Search: “research”

Accelerating Diffusion Sampling via Exploiting Local Transition Coherence

arXiv:2503.09675v3 Announce Type: replace Abstract: Text-based diffusion models have made significant breakthroughs in generating high-quality images and videos from textual descriptions. However, the

Accelerating Natural Gradient Descent for PINNs with Randomized Numerical Linear Algebra

DGX agent

arXiv:2505.11638v4 Announce Type: replace-cross Abstract: Natural Gradient Descent (NGD) has emerged as a promising optimization algorithm for training neural network-based solvers for partial differe

researcharxiv-cs-lg

28 May 2026

Accelerating Reinforcement Learning Training Using Simulation Surrogate Models

arXiv:2605.27556v1 Announce Type: cross Abstract: High-fidelity simulation models are widely used to analyze complex stochastic systems, but their high computational cost motivates the development of

Adapting, Fast and Slow: On Few-Shot Transportability of Compositions

arXiv:2512.22777v2 Announce Type: replace-cross Abstract: Generalization across domains requires stable structure that links the source and target distributions. Building on causal transportability th

Adaptive Coarse-to-Fine Subgoal Refinement for Long-Horizon Offline Goal-Conditioned Reinforcement Learning

arXiv:2605.28127v1 Announce Type: new Abstract: Offline goal-conditioned reinforcement learning (GCRL) is challenging in long-horizon tasks, where distant state--goal pairs provide weak supervision an

Adaptive Temporal Gating of Longitudinal Magnetic Resonance Imaging for Alzheimer's Prediction

arXiv:2605.28397v1 Announce Type: new Abstract: Predicting conversion from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) is critical for early intervention. Current deep learning paradig

Adjusted Shuffling SARAH: Advancing Complexity Analysis via Dynamic Gradient Weighting

arXiv:2506.12444v2 Announce Type: replace-cross Abstract: In this paper, we propose Adjusted Shuffling SARAH, a novel algorithm that integrates shuffling strategies into the recursive SARAH framework

Advancing Direct Training for Spiking Neural Networks with Circulate-Firing Neurons and Learnable Gradients

arXiv:2605.27412v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) have emerged with promising energy-efficient property, yet a substantial performance gap persists compared to Artificia

Alterbute: Editing Intrinsic Attributes of Objects in Images

arXiv:2601.10714v2 Announce Type: replace Abstract: We introduce Alterbute, a diffusion-based method for editing an object's intrinsic attributes in an image. We allow changing color, texture, materia

An analytic theory of convolutional neural network inverse problems solvers

arXiv:2601.10334v2 Announce Type: replace Abstract: Supervised convolutional neural networks (CNNs) are widely used to solve imaging inverse problems, achieving state-of-the-art performance in numerou

An Empirical Audit of k-NAF Budget Accounting for Anchored Decoding

arXiv:2605.28001v1 Announce Type: new Abstract: We empirically audit the k-NAF budget-accounting mechanism in Anchored Decoding using (i) a fixed, class-stratified workload (approximately 8,500 random

An Empirical Study on Variance-based MC Dropout Uncertainty-Error Correlation in 2D Brain Tumor Segmentation

arXiv:2510.15541v2 Announce Type: replace-cross Abstract: Accurate brain tumor segmentation from MRI is vital for diagnosis and treatment planning. Although Monte Carlo (MC) Dropout is widely used to

Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization

arXiv:2605.28428v1 Announce Type: cross Abstract: Detecting subtle visual anomalies in images remains challenging, particularly when only normal samples are available a priori. Such unsupervised anoma

Applications of temporal graph learning for predicting the dynamics of biological systems

arXiv:2605.28659v1 Announce Type: new Abstract: Biological foundation models have shown strong performance in single-cell representation learning by applying transformer architectures directly to gene

Asking Is Not Enough: Protocol Sensitivity in LLM Confidence Calibration

arXiv:2605.27752v1 Announce Type: new Abstract: LLM confidence calibration is often evaluated by comparing two signals: token-probability scores and verbalized confidence. These signals are sometimes

Atomic Skills are the Prerequisite: When Reinforcement Learning Synthesizes Compositional Reasoning, and When It Only Amplifies

arXiv:2512.01970v3 Announce Type: replace Abstract: Does Reinforcement Learning (RL) merely amplify existing skills, or synthesize novel skills? We investigate this question through the lens of Comple

