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Rethinking Layer Relevance in Large Language Models Beyond Cosine Similarity

arXiv:2605.14075v1 Announce Type: cross Abstract: Large language models (LLMs) have revolutionized natural language processing. Understanding their internal mechanisms is crucial for developing more i

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Rethinking the Good Enough Embedding for Easy Few-Shot Learning

DGX agent

arXiv:2605.14145v1 Announce Type: new Abstract: The field of deep visual recognition is undergoing a paradigm shift toward universal representations. The Platonic Representation Hypothesis suggests th

researcharxiv-cs-cv

15 May 2026

Robust Inference-Time Steering of Protein Diffusion Models via Embedding Optimization

arXiv:2602.05285v2 Announce Type: replace Abstract: A core challenge in structural biophysics is generating biomolecular conformations that are both physically plausible and consistent with experiment

RoSHAP: A Distributional Framework and Robust Metric for Stable Feature Attribution

arXiv:2605.15154v1 Announce Type: cross Abstract: Feature attribution analysis is critical for interpreting machine learning models and supporting reliable data-driven decisions. However, feature attr

RQ-MoE: Residual Quantization via Mixture of Experts for Efficient Input-Dependent Vector Compression

arXiv:2605.14359v1 Announce Type: cross Abstract: Vector quantization is a fundamental tool for compressing high-dimensional embeddings, yet existing multi-codebook methods rely on static codebooks th

SAGE3D: Soft-guided attention and graph excitation for 3D point cloud corner detection

arXiv:2605.15088v1 Announce Type: new Abstract: We present SAGE3D, a hybrid Transformer-based model for corner detection in airborne LiDAR point clouds. We propose a multi-stage solution built on a hi

Scalable Solution of the Stochastic Multi-path Traveling Salesman Problem via Neural Networks

arXiv:2605.14662v1 Announce Type: cross Abstract: The multi-path Traveling Salesman Problem with stochastic travel costs arises in hybrid vehicle routing applications designed for Smart City and City

Scalable Subset Selection in Linear Mixed Models

arXiv:2506.20425v3 Announce Type: replace-cross Abstract: Linear mixed models (LMMs), which incorporate fixed and random effects, are key tools for analyzing heterogeneous data, such as in personalize

Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model

arXiv:2605.14567v1 Announce Type: cross Abstract: We propose a simple mechanism by which scaling laws emerge from feature learning in multi-layer networks. We study a high-dimensional hierarchical tar

ScaLoRA: Optimally Scaled Low-Rank Adaptation for Efficient High-Rank Fine-Tuning

arXiv:2510.23818v2 Announce Type: replace Abstract: As large language models (LLMs) continue to scale in size, the computational overhead has become a major bottleneck for task-specific fine-tuning. W

SceneForge: Structured World Supervision from 3D Interventions

arXiv:2605.14399v1 Announce Type: new Abstract: Many multimodal learning tasks require supervision that remains consistent across edits, viewpoints, and scene-level interventions. However, such superv

SD-ReID: View-aware Stable Diffusion for Aerial-Ground Person Re-Identification

arXiv:2504.09549v3 Announce Type: replace Abstract: Aerial-Ground Person Re-IDentification (AG-ReID) aims to retrieve specific persons across cameras with different viewpoints. Previous works focus on

SeaVis: Modeling and Control of a Remotely Operated Towed Vehicle for Seabed Visualization and Mapping

arXiv:2605.14683v1 Announce Type: new Abstract: High-resolution seafloor mapping necessitates stable and precise positioning for underwater robots. This paper introduces a novel mathematical model for

researcharxiv-cs-ro

Semantic Feature Segmentation for Interpretable Predictive Maintenance in Complex Systems

arXiv:2605.14318v1 Announce Type: new Abstract: Predictive maintenance in complex systems is often complicated by the heterogeneity and redundancy of monitored variables,which can obscure fault-releva

Separating Intrinsic Ambiguity from Estimation Uncertainty in Deep Generative Models for Linear Inverse Problems

arXiv:2605.15050v1 Announce Type: new Abstract: Recently, deep generative models have been used for posterior inference in inverse problems, including high-stakes applications in medical imaging and s

