WikiSkill workflow report

Type: agentic-system-analyses/types/agentic-system-workflow-report.md

Workflow account

This account selects the ALFWorld skill-evolution workflow because SRC-1, §5.3, follows a named rejected proposal into a later accepted skill and subsequent refinement. It makes the handoff between retained failure evidence and later proposals concrete. The case is author-reported operation, simplified for presentation, not an inspectable execution transcript. The surrounding request-to-return sequence comes from the documented algorithm and prompts, not a reconstructed run. The boundary limits evidence to the frozen paper capture; harness implementation, actual tool calls and filesystem transactions remain uninspected.

Request and task execution

The request is a bounded optimization job, not a single open-ended household-task request: a caller supplies training tasks, validation tasks, a metric and an iteration budget. Appendix A.1 initializes empty skills and wiki and obtains an empty-skill validation baseline. Caller identity and interactive human control are unspecified. The harness owns iteration sequencing and the best-score state; there is no described per-proposal human approval. These mechanisms are documented in RT-RTE-validation-gate, SRC-1 §3.2.4 and Appendix A.1.

The harness delegates training tasks to the Inference Agent with the complete active skill text. For ALFWorld, each step supplies the current observation, recent observations/actions and admissible actions; the agent chooses a tagged action for the simulator. The task agent controls action selection, not skill admission. Simulator feedback enables further task actions, while the outer harness retains training trajectories for subsequent analysis. RT-RTE-task-execution, SRC-1 §3.2.1 and Appendix E.1, supplies this interface. The default mode restricts wiki access, so later knowledge reaches task behavior through active skills rather than direct wiki injection. History-window size, action enforcement and task exhaustion recovery are not established by the prompt.

Trace-to-wiki-to-proposal handoffs

After training rollouts, the harness supplies the Wiki Maintainer with current wiki context and sampled traces: up to five failures and three successes, capped at 15,000 characters per trace. This is a lossy handoff, not transmission of every trajectory. The maintainer is instructed to diagnose actions, causes and workarounds and returns JSON for pattern creation/patching, a replacement index and a log append. The surrounding system applies edits. RT-RTE-wiki-maintenance, SRC-1 §3.2.2 and Appendix C/E.2, establishes the described route; no wiki-content validation gate or malformed-edit recovery is established.

The proposer receives the updated index, skill-impact history and a summary of all training outcomes, including predictions and ground truth. It can select pattern pages and raw traces through read_file; it does not automatically inherit the maintainer's complete context. The prompt requests wiki-first inspection and at least four trace reads before proposing a change. finish submits one skill creation/patch or no_action. This is delegated proposal authority, not permission to accept its own output. RT-RTE-skill-proposal, SRC-1 §3.2.3 and Appendix E.3, retains the evidence. Enforcement of those reading rules and outer-loop handling of no_action remain uninspected.

Rejection and recovery across iterations

The source's case begins with a looping-pattern diagnosis and a rejected goal-directed-action proposal at Iteration 0. The retained diff and rejection result then inform an accepted break-repetition-loop proposal at Iteration 1; new loop evidence leads to further refinement at Iteration 4. WF-CLM-loop-evolution retains the case evidence. These are the source's iteration labels, not a claim that its case numbering matches Appendix A's one-based loop notation.

The verification owner is the harness. It provisionally applies the candidate, delegates validation task execution, computes domain scores against benchmark reference outcomes, and compares mean performance with the incumbent best. Only a strict improvement admits the candidate; ties and decreases reject it. This checks aggregate task performance, not the truth of each wiki explanation, compliance with every skill instruction or unseen-task reliability. On rejection the harness restores accepted skills, but keeps the wiki and appends the proposal diff, score and outcome to the impact history. Thus recovery removes the failed instruction change without deleting the evidence used for the next proposal. RT-RTE-validation-gate, SRC-1 §3.2.4 and Appendix A.1, supplies this policy. It is not evidence of crash-safe restoration, atomic edits or retries after parser/tool failure.

