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<p class="MsoNormal"><b><u><span style="font-family:"Cambria",serif;color:black">Presentation 1</span></u></b><span style="font-family:"Cambria",serif;color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><b><span style="color:black">Title:</span></b><span style="color:black"> FINAGENT: A BENCHMARK FOR EVALUATING LARGE LANGUAGE MODELS IN FINANCIAL ANALYSIS TASKS<o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><b><span style="color:black">Presenter:
</span></b><span style="color:black">Abiodun Adedeji<o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><b><span style="color:black">Supervisor:</span></b><span style="color:black"> Dr. Wenlu Wang<o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><span style="color:black"><o:p> </o:p></span></p>
<p class="MsoNormal" style="background:white"><b><span style="color:black">Abstract</span></b><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><span style="color:black">I present FinaGent, a benchmark for evaluating large language models (LLMs) on financial analysis tasks grounded in real U.S. Securities and Exchange Commission (SEC) filings. FinaGent
comprises 41 expert-validated question-context-answer triples across five task categories: Risk Analysis, Comparative Analysis, Strategic Reasoning, Causal Reasoning, and Financial Interpretation, and four major companies: JPMorgan Chase, Apple, Pfizer, and
Exxon Mobil. I evaluate four state-of-the-art LLMs using ROUGE, BERTScore, and LLM-as-Judge. Key findings are: (1) Task type, not model size, is the strongest predictor of LLM performance; (2) Risk Analysis is the most difficult task for all models; (3) LLM-as-Judge
provides more reliable evaluation than lexical or semantic measures for financial reasoning. Additionally, human-assigned difficulty labels do not align with computational difficulty, suggesting future benchmarks must better reflect actual task challenges.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:"Cambria",serif;color:black"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-family:"Cambria",serif;color:black"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><u><span style="font-family:"Cambria",serif;color:black">Presentation 2</span></u></b><span style="font-family:"Cambria",serif;color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><b><span style="color:black">Title: </span></b><i><span style="color:black">Reasoning Under Uncertainty: LLM-Based Coordination of Multi-Robot Systems with Intermittent Communication and Robot Failures</span></i><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><b><span style="color:black">Presenter:
</span></b><span style="color:black">Nyo Me Han<o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><b><span style="color:black">Supervisor:</span></b><span style="color:black"> Dr. Bozhen Liu<o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><span style="color:black"><o:p> </o:p></span></p>
<p class="MsoNormal" style="background:white"><b><span style="color:black">Abstract</span></b><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><span style="color:black">Multi-robot systems provide clear benefits for large-scale inspection, monitoring, and search-and-rescue tasks, but their effectiveness can be hindered by hardware issues, unreliable communication,
and too much reliance on fixed coordination methods. Current coordination structures often struggle to balance scalability with the intelligence of individual robots and do not have ways to continuously assess trust in each robot. This makes it hard to spot
performance drops and potential failures during missions. While large language models (LLMs) show promise for multi-robot planning, existing methods usually use static coordination patterns and do not effectively tackle the best way to coordinate when assessing
bridge structures under communication limits and structural uncertainty. This thesis presents a hybrid agent framework that promotes proactive coordination strategy selection, ongoing trust monitoring, clear decision-making, and flexible replanning during
missions. The framework includes four main components: (i) Proactive Mission Strategy Selection (PMSS), a pre-mission module that picks the best coordination strategy using a structured mission descriptor and rule-based scoring; (ii) a Trust Generalization
and Personalization Mechanism (TGPM) that continuously calculates a Trust-Theoretic Score for each robot to detect reduced sensing, mobility, or communication capabilities; (iii) LLM-Augmented Justification (LAJ), which gives natural language explanations
to support clarity and accountability; and (iv) LLM-Driven Adaptive Replanning (LDAR), which creates context-sensitive recovery plans in response to hardware, battery, and communication problems. The proposed model is used for autonomous assessments of bridge
structures and is tested with CommDeg-Bridge, a new benchmark for trust-aware, agent-based multi-robot bridge inspections. This benchmark measures damage distribution, resilience to communication issues, and the accuracy of coordination strategy selection.
Results show improved adaptability, resilience, and clarity compared to fixed-strategy methods. Although it is designed for bridge assessments, the framework’s reasoning abilities allow it to be applied in other inspection and post-disaster assessment fields.<o:p></o:p></span></p>
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