Paper

ColPackAgent: Agent-Skill-Guided Hard-Particle Monte Carlo Workflows for Colloidal Packing

Unreadunread

AI summary

arXiv:2605. 15625v1 Announce Type: new Abstract: We introduce ColPackAgent, an agent framework that autonomously runs Monte Carlo simulations of colloidal packing through a Model Context Protocol (MCP) tool server and an agent skill, whether as a standalone agent or inside an existing agent system.

Reading status

Priority

About this paper

Authors: Lijie Ding, Changwoo Do
Released: 2026-05-19 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because ColPackAgent: Agent-Skill-Guided Hard-Particle Monte Carlo Workflows for Colloidal Packing overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, text, rate, compare, without, alone, stage, language. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2605.15625

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to ColPackAgent: Agent-Skill-Guided Hard-Particle Monte Carlo Workflows for Colloidal Packing. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.