Search: “research”

2Mamba2Furious: Linear in Complexity, Competitive in Accuracy

arXiv:2602.17363v3 Announce Type: replace Abstract: Linear attention transformers have become a strong alternative to softmax attention due to their efficiency. However, linear attention tends to be l

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3DTMDet: A Dual-Path Synergy Network of Transformer and SSM for 3D Object Detection in Point Clouds

DGX agent

arXiv:2605.15546v1 Announce Type: new Abstract: A fundamental challenge in point cloud object detection lies in the conflict between the extreme sparsity of distant points and the need for remote cont

researcharxiv-cs-cv

18 May 2026

A mental model for working with coding agents is that they're blind squirrels running into a maze and bumping into walls. You must place the…

researchfrancois-chollet--x

A mental model for working with coding agents is that they're blind squirrels running into a maze and bumping into walls. You must place the walls (verifiable constraints) strategically so that they e

A Model Can Help Itself: Reward-Free Self-Training for LLM Reasoning

arXiv:2510.18814v3 Announce Type: replace-cross Abstract: Can language models improve their reasoning performance without external rewards, using only their own sampled responses for training? We show

A Multi-Layer Cloud-IDS Pipeline with LLM and Adaptive Q-Learning Calibration

arXiv:2605.15889v1 Announce Type: cross Abstract: Security in cloud computing has become a major concern due to several factors such as layered cloud architectures, dynamic environments, and exposure

A numerical study into neural network surrogate model performance for uncertainty propagation

arXiv:2605.16078v1 Announce Type: cross Abstract: Neural network surrogate models have emerged as a promising approach to model solution fields for a wide variety of boundary value problems encountere

A Retrieval-Enhanced Transformer for Multi-Step Port-of-Call Sequence Prediction in Global Liner Shipping

arXiv:2605.15937v1 Announce Type: new Abstract: Accurate multi-step port-of-call sequence prediction is vital for tactical resource orchestration and logistical efficiency. However, existing methods s

A Statistical Analysis for Per-Instance Evaluation of Stochastic Optimizers: Avoiding Unreliable Conclusions

arXiv:2503.16589v2 Announce Type: replace Abstract: A key trait of stochastic optimizers is that multiple runs of the same optimizer in attempting to solve the same problem can produce different resul

A Topology-Aware Spatiotemporal Handover Framework for Continuous Multi-UAV Tracking

arXiv:2605.15779v1 Announce Type: cross Abstract: The integration of Unmanned Aerial Vehicles(UAVs) into Intelligent Transportation Systems (ITS) offers synoptic visibility for traffic monitoring, yet

A Unified Generative-AI Framework for Smart Energy Infrastructure: Intelligent Gas Distribution, Utility Billing, Carbon Analytics, and Quantum-Inspired Optimisation

arXiv:2605.16232v1 Announce Type: cross Abstract: The accelerating convergence of smart metering, generative artificial intelligence, and quantum-inspired combinatorial optimisation is reshaping how e

A Unified Perturbation Framework for Analyzing Leaderboard Stability and Manipulation

arXiv:2605.15761v1 Announce Type: new Abstract: Evaluation leaderboards such as LMArena play a central role in benchmarking large language models by aggregating pairwise human preferences into model r

AdaEraser: Training-Free Object Removal via Adaptive Attention Suppression

arXiv:2605.15921v1 Announce Type: new Abstract: Object removal aims to eliminate specified objects from images while plausibly inpainting the affected regions with background content. Current training

ADAPT: A Self-Calibrating Proactive Autoscaler for Container Orchestration

arXiv:2605.15788v1 Announce Type: cross Abstract: Proactive autoscaling for containerized workloads depends on knowing the provisioning delay, i.e., the time between a scaling decision and the moment

Adaptive Conformal Prediction for Quantum Machine Learning

arXiv:2511.18225v2 Announce Type: replace Abstract: Quantum machine learning seeks to leverage quantum computers to improve upon classical machine learning algorithms. Currently, robust uncertainty qu

'AI is hiking your energy bill' is the most popular political talking point of 2026. The data doesn't support it. A thread:

This thread examines the 2026 political claim that AI is significantly increasing energy bills, arguing that available data does not support this assertion as a major economic impact. The post likely

researchyann-lecun--x

Amortized Energy-Based Bayesian Inference

arXiv:2605.15407v1 Announce Type: cross Abstract: We consider amortized Bayesian inference for nonlinear inverse problems in settings where only samples from the joint distribution of parameters and o

