FIELD NOTES TOPIC

decision making

8 notes, newest first.

  1. Scores Are Not Decisions: Cost-Aware Stopping for Tool Acquisition in LLM Agents

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    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…

  2. The Social Cost of an AI Teammate: How an Artificial Teammate Reshapes Human-Human Communication in Small-Team Decision-Making

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    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…

  3. The Social Cost of an AI Teammate: How an Artificial Teammate Reshapes Human-Human Communication in Small-Team Decision-Making

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    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…

  4. SAFETY SENTRY: Context-Aware Human Intervention via EXECUTE-ASK-REFUSE Routing

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    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…

  5. Can Induced Emotion Bias LLM Behaviors in Sequential Decision Making?

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    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…

  6. Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling

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    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)…

  7. Criticality-Based Guard Rail Validation for AI Agent Decisions in Autonomous Telecom Networks

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    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…

  8. MemSyco-Bench: Benchmarking Sycophancy in Agent Memory

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    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…