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Notes on ai
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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 large, locally deployable language models (LLMs) in data preparation tasks, specifically focused on…
Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks
The study introduces an open-source framework for evaluating the effectiveness of AI agents powered by open-weight large language models (LLMs) in data preparation tasks, particularly for longitudinal population…
Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems
The paper proposes a new approach for managing AI agent context, arguing that it's not just a storage-and-retrieval problem but a lifecycle issue involving deciding what to remember, structuring it, choosing the right…
Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems
The article proposes a new discipline called Agentic Context Management (ACM) to address issues faced by AI agents in managing their reasoning context, such as conversation histories and large prompts. The authors…
Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems
The paper presents Agentic Context Management (ACM), a solution to the issue of AI agents being overwhelmed by their own accumulating history and growing token cost in production settings. The authors argue that…
AREX: Towards a Recursively Self-Improving Agent for Deep Research
The paper introduces a new type of research agent called AREX, which can recursively improve its answers by verifying intermediate results and using the partially verified state to guide subsequent refinement. The…
AREX: Towards a Recursively Self-Improving Agent for Deep Research
The paper introduces a new family of agents called AREX for deep research. These agents are designed to recursively improve their answers by verifying intermediate results, guiding subsequent refinement, and learning…
Detecting LLM-Generated Tokens in Human--LLM Coauthored Text
A new method for detecting parts of text generated by a language model (LLM) within human-written text is introduced. This method operates at the token level, reducing variability in adjacent tokens while using an…
Detecting LLM-Generated Tokens in Human--LLM Coauthored Text
The paper presents a new method for identifying parts of a text generated by Language Model (LLM) at the token level, improving on existing document-level methods. The method is based on smoothing adjacent token scores…
Euclid-MCP: A Model Context Protocol Server for Deterministic Logical Reasoning via Prolog
The paper introduces Euclid-MCP, an open-source MCP server that provides deterministic logical reasoning via SWI-Prolog. It aims to improve the reliability of Large Language Models (LLMs) in multi-step logical…
Euclid-MCP: A Model Context Protocol Server for Deterministic Logical Reasoning via Prolog
The paper introduces Euclid-MCP, an open-source MCP server that provides deterministic logical reasoning via SWI-Prolog. It addresses the issue of unreliable multi-step logical reasoning in Large Language Models (LLMs)…
Euclid-MCP: A Model Context Protocol Server for Deterministic Logical Reasoning via Prolog
The paper introduces Euclid-MCP, an open-source MCP server that provides deterministic logical reasoning via SWI-Prolog. It aims to bridge the gap between Large Language Models (LLMs) and multi-step logical reasoning…
GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG
The paper introduces GRADRAG, a framework for improving Retrieval-Augmented Generation (RAG) systems by coordinating improvements across different components of the pipeline. It uses feedback from an Evaluator to…
GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG
The paper introduces GRADRAG, a framework that improves Retrieval-Augmented Generation (RAG) systems by coordinating improvements across the pipeline. Instead of optimizing components in isolation, GRADRAG models the…
GS-Agent: Creating 4D Physical Worlds With Generative Simulation
Researchers have developed GS-Agent, a system that uses natural language descriptions to automatically generate realistic, dynamic, and controllable 4D physical worlds. This is achieved through an end-to-end…
GS-Agent: Creating 4D Physical Worlds With Generative Simulation
Researchers have developed GS-Agent, a system that generates realistic, dynamic, and controllable 4D physical worlds from natural language descriptions using an end-to-end multi-agent framework. The system emulates…
ICAE-Bench: Evaluating Coding Agents as Interactive Project Builders
The paper introduces ICAE-Bench, a benchmark for evaluating coding agents in interactive project building settings. Instead of being limited to completing code under fully specified instructions, these agents are…
pAI-Econ-claude: A Gated Human-in-the-Loop Multi-Agent Architecture for AI-Assisted Economic Theory Development
A new multi-agent architecture, named pAI-Econ-claude, was proposed for AI-assisted economic theory development. This architecture uses a gated human-in-the-loop system to ensure reliability in tasks where no machine…
PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning
The authors propose a new training paradigm called PATS for reinforcement learning agents that learn long-term tasks. PATS converts rollout groups from the latest policy into evidence cards and adjusts the context used…
Regulating autonomous and agentic AI
This research paper explores the difficulties in regulating activities that use autonomous and agentic AI. The authors argue that existing regulatory assumptions no longer apply due to the distributed knowledge and…
Regulating autonomous and agentic AI
The paper explores the difficulties in regulating activities that use autonomous and agentic AI due to changes in regulatee knowledge and control, which often lie outside the AI supply chain. The authors investigate…
Scaling Up Formal Representation of Clinical Trial Protocols in Ensemble Logic Using LLMs: A Preliminary Study
Researchers present CT-TEL workflow, a scalable pipeline that uses Large Language Models (LLMs) to convert clinical trial protocols from unstructured free text into Temporal Ensemble Logic (TEL) formulas. The study…
Toward Continuous Assurance for the Democratization of AI Agent Creation in Industry
The paper discusses a reliability challenge caused by AI agents being created by non-engineering users within organizations, due to their dependency on various factors that can cause silent degradation over time. The…
Toward Continuous Assurance for the Democratization of AI Agent Creation in Industry
The paper proposes a solution to ensure reliability in AI agents created by non-engineering users within organizations, through democratized low-code, no-code, and conversational development environments. The proposed…
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