Portrait of Sara Vera Marjanovic is unavailable

Sara Vera Marjanovic

Alumni
Website
Google Scholar

Publications

Does The Way You Plan Matter? An Empirical Study of Planning Representations for LLM Web Agents
Alejandra Zambrano
Sara Vera Marjanovic
Imene Kerboua
Xing Han Lu
Leila Kosseim
Despite recent advances, LLM-based web agents still struggle with limited exploration, omission of critical steps, and sensitivity to task c… (see more)onstraints. Prior work suggests that many of these failures stem from weaknesses in planning, yet the impact of alternative natural language plan representation remains unexplored. To address this, we introduce PlanAhead, a static planner-executor framework that evaluates the impact of plan representation in agent performance. We first automatically categorize WebArena tasks into 3 difficulty levels, enabling consistent difficulty grading without human annotation. Then we systematically evaluate 4 different plan representations on the tasks categorized as hard: sequential subgoals, narrative, pseudocode, and checklist; across different families of multimodal LLM powered agents (OpenAI, Alibaba, and Google). To account for stochastic variability, we introduce two novel evaluation metrics: Achievement Rate (AR) and Solved-Task Consistency (STC). Our results show that both, the plan formulation and the underlying LLM generating the plan, significantly influence web-agent robustness and task success.
2026-05-27
arXiv (preprint)
doi.org
arxiv.org
DeepSeek-R1 Thoughtology: Let's think about LLM Reasoning
Sara Vera Marjanovi'c
Arkil Patel
Vaibhav Adlakha
Milad Aghajohari
Parishad BehnamGhader
Mehar Bhatia
Aditi Khandelwal
Austin Kraft
Benno Krojer
Xing Han Lu
Xing Han Lu
Nicholas Meade
Dongchan Shin
Amirhossein Kazemnejad
Gaurav Kamath
Marius Mosbach
Karolina Stanczak
Siva Reddy
Large Reasoning Models like DeepSeek-R1 mark a fundamental shift in how LLMs approach complex problems. Instead of directly producing an ans… (see more)wer for a given input, DeepSeek-R1 creates detailed multi-step reasoning chains, seemingly"thinking"about a problem before providing an answer. This reasoning process is publicly available to the user, creating endless opportunities for studying the reasoning behaviour of the model and opening up the field of Thoughtology. Starting from a taxonomy of DeepSeek-R1's basic building blocks of reasoning, our analyses on DeepSeek-R1 investigate the impact and controllability of thought length, management of long or confusing contexts, cultural and safety concerns, and the status of DeepSeek-R1 vis-\`a-vis cognitive phenomena, such as human-like language processing and world modelling. Our findings paint a nuanced picture. Notably, we show DeepSeek-R1 has a 'sweet spot' of reasoning, where extra inference time can impair model performance. Furthermore, we find a tendency for DeepSeek-R1 to persistently ruminate on previously explored problem formulations, obstructing further exploration. We also note strong safety vulnerabilities of DeepSeek-R1 compared to its non-reasoning counterpart, which can also compromise safety-aligned LLMs.
2025-04-01
ArXiv (preprint)
arxiv.org
arxiv.org