If the tool (backend) itself was at fault (for example, a timeout when accessing the system) — did you pass traceback errors to the models?
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147
Machine Learning / AI
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Was there a check for record duplication in the system after a failure following a successful write (idempotency)?
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147
What dataset was used to measure model selection metrics?
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146
How was the entire multi-agent system traced — at which stages do errors, latency, etc. occur?
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146
Have other embedding models been tested?
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146
What metrics were used to measure retrievability (which dataset and tool)?
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146
Why was Human-in-the-Loop not used in the multi-agent system, only in ReAct?
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144
What was the chunk size and did you do overlap?
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144
What is BERT and what tasks was it trained on?
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143
Describe the ReAct agent cycle: what were the steps and how did the cycle end?
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143
What types of annotations for NER tasks do you know? What have you worked with?
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142
What is VLM (Vision Language Model)?
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141
In terms of speed, which is faster — a regular agent with tools or a multi-agent system?
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141
Which part (BM25 or dense) has unnormalized values?
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140
Was the toolcalling native or through prompt (parsing)?
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139
Used one model for all agents or different ones?
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136
And if the model gave an invalid argument, what did you do?
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135
How do models report that an argument is invalid?
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135
What is the difference between fine-tuning and few-shot prompting? When and what to choose?
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134
Was there parallelism in the execution of agents or was everything sequential?
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133