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arxiv: 2605.19633 · v1 · pith:B5OTAX5Lnew · submitted 2026-05-19 · 💻 cs.CL · cs.AI· cs.LG· cs.NE· cs.SE

optimize_anything: A Universal API for Optimizing any Text Parameter

Lakshya A Agrawal , Donghyun Lee , Shangyin Tan , Wenjie Ma , Karim Elmaaroufi , Rohit Sandadi , Sanjit A. Seshia , Koushik Sen
show 6 more authors
Dan Klein Ion Stoica Joseph E. Gonzalez Omar Khattab Alexandros G. Dimakis Matei Zaharia
This is my paper

Pith reviewed 2026-05-20 05:38 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LGcs.NEcs.SE
keywords text optimizationLLM-based searchuniversal APIagent architecturesCUDA kernelsscheduling algorithmscross-task transferARC-AGI
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The pith

A single LLM-based system for optimizing text artifacts achieves state-of-the-art results across six diverse tasks including agent design and code generation.

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

The paper establishes that optimization problems can be reframed as improving a text artifact evaluated by a scoring function, letting one AI system handle single-task search, multi-task transfer, and generalization to new inputs. This unifies tasks that normally need separate domain-specific algorithms under a shared text-optimization framework. A sympathetic reader would care because the approach reportedly triples ARC-AGI accuracy, cuts cloud scheduling costs by 40 percent, and produces competitive CUDA kernels without hand-crafted code. Ablations show that side information beyond raw scores speeds improvement and that sharing search across related tasks boosts final performance. The work positions text-based LLM search as a general-purpose solver rather than a collection of narrow tools.

Core claim

When optimization problems are formulated as improving a text artifact evaluated by a scoring function, a single AI-based optimization system—supporting single-task search, multi-task search with cross-problem transfer, and generalization to unseen inputs—achieves state-of-the-art results across six diverse tasks.

What carries the argument

The optimize_anything API, which casts any parameter optimization as refinement of a text artifact guided by an external scoring function and solved via LLM-driven search.

If this is right

  • Actionable side information produces faster convergence and higher final scores than score-only feedback.
  • Multi-task search outperforms independent per-task optimization under equal total budget through cross-task transfer.
  • Benefits from multi-task search increase as the number of related tasks grows.
  • The system generalizes its discovered solutions to inputs not seen during optimization.
  • The same framework discovers agent architectures, scheduling policies, CUDA kernels, and geometric packings without task-specific redesign.

Where Pith is reading between the lines

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

  • The results suggest that representing candidate solutions as editable text may let LLMs serve as universal black-box optimizers across additional domains such as materials design or financial strategies.
  • If cross-task transfer continues to scale, future versions could maintain a shared library of successful text patterns that accelerate optimization on entirely new problems.
  • A practical test would measure how much the performance edge shrinks when the scoring functions are replaced by noisier or more expensive real-world evaluators.

Load-bearing premise

The scoring functions supplied for each task serve as reliable proxies for real performance that need no further domain-specific engineering to deliver the reported gains.

What would settle it

Applying the system to a new task outside the original six where it fails to match or exceed the best existing specialized method on that task would falsify the universality claim.

Figures

Figures reproduced from arXiv: 2605.19633 by Alexandros G. Dimakis, Dan Klein, Donghyun Lee, Ion Stoica, Joseph E. Gonzalez, Karim Elmaaroufi, Koushik Sen, Lakshya A Agrawal, Matei Zaharia, Omar Khattab, Rohit Sandadi, Sanjit A. Seshia, Shangyin Tan, Wenjie Ma.

Figure 1
Figure 1. Figure 1: The optimize_anything loop: a text artifact 𝑥 is passed to an evaluator 𝑓 (𝑥) which returns a score plus diagnostic feedback (SI), which is consumed by an LLM proposer to produce an improved artifact. The same API instantiates across domains: code optimization, prompt tuning, agent architecture search, and policy discovery. We observe that a wide range of problems can be formulated as optimizing a text art… view at source ↗
Figure 2
Figure 2. Figure 2: Claude Code on the Bleve repository. Optimized [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Optimization trajectories for cloud scheduling. Both [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: ARC-AGI agent architecture evolution with Gem [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: AIME prompt optimization for GPT-4.1-mini. Val [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 9
Figure 9. Figure 9: Ablation: prompt optimization with vs. without SI [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
Figure 8
Figure 8. Figure 8: Single-task vs. multi-task mode on 10 selected Ker [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 10
Figure 10. Figure 10: Architecture of the optimized ARC-AGI agent. The [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Qualitative comparison between zero-shot generations (left) and optimize_anything candidates (right) across four [PITH_FULL_IMAGE:figures/full_fig_p015_11.png] view at source ↗
read the original abstract

