cross-domain

This paper proposes a Multi-Modal Generative Fuzzy System (MMGFS) to enhance multimodal question answering by addressing modality bias and uncertainty through fuzzy inference and multi-hop reasoning, demonstrating improved performance on multiple benchmarks.

This paper introduces PAC-Bayes-regularized Meta-LoRA for cross-domain LLM personalization, enabling zero- and few-shot adaptation to user preferences while preventing overfitting under sparse evidence. Experiments on benchmarks like HiCUPID show significant improvements in cross-domain win rates and cold-start scenarios.

This paper introduces LUNAR, a benchmark for evaluating how large language models personalize responses from longitudinal app interaction histories across daily-life domains such as clothing, food, housing, and mobility. Experiments on 19 mainstream LLMs reveal that effective personalization depends on evidence selection and cross-domain integration, and that stronger personalization can come at the cost of privacy protection.

TenStrip/10Eros-Max is an experimental AI model that modifies the MiniMax H3 base model by transferring learned patterns from LTX 2.3, Wan 2.2, and Krea 2 video and image diffusion models using orthogonal projection, enhancing aesthetic and motion character while preserving core video and audio capabilities.

This arXiv paper introduces ProGFM, a Propagation-aware Graph Foundation Model that treats propagation relationships between edges and feature dimensions as transferable knowledge units, enabling adaptive aggregation and improved cross-domain generalization.

This paper proposes GenCDSR, a generative framework for cross-domain sequential recommendation with hybrid tokenization and serial-parallel decoding, achieving improved accuracy and significantly reduced inference latency compared to state-of-the-art baselines.

Presents NEXUS, a lightweight foundation model with ~3M parameters pre-trained on LHC collision data, demonstrating improved downstream performance and cross-domain transfer to gravitational waves, flood forecasting, and neural activity.

This paper introduces cross-domain off-policy evaluation and learning (OPE/L) for contextual bandits, allowing the use of logged data from multiple source domains to improve policy evaluation and learning in target domains with challenging conditions like few-shot data, deterministic logging policies, and new actions.

Moir is a method that improves cross-domain knowledge editing in LLMs by aligning the preservation distribution with the model's own decoding distribution, avoiding reliance on external corpora. It consistently preserves complex capabilities like mathematical reasoning across multiple models and editors.

This paper introduces a framework to test whether large language models exhibit consistent risk attitudes across domains. It finds that most LLMs show intra-task and cross-domain stability in risk attitude, converging to a narrower distribution than humans.

SinAE introduces a single-architecture flow-matching autoencoder using vanilla Transformers that achieves near-lossless reconstruction across molecules, crystals, and proteins, enabling cross-domain training and strong generative performance on standard benchmarks.

This paper evaluates whether hallucination signals from LLM internal representations generalize across languages and domains, focusing on Arabic↔English using TruthfulQA and HalluScore. Results show transferability for most models, with cross-lingual performance depending on class separability and language alignment.

Introduces IsoSci, a benchmark of isomorphic cross-domain science problem pairs that separates reasoning ability from domain knowledge retrieval in LLM evaluation. The study finds that 91.3% of reasoning-mode gains are knowledge-dependent, challenging common assumptions about chain-of-thought reasoning.

This paper introduces MedKGTab, a knowledge-injected framework that uses biomedical knowledge graphs to expand cross-domain features in tabular medical data, addressing data scarcity by generating high-fidelity biomedical profiles.

Proposes a blind visual paradigm using Three.js to test if procedural scaffolds extracted from large models can improve small model outputs without fine-tuning, validated by a blind judge model.

Proposes a knowledge-guided two-stage transfer learning framework using a lightweight GPT-2-style Transformer for cross-domain bearing fault diagnosis with limited data, achieving 92.61% accuracy with only 10% labeled data.

This paper presents Connect the Dots (CoD), a framework for training LLMs via reinforcement learning to develop meta-capabilities for long-lifecycle agents, enabling continuous learning and cross-domain generalization.

This article explores how abstract modeling ability helps people quickly transfer across different domains by extracting variables, relationships, and constraints to capture the core structure, rather than memorizing specific content.

The author shares a two-year experience deploying AI agents for investigating production incidents across team boundaries, highlighting that while the technical implementation was straightforward, the organizational politics posed the real challenge.

Kent C. Dodds shares advice from Grady Booch on reading source code outside your tech stack (e.g., MacPaint, Linux, MediaWiki) to build unique judgment through cross-domain curiosity.