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Introduces ReasoningFlow, a framework to capture discourse structures of large language model reasoning traces as directed acyclic graphs, enabling fine-grained analysis of reasoning behaviors like self-reflection and backtracking. Based on manual and automatic annotation of thousands of traces, it reveals structural similarities across models and that most erroneous steps do not contribute to final answers.
Yacine conducted a 1.5-hour in-depth interview with the founders of Paradigma, discussing how to use DAG (Directed Acyclic Graph) as the underlying infrastructure for autonomous research, covering core topics such as Agent operation, building large-scale public DAGs, and avoiding bad DAGs.
Interview discussing infrastructure for auto-research using DAGs, including how agents can execute DAGs and how to build large public DAGs.
PACER is a new scalable framework for causal discovery from large-scale interventional data that guarantees acyclicity by design, achieving up to two orders of magnitude speedups over penalty-based methods on benchmarks with thousands of variables.
GraphBit is a graph-based agentic framework that uses deterministic DAG orchestration with a Rust engine to eliminate hallucinations and infinite loops. It achieves 67.6% accuracy on GAIA benchmarks with zero framework-induced errors and low latency.
SPIN is a planning wrapper that ensures structurally valid DAG plans and uses prefix-based execution control to reduce task steps and tool calls in industrial LLM agent systems, improving plan validity and efficiency.