Beneath the Diff: Diagnosing and Mitigating Algorithmic Mode Collapse in Code-Level Autonomous Research Loops

# Beneath the Diff: Diagnosing and Mitigating Algorithmic Mode Collapse in Code-Level Autonomous Research Loops
Source: [https://arxiv.org/html/2609.00077](https://arxiv.org/html/2609.00077)
Weixu ZhangAffiliation:McGill UniversityEmail:[yili@mirrorspace\.tech](mailto:)Yili Jin††thanks:Corresponding to Dr\. Yili Jin\.Affiliation:MirrorSpace TechnologyAffiliation:Simon Fraser UniversityXue LiuAffiliation:MBZUAIAffiliation:McGill University

###### Abstract

Code\-level autonomous research loops \(ARLs\) have recently emerged as a concrete object of study in automated machine learning research\. In such loops, an LLM agent proposes modifications to an experimental training pipeline, executes the modified pipeline, and retains edits that improve a verifiable in\-loop metric\. Although executable metrics may appear to provide a reliable signal of progress, it remains unclear whether repeated metric\-driven code editing leads to genuine improvements that generalize beyond the loop\. We provide a systematic diagnosis of this question\. Across various experiment settings, we identify a robust failure mode that we callalgorithmic mode collapse\. In this regime, surface\-level edit diversity remains stable, but semantic and mechanism\-level diversity collapse: the agent continues to edit different lines of code while repeatedly proposing the same kinds of algorithmic changes\. This collapse is accompanied by a widening gap between in\-loop metric gains and gains measured on independent held\-out evaluations\. We then propose Diversity\-Aware Proposal Sampling \(DAPS\), a lightweight mitigation that combines category\-coverage reweighting, persistent edit memory, and a validation gate\. Under a three\-tier protocol separating the in\-loop metric, the audit metric read by the gate, and a blind metric no loop component ever accesses,DAPSreduces semantic\-cluster decay of edits by69\.1%69\.1\\%and improves relative faithfulness by83\.7%83\.7\\%blind and81\.6%81\.6\\%audited, while preserving in\-loop optimization speed\. We provide the code in Github[repository](https://github.com/BokwaiHo/arl-mode-collapse)\.

## 1Introduction

The recent open\-source release of iterative autonomous research agents, like the most prominentlyautoresearchby[Karpathy \(2026\)](https://arxiv.org/html/2609.00077#bib.bib15)and the broader AI\-Scientist family\([Lu et al\., 2024](https://arxiv.org/html/2609.00077#bib.bib22);[Schmidgall et al\., 2025](https://arxiv.org/html/2609.00077#bib.bib33);[Tang et al\., 2026](https://arxiv.org/html/2609.00077#bib.bib39);[Gottweis et al\., 2025](https://arxiv.org/html/2609.00077#bib.bib10)\), has made*code\-level autonomous research loops*\(ARLs\) a concrete object of study in automated machine learning research\. In such loops, an LLM proposer generates modifications to a training pipeline \(optimizer settings, architectural details, data preprocessing, or loss formulations\), the modified pipeline is executed, and a validation metric decides whether the edit is retained or reverted\. Thus, we can automate the iterative search for better training recipes with minimal human intervention\. Unlike recursive training\([Kovač et al\., 2025](https://arxiv.org/html/2609.00077#bib.bib17)\)which relies on self\-generated training samples to improve the model, code\-level ARLs can access optimization signals grounded in an external, executable evaluation\. Therefore, one might expect autonomous research loops to be immune to the diversity collapse in recursive training loops, which narrows the space of generated solutions and undermines generalization\([Shumailov et al\., 2024](https://arxiv.org/html/2609.00077#bib.bib36);[Zhu and Xie, 2026](https://arxiv.org/html/2609.00077#bib.bib46);[Mishra, 2026](https://arxiv.org/html/2609.00077#bib.bib26)\)\.

Figure 1:Algorithmic mode collapse in code\-level ARLs\.Left: surface\-level edit diversity, measured as mean pairwise normalized edit distance over a sliding window of 20 accepted diffs, remains essentially flat across500500iterations on all four tasks\. Right: semantic\-cluster count, obtained by Sentence\-BERT\([Reimers and Gurevych, 2019](https://arxiv.org/html/2609.00077#bib.bib32)\)embeddings of LLM\-summarized edit descriptions followed by HDBSCAN clustering\([Campello et al\., 2013](https://arxiv.org/html/2609.00077#bib.bib1)\), collapses by 50 to 70% over the same horizon\. The agent keeps editing different lines of code, but is increasingly proposing the same*kind*of change\. Shaded regions are±1\\pm 1standard deviation over 3 seeds\.However, we find that this expectation should be questioned\. Code\-level ARL, despite using a verifiable external metric, exhibits a previously undocumented failure mode we callalgorithmic mode collapse: the*surface*of the agent’s behavior, such as the lines it edits, the lexical form of its diffs, remains diverse, while the*semantics*of its proposals, what each edit is actually trying to accomplish, progressively concentrates on a small number of recurring patterns\. Figure[1](https://arxiv.org/html/2609.00077#S1.F1)shows the phenomenon at a glance: across four NLP\-relevant tasks, surface edit diversity is essentially flat over500500iterations, while the number of semantic clusters in the proposal stream collapses by5050to70%70\\%\. This narrowing changes the character of the loop\. Rather than continuing to search broadly over possible algorithmic interventions, the proposer increasingly returns to a small repertoire of edits that reliably improve the in\-loop metric\. Such concentration might be benign if it reflected convergence on genuinely useful mechanisms\. In our experiments, however, it is accompanied by a growing gap between optimization\-metric improvements claimed inside the loop and improvements measured on independent held\-out evaluations\. As the proposer’s repertoire narrows, it increasingly produces edits that overfit the in\-loop signal rather than improve the underlying system\. By iteration300300, average in\-loop gains overstate held\-out gains by a factor of2\.02\.0to2\.62\.6across our tasks\.

Building on this diagnosis, we introduce the mitigation strategy Diversity\-Aware Proposal Sampling \(DAPS\), that injects three lightweight components into an existing ARL loop: a category\-coverage reweighting that nudges the proposer toward under\-represented edit mechanisms, a persistent semantic memory that suppresses proposals too similar to recently accepted ones, and a validation gate that reverts the pipeline to its last*faithful*state when the gap between the in\-loop metric and a sparingly consulted audit metric exceeds a calibrated threshold\.DAPSreduces semantic\-cluster decay of edit descriptions by69\.1%69\.1\\%and improves the relative faithfulness by81\.6%81\.6\\%averaged across tasks, while matching or slightly trailing vanilla autoresearch on in\-loop optimization speed\. Because the gate consumes the audit metric, we also score every configuration on blind sets that no component ever accesses, where the advantage persists\.