Can a single LLM-based optimization system match specialized tools across fundamentally different domains? We show that when optimization problems are formulated as improving a text artifact evaluated by a scoring function, a single AI-based optimization system-supporting single-task search, multi-task search with cross-problem transfer, and generalization to unseen inputs-achieves state-of-the-art results across six diverse tasks. Our system discovers agent architectures that nearly triple Gemini Flash's ARC-AGI accuracy (32.5% to 89.5%), finds scheduling algorithms that cut cloud costs by 40%, generates CUDA kernels where 87% match or beat PyTorch, and outperforms AlphaEvolve's reported circle packing solution (n=26). Ablations across three domains reveal that actionable side information yields faster convergence and substantially higher final scores than score-only feedback, and that multi-task search outperforms independent optimization given equivalent per-problem budget through cross-task transfer, with benefits scaling with the number of related tasks. Together, we show for the first time that text optimization with LLM-based search is a general-purpose problem-solving paradigm, unifying tasks traditionally requiring domain-specific algorithms under a single framework. We open-source optimize\_anything with support for multiple backends as part of the GEPA project at https://github.com/gepa-ai/gepa .

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 manuscript introduces optimize_anything, a universal API for optimizing any text parameter via an LLM-based system. It claims that formulating optimization problems as improving a text artifact evaluated by a scoring function enables a single system—supporting single-task search, multi-task search with cross-problem transfer, and generalization to unseen inputs—to achieve state-of-the-art results across six diverse tasks. Specific results include nearly tripling Gemini Flash's ARC-AGI accuracy (32.5% to 89.5%), cutting cloud costs by 40% via improved scheduling, generating CUDA kernels where 87% match or beat PyTorch, and outperforming AlphaEvolve on circle packing (n=26). Ablations indicate that actionable side information outperforms score-only feedback and that multi-task search yields benefits through cross-task transfer that scale with the number of related tasks. The system is open-sourced as part of the GEPA project.

Significance. If the results hold after addressing methodological details, the work would be significant as a demonstration that LLM-based text optimization can serve as a general-purpose paradigm unifying tasks traditionally addressed by domain-specific algorithms. The empirical breadth across six tasks, the ablations on side information and multi-task transfer, and the open-source release with multiple backends are strengths that support reproducibility and potential adoption. The approach could reduce the need for specialized tools if the optimizer's contribution can be isolated from task-specific components.

major comments (3)
  1. [Abstract] Abstract: The central claim that a single system achieves SOTA results across six tasks rests on reported quantitative gains (e.g., ARC-AGI 32.5% to 89.5%, 40% cost reduction, 87% CUDA kernels matching or beating PyTorch). However, the abstract supplies no explicit construction of the scoring functions, no baselines, no statistical tests, and no ablation controls, making it impossible to determine whether these gains are attributable to the optimizer or to unstated properties of the scorers.
  2. [Methods] Scoring function definitions (Methods section): The universality argument requires that scoring functions serve as minimal, fixed, neutral proxies. If the paper does not demonstrate via sensitivity analysis or comparison to generic accuracy/latency metrics that equivalent gains cannot be obtained without task-specific test suites or reward shaping inside the scorer, then the reported advantage may partly reflect scorer engineering rather than the search procedure itself.
  3. [Results] Ablations (Results section): The claim that multi-task search outperforms independent optimization 'given equivalent per-problem budget' is load-bearing for the cross-problem transfer result. Without a precise description of how the per-problem budget is defined and allocated in the independent baseline (including total LLM calls or wall-clock time), the comparison cannot be evaluated for fairness.
minor comments (2)
  1. [Results] The manuscript should include a table summarizing the six tasks, their scoring functions, and the exact baselines used for each SOTA comparison.
  2. [Ablations] Notation for 'actionable side information' versus 'score-only feedback' should be defined explicitly in the first use to improve readability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment point by point below, clarifying the manuscript's contributions while making targeted revisions to improve transparency and rigor.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that a single system achieves SOTA results across six tasks rests on reported quantitative gains (e.g., ARC-AGI 32.5% to 89.5%, 40% cost reduction, 87% CUDA kernels matching or beating PyTorch). However, the abstract supplies no explicit construction of the scoring functions, no baselines, no statistical tests, and no ablation controls, making it impossible to determine whether these gains are attributable to the optimizer or to unstated properties of the scorers.

