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arxiv: 2606.01801 · v1 · pith:H7OQV2W4new · submitted 2026-06-01 · 💻 cs.MA

MetaForge: A Self-Evolving Multimodal Agent that Retrieves, Adapts, and Forges Tools On Demand

Shouang Wei , Houcheng Min , Xinpeng Dong , Xin Lin , Sen Cui , Bo Jiang , Zhongxiang Dai , Kun Kuang
show 3 more authors
Guandong Xu Fei Wu Min Zhang
This is my paper

Pith reviewed 2026-06-28 12:09 UTC · model grok-4.3

classification 💻 cs.MA
keywords multimodal agentstool useself-evolving agentsreinforcement learningtool forgingdynamic adaptationagent orchestration
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The pith

MetaForge lets multimodal agents decide when to forge new tools and recycle them via a closed RL-optimized loop.

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

The paper proposes MetaForge to overcome static tool inventories that fail to generalize and indiscriminate tool calls that add cost and errors. It breaks agent behavior into four coupled stages—Decide whether a tool is needed, Retrieve suitable ones, Adapt parameters to context, and Forge new skills online—linked in a judge-retrieve-adapt-forge-recycle loop. A single orchestration policy chooses among direct answers, existing tools, or new forging, and reinforcement learning jointly tunes invocation necessity, retrieval, execution, and reusability while applying an explicit cost penalty. This produces measurable gains in accuracy, efficiency, and generalization over fixed-tool baselines.

Core claim

MetaForge factorizes agentic behavior into four coupled stages—Decide, Retrieve, Adapt, and Forge—forming a closed judge-retrieve-adapt-forge-recycle loop. A unified orchestration policy enables the agent to choose among answering directly, reusing existing tools, or forging new ones. Joint reinforcement learning optimizes invocation necessity, retrieval accuracy, execution effectiveness, and forged-skill reusability with an explicit invocation-cost penalty, allowing on-demand self-evolution of the tool library.

What carries the argument

The closed judge-retrieve-adapt-forge-recycle loop with unified orchestration policy and joint RL optimization plus cost penalty.

If this is right

  • Agents generalize to unseen scenarios by forging and recycling new skills instead of relying on fixed inventories.
  • The decide stage plus cost penalty reduces redundant invocations and associated noise-induced errors.
  • Forged skills are added to the library and become available for future reuse, improving long-term efficiency.
  • End-to-end RL training aligns all four stages so that the agent selects the lowest-cost effective action at each step.

Where Pith is reading between the lines

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

  • The same decide-forge-recycle pattern could be applied to non-multimodal agents or to environments where tool needs shift over long horizons.
  • Dynamic tool creation might reduce the engineering burden of maintaining large hand-curated tool libraries in deployed systems.
  • If the RL loop remains stable, the framework could support continual learning agents that accumulate capabilities without explicit retraining.
  • Error accumulation in the forge step would be detectable by tracking whether newly forged tools are reused or discarded in subsequent episodes.

Load-bearing premise

The joint RL optimization of invocation necessity, retrieval accuracy, execution effectiveness, and forged-skill reusability with an explicit cost penalty will produce stable, generalizable behavior without the forged tools being overly task-specific or the loop introducing compounding errors.

What would settle it

Running MetaForge on a fresh benchmark suite where it fails to exceed static-tool baselines in accuracy or where forged tools increase error rates would falsify the effectiveness of the self-evolution loop.

Figures

Figures reproduced from arXiv: 2606.01801 by Bo Jiang, Fei Wu, Guandong Xu, Houcheng Min, Kun Kuang, Min Zhang, Sen Cui, Shouang Wei, Xin Lin, Xinpeng Dong, Zhongxiang Dai.

Figure 1
Figure 1. Figure 1: Comparison examples between existing tool [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of MetaForge, a self-evolving multimodal agent that retrieves, adapts, and forges tools on [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of TUE, Tool SR, and Task ACC [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Cumulative registered forged tools over the evaluation process under IID and OOD tool settings. [PITH_FULL_IMAGE:figures/full_fig_p016_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Tool call distribution and per-dataset Tool Usage Entropy (TUE) for each model under the IID tool setting. [PITH_FULL_IMAGE:figures/full_fig_p018_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Tool call distribution and per-dataset Tool Usage Entropy (TUE) for each model under the OOD tool [PITH_FULL_IMAGE:figures/full_fig_p019_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Per-dataset tool call success rates for each model under the IID tool setting. Existing baselines exhibit [PITH_FULL_IMAGE:figures/full_fig_p020_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Per-dataset tool call success rates for each model under the OOD tool setting. Most baselines show notable [PITH_FULL_IMAGE:figures/full_fig_p021_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Distribution of error types across models under IID (left) and OOD (right) tool settings. Errors are [PITH_FULL_IMAGE:figures/full_fig_p023_9.png] view at source ↗
read the original abstract

