Causal Discovery in the Era of Agents
Causal Discovery in the Era of Agents This paper addresses the growing trend of using Large Language Models (LLMs) to assist in causal discovery—the process...
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Causal Discovery in the Era of Agents This paper addresses the growing trend of using Large Language Models (LLMs) to assist in causal discovery—the process...
VeriEvol is a framework designed to improve how AI models learn visual mathematical reasoning.
Teaching LLMs String Matching, Backtracking, and Error Recovery to Deduce Bases and Truth Tables for the Combinatorially Exploding Bit Manipulation Puzzles L...
AI Exposure Scores: what they measure, what they miss, and what comes next examines the reliance of current labor policy debates on static "exposure scores"—...
Against Proxy Optimization This paper investigates the conditions under which relying on simplified metrics—or "proxies"—to make decisions leads to harmful o...
How Do Instructions Shape Speech? Cross-Attention Attribution for Style-Captioned Text-to-Speech Modern text-to-speech (TTS) systems allow users to control v...
Autonomous Event-Driven Multi-Agent Orchestration for Enterprise AI at Scale This research addresses the challenge of scaling multi-agent AI systems for ente...
Toward Calibrated Mixture-of-Experts Under Distribution Shift This paper investigates why Mixture-of-Experts (MoE) models—which combine multiple specialized...
Lagrange: An Open-Vocabulary, Energy-Based Sparse Framework for Generalized End-to-End Driving Autonomous driving systems currently face a difficult trade-of...
The Tao of Agency: Autotelic AI, Embedded Agency and Dissolution of the Self explores a shift in artificial intelligence from systems that follow designer-pr...
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