Abstract Artificial intelligence (AI) has the potential to address workforce shortages and workflow inefficiencies in radiation oncology, particularly in low-resourced settings where limited specialist capacity constrains access to care. This systematic review evaluated AI deployments in radiation oncology in low- and...
Doris Keziah Ndassi, Solomon Kibudde, Sizhuo Meng et al.· npj Digital Medicine· 0 citations
Generative artificial intelligence (GenAI) is increasingly embedded in the everyday work of statisticians and other quantitative scientists, yet practical experience often advances faster than formal guidance on where these tools add value and where they create new risks. This manuscript synthesizes a moderated discuss...
Victoria L Prince· Zenodo (CERN European Organi...· 0 citations
This study examines Technology Education Students Perceptions of ChatGPT use in distance learning, focusing on its benefits, ease of use, and risks. The study employs a descriptive quantitive approach. The participants are Technology Education students who have experience using ChatGPT for learning activities. Data are...
Ida Royanti, Vendyah Trisnaningtyas· Edumaspul - Jurnal Pendidika...· 0 citations
Dynamic Time Warping (DTW)-based Nearest-Neighbor (NN) classifiers are effective for time-series classification but are vulnerable to mislabeled training samples and require numerous DTW computations during inference. We propose DTW-based Granular Ball Computing (DTW-GBC), which organizes temporally similar training sa...
Ziqiang Li, Yun Liu, Gouhei Tanaka· 0 citations
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Choosing the right large language model (LLM) backbone is the most consequential decision when building a vision-language model (VLM), yet it remains fundamentally unprincipled: compute-based scaling laws fail to generalize across model families, and no framework exists for directly predicting VLM performance before tr...
Ziran Li, Qiang Wang, Zhengyu Chen et al.· 0 citations
This paper proposes a human-centered artificial intelligence (HCAI) framework for AI-assisted lexicography. While generative AI offers significant opportunities to enhance lexicographic work, it also raises concerns regarding the future role of lexicographers and the preservation of linguistic and cultural diversity. D...
Antonio San Martin, Catherine Trekker· 0 citations
Reinforcement learning (RL) has become a dominant paradigm for enhancing LLMs' reasoning capabilities. However, RL algorithms with PPO-Clip are inherently limited by exploration collapse. Subsequent works remain primarily heuristic and fail to identify the essential cause of PPO-Clip's failure. This work reveals the fu...
Zhicheng Cai, Xinyuan Guo, Hanlin Wu et al.· 0 citations
Decision-time planning with action-conditioned world models has become a popular paradigm for embodied control. However, the standard planning cost judges a candidate solely by how close its predicted terminal state lies to the goal, leaving the realizability of the intermediate transitions unchecked--a predicted traje...
We study the closed-loop properties of a recurrent state-space model (RSSM) world model trained on human demonstrations in Gymnasium's LunarLander-v3. We use the trained world model for zero-shot CEM model-predictive control (MPC) and for actor-critic (A2C) training in imagination. Scored on 100 held-out episodes, the...
Self-improving agents are state-of-the-art on agentic coding benchmarks, yet their search methods assume a stationary evaluation criterion. This ignores a central feature of evolution: species adapt as their environments change with them. We introduce the Red Queen G\"odel Machine (RQGM), an evolutionary framework for...
Alex Iacob, Andrej Jovanovi\'c, William F. Shen et al.· 0 citations
We study regression-based data fusion under uncertainty, where multiple noisy and biased measurement sources are available but ground-truth labels are absent during training. This setting arises in sensor networks, simulation ensembles, and scientific monitoring systems where supervision is costly or infeasible. We pro...
Muhammed Faruk Aytin, Zehra Demir, Alper Unal et al.· 0 citations
Reinforcement learning with verifiable rewards (RLVR) has driven substantial progress in training LLMs for reasoning tasks, but representative methods such as GRPO assign uniform credit across all tokens, wasting gradient on routine tokens while under-crediting pivotal reasoning steps. Existing token-level credit assig...
Yingyu Shan, Yuhang Guo, Zihao Cheng et al.· 0 citations
