research

Q-MambaIR: Accurate Quantized Mamba for Efficient Image Restoration

arXiv:2503.21970v3 Announce Type: replace Abstract: State-Space Models (SSMs) have attracted considerable attention in Image Restoration (IR) due to their ability to scale linearly sequence length whi

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QualiaNet: An Experience-Before-Inference Network

DGX agent

arXiv:2604.14193v1 Announce Type: new Abstract: Human 3D vision involves two distinct stages: an Experience Module, where stereo depth is extracted relative to fixation, and an Inference Module, where

researcharxiv-cs-cv

17 Apr 2026

Quantitative Approximation Rates for Group Equivariant Learning

arXiv:2602.20370v2 Announce Type: replace Abstract: The universal approximation theorem establishes that neural networks can approximate any continuous function on a compact set. Later works in approx

Quantization of Spiking Neural Networks Beyond Accuracy

arXiv:2604.14487v1 Announce Type: new Abstract: Quantization is a natural complement to the sparse, event-driven computation of Spiking Neural Networks, reducing memory bandwidth and arithmetic cost f

Quantum-inspired tensor networks in machine learning models

arXiv:2604.14287v1 Announce Type: new Abstract: Tensor networks were developed in the context of many-body physics as compressed representations of multiparticle quantum states. These representations

RACER: Retrieval-Augmented Contextual Rapid Speculative Decoding

arXiv:2604.14885v1 Announce Type: new Abstract: Autoregressive decoding in Large Language Models (LLMs) generates one token per step, causing high inference latency. Speculative decoding (SD) mitigate

Random Matrix Theory for Deep Learning: Beyond Eigenvalues of Linear Models

arXiv:2506.13139v2 Announce Type: replace-cross Abstract: Modern Machine Learning (ML) and Deep Neural Networks (DNNs) often operate on high-dimensional data and rely on overparameterized models, wher

ReasonScaffold: A Scaffolded Reasoning-based Annotation Protocol for Human-AI Co-Annotation

arXiv:2603.21094v3 Announce Type: replace Abstract: Human annotation is central to NLP evaluation, yet subjective tasks often exhibit substantial variability across annotators. While large language mo

Rethinking LLM-Driven Heuristic Design: Generating Efficient and Specialized Solvers via Dynamics-Aware Optimization

arXiv:2601.20868v2 Announce Type: replace Abstract: Large Language Models (LLMs) have advanced the field of Combinatorial Optimization through automated heuristic generation. Instead of relying on man

Revisiting Token Compression for Accelerating ViT-based Sparse Multi-View 3D Object Detectors

arXiv:2604.14563v1 Announce Type: new Abstract: Vision Transformer (ViT)-based sparse multi-view 3D object detectors have achieved remarkable accuracy but still suffer from high inference latency due

Robustness of Vision Foundation Models to Common Perturbations

arXiv:2604.14973v1 Announce Type: cross Abstract: A vision foundation model outputs an embedding vector for an image, which can be affected by common editing operations (e.g., JPEG compression, bright

S2AM3D: Scale-controllable Part Segmentation of 3D Point Cloud

arXiv:2512.00995v3 Announce Type: replace Abstract: Part-level point cloud segmentation has recently attracted significant attention in 3D computer vision. Nevertheless, existing research is constrain

Sandwich: Joint Configuration Search and Hot-Switching for Efficient CPU LLM Serving

arXiv:2507.18454v2 Announce Type: replace-cross Abstract: CPUs are critical for LLM serving due to their availability, cost efficiency, and edge applicability. However, efficient CPU serving is hinder

Scalable Model-Based Clustering with Sequential Monte Carlo

arXiv:2604.14810v1 Announce Type: cross Abstract: In online clustering problems, there is often a large amount of uncertainty over possible cluster assignments that cannot be resolved until more data

Seen-to-Scene: Keep the Seen, Generate the Unseen for Video Outpainting

arXiv:2604.14648v1 Announce Type: new Abstract: Video outpainting aims to expand the visible content of a video beyond the original frame boundaries while preserving spatial fidelity and temporal cohe

Segment-Level Coherence for Robust Harmful Intent Probing in LLMs

arXiv:2604.14865v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly exposed to adaptive jailbreaking, particularly in high-stakes Chemical, Biological, Radiological, and Nucl