Our contributions can be summarized as follows: \(i\) We conduct the first controlled empirical study of code\-level ARL dynamics over long horizons across NLP tasks, producing ARL trajectories with logged proposals, diffs, executions, and held\-out evaluations\. \(ii\) We introduce a four\-axis diagnostic instrument: surface, semantic, mechanism, and corpus\-similarity diversity, together with a faithfulness audit that operationalize*algorithmic mode collapse*and reveal its link to in\-loop/held\-out divergence\. \(iii\) We proposeDAPS, a drop\-in mitigation that materially reduces collapse and improves faithfulness across three proposer LLMs without slowing optimization\. \(iv\) We show that surface\-only diversity measurements that are common in prior data\-level collapse analyses\([Li et al\., 2026](https://arxiv.org/html/2609.00077#bib.bib20);[Mishra, 2026](https://arxiv.org/html/2609.00077#bib.bib26)\), systematically miss algorithmic mode collapse, motivating semantic\-aware monitoring for future ARL deployment\.

## 2Related Work

Autonomous Research Agents\.The AI\-Scientist\([Lu et al\., 2024](https://arxiv.org/html/2609.00077#bib.bib22)\), Agent Laboratory\([Schmidgall et al\., 2025](https://arxiv.org/html/2609.00077#bib.bib33)\), AI\-Researcher\([Tang et al\., 2026](https://arxiv.org/html/2609.00077#bib.bib39)\), and AI Co\-Scientist\([Gottweis et al\., 2025](https://arxiv.org/html/2609.00077#bib.bib10)\)systems orchestrate LLM agents through hypothesis generation, experimentation, and writeup\.[Karpathy \(2026\)](https://arxiv.org/html/2609.00077#bib.bib15)distilled the core experiment\-loop pattern into a 630\-line script that operates at single\-GPU scale, popularizing the term*autoresearch*and motivating a wave of variants\([Walker, 2026](https://arxiv.org/html/2609.00077#bib.bib42)\)\. Concurrently,[Trehan and Chopra \(2026\)](https://arxiv.org/html/2609.00077#bib.bib41)document six recurring failure modes across four end\-to\-end autonomous ML research attempts, while[Xiong et al\. \(2026\)](https://arxiv.org/html/2609.00077#bib.bib43)report that even frontier models reach only9\.4%9\.4\\%accuracy on rigorous scientific\-literature discovery\. Our work is complementary: where prior efforts evaluate end\-to\-end research quality, we analyze the*dynamics*of the experimentation loop\.

Diversity Collapse in Recursive Training\.[Shumailov et al\. \(2024\)](https://arxiv.org/html/2609.00077#bib.bib36)established that recursively training on model\-generated data leads to distributional narrowing\. Subsequent work has refined this picture for self\-play and self\-training\([Kovač et al\., 2025](https://arxiv.org/html/2609.00077#bib.bib17);[Zhu and Xie, 2026](https://arxiv.org/html/2609.00077#bib.bib46)\)\. Most directly related,[Li et al\. \(2026\)](https://arxiv.org/html/2609.00077#bib.bib20)identify a*Diversity Illusion*in Challenger and Solver self\-play\. i\.e\., surface variation persists while underlying patterns collapse\.[Mishra \(2026\)](https://arxiv.org/html/2609.00077#bib.bib26)diagnose*curriculum collapse*in self\-evolving reasoning systems, proposing semantic cluster\-coverage rewards\. We adopt their semantic\-vs\-surface framing but transport it to a fundamentally different setting: the optimization signal in code\-level ARL is an external execution, not a learned judge or self\-generated label, so prior collapse mechanisms \(model\-on\-its\-own\-data\) do not directly apply\. Our diagnosis identifies a distinct mechanism rooted in the*proposer’s*prior distribution interacting with metric overfitting\.

Reward Hacking and Goodharting\.That optimizers exploit proxy metrics is a classical observation\([Christiano et al\., 2017](https://arxiv.org/html/2609.00077#bib.bib2);[Skalse et al\., 2022](https://arxiv.org/html/2609.00077#bib.bib37);[Pan et al\., 2022](https://arxiv.org/html/2609.00077#bib.bib27)\)\. Closer to our setting,[Gao et al\. \(2023\)](https://arxiv.org/html/2609.00077#bib.bib8)document that the gap between proxy and gold rewards widens with optimization pressure\. Algorithmic mode collapse can be read as a Goodhart\-style phenomenon at the*proposal*layer: a narrowing repertoire of edits is selected precisely because it reliably moves the in\-loop metric, including when the underlying system has not improved\.

Plagiarism of AI\-Generated Research\.[Gupta and Pruthi \(2025\)](https://arxiv.org/html/2609.00077#bib.bib12)show that AI\-generated research ideas frequently recapitulate prior work without attribution\. We operationalize a related diagnosis, asking whether a proposed modification is essentially retrieved from the proposer’s training data\.

## 3Methodology

### 3\.1Code\-Level ARL: Setup and Notation

A code\-level ARL loop is a tuple\(𝒫,πθ,ℰ,R\)\(\\mathcal\{P\},\\pi\_\{\\theta\},\\mathcal\{E\},R\)where𝒫\\mathcal\{P\}is a training pipeline represented as a set of source files,πθ\\pi\_\{\\theta\}is an LLM proposer,ℰ\\mathcal\{E\}is an executor that runs a candidate pipeline to obtain a metric value, andR:ℝ→\{0,1\}R:\\mathbb\{R\}\\to\\\{0,1\\\}is an acceptance rule\. At iterationtt, the proposer samples a textual modification descriptiondt∼πθ\(⋅∣𝒫t,ht\)d\_\{t\}\\sim\\pi\_\{\\theta\}\(\\cdot\\mid\\mathcal\{P\}\_\{t\},h\_\{t\}\)together with a code patchρt\\rho\_\{t\}, wherehth\_\{t\}summarizes the loop history\. The executor evaluates𝒫t⊕ρt\\mathcal\{P\}\_\{t\}\\oplus\\rho\_\{t\}on an optimization metricmoptm^\{\\text\{opt\}\}; the new pipeline is retained ifR⁡\(mopt​\(𝒫t⊕ρt\)−mopt​\(𝒫t\)\)=1R\(m^\{\\text\{opt\}\}\(\\mathcal\{P\}\_\{t\}\\oplus\\rho\_\{t\}\)\-m^\{\\text\{opt\}\}\(\\mathcal\{P\}\_\{t\}\)\)=1, typically when the metric improves by more than a noise thresholdϵ\\epsilon\. Crucially,moptm^\{\\text\{opt\}\}is what the agent sees; §[3\.3](https://arxiv.org/html/2609.00077#S3.SS3)defines the two evaluations that it does*not*optimize\.

### 3\.2Multi\-Axis Diversity Instrumentation

Existing analyses of diversity collapse measure it primarily through token\- or embedding\-level signals over generated*text*\([Li et al\., 2026](https://arxiv.org/html/2609.00077#bib.bib20);[Mishra, 2026](https://arxiv.org/html/2609.00077#bib.bib26)\)\. In a code\-level autonomous research loop, the natural unit is the edit, which admits multiple, partially independent notions of diversity\. We instrument four axes as follows\.

Surface diversity \(St\\mathrm\{S\}\_\{t\}\)\.For each accepted edit, we extract its unified diff and compute the normalized Levenshtein distance between every pair of diffs in a sliding window of theW=20W=20most recent accepted edits\.St\\mathrm\{S\}\_\{t\}is the window\-mean\. This captures whether the agent is editing different lines of code in lexically different ways\.

