arXiv AI
4d ago

Behaviour analysis for large language model-driven agents

The paper proposes a general framework for analysing the behaviour of large language model-driven agents. It connects data-driven behavioural discovery with analysis and experimental investigation, using categories derived from recorded activity to measure behaviours and preserve their positions in the process. The framework includes statistical assessment in held-out contexts and experiments such as prompt steering to test how behaviours and outcomes change under varied conditions, illustrated by a study of iterative self-reflection.

By Tianqiang Yan, Lizhen Qu
arXiv AI
Aug 26

Comparing Explanations is Not Enough, Explain the Change: New Standards are Needed to Explain Behavioral Shifts in Large Language Models

arXiv:2602. 02304v3 Announce Type: replace Abstract: Large-scale foundation models exhibit behavioral shifts when subjected to interventions such as scaling, fine-tuning, reinforcement learning with human feedback, or in-context learning.

By Martino Ciaperoni, Marzio Di Vece, Roberto Pellungrini, Luca Pappalardo, Fosca Giannotti, Francesco Giannini
arXiv AI
Oct 19

Testing the Planning Claims of LLM-Based Agents against Formal Semantics

The paper evaluates the planning abilities of large language model (LLM) agents by comparing their performance against formal semantics defined in hidden PDDL models. Across five domains, four LLMs, six prompting strategies, and three agent architectures, the best setup solved 78% of solvable tasks offline but only 40% online, with limited recognition of unsolvable tasks and no clear benefit from more elaborate prompting. The study concludes that claims of LLM planning competence require validated action traces rather than end‑to‑end success rates.

By Oriol Miro-Lopez-Feliu, Oscar Mir\'o L\'opez-Feliu, Victor Gimenez-Abalos, Javier Vazquez-Salceda, Victor Carballo Araruna, Sergio Alvarez-Napagao
arXiv Computation and Language
Oct 21

From Abstraction to Action: DSLs for Natural Language Abstraction Interpretation and Execution

The paper introduces domain-specific languages (DSLs) as an explicit representation layer to bridge the gap between high-level natural-language abstractions and concrete actions. Experiments on the visually-grounded “Hexagons” task and the “Text2CAD” benchmark show that code generation using a human-curated DSL yields dramatic performance gains, especially at higher abstraction levels, while automatically induced DSLs only partially recover these gains. The study highlights that the right representation can close much of the performance gap caused by abstraction, but that automatically learning such representations remains a challenge.

By Amit Lisha, Reut Tsarfaty