TROVE: Adaptive Agent Skill Orchestration via Trace-Grounded Route Validation and Editing

A multi-step product task fails because the agent loses track of dependencies.

What you would change

Try the proposed planning step on tasks with checkable intermediate outcomes.

Try the proposed planning step on tasks with checkable intermediate outcomes.

Compare against

The same agent solving the same tasks without the new planning step.

1. Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.
2. Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.
3. Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.
4. Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test.

What to measure

Task completion, unnecessary steps, planning tokens, response time, and total cost.

When to keep it

Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation.

recommendation

test

research area

planning and decomposition

technical change

Trace-grounded adaptive route editing for agents

published

2026-09-04

The approach is implementable on a single GPU or via APIs, but its benefit is conditional on having representative execution traces and workloads where runtime evidence changes the appropriate continuation, so it warrants a scoped pilot rather than direct adoption.

1. claim 01Paperraft inference
   Read the brief interpretation

   TROVE replaces pre-execution commitment to a fixed agent execution plan (or broad replanning when evidence invalidates it) with provisional routes that are locally validated and edited at runtime, using offline-distilled skills and an outcome-conditioned transition graph built from workflow-search traces. The cost is an offline pipeline that requires evaluated search traces to distill atomic and composite skills plus a transition graph, added controller logic, and extra runtime inference for validation and editing decisions; quality gains depend on outcome-dependent continuations actually occurring in the workload. It can fail where tasks are near-saturated (editing yields mainly early-termination efficiency, not quality), where available traces do not cover the failure modes encountered online, or where trace-supported insertions are invalid for the current state, and gains on new backbones or domains are unverified without re-distillation.

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2. claim 02Paperraft inference

   Abstract reports a stronger quality-efficiency trade-off than dataset-level optimization, query-level architecture selection, and graph-constrained scheduling baselines across code generation, QA, and math benchmarks, but provides no specific multiplicative figure.

   This is a Paperraft inference. It is not pinned to a verified PDF passage.

   Inspect the source