AutoResearch: Insight In, Hallucination Out
AutoResearch is a two-stage autonomous system designed to improve the scientific grounding of AI-driven research.
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Understand new AI research with readable explanations and links to the original papers.
AutoResearch is a two-stage autonomous system designed to improve the scientific grounding of AI-driven research.
EvoTS-Agent is an autonomous system designed to identify structural changes in financial time series—such as shifts in volatility or market regimes—without r...
Adaptive Policy Portfolios for Robust Markov Decision Processes introduces a method to reduce the conservativeness of robust decision-making by using a set o...
StagedWorkspace: A Versioned Workspace for Knowledge-Work Agents addresses the problem of version drift in AI agents that perform knowledge work.
On the Fragility of Self-Improving Agents: Variance, Task Order, and Underspecification evaluates the reliability of memory-based self-improving agents—syste...
Delegation Asymmetry in Agentic Recommender Systems: Measuring Two-Sided Receptivity in Online Dating investigates whether users of dating platforms are will...
Can Large Language Models Explain Flight Safety Events? A Prior-Guided Semantic LLM-based Approach introduces FlightLLM, a framework designed to interpret th...
Towards Zero-Shot Task Transfer with Neurosymbolic World Models introduces a method to enable reinforcement learning agents to adapt to new tasks without fur...
HLSR (Hybrid Live–Forecast Selective Dynamic Vehicle Rerouting) is a framework designed to reduce urban traffic congestion by selectively rerouting vehicles...
Procedural Content Metageneration via Program Search and Continual Abstraction Discovery introduces a method to automate the creation of game level generator...
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