Large language models are increasingly deployed at scale as API-accessible, tool-augmented agents, forming a heterogeneous, fast-evolving agent ecosystem. A central challenge is query-level identification: selecting the most suitable agent per query from candidates provided as black-box services, where costly input-output evaluation makes exhaustive profiling and router retraining impractical at scale. Predating LLMs, the learnware paradigm provides a principled perspective on this challenge by advocating capability specifications and reducing identification to specification matching, avoiding pool-dependent retraining and exhaustive supervision. We operationalize it with constructive specification, which builds hierarchical capability representations from limited profiling over diverse benchmarks, using an optimism-guided profiler that prioritizes informative regions and prunes low-utility areas with guarantees. At serving time, we enrich query context with system-maintained benchmarks and map queries into the same specification space for multi-granularity similarity matching, enabling plug-and-play identification without accessing agent internals or training any additional selector. Experiments show that our approach, selecting among lightweight agents, outperforms contenders and matches or surpasses much larger models on several tasks.
Jian-Dong Liu, Zi-Chen Zhao, Haodong Sun et al.· Proceedings of the 32nd ACM...· 3 citations· ⚡1
Agentic systems persist model-visible memory while mutating workspaces, while a runtime, registry, or approval service may hold authority state outside both. Identical final files can then require opposite safe actions. We call this the cross-substrate authority gap: decision- relevant authorization information resides outside the planner-visible workspace or memory state. Across two controlled mini-benchmark families, three experiments compare planner-observation augmentation with an execution-time authority check using real Git lineage, durably recorded agent execution attempts, deterministic oracles, and two model routes. Experiment 1 is a 128-cell controlled evidence ablation: authority-blind candidate evidence obtains 0/32 final semantic success, while raw receipts and a typed relation both obtain 32/32. The missing authority fact accounts for the gain; typed packaging provides no observed planning-accuracy gain over equal raw information. Experiment 2 uses 96 planning calls: workspace-visible evidence yields 12/16 unsafe publication decisions, and planning with the typed relation remains unreliable (15/32 first actions correct; 11/32 invalid or absent). Experiment 3 replays the same 32 fixed model-generated first-action intents with zero additional model calls; a deterministic execution guard prevents all six unsafe intents from becoming effects and permits all 12 valid authorized publish intents. These results position authority enforcement at the mutation boundary as the operational endpoint of memory governance.
Yang Li, Sergey Volkov, Hai Liu et al.· 0 citations
This work operationalizes constructive specification with constructive specification, which builds hierarchical capability representations from limited profiling over diverse benchmarks, using an optimism-guided profiler that prioritizes informative regions and prunes low-utility areas with guarantees, and enables plug-and-play identification of lightweight agents.
Jian-Dong Liu, Zi-Chen Zhao, Haodong Sun et al.· Proceedings of the 32nd ACM...· 1 citation
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