An LLM-RAG Approach for Healthy Eating Index-Informed Personalized Food Recommendations

arXiv:2605.15213v1 Announce Type: cross Abstract: Diet quality is a leading determinant of chronic disease risk. Advances in artificial intelligence (AI) have enabled food recommendation systems to ad

Antidistillation Fingerprinting

arXiv:2602.03812v2 Announce Type: replace-cross Abstract: Model distillation enables efficient emulation of frontier large language models (LLMs), creating a need for robust mechanisms to detect when

AOT-POT: Adaptive Operator Transformation for Large-Scale PDE Pre-training

arXiv:2605.15793v1 Announce Type: new Abstract: Pre-training neural operators on diverse partial differential equation (PDE) datasets has emerged as a promising direction for building general-purpose

Approximating Global Contact-Implicit MPC via Sampling and Local Complementarity

arXiv:2505.13350v2 Announce Type: replace Abstract: To achieve general-purpose dexterous manipulation, robots must rapidly devise and execute contact-rich behaviors. Existing model-based controllers a

researcharxiv-cs-ro

Are VLMs Seeing or Just Saying? Uncovering the Illusion of Visual Re-examination

arXiv:2605.15864v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) often produce self-reflective statements like 'let me check the figure again' during reasoning. Do such statements trigg

ART: Articulated Reconstruction Transformer

arXiv:2512.14671v3 Announce Type: replace Abstract: We introduce ART, Articulated Reconstruction Transformer -- a category-agnostic, feed-forward model that reconstructs complete 3D articulated object

Artificial Aphasias in Lesioned Language Models

arXiv:2605.16222v1 Announce Type: new Abstract: Aphasias, selective language impairments which can arise from brain damage, reveal the functional organization of human language by providing causal lin

Asking the Right Questions: Improving Reasoning with Generated Stepping Stones

arXiv:2602.19069v2 Announce Type: replace Abstract: Recent years have witnessed tremendous progress in enabling LLMs to solve complex reasoning tasks such as math and coding. As we start to apply LLMs

Attention Dispersion in Dynamic Graph Transformers: Diagnosis and a Transferable Fix

arXiv:2605.16112v1 Announce Type: cross Abstract: Transformer-based architectures have become the dominant paradigm for Continuous-Time Dynamic Graph (CTDG) learning, yet their performance remains lim

Autoguided Online Data Curation for Diffusion Model Training

arXiv:2509.15267v2 Announce Type: replace-cross Abstract: The costs of generative model compute rekindled promises and hopes for efficient data curation. In this work, we investigate whether recently

BARRIER: Bounded Activation Regions for Robust Information Erasure

arXiv:2605.15737v1 Announce Type: new Abstract: Machine unlearning has reached a critical bottleneck. As traditional weight-space interventions focus primarily on erasing targeted concepts, they often

Beyond Binary Rewards: Training LMs to Reason About Their Uncertainty

arXiv:2507.16806v2 Announce Type: replace-cross Abstract: When language models (LMs) are trained via reinforcement learning (RL) to generate natural language 'reasoning chains', their performance impr

Beyond Content: A Comprehensive Speech Toxicity Dataset and Detection Framework Incorporating Paralinguistic Cues

arXiv:2605.15984v1 Announce Type: cross Abstract: Toxic speech detection has become a crucial challenge in maintaining safe online communication environments. However, existing approaches to toxic spe

Beyond Forgetting: Machine Unlearning Elicits Controllable Side Behaviors and Capabilities

arXiv:2601.21702v3 Announce Type: replace-cross Abstract: We consider Representation Misdirection (RM), a class of large language model (LLM) unlearning methods that achieve forgetting by redirecting

Beyond Sunk Costs: Boosting LLM Pre-training Efficiency via Orthogonal Growth of Mixture-of-Experts

arXiv:2510.08008v2 Announce Type: replace Abstract: As the computational demands for pre-training Large Language Models (LLMs) continue to surge, the need for efficient training paradigms becomes crit

BioBlobs: Unsupervised Discovery of Functional Substructures for Protein Function Prediction

arXiv:2510.01632v3 Announce Type: replace-cross Abstract: Protein function is driven by cohesive substructures, such as catalytic triads, binding pockets, and structural motifs, that occupy only a sma

