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Notes on decision making
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Scores Are Not Decisions: Cost-Aware Stopping for Tool Acquisition in LLM Agents
The paper proposes a method called Cost-Aware Marginal Decision-Focused Stopping (CAM-DF) for selecting tools in large language model agents. This method aims to balance between acquiring enough tools and keeping costs…
The Social Cost of an AI Teammate: How an Artificial Teammate Reshapes Human-Human Communication in Small-Team Decision-Making
A study found that using conversational AI as a teammate in small group decision-making reshapes human communication in several ways. The AI was the most talkative and self-cohesive member but contributed less new…
The Social Cost of an AI Teammate: How an Artificial Teammate Reshapes Human-Human Communication in Small-Team Decision-Making
A study found that when an AI teammate is introduced in small decision-making teams, it becomes the most talkative and self-cohesive member but its contributions lack new information and density. Human teammates in…
SAFETY SENTRY: Context-Aware Human Intervention via EXECUTE-ASK-REFUSE Routing
Researchers present Safety Sentry, a lightweight guard model that makes decisions about executing, asking for help, or refusing actions in real-world environments. This model aims to improve safety and autonomy by…
Can Induced Emotion Bias LLM Behaviors in Sequential Decision Making?
Researchers investigated if emotions induced in large language models (LLMs) could influence their sequential decision-making using the Iowa Gambling Task (IGT). They found that, unlike humans, induced emotion did not…
Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling
Researchers investigate if large language models (LLMs) can reproduce human biases in decision-making, specifically in route choice, without explicitly specifying parameters for Cumulative Prospect Theory (CPT)…
Criticality-Based Guard Rail Validation for AI Agent Decisions in Autonomous Telecom Networks
The paper proposes a Guard Rail Validation (GRV) framework for ensuring the accuracy of AI-driven decisions in fully autonomous telecom networks. The framework evaluates decisions based on several factors, such as…
MemSyco-Bench: Benchmarking Sycophancy in Agent Memory
The authors propose MemSyco-Bench, a new benchmark to evaluate how memory affects decision-making in language models (LLMs) used in AI agents. The benchmark aims to assess if the agents can handle issues of sycophancy…
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