Large language models (LLMs) have enabled AI scientist systems to automate scientific discovery, yet existing approaches most rely on static prompting or fixed workflows and fail to accumulate experience for continual improvement. We propose HypoForge, an experience-guided multi-agent framework that learns reusable scientific skills for automated hypothesis generation and hypothesis testing. HypoForge is built on the observation that these two stages involve different supervision signals. For hypothesis generation, where explicit feedback is unavailable, HypoForge adopts an adversarial generator--discriminator mechanism to improve reasoning through comparative critique. For hypothesis testing, where empirical feedback is available, HypoForge learns testing skills from execution outcomes and ground-truth results. By matching skill learning strategies with stage-specific supervision, HypoForge enables continual improvement without fine-tuning foundation models. Experiments on hypothesis generation and testing benchmarks show that HypoForge consistently outperforms existing AI scientist frameworks and skill-level variants. Further analysis demonstrates the effectiveness of the proposed stage-specific skill learning paradigms.
Ziqing Qian, Jiaying Lei, Yi-Fang Wang et al.· 0 citations
Large language models (LLMs) have recently been used as frozen backbones for time-series forecasting, aiming to transfer general sequence priors through prompts and embedding-based interfaces. However, recent evidence suggests that many LLM-based forecasting pipelines do not reliably benefit from the language model itself, raising a fundamental question: under what design constraints does a frozen LLM provide measurable and attributable value for numerical time series prediction. Thus, we present Align-TS, a modular framework that makes the role of the frozen LLM explicit and testable by separating structure learning from semantic conditioning. First, a structure-guided encoder models inter-variable dependencies by operating attention over variable tokens, producing representations that preserve multivariate structure. Second, a languagespace recasting module converts structure-aware patches into embeddings that are compatible with the frozen LLM without requiring backbone updates. Third, an alignment-driven conditioning module produces task-aware prompt representations and aligns them with numerical embeddings to reduce entanglement and stabilize inference. This design yields a controllable pathway in which the LLM functions as a semantic conditioner rather than a monolithic predictor. Experiments on multiple public multivariate time-series forecasting benchmarks demonstrate that Align-TS achieves competitive and often superior forecasting performance across diverse domains while keeping the language model fully frozen.
Yi-Fang Wang, Shu Miao· 2026 IEEE 9th International...· 0 citations