    Authors: We acknowledge that the abstract's brevity limits inclusion of full methodological details. Scoring functions are defined in the Methods section as minimal, task-appropriate metrics (e.g., test-set accuracy for ARC-AGI without additional shaping, measured cost for scheduling). Baselines, statistical comparisons where applicable, and ablations appear in Results. To improve accessibility, we will revise the abstract to briefly note the scoring-function formulation and reference the main text for baselines and controls. The controlled single- vs. multi-task experiments and side-information ablations are designed to isolate the optimizer's contribution from scorer properties. revision: partial

  2. Referee: [Methods] Scoring function definitions (Methods section): The universality argument requires that scoring functions serve as minimal, fixed, neutral proxies. If the paper does not demonstrate via sensitivity analysis or comparison to generic accuracy/latency metrics that equivalent gains cannot be obtained without task-specific test suites or reward shaping inside the scorer, then the reported advantage may partly reflect scorer engineering rather than the search procedure itself.

    Authors: Scoring functions use standard, fixed metrics without reward shaping: accuracy on held-out ARC-AGI examples, runtime and correctness for CUDA kernels, and direct cost for scheduling. The same optimizer is applied across all tasks, with ablations showing gains from search strategy rather than scorer changes. While a dedicated sensitivity analysis to generic metrics was not present in the original submission, the cross-task transfer results and side-information comparisons provide evidence that the optimizer drives performance. We will add a subsection in Methods explicitly discussing scorer neutrality and comparing against generic metrics where feasible. revision: yes

  3. Referee: [Results] Ablations (Results section): The claim that multi-task search outperforms independent optimization 'given equivalent per-problem budget' is load-bearing for the cross-problem transfer result. Without a precise description of how the per-problem budget is defined and allocated in the independent baseline (including total LLM calls or wall-clock time), the comparison cannot be evaluated for fairness.

    Authors: The per-problem budget is defined as an equal allocation of total LLM calls (optimization steps) to each task in the independent baseline, with the multi-task setting using the same aggregate budget but allowing information sharing. This is stated in Results, but we agree a more precise specification would strengthen the claim. We will revise the section to include an explicit definition, a budget-allocation table, and pseudocode distinguishing the two regimes, ensuring the comparison accounts for total compute and wall-clock time. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical results rest on independent experimental outcomes

full rationale

The paper formulates optimization as improving text artifacts under provided scoring functions and reports empirical gains across six tasks (e.g., ARC-AGI accuracy lift, cloud-cost reduction, CUDA kernel performance). No equations, derivations, or self-referential definitions appear. Claims of universality and cross-task transfer are supported by ablations and observed performance rather than reducing by construction to fitted inputs, self-citations, or renamed known results. Scoring functions are treated as external inputs whose quality is an assumption, not a load-bearing derivation step internal to the optimizer. The work is therefore self-contained against external benchmarks and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review prevents exhaustive enumeration; the approach implicitly assumes LLMs can perform effective black-box search over text spaces when given a scorer, with no explicit free parameters or new physical entities described.

axioms (1)
  • domain assumption LLM-based iterative text editing guided by scalar scores can discover high-performing solutions across unrelated domains
    This is the load-bearing premise enabling the universal API claim.

pith-pipeline@v0.9.0 · 5826 in / 1184 out tokens · 45038 ms · 2026-05-20T05:38:42.642229+00:00 · methodology

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Reference graph

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    Pareto-Frontier Candidate Selection: Explicitly keeps candidates that offer distinct cost/time tradeoffs

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    Diverse Steiner Strategies: Includes MST-like approximations for cost and bottleneck-widest paths for throughput

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    ""Bilevel L-BFGS with exact LP sensitivities + SLP block boosts + CMA/Evolution fallback

    Robust Greedy Allocation: Accurately models bandwidth contention across partitions. """ # --- Constants & Configuration --- EST_DATA_VOL_GB = 300.0 EST_INSTANCE_COST_PER_HR = 10.0 PARTITION_VOL_GB = EST_DATA_VOL_GB /max(1, num_partitions) # Sweep parameters for Cost vs Time tradeoff alphas = [0.0, 1e-5, 0.001, 0.01, 0.05, 0.1, 0.5, 2.0] bw_thresholds = [0...

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