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Notes on artificial intelligence
262 notes, page 10 of 11
TRIAGE: Role-Typed Credit Assignment for Agentic Reinforcement Learning
The paper proposes a new framework called TRIAGE for credit assignment in agentic reinforcement learning. This framework adds a semantic role axis to outcome credit, aiming to correct the issues of standard GRPO where…
BayesEvolve: Explicit Belief States for Autonomous Scientific Discovery
BayesEvolve is a new framework for autonomous scientific discovery. It uses large language models (LLMs) to maintain explicit, uncertainty-aware beliefs about the quality of hypotheses, and it uses these beliefs to…
Linguistic Firewall: Geometry as Defense in Multi-Agent Systems Routing
A new routing architecture called ANTAP (Automatic Non-Textual Agent Picker) was introduced in this research. It aims to address security vulnerabilities in multi-agent systems by discarding indirect proxies and…
MESA: Prioritizing Vulnerable Communication Channels for Securing Multi-Agent Systems
The paper presents Mesa, a framework for prioritizing vulnerable communication channels in multi-agent systems (MAS). Mesa uses graph-theoretic metrics and dynamic probes to identify the edges most likely to affect the…
SIMAX: A Scalable and Interpretable Framework for Multi-Fidelity and Annotated Clinician-Patient Dialogue Simulation
The study introduces SIMAX, a framework that generates controlled clinical dialogue data with reference behavioral annotations for evaluating AI-driven communication coding systems. The generated dialogues are of…
TRACE: Temporal Relationship-Aware Conversational Entrainment Detection in Dyadic Speech
Researchers introduce a dataset (DyadEE) for studying emotional entrainment in dyadic speech interactions, including both entrained and disrupted conversations. They also propose TRACE, a new framework that models…
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A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models
A new synthetic clinical notes pipeline is introduced to generate a dataset that can be used for developing and evaluating AI tools in healthcare. The pipeline generates structured patient information, simulates…
A Process Harness for Uplifting Legacy Workflows to Agentic BPM: Design and Realization in CUGA FLO
The authors introduce a new mechanism called 'process harness' for upgrading legacy workflows to an Agentic Business Process Management (Agentic BPM) without replacing the existing engine. The process harness involves…
AI Healthcare Chatbots as Information Infrastructure: A Large-Scale Study of User-Reported Breakdowns
A study analyzing over 15,000 user reviews of AI healthcare chatbot apps found recurring issues with access barriers, service unreliability, user experience, interaction quality, billing, and customer support. Privacy…
Ask, Don't Judge: Binary Questions for Interpretable LLM Evaluation and Self-Improvement
Researchers have developed a new system called BINEVAL for evaluating large language models (LLMs). This system breaks down the evaluation criteria into binary questions, making it easier to understand the results and…
Auditing Framing-Sensitive Behavioral Instability in Large Language Models for Mental Health Interactions
This research investigates how different contexts (framings) can affect the responses of large language models used in mental health support tools. The study finds that framing does systematically alter interpretive…
Bridging Talk and Thought: Understanding Dialogue Dynamics Across Collaborative Problem-Solving Contexts
The authors present a framework for analyzing dialogue in collaborative problem-solving contexts, especially in human-AI and multi-agent collaboration. The framework aims to understand the emerging dynamics of these…
Bridging Talk and Thought: Understanding Dialogue Dynamics Across Collaborative Problem-Solving Contexts
Researchers present a framework for analyzing dialogues during collaborative problem-solving tasks involving human-AI and multi-agent interactions. The framework aims to optimize and evaluate these partnerships by…
Chai: Agentic Discovery of Cryptographic Misuse Vulnerabilities
Researchers have developed Chai, an AI-based system that discovers and validates cryptographic misuse vulnerabilities through naturally occurring signals. Instead of auditing one codebase for many flaws, Chai catalogs…
Confidence-Aware Tool Orchestration for Robust Video Understanding
The paper presents a new framework called Robust-TO for video understanding that addresses the issue of 'Blind Trust Problem' in current language models. These models assume every input frame is equally reliable, but…
From Celebrities to Anyone: Characterizing AI Nudification Content, Technology, and Community Dynamics on 4chan
Researchers studied AI-generated explicit content on 4chan and found a shift from targeting celebrities to non-celebrity individuals (55.8% vs. 4.7% in prior studies). Open-source models are widely used, with Stable…
In-Context Model Predictive Generation: Open-Vocabulary Motion Synthesis from Language Models to Physics
The paper proposes a new method called In-Context Model Predictive Generation (ICMPG) for generating human motions based on textual descriptions in digital applications. The method combines language model planning with…
Look-Before-Move: Narrative-Grounded World Visual Attention in Dynamic 3D Story Worlds
Researchers have developed a new camera planning framework called Look-Before-Move for embodied AI operating in dynamic 3D environments. The goal is to enable the camera to actively decide what to observe, not just…
Parametric Open Source Games
The authors introduce a new concept called parametric open-source games, which are continuous versions of game equilibria where players choose parameter vectors to determine their actions in an underlying finite game…
ReaORE: Reasoning-Guided Progressive Open Relation Extraction Empowered by Large Reasoning Models
The paper proposes a new method called Reasoning-guided progressive Open Relation Extraction (ReaORE) to improve the performance of Open Relation Extraction (OpenRE). This method addresses the limitations of current…
Semantic Early-Stopping for Iterative LLM Agent Loops
The research presents a method called semantic early-stopping for multi-agent large language model (LLM) loops, such as a Writer and a Critic. The goal is to halt the loop when the consecutive draft embeddings no…
State Representation Matters in Deep Reinforcement Learning: Application to Energy Trading
Researchers studied the impact of state representation in deep reinforcement learning (DRL) for energy trading decisions, using a fixed Double DQN agent in the HydroDam environment. They found that combining absolute…
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