Researchers release CaSKG
CaSKG released — points out that, in retrieval from an agent skill library, feeding in the whole prompt overloads the context, vector search treats skills independently, and graph search recovers workflow context only when edge reliability is high; presents a method that builds a high-recall candidate graph from semantic, lexical, input-output and structural evidence, then uses direction-conditioned counterfactual probes to remove, substitute and reorder skill pairs, aggregates the evidence with Bayesian smoothing, and so constructs a state-filtered weighted directed graph offline
Why it matters
A strand that does not take LLM-extracted graph edges at face value but recalibrates edge weights after the fact through counterfactual intervention — tackling, before the retrieval stage, the edge-reliability problem long singled out as a chronic weakness of graph retrieval. In the authors' own reporting, across 6 LLMs and compared with Graph-of-Skills, the ScienceWorld macro average rose from 72.62 to 80.50 and the ALFWorld success rate from 80.01% to 86.79%, with fewer environment steps.
Source — Primary sources first; secondary reports are labelled “Press”
Paperarxiv.orghttps://arxiv.org/abs/2608.25500Added in Week 36, 2026 · See that week