Search: “models”

Context-aware child-directed speech detection from long-form recordings

arXiv:2606.01134v1 Announce Type: cross Abstract: Automatically distinguishing child-directed speech from adult-directed speech in long-form recordings is key to scalable analyses of children's langua

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Research

Contrastive meta-domain adaptation for robust skin lesion classification across clinical and acquisition conditions

DGX agent

arXiv:2602.19857v2 Announce Type: replace Abstract: Deep learning models for dermatological image analysis remain sensitive to acquisition variability and domain-specific visual characteristics, leadi

researcharxiv-cs-cv

2 Jun 2026

CORE-MTL: Rethinking Gradient Balancing via Causal Orthogonal Representations

arXiv:2606.02221v1 Announce Type: new Abstract: Multi-task learning (MTL) aims to construct a joint model for multiple tasks by sharing a common representation across domains. To achieve this goal, ex

Counterfactual Intervention Feature Transfer for Visible-Infrared Person Re-identification

arXiv:2208.00967v4 Announce Type: replace Abstract: Graph-based models have achieved great success in person re-identification tasks recently, which compute the graph topology structure (affinities) a

Deep Learning as the Disciplined Construction of Tame Objects

arXiv:2509.18025v2 Announce Type: replace-cross Abstract: One can see deep-learning models as compositions of functions within the so-called tame geometry. In this expository note, we give an overview

Defenses & Enablers For Skill Injection Attacks on Terminal Based Agents

arXiv:2606.01567v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly rely on reusable skills i.e. documents describing task-specific procedures. However, this introduces a

Demystifying Multi-Agent Debate: The Role of Confidence and Diversity

arXiv:2601.19921v2 Announce Type: replace-cross Abstract: Multi-agent debate (MAD) is widely used to improve large language model (LLM) performance through test-time scaling, yet recent work shows tha

Local Ai

DerMAE: Improving skin lesion classification through conditioned latent diffusion and MAE distillation

arXiv:2602.19848v2 Announce Type: replace Abstract: Skin lesion classification datasets often suffer from severe class imbalance, with malignant cases significantly underrepresented, leading to biased

local-aiarxiv-cs-cv

Dimension Reduction via Sum-of-Squares and Improved Clustering Algorithms for Non-Spherical Mixtures

arXiv:2411.12438v2 Announce Type: replace-cross Abstract: We develop a new approach for clustering non-spherical (i.e., arbitrary component covariances) Gaussian mixture models via a subroutine, based

Dive into Ambiguity: A*-Inspired Multi-Agents Commonsense Obfuscation Attack on LLM Prompts

arXiv:2606.01441v1 Announce Type: new Abstract: Large language models (LLMs) excel in reasoning and knowledge-intensive tasks but remain vulnerable to prompt-level adversarial attacks that preserve in

Dive into Waves: Morlet Spectral Transformer for Cross-Subject Emotion Decoding from EEG

arXiv:2606.00884v1 Announce Type: cross Abstract: We study cross-subject emotion recognition from EEG, a practically important yet challenging problem in brain-computer interfaces. Unlike tasks with c

DyLLM: Efficient Diffusion LLM Inference via Saliency-based Token Selection and Partial Attention

arXiv:2603.08026v2 Announce Type: replace-cross Abstract: Masked diffusion language models enable parallel token decoding, providing a promising alternative to the sequential nature of autoregressive

Dynamic Coordination Strategy Selection for Enterprise Multi-Agent Systems

arXiv:2606.00804v1 Announce Type: cross Abstract: Enterprise multi-agent systems increasingly expose multiple coordination patterns, but deployments often lack evidence for when to use consensus, deba

Dynamic Trust-Aware Sparse Communication Topology for LLM-Based Multi-Agent Consensus

arXiv:2606.01828v1 Announce Type: cross Abstract: Large language model-driven multi-agent systems enhance the reliability of complex reasoning tasks through multi-round deliberation, role specializati

Edge-aware Decoding for Neural Asymmetric Routing

arXiv:2606.02136v1 Announce Type: new Abstract: Neural asymmetric routing models increasingly encode directionality through matrix representations and asymmetry-aware attention. The final routing acti

Edge Prediction for Roof Wireframe Reconstruction with Transformers

arXiv:2606.02406v1 Announce Type: new Abstract: This paper presents a competitive solution to the S23DR Challenge 2026, which aims to reconstruct 3D house roof wireframe models from sparse SfM point c

