{"total":13,"items":[{"citing_arxiv_id":"2606.30997","ref_index":10,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"A Three-Phase Foundation Model for Tax-Aware Personalized Portfolio Management","primary_cat":"cs.AI","submitted_at":"2026-06-30T00:19:59+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A three-phase DRL framework for personalized portfolio management using a ticker-free encoder pretrained with a time series foundation model, an objective-conditioned MoE actor-critic, and inference-time LoRA adaptation from brokerage data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.10448","ref_index":22,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Mitigating Bias in Low-SNR Financial Reinforcement Learning via Quantum Representations","primary_cat":"cs.LG","submitted_at":"2026-06-09T05:55:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"FPQC-SAC adds a bounded parameterized quantum circuit to SAC to constrain representations in low-SNR financial environments, reporting 66.89% higher cumulative returns than standard SAC on real portfolio tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.09115","ref_index":66,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Counterfactual Transport Flows for Offline Conservative Trajectory Refinement","primary_cat":"cs.LG","submitted_at":"2026-06-08T07:11:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Counterfactual transport flows enable conservative, instance-specific trajectory refinement in offline RL by constructing local preference pairs in latent space from offline data and learning refinement directions controlled by a strength parameter.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.09104","ref_index":15,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Addressing Market Regime Changes and Heavy-Tailed Returns in Portfolio Optimization via Bayesian VAR and Elliptical Black-Litterman","primary_cat":"cs.LG","submitted_at":"2026-06-08T06:58:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"BAVAR-BLED combines BAVAR for regime-aware priors and BLED with Student's t-distributions inside TD3, reporting Sharpe 1.72 and Sortino 2.70 on 29 DJIA stocks over 10 years.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.08283","ref_index":32,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Macro Economists in the Machine: A Multi-Agent LLM Framework for Commodity-Related ETF Portfolio Construction","primary_cat":"q-fin.PM","submitted_at":"2026-06-06T18:07:28+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":4.0,"formal_verification":"none","one_line_summary":"LLM agents (hawkish, dovish, debate) outperform a deterministic z-score rule agent in Sharpe ratio for commodity ETF portfolios by 0.04-0.044, with advantage concentrated in the soft-landing sub-period and preserved up to 30bp trading costs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.04574","ref_index":22,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning","primary_cat":"cs.LG","submitted_at":"2026-06-03T08:10:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A hybrid DRL system for multi-pair crypto trading with deterministic risk shielding outperforms a heuristic baseline at 10% significance on Binance futures data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.00143","ref_index":23,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Regime-Adaptive Continual Learning for Portfolio Management","primary_cat":"q-fin.PM","submitted_at":"2026-05-29T02:24:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"ReCAP segments markets into regimes, builds a policy library via continual learning, and uses a regime-gate to adapt trading policies, claiming superior returns and fast adaptation on five real datasets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.02300","ref_index":83,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Meta Reinforcement Learning Approach to Goals-Based Wealth Management","primary_cat":"cs.LG","submitted_at":"2026-05-04T07:48:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"MetaRL pre-trained on GBWM problems delivers near-optimal dynamic strategies in 0.01s achieving 97.8% of DP optimal utility and handles larger problems where DP fails.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.01384","ref_index":1,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"SBCA: Cross-Modal BERT-driven Actor-Critic for Multi-Asset Portfolio Optimization","primary_cat":"q-fin.CP","submitted_at":"2026-05-02T11:16:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SBCA is a reinforcement learning framework using BERT cross-modal fusion and Actor-Critic to integrate price data with sentiment text for multi-asset portfolio optimization with practical trading constraints.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.27610","ref_index":61,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Systematic Review of Recent Advancements in PINN Augmented Deep Learning and Mathematical Modeling for Efficient Portfolio Management","primary_cat":"math.OC","submitted_at":"2026-04-30T09:02:09+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"A systematic review of physics-informed neural networks and mathematical modeling approaches for portfolio optimization and management in finance.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.14333","ref_index":15,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"When Missing Becomes Structure: Intent-Preserving Policy Completion from Financial KOL Discourse","primary_cat":"cs.LG","submitted_at":"2026-04-15T18:39:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"KICL completes execution decisions in KOL financial discourse using offline RL, achieving top returns and Sharpe ratios with no unsupported trades or direction changes on YouTube and X data from 2022-2025.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.14206","ref_index":8,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Portfolio Optimization Proxies under Label Scarcity and Regime Shifts via Bayesian and Deterministic Students under Semi-Supervised Sandwich Training","primary_cat":"cs.LG","submitted_at":"2026-04-04T06:42:38+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A semi-supervised teacher-student framework enables neural networks to proxy CVaR portfolio optimization using synthetic data augmentation for scarce labels and regime shifts.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"pretable baseline; for Indian equity markets specifi- cally, [19] provide an adapted four-factor framework that informs our feature construction for the Indian as- sets in our training universe. Direct portfolio construc- tion using neural architectures - including recurrent networks, temporal convolutional networks, and atten- tion mechanisms [6, 7] - and reinforcement learning formulations [8, 20-22] have also been explored, but these approaches typically sacrifice interpretability and require extensive reward shaping, large training sets, and careful hyperparameter tuning to achieve stable behavior under realistic constraints. Probabilistic and Bayesian approaches offer a prin- cipled remedy to the brittleness of point-estimate neural models."},{"citing_arxiv_id":"2604.07355","ref_index":3,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Prediction Arena: Benchmarking AI Models on Real-World Prediction Markets","primary_cat":"cs.LG","submitted_at":"2026-03-28T06:13:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Frontier AI models lose 16-31% trading on Kalshi over 57 days but show better results on Polymarket, with platform design strongly affecting outcomes and prediction accuracy mattering more than research volume.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}