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- PaCo-RL: Advancing Reinforcement Learning for Consistent Image Generation with Pairwise Reward Modeling9
Extensive experiments show that PaCo-Reward significantly improves alignment with human perceptions of visual consistency, and PaCo-GRPO achieves state-of-the-art consistency performance with improved training efficiency and stability, highlighting the promise of PaCo-RL as a practical and scalable solution for consistent image generation.
- GenMatLab: A Generative Platform for Inverse Materials Design1
GenMatLab is a user-friendly web platform that makes latest AI techniques accessible for inverse materials design and generative models that support interactive operations, allowing users to conduct inverse design and investigate generated candidates in an intuitive and exploratory way.
- Uncover and unlearn nuisances: agnostic fully test-time adaptation1
This work exploits a dual perspective on FTTA, and proposes Agnostic FTTA (AFTTA) as a novel formulation that enables the usage of off-the-shelf domain transformations during test-time to enable direct generalization to unforeseeable target data.
- Why Settle for One? Text-to-ImageSet Generation and Evaluation1
A training-free framework that maximally leverages pretrained Diffusion Transformers'in-context capabilities to harmonize visual elements to satisfy both image-level prompt alignment and set-level visual consistency, and significantly outperforms current generalized and even specialized approaches.
- Multi-Modal Dataset Distillation in the Wild1
Multi-modal dataset Distillation in the Wild is proposed, the first framework to distill noisy multi-modal datasets into compact clean ones for effective and efficient model training and introduces learnable fine-grained correspondences during distillation and adaptively optimizes distilled data to emphasize correspondence-discriminative regions, thereby enhancing distilled data's information density and efficacy.
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- BadSKP: Backdoor Attacks on Knowledge Graph-Enhanced LLMs with Soft Prompts–
This work proposes BadSKP, a backdoor attack that targets the graph-to-prompt interface through a multi-stage optimization strategy: it constructs adversarial target embeddings, optimizes poisoned node embeddings to steer the induced soft prompt, and approximates the optimized representations with fluent adversarial node attributes.
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- Exploring the Effectiveness and Interpretability of Texts in LLM-based Time Series Models–
The analysis reveals the misalignment and limited interpretability of texts in current time-series LLMs, and proposes a novel metric named Semantic Matching Index (SMI) to better evaluate the matching degree between time series and texts during the post hoc interpretability investigation.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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