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Transformer-based few-shot learning for modeling Electricity Consumption Profiles with minimal data across thousands of domains

arXiv:2408.08399v3 Announce Type: replace Abstract: Electricity Consumption Profiles (ECPs) are crucial for operating and planning power distribution systems, especially with the increasing number of

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Transformers over-extend what humans underlearn: the case of Spanish L-shaped morphome

DGX agent

arXiv:2507.21556v3 Announce Type: replace Abstract: The cognitive reality of irregular morphological patterns has been debated for decades: do speakers extend them to novel forms, or are they lexical

applicationsarxiv-cs-cl

26 May 2026

Translators as Invisible Teachers of AI: Copyright, Translation Memory, and the Political Economy of Linguistic Data

arXiv:2605.24842v1 Announce Type: new Abstract: This paper examines how the labour of translators has been transformed into foundational data capital for the age of artificial intelligence (AI). Trans

applicationsarxiv-cs-cl

Local Ai

Treatment Effect Estimation with Differentiated Networked Effect on Graph Data

arXiv:2605.24358v1 Announce Type: cross Abstract: Estimating individual treatment effect (ITE) from observational graph data is crucial for decision-making in the fields such as commerce and medicine.

local-aiarxiv-cs-ai

Triplet-Block Diffusion RWKV

arXiv:2605.25969v1 Announce Type: new Abstract: Causal Transformer language models suffer from strictly sequential decoding and a quadratic per-step attention cost. While linear-time causal models and

researcharxiv-cs-cl

TriVAL: A Tri-Validation Framework for Faithful Automatic Optimization Modeling

arXiv:2605.23966v1 Announce Type: cross Abstract: Optimization modeling serves as the pivotal bridge between natural-language problem descriptions and optimization solvers, and remains a cornerstone f

Trust-Aware Joint Feature-Prediction Discrepancy for Robust Domain Adaptation

arXiv:2605.25119v1 Announce Type: cross Abstract: Domain adaptation aims to mitigate performance degradation caused by distribution shifts between a labeled source domain and an unlabeled or sparsely

Trust but Verify: Prover-Verifier Deliberation for Selective LLM Prediction

arXiv:2605.25133v1 Announce Type: new Abstract: Reliably knowing when a language model is correct is almost as important as being correct. We introduce prover-verifier deliberation (PVD), an inference

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing

arXiv:2605.24052v1 Announce Type: cross Abstract: To better serve users' demands in mobile applications (e.g., navigation), mobile crowdsourcing platforms can iteratively align large language model (L

TS-Skill: A Benchmark for Evaluating Analytical Skills in Time-Series Question Answering

arXiv:2605.24703v1 Announce Type: cross Abstract: Large language models (LLMs) and time-series language models (TSLMs) are increasingly applied to time-series question answering (TSQA). Unlike text-on

TSFLora: Token-Compressed Split Fine-Tuning for Wireless Edge Networks

arXiv:2605.23988v1 Announce Type: cross Abstract: Adapting large AI models (LAMs) to personalized edge data is challenging because wireless devices have limited memory, computation, and uplink capacit

TTPrint: Evidence-Grounded TTP Extraction via Diverge-then-Converge Verification

arXiv:2605.25836v1 Announce Type: cross Abstract: Extracting MITRE ATT&CK techniques from cyber threat intelligence (CTI) reports is an open-set, multi-label problem requiring both high recall (not mi

TUBE: Tangent Upper Bound on Evidence for Discrete Diffusion Language Models

arXiv:2605.24292v1 Announce Type: new Abstract: Log-likelihood is a standard metric for evaluating generative models. Unfortunately, in contrast to autoregressive models (ARMs), discrete diffusion mod

Turn-Based Structural Triggers: Prompt-Free Backdoors in Multi-Turn LLMs

arXiv:2601.14340v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are widely integrated into interactive systems such as dialogue agents and task-oriented assistants. This growing

Tutorials

Two-Sided Time-Independent Regret for Matching Markets with Limited Interviews

arXiv:2602.12224v2 Announce Type: replace-cross Abstract: Two-sided matching platforms rely on preferences from both sides, yet participants can evaluate only a small fraction of potential partners. I

tutorialsarxiv-cs-ai

TypedCSIP: Typed Counterfactual Pretraining for Chinese Legislative Conflict Classification

model-releasesarxiv-cs-cl

arXiv:2605.25474v1 Announce Type: new Abstract: TypedCSIP is a typed counterfactual pretraining method for the conflict-classification task of the LCR-CN benchmark (Zhao et al., 2026): given a (superi