SliceGraph: Mapping Process Isomers in Multi-Run Chain-of-Thought Reasoning

arXiv:2605.14619v1 Announce Type: new Abstract: Multi-run chain-of-thought reasoning is usually collapsed to final-answer aggregates, which discard howsampled trajectories share, split, and rejoin thr

Slower Generalization, Faster Memorization: A Sweet Spot in Algorithmic Learning

arXiv:2605.14659v1 Announce Type: new Abstract: Critical-data-size accounts of grokking suggest a natural post-threshold intuition: once training data is sufficient to identify the underlying rule, ad

SparseOIT: Improving Order-Independent Transparency 3DGS via Active Set Method

arXiv:2605.13855v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) has received tremendous popularity over the past few years due to its photorealistic visual appearance. However, 3DGS use

Spontaneous symmetry breaking and Goldstone modes for deep information propagation

arXiv:2605.14685v1 Announce Type: cross Abstract: In physical systems, whenever a continuous symmetry is spontaneously broken, the system possesses excitations called Goldstone modes, which allow cohe

Stochastic Matching via Local Sparsification

arXiv:2605.14195v1 Announce Type: cross Abstract: The classic online stochastic matching problem typically requires immediate and irrevocable matching decisions. However, in many modern decentralized

Streaming Speech-to-Text Translation with a SpeechLLM

arXiv:2605.14766v1 Announce Type: cross Abstract: Normally, a system that translates speech into text consists of separate modules for speech recognition and text-to-text translation. Combining those

TAPIOCA: Why Task- Aware Pruning Improves OOD model Capability

arXiv:2605.14738v1 Announce Type: cross Abstract: Recent work has promoted task-aware layer pruning as a way to improve model performance on particular tasks, as shown by TALE. In this paper, we inves

Teaching Large Language Models When Not to Know: Learning Temporal Critique for Ex-Ante Reasoning

arXiv:2605.14636v1 Announce Type: new Abstract: Large language models (LLMs) often fail to reason under temporal cutoffs: when prompted to answer from the standpoint of an earlier time, they exploit k

TeDiO: Temporal Diagonal Optimization for Training-Free Coherent Video Diffusion

arXiv:2605.14136v1 Announce Type: new Abstract: Recent text-to-video diffusion transformers generate visually compelling frames, yet still struggle with temporal coherence, often producing flickering,

TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning

arXiv:2603.12529v2 Announce Type: replace-cross Abstract: Large Reasoning Models (LRMs) achieve impressive performance on complex reasoning tasks via Chain-of-Thought (CoT) reasoning, which enables th

Text-Dependent Speaker Verification (TdSV) Challenge 2024: Team Naive System Report

arXiv:2605.14896v1 Announce Type: cross Abstract: This paper presents a system for the 2024 Text-Dependent Speaker Verification (TdSV) Challenge. The system achieved a Minimum Detection Cost Function

The Download: China’s AI drama factory and the WHO’s missing health targets

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. How Chinese short dramas became AI content machines China’s sh

researchmit-tech-review

The Scientific Contribution Graph: Automated Literature-based Technological Roadmapping at Scale

arXiv:2605.15011v1 Announce Type: new Abstract: Scientific contributions rarely develop in isolation, but instead build upon prior discoveries. We formulate the task of automated technological roadmap

The Spheres Dataset: Multitrack Orchestral Recordings for Music Source Separation and Information Retrieval

arXiv:2511.21247v2 Announce Type: replace-cross Abstract: This paper introduces The Spheres dataset, multitrack orchestral recordings designed to advance machine learning research in music source sepa

The Velocity Deficit: Initial Energy Injection for Flow Matching

arXiv:2605.14819v1 Announce Type: new Abstract: While Flow Matching theoretically guarantees constant-velocity trajectories, we identify a critical breakdown in high-dimensional practice: the Velocity

The world is on track to miss its health targets

Every year the World Health Organization publishes a global health statistics report. It features the numbers behind world health trends and, importantly, assesses whether we’re on track to reach ambi

researchmit-tech-review

There is a case to be made that the future of Mathematics is very bright. In my mind, proofs have always been a tool to achieve a goal. The …

There is a case to be made that the future of Mathematics is very bright. In my mind, proofs have always been a tool to achieve a goal. The goal was and still is to understand, and reading/writing pro

researchyann-lecun--x

Tokenizing Single-Channel EEG with Time-Frequency Motif Learning

arXiv:2502.16060v5 Announce Type: replace-cross Abstract: Foundation models are reshaping EEG analysis, yet an important problem of EEG tokenization remains a challenge. This paper presents TFM-Tokeni