Semantic diversity \(Ct\\mathrm\{C\}\_\{t\}\)\.The proposer emits, alongside each patch, a one\-sentence natural\-language description of its intent \(or we elicit one post\-hoc from a fixed summarizer LLM\)\. We embed all descriptions accumulated up to iterationttwith Sentence\-BERT\([Reimers and Gurevych, 2019](https://arxiv.org/html/2609.00077#bib.bib32)\)and cluster them with HDBSCAN\([Campello et al\., 2013](https://arxiv.org/html/2609.00077#bib.bib1)\)at a fixed minimum cluster size of55\.Ct\\mathrm\{C\}\_\{t\}is the number of clusters whose youngest member was added within the lastWWiterations\. The motivation is direct:*what*the agent is trying to accomplish, like learning\-rate adjustment, attention re\-formulation, regularization tweak, is exactly what surface metrics fail to capture\.

Mechanism entropy \(Ht\\mathrm\{H\}\_\{t\}\)\.Surface and semantic axes are continuous; we add a discrete taxonomy for interpretability\. To derive the taxonomy we conducted a pilot study on187187edit descriptions sampled uniformly from publicly available code\-level ARL logs, embedded with Sentence\-BERT and clustered with affinity propagation\([Frey and Dueck, 2007](https://arxiv.org/html/2609.00077#bib.bib7)\); resulting clusters were iteratively merged when their natural\-language summaries overlapped, yielding nine categories that jointly cover96\.4%96\.4\\%of pilot edits \(a residual “other” bucket absorbs the long tail\)\. Because the pilot logs come from other ARL systems, the categories are frozen before any of our own runs\. Table[1](https://arxiv.org/html/2609.00077#S3.T1)lists the categories with a representative paraphrased example each\. At loop time each edit description is assigned by a rule\-based parser over a curated keyword inventory, cross\-validated against parallel labelling by a frontier LLM annotator; Cohen’sκ\\kappabetween the two on a200200\-edit held\-out validation set is0\.840\.84, indicating almost perfect agreement\([Landis and Koch, 1977](https://arxiv.org/html/2609.00077#bib.bib19)\)\.Ht\\mathrm\{H\}\_\{t\}is the Shannon entropy\([Shannon, 1948](https://arxiv.org/html/2609.00077#bib.bib34)\)of the category distribution over a window ofWWaccepted edits\. LowHt\\mathrm\{H\}\_\{t\}in the presence of highSt\\mathrm\{S\}\_\{t\}is the signature of algorithmic mode collapse: the agent edits diverse lines of code but is increasingly trying to do the same kind of thing\. Appendix[J](https://arxiv.org/html/2609.00077#A10)shows the collapse to be invariant to taxonomy granularity and to human labelling\.

Table 1:Mechanism taxonomyused forHt\\mathrm\{H\}\_\{t\}\. Categories were derived from a pilot study \(see text\) and cover96\.4%96\.4\\%of observed edits\.Corpus similarity \(Vt\\mathrm\{V\}\_\{t\}\)\.Each edit description is encoded and matched, via nearest\-neighbor cosine similarity, against a corpus of1\.11\.1M arXiv cs\.CL/cs\.LG abstracts published before January 2024\.Vt\\mathrm\{V\}\_\{t\}is the mean top\-1 similarity over the window\. We treat this axis as descriptive: a risingVt\\mathrm\{V\}\_\{t\}is consistent with the proposer leaning on familiar, frequently described patterns rather than exploring\([Gupta and Pruthi, 2025](https://arxiv.org/html/2609.00077#bib.bib12)\), but it does not by itself establish retrieval, since the same mechanism can be phrased in standard or in unusual ML vocabulary and the corpus is abstracts only, pre\-2024, and field\-restricted\.

### 3\.3The Three\-Tier Faithfulness Audit

We separate three evaluation roles\. The*in\-loop*metricmoptm^\{\\text\{opt\}\}is the only signal the proposer optimizes and the acceptance rule consumes\. The*audit*metricmauditm^\{\\text\{audit\}\}, defined on data the agent cannot infer from𝒫t\\mathcal\{P\}\_\{t\}and targeting the*intended*capability rather than the proxy, is never observed by the proposer and is read only by the gate of §[3\.4](https://arxiv.org/html/2609.00077#S3.SS4)and its threshold calibration, everyK=10K=10iterations, through a scalar test whose only effects are a revert and a templated, value\-free notice \(2\.7±0\.92\.7\\pm 0\.9reverts per run\)\. Since it can thus influence the trajectory, we call it a validation signal rather than a fully held\-out test\. The*blind*metricmblindm^\{\\text\{blind\}\}is read by no component at any point, gate and calibration included, and is evaluated once per run; it is our headline faithfulness evaluation \(partitions in Appendix[G](https://arxiv.org/html/2609.00077#A7)\)\.

EveryK=10K=10iterations, we evaluate the best\-so\-far pipeline onmauditm^\{\\text\{audit\}\}and compare againstmoptm^\{\\text\{opt\}\}\. Concretely, we define the*faithfulness gap*

Δtfaith=g¯topt⏟in\-loop gain−g¯taudit⏟audited gain,\\Delta^\{\\text\{faith\}\}\_\{t\}=\\underbrace\{\\bar\{g\}^\{\\text\{opt\}\}\_\{t\}\}\_\{\\text\{in\-loop gain\}\}\-\\underbrace\{\\bar\{g\}^\{\\text\{audit\}\}\_\{t\}\}\_\{\\text\{audited gain\}\},\(1\)where each gain is the improvement over the starting pipeline, signed so that positive means better\. A wideningΔtfaith\\Delta^\{\\text\{faith\}\}\_\{t\}implies that the loop is increasingly optimizing the proxy without commensurate downstream effect;ΔTblind\\Delta^\{\\text\{blind\}\}\_\{T\}is defined analogously\.

### 3\.4DAPS: Diversity\-Aware Proposal Sampling

To mitigate such identified algorithmic mode collapse, we propose theDAPSframework which composes three components that target the diagnosed pathology directly\.

Category\-Coverage Reweighting \(ccr\)\.Letpt​\(c\)p\_\{t\}\(c\)denote the empirical frequency of mechanism categoryccin the lastWWaccepted edits\. When sampling proposals we drawNNcandidates fromπθ\\pi\_\{\\theta\}, classify them on the fly, and re\-weight them byw\(ρ\)=exp\(−logpt\(c\(ρ\)\)/τc\)w\(\\rho\)=\\exp\\big\(\-\\log p\_\{t\}\(c\(\\rho\)\)/\\tau\_\{c\}\\big\)before acceptance\.ccrdoes not alter the executor; it only changes which candidate is presented for execution\. The motivation is that the proposer’s prior over edit*types*is sharply peaked toward optimizer/learning\-rate edits, and acceptance pressure amplifies skew\.

Persistent Edit Memory \(pem\)\.We maintain a first\-in\-first\-out \(FIFO\) buffer of the Sentence\-BERT embeddings of the lastM=200M=200accepted edit descriptions and reject any candidate whose cosine similarity to its nearest neighbor in memory exceedsτm\\tau\_\{m\}\.pemprevents the loop from cycling on near\-duplicate semantics regardless of whether the underlying code differs\. This is the code\-level analog of the memory\-augmented penalty of[Li et al\. \(2026\)](https://arxiv.org/html/2609.00077#bib.bib20), applied to edit descriptions rather than self\-play questions\.

