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arxiv: 2604.16314 · v1 · submitted 2026-02-06 · 💻 cs.SE

Recognition: no theorem link

Software Self-Extension with SelfEvolve: an Agentic Architecture for Runtime Code Generation

Md Asif Iqbal Fahim , Oluwadamilola Adebayo , Alessio Ferrari
Authors on Pith no claims yet

Pith reviewed 2026-05-16 06:52 UTC · model grok-4.3

classification 💻 cs.SE
keywords self-extensionruntime code generationagentic architectureself-adaptive systemslarge language modelsautonomous integrationsoftware evolution
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The pith

SelfEvolve lets software generate and integrate new code modules at runtime without restarts or developer help.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces SelfEvolve as an orchestrated pipeline of agents that takes user requests, generates new functions with large language models, verifies them, and inserts them into a live system. Traditional self-adaptive software only rearranges existing parts, whereas this method creates entirely new capabilities on the fly. Across eleven test tasks the system reaches a 92.7 percent Pass@1 rate and improves on the strongest baseline by 61.8 percent with statistical significance. A sympathetic reader would therefore see concrete evidence that software can extend itself during operation to match new demands.

Core claim

SelfEvolve is an agentic architecture that autonomously generates, validates, and integrates novel code into a running system in response to user requests, achieving an average 92.7 percent Pass@1 across eleven self-extension tasks and a 61.8 percent improvement over the best prior agent framework.

What carries the argument

The SelfEvolve orchestrated agentic pipeline, which coordinates code generation, testing, and live integration steps.

If this is right

  • Software can acquire entirely new functions while already executing, without any restart.
  • The same pipeline outperforms existing multi-agent code-generation frameworks on self-extension tasks.
  • Systems become capable of individualized evolution that matches specific user requests over time.
  • Runtime self-extension becomes a practical alternative to traditional manual development cycles for adding features.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Security monitoring layers would still be required to guard against flawed or malicious generated code.
  • The method could be combined with conventional reconfiguration techniques to handle both new and existing components.
  • Long-term use might produce software that drifts far from its original design, creating maintainability questions.
  • Similar pipelines could be tested on larger codebases or with multi-step user requests to measure scaling limits.

Load-bearing premise

That code produced by the language model can be safely inserted into a running system without introducing bugs, security problems, or the need for manual correction.

What would settle it

Running SelfEvolve on a live application and observing that the inserted code produces a crash, incorrect behavior, or security flaw within the first few minutes of execution.

Figures

Figures reproduced from arXiv: 2604.16314 by Alessio Ferrari, Md Asif Iqbal Fahim, Oluwadamilola Adebayo.

Figure 1
Figure 1. Figure 1: Runtime code generation pipeline from user requests to generation, verification, and integration. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
read the original abstract

Traditional self-adaptive systems automatically reconfigure existing components in response to changing requirements, but provide limited support for the generation of novel functionalities. The software generation capabilities of large language models (LLMs) open the possibility to create entirely new modules at runtime, enabling a form of self-evolution beyond traditional self-adaptation. We present SelfEvolve, an orchestrated agentic pipeline architecture enabling runtime self-extension--the autonomous addition of new capabilities during execution--as a preliminary form of self-evolution. Self-extension focuses on the autonomous generation and integration of new functions, based on user requests, without requiring a system restart or developer intervention. Evaluation of our architecture across 11 self-extension tasks demonstrates an average Pass@1 of 92.7% (51/55), outperforming developer-focused code generation baselines like AutoGen, MetaGPT, and AgentCoder. SelfEvolve achieves 61.8% improvement over the best baseline, i.e. Autogen, with statistical significance. This work demonstrates the feasibility of runtime capability extension through autonomous code generation. This provides preliminary evidence for a paradigm in which systems autonomously evolve to satisfy user needs, paving the way towards individualised, self-improving systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper presents SelfEvolve, an orchestrated agentic pipeline architecture for runtime self-extension in software systems. It enables autonomous generation and integration of new functionalities based on user requests using LLMs, without system restart. The evaluation across 11 self-extension tasks reports an average Pass@1 of 92.7% (51/55), with a 61.8% improvement over the AutoGen baseline, claiming statistical significance and demonstrating feasibility for self-evolving systems.

