Learning Loop Research Basis

Use this reference only when reviewing or changing Learning Capture, Loop Discovery, intervention-ledger, or Skill-evolution behavior. It records the external research basis behind those contracts. It is not an evidence source for scoring a project and never substitutes for opened local instructions, code, tests, delivery state, or reviewed Task Episodes.

This file owns the primary-source rationale and the known stability conditions for evolving a durable asset from experience. It does not own pattern signatures, recurrence thresholds, or coverage codes; use Learning Loop Detection Patterns. It does not own owner selection; use Loop Discovery.

Load When

• A Learning Capture, Loop Discovery, or intervention-ledger contract change needs a primary-source rationale.
• A proposal wants automatic Skill generation, asset merging, or unattended self-revision, and the review needs the published stability conditions.
• A reviewer must keep opportunity and readiness claims separate from an effectiveness claim and needs the comparison designs that support each.

Improvement axes

Self-Improvements in Modern Agentic Systems models an agent as a foundation model coupled to an operational scaffold of prompts, memory, tools, and control logic, and formalizes self-improvement as a self-induced update operator that commits changes to one of those targets. The two axes are orthogonal:

Axis	Update target	Repository position
Foundation model improvement	Parameters, driven by self-generated demonstrations, self-evaluation, or environment exploration	Out of scope. This repository never updates model weights.
Scaffolding improvement	Prompts, memory, tools, workflows, or the full scaffold	In scope. Every durable owner (Memory, Rule, Skill, Hook, Gate, Workflow, Agent, Eval) is a scaffold component.

Keep the boundary explicit in reader-facing output. Repository-side evolution changes reviewable scaffold artifacts; it does not change model capability, and a scaffold gain must never be reported as a capability gain.

Distillation shape

Skill-artifact work converges on the same progression, which is why the repository's stage vocabulary (capture, generalize, codify, route, exercise, evaluate, maintain) can carry these findings without a new model.

Stage	Research position	Repository owner
capture	Raw trajectory stores are redundant and noise-heavy; distill instead. Successful runs become strategic patterns, failed runs become concise lessons (SkillRL).	Normalized Task Episodes and learning signals.
generalize	Recurring lessons are induced across many traces in parallel, not from one trace (Trace2Skill).	Two comparable episodes as the default recurrence threshold.
codify	Lessons consolidate hierarchically into one unified, conflict-free skill directory (Trace2Skill); failures materialize into structured skill folders (EvoSkill).	recommendedOwner selection. Consolidation is reported, not performed.
route	Retrieval separates general heuristics from task-specific ones (SkillRL); guidance splits into task-level and step-level granularity (D2Skill).	Smallest-durable-owner selection in Loop Discovery.
exercise	An artifact only counts once the agent actually runs under it.	routed-but-not-applied and asset-updated-not-reexercised.
evaluate	A held-out or paired comparison decides whether the artifact is kept.	Intervention ledger.
maintain	Prune by measured utility, not by age (D2Skill).	stale-or-conflicting-asset freshness review.

Stability conditions

Every published result that reliably improves over its starting point adds an acceptance gate. Loosely controlled self-revision is reported as the failure mode, not the baseline. Treat the following as review questions for any evolution proposal.

Condition	Source	Current repository state
Accept an edit only when a held-out score strictly improves	SkillOpt	Absent. stopOrRevertCondition records a revert trigger; there is no held-out acceptance gate.
Retain only artifacts that stay on a validation Pareto frontier	EvoSkill	Absent.
Keep the verifier isolated from ground-truth test content while it still returns actionable feedback	CoEvoSkills	Partial. Read-only specialist passes are separated from Lead reconciliation, but the verifier does not co-evolve.
Compare paired baseline and artifact-injected runs under one policy, and use the gap as the update signal	D2Skill	Absent. comparisonWindow.taskMix compares observation windows, not controlled pairs.
Bound each edit and buffer rejected edits so the optimizer stops re-proposing them	SkillOpt	Absent. Related patterns (unvalidated-intervention, correction-not-promoted) detect the symptom only.
Assign credit below episode granularity before blaming a step	GiGPO	Absent. Findings attribute to a primary check at Task Episode level.
Generate held-out tasks rather than assuming a fixed suite exists	AgentEvolver	Absent. The eval owner may be recommended but is not produced.

The paired-comparison gap explains a current honest limit: with only window comparison available, improving and unchanged ledger results project later-validation to Exercised, not Outcome-supported. Closing that gap requires a controlled comparison, not a scoring change.

Transfer

Evolved skill artifacts retain value across model scales, across model families, across execution harnesses, and on out-of-distribution tasks, with the underlying model frozen and without test-time retrieval. Human-authored skills can also underperform because of human-machine cognitive misalignment rather than missing content.

Both findings support treating a repository-local artifact as a durable asset rather than model-specific tuning, and they justify reviewing a human-written asset against observed agent behavior instead of assuming authorship implies fitness. Neither finding licenses a claim about a specific asset in a specific repository.

Interpretation boundary

These sources explain the shape of the contracts. They do not freeze terminology, provider features, thresholds, or numeric scores.

• Do not cite a published gain as evidence about this repository. Reported numbers come from bounded benchmark suites under a fixed harness.
• Do not raise later-validation above Exercised without a comparable held-out or paired result recorded in the intervention ledger.
• Do not auto-merge, auto-rewrite, or auto-delete a named durable asset from a single observation window. Consolidation findings stay advisory until an acceptance gate exists.
• Do not convert an absent eval suite into a low score. Without a comparison set the honest state is Unobserved.
• Weight-space methods stay out of scope even when cited here; they are recorded to mark the boundary, not to widen it.

References

Scaffolding improvement, skill artifacts:

• Ni et al., Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills, 2026. https://arxiv.org/abs/2603.25158
• Alzubi et al., EvoSkill: Automated Skill Discovery for Multi-Agent Systems, 2026. https://arxiv.org/abs/2603.02766
• Yang et al., SkillOpt: Executive Strategy for Self-Evolving Agent Skills, 2026. https://arxiv.org/abs/2605.23904
• Zhang et al., CoEvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification, 2026. https://arxiv.org/abs/2604.01687

Skill banks coupled to policy training:

• Xia et al., SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning, 2026. https://arxiv.org/abs/2602.08234
• Tu et al., Dynamic Dual-Granularity Skill Bank for Agentic RL, 2026. https://arxiv.org/abs/2603.28716

Credit assignment, task generation, and supervision, recorded as the out-of-scope boundary:

• Feng et al., Group-in-Group Policy Optimization for LLM Agent Training, NeurIPS 2025. https://arxiv.org/abs/2505.10978
• Zhai et al., AgentEvolver: Towards Efficient Self-Evolving Agent System, 2025. https://arxiv.org/abs/2511.10395
• Zhao et al., Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models, 2026. https://arxiv.org/abs/2601.18734

Umbrella framing:

• Ren et al., Self-Improvements in Modern Agentic Systems: A Survey, 2026. https://arxiv.org/abs/2607.13104