“The article likely contains details on the architecture used by AutoScout24, providing a practical example of how to build a scalable AI agent development framework.”

“This paper introduces an Information-Obfuscation Reversible Adversarial Example (IO-RAE) framework, the pioneering method designed to safeguard audio privacy using reversible adversarial examples.”

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“The framework creates the image-encoding state using a unitary gate, which can later be transpiled to target quantum backends.”

“BEDA consistently outperforms strong baselines: on CKBG it improves success rate by at least 5.0 points across backbones and by 20.6 points with GPT-4.1-nano; on Mutual Friends it achieves an average improvement of 9.3 points; and on CaSiNo it achieves the optimal deal relative to all baselines.”

“AstroReview correctly identifies genuinely accepted proposals with an accuracy of 87% in the meta-review stage, and the acceptance rate of revised drafts increases by 66% after two iterations with the Proposal Authoring Agent.”

“Experiments demonstrate that Youtu-Agent achieves state-of-the-art performance on WebWalkerQA (71.47%) and GAIA (72.8%) using open-weight models.”

“Novice endoscopists exposed to EndoRare-generated cases achieved a 0.400 increase in recall and a 0.267 increase in precision.”

“The paper presents an activation-steering framework for MDLMs that computes layer-wise steering vectors from a single forward pass using contrastive examples, without simulating the denoising trajectory.”

“VisU autonomously orchestrates a four-stage workflow comprising Preprocessing, Recursive Channel Discovery, Important-Motion Identification, and Validation/Summary.”

“LoongFlow outperforms leading baselines (e.g., OpenEvolve, ShinkaEvolve) by up to 60% in evolutionary efficiency while discovering superior solutions.”

“ROAD achieved a 5.6 percent increase in success rate and a 3.8 percent increase in search accuracy within just three automated iterations.”

“The approach yields significant improvements in both accuracy and efficiency and, crucially, demonstrates strong cross-domain generalization while preserving the interpretability of chain-of-thought reasoning.”

“The paper argues that 'stochastic generative models can be fragile in operational domains unless paired with mechanisms that provide verifiable feasibility, robustness to distribution shift, and stress testing under high-consequence scenarios.'”

“McCoy orchestrates an LLM to translate medical literature into ASP code, combines it with patient data, and processes it using an ASP solver to arrive at the final diagnosis.”

“NestBrowse introduces a minimal and complete browser-action framework that decouples interaction control from page exploration through a nested structure.”

“The model reaching 96.23% accuracy, 95.58% F1-score and 94.83% specificity.”

“ClinDEF effectively exposes critical clinical reasoning gaps in state-of-the-art LLMs, offering a more nuanced and clinically meaningful evaluation paradigm.”

“KernelEvolve reduces development time from weeks to hours and achieves substantial performance improvements over PyTorch baselines.”

“GeoTeacher enhances the student model's ability to capture geometric relations of objects with limited training data, especially unlabeled data.”

“UniReg exhibits robust cross-domain and multi-modal performance comparable to optimization-based methods.”

“PI-MFM consistently outperforms purely data-driven counterparts, especially with sparse labeled spatiotemporal points, partially observed time domains, or few labeled function pairs.”

“It’s a pretty interesting take on making agents function more as long-lived entities.”

“WeDLM preserves the quality of strong AR backbones while delivering substantial speedups, approaching 3x on challenging reasoning benchmarks and up to 10x in low-entropy generation regimes; critically, our comparisons are against AR baselines served by vLLM under matched deployment settings, demonstrating that diffusion-style decoding can outperform an optimized AR engine in practice.”

“DICE achieves 85.7% agreement with human experts, substantially outperforming existing LLM-based metrics such as RAGAS.”

“T3LLM achieves state-of-the-art performance over strong LLM-based baselines.”

“OBEYED-VLA substantially improves robustness over strong VLA baselines across four challenging regimes and multiple difficulty levels: distractor objects, absent-target rejection, background appearance changes, and cluttered manipulation of unseen objects.”

“AMBIT demonstrates that physics-grounded residuals approach the accuracy of a strong tree-based predictor while retaining interpretable structure.”

“本データセットは、総務省のポータルサイト e-Gov などで公開されている法令文書などを参照した質問・回答ペアをまとめたデータセットであり、全ての質問が a ~ d の4択式の問題で構成されています。”

“本データセットは、総務省のポータルサイト e-Gov などで公開されている法令文書などを参照した質問・回答ペアをまとめたデータセットであり、全ての質問が a ~ d の4択式の問題で構成されています。”

“CDA captures cross-channel interdependencies while providing interpretable, privacy-preserving attribution insights, offering a scalable and future-proof alternative to traditional path-based models.”

“Agentic Multi-Persona Framework for Evidence-Aware Fake News Detection”

“"PRISM achieves superior personality consistency aligned with human ground truth, significantly outperforming standard homogeneous and Big Five benchmarks."”

“The paper introduces PRISM, a personality-driven multi-agent framework.”

“The article is sourced from ArXiv.”

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“TriFlow is a Progressive Multi-Agent Framework for Intelligent Trip Planning.”

“The article emphasizes a structured approach to daily learning and preparation, focusing on consistent effort and efficient use of time.”

“ART utilizes a multi-agent, tournament-based approach.”

“The context provided is from ArXiv, indicating a research paper.”

“Build Production-Ready Agentic-RAG Applications From Scratch!”

“The framework uses pure vision instead of error prone "set-of-marks" system, uses tiny VLM (Moondream) instead of OpenAI/Anthropic, and uses two agents: one for planning and adapting test cases and one for executing them quickly and consistently.”

“Our framework enables cybersecurity experts to identify which defenses are necessary—and how to prioritize them”

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