SegviGen: Repurposing 3D Generative Model for Part Segmentation

arXiv:2603.16869v2 Announce Type: replace Abstract: We introduce SegviGen, a framework that repurposes native 3D generative models for 3D part segmentation. Existing pipelines either lift strong 2D pr

SegWithU: Uncertainty as Perturbation Energy for Single-Forward-Pass Risk-Aware Medical Image Segmentation

arXiv:2604.15271v1 Announce Type: new Abstract: Reliable uncertainty estimation is critical for medical image segmentation, where automated contours feed downstream quantification and clinical decisio

Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go?

arXiv:2604.13826v1 Announce Type: cross Abstract: Sentiment analysis in software engineering focuses on understanding emotions expressed in software artifacts. Previous research highlighted the limita

Similarity-Distance-Magnitude Activations

arXiv:2509.12760v4 Announce Type: replace-cross Abstract: We introduce the Similarity-Distance-Magnitude (SDM) activation function, a more robust and interpretable formulation of the standard softmax

Social Story Frames: Contextual Reasoning about Narrative Intent and Reception

arXiv:2512.15925v2 Announce Type: replace Abstract: Reading stories evokes rich interpretive, affective, and evaluative responses, such as inferences about narrative intent or judgments about characte

SPaCe: Unlocking Sample-Efficient Large Language Models Training With Self-Pace Curriculum Learning

arXiv:2508.05015v2 Announce Type: replace Abstract: Large language models (LLMs) have shown strong reasoning capabilities when fine-tuned with reinforcement learning (RL). However, such methods requir

Stability and Generalization in Looped Transformers

arXiv:2604.15259v1 Announce Type: new Abstract: Looped transformers promise test-time compute scaling by spending more iterations on harder problems, but it remains unclear which architectural choices

STEP-Parts: Geometric Partitioning of Boundary Representations for Large-Scale CAD Processing

arXiv:2604.14927v1 Announce Type: cross Abstract: Many CAD learning pipelines discretize Boundary Representations (B-Reps) into triangle meshes, discarding analytic surface structure and topological a

StreamCacheVGGT: Streaming Visual Geometry Transformers with Robust Scoring and Hybrid Cache Compression

arXiv:2604.15237v1 Announce Type: new Abstract: Reconstructing dense 3D geometry from continuous video streams requires stable inference under a constant memory budget. Existing O(1) frameworks primar

Structural interpretability in SVMs with truncated orthogonal polynomial kernels

arXiv:2604.15285v1 Announce Type: cross Abstract: We study post-training interpretability for Support Vector Machines (SVMs) built from truncated orthogonal polynomial kernels. Since the associated re

Structure as Computation: Developmental Generation of Minimal Neural Circuits

arXiv:2604.15143v1 Announce Type: cross Abstract: This work simulates the developmental process of cortical neurogenesis, initiating from a single stem cell and governed by gene regulatory rules deriv

Style Amnesia: Investigating Speaking Style Degradation and Mitigation in Multi-Turn Spoken Language Models

arXiv:2512.23578v3 Announce Type: replace Abstract: In this paper, we show that when spoken language models (SLMs) are instructed to speak in a specific speaking style at the beginning of a multi-turn

Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging

arXiv:2410.08329v3 Announce Type: replace Abstract: Computational wave imaging (CWI) extracts hidden structure and physical properties of a volume of material by analyzing wave signals that traverse t

The Acoustic Camouflage Phenomenon: Re-evaluating Speech Features for Financial Risk Prediction

arXiv:2604.14619v1 Announce Type: cross Abstract: In computational paralinguistics, detecting cognitive load and deception from speech signals is a heavily researched domain. Recent efforts have attem

The case for fixing everything

The handsome new book Maintenance: Of Everything, Part One, by the tech industry legend Stewart Brand, promises to be the first in a series offering “a comprehensive overview of the civilizational imp

researchmit-tech-review

The Code Whisperer: LLM and Graph-Based AI for Smell and Vulnerability Resolution

arXiv:2604.13114v1 Announce Type: cross Abstract: Code smells and software vulnerabilities both increase maintenance cost, yet they are often handled by separate tools that miss structural context and

