arXiv AI
Position, Role or Label? A Controlled Study of Instruction Conflicts in LLM Agents
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arXiv AI
Sep 1
How Language Models Choose Sides: Internal Representations of Instruction Hierarchy
arXiv:2608.28648v1 Announce Type: new Abstract: We study how instruction-tuned LLMs arbitrate direct conflicts between system and user instructions. We introduce a benchmark of 41 paired constraints...
arXiv AI
Oct 9
One Skill Too Many: How Co-Installed Skills Conflict in Coding Agents
The paper investigates how co-installed coding‑agent skills can conflict, causing the model to choose a similar skill over the intended one based solely on name and description. An empirical study of 20,947 repositories uncovered 822,109 candidate skill pairs, with 312 confirmed conflicts tested across three models, revealing that nearly one in four installed skills is overridden by a similar skill. Key findings include that conflicts often involve normative skills, that the installed skill loses core functions in about one‑fifth of runs, and that the first skill read determines the outcome, suggesting benchmarks should evaluate exclusive core functions and platforms should guard the initial read.
arXiv AI
Sep 10
Many-Tier Instruction Hierarchy in LLM Agents
The paper introduces Many-Tier Instruction Hierarchy (ManyIH), a new framework for resolving conflicts among instructions with arbitrarily many privilege levels in large language model agents. It presents ManyIH-Bench, a benchmark featuring 853 agentic tasks that require navigating up to 12 levels of conflicting instructions across 46 real-world agents. Experiments show current models achieve only about 40% accuracy when instruction conflict scales, highlighting a gap in fine-grained, scalable conflict resolution.
arXiv AI
Jul 3
AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents
arXiv:2607. 02255v1 Announce Type: new Abstract: Memory for a long-horizon LLM agent is a contract about what each future decision is allowed to see.
Hugging Face Trending Papers
Sep 13
Loop-Back Authority in LLM Agent Teams: A Paired Experiment on Flat and Hierarchical Coordination
Hierarchical orchestration, in which a Manager agent reviews worker output and can send it back for revision, is the default coordination pattern in production multi-agent LLM frameworks. Classical or...
arXiv AI
3d ago
AOS-Bench: Can LLM Agents Manage Dynamic Workloads?
AOS-Bench evaluates large language model agents as dynamic workload managers, testing their ability to handle multiple concurrent tasks under shared CPU and memory limits. The benchmark shows that isolated task success does not predict performance in a streaming workload, with significant drops in heavy and medium scenarios. Execution‑policy experiments reveal that simple concurrency controls can rival more complex management strategies, highlighting the importance of workload management as a distinct agent capability.