TopoPrimer: The Missing Topological Context in Forecasting Models

arXiv:2605.15035v1 Announce Type: new Abstract: We introduce TopoPrimer, a framework that makes the global topological structure of the series population an explicit input to any forecasting model. To

Towards Accurate Single Panoramic 3D Detection: A Semantic Gaussian Centric Approach

arXiv:2605.14601v1 Announce Type: new Abstract: Three-dimensional object detection in panoramic imagery is crucial for comprehensive scene understanding, yet accurately mapping 2D features to 3D remai

Towards Gaze-Informed AI Disclosure Interfaces: Eye-Tracking Attentional and Cognitive Load While Reading AI-Assisted News

arXiv:2605.14999v1 Announce Type: cross Abstract: As generative AI becomes increasingly integrated into journalism, designing effective AI-use disclosures that inform readers without imposing unnecess

Towards Label-Free Single-Cell Phenotyping Using Multi-Task Learning

arXiv:2605.14717v1 Announce Type: cross Abstract: Label-free single-cell imaging offers a scalable, non-invasive alternative to fluorescence-based cytometry, yet inferring molecular phenotypes directl

Towards Robotic Dexterous Hand Intelligence: A Survey

arXiv:2605.13925v1 Announce Type: new Abstract: Robotic dexterous hands are central to contact-rich manipulation, with rapid progress driven by advances in hardware, sensing, control, simulation, and

researcharxiv-cs-ro

TRIM: Token-wise Attention-Derived Saliency for Data-Efficient Instruction Tuning

arXiv:2510.07118v3 Announce Type: replace Abstract: Instruction tuning is essential for aligning large language models (LLMs) to downstream tasks and commonly relies on large, diverse corpora. However

Uncertainty Quantification for Large Language Diffusion Models

arXiv:2605.14570v1 Announce Type: new Abstract: Large Language Diffusion Models (LLDMs) are emerging as an alternative to autoregressive models, offering faster inference through higher parallelism. S

Uncovering Trajectory and Topological Signatures in Multimodal Pediatric Sleep Embeddings

arXiv:2605.14156v1 Announce Type: new Abstract: While generative models have shown promise in pediatric sleep analysis, the latent structure of their multimodal embeddings remains poorly understood. T

Understanding Imbalanced Forgetting in Rehearsal-Based Class-Incremental Learning

arXiv:2605.14785v1 Announce Type: cross Abstract: Neural networks suffer from catastrophic forgetting in class-incremental learning (CIL) settings. Rehearsalnicode{x2013}replaying a subset of past sam

Unsupervised simulation of incompressible flows with physics- and equality- constrained artificial neural networks

arXiv:2511.18820v2 Announce Type: replace-cross Abstract: Physics-informed neural networks (PINNs) have shown promise for solving partial differential equations, yet their success in simulating incomp

VGGT-360: Geometry-Consistent Zero-Shot Panoramic Depth Estimation

arXiv:2603.18943v2 Announce Type: replace Abstract: This paper presents VGGT-360, a novel training-free framework for zero-shot, geometry-consistent panoramic depth estimation. Unlike prior view-indep

VGGT-Edit: Feed-forward Native 3D Scene Editing with Residual Field Prediction

arXiv:2605.15186v1 Announce Type: cross Abstract: High-quality 3D scene reconstruction has recently advanced toward generalizable feed-forward architectures, enabling the generation of complex environ

Video-Zero: Self-Evolution Video Understanding

arXiv:2605.14733v1 Announce Type: new Abstract: Self-evolution offers a promising path for improving reasoning models without relying on intensive human annotation. However, extending this paradigm to

Vision-Based Water Level and Flow Estimation

arXiv:2605.14645v1 Announce Type: cross Abstract: With the rapid evolution of computer vision, vision-based methodologies for water level and river surface velocity estimation have reached significant

VMU-Diff: A Coarse-to-fine Multi-source Data Fusion Framework for Precipitation Nowcasting

arXiv:2605.14597v1 Announce Type: new Abstract: Precipitation nowcasting is a vital spatio-temporal prediction task for meteorological applications but faces challenges due to the chaotic property of