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cs.IR updates on arXiv.org

From Top-1 to Top-K: A Reproducibility Study and Benchmarking of Counterfactual Explanations for Recommender Systems Impact of large language models on peer review opinions from a fine-grained perspective: Evidence from top conference proceedings in AI Diagnosable ColBERT: Debugging Late-Interaction Retrieval Models Using a Learned Latent Space as Reference Enhancing Unsupervised Keyword Extraction in Academic Papers through Integrating Highlights with Abstract CAST: Modeling Semantic-Level Transitions for Complementary-Aware Sequential Recommendation IndiaFinBench: An Evaluation Benchmark for Large Language Model Performance on Indian Financial Regulatory Text Think Before Writing: Feature-Level Multi-Objective Optimization for Generative Citation Visibility RARE: Redundancy-Aware Retrieval Evaluation Framework for High-Similarity Corpora Personalized Benchmarking: Evaluating LLMs by Individual Preferences Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations JFinTEB: Japanese Financial Text Embedding Benchmark UsefulBench: Towards Decision-Useful Information as a Target for Information Retrieval SIMMER: Cross-Modal Food Image--Recipe Retrieval via MLLM-Based Embedding Rethinking the Necessity of Adaptive Retrieval-Augmented Generation through the Lens of Adaptive Listwise Ranking BioHiCL: Hierarchical Multi-Label Contrastive Learning for Biomedical Retrieval with MeSH Labels Learning Behaviorally Grounded Item Embeddings via Personalized Temporal Contexts Collaborative Filtering Through Weighted Similarities of User and Item Embeddings IG-Search: Step-Level Information Gain Rewards for Search-Augmented Reasoning Metric-agnostic Learning-to-Rank via Boosting and Rank Approximation GenRec: A Preference-Oriented Generative Framework for Large-Scale Recommendation Uncertainty-aware Generative Learning Path Recommendation with Cognition-Adaptive Diffusion CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations Don't Retrieve, Navigate: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG NewsTorch: A PyTorch-based Toolkit for Learner-oriented News Recommendation Controlling Authority Retrieval: A Missing Retrieval Objective for Authority-Governed Knowledge APEX-MEM: Agentic Semi-Structured Memory with Temporal Reasoning for Long-Term Conversational AI ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendation Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends Dual-Enhancement Product Bundling: Bridging Interactive Graph and Large Language Model Evaluation of Agents under Simulated AI Marketplace Dynamics
Can Evolutionary Computation Help us to Crib the Voynich ...
Daniel Devatman Hromada · 2021-07-08 · via cs.IR updates on arXiv.org

Departing from the postulate that Voynich Manuscript is not a hoax but rather encodes authentic contents, our article presents an evolutionary algorithm which aims to find the most optimal mapping between voynichian glyphs and candidate phonemic values. Core component of the decoding algorithm is a process of maximization of a fitness function which aims to find most optimal set of substitution rules allowing to transcribe the part of the manuscript -- which we call the Calendar -- into lists of feminine names. This leads to sets of character subsitution rules which allow us to consistently transcribe dozens among three hundred calendar tokens into feminine names: a result far surpassing both ``popular'' as well as "state of the art" tentatives to crack the manuscript. What's more, by using name lists stemming from different languages as potential cribs, our ``adaptive'' method can also be useful in identification of the language in which the manuscript is written. As far as we can currently tell, results of our experiments indicate that the Calendar part of the manuscript contains names from baltoslavic, balkanic or hebrew language strata. Two further indications are also given: primo, highest fitness values were obtained when the crib list contains names with specific infixes at token's penultimate position as is the case, for example, for slavic \textbf{feminine diminutives} (i.e. names ending with -ka and not -a). In the most successful scenario, 240 characters contained in 35 distinct Voynichese tokens were successfully transcribed. Secundo, in case of crib stemming from Hebrew language, whole adaptation process converges to significantly better fitness values when transcribing voynichian tokens whose order of individual characters have been reversed, and when lists feminine and not masculine names are used as the crib.