AI Investment Frontier From Filing to Evidence Map: AI Makes Research Faster—and Verification Harder AI can turn a long filing into a research map, but investors still need point-in-time evidence, citations, and a human audit.
AI Investment Frontier AI Investment Frontier — Investment AI Needs a Decision Trace A new benchmark argues that investment agents should be judged by point-in-time evidence, investor profile, reasoning, decisions, and delayed outcomes—not returns alone.
AI Investment Frontier AI Investment Frontier — The Portfolio Solver Needs a Contract A new comparison of photonic, classical, and reinforcement-learning optimizers points to a practical rule: make the solver’s contract explicit before comparing performance.
AI Investment Frontier AI Investment Frontier — The Missing Layer Is Decision Knowledge A new investment-process paper argues that data and models matter only when their contribution to the decision can be traced and assessed.
AI Investment Frontier AI Investment Frontier — The Real Test for Financial Foundation Models Is Temporal Integrity Financial foundation models can improve research workflows, but only if their priors, labels, and evaluation windows respect the information available at the time.
AI Investment Frontier AI 投资前沿 — SciPhy RL 把组合 AI 拉回成本压力之下 一篇新的 arXiv 论文把组合优化写成成本感知的强化学习问题，提醒我们：投资 AI 的关键不只是预测，更是能否在执行成本和仓位约束下把决策真正落地。
AI Investment Frontier AI Investment Frontier — SciPhy RL Puts Portfolio AI Back Under Cost Pressure A new arXiv paper on science-informed reinforcement learning for portfolio optimization is a useful reminder that the real problem in portfolio AI is not prediction alone, but reaching the target position under execution cost and volatility constraints.
AI Investment Frontier AI Investment Frontier — State-First Microstructure Models Beat Fancy Friction A new crypto futures paper argues that liquidity state should come before order flow, and that latency is a separate frontier from prediction accuracy.
AI Investment Frontier AI Investment Frontier — Learned Policies Still Live or Die on Turnover A new cross-asset futures paper shows end-to-end AI policies can beat simple rules, but the edge depends on turnover, costs, and whether the model really trades less.
AI Investment Frontier ML Term-Structure Forecasts Should Feed Duration Policy A June 2026 preprint shows neural term-structure models can improve bond-curve forecasts, but the real value is in duration control and portfolio policy.
AI Investment Frontier 时间序列基础模型是先验，不是阿尔法引擎 一篇新的 arXiv 基准研究提醒投资 AI 团队：预训练时间序列基础模型能降低建模成本，但相对随机游走的优势仍然稀疏。
AI Investment Frontier Time-Series Foundation Models Are Priors, Not Alpha Engines A new arXiv benchmark finds pretrained time-series foundation models can reduce modeling work in return forecasting, but their gains over random walk remain sparse.
AI Investment Frontier LLM Factor Forecasts Need Decision-Time Inputs A new leakage-aware benchmark shows why investment LLMs must separate forecast skill from information timing before entering factor allocation workflows.
AI Investment Frontier 金融 AI 需要确定性的生产内核 一篇关于 Mojo 与资本市场 AI 的论文提醒投资团队：速度只是生产化的一半，真正进入投研、风控和审计流程的 AI，还需要可复现的计算内核。
AI Investment Frontier Financial AI Needs Deterministic Production Kernels A new arXiv paper on Mojo for capital markets argues that speed is only half the production problem. Investment AI also needs reproducible kernels that survive audit, backtesting, and model handoff.
AI Investment Frontier Fundamental Research Needs Independence-Preserving Agent Architectures FundaPod shows why institutional investment research needs isolated personas, knowledge-graph memory, and post-hoc disagreement surfacing rather than consensus-seeking agent teams.
AI Investment Frontier 执行模型需要“时间意外”遥测 一篇 2026 年 6 月的 arXiv 论文提醒投资 AI 构建者：执行系统不能只等滑点统计显著后才反应，而应记录成交后不利价格事件是否过快出现。
AI Investment Frontier Execution Models Need Timing-Surprise Telemetry A new arXiv proposal argues that execution systems should detect market impact from the timing of adverse prints after fills, not only from slow slippage statistics.