Bounded-Rationality, Hedging, and Generalization

arXiv:2605.15340v1 Announce Type: new Abstract: A learner does not only fit data; it also determines how strongly the training sample may shape its output and how much distortion it can hedge. We stud

BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models

arXiv:2602.04163v2 Announce Type: replace Abstract: Large language model inference is often bounded by memory footprint and bandwidth in resource-constrained deployments, making quantization fundament

Brain-OF: An Omnifunctional Foundation Model for fMRI, EEG and MEG

arXiv:2602.23410v3 Announce Type: replace-cross Abstract: Brain foundation models have achieved remarkable advances across a wide range of neuroscience tasks. However, most existing models are limited

Capability Conditioned Scaffolding for Professional Human LLM Collaboration

arXiv:2605.15404v1 Announce Type: new Abstract: Large language model personalization typically adapts outputs to user preferences and style but does not account for differences in user evaluation capa

CAPS: Cascaded Adaptive Pairwise Selection for Efficient Parallel Reasoning

arXiv:2605.15513v1 Announce Type: new Abstract: Parallel reasoning, where a generator samples many candidate solutions and an aggregator selects the best, is one of the most effective forms of test-ti

CG-MLLM: Captioning and Generating 3D content via Multi-modal Large Language Models

arXiv:2601.21798v2 Announce Type: replace Abstract: Large Language Models(LLMs) have revolutionized text generation and multimodal perception,but their capabilities in 3D content generation remain und

Community-aware evaluation and threshold calibration for open-set plankton image recognition

arXiv:2605.15835v1 Announce Type: new Abstract: Automated plankton image recognition is increasingly used in aquatic ecosystem monitoring, but deployed classifiers inevitably encounter unseen taxa and

CompactQE: Interpretable Translation Quality Estimation via Small Open-Weight LLMs

arXiv:2605.15763v1 Announce Type: cross Abstract: Current state-of-the-art Quality Estimation (QE) in machine translation relies on massive, proprietary LLMs, raising data privacy concerns. We demonst

Complexity of Non-Log-Concave Sampling in Fisher Information

arXiv:2605.15859v1 Announce Type: cross Abstract: We study the query complexity of obtaining a relative Fisher information guarantee for sampling from a log-smooth non-log-concave distribution; this i

Convergent Representations of Linguistic Constructions in Human and Artificial Neural Systems

arXiv:2603.29617v2 Announce Type: replace-cross Abstract: Understanding how the brain processes linguistic constructions is a central challenge in cognitive neuroscience and linguistics. Recent comput

Conversations in Space: Structuring Non-Linear LLM Interactions on a Canvas

arXiv:2605.15848v1 Announce Type: cross Abstract: Conversational interfaces powered by large language models (LLMs) are widely used for ideation and analysis, yet their linear structure limits explora

CrystalBoltz: End-to-End Protein Structure Determination via Experiment-Guided Diffusion for X-Ray Crystallography

arXiv:2605.15564v1 Announce Type: new Abstract: Generative models trained on public databases of protein structures, most of which have been determined by X-ray crystallography, now provide powerful p

CUBE: Contrastive Understanding by Balanced Experiments

arXiv:2509.10825v5 Announce Type: replace-cross Abstract: Explaining a trained model requires a clear account of how explanatory evidence is generated. We propose CUBE, a post-hoc explanation framewor

DealMaTe: Multi-Dimensional Material Transfer via Diffusion Transformer

arXiv:2605.15681v1 Announce Type: cross Abstract: Recently, diffusion-based material transfer methods rely on image fine-tuning or complex architectures with auxiliary networks but face challenges suc

Decision making was the bottleneck all along. Productivity is the rate at which you make open-ended decisions, the rate at which you reduce …

researchfrancois-chollet--x

Francois Chollet argues that decision-making, rather than execution, is the true limiting factor in productivity. He defines productivity as the rate at which one makes open-ended decisions and reduce

DecomPose: Disentangling Cross-Category Optimization Contention for Category-Level 6D Object Pose Estimation

arXiv:2605.15728v1 Announce Type: cross Abstract: Category-level 6D object pose estimation is typically formulated as a multi-category joint learning problem with fully shared model parameters. Howeve