Audit\-Based Validation Gate \(hvg\)\.EveryK=10K=10iterations we computeΔtfaith\\Delta^\{\\text\{faith\}\}\_\{t\}from Eq\.[1](https://arxiv.org/html/2609.00077#S3.E1)\. IfΔtfaith\\Delta^\{\\text\{faith\}\}\_\{t\}exceeds a task\-calibrated thresholdτh\\tau\_\{h\}, the gate reverts𝒫\\mathcal\{P\}to the last checkpoint withΔfaith≤τh\\Delta^\{\\text\{faith\}\}\\leq\\tau\_\{h\}and feeds a structured failure summary back to the proposer\.hvgis the only component that consumes the audit metric; it does so sparingly, through the scalar test of §[3\.3](https://arxiv.org/html/2609.00077#S3.SS3), and is the mechanism by which Goodhart\-style edits are eventually undone\.

The full procedure has been summarized in Algorithm[1](https://arxiv.org/html/2609.00077#alg1)\.

Algorithm 1Diversity\-Aware Proposal Sampling1:Input:pipeline

𝒫0\\mathcal\{P\}\_\{0\}, proposer

πθ\\pi\_\{\\theta\}, executor

ℰ\\mathcal\{E\}, metrics

mopt,mauditm^\{\\text\{opt\}\},m^\{\\text\{audit\}\}
2:Initialize memory

ℳ←∅\\mathcal\{M\}\\leftarrow\\emptyset, history

h←∅h\\leftarrow\\emptyset
3:for

t=1,…,Tt=1,\\ldots,Tdo

4:Sample

NNcandidates

\{\(ρi,di\)\}∼πθ\(⋅∣𝒫t−1,h\)\\\{\(\\rho\_\{i\},d\_\{i\}\)\\\}\\sim\\pi\_\{\\theta\}\(\\cdot\\mid\\mathcal\{P\}\_\{t\-1\},h\)
5:Compute

wi←exp\(−logpt\(c\(ρi\)\)/τc\)w\_\{i\}\\leftarrow\\exp\(\-\\log p\_\{t\}\(c\(\\rho\_\{i\}\)\)/\\tau\_\{c\}\)⊳\\trianglerightccr

6:Drop

ρi\\rho\_\{i\}if

maxe∈ℳ⁡cos⁡\(ϕ⁡\(di\),e\)\>τm\\max\_\{e\\in\\mathcal\{M\}\}\\cos\(\\phi\(d\_\{i\}\),e\)\>\\tau\_\{m\}⊳\\trianglerightpem

7:Choose

ρ⋆←\\rho^\{\\star\}\\leftarrowweighted\-sample remaining candidates by

wiw\_\{i\}
8:

m←ℰ⁡\(𝒫t−1⊕ρ⋆\)m\\leftarrow\\mathcal\{E\}\(\\mathcal\{P\}\_\{t\-1\}\\oplus\\rho^\{\\star\}\)
9:if

R⁡\(m−mopt​\(𝒫t−1\)\)=1R\(m\-m^\{\\text\{opt\}\}\(\\mathcal\{P\}\_\{t\-1\}\)\)=1then

10:

𝒫t←𝒫t−1⊕ρ⋆\\mathcal\{P\}\_\{t\}\\leftarrow\\mathcal\{P\}\_\{t\-1\}\\oplus\\rho^\{\\star\}; add

ϕ⁡\(d⋆\)\\phi\(d^\{\\star\}\)to

ℳ\\mathcal\{M\}
11:else

12:

𝒫t←𝒫t−1\\mathcal\{P\}\_\{t\}\\leftarrow\\mathcal\{P\}\_\{t\-1\}
13:endif

14:if

tmodK=0t\\bmod K=0and

Δtfaith\>τh\\Delta^\{\\text\{faith\}\}\_\{t\}\>\\tau\_\{h\}then⊳\\trianglerighthvg

15:Revert

𝒫t\\mathcal\{P\}\_\{t\}to last faithful checkpoint

16:Append failure summary to

hh
17:endif

18:endfor

## 4Experiments

### 4\.1Experiment Setup

#### 4\.1\.1Tasks and Datasets

We instantiate code\-level ARL on four NLP\-relevant tasks spanning pretraining, post\-training, reasoning, and inference\-time configuration\.

T1: Small\-LM Pretraining\.A GPT\-2\-small style model\([Radford et al\., 2019](https://arxiv.org/html/2609.00077#bib.bib30)\)\(∼\\sim124124M parameters\) trained on a0\.50\.5B\-token subset of OpenWebText for a fixed55\-minute budget per execution, following theautoresearchsetup of[Karpathy \(2026\)](https://arxiv.org/html/2609.00077#bib.bib15)\.moptm^\{\\text\{opt\}\}: in\-distribution validation loss\.mauditm^\{\\text\{audit\}\}: perplexity on LAMBADA\([Paperno et al\., 2016](https://arxiv.org/html/2609.00077#bib.bib28)\)and a held\-out C4\([Raffel et al\., 2020](https://arxiv.org/html/2609.00077#bib.bib31)\)subset\.

T2: Instruction Tuning\.A Llama\-3\.2\-1B base model\([Grattafiori et al\., 2024](https://arxiv.org/html/2609.00077#bib.bib11)\)fine\-tuned on a2020k\-example Alpaca subset\([Taori et al\., 2023](https://arxiv.org/html/2609.00077#bib.bib40)\)via LoRA\.moptm^\{\\text\{opt\}\}: held\-in instruction\-following win\-rate \(AlpacaEval\-style\([Li et al\., 2023](https://arxiv.org/html/2609.00077#bib.bib21)\)against a fixed reference\) on a500500\-example development split\.mauditm^\{\\text\{audit\}\}: MMLU\([Hendrycks et al\., 2021a](https://arxiv.org/html/2609.00077#bib.bib13)\)5\-shot accuracy and the IFEval prompt\-following benchmark\([Zhou et al\., 2023](https://arxiv.org/html/2609.00077#bib.bib45)\)\.

T3: Reasoning Fine\-Tuning\.A Qwen\-2\.5\-1\.5B\([Yang et al\., 2024](https://arxiv.org/html/2609.00077#bib.bib44)\)model fine\-tuned on GSM8K\([Cobbe et al\., 2021](https://arxiv.org/html/2609.00077#bib.bib4)\)training split\.moptm^\{\\text\{opt\}\}: GSM8K dev\-set exact\-match accuracy\.mauditm^\{\\text\{audit\}\}: ARC\-Easy\([Clark et al\., 2018](https://arxiv.org/html/2609.00077#bib.bib3)\)and MATH\-500\([Hendrycks et al\., 2021b](https://arxiv.org/html/2609.00077#bib.bib14)\)subset accuracy\.

T4: Prompt Optimization\.Frozen Llama\-3\.2\-3B prompted on a question\-answering subset, where the autonomous research loop edits a Python DSPy\-style prompt program\([Khattab et al\., 2023](https://arxiv.org/html/2609.00077#bib.bib16)\)\.moptm^\{\\text\{opt\}\}: dev\-set accuracy on a sampled ARC\-Easy split\.mauditm^\{\\text\{audit\}\}: ARC\-Challenge\([Clark et al\., 2018](https://arxiv.org/html/2609.00077#bib.bib3)\)and CommonsenseQA\([Talmor et al\., 2019](https://arxiv.org/html/2609.00077#bib.bib38)\)\.

