Tag
This paper presents a workload-aware cache eviction policy for multi-agent systems that uses recomputation cost, DAG dependency count, and agent invocation frequency to retain valuable cached entries, reducing latency by up to 64.7% over uncached baselines and 31.1% over the next best finite-capacity method.
BatchDAG introduces a system where an LLM generates typed directed acyclic graphs of operations for scalable ad-hoc analysis over enterprise data, achieving up to 47x reduction in LLM calls and sub-60-second query times over 50,000+ meetings.
The paper introduces a knowledge-centric framework for generating ComfyUI workflows by distilling hierarchical knowledge (pseudo-codes, skeletons, strategies) from real workflows and using LLMs to perform reasoning from task descriptions to executable structures, achieving higher node diversity and execution success rates.
DataFlow-Harness introduces a platform that guides LLM agents to construct editable DAG-based data pipelines through incremental mutations, achieving high pass rates and reduced costs compared to script-generation baselines.
A thread introducing the Atomic Task Graph (ATG), a DAG-based execution substrate that allows a small 8B model to outperform GPT-4 on complex agent tasks by storing plan structure in a graph instead of linear text, reducing context bloat and enabling localized failure recovery.
The Marble Curriculum, a comprehensive open-source dataset covering 1,590 primary school concepts with 3,221 connections across 8 subjects, has been released to enable building learning paths and AI-driven educational tools.
Proposes a finite-sample method for recovering the sparsest DAG in linear non-Gaussian acyclic models with latent confounders using higher-order cumulants, without restricting the number of latents.
A developer built a deterministic firewall in Rust for AI agents that runs under 5ms, using plans, MCP lists, and taint tracking to enforce security without hallucination or latency issues.
A blog post explaining PyTorch FX graphs, which are an intermediate representation used throughout the PyTorch 2.0 compile ecosystem. It covers the core objects Graph, Node, and GraphModule, and how to understand and work with them.
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.