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arXiv cs.CL92

MemPlan: A Memory-Augmented PDDL Planning Framework for Partially Observable Textual Environments

The paper was published on arXiv on October 24, 2026 (ID 2610.30379v1), introducing MemPlan—a memory-conditioned PDDL planning framework that uses large language models (LLMs) as a constrained bridge between linguistic and symbolic planning, suitable for partially observable interactive text-based environments.

MemPlan: Memory-Conditioned PDDL Planning for Partially Observable Text Environments

Why it matters：MemPlan dynamically reformulates PDDL problems using forward-graph memory and backward-memory mechanisms, significantly improving task success rates and step efficiency while substantially reducing online token consumption. Its offline fine-tuned language–symbol interface can be reused across TextWorld task types without requiring task-specific fine-tuning, demonstrating the method’s generalizability and practicality.

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arXiv cs.CL82

IFC-MCQ: A Method for Automatically Generating Multiple-Choice Question Benchmarks for LLM Agents in Building Information Modeling

On October 24, 2026, researchers published the IFC-MCQ method on arXiv, aiming to automatically generate a multi-choice question benchmark with precise scoring for Industry Foundation Classes (IFC) data retrieval systems based on large language models (LLMs) in the architecture, engineering, and construction (AEC) sector.

IFC-MCQ: Automatically Generating Multiple-Choice Benchmarks for Evaluating LLM Agents on IFC Data Retrieval

Why it matters：This work directly addresses the pain points of LLMs—namely, the difficulty and high cost of evaluating them on specialized structured data such as IFC—by proposing the first automated multiple-choice question-generation framework for IFC files. It covers 15 categories and 36 subcategories, supporting reproducible and scalable evaluations of agent capabilities.

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arXiv cs.CL87

FusionEval Released: A New Benchmark for Large Language Model Fusion

On October 24, 2026, researchers published the paper arXiv:2610.30162v1 on arXiv, introducing FusionEval—the first unified benchmark specifically designed to evaluate fusion methods for large language models. It covers vulnerable aspects such as reasoning chain length and tool invocation, and supports comparisons between fine-tuning-based and zero-shot fusion approaches.

FusionEval: Benchmarking Model Fusion for Large Language Models

Why it matters：This benchmark fills the gap left by existing evaluations, which inadequately cover long-chain reasoning and tool usage; it clearly distinguishes between average performance and worst-case capability retention, provides a reproducible expert pool (based on Qwen3/Llama), multi-dimensional metrics (including GPU memory and runtime), and open-source code, offering strong practical guidance.

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