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- Large Language Model Agent: A Survey on Methodology, Applications and Challenges234
This survey systematically deconstructs LLM agent systems through a methodology-centered taxonomy, linking architectural foundations, collaboration mechanisms, and evolutionary pathways, and unify fragmented research threads by revealing fundamental connections between agent design principles and their emergent behaviors in complex environments.
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- Not All Parts Are Created Equal: 3D Pose Estimation by Modeling Bi-Directional Dependencies of Body Parts66
A progressive approach is proposed that explicitly accounts for the distinct DOFs among the body parts, and introduces a pose-attribution estimation, where the relative location of a limb joint with respect to the torso, which has the least DOF of a human body, is explicitly estimated and further fed to the joint-estimation module.
- A Comprehensive Survey of Data Augmentation in Visual Reinforcement Learning63
This work proposes a unified framework for analyzing visual RL and understanding the role of DA and presents a principled taxonomy of the existing augmentation techniques used in visual RL and conducts an in-depth discussion on how to better leverage augmented data in various scenarios.
- Patch Alignment Manifold Matting46
A manifold matting framework named Patch Alignment Manifold Matting is proposed for image matting, and a part modeling of color space in the local image patch and whole alignment optimization for approximating the alpha results using subspace reconstructing error are proposed.
- GenderBias-VL: Benchmarking Gender Bias in Vision Language Models via Counterfactual Probing39
This paper introduces the GenderBias-VL benchmark, a comprehensive dataset for occupation-related gender bias evaluation, an up-to-date leaderboard on LVLM biases, and a nuanced understanding of the biases presented by these models.
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- SCE: A Manifold Regularized Set-Covering Method for Data Partitioning22
A Structural Cluster Ensemble (SCE) algorithm for data partitioning formulated as a set-covering problem is proposed and a Laplacian regularized objective function is constructed to capture the structure information among clusters.
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- DATABench: Evaluating Dataset Auditing in Deep Learning from an Adversarial Perspective15
A comprehensive study evaluating dataset auditing from an adversarial perspective, including a new benchmark, DATABench, comprising 17 evasion attacks, 5 forgery attacks, and 9 representative auditing methods that reveal that none of the evaluated auditing methods are sufficiently robust or distinctive under adversarial settings.
- NoVo: Norm Voting off Hallucinations with Attention Heads in Large Language Models11
NoVo is presented, which harnesses the untapped potential of attention head norms to dramatically enhance factual accuracy in zero-shot multiple-choice questions (MCQs) and opens new frontiers in LLM interpretability, robustness and reliability.
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- Erasing Without Remembering: Implicit Knowledge Forgetting in Large Language Models6
This paper identifies a broader unlearning scope that includes both target data and logically associated samples, including rephrased, subject-replaced, relation-reversed, and one-hop reasoned data, and proposes PerMU, a novel probability perturbation-based unlearning paradigm.
- Reverse Prompt: Cracking the Recipe Inside Text-to-Image Generation4
This paper explores how to decode textual prompts from reference images, a process the authors refer to as image reverse prompt engineering, and proposes a method known as automatic reverse prompt optimization (ARPO), which refines an initial prompt into a high-quality prompt through an iteratively imitative gradient prompt optimization process.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; 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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