Agents

AutoScientists: Self-Organizing Agent Teams for Long-Running Scientific Experimentation

arXiv:2605.28655v1 Announce Type: new Abstract: Scientific research proceeds through iterative cycles of hypothesis generation, experiment design, execution, and revision. AI agents can automate parts

agentsarxiv-cs-ai

Balancing Fidelity and Diversity in Diffusion Models via Symmetric Attention Decomposition: Hopfield Perspective

arXiv:2605.27476v1 Announce Type: cross Abstract: We characterize the pre-softmax attention matrix mathbf{QK^op} in transformers as an associative memory matrix encoding pairwise associations between

Better heads do not guarantee better binarized constituency parsing

arXiv:2605.28131v1 Announce Type: new Abstract: We revisit punctuation-aware tree binarization for constituency parsing and ask whether dependency-induced headedness improves binary parser supervision

Beyond Chunk-Local Extraction: Cross-Chunk Graph Augmentation for GraphRAG

arXiv:2605.28004v1 Announce Type: new Abstract: GraphRAG extends retrieval-augmented generation by organizing corpora as explicit knowledge graphs, enabling graph-based retrieval for complex question

Beyond Input Understanding: Diagnosing Multilingual Mathematical Reasoning with Directed Acyclic Trace Graphs

arXiv:2605.27715v1 Announce Type: new Abstract: Large reasoning models (LRMs) achieve strong mathematical reasoning performance in English, but remain much less reliable in many low- and medium-resour

Beyond pass@k: Redundancy-Aware RLVR for Multi-Sample Code Generation

arXiv:2605.28022v1 Announce Type: new Abstract: LLMs for code generation are commonly evaluated in repeated-sampling settings using Pass@k, where multiple candidate programs are executed against unit

Beyond Surrogate Gradients: Fully Differentiable Token Pruning for Vision-Language Models

arXiv:2605.28051v1 Announce Type: new Abstract: Visual token pruning reduces the computational cost of Vision-Language Models (VLMs) by removing redundant visual tokens. Existing methods typically rel

Bio-Inspired Self-Supervised Learning for Wrist-worn Accelerometer Data

arXiv:2603.10961v2 Announce Type: replace Abstract: Wearable accelerometers enable large-scale health monitoring, yet learning robust human-activity representations has been constrained by scarce labe

BIRDNet: Mining and Encoding Boolean Implication Knowledge Graphs as Interpretable Deep Neural Networks

arXiv:2605.28739v1 Announce Type: cross Abstract: Tabular data in knowledge-rich domains often carries a latent prior in the form of Boolean implication relationships (BIRs) between pairs of features.

BIRDS: Characterizing and Understanding Biodiversity Impact of Large Language Model Serving

arXiv:2605.27480v1 Announce Type: cross Abstract: Large language model (LLM) serving creates environmental impacts beyond carbon and water, including ecosystem damage through biodiversity-related path

Bound-Constrained Sparse Representation for Electrical Impedance Tomography

arXiv:2605.28392v1 Announce Type: new Abstract: This study proposes a bound-constrained sparse representation (BC-SR) framework for electrical impedance tomography (EIT), aimed at improving conductivi

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models

arXiv:2605.28488v1 Announce Type: cross Abstract: We study inference in stochastic block models (SBMs) through the lens of optimal transport (OT). We first establish that maximum likelihood variationa

Bridging the Generalization Gap in Adverse Weather Segmentation: A Training Recipe Perspective

arXiv:2605.27962v1 Announce Type: new Abstract: This paper describes our approach for the 8th UG2+ Workshop (CVPR 2026) Track~2, which targets semantic segmentation of outdoor scenes degraded by five

Bridging the Sampling Distribution Shift in Radio Map Estimation: A Trajectory-Aware Paradigm

arXiv:2605.28234v1 Announce Type: new Abstract: Learning-based radio map estimation (RME) plays a critical role in UAV-assisted wireless sensing, enabling tasks such as coverage prediction and network

Bullet Trains: Parallelizing Training of Temporally Precise Spiking Neural Networks

arXiv:2603.13283v2 Announce Type: replace-cross Abstract: Continuous-time, event-native spiking neural networks (SNNs) operate strictly on spike events, treating spike timing and ordering as the repre

CALM-IT: Generating Realistic Long-Form Motivational Interviewing Dialogues with Dual-Actor Conversational Dynamics Tracking

arXiv:2601.10085v2 Announce Type: replace Abstract: Therapeutic dialogue is not a sequence of isolated responses: client goals, motivation, resistance, and therapeutic alliance evolve over time. Yet c