Tutorials

UNATE: UNsupervised ATomic Embedding for crystal structures property prediction

arXiv:2605.25866v1 Announce Type: new Abstract: Accurately predicting crystal properties is critical for accelerating materials discovery, but it is often limited by scarce labeled data and costly the

tutorialsarxiv-cs-lg

Unbalanced Incomplete Multi-view Clustering via the Scheme of View Evolution: Weak Views are Meat; Strong Views do Eat

arXiv:2011.10254v3 Announce Type: replace-cross Abstract: Incomplete multi-view clustering is an important technique to deal with real-world incomplete multi-view data. Previous works assume that all

applicationsarxiv-cs-ai

Uncertainty Decomposition via Cyclical SG-MCMC and Soft-label Learning for Subjective NLP

arXiv:2605.24773v1 Announce Type: new Abstract: Annotator disagreement in emotion classification reflects ambiguity intrinsic to emotion concepts and is essential for predictor-quality assessment in s

Uncertainty-DTW for Sequences and Visual Tokens

arXiv:2605.25110v1 Announce Type: cross Abstract: Aligning structured data is a fundamental problem in computer vision and machine learning, underlying tasks such as time series analysis, human action

Tutorials

Uncertainty Reasoning with Large Language Models for Explainable Disease Diagnosis

arXiv:2605.25566v1 Announce Type: new Abstract: Clinical decision-making requires reasoning over incomplete, imprecise, and linguistically expressed patient narratives. While large language models (LL

tutorialsarxiv-cs-ai

Uncovering Autoregressive LLM Knowledge of Thematic Fit in Event Representation

arXiv:2410.15173v4 Announce Type: replace-cross Abstract: The thematic fit estimation task measures semantic arguments' compatibility with a given semantic role for a given predicate. We investigate i

Uncovering Vulnerabilities of LLM-Assisted Cyber Threat Intelligence

arXiv:2509.23573v4 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used to help security analysts manage the surge of cyber threats, automating tasks from vulnerab

Understanding, Accelerating, and Improving MeanFlow Training

arXiv:2511.19065v2 Announce Type: replace-cross Abstract: MeanFlow promises high-quality generative modeling in few steps, by jointly learning instantaneous and average velocity fields. Yet, the under

Understanding and Mitigating Premature Confidence for Better LLM Reasoning

arXiv:2605.24396v1 Announce Type: new Abstract: Long chains of thought (CoT) from current language models frequently contain logical gaps and unjustified leaps, limiting the gains from additional test

Understanding Conversational Patterns in Multi-agent Programming: A Case Study on Fibonacci Game Development

arXiv:2605.24138v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly applied to software engineering (SE), yet their potential for autonomous, role-oriented collaboration re

Understanding the Impact of Geometric Foundation Models on Vision-Language-Action Models

arXiv:2605.24642v1 Announce Type: cross Abstract: Recent work explores new opportunities at the intersection of vision-language-action models (VLAs) and geometric foundation models (GFMs) for 3D recon

researcharxiv-cs-ro

Uni-DPO: A Unified Paradigm for Dynamic Preference Optimization of LLMs

arXiv:2506.10054v4 Announce Type: replace-cross Abstract: Direct Preference Optimization (DPO) has emerged as a cornerstone of reinforcement learning from human feedback (RLHF) due to its simplicity a

Unifying Value Alignment and Assignment in Cross-Domain Offline Reinforcement Learning with Heterogeneous Datasets

arXiv:2605.24862v1 Announce Type: new Abstract: Cross-domain offline reinforcement learning (RL) aims to learn a policy in the target domain with a limited target domain dataset and a source domain da

safetyarxiv-cs-lg

UniRank: End-to-End Domain-Specific Reranking of Hybrid Text-Image Candidates

arXiv:2603.29897v2 Announce Type: replace-cross Abstract: Reranking is a critical component in many information retrieval pipelines. Despite remarkable progress in text-only settings, multimodal reran

