propagate-uncertainty-over-trajectory-dependency-graph TEC-0286 · proposedPropagate uncertainty over a trajectory dependency graph
Represent an agent's execution history as a directed graph of reasoning, tool-call and environment nodes with typed dependency edges (sequential, repetition, progression, feedback, goal alignment), then propagate each node's local uncertain
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
Represent an agent's execution history as a directed graph of reasoning, tool-call and environment nodes with typed dependency edges (sequential, repetition, progression, feedback, goal alignment), then propagate each node's local uncertain Filed by the drafting stage from From Sequence to Structure: Relational Uncertainty Propagation for LLM Agents (2608.16002) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: AUROC/AUPRC/best-F1 for trajectory failure prediction against entropy, sequence probability, SAUP, Tracer and UProp on three benchmarks and six models; average AUROC rose e.g. 0.608 to 0.656 (Qwen3.5-27B) and 0.694 to 0.718 (MiniMax-M2.7). Prefix-truncated trajectories show earlier detection, and selecting the lowest-risk sampled action raised task accuracy (Terminal-Bench-2 Qwen3.5-27B 0.105 random to 0.213). Ablations: removing graph modeling drops AUROC to 0.678; a random graph topology gives a similar drop, and entropy-binned analysis shows ~0.85 AUROC where entropy alone is at chance.. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
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