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New ArchOR agent enhances LLM reliability in optimization tasks

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ArchOR, a new skill agent designed to improve the reliability of Large Language Models (LLMs) in optimization tasks. ArchOR addresses brittleness issues with narrative rewrites and instance-level experience storage by incorporating two novel retrieval modules: Centroid-Aware Representation Learning (CARL) and Signature-Guided Retrieval (S3RL). CARL refines skill representation ordering, while S3RL uses structural signatures and weighted Jaccard overlap for candidate prioritization. Tested with the DeepSeek-V4.1-Flash backbone, ArchOR achieved a 65.00% Micro-Avg. Pass@1 across five benchmarks, outperforming its predecessor, OptSkills. AI

IMPACT Enhances LLM capabilities in structured problem-solving and optimization, potentially improving agent reliability.

RANK_REASON The cluster contains an academic paper detailing a new method and its performance evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New ArchOR agent enhances LLM reliability in optimization tasks

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The cluster contains an academic paper detailing a new method and its performance evaluation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Anran Liu, Benzhao Hou, Zongsheng Cao, Daichao Wang, Lang Chen, Shitong Chen ·

    Structure-Aware Skill Retrieval and Arbitration for Optimization Modeling

    arXiv:2610.41909v1 Announce Type: new Abstract: Large Language Models (LLMs) can turn natural-language optimization tasks into solver-checked answers, but current agents remain brittle under narrative rewrites and often store reusable experience at the instance level. We introduc…

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