Multimodal agents have achieved notable progress on complex reasoning tasks through tool use, yet remain limited by two issues: statically predefined tool inventories fail to generalize to unseen scenarios, and indiscriminate tool invocation incurs redundant cost and noise-induced errors. We propose MetaForge, a multimodal agent framework that learns when to invoke tools and how to evolve its toolset on demand. MetaForge factorizes agentic behavior into four coupled stages: Decide (judging whether tool use is warranted), Retrieve (selecting suitable tools), Adapt (grounding tool parameters in task context), and Forge (synthesizing new skills online and recycling them into the tool library for reuse), forming a closed judge-retrieve-adapt-forge-recycle loop. A unified orchestration policy enables the agent to choose among answering directly, reusing existing tools, or forging new ones. We jointly optimize invocation necessity, retrieval accuracy, execution effectiveness, and forged-skill reusability via reinforcement learning, with an explicit invocation-cost penalty discouraging redundant calls. Across 12 benchmarks, MetaForge consistently surpasses 16 baselines in accuracy, efficiency, and generalization, validating a paradigm shift from static tool inventories to on-demand self-evolution.

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 manuscript proposes MetaForge, a multimodal agent that factorizes behavior into a closed judge-retrieve-adapt-forge-recycle loop, uses a unified orchestration policy to decide among direct answering, tool reuse, or forging new skills, and jointly optimizes the four factors plus an explicit cost penalty via reinforcement learning. It reports consistent outperformance over 16 baselines on 12 benchmarks in accuracy, efficiency, and generalization.

Significance. If the empirical results are robust and the RL loop produces reusable rather than brittle tools, the work would meaningfully advance multimodal agents by replacing static tool inventories with on-demand self-evolution. The explicit cost penalty and four-stage factorization are conceptually clean contributions.

major comments (2)
  1. [Experiments] The central empirical claim (surpassing 16 baselines on 12 benchmarks) rests on the joint RL objective producing stable, generalizable behavior. No convergence analysis, ablation on the cost-penalty term, or quantitative measurement of forged-tool reuse rates versus novelty appears in the experimental section, leaving the risk of compounding errors or task-specific overfitting unaddressed.
  2. [Method] The abstract states that the unified policy optimizes invocation necessity, retrieval accuracy, execution effectiveness, and forged-skill reusability, yet the manuscript supplies no equations or pseudocode showing how these four terms are combined into a single reward or how the closed loop is prevented from error accumulation across iterations.
minor comments (2)
  1. [Tables and Figures] Figure captions and table headers should explicitly list the 12 benchmarks and 16 baselines so readers can assess coverage without cross-referencing the text.
  2. [Evaluation Protocol] Clarify whether the forged tools are evaluated for reusability on held-out tasks or only on the training distribution.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and commit to revisions that strengthen the empirical and methodological sections.

read point-by-point responses

  1. Referee: [Experiments] The central empirical claim (surpassing 16 baselines on 12 benchmarks) rests on the joint RL objective producing stable, generalizable behavior. No convergence analysis, ablation on the cost-penalty term, or quantitative measurement of forged-tool reuse rates versus novelty appears in the experimental section, leaving the risk of compounding errors or task-specific overfitting unaddressed.

    Authors: We agree that these analyses are absent and would strengthen the claims. The revised manuscript will include RL convergence curves, an ablation removing the cost-penalty term, and quantitative reuse-versus-novelty statistics for forged tools across all 12 benchmarks to address concerns about stability and overfitting. revision: yes

  2. Referee: [Method] The abstract states that the unified policy optimizes invocation necessity, retrieval accuracy, execution effectiveness, and forged-skill reusability, yet the manuscript supplies no equations or pseudocode showing how these four terms are combined into a single reward or how the closed loop is prevented from error accumulation across iterations.

    Authors: The referee correctly notes the absence of explicit equations and pseudocode. We will add a subsection to the Method section containing the reward function that linearly combines the four terms with the cost penalty, plus pseudocode for the full judge-retrieve-adapt-forge-recycle loop and safeguards such as forged-tool validation to limit error accumulation. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical framework evaluated on external benchmarks

full rationale

The paper presents a methodological framework (four-stage loop with unified RL policy and cost penalty) whose performance claims rest on direct comparisons against 16 external baselines across 12 benchmarks. No equations, self-definitional reductions, fitted-input predictions, or load-bearing self-citations are visible in the provided text that would make any result equivalent to its inputs by construction. The RL optimization and empirical superiority are standard experimental claims, not derivations that collapse into the design itself.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities beyond the high-level framework description can be extracted or verified.

pith-pipeline@v0.9.1-grok · 5772 in / 1080 out tokens · 26091 ms · 2026-06-28T12:09:28.434377+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

29 extracted references · 3 canonical work pages · 2 internal anchors

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