Delivery and its limits

Acceptance makes candidate skill text active for subsequent task prompts through RT-BAP-skill-injection. That state transition differs from delivery of the final optimization product. Appendix A.1 stops at the iteration budget or a best validation score of 1.0 and returns skills and wiki. Its caption states:

Algorithm 1 WikiSkill evolution loop. At each iteration kk, the inference agent rolls out on training tasks using active skills Sk−1S_{k-1}, the maintainer consolidates sampled traces into the intermediate wiki Wk′W^{\prime}{k}, the proposer generates candidate skill modifications PkP{k}, validation gating determines whether to accept Sk′S^{\prime}{k} or roll back to Sk−1S{k-1}, and the system appends the proposal outcome and skill diff to produce the final wiki state WkW_{k}. --- /home/zby/llm/commonplace/.commonplace/worktrees/wikiskill-65116a9d0de7/kb/agentic-system-analyses/state/AAS-2026-10-15-2608-27454-65116a9d0de7-01/sources/arxiv-2608.27454v1-text.txt

The return is the documented delivery boundary, not a demonstrated publication, deployment or human sign-off. The algorithm writes a final indexed return even after early stopping; concrete handling is unavailable. The case does not supply final exported files, scores for every cited proposal or a task-by-task execution bundle. It supports the attributed revision sequence, not independent confirmation that retained knowledge caused improved later behavior. Training summaries and validation references make this a benchmark-guided experiment; they do not establish an equivalent oracle for arbitrary future requests.

Shared records

Components

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Operative objects

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Routes

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Claims

WF-CLM-loop-evolution

Label: Reported loop-skill evolution

Claimed operation: SRC-1, §5.3 and Figure 3 caption, reports a Qwen-3.6-27B ALFWorld sequence in which rejection evidence is retained and informs a later accepted anti-loop skill, followed by refinement with additional pattern evidence. The distinct referent is the particular reported sequence, not another declaration of the general proposal or admission mechanism in RT-RTE-skill-proposal and RT-RTE-validation-gate. The closest supplied claim, RT-CLM-wiki-benefit, concerns an aggregate ablation contrast, not this case.

At Iteration 0, the Wiki Maintainer identifies a basic looping behavior (take-examine-move-loop.md), while the Skill Proposer proposes goal-directed-action, which fails to improve performance on the validation set and is rejected. Crucially, skill-impact.md preserves the proposal diff and rejection outcome, allowing subsequent skill updates to account for this failed attempt. --- /home/zby/llm/commonplace/.commonplace/worktrees/wikiskill-65116a9d0de7/kb/agentic-system-analyses/state/AAS-2026-10-15-2608-27454-65116a9d0de7-01/sources/arxiv-2608.27454v1-text.txt

Informed by this audit trail, the Skill Proposer creates break-repetition-loop at Iteration 1 with a concrete action rule (Never Return an Item to Its Origin Location), which is accepted. As new loop variants emerge across rollouts (multi-operation-loop.md), the Wiki Maintainer accumulates new evidence in the persistent wiki. Guided by these accumulated wiki patterns and newly stored trajectories (not shown in the figure), the Skill Proposer further refines the skill at Iteration 4 with a new rule (Each Operation Type ONCE Per Item). This example illustrates how persistent knowledge from prior iterations informs subsequent skill refinement. --- /home/zby/llm/commonplace/.commonplace/worktrees/wikiskill-65116a9d0de7/kb/agentic-system-analyses/state/AAS-2026-10-15-2608-27454-65116a9d0de7-01/sources/arxiv-2608.27454v1-text.txt

Evidence layer: reported operation. Figure 3 is simplified and the prose explicitly omits some trajectories. The account does not independently establish exercised content dependence, successful implementation of rollback or the gate outcome of the Iteration 4 refinement. Those limits prevent treating this narrative as an observed full-loop execution or a causal experiment.

Evidenced absences

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Behavioral-authority paths

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Annotations

On RT-RTE-validation-gate

Workflow role: verification and rejection recovery separate provisional skill application from durable active-skill admission. A failed candidate leaves a retained impact entry and wiki context for another proposer invocation rather than ending the optimization job. The gate's performance decision is not a wiki-admission decision. Final product delivery occurs at the outer-loop return, not at each acceptance.

On RT-RTE-skill-proposal

Workflow handoff: the proposer receives a separately selected context rather than the maintainer's entire analytical session. Its finish response transfers control to harness verification; it is not itself delivery of an accepted skill. The reported case supplies an example of cross-iteration reconsideration, but no inspectable transcript proves compliance with the minimum trace-reading instruction.