The paper introduces F-ICL, a benchmark that measures in‑context algorithmic reasoning in language models by exhaustively enumerating 86 million valid programs of length ≤13 on a Turing‑complete machine and computing the exact posterior under a bounded Levin–Solomonoff prior. Unlike typical benchmarks, F‑ICL provides a distributional reference rather than just answers, allowing the evaluation of models’ inductive priors. Across 105 configurations of models ranging from 0.8 B to 675 B parameters, models achieve up to 92 % accuracy, yet many still deviate from the Bayes‑optimal reference, and the study derives theoretical bounds on cumulative loss for predictors with positive prior weight on the reference.
By Luan Ozelim, Hector Zenil
arXiv:2608.21914v1 Announce Type: new
Abstract: We align three upper ontologies that sit under UK and NATO defence data infrastructure: the Information Exchange Standard (IES), the Higher Quality Dat...
By Fabio Rovai
arXiv:2608.21729v1 Announce Type: new
Abstract: Simulation-Based Inference (SBI) serves as a vital framework for parameter inference in scientific fields where simulators involve intractable likeliho...
By Yichen Zang, Song Liu, Jiun-Yi Lin
arXiv:2608.22068v1 Announce Type: new
Abstract: Geothermal well arrays, which organize multiple geothermal wells into carefully planned geometric configurations, provide opportunities to enhance ener...
By Edwin Ouko, Emmanuel Lujan, Alan Edelman, Robert Metcalfe
The study examines how Theory of Mind (ToM) reasoning and prosocial beliefs influence large language models (LLMs) in the ultimatum game. By initializing LLM agents with Greedy, Fair, or Selfless beliefs and applying chain‑of‑thought or varying levels of ToM reasoning, the authors ran 2,700 simulations across several models, including o3‑mini and DeepSeek‑R1 Distilled Qwen 32B. Results show that ToM‑enhanced LLMs align more closely with human decision patterns, exhibit greater consistency, and achieve better negotiation outcomes, with Llama 3.3 70B producing the most belief‑consistent reasoning.
whyItMatters":"The findings clarify the importance of incorporating Theory of Mind into LLMs to improve their alignment with human norms in cooperative decision‑making tasks."
By Neemesh Yadav, Yihuai Lan, Shan Dong, Mai Hieu Hien, Palakorn Achananuparp, Jing Jiang, Ee-Peng Lim
arXiv:2608.22960v1 Announce Type: new
Abstract: Coding agents are increasingly evaluated not only by whether they solve a task, but also by how they execute it. However, existing process-level evalua...
By Jiawei He, Mengyu Shi, Jie jia, Xikai Yang, Dong Sun
The paper introduces Hierarchical Exponential-Gaussian Mixture (HEGM) models to predict watch-time distributions for short-video recommendations, addressing issues such as variance collapse, component redundancy, and inactive components found in the prior Exponential-Gaussian Mixture Network (EGMN). HEGM employs a hierarchical skip-watch decomposition, KL-based variance regularization, structured initialization, and removes certain regularizers to improve mixture stability and interpretability. Experiments on public and large-scale industrial datasets show that HEGM enhances ranking accuracy and threshold-event prediction while maintaining competitive point-estimation accuracy, and a 1.5‑month production A/B test demonstrates statistically significant engagement lifts.
By Sofia Gulevskaia, Mikhail Trapeznikov, Aleksandr Poslavsky, Alexander D'yakonov
arXiv:2503.05684v2 Announce Type: replace-cross
Abstract: Pre-trained foundation models can be efficiently adapted for specific tasks using Low-Rank Adaptation (LoRA), but the fairness properties of...
By Parameswaran Kamalaruban, Mark Anderson, Stuart Burrell, Maeve Madigan, Piotr Skalski, David Sutton
arXiv:2608.22076v1 Announce Type: cross
Abstract: Rising energy demand, fossil fuel depletion and climate change highlight the need for more efficient energy production and consumption. Offshore oil...
By Khivishta Boodhoo, Josh Plumbly, Nicholas Watson
arXiv:2608.23383v1 Announce Type: new
Abstract: Video generation is progressing beyond isolated clips toward long-form narratives and interactive worlds, requiring models to preserve identities, foll...
By Nan Duan, Haoyang Huang, Weiyang Jin, Haoran Li, Yaowei Li, Yuming Li, Yijun Liu, Xin Lu, Xiaoxiao Ma, Yanwen Ma, Yaofeng Su, Yilang Sun, Haoyu Wang, Zeyue Xue, Songchun Zhang, Junhao Zhuang
arXiv:2608.22965v1 Announce Type: new
Abstract: Accurate extrinsic calibration between event-based and frame-based cameras remains a practical bottleneck for heterogeneous stereo systems. Existing ap...
By Nico Hessenthaler, Adam T. M\"uller, Nicolaj C. Stache
arXiv:2606.21596v2 Announce Type: replace
Abstract: Recent image-to-3D scene methods recover high-fidelity 3D objects with plausible arrangements, but often leave floatings and interpenetrations that...
By Haodong Li, Lulu Shao, Haolin Lu, Yu Fu, Yen-Ru Chen, Seemandhar Jain, Manmohan Chandraker
arXiv:2606.11269v2 Announce Type: replace
Abstract: Personality assessment aims to infer stable traits from dynamic behaviors across modalities like language, voice, and facial expressions. Existing...
By Jia Li, Qian Chen, Wei Wang, Xinyu Li, Zhenzhen Hu, Dongsheng Shao, Richang Hong, Meng Wang
arXiv:2605.07785v3 Announce Type: replace
Abstract: Concept Bottleneck Models (CBMs) in medical imaging aim to improve model interpretability by predicting intermediate clinical concepts before final...
By Amy Rafferty, Rishi Ramaesh, Ajitha Rajan
arXiv:2604.13183v4 Announce Type: replace
Abstract: Generalizable cross-view geo-localization aims to match the same location across views in unseen regions and conditions without GPS supervision. It...
By Hongyang Zhang, Yinhao Liu, Haitao Zhang, Zhongyi Wen, Zhenyu Kuang, Shuxian Liang, Xian-Sheng Hua
arXiv:2603.15525v4 Announce Type: replace
Abstract: Deep learning models for chest X-ray diagnosis are constrained by limited coverage of clinically meaningful concept combinations in publicly availa...
By Amy Rafferty, Rishi Ramaesh, Ajitha Rajan
arXiv:2511.05782v3 Announce Type: replace
Abstract: Unsupervised domain adaptation (UDA) for medical image segmentation remains challenging due to substantial domain shifts across imaging modalities,...
By Lalit Maurya, Honghai Liu, Reyer Zwiggelaar
arXiv:2504.03337v2 Announce Type: replace
Abstract: Existing bias mitigation methods for Visual Question Answering (VQA), a typical Artificial intelligence application, endure two main limitations. F...
By Quanxing Xu, Ling Zhou, Xian Zhong, Feifei Zhang, Rubing Huang
arXiv:2608.21438v1 Announce Type: new
Abstract: Text guided 3D scene editing provides an intuitive interface for modifying reconstructed environments, but remains difficult because natural language d...
By Xiujin Liu, Tianyu Yang, Yilun Zhao, Xiangliang Zhang
arXiv:2601.23182v2 Announce Type: replace
Abstract: Despite the non-autoregressive potential of diffusion language models (dLLMs), existing decoding strategies demonstrate positional bias, failing to...
By Siyang He, Qiqi Wang, Xiaoran Liu, Hongnan Ma, Yiwei Shi, Yuerong Song, Ying Zhu, Tianyi Liang, Zengfeng Huang, Ziwei He, Xipeng Qiu