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arxiv: 2604.13120 · v1 · submitted 2026-04-13 · 💻 cs.SE · cs.AI

AgentForge: Execution-Grounded Multi-Agent LLM Framework for Autonomous Software Engineering

Rajesh Kumar , Waqar Ali , Junaid Ahmed , Najma Imtiaz Ali , Shaban Usman This is my paper

Pith reviewed 2026-05-10 15:55 UTC · model grok-4.3

classification 💻 cs.SE cs.AI
keywords multi-agent frameworkslarge language modelssoftware engineeringexecution feedbackcode verificationautonomous agentsbenchmark evaluationsandboxed execution
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The pith

Mandatory execution verification in a multi-agent LLM setup resolves 40 percent of tasks on a standard software benchmark, exceeding single-agent baselines by 26 to 28 points.

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

The paper establishes that requiring every code change to pass through sandboxed execution before acceptance supplies a more reliable decision signal than model predictions alone. This principle is realized through a team of specialized agents that coordinate via shared memory while decomposing the work of planning, coding, testing, debugging, and critiquing. A reader would care because current language models frequently generate code that appears correct yet fails on actual runs, restricting autonomous software engineering. The reported gains come from ablations showing that both the execution loop and the role split contribute independently to the 40 percent resolution rate.

Core claim

Software engineering with large language models is formalized as an iterative decision process over repository states in which execution feedback provides a stronger supervision signal than next-token likelihood. AgentForge instantiates this by routing every modification through a mandatory Docker sandbox and through coordinated Planner, Coder, Tester, Debugger, and Critic agents that share memory, achieving 40.0 percent resolution on SWE-Bench Lite while outperforming single-agent baselines by 26 to 28 points.

What carries the argument

Execution-grounded verification, the rule that every code change must survive sandboxed execution before propagation, carried by the multi-agent coordination structure with shared memory.

If this is right

  • Execution feedback alone improves performance over reliance on model likelihood.
  • Role decomposition adds measurable gains when paired with execution grounding.
  • The iterative repository-state process becomes more reliable when every change is verified by actual runs before acceptance.
  • The framework can be applied to other benchmarks that involve real code modifications and test execution.

Where Pith is reading between the lines

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

  • Other LLM agent systems could adopt mandatory execution loops to reduce the gap between generated and working code without requiring new model training.
  • Scaling the approach to larger repositories would require efficient sandbox isolation that preserves speed while still blocking propagation of faulty changes.
  • The same execution-as-supervision idea might transfer to non-software domains where agents must act in environments that return observable outcomes.
  • Treating repository states as the core object of iteration opens the possibility of formalizing progress as reduction in a distance metric defined by test failures.

Load-bearing premise

Mandatory sandboxed execution feedback supplies a reliably stronger and less noisy supervision signal than the LLM's next-token likelihood for guiding iterative repository-state decisions across diverse real-world tasks.

What would settle it

Removing the sandbox requirement while keeping the same agents and shared memory on SWE-Bench Lite and observing whether resolution falls to single-agent levels would directly test whether execution feedback is the driver of the reported gains.

Figures

Figures reproduced from arXiv: 2604.13120 by Junaid Ahmed, Najma Imtiaz Ali, Rajesh Kumar, Shaban Usman, Waqar Ali.

Figure 1
Figure 1. Figure 1: Overview of the AgentForge multi-agent coding framework, illustrating the sequential handover between specialized [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Retrieval-Augmented Generation (RAG) architecture: (a) Offline repository indexing phase into the vector store; (b) [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Isolated Docker sandbox execution environment. The 512 MB memory limit and disabled networking ensure security [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Performance of AGENTFORGE across three evaluation axes on SWE-BENCH Lite. Left: Resolution rate as a function of debug retries (N). Shaded regions denote ±1 standard deviation across runs. Iterative execution and repair yield consistent gains, with diminishing returns after N = 2. Center: Resolution under k independent runs with majority voting (Pass@k). Performance scales with k without increasing per-run… view at source ↗
Figure 6
Figure 6. Figure 6: Resolve rates across ablation conditions. Removing any [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
read the original abstract

Large language models generate plausible code but cannot verify correctness. Existing multi-agent systems simulate execution or leave verification optional. We introduce execution-grounded verification as a first-class principle: every code change must survive sandboxed execution before propagation. We instantiate this principle in AGENTFORGE, a multi-agent framework where Planner, Coder, Tester, Debugger, and Critic agents coordinate through shared memory and a mandatory Docker sandbox. We formalize software engineering with LLMs as an iterative decision process over repository states, where execution feedback provides a stronger supervision signal than next-token likelihood. AGENTFORGE achieves 40.0\% resolution on SWE-BENCH Lite, outperforming single-agent baselines by 26--28 points. Ablations confirm that execution feedback and role decomposition each independently drive performance. The framework is open-source at https://github.com/raja21068/AutoCodeAI.

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

2 major / 2 minor

Summary. The paper introduces AGENTFORGE, a multi-agent LLM framework for autonomous software engineering that treats execution-grounded verification as a first-class principle: every code change must pass sandboxed execution in a Docker environment before propagation. It defines five specialized agents (Planner, Coder, Tester, Debugger, Critic) that coordinate via shared memory and formalizes the task as an iterative decision process over repository states in which execution feedback supplies a stronger supervision signal than next-token likelihood. The central empirical claim is that AGENTFORGE attains 40.0% resolution on SWE-Bench Lite, outperforming single-agent baselines by 26–28 points, with ablations indicating that both execution feedback and role decomposition contribute independently to the gains. The framework is released as open source.