Themoptm^\{\\text\{opt\}\}andmauditm^\{\\text\{audit\}\}are computed with independent prompts and data partitions\. Here, audit tasks are chosen to be*capability\-overlapping but distribution\-different*relative to in\-loop ones: a genuine capability improvement should transfer, while a metric\-specific overfitting should not\. The blind sets, evaluated once per run and read by no component of the loop, are WikiText\-103 log\-perplexity\([Merity et al\., 2017](https://arxiv.org/html/2609.00077#bib.bib24)\)for T1, win\-rate on500500held\-out Dolly instructions\([Conover et al\., 2023](https://arxiv.org/html/2609.00077#bib.bib5)\)under the same judging protocol for T2, SVAMP accuracy\([Patel et al\., 2021](https://arxiv.org/html/2609.00077#bib.bib29)\)for T3, and OpenBookQA accuracy\([Mihaylov et al\., 2018](https://arxiv.org/html/2609.00077#bib.bib25)\)for T4\.

#### 4\.1\.2Baselines

We compareDAPSagainst eight baselines spanning the major existing strategies\. \(B1\)Vanilla AR: a faithful reimplementation of[Karpathy \(2026\)](https://arxiv.org/html/2609.00077#bib.bib15)with the same proposer LLM\. \(B2\)HiTemp: vanilla autoresearch with proposer temperature raised from0\.70\.7to1\.21\.2, a frequently suggested ad\-hoc fix for low diversity\. \(B3\)R\-Diverse\-A: vanilla autoresearch augmented with the Memory\-Augmented Penalty of[Li et al\. \(2026\)](https://arxiv.org/html/2609.00077#bib.bib20), applied to edit descriptions \(the strongest published mitigation transposed to our setting\)\. \(B4\)Prism\-A: vanilla autoresearch with the semantic cluster\-coverage reward of[Mishra \(2026\)](https://arxiv.org/html/2609.00077#bib.bib26)\. \(B5\)Reflexion: vanilla autoresearch with an additional reflective summary\([Shinn et al\., 2023](https://arxiv.org/html/2609.00077#bib.bib35)\)prepended tohhat every step\. \(B6\)RandSearch: a proposer\-free baseline that samples edits uniformly from a corpus of code modifications harvested from the proposer LLM in iteration11; this isolates the contribution of LLM’s adaptive proposals beyond a static edit distribution\. \(B7\)HO\-EarlyStop: vanilla autoresearch that readsmauditm^\{\\text\{audit\}\}everyK=10K\{=\}10iterations and returns the best\-audit checkpoint\. \(B8\)HO\-Revert: audit\-based reversion alone, that ishvgwithoutccrorpem\. B7 and B8 matchDAPSin audit frequency, compute, and feedback format, whileHiTempandRandSearchare diagnostic controls rather than competitors\. All baselines shareDAPS’s tuning budget \(Appendix[N](https://arxiv.org/html/2609.00077#A14)\)\.

#### 4\.1\.3Evaluation Metrics

For each method/task/seed trajectory, we report: \(i\)In\-loop gaing¯Topt\\bar\{g\}^\{\\text\{opt\}\}\_\{T\}; \(ii\)Audited gaing¯Taudit\\bar\{g\}^\{\\text\{audit\}\}\_\{T\}andblind gaing¯Tblind\\bar\{g\}^\{\\text\{blind\}\}\_\{T\}; \(iii\)Faithfulness ratiosρTaudit=g¯Taudit/g¯Topt\\rho^\{\\text\{audit\}\}\_\{T\}=\\bar\{g\}^\{\\text\{audit\}\}\_\{T\}/\\bar\{g\}^\{\\text\{opt\}\}\_\{T\}andρTblind=g¯Tblind/g¯Topt\\rho^\{\\text\{blind\}\}\_\{T\}=\\bar\{g\}^\{\\text\{blind\}\}\_\{T\}/\\bar\{g\}^\{\\text\{opt\}\}\_\{T\}\(closer to11is better;<1<1indicates Goodharting\), where all gains are signed improvements oriented so that positive means better: on T1 numerator and denominator are both reductions in nats per token \(0\.058/0\.142=0\.410\.058/0\.142=0\.41underVanilla AR\), on T2 to T4 both are percentage points, and ratios are reported only wheng¯Topt\>ϵ\\bar\{g\}^\{\\text\{opt\}\}\_\{T\}\>\\epsilon, as held throughout\. We writeρT\\rho\_\{T\}without a superscript when a statement holds for both; \(iv\)Faithfulness gapΔTfaith\\Delta^\{\\text\{faith\}\}\_\{T\}at end of run \(Eq\.[1](https://arxiv.org/html/2609.00077#S3.E1)\); \(v\) The four diagnostic axes of §[3\.2](https://arxiv.org/html/2609.00077#S3.SS2)evaluated at end of run:surface diversityST\\mathrm\{S\}\_\{T\},semantic cluster countCT\\mathrm\{C\}\_\{T\},mechanism entropyHT\\mathrm\{H\}\_\{T\}, andcorpus similarityVT\\mathrm\{V\}\_\{T\}; \(vi\)Cluster decayΔ​C=\(CT/5−CT\)/CT/5\\Delta\\mathrm\{C\}=\(\\mathrm\{C\}\_\{T/5\}\-\\mathrm\{C\}\_\{T\}\)/\\mathrm\{C\}\_\{T/5\}, the fractional drop in clusters from the early\-warm\-up to the final window, used as a scalar summary of trajectory\-level collapse\. We report mean±\\pms\.d\. over33seeds for the main configurations and aggregate55seeds for T1 \(where per\-trajectory variance is largest\)\.

Table 2:Main results\.In\-loop gaing¯opt\\bar\{g\}^\{\\text\{opt\}\}and audited faithfulness ratioρTaudit\\rho^\{\\text\{audit\}\}\_\{T\}atT=300T=300across four tasks\. Both are oriented so that higher is better:g¯opt\\bar\{g\}^\{\\text\{opt\}\}is the loss reduction in nats per token on T1 and the win\-rate or accuracy gain in percentage points on T2 to T4\.ρTaudit\\rho^\{\\text\{audit\}\}\_\{T\}closer to11indicates that in\-loop gains transfer to the audit evaluation; blind\-set counterparts are in Table[3](https://arxiv.org/html/2609.00077#S4.T3)and Appendix[G](https://arxiv.org/html/2609.00077#A7)\. Subscripts denote±1\\pm 1standard deviation over33seeds \(5 for T1\)\.
#### 4\.1\.4Implementation Details

