Papers

Large language models (LLMs) are rapidly transforming scientific discovery, showing promise in hypothesis generation, literature understanding, and symbolic reasoning. Yet, their capacity to conduct iterative, feedback-driven molecular design---a hallmark of real-world drug and materials discovery---remains underexplored. Existing benchmarks typically cast molecular tasks as one-shot question-answering or text-to-molecule translation, neglecting the iterative propose-evaluate-refine process central to scientific practice. We propose \textbf{ICIMBench}, an \textit{In-Context Iterative Molecular Design Benchmark} that evaluates LLMs in multi-turn molecular design episodes. In each task, the model receives a natural-language specification, generates candidate molecules in SMILES format, and iteratively refines them based on deterministic oracle feedback from RDKit. We introduce the \textbf{NumEval} metric---the number of evaluations required to satisfy the target---which captures both performance efficiency and robustness under realistic evaluation budgets. Experiments on frontier models (GPT-5, DeepSeek-V3.2, Intern-S1) show that while single-property design is largely solved (NumEval $=1$) by state-of-the-art LLMs like GPT-5, multi-property optimization remains a strong challenge, especially under coupled constraints such as lipophilicity and scaffold similarity. ICIMBench provides a principled framework for probing the in-context reasoning and adaptive optimization abilities of LLMs, paving the way toward autonomous, language-driven molecular discovery.

Mega-cap technology equities, cryptocurrencies, and gold increasingly co-determine modern portfolio outcomes, yet their joint dynamics are regime-dependent and incompletely understood. This paper studies the state-dependent comovement among Apple Inc. (AAPL), Bitcoin (BTC), and gold (XAU) using daily data from 2015 to 2025. We triangulate three complementary lenses: (i) rolling Pearson correlations to trace smooth co-movement and structural shifts; (ii) a Diebold–Yilmaz connectedness framework based on rolling 252-day VARs and generalized forecast-error variance decompositions to identify directional risk transmitters and receivers; and (iii) empirical copulas to estimate lower- and upper-tail dependence at the 5% threshold, contrasting crash versus rally dynamics. We document three core results. First, the AAPL–BTC correlation rose from near zero before 2020 to roughly 0.30 during the COVID-19 period and has remained persistently elevated through 2025, indicating a lasting post-pandemic regime. Second, gold emerges as a net transmitter of shocks while BTC is a net receiver, revising the canonical view of gold as a purely passive safe haven. Third, AAPL–BTC exhibits pronounced asymmetry in the tails, with downside dependence considerably stronger than upside (“crash correlation”). Robustness checks spanning window sizes, VAR lags, alternative dependence metrics, and tail thresholds corroborate these findings. The portfolio implication is that static diversification across tech, crypto, and gold underperforms precisely when insurance is most needed. We advocate regime-aware allocation that monitors connectedness and transmitter identity, budgets explicitly for joint-tail risk, and uses dynamic overlays. The results also inform macro-prudential monitoring: supervisors should track transmitter rotations and connectedness spikes that presage cross-market stress transmission.

This research presents a comprehensive diagnostic study of confidence-gated iterative induction for zero-shot Named Entity Recognition (NER) in crisis scenarios. While existing approaches struggle to adapt to novel disaster lexicons without manually curated resources, we investigate whether iterative knowledge distillation can overcome these limitations. Our framework leverages a pretrained language model to extract high-recall entity candidates, then iteratively distills domain knowledge through a self-correcting loop that uses high-confidence seeds to induce micro-gazetteers and syntactic rules. Comprehensive evaluations on synthetic crisis data reveal that the framework maintains a constant zero-shot F1-score of approximately 0.295 across all experimental configurations, demonstrating that the iterative mechanism provides no measurable improvement over baseline approaches. This negative result offers valuable diagnostic insights into the fundamental challenges of adaptive NER in dynamic crisis domains, including confidence threshold calibration difficulties, clustering algorithm limitations, and error propagation risks. The findings provide a cautionary tale for researchers working on adaptive NER systems and establish a foundation for future research on more robust zero-shot approaches in crisis scenarios.

Training small language models is challenging due to their limited capacity to capture complex patterns and their susceptibility to overfitting. To address these issues, we investigate gradient noise injection as a regularization strategy, building on prior work while introducing a noise schedule that decays exponentially over training. Unlike existing techniques, our method explicitly controls the trade-off between exploration and stability during optimization. We compare the exponential decay schedule with linear and adaptive variants, demonstrating empirically that the exponential schedule yields superior convergence and generalization. Extensive experiments on diverse text corpora, including shakespeare\_char, enwik8, text8, and larger benchmark datasets, show consistent improvements in training dynamics, validation loss, and final performance. We report error bars and statistical significance tests to ensure robustness of the results. Detailed implementation information, including model architectures, hyperparameter settings, dataset sizes, and optimization strategies, is provided to support reproducibility, and we release our code and trained models publicly. Furthermore, we compare gradient noise injection with other regularization methods such as dropout, weight decay, and data augmentation, both in isolation and in combination, revealing complementary effects on training stability and generalization. Finally, we analyze the computational cost of gradient noise injection relative to these baselines, highlighting its practical efficiency in resource-constrained environments. Together, these contributions position gradient noise injection as a theoretically grounded, empirically validated, and computationally practical method for improving the robustness of small language models.