Effects of Varying LLM Access on Essay Writing Behavior

arXiv:2606.00250v1 Announce Type: cross Abstract: Investigating the degree to which large language models (LLMs) affect teaching and learning in universities can help identify strategies for integrati

Efficient Synthetic Network Generation via Latent Embedding Reconstruction

arXiv:2606.00934v1 Announce Type: cross Abstract: Network data are ubiquitous across the social sciences, biology, and information systems. Generating realistic synthetic network data has broad applic

ETC: Extreme Token Compression via Task-aware Visual Information Distillation in VLMs

arXiv:2606.00543v1 Announce Type: new Abstract: In Vision-Language Models (VLMs), high-resolution images produce a large number of visual tokens, resulting in high computational costs and KV-cache ove

Applications

Evaluating Bivariate Causal Statements Based on Mutual Compatibility

arXiv:2606.00278v1 Announce Type: new Abstract: For many real-world systems, causal ground truth is difficult to obtain, making claims about causal effects hard to assess. We develop methods for evalu

applicationsarxiv-cs-ai

Evidence-Gated LLM Priors for Multi-Objective Bayesian Optimization

arXiv:2606.01730v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as heuristic advisors for black-box optimization, yet their suggestions and self-reported confidence

tutorialsarxiv-cs-ai

Family Matters: A Systematic Study of Spatial vs. Frequency Masking for Continual Test-Time Adaptation

arXiv:2512.08048v3 Announce Type: replace Abstract: Recent continual test-time adaptation (CTTA) methods adopt masked image modeling to stabilize learning under distribution shift, yet each treats its

safetyarxiv-cs-cv

Fast and Lightweight Novel View Synthesis with Differentiable Multiplane Image

arXiv:2606.02068v1 Announce Type: cross Abstract: Recently, novel view synthesis has witnessed remarkable progress, with mainstream methods such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatt

Finding What Matters: Anchoring Context Knowledge with Evolving Indices for Iterative Retrieval

arXiv:2601.16462v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) has become a dominant paradigm for mitigating hallucinations in Large Language Models (LLMs) by incorporating e

tutorialsarxiv-cs-cl

Frequentist Consistency of Prior-Data Fitted Networks for Causal Inference

arXiv:2603.12037v2 Announce Type: replace Abstract: Foundation models based on prior-data fitted networks (PFNs) have shown strong empirical performance in causal inference by framing the task as an i

safetyarxiv-cs-lg

From Global to Local: Learning Context-Aware Graph Representations for Document Classification and Summarization

arXiv:2603.00021v2 Announce Type: replace Abstract: Recent NLP systems commonly represent documents as linear token sequences. Although this captures sequential order, it can hinder modeling long-rang

researcharxiv-cs-cl

From Layers to Submodules: Rethinking Granularity in Replacement-Based LLM Compression

arXiv:2606.02559v1 Announce Type: cross Abstract: Post-training compression of Large Language Models (LLMs) removes entire architectural components, either deleting them or replacing them with fitted

Applications

Graph-Augmented Retrieval for Cross-Entity Financial Sentiment Analysis: A Comparative Study

arXiv:2606.00062v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become foundational for grounding large language models in domain-specific corpora, yet conventional vector-bas

applicationsarxiv-cs-cl

Graph Transfer Learning via Shared Latent Geometry: Theory and Applications

arXiv:2606.00716v1 Announce Type: new Abstract: Inference and control in engineered physical systems pay a heavy physics cost at deployment: state estimators, inverse-problem solvers, model-predictive

Hard Labels In! Rethinking the Role of Hard Labels in Mitigating Local Semantic Drift

arXiv:2512.15647v3 Announce Type: replace Abstract: Soft labels from teacher models are a de facto practice for knowledge transfer and large-scale dataset distillation (e.g., SRe2L, LPLD). However, wh

safetyarxiv-cs-cv

HASTE: Hardware-Aware Dynamic Sparse Training for Large Output Spaces

arXiv:2606.01117v1 Announce Type: cross Abstract: Extreme multi-label classification (XMC) involves learning models over large output spaces with millions of labels, making the output layer a memory-c

Honey, I Shrunk the Arc de Triomphe!

arXiv:2606.02379v1 Announce Type: new Abstract: Metric scale monocular geometry estimation has seen significant progress through large-scale data aggregation, yet current foundation models suffer from

