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Notes on ai
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Do Latent Channels Actually Communicate? A Causal Audit of Latent Multi-Agent LLM
Researchers introduce a method to study how latent channels in multi-agent systems, which transmit information without using text, impact the receiver's performance. They apply this method to Qwen3-4B and Qwen3-8B on…
From Passive Video to Editable Experience: Physically Grounded Experience Synthesis for Embodied Intelligence
Jia Luo introduces Pegasus, a framework to help robots learn from human manipulation videos by translating the human demonstrations into data that can be understood by robots. The method involves creating an…
OmegaUse-OfficeVal: Benchmarking LLM Agents on Long-Horizon Office-Suite Tasks with Economic Grounding
Researchers have developed a new benchmark called OmegaUse-OfficeVal to test the performance of large language model (LLM) agents in completing long-horizon office-suite tasks. The benchmark includes 100 tasks that…
Scores Are Not Decisions: Cost-Aware Stopping for Tool Acquisition in LLM Agents
The paper presents a new approach, Cost-Aware Marginal Decision-Focused Stopping (CAM-DF), for tool selection in Large Language Model (LLM) agents that use various external services. The approach addresses the…
SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response
The study introduces SecRespond, a new benchmark for evaluating AI agents in post-compromise incident response scenarios. The benchmark tests the ability of agents to produce forensic reports and remediation plans…
Setoka: A Benchmark for Hierarchical User Understanding in Personalized Agents over Heterogeneous Data
The researchers propose a benchmark called Setoka to evaluate how well personalized agents understand users' abstract characteristics, such as personality traits and behaviors, beyond just retrieving explicit facts…
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…
A corrective agentic hybrid RAG and an operations-grounded evaluation for a scientific facility
Researchers have developed APS-RAG, a platform that helps scientists at the Advanced Photon Source (APS) access institutional knowledge through natural language queries. The platform uses a retrieval engine that fuses…
Artificial Intelligence and Innovation Ecosystem: Evolutionary Developments, Challenges, and Future Directions
This research investigates the influence of Artificial Intelligence (AI) on the Innovative Ecosystem (IE), a new paradigm for economic integration and collaboration. The study analyzes AI's unique contributions to IE…
Closed-Loop Validation-Repair for Healthcare Interoperability: A Multi-Model Study of Schema Compliance in Clinical LLMs
The study evaluates three large language models (Qwen2.5 7B, Llama 3.1 8B, and Gemma2 9B) in the context of healthcare interoperability. The models were assessed across 320 clinical scenarios for their compliance with…
ERUnderstand: Evaluating Vision-Language Models on Structured ER Diagrams
A new benchmark called ERUnderstand has been introduced for evaluating the ability of Vision-Language Models (VLMs) to understand Entity-Relationship Diagrams (ERDs), which are important in database design. The…
Eviction as Estimation: A Fixed-Lag Smoothing View of Test-Time Memory, and When Measuring Beats Accumulating
The authors present a new approach for managing a language model's working memory by treating the decision of which stored items to keep as an estimation problem. They introduce a method called 'fixed-lag smoothing'…
Gubernaut: A Deterministic Homeostatic Controller for Affect-Regulated LLM Agents, Validated Across Independent Model Families
The research presents a new model-agnostic controller, Gubernaut Cognitive Controller (GCC), designed to control the behavior of Large Language Model (LLM) agents. The GCC aims to mitigate reactive failure modes in…
LLM-SoccerArena: Benchmarking LLMs on Real-World Predictions in Sports
LLM-SoccerArena is a new platform that allows for the prospective evaluation of large language models' abilities to forecast real-world sports events before their outcomes are known. The platform provides a live…
Looping Is Not Reliability: State-Bound Evidence and Typed Revision Contracts for Agentic Code Repair
The paper explores the lack of reliability guarantees in coding agents that use generate-test-revise loops. The authors conduct studies to understand the gap between finding a correct patch and retaining, verifying…
Myopia Prevention and Control 3.0: Artificial Intelligence--Driven Risk Stratification, Proactive Monitoring, and Personalized Intervention
The authors discuss Myopia Prevention and Control 3.0, a new approach that leverages artificial intelligence (AI), digital sensing, and ubiquitous computing to transform the way myopia is managed. This approach aims to…
Regulating for AI Legitimacy
The article discusses the issue of AI governance, specifically focusing on the question of legitimacy rather than just alignment. The author argues that AI systems already have significant influence in areas like…
SIREN: Towards End-to-End Extreme-Weather Early Warning with Experience-Grounded LLM Agents
The study presents SIREN, a framework for automating end-to-end extreme-weather early warning using Large Language Model (LLM) agents. The researchers developed SIREN-Bench, a comprehensive benchmark, and found…
SIREN: Towards End-to-End Extreme-Weather Early Warning with Experience-Grounded LLM Agents
Researchers are working on an automated system called SIREN to improve extreme weather early warning systems using Large Language Model (LLM) agents. The system is designed to bridge the gap between isolated scientific…
Task-Conditional Faithfulness Auditing of Multimodal LLMs for Grid Diagnosis
The paper proposes a framework for auditing multimodal large language models used in grid diagnosis tasks to ensure they are using task-appropriate evidence. The framework compares self-reported reliance, behavior…
Teacher Knows It Best: Spontaneous Symmetry Breaking and Tipping Points in Networked Langevin Dynamics AI Sycophancy
Researchers have developed a statistical physics framework to study a networked stochastic system exhibiting bistability, which can be used to understand and address AI-induced delusional spiraling, a phenomenon where…
Teacher Knows It Best: Spontaneous Symmetry Breaking and Tipping Points in Networked Langevin Dynamics AI Sycophancy
Researchers present a statistical physics model for a networked stochastic system with bistability, applied to address AI-induced delusional spiraling. The model partitions the network into regular agents and 'aware'…
Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks
Researchers have developed an open-source framework to evaluate the effectiveness of AI agents powered by open-weight large language models (LLMs) in data preparation tasks for longitudinal population studies. The…
Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks
Researchers have developed an open-source framework for evaluating the effectiveness of AI agents powered by open-weight large language models in data preparation tasks, specifically focusing on longitudinal population…
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