Large Language Models have shown promise in translating natural language into executable optimization models, yet they often suffer from the Sisyphus Dilemma: a memoryless cycle where identical errors are repeated across structurally similar problems. Existing retrieval-augmented strategies primarily fetch static problem-model pairs as few-shot demonstrators, failing to capture the dynamic reasoning required to resolve execution failures. To bridge this gap, we propose EOM, a framework that implements Experience Replay to transform transient rectification steps into persistent knowledge. EOM distills interaction histories into Causal Correction Mappings, indexing both diagnostic insights and prohibitive traps. By utilizing a structure-aware retrieval mechanism that aligns semantic intent with abstract syntax trees and solver tracebacks, the system enables models to recall specific correction strategies for isomorphic errors. Extensive experiments across seven benchmarks demonstrate that EOM improves modeling accuracy by 8.45% on complex tasks while reducing token consumption by 28.65% and interaction turns by 25.82%, validating the efficiency of a “Rectify Once, Solve Many” paradigm.

Large language models (LLMs) and vision-language models (VLMs) are increasingly used as optimization assistants to produce solutions, generate solver-executable programs, or both. However, current evaluations are misaligned with deployment in three ways: they (P1) fail to represent multimodal problem specifications, (P2) score outcomes only and cannot localize where failures occur along the modeling pipeline, and (P3) rarely report inference cost, obscuring reliability–cost trade-offs. We introduce Graph Optimization benchmark (GOBench), an aligned multimodal benchmark with solver-derived oracles and a four-layer diagnostic protocol that evaluates intermediate artifacts as well as end results, together with the Visual Inference Penalty (VIP) to measure multimodal overhead. Across frontier and open-weight models under paired text-only vs. T+V settings, we find that vision reliably increases inference cost, while its reliability impact is regime-dependent: frontier models often benefit from visual grounding, whereas several mid-tier/open models exhibit a Visual Paradox where vision reduces downstream executability and verification coverage. End-to-end success is frequently bottlenecked by intermediate-stage dropout; supervised fine-tuning on intermediate targets can mitigate this attrition in open models, enabling a reproducible harness for diagnosing failure modes and quantifying reliability–cost trade-offs.

Mixture-of-Experts (MoE) models offer a promising path for scaling model capacity, yet their massive memory footprint poses significant challenges for deployment on resource-constrained edge devices. Existing solutions, such as static pruning or dynamic offloading, often struggle to balance model accuracy with inference latency due to irreversible information loss or prohibitive I/O overhead. In this paper, we propose LightMoE, a novel framework for memory-efficient MoE inference that exploits the inherent functional redundancy and temporal locality of expert activation. LightMoE employs a frequency-aware expert initialization strategy to retain a compact core of resident experts and introduces a similarity-based redirection mechanism to compensate for missing experts without incurring I/O costs. Furthermore, it incorporates a lightweight runtime manager that performs coarse-grained, task-level expert replacement to adapt to shifting data distributions. Empirical evaluations on representative edge platforms demonstrate that LightMoE achieves a superior accuracy-efficiency trade-off, improving average accuracy by 4.3% over static pruning and 2.4% over dynamic swapping methods, while maintaining inference latency comparable to strictly pruned models.

Pre-trained language models (PLMs) are widely used in NLP but struggle with capturing entity knowledge. To address this, knowledge enhancement techniques have been proposed. However, existing methods rely heavily on external knowledge bases embedding and often introduce noisy entity representations. In this work, we propose a novel Knowledge Enhancement Filtering Framework named KEFF, which contains both knowledge enhancement and knowledge enhancement filtering modules for PLM. We find that there are certain redundant bits in the embedding space of PLMs. Building on this insight, we implement knowledge-enhanced mapping of redundant bit values in entity span tokens. In order to solve the knowledge enhancement problem of existing methods that introduce noisy entity representation knowledge, we further propose a novel knowledge enhancement filter based on our knowledge enhancement method. Finally, experiments on four knowledge-driven NLP tasks show that our method effectively improves the ability of PLMs on downstream tasks. Compared to state-of-the-art approachs, our method achieves the highest F1-score and accuracy, while reducing the computational cost by 1.7-2.5x.