Mixture-of-Experts (MoE) large language models decouple capacity from compute through sparse routing, but their large parameter count creates storage and serving challenges. We analyze three MoE compression families: expert pruning, expert merging, and weight reconstruction, and derive structural error bounds showing that pruning and merging can incur non-vanishing errors tied to routing and expert heterogeneity. In contrast, weight reconstruction avoids these structural costs by preserving expert structure and routing. Motivated by the analysis, we propose Shared Low-rank Basis Factorization (SLBF), a data-free weight reconstruction method that uses rank-k bases shared among experts, enabling richer cross-expert sharing, faster convergence, and lower reconstruction error. A post-hoc gauge fixing removes redundant parameters at no representational cost. Across five MoE architectures spanning 16B to 122B parameters, SLBF consistently outperforms methods from all three compression families.

LLM-as-a-Judge, which uses LLMs to evaluate responses to open-ended questions, has seen significant growth in recent years. It has been adopted as a scalable alternative to manual human evaluation, such as crowdsourcing, which is often time-consuming and costly. However, the discrepancy between LLM-generated evaluations and human evaluations remains a critical problem in this field. To bridge this gap, we propose Multi-Aspect Panels of LLM Evaluators (MAPLE), a framework that orchestrates evaluations across multiple criteria using multiple LLMs. MAPLE integrates criterion-wise pairwise evaluations from multiple LLMs by estimating the importance of criteria and the reliability of individual evaluators. We conduct experiments with both open-source and closed-source models. Our results demonstrate that MAPLE achieves superior alignment with human evaluations compared to baselines, highlighting the importance of employing multi-agent and multi-criteria evaluation strategies.

Formulating optimization problems from raw inputs of problem descriptions and data is time-consuming and costly. While Large Language Models (LLMs) offer a promising way to automate this process, existing benchmarks overestimate their capabilities by relying on idealized inputs where all data is relevant and complete. This diverges from reality, where data-centric reasoning is required to extract, compute, and predict parameters from noisy sources. To address this, we introduce NLData2Opt, a benchmark to evaluate LLMs on the multi-stage reasoning inherent in practical optimization workflows. NLData2Opt decomposes problems into problem descriptions and data, and requires models to (1) extract relevant parameters from data containing misleading attributes, (2) compute missing parameters derived by transforming existing data attributes, and (3) predict unavailable future values from historical observations. Evaluation results show substantial performance decline relative to existing benchmarks, indicating that benchmark progress does not directly translate to formulation accuracy in practical optimization workflows. NLData2Opt is available at https://github.com/sumarivyn/NLData2Opt.

This paper investigates defenses in LLM-based evaluation, where prompt injection attacks can manipulate scores by deceiving the evaluation system. We formalize blind attacks as a class in which candidate answers are crafted independently of the true answer. To counter such attacks, we propose an evaluation framework that combines standard and counterfactual evaluation. Experiments show it significantly improves attack detection with minimal performance trade-offs for recent models.