Significance. If the performance numbers and ablation attributions hold after proper controls, the work would advance LLM-based software engineering by demonstrating that mandatory sandboxed execution can materially reduce plausible-but-incorrect code. The explicit formalization of repository-state iteration and the open-source release are concrete strengths that enable reproducibility and follow-on research. The significance is currently limited by incomplete experimental reporting that prevents clear isolation of the proposed mechanisms from increased inference budget.

major comments (2)
  1. [§4] §4 (Experimental Evaluation): The manuscript reports a 40.0% resolution rate and a 26–28 point improvement over single-agent baselines on SWE-Bench Lite, yet supplies no details on baseline implementations, number of independent runs, statistical significance tests, temperature settings, or failure-mode categorization. Without these elements the central performance claim remains only partially supported.
  2. [Ablation studies] Ablation studies (reported in §4): The claim that execution feedback and role decomposition each independently drive performance is not yet isolated from confounding factors. The full multi-agent system (Planner + Coder + Tester + Debugger + Critic) necessarily increases the number of LLM invocations and shared-memory iterations relative to the ablated variants; the paper does not report total tokens or calls per task, so the observed gains could arise from greater sampling opportunity rather than a stronger supervision signal over next-token likelihood.
minor comments (2)
  1. [Abstract / Introduction] The abstract and introduction use the phrase 'first-class principle' for execution-grounded verification; a brief comparison table against prior multi-agent SE systems that treat execution as optional would help readers locate the novelty.
  2. [Abstract] The GitHub link is welcome; the repository should include the exact prompts, agent orchestration code, and SWE-Bench Lite task IDs used in the reported experiments to support direct replication.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the constructive feedback on our manuscript. We address each major comment point by point below, with proposed revisions to strengthen the experimental details and ablation analysis while preserving the core claims.

read point-by-point responses

  1. Referee: [§4] §4 (Experimental Evaluation): The manuscript reports a 40.0% resolution rate and a 26–28 point improvement over single-agent baselines on SWE-Bench Lite, yet supplies no details on baseline implementations, number of independent runs, statistical significance tests, temperature settings, or failure-mode categorization. Without these elements the central performance claim remains only partially supported.

    Authors: We agree that the experimental section requires additional details to fully substantiate the reported results. In the revised manuscript, §4 will be expanded to specify: baseline implementations (single-agent variants using identical LLM backbones and prompting as the corresponding AGENTFORGE agents); three independent runs per configuration with mean and standard deviation; paired t-test results confirming statistical significance (p < 0.05) for the 26–28 point gains; temperature fixed at 0.7 across all calls; and a failure-mode breakdown table categorizing issues such as syntax errors, test failures, and timeouts. These additions directly address the gaps and provide stronger support for the 40.0% resolution rate. revision: yes

  2. Referee: [Ablation studies] Ablation studies (reported in §4): The claim that execution feedback and role decomposition each independently drive performance is not yet isolated from confounding factors. The full multi-agent system (Planner + Coder + Tester + Debugger + Critic) necessarily increases the number of LLM invocations and shared-memory iterations relative to the ablated variants; the paper does not report total tokens or calls per task, so the observed gains could arise from greater sampling opportunity rather than a stronger supervision signal over next-token likelihood.

    Authors: We acknowledge the potential confounding from increased inference budget in the full system. In the revision, we will add a table in §4 reporting average LLM calls and total tokens per task for the complete framework and each ablation. This data indicates that execution feedback and role-specific decomposition yield gains beyond raw sampling volume, as variants retaining mandatory sandbox verification outperform others at comparable token budgets. The execution signal supplies explicit pass/fail outcomes that guide iterative state transitions, distinct from additional next-token sampling. We are prepared to include further controls if required. revision: partial

Circularity Check

0 steps flagged

No significant circularity; empirical results on external benchmark with conceptual framing only.

full rationale

The paper's central claims rest on empirical performance (40% on SWE-Bench Lite) and ablations rather than any mathematical derivation chain. The formalization of software engineering as an iterative decision process over repository states is presented as a conceptual principle, not an equation or fitted model that reduces to its own inputs by construction. No self-citations, uniqueness theorems, ansatzes, or predictions equivalent to fitted parameters are load-bearing in the provided text. Results are externally benchmarked and falsifiable, satisfying the criteria for non-circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The framework rests on the domain assumption that execution outcomes constitute an objective and stronger training signal than language-model likelihood, plus the practical assumption that Docker sandboxes can be reliably instantiated for arbitrary repository states without introducing confounding errors.

axioms (1)
  • domain assumption Execution feedback provides a stronger supervision signal than next-token likelihood for iterative software engineering decisions
    Explicitly stated in the abstract as the formalization of the software engineering process.
invented entities (1)
  • Planner, Coder, Tester, Debugger, and Critic agents no independent evidence
    purpose: Role decomposition for coordinating code changes through shared memory
    These are defined roles instantiated in the framework; no independent evidence outside the system description is supplied.

pith-pipeline@v0.9.0 · 5462 in / 1346 out tokens · 43491 ms · 2026-05-10T15:55:52.522190+00:00 · methodology

discussion (0)

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