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Notes on ai
458 notes, page 15 of 20
LLM-as-a-Verifier: A General-Purpose Verification Framework
In this research, a general-purpose verification framework called LLM-as-a-Verifier is introduced. This framework provides detailed feedback for agentic tasks without requiring additional training. The framework…
LLM-Based Test Oracles: Source-of-Authority Taxonomy -- A Systematic Literature Review
A study on Large Language Models (LLMs) used to determine correct behavior in tests, identifies a gap in understanding the source of these models' authority. The study conducts a systematic literature review and finds…
OptiAgent: End-to-End Optimization Modeling via Multi-Agent Iterative Refinement
The authors propose OptiAgent, a multi-agent framework that takes a natural language description of an Operations Research problem and produces a solver-ready mathematical formulation and executable code. The…
SovereignPA-Bench: Evaluating User-Owned Personal Agents under Evolving Intent, Platform Mediation, and Consent Constraints
The text introduces a new benchmark called SovereignPA-Bench for evaluating user-owned personal agents. The benchmark focuses on aspects like preserving user sovereignty, privacy, consent, and resisting manipulative…
TacReasoner: A Dynamic Tactile-Language Framework for Interactive Reasoning in Real-World Scenarios
The paper proposes TacReasoner, a new framework that enhances the perception and representation of dynamic tactile signals for intelligent systems. It addresses two challenges: insufficient modeling of such signals and…
The Changing Role of Symbolic Methods in Artificial Intelligence
The article discusses the changing role of symbolic reasoning in artificial intelligence (AI), arguing that it is not a fundamental property of intelligence but a computational consequence of operating on simplified…
Three-Phase Evaluation of AI-Assisted Software Development Life Cycle
The paper evaluates the impact of increasing levels of AI autonomy on software development productivity, adherence to requirements, and developer cognitive workload using a web application case study. The study…
TREK: Distill to Explore, Reinforce to Refine
The research presents TREK (Teacher-Routed Exploration via Forward KL), a method that uses distillation for exploration support expansion, rather than imitation. This method can be used with various types of teachers…
TREK: Distill to Explore, Reinforce to Refine
The paper proposes a method called TREK (Teacher-Routed Exploration via Forward KL) to improve Group Relative Policy Optimization (GRPO). TREK uses distillation for exploration support expansion and can work with…
AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents
The authors introduce a new bounded contract for long-horizon Large Language Model (LLM) agents in the game Slay the Spire 2. The contract allows decisions to be made from a fresh user message, with no raw…
Copewell: A Multi-Agent Swarm Architecture for Equitable Mental Wellness Support
The paper presents Copewell, a multi-agent swarm system designed to expand access to mental wellness support through human-centered AI principles. The system is aimed at addressing the lack of treatment for mental…
Criticality-Based Guard Rail Validation for AI Agent Decisions in Autonomous Telecom Networks
The paper proposes a Guard Rail Validation (GRV) framework for ensuring the accuracy of AI-driven decisions in fully autonomous telecom networks. The framework evaluates decisions based on several factors, such as…
Distributed Attacks in Persistent-State AI Control
Researchers introduce Iterative VibeCoding, a setting for AI control where an autonomous coding agent builds software over a series of pull requests in a persistent codebase while pursuing a covert side task. The study…
Distributed Attacks in Persistent-State AI Control
A study on distributed attacks in AI control where autonomous coding agents build software over a sequence of pull requests (PRs) in a persistent codebase while pursuing a covert side task. The study found that no…
Distributed Attacks in Persistent-State AI Control
Researchers explore the issue of distributed attacks on autonomous AI coding agents that persistently ship code iteratively. They introduce Iterative VibeCoding, a setting for studying AI control, where an AI agent…
EvoPolicyGym: Evaluating Autonomous Policy Evolution in Interactive Environments
A new evaluation setting called Autonomous Policy Evolution (APE) is introduced in a benchmark named EvoPolicyGym, which is used to test how autonomous agents iteratively improve their policy systems within a fixed…
EvoPolicyGym: Evaluating Autonomous Policy Evolution in Interactive Environments
In the research paper titled 'EvoPolicyGym', the authors introduce a controlled evaluation setting called Autonomous Policy Evolution (APE) for assessing how autonomous agents iteratively improve their executable…
Hardware-Enforced Semantic Coordination for Safety-Critical Real-Time Autonomous Systems
The paper proposes a hardware-enforced semantic coordination architecture for safety-critical real-time autonomous systems. This approach aims to overcome limitations of software-mediated coordination by implementing…
Hardware-Enforced Semantic Coordination for Safety-Critical Real-Time Autonomous Systems
The authors propose a hardware-enforced semantic coordination architecture for safety-critical real-time autonomous systems. This new approach aims to address limitations of software-mediated coordination by…
Human Capital, Not Model Benchmarks, Predicts Hybrid Intelligence in Forecasting
The study shows that the effectiveness of human-AI collaboration in forecasting depends on specific forms of human capital such as perspective-taking, intellectual humility, and curiosity. Most people either deferred…
Human Capital, Not Model Benchmarks, Predicts Hybrid Intelligence in Forecasting
The study examines the performance of human-AI collaboration in forecasting using real-money prediction markets as a benchmark. It finds that the effectiveness of this collaboration is dependent on a specific type of…
Human Capital, Not Model Benchmarks, Predicts Hybrid Intelligence in Forecasting
A study using real-money prediction market Polymarket finds that the performance of human-AI collaboration in forecasting depends on specific forms of human capital such as perspective-taking, intellectual humility…
Learning to Move Before Learning to Do: Task-Agnostic pretraining for VLAs
Researchers propose Task-Agnostic Pretraining (TAP), a two-stage framework to train Vision-Language-Action (VLA) models. TAP first learns transferable motor priors from unlabeled interaction data using self-supervised…
Learning to Move Before Learning to Do: Task-Agnostic pretraining for VLAs
Researchers propose Task-Agnostic Pretraining (TAP) as a solution to the scarcity of expert demonstrations in Vision-Language-Action (VLA) models. TAP aims to separate learning physical competence and semantic…
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