In experiments, our primary pipeline forks the publicautoresearchrepository\([Karpathy, 2026](https://arxiv.org/html/2609.00077#bib.bib15)\)and adds task adapters, the four diagnostic axes, and theDAPScomponents\. To verify that our findings are not artifacts of a single implementation, we additionally instantiate the same ARL specification on top of Aider\([Gauthier, 2024](https://arxiv.org/html/2609.00077#bib.bib9)\), a popular open\-source code\-editing agent whose proposal mechanism differs substantially fromautoresearch: Aider emits structuredSEARCH/REPLACEblocks committed via git rather than direct unified diffs, manages its own multi\-file repository map and chat history, and decouples “decide what to change” from “apply the change” through separate LLM passes\. We wrap Aider as a drop\-in proposer\-and\-editor module within our loop while keeping the executor, acceptance rule, history summary, and diagnostic instrumentation identical\. The diagnostic axes are computed in exactly the same way on both frameworks: edit descriptions are extracted from Aider’s commit messages \(which it auto\-generates\) and re\-summarized by the same fixed summarizer LLM for consistency with theautoresearchpipeline\. The proposer LLM for our main configuration is Claude Opus 4\.7 withtemperature=0\.7\\text\{temperature\}\{=\}0\.7, sampled through the Anthropic API; robustness ablations additionally use GPT\-5\.2 and Llama\-3\.3\-70B served via vLLM\. For semantic embedding we useall\-mpnet\-base\-v2\([Reimers and Gurevych, 2019](https://arxiv.org/html/2609.00077#bib.bib32)\); clustering uses HDBSCAN withmin\_cluster\_size=5\\text\{min\\\_cluster\\\_size\}=5andmin\_samples=2\\text\{min\\\_samples\}=2\([Campello et al\., 2013](https://arxiv.org/html/2609.00077#bib.bib1)\); UMAP\([McInnes et al\., 2018](https://arxiv.org/html/2609.00077#bib.bib23)\)is used only for visualization\. Hyperparameters ofDAPSare fixed across all tasks and both frameworks atτc=1\.0\\tau\_\{c\}\{=\}1\.0,τm=0\.85\\tau\_\{m\}\{=\}0\.85,M=200M\{=\}200,K=10K\{=\}10, and a task\-calibratedτh\\tau\_\{h\}chosen on the first3030iterations to be the8080th percentile of\|Δfaith\|\|\\Delta^\{\\text\{faith\}\}\|observed underVanilla AR\. Each ARL execution is bounded at55minutes on a single A100; full sweeps used approximately6,2006\{,\}200A100\-hours\.

### 4\.2Main Results and Analysis

We report our main results in Table[2](https://arxiv.org/html/2609.00077#S4.T2)\. Three findings stand out\.First, vanilla autoresearch overfits\.Across all four tasks,ρTaudit\\rho^\{\\text\{audit\}\}\_\{T\}forVanilla ARranges from0\.380\.38to0\.490\.49, meaning roughly half to two\-thirds of the gain claimed inside the loop fails to materialize on held\-out evaluation\. This is the quantitative form of the gap previewed in §[1](https://arxiv.org/html/2609.00077#S1)\.Second, data\-level diversity mitigations transfer only partially\.R\-Diverse\-AandPrism\-A, the strongest published collapse mitigations from the data\-level literature, improve faithfulness \(ρTaudit\\rho^\{\\text\{audit\}\}\_\{T\}rises from≈0\.43\\approx 0\.43to≈0\.58\\approx 0\.58averaged across tasks\) but neither fully closes the gap nor preserves in\-loop performance \(g¯opt\\bar\{g\}^\{\\text\{opt\}\}degrades by3%3\\%to13%13\\%relative toVanilla ARon T2 and T3\)\. This supports our claim that code\-level ARL involves a distinct failure mode requiring code\-level instrumentation\.Third, raising temperature is not a remedy\.HiTempachieves marginally higher faithfulness at marginally lower in\-loop gain, but the effect is well within the standard deviation\. Increased proposer entropy does not, in our experiments, translate to increased*semantic*diversity, a phenomenon we examine in §[4\.4](https://arxiv.org/html/2609.00077#S4.SS4)\.DAPSachieves the highestρTaudit\\rho^\{\\text\{audit\}\}\_\{T\}on all four tasks while remaining within one standard deviation of the bestg¯opt\\bar\{g\}^\{\\text\{opt\}\}\. We attribute the absence of an in\-loop cost to two facts:ccr/pemreject duplicate proposals*before*executor calls, so they do not waste budget; andhvgreverts only when in\-loop and audit metrics diverge, so a faithful trajectory is unaffected\.

Table 3:Faithfulness under matched audit access atT=300T=300, averaged across tasks and seeds, withg¯opt\\bar\{g\}^\{\\text\{opt\}\}normalized byVanilla ARandρaud\\rho^\{\\text\{aud\}\},ρbli\\rho^\{\\text\{bli\}\}abbreviatingρaudit\\rho^\{\\text\{audit\}\},ρblind\\rho^\{\\text\{blind\}\}\. Methods in the upper block never read any external metric; those in the lower block readmauditm^\{\\text\{audit\}\}everyKKiterations under the same evaluation frequency, compute, and feedback format\.
### 4\.3Faithfulness Under Matched Audit Access

Table[3](https://arxiv.org/html/2609.00077#S4.T3)studies whether the faithfulness advantage survives on data the gate never touches, and whether it follows from the method or from audit access that no baseline is granted\. Every configuration loses only0\.030\.03to0\.050\.05between the audit and the blind tier, including the four that read no external metric at all, so that offset reflects benchmark idiosyncrasy rather than leakage through the gate:DAPSkeeps its advantage blind \(0\.740\.74against0\.790\.79audited\), and per task the relative improvement overVanilla ARis83\.7%83\.7\\%blind against81\.6%81\.6\\%audited\. On equal footing, three conclusions follow\.*First*, with zero audit accessccr\+pemalready surpasses both published diversity mitigations on both faithfulness tiers and onΔ​C\\Delta\\mathrm\{C\}at a smaller in\-loop cost\.*Second*, audit access alone reaches at mostρTaudit=0\.67\\rho^\{\\text\{audit\}\}\_\{T\}=0\.67\(0\.620\.62blind\) and barely reduces collapse \(Δ​C≥0\.55\\Delta\\mathrm\{C\}\\geq 0\.55\), so the extra signal explains only part of the advantage\.*Third*, the two ingredients are complementary, as §[3\.4](https://arxiv.org/html/2609.00077#S3.SS4)intends\. Appendix[F](https://arxiv.org/html/2609.00077#A6)indexes the validity, robustness, and scope checks behind these numbers\.