The Cognitive Circuit Breaker: A Systems Engineering Framework for Intrinsic AI Reliability

arXiv:2604.13417v1 Announce Type: cross Abstract: As Large Language Models (LLMs) are increasingly deployed in mission-critical software systems, detecting hallucinations and ``faked truthfulness'' ha

The Download: bad news for inner Neanderthals, and AI warfare’s human illusion

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. The problem with thinking you’re part Neanderthal There’s a th

researchmit-tech-review

Threshold Differential Attention for Sink-Free, Ultra-Sparse, and Non-Dispersive Language Modeling

arXiv:2601.12145v2 Announce Type: replace Abstract: Softmax attention struggles with long contexts due to structural limitations: the strict sum-to-one constraint forces attention sinks on irrelevant

Tight Bounds for Learning Polyhedra with a Margin

arXiv:2604.14614v1 Announce Type: cross Abstract: We give an algorithm for PAC learning intersections of k halfspaces with a rho margin to within error arepsilon that runs in time extsf{poly}(k, areps

TokenFormer: Unify the Multi-Field and Sequential Recommendation Worlds

arXiv:2604.13737v1 Announce Type: cross Abstract: Recommender systems have historically developed along two largely independent paradigms: feature interaction models for modeling correlations among mu

TokenGS: Decoupling 3D Gaussian Prediction from Pixels with Learnable Tokens

arXiv:2604.15239v1 Announce Type: new Abstract: In this work, we revisit several key design choices of modern Transformer-based approaches for feed-forward 3D Gaussian Splatting (3DGS) prediction. We

TokenLight: Precise Lighting Control in Images using Attribute Tokens

arXiv:2604.15310v1 Announce Type: new Abstract: This paper presents a method for image relighting that enables precise and continuous control over multiple illumination attributes in a photograph. We

Towards Design Compositing

arXiv:2604.14605v1 Announce Type: new Abstract: Graphic design creation involves harmoniously assembling multimodal components such as images, text, logos, and other visual assets collected from diver

Towards Faster Language Model Inference Using Mixture-of-Experts Flow Matching

arXiv:2604.15009v1 Announce Type: cross Abstract: Flow matching retains the generation quality of diffusion models while enabling substantially faster inference, making it a compelling paradigm for ge

Tracking the Temporal Dynamics of News Coverage of Catastrophic and Violent Events

arXiv:2604.14315v1 Announce Type: new Abstract: The modern news cycle has been fundamentally reshaped by the rapid exchange of information online. As a result, media framing shifts dynamically as new

Trump is reportedly negotiating a deal which would stop Iran from producing a nuclear weapon in exchange for $20 Billion in Iranian assets b…

Trump is reportedly negotiating a deal which would stop Iran from producing a nuclear weapon in exchange for 20 Billion in Iranian assets being unfrozen. Note that The Iranian Nuclear agreement (JCPOA

researchyann-lecun--x

Tug-of-War within A Decade: Conflict Resolution in Vulnerability Analysis via Teacher-Guided Retrieval-Augmented Generations

arXiv:2604.14172v1 Announce Type: new Abstract: Large Language Models (LLMs) are essential for analyzing and addressing vulnerabilities in cybersecurity. However, among over 200,000 vulnerabilities we

Universal hidden monotonic trend estimation with contrastive learning

arXiv:2210.09817v3 Announce Type: replace Abstract: In this paper, we describe a universal method for extracting the underlying monotonic trend factor from time series data. We propose an approach rel

Unsupervised feature selection using Bayesian Tucker decomposition

arXiv:2604.14949v1 Announce Type: cross Abstract: In this paper, we proposed Bayesian Tucker decomposition (BTuD) in which residual is supposed to obey Gaussian distribution analogous to linear regres

Unsupervised Learning of Local Updates for Maximum Independent Set in Dynamic Graphs

arXiv:2505.13754v3 Announce Type: replace Abstract: We present the first unsupervised learning model for Maximum-Independent-Set (MaxIS) in dynamic graphs where edges change over time. Our method comb

VisPCO: Visual Token Pruning Configuration Optimization via Budget-Aware Pareto-Frontier Learning for Vision-Language Models

arXiv:2604.15188v1 Announce Type: new Abstract: Visual token pruning methods effectively mitigate the quadratic computational growth caused by processing high-resolution images and video frames in vis