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Notes on ai
458 notes, page 6 of 20
CoPlan: A Trustworthy Co-Intelligence Interface for Care Planning through Role-Based Contestable Argument Graphs
CoPlan is a new AI system designed to help with care planning for clinicians, patients, caregivers, and care teams. It allows stakeholders to inspect, challenge, and revise plans if they conflict with clinical…
Hierarchical Graph Memory for LLM Agents with Path-level Localization and Rewrite
The authors propose HiGram, a hierarchical graph memory framework for long-term reasoning agents. This framework aims to efficiently update memories over time by organizing them into a coarse-to-fine architecture…
Hierarchical Graph Memory for LLM Agents with Path-level Localization and Rewrite
The authors propose HiGram, an evolving hierarchical graph memory framework that aims to improve the efficiency and effectiveness of long-term reasoning agents by reducing irrelevant information during retrieval and…
InsightEmb: Learning Action-Intent Embeddings for Agentic Insight Retrieval
The paper proposes a new method called InsightEmb for learning action-intent embeddings to improve agentic insight retrieval, which helps self-improving agents make better decisions by using insights from prior…
The Transformer Revolution, Part 1: Dynamic Processing through Output- Weight Interconnections
The paper proposes a new interpretation of Transformers during inference, suggesting they construct prompt-dependent transformations whose parameters are generated during inference. This form of computation is named…
Agentic Metaverse Services: A New As-a-Service Paradigm
The paper discusses a new concept called Agentic Metaverse Services (AMServ), which combines advanced Generative Artificial Intelligence (GenAI) with the metaverse to create a virtual ecosystem for various aspects of…
Agentic Metaverse Services: A New As-a-Service Paradigm
The paper discusses Agentic Metaverse Services (AMServ), a new paradigm of agentic services and service computing, that combines Generative Artificial Intelligence (GenAI) with the metaverse to provide customized agent…
AISPA: User-Centric System Prompt Auditing for Large Language Model Applications
A new framework called AISPA is introduced to audit system prompts in AI systems, which are instructions used by developers to control the behavior of AI applications. The framework examines specific parts of a system…
AskChem: Claim-Centered Infrastructure for Chemistry Literature Synthesis
The paper introduces AskChem, a system that aims to simplify chemistry literature synthesis by converting papers into atomic, typed claims. Each claim is linked to its source and can be searched and synthesized using…
CACHE-UK: A Stability-Aware Memory Editor for Sequentially Updated Quantized LLMs in Finance
Researchers introduce CACHE-UK, a framework designed for efficiency and accuracy in deploying large language models (LLMs) in the dynamic field of finance. The system addresses the 'quantization stability crisis' that…
EMBL AI Librarian: Life-Sciences Knowledge Layer for AI Agents
The paper introduces EMBL AI Librarian, a system that enhances the Europe PMC interface for AI agents to enable them to search and retrieve evidence in natural language. The system was evaluated across four benchmarks…
MANTA: Multi-Agent Network Topology Adaptation for Self-Evolving Multi-Agent Systems
The paper introduces MANTA, a framework for adapting communication structures in multi-agent systems during inference time. This allows for more efficient problem solving by enabling agents to adjust their roles…
MANTA: Multi-Agent Network Topology Adaptation for Self-Evolving Multi-Agent Systems
The paper introduces MANTA, a framework for Multi-Agent Network Topology Adaptation that allows communication structures in large language model-based multi-agent systems to evolve during deployment. The goal is to…
One Human, $N$ Agents: Audit-Budget Allocation for LLM Agent Fleets under Miscalibrated, Correlated Confidence
The paper presents a study on auditing multiple Language Learning Models (LLMs) with limited budgets, where human auditors have to evaluate self-reported confidence that may be miscalibrated and correlated. The…
ORCA-bench: How Ready Are Language Model Agents for Oncall?
The paper introduces ORCA-bench, a benchmark for testing the performance of large language models in oncall root cause analysis (RCA). The benchmark includes a live microservice system with six days of metrics, logs…
ORCA-bench: How Ready Are Language Model Agents for Oncall?
The study presents ORCA-bench, a benchmark for evaluating the performance of large language models in oncall root cause analysis (RCA). The benchmark exposes a microservice system with six days of data and 1,079 RCA…
Rethinking Inference-Time Scaling in Local Computer-Use Agents: Failure Modes and Compute Tradeoffs
The paper presents an empirical study on the effectiveness of inference-time scaling in locally deployed autonomous computer-use agents (CUAs) under strict hardware constraints. The research focuses on four dimensions…
Selective Credibility-Limited Belief Update
The paper introduces a new method called Selective Credibility-Limited Belief Update. This method allows an agent to update its beliefs based on changes in the world, by transforming the epistemic input (information)…
Selective Credibility-Limited Belief Update
The paper introduces a new method for belief update called Selective Credibility-Limited Belief Update. This method allows for partial incorporation of an epistemic input (information) from a source world, addressing a…
Selective Credibility-Limited Belief Update
The paper introduces a new method for updating an agent's beliefs called Selective Credibility-Limited Belief Update. This method allows for partial incorporation of epistemic inputs, meaning only part of a compound…
TCA-SIR: Learning Target-Conditioned Abstractions for Scientific Inspiration Retrieval
Researchers developed a new method called TCA-SIR for AI to generate scientific hypotheses. This method focuses on transferable abstract principles rather than topical similarity when retrieving inspirations for a…
Tycho: Active Abstraction with Programmatic World Models for ARC-AGI-3
The paper introduces Tycho, a system that helps an artificial agent learn the rules and goals of unfamiliar games. The system separates actionable observations from other frames and uses them to construct game-specific…
When Specifications Conflict: A Symmetry-Based Framework for Measuring LLM Preferences
The researchers propose a framework to analyze how large language models (LLMs) handle conflicting specifications when integrating multiple sources of information. They compare four types of representation: pure…
AI as Friction for Reflection Support in Ideation
The authors suggest that AI tools used for creative work, designed to reduce friction, may not be ideal as they overlook an essential aspect of the design process: reflection-in-action during ideation. They propose…
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