arXiv subjects

Zixun Fu

Publications and source records attributed to Zixun Fu.

3 recordsLinked to original sources

arXiv2026

olBlend: Language-Guided Molecular Blend Retrieval with Mixture Odor Prediction

Converting open-ended natural-language odor descriptions into molecular mixtures that can be physically prepared from an available inventory remains an important challenge in computational olfaction. We propose olBlend, a language-guided framework for molecular mixture retrieval. olBlend first uses MixPOM to predict odor probabilities for candidate molecular pairs and stores these predictions for retrieval. It then uses a language model to translate each odor description into three types of semantic requirements: lead attributes that define the main odor, ambient attributes that describe its background, and absent attributes that should be avoided. A structured scoring algorithm uses the precomputed odor probabilities to score how well each candidate mixture meets these requirements, then retrieves and ranks mixtures from the available inventory. Evaluation on established odor datasets shows that MixPOM improves mixture odor prediction over averaged single-molecule predictions. Retrieval comparisons across natural-language queries demonstrate how background and exclusion requirements guide mixture selection. We further prepare retrieved formulations as physical odor samples, demonstrating the workflow from natural-language descriptions to realizable molecular formulations.

cs.HC↗

arXiv2026

Do the Numbers Match the Plot? Statistical-Visual Verification for Automated Exploratory Data Analysis

Automated exploratory data analysis (EDA) systems can execute statistics correctly while producing misleading interpretations. For example, correlations may be driven by influential observations, and mean differences may arise from skewness, overlap, or sparse subgroups rather than broad distributional shifts. We present EDAgent, a sequential framework that performs targeted visual verification before report synthesis. For each finding, the system records the statistical result, generates a visualization request identifying relevant distributional features, creates the corresponding chart, and uses the observed pattern to retain, qualify, or reconsider the interpretation. We call this process statistical-visual verification. EDAgent includes VisPlan, an 8B-parameter model that converts visualization rationales into detailed chart plans. We evaluate 87 end-to-end reports on EDA-Bench using a six-dimension rubric and GPT-5.1 multimodal evaluation. Maximum-score proportions reached 87.4% for factuality and visual consistency, 93.1% for statistical-visual cross-validation, and 96.6% for analytical narrative. On 166 open-ended VisEval tasks, VisPlan improved the mean overall score from 4.55 (Qwen3-8B) to 4.62, with the largest gain in aesthetics. These descriptive results indicate that visual verification can make automated EDA reports more transparent about how statistical findings relate to underlying data structure.

cs.HC↗

arXiv2026

PrunePath: Towards Highly Structured Sparse Language Models

Feed-forward networks (FFNs) dominate the parameter count and computation of modern language models, yet existing pruning methods often struggle to convert sparsity into hardware-friendly inference efficiency gains. We introduce \textbf{PrunePath}, a budget-adaptive structured sparsification framework for FFN layers. Built on MoEfication, PrunePath replaces independent expert-wise thresholding with a softmax-normalized routing distribution and activates important experts under a cumulative-mass threshold. This formulation imposes a token-level probability budget, enabling adaptive expert counts and a direct inference-time sparsity knob from a single checkpoint. Across NLU, NLG, and instruction-tuning evaluations, PrunePath achieves a favorable sparsity--performance trade-off compared with existing static pruning and MoEfication-based methods. We further implement Triton kernels for KV-cache decoding to translate the resulting structured sparsity into practical memory savings and measurable decoding-speed improvements. These results demonstrate the superior performance of PrunePath for building highly sparse, deployment-friendly large language models.

cs.CL↗