Adaptive Consistency Graph for Long-Horizon Agents
Quick summary
arXiv:2609.32754v2 Announce Type: replace Abstract: Large language model agents can often make reasonable local decisions on short tasks, yet their performance degrades when success requires long sequences of dependent actions and tool calls. During execution, task requirements, historical evidence, and the current execution state may gradually become disconnected, so later decisions can drift from the original objective. We study this problem by introducing the Adaptive Consistency Graph (ACG) for long-horizon execution. ACG incrementally organizes execution evidence and its provenance in a p
Key takeaways
- arXiv:2609.32754v2 Announce Type: replace Abstract: Large language model agents can often make reasonable local decisions on short tasks, yet their performance degrades when success requires long sequences of dependent actions and tool calls.
- During execution, task requirements, historical evidence, and the current execution state may gradually become disconnected, so later decisions can drift from the original objective.
- We study this problem by introducing the Adaptive Consistency Graph (ACG) for long-horizon execution.
Why it matters
“Adaptive Consistency Graph for Long-Horizon Agents” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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