The paper introduces skilder, a framework that organizes LLM agent capabilities into role‑scoped bundles of skills, tools, and instructions, with explicit limits. Agents start with a minimal role catalog, discover the roles needed for a task, and receive the associated tools only through a single MCP server, ensuring deterministic enforcement of scope. Experiments on 13 tasks with six models show that skilder’s authorization layer prevents unauthorized tool calls and parameter violations while maintaining flexibility through dynamic cross‑role capability acquisition.
By Michael Stettler, Benjamin Girardet, Jonas Canton, Nicolas Corod
arXiv:2511. 04694v5 Announce Type: replace-cross Abstract: As large language model (LLM) based systems take on high-stakes roles in real-world decision-making, they must reconcile competing instructions from multiple sources within a single prompt context.
By Zishuo Zheng, Vidhisha Balachandran, Chan Young Park, Faeze Brahman, Sachin Kumar
arXiv:2606. 07808v1 Announce Type: new Abstract: Reasoning language models deployed in agentic workflows must follow an instruction hierarchy: when instructions from different sources conflict, the model should obey the highest-privilege applicable instruction.
By Sanjay Kariyappa, G. Edward Suh
SkillEffect is a checked‑lowering runtime that ensures agent tool calls stay within memory limits by verifying each proposed program against an immutable input before execution. It uses audited relation plugins to provide source recognition, bounded intermediate representation construction, and postconditions, while a shared runtime handles selection, bounded VM execution, and atomic capacity leasing. Experiments across six operator families show that bounded access significantly reduces peak memory usage and improves completion rates under fixed memory caps.
By Yinuo Wang, Yiyu Shi
Agent Skills can specify procedural and resource obligations for tool use, and language models instantiate them as concrete programs. However, when models turn this guidance into code for existing too...
arXiv:2602. 12430v4 Announce Type: replace-cross Abstract: The transition from monolithic language models to modular, skill-equipped agents marks a defining shift in how large language models (LLMs) are deployed in practice.
By Renjun Xu, Yang Yan
arXiv:2603. 17673v2 Announce Type: replace-cross Abstract: LLM agents are becoming increasingly important in the security domain, but leading systems are often closed-source, cloud-based, hard to reproduce or use with sensitive code.
By Philipp Normann, Andreas Happe, J\"urgen Cito, Daniel Arp
The paper introduces AgentLeak, a black‑box attack that clones the task‑solving capabilities of a strong LLM agent onto a weaker one by exploiting differences between successful and failed executions. Unlike prior skill‑stealing methods that only recover explicit skill artifacts, AgentLeak identifies and incorporates missing procedural behaviors, boosting task pass rates by over 40% and closing more than 80% of the capability gap across 20 scenarios. The study demonstrates that observable execution behavior can leak proprietary procedural knowledge, posing a confidentiality risk for LLM agents.
By Xiaoting Lyu, Yuhong Wu, Yufei Han, Shichang Liu, Liang Zhang, Bin Wang, Bin Wang, Xiaobo Ma, Wei Wang
arXiv:2609.14992v1 Announce Type: new
Abstract: Recently, the rapid development of large language models (LLMs) has reshaped software engineering by enabling autonomous code agents that plan, execute...
By Bosi Wen, Cunxiang Wang, Jiayi Gui, Haoke Zhang, Yilin Niu, Pei Ke, Dayong Yang, Hongning Wang, Minlie Huang
arXiv:2605. 09163v3 Announce Type: replace Abstract: Large language model agents increasingly operate through an intermediate skill layer that mediates between user intent and concrete task execution.
By Shawn Li, Chenxiao Yu, Han Wang, Wei Yang, Ryan Rossi, Franck Dernoncourt, Xiyang Hu, Philip Yu, Chaowei Xiao, Huan Zhang, Yue Zhao
The paper introduces Skillware, an open architectural framework that separates an agent’s intent from its execution by providing modular, versioned software capability bundles. These bundles include typed JSON contracts, deterministic Python code, task directives, datasets, and unit tests, allowing agents to perform tasks reliably without relying on fragile prompt engineering. Experiments across 23 models and 368 benchmarks show that small local models equipped with Skillware outperform larger frontier models, reducing token usage by up to 84.8%, cutting inference costs by up to 97.3%, and eliminating parameter errors.
By Vladimiros Peilivanidis (ARPA Hellenic Logical Systems)
The paper introduces Probabilistic Agentic Programming (PAP), a framework that explicitly defines responsibility boundaries for language‑model agents by linking mandate, authority, effects, and evidence. It compares PAP to various programming paradigms and agent systems across seven dimensions, and presents a mediation kernel that enforces checks before tool execution, operation‑bound approval, and resource limits. An exploratory study with seven business cases and multiple models demonstrates that PAP’s native Touba implementation achieves the highest or tied observation counts, supporting its effectiveness in managing agentic behavior.
By Khadim Lo, Soukaina Alaoui Ismaili, Jean-S\'{e}bastien Dessureault