SkillLift report verification

Type: agentic-system-analyses/types/agentic-system-verification.md

Verification

The supplied reports support publication of a bounded improvement-plane account. Both previous blockers are repaired. I judged the current reports and correction answers against source behavior rather than treating either the earlier verification or the analysts' answers as established findings. The supplied report-check contains no structural failures. Runtime and memory each supply one corrected answer; epistemic and workflow answers are empty.

The frozen checkout HEAD matches the boundary, and its working tree is unchanged. Evidence below is SRC-1 implementation, except explicitly attributed README and prompt doctrine. No target program, test, model service or benchmark was executed. The complete-artifact, partial-loop classification remains appropriate: shipped drivers and local host-launch code are included, while benchmark gitlinks, sibling tau2 and external hosts/providers do not supply inspectable end-to-end evidence.

Corrections and reconciliation

The memory correction holds across its receipt annotation, read-back table, integration issues and limitations. Independent tracing of SRC-1, skilllift/skilllift/coordinator/modes.py and skilllift/skilllift/rubricator.py, confirms three distinct conditions. Portfolio conversion retains manifest-relative paths. Evidence packing protects only the exact SKILL.md key; prefixed instruction files enter the auxiliary list and can be truncated or omitted under a configured budget. Ordinary task Mode B passes no evidence budget to revision; its separate character preflight rejects oversized requests rather than truncating that packed evidence. The corrected annotation on RT-RTE-receipt-revision in memory Annotations now agrees with EPI-RTE-evidence-packing and EPI-OBJ-evidence-package. The earlier universal-preservation objection is discharged, without substituting a default-truncation claim.

The runtime correction also holds. SRC-1, scripts/skilllift_tau2_run_domain.py, passes each cluster's final training artifact; SRC-1, skilllift_eval/runners/skilllift_tau2.py, exports every current skill and loads every metadata entry without consulting aggregate oracle rank. RT-RTE-domain-fusion now states this selector in both Later read-back and Selection predicate and retains the handoff, producer and consumer passages. Lower-ranked current skills can enter fusion. MEM-OBJ-master-skill and the epistemic domain-consumption account do not depend on winner-only lineage. Category fusion remains a different, winner-fed path. The earlier runtime blocker is discharged.

The current reconciliation preserves both repairs and correctly withdraws the former evidence-preservation conflict. Its other integration dispositions retain compatible distinctions: rationale delivery versus reliance, stored summaries versus revision inputs, and full-snapshot recovery versus task patch replay. No unresolved cross-report contradiction remains. No supersession is declared or needed; containers remain valid beside their parts.

Runtime and workflow

Independent reads of SRC-1, skilllift_eval/runners/skilllift_wildclaw.py, skilllift/skilllift/coordinator/task.py and skilllift/skilllift/coordinator/modes.py, support RT-RTE-task-evolution, RT-RTE-branch-refinement, RT-RTE-receipt-revision and RT-RTE-cell-recovery. Code schedules explicit model handoffs. Verifier nondecrease admits inner refinements; benchmark improvement or an oracle tie plus verifier advantage admits a successor. Calibration follows selection and receives the pre-round champion. It retains measured-best alignment, not guaranteed convergence. Logical-cell recovery authenticates/reuses results or retries physical attempts; a fuse does not permanently close an indeterminate cell. Later task control flow still determines whether a cell is revisited.

The workflow account accurately joins this one coordinator/store path rather than combining unrelated drivers. It preserves invalid/terminal anchor exits, rejection boundaries, final-score reporting and runner-written delivery. Ordinary completion uses two final logical trials and reports their maximum valid reward, not an assurance of improvement or transfer.

SRC-1, skilllift/skilllift/portfolio/store.py, independently confirms the conditional mismatch in RT-CLM-refinement-replay: refined branches retain their original candidate record, while accepted IDs replay its original patch. This is an implementation risk when changed refinements are accepted, not an observed incident. RT-CLM-mode-a correctly limits zero oracle calls to inner refinement.

SRC-1, skilllift/skilllift/portfolio/ref.py, supports staged diff application, changed-path/forbidden-path validation, identity preservation and atomic tree publication. SRC-1, skilllift/skilllift/adapters/wildclawbench.py and skilllift_eval/runners/skillsbench_portfolio_adapter.py, supports the declared identity, fingerprint and loading-receipt protocols. SRC-1, skilllift_eval/runners/wildclaw_engine/run_batch.py, confirms gateway/agent launch, timeout handling and conditional grading in the finally path. None proves semantic safety, honest receipt production or host isolation. The runtime's invariant/protocol strengths and unobserved-operation qualifications are therefore retained.

RT-CMP-llm-client matches SRC-1, skilllift/skilllift/llm_client.py: local request configuration/counters, explicit system/user calls and no authenticated frozen weight revision. Provider mutability is unresolved independently of text adaptation. RT-OBJ-portfolio-ref, RT-OBJ-receipt, RT-BAP-skill-delivery and RT-BAP-receipt-scoring correctly separate symbolic identity, interpreted instructions, model judgments and enforced numerical decisions.

Memory objects and alternative routes

The declarations and supporting passages for MEM-OBJ-skill-instructions, MEM-OBJ-candidate-record, MEM-OBJ-search-state, MEM-OBJ-receipt-rubrics, MEM-OBJ-receipt-metadata and MEM-OBJ-revision-attempts support their distinct consumers and authority. Independent producer/consumer reads in SRC-1, skilllift/skilllift/portfolio/store.py, skilllift/skilllift/portfolio/prompts.py, skilllift/skilllift/coordinator/modes.py, skilllift/skilllift/rubricator.py and skilllift/skilllift/baselines/prompts.py, confirm rejected-proposal retention without promotion, accepted-ID reconstruction, history/novelty feedback, full receipt serialization and within-episode repair. Retaining attempt files does not establish later general retrieval. Receipt metadata can reach later models without proving reliable diagnosis or reliance.