CaMBRAIN: Real-time, Continuous EEG Inference with Causal State Space Models

arXiv:2605.28792v1 Announce Type: new Abstract: Electroencephalography (EEG) is a critical, non-invasive method to monitor electrical brain activity. EEGs can span anywhere from a couple seconds to mu

Can Entry-Wise Clipping Give Spectral Control of Stochastic Gradients?

arXiv:2605.27733v1 Announce Type: new Abstract: Training instabilities such as loss spikes are frequently the result of stochastic gradient noise. Because of rare expressions in language training data

Can Hallucinations Be Useful? Solving Multi-Hop Questions With SLMs By Chaining System-I/II Reasoning

arXiv:2605.27596v1 Announce Type: new Abstract: Recently, there has been increased interest in Small Language Models (SLMs), which are fast, show good performance, and have lower hardware demands than

Can I Have Your Order? Monte-Carlo Tree Search for Slot Filling Ordering in Diffusion Language Models

arXiv:2602.12586v2 Announce Type: replace Abstract: While plan-and-infill decoding in Masked Diffusion Models (MDMs) shows promise for mathematical and code reasoning, performance remains highly sensi

Can LLMs Use Linguistic Uncertainty Markers to Reliably Reflect Intrinsic Confidence?

arXiv:2605.28778v1 Announce Type: new Abstract: LLMs' linguistically expressed confidence should faithfully reflect their intrinsic uncertainty. While recent work shows LLMs struggle to use epistemic

Can Quantum Federated Learning Withstand Circuit-Level Backdoors?

arXiv:2605.27416v1 Announce Type: cross Abstract: Quantum Federated Learning (QFL) inherits the core vulnerability of federated optimization to malicious clients, while also introducing an attack surf

Challenges in Explaining Pretrained Clinical Text Classifiers

arXiv:2605.28060v1 Announce Type: new Abstract: Explaining the predictions of neural models in clinical NLP remains a significant challenge, especially for complex tasks involving long, unstructured m

Chreode: A Cell World Model for One-Step Temporal Dynamics and Perturbation Prediction

arXiv:2605.28111v1 Announce Type: new Abstract: Predicting how a cell will change its transcriptional state under a developmental signal or a genetic perturbation is the computational core of in-silic

Clark Hash: Stateless Sparse Johnson-Lindenstrauss Quantization for Neural Embeddings

arXiv:2605.28034v1 Announce Type: new Abstract: Clark Hash is a small method for storing neural embeddings in less space. It normalizes each database vector, applies a deterministic sparse signed John

Climate tech companies are going public. What’s next?

This year, there’s been a wave of notable energy companies going public via IPO in the US. The solar and battery company Solv Energy went public in February, to the tune of $6 billion. X-energy, which

researchmit-tech-review

Clinical Validation of the Melanoscope AI Mobile Dermoscopy Clinical Decision Support System

arXiv:2605.27561v1 Announce Type: cross Abstract: Introduction. Early detection of malignant skin lesions is critical for prognosis, yet dermatologist shortages in Russian regions limit screening cove

ClothTransformer: Unified Latent-Space Transformers for Scalable Cloth Simulation

arXiv:2605.27852v1 Announce Type: cross Abstract: Unified and scalable Transformers have recently achieved remarkable success in modeling diverse phenomena traditionally associated with computer graph

Comonadic Morphophonology: A Compositional Framework for Context-Dependent Morphological Rules in Finnish

arXiv:2605.28484v1 Announce Type: new Abstract: Composing finite-state transducers (FSTs) for context-dependent morphophonological rules -- consonant gradation, vowel harmony, possessive suffix assimi

Compositional Generalization in Autoregressive Models via Logit Composition

arXiv:2605.28304v1 Announce Type: new Abstract: Composing autoregressive models remains a core challenge in understanding how large language models can combine behaviors or skills learned across tasks

Conditionally Site-Independent Neural Evolution of Antibody Sequences

arXiv:2602.18982v4 Announce Type: replace Abstract: Common deep learning approaches for antibody engineering focus on modeling the marginal distribution of sequences. By treating sequences as independ

Conformal Prediction for Hierarchical Data

arXiv:2411.13479v4 Announce Type: replace-cross Abstract: We consider conformal prediction for multivariate data and focus on hierarchical data, where some components are linear combinations of others