Figure 2:Diagnostic axes over iterations on T2\. Mechanism entropyHt\\mathrm\{H\}\_\{t\}collapses by∼\\sim0\.60\.6nats while surface diversitySt\\mathrm\{S\}\_\{t\}remains flat\. Corpus similarityVt\\mathrm\{V\}\_\{t\}rises, which is consistent with reliance on patterns close to pretraining text; the in\-loop/audit gapΔtfaith\\Delta^\{\\text\{faith\}\}\_\{t\}widens monotonically underVanilla ARand is materially shrunk underDAPS\.
### 4\.4Dissecting the Collapse

Figure[2](https://arxiv.org/html/2609.00077#S4.F2)traces our four diagnostic axes on T2 underVanilla ARandDAPS\. We have three observations as follows\. \(a\)*Surface diversity is a poor warning sign\.*St\\mathrm\{S\}\_\{t\}stays in\[0\.66,0\.68\]\[0\.66,0\.68\]for both methods throughout, whileHt\\mathrm\{H\}\_\{t\}underVanilla ARdrops from1\.761\.76nats att=30t=30to1\.181\.18nats att=300t=300\. Any monitor that watched onlySt\\mathrm\{S\}\_\{t\}would have raised no flag\. \(b\)*Corpus similarity rises with collapse\.*Vt\\mathrm\{V\}\_\{t\}increases from0\.600\.60to0\.720\.72underVanilla AR, so the agent concentrates on edits whose descriptions are increasingly close to commonly published ML text\([Gupta and Pruthi, 2025](https://arxiv.org/html/2609.00077#bib.bib12)\)\. This is consistent with the proposer relying on its prior rather than exploring, although as a descriptive statistic it does not on its own establish retrieval \(§[3\.2](https://arxiv.org/html/2609.00077#S3.SS2)\)\. \(c\)*Faithfulness erodes monotonically\.*Δtfaith\\Delta^\{\\text\{faith\}\}\_\{t\}grows from≈0\.05\\approx 0\.05att=60t=60to≈0\.17\\approx 0\.17att=300t=300underVanilla AR, mirroring the entropy collapse with almost no iteration lag\. UnderDAPS, the gap stabilizes at≈0\.07\\approx 0\.07\. Table[4](https://arxiv.org/html/2609.00077#S4.T4)reports the axis statistics across all tasks and methods\. The pattern is consistent: methods that close the entropy gap \(Prism\-A,DAPS\) also close the faithfulness gap; methods that do not \(HiTemp,Reflexion\) do not\. Surface diversity correlates with neither\.

Table 4:Diagnostic axes atT=300T=300, averaged across tasks and seeds\. Surface diversityST\\mathrm\{S\}\_\{T\}is essentially constant across methods; mechanism entropyHT\\mathrm\{H\}\_\{T\}, cluster decayΔ​C\\Delta\\mathrm\{C\}, and faithfulness gapΔTfaith\\Delta^\{\\text\{faith\}\}\_\{T\}co\-vary tightly\.
### 4\.5Ablation Study

Table 5:Ablation study ofDAPScomponents averaged across tasks\.g¯opt\\bar\{g\}^\{\\text\{opt\}\}is normalized byVanilla AR\. Each component contributes;hvgcontributes most to faithfulness,ccr\+pemcontribute most to cluster decay\.Table[5](https://arxiv.org/html/2609.00077#S4.T5)ablates the threeDAPScomponents\.ccrandpemattack semantic concentration directly and together reduceΔ​C\\Delta\\mathrm\{C\}from0\.680\.68to0\.280\.28\.hvgattacks Goodharting and yields the single largest jump inρTaudit\\rho^\{\\text\{audit\}\}\_\{T\}\(0\.44→0\.670\.44\\to 0\.67\); however, used alone it leaves cluster decay almost untouched \(0\.68→0\.550\.68\\to 0\.55\), because the proposer’s prior is not modified\. The three contributions are sub\-additive: alone they raiseρTaudit\\rho^\{\\text\{audit\}\}\_\{T\}by0\.140\.14,0\.170\.17, and0\.230\.23, which would predict0\.980\.98rather than the observed0\.790\.79, with the largest shortfall for the pairs that includehvg\. The same overlap shows in the in\-loop column, wherehvgalone is the costliest variant \(0\.9530\.953\) while the full system recovers most of that cost, consistent with a broader proposal pool leaving the gate fewer proxy\-only edits to revert\. The full combination achieves the highestρTaudit\\rho^\{\\text\{audit\}\}\_\{T\}at the lowest cluster decay, with in\-loop gain within2%2\\%ofVanilla AR\.

### 4\.6Robustness Across Proposer LLMs

Figure 3:Mechanism entropyHT\\mathrm\{H\}\_\{T\}\(left\) and faithfulness gapΔTfaith\\Delta^\{\\text\{faith\}\}\_\{T\}\(right\) atT=300T\{=\}300on T2, evaluated underVanilla ARandDAPSfor three proposer LLMs\. Error bars show±1\\pm 1s\.d\. over 3 seeds\.Figure[3](https://arxiv.org/html/2609.00077#S4.F3)repeats T2 with three proposer LLMs\. The pattern is uniform: every proposer exhibits algorithmic mode collapse underVanilla AR, andDAPSreduces both mechanism\-entropy collapse and the faithfulness gap for each\. Llama\-3\.3\-70B shows the most severe baseline collapse, consistent with its narrower prior over ML edits, while Claude Opus 4\.7 produces the highest in\-loop gain at every iteration count\. Besides, the relative ranking of methods \(Table[4](https://arxiv.org/html/2609.00077#S4.T4)\) is preserved across proposers, suggesting that our findings are not artifacts of any single model’s idiosyncrasies\.

### 4\.7Robustness Across ARL Frameworks

Table 6:Surface diversityST\\mathrm\{S\}\_\{T\}, faithfulness ratioρTaudit\\rho^\{\\text\{audit\}\}\_\{T\}, mechanism entropyHT\\mathrm\{H\}\_\{T\}, and faithfulness gapΔTfaith\\Delta^\{\\text\{faith\}\}\_\{T\}atT=300T\{=\}300on T2 for the two ARL frameworks \(autoresearchand Aider\), each evaluated under a vanilla baseline and theDAPSvariant\. Subscripts denote±1\\pm 1s\.d\. over 3 seeds\.Sinceautoresearchand Aider differ in how they generate, scope, and commit edits, a reasonable concern is that the collapse pattern we report is an artifact ofautoresearch’s direct\-diff proposer rather than a property of code\-level ARL in general\. To address this, we re\-ran T2 with our Aider\-based loop under bothVanillaandDAPSconfigurations, holding executor, acceptance rule, history summary, and diagnostic instrumentation fixed \(§[4\.1\.4](https://arxiv.org/html/2609.00077#S4.SS1.SSS4)\)\. Results are provided in Table[6](https://arxiv.org/html/2609.00077#S4.T6)\.

We have three observations as follows\.*First*, underVanilla\-Aider, the faithfulness ratio \(ρTaudit=0\.48\\rho^\{\\text\{audit\}\}\_\{T\}=0\.48\) and mechanism entropy \(HT=1\.22\\mathrm\{H\}\_\{T\}=1\.22nats\) matchVanilla ARto within one standard deviation, despite Aider’s edits being structurally different at the diff level\. The surface diversityST\\mathrm\{S\}\_\{T\}is in fact higher under Aider \(0\.740\.74vs\.0\.660\.66\) because its multi\-lineSEARCH/REPLACEblocks span more tokens, yet semantic and mechanism diversity trackautoresearchclosely\. This is exactly the dissociation predicted by §[4\.4](https://arxiv.org/html/2609.00077#S4.SS4): surface differences between frameworks do not translate into differences in what the agent is actually*trying*to do\.*Second*,DAPStransfers without modification:DAPS\-AiderimprovesρTaudit\\rho^\{\\text\{audit\}\}\_\{T\}from0\.480\.48to0\.760\.76and lifts mechanism entropy by0\.430\.43nats, mirroring the effect onautoresearch\.*Third*, the small residual gap betweenDAPS\-AiderandDAPS\(ρTaudit\\rho^\{\\text\{audit\}\}\_\{T\}0\.760\.76vs\.0\.800\.80\) is consistent with Aider’s coarser\-grained edits being slightly harder to deduplicate at the description level\. Overall, algorithmic mode collapse and its mitigation are not framework\-specific\.

