time inference on small devices needs low activation memory without extra latency, which makes operator order the most valuable memory lever: it is free in time.HiPredict: Predicting the missing HiRISE RED4 spectral channel with machine learning
The High Resolution Imaging Science Experiment (HiRISE) onboard the Mars Reconnaissance Orbiter (MRO) acquires imagery of Mars at 25--30 cm/pixel, supporting studies of martian geology, climate…Minimax-optimal Scaling Laws in Ridge Regression via Transfer Learning
Pretrained models are often reused across downstream tasks, motivating the question of whether this transfer learning procedure can provide a substantial performance improvement, namely, in terms of…SETAREH Transform: A Sorting-Assisted Haar-Wavelet Sparsification Method for Approximate Inner-Product Computation
Modern deep learning systems increasingly rely on high-dimensional vector operations, in which large input and parameter vectors are repeatedly stored, transferred, and processed.How Finite-Rank Bottlenecks Shape the Low-Rank Adaptation Landscape
Low-rank adaptation (LoRA) has become the standard method for parameter-efficient fine-tuning of large pretrained models, yet theoretical explanations for why low-rank updates suffice remain…RTSM: Adapting Recursive Models to Multivariate Time-Series Classification
Accurate multivariate time-series classification requires extracting temporal features and integrating information across a sequence.Beyond Success Rate: Interventional Evaluation of Memory in Vision-Language-Action Systems
When we say that a robot uses memory, does that memory actually play a role?