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2,348 notes, page 25 of 98
Hardware Keystores for AI Agent Signing Workflows: A Zero-Trust MCP Enforcement Architecture
The paper proposes a system to secure AI agents' private keys by replacing software-stored keys with hardware-confined ones, accessible through a standard PKCS#11 interface. The system aims to ensure key…
Hardware Keystores for AI Agent Signing Workflows: A Zero-Trust MCP Enforcement Architecture
The authors propose a new architecture to improve security for AI agents that perform cryptographic operations by replacing software-stored private keys with hardware-confined ones, using a vendor-neutral PKCS#11…
Hardware Keystores for AI Agent Signing Workflows: A Zero-Trust MCP Enforcement Architecture
The researchers propose a system that uses hardware-confined keys instead of software-resident keys to enhance security for AI agents performing cryptographic operations. The system aims to protect against key…
HarnessOpt-Bench: Evaluating LLMs at Harness Optimization
Researchers introduce HarnessOpt-Bench, a benchmark for evaluating the ability of Large Language Models (LLMs) to optimize their own systems. The study compares five frontier LLMs across four tasks and finds that they…
HarnessOpt-Bench: Evaluating LLMs at Harness Optimization
Researchers have introduced HarnessOpt-Bench, a benchmark to measure the performance of Large Language Models (LLMs) in optimizing their own harnesses (the prompts, tools, etc. surrounding them). The research shows…
HarnessOpt-Bench: Evaluating LLMs at Harness Optimization
The paper introduces HarnessOpt-Bench, a benchmark for evaluating the ability of Language Models (LLMs) to optimize the harnesses surrounding them, which includes prompts, tools, control flow, memory, and orchestration…
HarnessOpt-Bench: Evaluating LLMs at Harness Optimization
Researchers have introduced HarnessOpt-Bench, a benchmark for evaluating the performance of language models (LLMs) in optimizing harnesses (the tools and code surrounding them). The study tests 5 leading LLMs across 4…
HarnessOpt-Bench: Evaluating LLMs at Harness Optimization
The text introduces HarnessOpt-Bench, a benchmark for evaluating the performance of Large Language Models (LLMs) in optimizing AI systems. The benchmark involves an iterative process where an LLM edits a given harness…
HarnessOpt-Bench: Evaluating LLMs at Harness Optimization
Researchers introduce HarnessOpt-Bench, a benchmark for evaluating the performance of Large Language Models (LLMs) in optimizing their own harnesses. The study examines five frontier LLMs on four tasks and finds that…
iARCS: Iterative Agentic RL for Controllable 3D Scene Generation
The paper presents iARCS, an iterative agentic reinforcement learning framework for enhancing the performance of synthetic 3D scene generators by adapting them to meet task-specific functional constraints, such as…
iARCS: Iterative Agentic RL for Controllable 3D Scene Generation
Researchers present iARCS, an iterative agentic reinforcement learning framework to adapt a pre-trained 3D scene generator for meeting task-specific requirements in natural language. The system uses a two-stage…
Improving the Realism of Synthetic Clinical Benchmarks Under Utility Constraints
Researchers propose a method to improve the realism of synthetic clinical benchmarks for AI agents in healthcare settings without compromising their downstream utility checks. They develop a framework that ensures…
Improving the Realism of Synthetic Clinical Benchmarks Under Utility Constraints
The authors propose a method to improve the realism of synthetic clinical benchmarks for enterprise AI agents while maintaining their utility. They focus on healthcare settings where operational data is hard to access…
Improving the Realism of Synthetic Clinical Benchmarks Under Utility Constraints
Researchers propose a method to create more realistic synthetic clinical benchmarks for AI agents in healthcare settings, where operational data is hard to access. The method aims to improve the benchmark's realism…
Improving the Realism of Synthetic Clinical Benchmarks Under Utility Constraints
Researchers propose a method to improve the realism of synthetic clinical benchmarks for AI agents in privacy-sensitive healthcare settings without compromising their practical utility. They formulate this as a problem…
Investigating Artificial Intelligence Digital Sovereignty in Mobile Shopping Apps: A Case Study of Nigeria
This research investigates the impact of Artificial Intelligence (AI) in Nigerian mobile shopping apps on digital sovereignty, focusing on platform transparency as a key indicator of user awareness and control. The…
Investigating Artificial Intelligence Digital Sovereignty in Mobile Shopping Apps: A Case Study of Nigeria
This research investigates how Artificial Intelligence (AI) affects digital sovereignty in mobile shopping apps in Nigeria. The study finds that while AI is widely used, transparency about its implementation is…
Investigating Artificial Intelligence Digital Sovereignty in Mobile Shopping Apps: A Case Study of Nigeria
The research investigates how Artificial Intelligence (AI) in Nigerian mobile shopping apps affects digital sovereignty, focusing on platform transparency as a key indicator of user awareness and control. The findings…
Learning Globally Reusable Skills for Coding Agents
Researchers present a framework called GSE (Globalized Skill Evolution) that aims to improve automated skill evolution for Large Language Model (LLM) agents in software engineering tasks. The proposed method addresses…
Learning Globally Reusable Skills for Coding Agents
Researchers propose a new method called GSE (Globalized Skill Evolution) to help Large Language Model agents continuously improve their skills without needing expensive retraining. The proposed method optimizes skill…
Learning Globally Reusable Skills for Coding Agents
Researchers have developed a new approach called Globalized Skill Evolution (GSE) to help Large Language Model (LLM) agents continuously improve their coding skills without needing expensive retraining. This method is…
MicroEvo: Knowledge-Guided LLM Sampling for Efficient Microarchitecture Design Space Exploration
A new method called MicroEvo is proposed for efficient exploration of microarchitecture design spaces. This approach utilizes a combination of large language models (LLMs) and Monte Carlo Tree Search (MCTS) to optimize…
Poli-Bias: Understanding and Measuring Large Language Model Biases in International Political Conflicts
Researchers introduce Poli-Bias, a method to measure political bias in large language models (LLMs) by comparing responses to paired prompts where country identities are swapped across various geopolitical…
Poli-Bias: Understanding and Measuring Large Language Model Biases in International Political Conflicts
The text introduces a new method called Poli-Bias, which is designed to measure bias in large language models (LLMs) when dealing with international political conflicts. This framework compares responses to paired…
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