Hidden in Plain Sight: Visual-to-Symbolic Analytical Solution Inference from Field Visualizations

Recovering analytical solutions of physical fields from visual observations is a fundamental yet underexplored capability for AI-assisted scientific reasoning. We study visual-to-symbolic analytical solution inference (ViSA) for two-dimensional linear steady-state fields: given field visualizations (and first-order derivatives) plus minimal auxiliary metadata, the model must output a single executable SymPy expression with fully instantiated numeric constants. We introduce ViSA-R2 and align it with a self-verifying, solution-centric chain-of-thought pipeline that follows a physicist-like pathway: structural pattern recognition solution-family (ansatz) hypothesis parameter derivation consistency verification. We also release ViSA-Bench, a VLM-ready synthetic benchmark covering 30 linear steady-state scenarios with verifiable analytical/symbolic annotations, and evaluate predictions by numerical accuracy, expression-structure similarity, and character-level accuracy. Using an 8B open-weight Qwen3-VL backbone, ViSA-R2 outperforms strong open-source baselines and the evaluated closed-source frontier VLMs under a standardized protocol.

Retrieving Event-related Human Brain Dynamics from Natural Sentence Reading

Electroencephalography (EEG) signals recordings when people reading natural languages are commonly used as a cognitive method to interpret human language understanding in neuroscience and psycholinguistics. Previous studies have demonstrated that the human fixation and activation in word reading associated with some brain regions, but it is not clear when and how to measure the brain dynamics across time and frequency domains. In this study, we propose the first analysis of event-related brain potentials (ERPs), and event-related spectral perturbations (ERSPs) on benchmark datasets which consist of sentence-level simultaneous EEG and related eye-tracking recorded from human natural reading experiment tasks. Our results showed peaks evoked at around 162 ms after the stimulus (starting to read each sentence) in the occipital area, indicating the brain retriving lexical and semantic visual information processing approaching 200 ms from the sentence onset. Furthermore, the occipital ERP around 200ms presents negative power and positive power in short and long reaction times. In addition, the occipital ERSP around 200ms demonstrated increased high gamma and decreased low beta and low gamma power, relative to the baseline. Our results implied that most of the semantic-perception responses occurred around the 200ms in alpha, beta and gamma bands of EEG signals. Our findings also provide potential impacts on promoting cognitive natural language processing models evaluation from EEG dynamics.

CogniFNN: A Fuzzy Neural Network Framework for Cognitive Word Embedding Evaluation

Word embeddings can reflect the semantic representations, and the embedding qualities can be comprehensively evaluated with human natural reading-related cognitive data sources. In this paper, we proposed the CogniFNN framework, which is the first attempt at using fuzzy neural networks to extract non-linear and non-stationary characteristics for evaluations of English word embeddings against the corresponding cognitive datasets. In our experiment, we used 15 human cognitive datasets across three modalities: EEG, fMRI, and eye-tracking, and selected the mean square error and multiple hypotheses testing as metrics to evaluate our proposed CogniFNN framework. Compared to the recent pioneer framework, our proposed CogniFNN showed smaller prediction errors of both context-independent (GloVe) and context-sensitive (BERT) word embeddings, and achieved higher significant ratios with randomly generated word embeddings. Our findings suggested that the CogniFNN framework could provide a more accurate and comprehensive evaluation of cognitive word embeddings. It will potentially be beneficial to the further word embeddings evaluation on extrinsic natural language processing tasks.