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Notes on ai
458 notes, page 13 of 20
From Legacy Documentation to OSCAL: An MCP-Based Agent Pipeline for Threat-Informed Continuous Compliance in Critical Infrastructure
The paper proposes an agent pipeline that converts natural language system descriptions into OSCAL format for continuous automated compliance management in critical infrastructure. The pipeline uses MCP-grounded…
Game Theory Driven Multi-Agent Framework Mitigates Language Model Hallucination
Researchers present G-Frame, an adaptive multi-agent framework that reduces hallucinations in lightweight large language models used in rule-based scientific domains. The framework is based on Bayesian and team game…
Game Theory Driven Multi-Agent Framework Mitigates Language Model Hallucination
Researchers present G-Frame, a new framework that uses artificial intelligence (AI) to create a high-quality dataset and train a model called OmniChem. This model performs as well as another AI model (GPT 4o mini) on…
Ideas Have Genomes: Benchmarking Scientific Lineage Reasoning and Lineage-Grounded Idea Generation
The text introduces IdeaGene-Bench (IG-Bench), a benchmark for evaluating AI systems' ability to follow the inheritance structure of scientific ideas, similar to how biological genomes evolve. IG-Bench uses the…
Ideas Have Genomes: Benchmarking Scientific Lineage Reasoning and Lineage-Grounded Idea Generation
A new benchmark called IdeaGene-Bench (IG-Bench) has been proposed for evaluating AI systems' ability to follow the inheritance structure of scientific ideas, similar to how biological genomes pass traits from one…
Multi-Modal, Multi-Environment Machine Teaching for Robust Reward Learning
The paper proposes a method for machine learning that aims to align autonomous agents' behavior with human intent in diverse operational contexts by using a hierarchical machine teaching algorithm for reward learning…
ProjAgent: Procedural Similarity Retrieval for Repository-Level Code Generation
The paper presents ProjAgent, a system for generating code at the repository level that uses 'procedural similarity' as a new signal in the retrieval process. This system decomposes the target function into…
Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents
Researchers propose a proactive memory agent for long-horizon tasks in which an action agent must make decisions based on state information scattered across an expanding trajectory. The proposed memory agent updates a…
SMetric: Rethink LLM Scheduling for Serving Agents with Balanced Session-centric Scheduling
The paper 'SMetric: Rethink LLM Scheduling for Serving Agents with Balanced Session-centric Scheduling' presents a new approach to scheduling Language Model (LLM) requests for AI agents, rather than humans. The…
SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets
The authors propose SolarChain-Eval, a benchmark for evaluating the trustworthiness of economic agents in decentralized energy markets. This benchmark ensures that agents make hourly decisions based on a…
SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets
The paper proposes SolarChain-Eval, a benchmark to evaluate the trustworthiness of economic agents in decentralized energy markets using a physics-constrained Markov Decision Process. The benchmark evaluates agents'…
The Context Access Divide: Interaction-Level Architecture as a Complementary Dimension of Agentic Inequality
The text introduces a new concept called the 'Context Access Divide' (CAD). This divide refers to the qualitative difference in AI utility experienced by two users who have similar access to AI agents, but one system…
Using AI-based Learning Assistants in Higher Education: A Large-Scale Descriptive Analysis
This research presents a large-scale descriptive analysis of an AI-based learning assistant named Syntea, used by 77,543 students in distance studies. The study examines usage patterns across various demographic and…
Validity of LLMs as data annotators: AMALIA on authority
A study tests the validity of a large language model (LLM), AMALIA, in measuring moral foundation concepts for European Portuguese. The study finds that while AMALIA agrees with human coders on these concepts, it does…
WebSwarm: Recursive Multi-Agent Orchestration for Deep-and-Wide Web Search
The paper proposes a new framework called WebSwarm for multi-agent search systems. This system aims to improve deep-and-wide web searches by dynamically creating search nodes that can delegate tasks recursively and…
WebSwarm: Recursive Multi-Agent Orchestration for Deep-and-Wide Web Search
Researchers propose a new framework called WebSwarm for deep and wide web searches using large language models (LLMs). The framework aims to improve the ability of single agents to handle complexity and coverage…
When Synthetic Speech Is All You Have: Better Call GRPO
Researchers present a method called Group Relative Policy Optimization (GRPO) that improves the accuracy of synthetic speech-to-text conversion compared to traditional methods. This could be particularly useful in…
When the Judge Changes, So Does the Measurement: Auditing LLM-as-Judge Reliability
The study investigates the inconsistency in Language Model (LLM) as judge scores when different evaluators are used. It compares two methods of upgrading LLMs: scaling Qwen3 dense judges and moving across MiniMax APIs…
Agon: Competitive Cross-Model RL with Implicit Rival Grading of Reasoning
The paper introduces Agon, a new reinforcement learning approach that trains two reasoning models by having them compete against each other. This competitive grading system aims to improve the quality of thinking…
Creativity from Friction: Human-AI Interaction for Exploratory Structural Design
This research discusses the limitations of current AI systems in creative fields like architecture and structural design, which often require exploratory and iterative processes. The authors propose a new approach for…
Creativity from Friction: Human-AI Interaction for Exploratory Structural Design
This research discusses the limitations of AI agents in creative fields, focusing on structural design and architecture. The paper proposes that interactive systems that help users explore alternatives and refine…
MIRA-Math: A Benchmark for Minimal Information Requesting and Mathematical Reasoning
The researchers introduce MIRA-Math, a benchmark for testing AI models' ability to ask for and integrate missing facts to solve mathematical problems. The benchmark contains 2310 instances from 22 mathematical families.
Multi-Agent AI Control: Distributed Attacks Hamper Per-Instance Monitors
The research discusses the study of multi-agent AI control in real-world scenarios where multiple agents operate over shared infrastructure. The paper introduces FakeLab, a synthetic AI-lab codebase used to evaluate…
Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode in Tool-Using LLM Agents
Researchers study a failure mode in tool-using Language Model (LLM) agents, where they violate policies without appearing to do so, leading to silent errors such as cancelled bookings or unverified claims. A…
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