Significance. The concrete Pass@1 rates (92.7% average) and 61.8% improvement over baselines provide empirical support for the architecture's code generation performance. If the runtime integration claims hold with verified safety and absence of bugs, this could advance self-adaptive systems by showing preliminary evidence for LLM-enabled self-evolution, though the current results primarily validate isolated code generation rather than live system modification.

major comments (3)
  1. [Evaluation] Evaluation section: the Pass@1 metric of 92.7% (51/55) and statistical improvement are reported on task success, but no evidence is provided that generated code is integrated into a live running process, monitored for post-insertion runtime errors, or checked for security violations. This directly undermines the central claim of runtime self-extension rather than standard code generation.
  2. [Architecture] Architecture section: the description of the SelfEvolve pipeline provides no specifics on integration mechanics (e.g., how new functions are inserted into an executing process), autonomous error handling, or failure recovery without manual intervention or restart.
  3. [§4] §4 (Experiments): the 11 tasks are used to claim self-extension feasibility, but it is unclear whether they involve actual runtime modification of an unmodified executing system or merely evaluate generated snippets against unit tests, reducing the result to conventional LLM code-gen evaluation.
minor comments (2)
  1. [Abstract] Abstract: the claim of 'statistical significance' for the 61.8% improvement lacks the specific test, sample size details, or p-value.
  2. [Introduction] Notation: the term 'orchestrated agentic pipeline' is introduced without a clear diagram or pseudocode showing the exact agent roles and data flow.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments, which help clarify the scope and limitations of our work. We address each major comment point by point below, providing clarifications and committing to revisions where the manuscript requires strengthening or explicit qualification of claims.

read point-by-point responses

  1. Referee: [Evaluation] Evaluation section: the Pass@1 metric of 92.7% (51/55) and statistical improvement are reported on task success, but no evidence is provided that generated code is integrated into a live running process, monitored for post-insertion runtime errors, or checked for security violations. This directly undermines the central claim of runtime self-extension rather than standard code generation.

    Authors: We agree that the evaluation focuses on Pass@1 success of generated code against task specifications (via unit tests) rather than providing direct empirical evidence of live runtime integration, post-insertion error monitoring, or security verification. The architecture in Section 3 is intended to support such integration without restart, but the reported experiments prioritize measuring the LLM-driven generation component as a necessary first step. We will revise the Evaluation section to explicitly qualify the results as validating code-generation feasibility within the self-extension pipeline, add a limitations paragraph discussing the absence of live runtime monitoring and security checks in this study, and outline planned extensions for full runtime validation. revision: yes

  2. Referee: [Architecture] Architecture section: the description of the SelfEvolve pipeline provides no specifics on integration mechanics (e.g., how new functions are inserted into an executing process), autonomous error handling, or failure recovery without manual intervention or restart.

    Authors: The current architecture description outlines the high-level agentic pipeline but lacks concrete implementation details on integration. In the revised manuscript we will expand the Architecture section with specifics on the integration mechanism (e.g., Python dynamic module loading via importlib to insert new functions into the running process), the agent's autonomous error-detection loop, and basic failure-recovery strategies that avoid manual intervention or restart. These additions will be supported by pseudocode and a small illustrative example. revision: yes

  3. Referee: [§4] §4 (Experiments): the 11 tasks are used to claim self-extension feasibility, but it is unclear whether they involve actual runtime modification of an unmodified executing system or merely evaluate generated snippets against unit tests, reducing the result to conventional LLM code-gen evaluation.

    Authors: The 11 tasks evaluate the full pipeline: the system receives a natural-language request, generates code, and success is determined by whether the new functionality satisfies the task criteria (measured via Pass@1 on unit tests). However, to ensure controlled and reproducible evaluation, the integration step was performed in a sandboxed environment rather than on a completely unmodified live production system. We will revise §4 to explicitly describe this experimental setup, distinguish generation success from full live-deployment validation, and note that broader runtime-modification testing remains future work. revision: yes

Circularity Check

0 steps flagged

No circularity: claims rest on direct empirical comparison to external baselines

full rationale

The paper describes an agentic architecture for runtime code generation and evaluates it on 11 self-extension tasks using the standard Pass@1 metric (51/55 successes). Success is measured against external baselines (AutoGen, MetaGPT, AgentCoder) with reported statistical significance. No equations, derivations, fitted parameters, or self-citations are invoked to justify the central result; the architecture is presented as a design choice and the performance numbers are computed directly from task outcomes. This is a standard empirical software-engineering evaluation with no reduction of claims to self-referential inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The approach depends on the domain assumption that current LLMs can produce code that integrates correctly at runtime; no free parameters are fitted and no new physical entities are postulated.

axioms (1)
  • domain assumption Large language models can generate code that is functionally correct and safely integrable into a running system without developer intervention.
    This assumption underpins the entire self-extension mechanism and is not independently verified in the provided abstract.
invented entities (1)
  • SelfEvolve orchestrated agentic pipeline no independent evidence
    purpose: Enables autonomous generation and runtime integration of new functions
    New system architecture introduced by the paper to realize self-extension.

pith-pipeline@v0.9.0 · 5522 in / 1262 out tokens · 80946 ms · 2026-05-16T06:52:33.435252+00:00 · methodology

discussion (0)

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