Spectroscopic structure elucidation is central to molecular analysis, but recent Large Language Model (LLM)-based methods mostly formulate it as direct spectrum-to-SMILES generation. Although this paradigm can leverage paired spectral data, it does not explicitly model the analytical workflow used by spectroscopists, such as diagnostic peak interpretation, fragment reasoning, formula constraints, and chemical consistency checking. In this paper, we introduce S3C-LLM, a skill-guided and code-grounded agentic LLM for spectrum-to-structure elucidation. Rather than directly predicting a molecule, S3C-LLM retrieves modality-specific spectroscopy skills, executes analysis code to instantiate these skills on the input spectra, and integrates the resulting peak-level evidence and formula constraints before generating SMILES. Specifically, we contribute a self-evolving spectroscopy skill library, a thinking-augmented skill-code trajectory construction pipeline, and a two-stage training strategy that teaches Qwen3-4B through supervised fine-tuning (SFT) followed by our proposed step-level reinforcement learning (RL). Experiments on diverse benchmarks show that S3C-LLM consistently outperforms current general LLM and spectrum-specific models across spectrums, while using less than 1/10th of SpectraLLM’s training corpus.

While Vision-Language Models (VLMs) have demonstrated significant potential in chemical visual understanding, current models are predominantly optimized for direct visual question-answering tasks. This paradigm often results in “black-box” systems that fail to utilize the inherent capability of Large Language Models (LLMs) to infer underlying reaction mechanisms. In this work, we introduce ChemVLR, a chemical VLM designed to prioritize reasoning within the perception process. Unlike conventional chemical VLMs, ChemVLR analyzes visual inputs in a fine-grained manner by explicitly identifying granular chemical descriptors, such as functional groups, prior to generating answers. This approach ensures the production of explicit and interpretable reasoning paths for complex visual chemical problems. To facilitate this methodology, we implement a cross-modality reverse-engineering strategy combined with a rigorous filtering pipeline to curate a large-scale reasoning and caption dataset, comprising 760k high-quality samples across molecular and reaction tasks. Furthermore, we adopt a three-stage training framework that systemically builds model perception and reasoning capacity. Experiments demonstrate that ChemVLR achieves state-of-the-art (SOTA) performance, surpassing both leading proprietary models and domain-specific open-source baselines. We also provide comprehensive ablation studies to validate our training strategy and data generation designs.

Large Language Models (LLMs) have recently shown strong reasoning capabilities, motivating their use in complex decision-making environments. StarCraft II (SC2), with its massive state-action space and partial observability, is a challenging testbed. However, existing LLM-based SC2 agents primarily focus on improving the policy itself, leaving the integration of a learnable, action-conditioned dynamics model into the decision loop largely unexplored. In this work, we propose StarWM and StarWM-Agent, and conduct the first systematic study of the learnability and decision utility of player-view, action-conditioned textual world models for SC2. StarWM predicts short-horizon future observations under partial observability. StarWM-Agent integrates StarWM into a lightweight Generate-Simulate-Refine loop for foresight-driven policy refinement. Extensive experiments show that StarWM substantially outperforms zero-shot baselines across multiple dimensions, while StarWM-Agent achieves consistent win-rate gains of 30%, 15%, and 30% against the SC2 built-in AI at Hard (LV5), Harder (LV6), and VeryHard (LV7), respectively. Additional analyses show that our method is complementary to existing history-summarization SC2-agent approaches, with their combination achieving the strongest online performance in our evaluation. Code is available at https://github.com/yxzzhang/StarWM.

Vanilla spiking neurons are simplified from complex biological neurons with dendrites, soma, and synapses, into single somatic compartments. Due to limitations in performance and training efficiency, vanilla spiking neurons face significant challenges in modeling long sequences. In terms of performance, the oversimplified dynamics of spiking neurons omit long-term temporal dependencies. Additionally, the long-tail membrane potential distribution and binary activation discretization errors further limit their capacity to model long sequences. In terms of efficiency, the serial mechanism of spiking neurons leads to excessively long training times for long sequences. Though parallel spiking neurons are an efficient solution, their number of parameters is often tied to the hidden dimension or sequence length, which makes current parallel neurons unsuitable for large architectures. To address these issues, we propose MMDEND: a Multi-Branch Multi-Compartment Parallel Spiking Dendritic Neuron. Its proportion-adjustable multi-branch, multi-compartment structure enables long-term temporal dependencies. Additionally, we introduce a Scaling-Shifting Integer Firing (SSF) mechanism that fits the long-tail membrane potential distribution, retains efficiency, and mitigates discretization errors. Compared with parallel neurons, MMDEND achieves better long-sequence modeling capability with fewer parameters and lower energy consumption. Visualization also confirms that the SSF mechanism effectively fits long-tail distributions.