CV-CCI combines observational and randomized data for reliable counterfactual KPI inference in wireless networks.

Abdessamed Qchohi, Jessica Moysen Cortes, Matteo Zecchin

AlleCompanion framework combines Two Tower architecture and ComCat mapping for improved recommendation accuracy.

Aleksandra Osowska-Kurczab, Klaudia Nazarko, Eliška Kosturová et al.

Repeated queries exhaust an LLM's brand recommendations but not its sources.

Dmitrij Żatuchin

The paper introduces Q-aggregation, reconciling universal and uniform learning to achieve exponential rates.

Mikael Møller Høgsgaard, Patrick Rebeschini, Tobias Wegel

Achieved efficient ETO evaluation via accumulation and blending matrix reformulations, with up to 256.72x speedup.

Yanchen Li, Xiaoming Xue, Kay Chen Tan

Using symmetrized Gamma noise for α-TV-LDP, achieving faster estimation rates.

Cristina Butucea, Huiyun Tang, Marie-Luce Taupin

Study shows pooling dependent samples in self-supervised learning is more effective than partitioning independent samples.

Maximilian Fleissner, Debarghya Ghoshdastidar, Samory Kpotufe

TeQHallu uses low-level symbolic competence for unsupervised hallucination detection, excelling on RAGTruth dataset.

Renato Vukovic, Hsien-chin Lin, Carel van Niekerk et al.

APT-RAG framework excels in evidence-intensive QA, achieving a 40.69% F1 score improvement.

Songeun Lee, Kyungjin Min, Injae Na et al.

SAM-D2Q enhances e-commerce search with multimodal document expansion, boosting GMV by 3.38%.

Hui Zhou, Jian Hui Ji, Lei Ma et al.

The paper proposes a diffusion-based method for estimating local intrinsic dimension with a minimax lower bound.

Jaehee Seo, Wontae Jeong, Jisu Kim

Using susceptibility techniques to analyze Turing machines' algorithmic structures, revealing symmetry and path separation features.

Billy Snikkers, Rumi Salazar, Daniel Murfet et al.

Proposed a cost-aware single-generation architecture achieving 91.7% accuracy on the DevRev benchmark.

Yoga Sri Varshan Varadharajan, Ajay Yadav, Ritesh Goru et al.

The paper proposes an optimal stratified allocation method for rare-event forecasting in dependent sequences, validated on U.S. equities dataset.

Jaskaran Singh

Principia evaluates physical reasoning in videos via relational consistency across 8 phenomena, independent of calibration, with scores below 0.42 for SOTA models.

Varun Varma Thozhiyoor, Shivam Tripathi, Venkatesh Babu Radhakrishnan et al.

Proposes 'Compile by Training' to convert natural language specs into local neural functions with 83.6% accuracy, using teacher models and adapter fine-tuning.

Yuntian Deng, Pengyu Nie, Stuart Shieber

This study reveals instability in black-box LLM observers on shared endpoints, validating that fixed thresholds often fail in real-world settings.

Haoyaun Zhu, Jie Zhang

ESPO enhances prompt accuracy by 3.76% and shortens prompts by 47% through structured error diagnosis, multi-strategy candidate generation, and bootstrap stability selection.

Lihao Liu, Peng Tang, Kunwar Yashraj Singh et al.

This study quantifies reasoning step importance via advantage, revealing models struggle to decode this solely from text.

Kevin Du, Alexander Hoyle, Laura Ruis et al.

This study demonstrates that auxiliary views (e.g., paraphrases, analogies) significantly improve knowledge acquisition in large language models, especially at larger scales.

Joseph Lee, Yidi Huang, Dokyoon Kim et al.