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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
Taming the Black Swan: A Momentum-Gated Hierarchical Opti...
Arya Chakraborty, Randhir Singh · 2026-04-10 · via cs.IR updates on arXiv.org

Conventional momentum strategies, despite their proven efficacy in generating alpha, frequently suffer from the "Winner's Curse", a structural vulnerability in which high performing assets exhibit clustered volatility and severe drawdowns during market reversals. To counteract this propensity for momentum crashes, this study presents the Adaptive Equity Generation and Immunisation System (AEGIS), a novel framework that fundamentally reengineers the trade-off between growth and stability. By leveraging a volatility-adjusted momentum filter to identify trend strength and employing a minimax correlation algorithm to enforce structural diversification, the model utilises sequential least squares programming (SLSQP) to optimise capital allocation for the sortino ratio. This architecture allows the portfolio to dynamically adapt to distinct market regimes: explicitly lowering the intensity of crashes during bear markets by decoupling correlated risks, while retaining asymmetric upside participation during bull runs. Empirical validation via a comprehensive 20-year walk-forward backtest (2006-2025), which covers significant stress events like the 2008 Global Financial Crisis, confirms that the framework produces substantial excess alpha relative to the standard S&P 500 benchmark. Notably, the strategy successfully matched the capital appreciation of the high-beta NASDAQ-100 index while achieving significantly reduced downside volatility and improved structural resilience. These results suggest that synthetic beta can be effectively engineered through mathematical regularisation, enabling investors to capture the high-growth characteristics of concentrated portfolios while preserving the defensive stability typically associated with broad-market diversification.