“Unlike black-box methods, MCEMOL delivers dual value: interpretable transformation rules researchers can understand and trust, alongside high-quality molecular libraries for practical applications.”

“The paper introduces an explicit formulation of object arrangement preferences along four interpretable constructs: spatial practicality, habitual convenience, semantic coherence, and commonsense appropriateness.”

“Probes with a small supervisor model can achieve low calibration error and Brier Skill Score of approx 0.2 estimating edited lines on code generated by models many orders of magnitude larger.”

“WM-SAR consistently outperforms existing deep learning and LLM-based methods.”

“CogRec leverages Soar as its core symbolic reasoning engine and leverages an LLM for knowledge initialization to populate its working memory with production rules.”

“Out-of-distribution prompts can manipulate the routing strategy such that all tokens are consistently routed to the same set of top-$k$ experts, which creates computational bottlenecks.”

“The RL-GOAL attacker achieves higher mean OGF (up to 2.81 +/- 1.38) across victims, demonstrating its effectiveness.”

“KANO provides a transparent and structured representation of the latent degradation fitting process.”

“By reframing LLMs as knowledge curation engines rather than black-box predictors, this work demonstrates a scalable, interpretable, and workflow-compatible pathway for advancing AI-driven decision support in oncology.”

“Instructors can adjust concept predictions and instantly view the updated grade, enabling accountable human-in-the-loop evaluation.”

“"Our findings indicate that the UDE model is much more data efficient, needing only 20% of data for a correct forecast, whereas the Neural ODE requires 90%."”

“PHANTOM achieves over 90\% attack success rate under optimal conditions and maintains 60-80\% effectiveness even in degraded environments.”

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“The article's source is ArXiv, suggesting that it's a pre-print of a scientific research paper.”

“”

“The research is sourced from ArXiv, indicating a pre-print publication.”

“The paper focuses on rank-based zeroth-order algorithms and their query complexities.”

“The article likely presents a novel approach to financial analysis, potentially offering advantages in terms of transparency and interpretability compared to existing methods.”

“”

“The paper is available on ArXiv.”

“The research is published on ArXiv.”

“The research focuses on auto-tuning safety guardrails.”

“OPAL addresses optimization problems.”

“The article focuses on visualizing token importance.”

“”

“Black-Box Behavioral Distillation Breaks Safety Alignment in Medical LLMs”

“The research is published on ArXiv.”

“The article is based on a paper published on ArXiv, suggesting a peer-reviewed or pre-print research.”

“”

“The paper focuses on interpreting hard labels in black-box text classifiers.”

“The study focuses on classifying the structure and magnetism in Delafossite compounds.”

“The article likely discusses the use of black-box optimization, which means the internal workings of the LLM are not directly accessed. Instead, the optimization process relies on evaluating the LLM's output based on different prompt inputs.”

“Nanda reckons that machine learning is unique because we create neural networks that can perform impressive tasks (like complex reasoning and software engineering) without understanding how they work internally.”

“Stochastic gradient descent is a universal choice for optimizing deep learning models. However, it is not the only option. With black-box optimization algorithms, you can evaluate a target function $f(x): \mathbb{R}^n \to \mathbb{R}$, even when you don’t know the precise analytic form of $f(x)$ and thus cannot compute gradients or the Hessian matrix.”

“Further analysis would require the full article content. Potential areas of focus could include specific attack methodologies (e.g., model extraction, membership inference), defenses against such attacks, and the ethical considerations surrounding model security.”

“The article is an introduction to model-based machine learning.”