CauProphet is a new agent that learns and updates causal knowledge within a Causal Cognition framework. It constructs a structured Causal Belief for each task by combining relevant knowledge with current evidence, uses this belief for decision-making, and continuously refines both the belief and its underlying causal knowledge as new observations arrive. Experiments show that CauProphet progressively acquires causal knowledge, transfers it to future decisions, and outperforms baseline agents in financial prediction tasks and other scientific and everyday scenarios.
arXiv:2606. 17591v1 Announce Type: new Abstract: Training-free verbal reinforcement learning enables LLM agents to learn from world feedback -- objective signals such as dynamic task outcomes, market returns, or demand forecasts -- by extracting verbal rules from experience and injecting them as context, updating the agent's behavior without parameter changes.
arXiv:2606. 11816v1 Announce Type: cross Abstract: Forecasting real-world events requires language-model agents to reason under uncertainty from incomplete, time-bounded information.
arXiv:2610.34094v1 Announce Type: new Abstract: Large language models (LLMs) generally lack deep causal reasoning capabilities in temporal reasoning. Inspired by document layout reordering, this pape...
The paper introduces a framework that uses large language models (LLMs) to generate natural‑language narratives explaining cross‑sectional stock return predictions. It combines temporal Shapley additive explanations (SHAP) from an XGBoost model with historical regime analogs to provide context. A controlled study shows that progressively externalizing numerical and relational reasoning improves evidence faithfulness and accuracy, while historical analogs boost human‑rated usefulness.
arXiv:2607. 04293v1 Announce Type: cross Abstract: Building AI Scientist agents with Large Language Models (LLMs) has recently attracted growing attention.
ForecastCompass (FoCo) is an adaptive factor-based memory framework designed to enhance agentic forecasting. It structures forecasting experience using a hierarchical task taxonomy and maintains two memory components: factor memory for reusable predictive dimensions and reasoning memory for probability updating, uncertainty handling, and calibration. Through a verbalized memory-revision process guided by retrospective analyses, FoCo iteratively refines its knowledge, leading to improved probabilistic accuracy and calibration in experiments with GPT-5-mini and Gemini-2.5-Flash on Prophet Arena and FutureX.
arXiv:2608. 06128v1 Announce Type: new Abstract: Search agents extend large language models beyond static parametric memory by enabling them to acquire and use ex ternal evidence during multi-step reasoning.
META (Memory Enhanced Trading Agent) is a new agent-based trading framework that augments large language models with episodic memory. It combines specialized indicator agents—such as Trend, MACD, Stochastic, RSI, SMA, AVWAP, and Heikin‑Ashi—with a Decision Agent that fuses their reports, while a Memory module retrieves and updates past trading episodes encoded as market state embeddings with outcomes and reflections. By recalling relevant experiences and adaptively reweighting signals under similar market regimes, META improves directional accuracy and robustness in short‑horizon evaluations, offering regime‑aware, interpretable, and low‑latency decision‑making for financial trading.
CastFlow is a role‑specialized agentic forecasting framework that combines general‑purpose tool reasoning with specialized temporal forecasting. It uses an Experience Memory to guide a frozen LLM in acquiring evidence via a multi‑view toolkit, then a lightweight specialized LLM refines predictions through evidence‑grounded reasoning. The two‑stage training strategy—supervised fine‑tuning followed by reinforcement learning with verifiable rewards—optimizes the specialized model based on final forecasting outcomes, achieving superior performance on nine out of ten real‑world benchmarks.
arXiv:2610.14381v1 Announce Type: new Abstract: Large language models are increasingly used as scientific assistants, yet we still lack reliable ways to evaluate whether they can investigate a system...
arXiv:2606. 24160v1 Announce Type: new Abstract: Causal inference provides a set of principles and tools that allow one to combine data and knowledge about an environment to reason with questions of counterfactual nature, i.
FinLLM-KG is a model‑agnostic framework that enhances financial sentiment analysis by integrating causal large language models with a financial knowledge graph derived from SEC 10‑K filings. It injects temporally grounded, company‑level context at inference time without updating model weights, allowing sentiment predictions to consider broader firm‑level information. The framework also attributes sentiment scores to individual sentences and retrieved knowledge‑graph facts, improving explainability and supporting human‑in‑the‑loop review, and simulations show it consistently outperforms state‑of‑the‑art baselines in portfolio performance.