How Far Do Auto-Interpretation Labels Generalize: A Controlled Study Across Languages, Scripts, and Rewordings

arXiv:2606.00356v1 Announce Type: new Abstract: Sparse autoencoder (SAE) features are increasingly used to interpret language models, with auto-generated natural-language labels serving as the primary

researcharxiv-cs-cl

Hybrid Probabilistic Forecasting of Under-Five Malaria Admissions in Ghana: A Gaussian Process Regression with Holt-Winters Smoothing

arXiv:2606.00834v1 Announce Type: cross Abstract: Accurate malaria forecasting remains a major challenge in sub-Saharan Africa, where strong seasonality, reporting uncertainty, and non-stationary tran

Improving Visual Token Reduction via Rectifying Distortions for Efficient Multimodal LLM Inference

arXiv:2606.01711v1 Announce Type: new Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have achieved remarkable success in vision-language tasks, yet the quadratic computation

Information-Theoretic Lower Bounds for Bit-Constrained Stochastic Optimization via a Reduction to Compressed Gaussian Mean Estimation

arXiv:2606.00703v1 Announce Type: cross Abstract: Low-precision pretraining (FP8, MXFP4, NVFP4) is now standard for frontier language models, yet the literature is almost entirely achievability -- alg

Interpretable Self-Supervised Learning via Representer Landmarks and Nystrom Approximation

arXiv:2509.24467v3 Announce Type: replace Abstract: Self-supervised learning (SSL) learns representations from massive unlabeled data, yet the resulting models typically operate as black boxes, necess

safetyarxiv-cs-lg

Interpreto: An Explainability Library for Transformers

arXiv:2512.09730v3 Announce Type: replace Abstract: Interpreto is an open-source Python library for interpreting HuggingFace language models, from early BERT variants to LLMs. It provides two compleme

researcharxiv-cs-cl

Is Zero-Shot Super-Resolution Possible in Operator Learning?

arXiv:2606.00296v1 Announce Type: cross Abstract: Neural operators are often reported to exhibit zero-shot super-resolution, a phenomenon in which a model trained on coarse grids produces accurate pre

Iteris: Agentic Research Loops for Computational Mathematics

arXiv:2606.02484v1 Announce Type: new Abstract: Recent advances in large language models and agentic AI systems have enabled significant progress in mathematical discovery, from solving competition pr

KACE: Knowledge-Adaptive Context Engineering for Mathematical Reasoning

arXiv:2606.00532v1 Announce Type: new Abstract: Context engineering can improve large language models without updating their weights, but mathematical reasoning exposes a key limitation: feedback accu

Last Layer Logits to Logic: Empowering LLMs with Logic-Consistent Structured Knowledge Reasoning

arXiv:2511.07910v2 Announce Type: replace Abstract: Large Language Models (LLMs) achieve excellent performance in natural language reasoning tasks through pre-training on vast unstructured text, enabl

tutorialsarxiv-cs-cl

LeARN: Learnable and Adaptive Representations for Nonlinear Dynamics in System Identification

arXiv:2412.12036v2 Announce Type: replace Abstract: System identification, the process of deriving mathematical models of dynamical systems from observed input-output data, has undergone a paradigm sh

agentsarxiv-cs-lg

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach

arXiv:2509.24627v2 Announce Type: replace Abstract: Embedding physical intuition into network architectures allows the learning of dynamics that enforce fundamental properties, such as energy conserva

Learning When Not to Act: Mitigating Tool Abuse in Agentic Reinforcement Learning

arXiv:2606.02132v1 Announce Type: new Abstract: Agentic reinforcement learning can induce tool abuse, where models overuse external tools even for queries solvable by internal reasoning. Existing appr

LLM Trainer: Automated Robotic Data Generation via Demonstration Augmentation using LLMs

arXiv:2509.20070v2 Announce Type: replace Abstract: We present LLM Trainer, a fully automated pipeline that leverages the world knowledge of Large Language Models (LLMs) to transform a small number of

safetyarxiv-cs-ro

LLMs Need Encoders for Semantic IDs Too

arXiv:2606.00324v1 Announce Type: cross Abstract: Multimodal LLMs use dedicated encoders to bridge non-language modalities (vision encoders for images, depth models for audio codec tokens) because raw

tutorialsarxiv-cs-ai

Hardware

Lodestar: An Online-Learning LLM Inference Router

arXiv:2606.00946v1 Announce Type: cross Abstract: Efficiently serving large language model (LLM) inference tasks is crucial both for user-perceived latency such as time-to-first-token (TTFT) and for G

hardwarearxiv-cs-ai