Search: “models”

CA-BED: Conversation-Aware Bayesian Experimental Design

arXiv:2606.01182v1 Announce Type: cross Abstract: Large Language Models (LLMs) excel at static reasoning tasks, yet their performance often degrades in interactive scenarios where information must be

CARES: Context-Aware Resolution Selector for VLMs

arXiv:2510.19496v3 Announce Type: replace-cross Abstract: Large vision-language models (VLMs) commonly process images at native or high resolution to remain effective across tasks. This inflates visua

researcharxiv-cs-ai

2 Jun 2026

Characterizing the Effect of Noise in Language Generation in the Limit

arXiv:2601.21237v2 Announce Type: replace-cross Abstract: Kleinberg and Mullainathan recently proposed a formal framework for studying the phenomenon of language generation, called language generation

Chunking Methods on Retrieval-Augmented Generation - Effectiveness Evaluation Against Computational Cost and Limitations

arXiv:2606.00881v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has demonstrated significant capabilities in enhancing the performance of Large Language Models (LLMs). One of the

Code2Math: Can Your Code Agent Effectively Evolve Math Problems Through Exploration?

arXiv:2603.03202v3 Announce Type: replace Abstract: As large language models (LLMs) advance their mathematical capabilities toward the IMO and research level, the scarcity of challenging, high-quality

agentsarxiv-cs-cl

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

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

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

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

safetyarxiv-cs-ai

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

safetyarxiv-cs-ai

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

Tutorials

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

Tutorials

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

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

safetyarxiv-cs-ai

Tools

Holo3.1: Fast & Local Computer Use Agents

Holo3.1 is a computer use agent developed by Hugging Face that enables fast, local execution of tasks on computing systems without requiring cloud infrastructure. The model is designed to interpret an

toolshugging-face

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

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

Industry

If your daughter needs tutoring in algebra, you can probably find someone cheaper than Albert Einstein. Giving every task to GPT5.5 or Opus …

If your daughter needs tutoring in algebra, you can probably find someone cheaper than Albert Einstein. Giving every task to GPT5.5 or Opus 4.8 is overkill. Often times you can get the task done just

industryclem-delangue--x

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

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

Applications

It is difficult to know how good MAI-Thinking-1 is from the scores alone (like weirdly low GPQA & Terminal Bench 2.0) But Microsoft makes it…

applicationsethan-mollick--x

It is difficult to know how good MAI-Thinking-1 is from the scores alone (like weirdly low GPQA & Terminal Bench 2.0) But Microsoft makes it really hard to try its models upon release (a general issue

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