Universal Activation Verbalizer: A Unified Framework for Cross-Model Activation Explanation

arXiv:2605.25903v1 Announce Type: new Abstract: Activation verbalization explains hidden representations in natural language, but existing methods are mostly limited to self-explanation, where each mo

researcharxiv-cs-cl

Universal Boosts, Specific Suppressors: Sparse Autoencoder Steering of Medical Vision-Language Models

arXiv:2605.24977v1 Announce Type: cross Abstract: Medical vision-language models (VLMs) often hallucinate findings when generating chest X-ray reports: they fabricate findings that are not present in

safetyarxiv-cs-cl

Local Ai

Unlocking Apple's Private Cloud Compute: An Analysis of Privacy-Preserving Artificial Intelligence

arXiv:2605.24239v1 Announce Type: cross Abstract: Many existing Artificial Intelligence (AI) solutions on mobile devices rely on an extensive collection of sensitive data, raising privacy concerns and

local-aiarxiv-cs-ai

Unveil: Unified Visual-Textual Integration and Distillation for Multi-modal Document Retrieval

arXiv:2605.24530v1 Announce Type: new Abstract: Document retrieval in real-world scenarios faces significant challenges due to diverse document formats and modalities. Traditional text-based approache

applicationsarxiv-cs-cl

URS: A Unified Neural Routing Solver for Cross-Problem Zero-Shot Generalization

model-releasesarxiv-cs-lg

arXiv:2509.23413v2 Announce Type: replace Abstract: Multi-task neural routing solvers have emerged as a promising paradigm for their ability to solve multiple vehicle routing problems (VRPs) using a s

UtilityMax Prompting: A Formal Framework for Multi-Objective Large Language Model Tasks

arXiv:2603.11583v4 Announce Type: replace-cross Abstract: The success of a Large Language Model (LLM) task depends heavily on its prompt. Most use-cases specify prompts using natural language, which i

UWM-JEPA: Predictive World Models That Imagine in Belief Space

arXiv:2605.25313v1 Announce Type: cross Abstract: World models for partially observed environments must imagine multiple compatible hidden futures and steer between them under counterfactual actions.

V3H: View Variation and View Heredity for Incomplete Multi-view Clustering

model-releasesarxiv-cs-lg

arXiv:2011.11194v4 Announce Type: replace Abstract: Real data often appear in the form of multiple incomplete views. Incomplete multi-view clustering is an effective method to integrate these incomple

VaaWIT: Visual-Aware Adaptation of Large Language Models for Multilingual Web Image Translation

arXiv:2605.24675v1 Announce Type: cross Abstract: Translating text embedded in Web images is crucial for improving content accessibility and cross-lingual information retrieval, particularly within so

Variable Clustering via Distributionally Robust Nodewise Regression

arXiv:2212.07944v3 Announce Type: replace Abstract: We study a multi-factor block model for variable clustering and connect it to regularized subspace clustering through a distributionally robust vers

vAttention: Verified Sparse Attention

arXiv:2510.05688v2 Announce Type: replace-cross Abstract: State-of-the-art sparse attention methods for reducing decoding latency fall into two main categories: approximate top-k (and its extension, t

VectorArk: Learning Practical Image Vectorization with Rounded Polygon Representation

arXiv:2605.24398v1 Announce Type: cross Abstract: Recent vision-language model (VLM)-based approaches have achieved impressive results on image vectorization tasks. However, they are typically evaluat

applicationsarxiv-cs-ai

VEN-VL: A Visual Ensemble MoE Framework for Effective and Efficient Multi-Modal Understanding

arXiv:2605.25952v1 Announce Type: cross Abstract: Despite the remarkable progress achieved by recent efficient methods in accelerating multimodal understanding, they still suffer from noticeable perfo

Verified SHAP: Provable Bounds for Exact Shapley Values of Neural Networks

arXiv:2605.24084v1 Announce Type: cross Abstract: Shapley additive explanations (SHAP) are widely recognised as computationally intractable for neural networks, since they induce an exponential search

VeriTrace: Evolving Mental Models for Deep Research Agents

arXiv:2605.26081v1 Announce Type: new Abstract: Deep research agents face vast, interdependent, and pervasively uncertain information. Existing systems explore what evolving intermediate representatio

Agents

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows

arXiv:2605.23914v1 Announce Type: cross Abstract: Agentic workflows interleave configurable LLM stages with tool stages and often include retries or refinement loops. Existing workflow managers profil

agentsarxiv-cs-ai

ViroBench: Benchmarking Nucleotide Foundation Models on Viral Genomics Tasks

model-releasesarxiv-cs-lg

arXiv:2605.25388v1 Announce Type: new Abstract: Nucleotide sequences constitute the fundamental genetic basis of biological systems, rendering viral genomic analysis critical for biomedical advancemen

Vision-Guided Outdoor Flight and Obstacle Evasion via Reinforcement Learning

arXiv:2605.24449v1 Announce Type: cross Abstract: Although quadcopters boast impressive traversal capabilities enabled by their omnidirectional maneuverability, the need for continuous pilot control i

safetyarxiv-cs-lg