SRC-1, skilllift_eval/runners/bounded_edits_skill_generator.py, supports full scoped-input attempts, mandatory-file preservation, model-selected optional IDs, count/byte bounds and character preflight for editor and repair requests. This is explicit context selection, not host chat compaction or dereferenceable model memory. Planner overflow fallback is a separate mechanism.

SRC-1, skilllift_eval/runners/skilllift_skillsbench.py, supports RT-RTE-category-training, MEM-OBJ-category-round and EPI-RTE-category-fusion: completed-round snapshots reload content and receipt; immediate update admission requires verifier nondecrease; batch winners come from measured rounds; a newly fused package is parsed/name-checked and written before held-out evaluation. Its measured inputs do not certify the fused output.

SRC-1, scripts/skilllift_tau2_run_domain.py, scripts/skilllift_tau2_real_llm_train_evolve_smoke.py, skilllift_eval/runners/skilllift_tau2.py and skilllift_eval/skills/tau2_loader.py, support MEM-OBJ-cluster-checkpoint, MEM-OBJ-master-skill and the corrected domain route. The checkpoint loader has no configuration-signature comparison; the update helper admits structurally validated content without category's immediate nondecrease test. Fusion copies upstream hash labels, while held-out loading checks output equality/hash and renders retained skill text beside public policy. Structural admission and output association do not authenticate all source rules or their preservation.

MEM-OBJ-behavior-summary is supported by SRC-1, skilllift_eval/runners/skilllift_tau2.py, skilllift/skilllift/oracle_contrast.py and skilllift/skilllift/baselines/prompts.py: message heuristics produce retained feedback summaries, but shared public-score projection supplies scalar score/pass/rank rather than that feedback to receipt revision. Retention is not summary-driven instruction induction. Scoped checkpoint/reset call-site searches and archive-caller searches support the memory report's capability-versus-consumer distinction; the known pruning caller in SRC-1, scripts/run_skilllift.sh, launches coevoskills.

All declared Part of relations are supported: skill instructions within PortfolioRef; rubric rules and metadata within Receipt; category/domain snapshots within their routes; category fusion and tau2 evaluator configuration within their respective enclosing routes. Recipes, diagnostics, scores and multi-input evidence views are not duplicate containers. Cross-kind containment is explained. No missing part was identified that changes the retained conclusions.

Epistemic checks and evidence strength

Independent SRC-1 reads of skilllift/skilllift/verifier.py, skilllift/skilllift/normalization.py and skilllift/skilllift/rubricator.py support EPI-OBJ-verifier-score, EPI-RTE-verifier-check and EPI-RTE-receipt-check. Exact ID/bool parsing and conditional arithmetic do not certify criterion-hit truth. The lexical fallback counterexample is valid within its stated conditions. Receipt removals require reasons/references structurally, not authenticated semantic support; nonbinary wording can remain a warning.

SRC-1, skilllift_eval/cli.py, supports EPI-RTE-artifact-audit as reporting and process-exit force, not optimizer rollback. SRC-1, skilllift_eval/runners/skilllift_tau2.py, supports EPI-RTE-tau2-evaluator as external evaluator configuration and imported reward, not proof NL assertions actually ran or all rewards are model judgments.

SRC-1, README.md, confirms the claim sources for EPI-CLM-local-safety, EPI-CLM-ranking-surrogate and EPI-CLM-ablation-mechanism. The report correctly withholds broad safety, guaranteed alignment, universal single-call portfolio scoring and independently established ablation causality. Operational policy retention is not accepted ampliative factual knowledge. The theory-condition account keeps inspectable rule exposure/consumption/retention separate from requested content-directed criticism. Reflection architecture, learning, autonomy and exercised self-improvement are not collapsed into one conclusion; missing execution evidence does not erase wired feedback capacity.

Every declared record, its source anchor, evidence layer, status, load-bearing passage and relation was assessed. The reports declare no evidenced absences or supersessions. None of their route-scoped uncertainties is converted into a system-complete negative. No unsupported emitted classification remains that needs a publication caveat to rescue it.

Omission questions and stopping decision

The frozen tracked tree, alternate category/domain drivers and producer/consumer dependencies selected three consequential challenges: whether instruction bodies survive evidence packing; whether fusion inputs inherit a winner-only benchmark gate; and whether saved execution summaries bypass trajectory exclusion into scoring-policy revision. Direct caller, packing, artifact-export, fusion-loader and score-projection reads dispose all three as described above. Category snapshot/fusion and domain checkpoint/update/held-out reads additionally test the temptation to project task admission or recovery guarantees onto alternate shipped paths. The post-run audit is accounted for separately rather than mistaken for skill withdrawal.

Further optional omission expansion stopped because these challenges have supported dispositions and no identified material challenge remains unresolved. Other baseline internals, every launcher, low-level edit/package validator semantics, all injection branches and external host processing remain incompletely inspected. The reports already withhold exhaustive repository, semantic-validator, security and host-context comparisons; those unsearched alternatives are not evidenced absences. The positive bounded route findings remain supported. Unavailable dependencies and missing run/experiment evidence already prevent end-to-end compliance, grader-validity, observed reliability and causal-benefit conclusions. These are faithfully scoped uncertainties, not additional verifier findings requiring Limits.

Blockers

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Limits

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