### 4\.8Efficiency Analysis

Figure 4:In\-loop gain on T2 overT=300T\{=\}300iterations forVanilla ARandDAPS\. Mean±\\pms\.d\. over 3 seeds; dotted line marks80%80\\%ofVanilla AR’s terminal gain\.Here, we demonstrate thatDAPSalso matchesVanilla AR’s convergence rate, and that its three components add bounded wall\-clock cost\. Figure[4](https://arxiv.org/html/2609.00077#S4.F4)plotsmtoptm^\{\\text\{opt\}\}\_\{t\}over T2’s full trajectory: the two methods are statistically indistinguishable through∼\\sim150150iterations and remain within one standard deviation atT=300T\{=\}300\. The number of iterations to reach80%80\\%ofVanilla AR’s terminal gain is154±8154\_\{\\pm 8\}forVanilla ARand163±9163\_\{\\pm 9\}forDAPS, a difference close to the seed\-to\-seed variance\. Convergence on T1, T3, and T4 follows the same pattern \(Appendix[B](https://arxiv.org/html/2609.00077#A2)\)\. On per\-iteration wall\-clock,ccrreuses each proposer\-emitted edit description through a rule\-based parser,pemperforms one Sentence\-BERT embedding plus a200200\-vector nearest\-neighbor lookup, andhvgamortizes one audit evaluation acrossK=10K\{=\}10iterations\. Aggregated against the55\-minute executor budget,DAPSadds approximately1\.4%1\.4\\%to per\-iteration wall\-clock on T2, with the amortizedhvgcost accounting for the bulk\. Per\-component breakdowns and cross\-task timings are in Appendix[B](https://arxiv.org/html/2609.00077#A2)\.

## 5Conclusion and Future Work

In this paper, we illustrate that code\-level autonomous research loops, despite using executable external metrics, exhibit algorithmic mode collapse: surface edit diversity remains intact while semantic and mechanism\-level diversity progressively concentrate, and in\-loop gains decreasingly transfer to held\-out evaluation\. A four\-axis diagnostic instrument makes the phenomenon measurable, and a lightweight interventionDAPSsubstantially mitigates it without harming optimization speed\. Under a three\-tier protocol separating the in\-loop metric, the audit signal read by the gate, and a blind evaluation no component accesses, the advantage persists on data the loop never influenced\. Future work could examine collapse dynamics under frontier\-scale executors and much longer horizons, extend the diagnosis beyond the preliminary non\-ML loop, and study how multi\-agent loops alter the picture\.

## Limitations

Our study is confined to model and data scales accessible on a single A100 machine; whether algorithmic mode collapse looks the same when proposer and executor models are both at the frontier, or when budgets allow1010k\+\+iterations, remains open, although the1,0001\{,\}000\-iteration run of Appendix[L](https://arxiv.org/html/2609.00077#A12)shows the collapse deepening rather than self\-correcting\. Our headline claims are scoped to code\-level ARLs that edit ML pipelines: the performance\-engineering loop of Appendix[M](https://arxiv.org/html/2609.00077#A13)is a single preliminary probe outside that scope, and our mechanism taxonomy is ML\-centric and would need re\-derivation to study ARL in non\-ML domains \(e\.g\., theorem proving, scientific simulation\), even though the derivation protocol of §[3\.2](https://arxiv.org/html/2609.00077#S3.SS2)is domain\-general\. The audit metric read byhvgis a validation signal rather than an untouched final test, which is why we report blind evaluations as headline numbers; those evaluations, though independent, are themselves benchmarks and may share idiosyncratic biases with the in\-loop metric\. Finally, corpus similarityVT\\mathrm\{V\}\_\{T\}is descriptive and agrees only moderately with human novelty judgements \(Appendix[K](https://arxiv.org/html/2609.00077#A11)\), so it should not be read as direct evidence of retrieval from pretraining\.

## Ethical Considerations

Autonomous research agents that appear to improve themselves but in fact overfit their own evaluations pose a misinformation risk in scientific contexts\. Our diagnostic instrument andDAPSare intended as cautionary tools: they should not be read as endorsements of unrestricted ARL deployment, but as steps toward making such systems more transparent about whether their claimed gains generalize\. All artifacts we plan to release are training\-pipeline edits and analysis scripts; no personal or sensitive data is involved\. We use only public benchmarks under their respective licenses\. The human annotation studies reported in Appendices[I](https://arxiv.org/html/2609.00077#A9)and[K](https://arxiv.org/html/2609.00077#A11)were conducted by volunteer researchers who consented to the use of their labels and who annotated only code\-edit descriptions containing no personal data\.

## References

- Campello et al\. \(2013\)Ricardo JGB Campello, Davoud Moulavi, and Jörg Sander\. 2013\.Density\-based clustering based on hierarchical density estimates\.In*Pacific\-Asia conference on knowledge discovery and data mining*, pages 160–172\. Springer\.
- Christiano et al\. \(2017\)Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei\. 2017\.Deep reinforcement learning from human preferences\.*Advances in neural information processing systems*, 30\.
- Clark et al\. \(2018\)Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord\. 2018\.Think you have solved question answering? try arc, the ai2 reasoning challenge\.*arXiv preprint arXiv:1803\.05457*\.
- Cobbe et al\. \(2021\)Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, and 1 others\. 2021\.Training verifiers to solve math word problems\.*arXiv preprint arXiv:2110\.14168*\.
- Conover et al\. \(2023\)Mike Conover, Matt Hayes, Ankit Mathur, Jianwei Xie, Jun Wan, Sam Shah, Ali Ghodsi, Patrick Wendell, Matei Zaharia, and Reynold Xin\. 2023\.Free dolly: Introducing the world’s first truly open instruction\-tuned llm\.Databricks Blog\.
- Ford \(2004\)John M Ford\. 2004\.Content analysis: An introduction to its methodology\.*Personnel psychology*, 57\(4\):1110\.
- Frey and Dueck \(2007\)Brendan J Frey and Delbert Dueck\. 2007\.Clustering by passing messages between data points\.*science*, 315\(5814\):972–976\.
- Gao et al\. \(2023\)Leo Gao, John Schulman, and Jacob Hilton\. 2023\.Scaling laws for reward model overoptimization\.In*International conference on machine learning*, pages 10835–10866\. PMLR\.
- Gauthier \(2024\)Paul Gauthier\. 2024\.Aider: Ai pair programming in your terminal\.GitHub repository\.[https://github\.com/Aider\-AI/aider](https://github.com/Aider-AI/aider)\.
- Gottweis et al\. \(2025\)Juraj Gottweis, Wei\-Hung Weng, Alexander Daryin, Tao Tu, Anil Palepu, Petar Sirkovic, Artiom Myaskovsky, Felix Weissenberger, Keran Rong, Ryutaro Tanno, and 1 others\. 2025\.Towards an ai co\-scientist\.*arXiv preprint arXiv:2502\.18864*, 2\.
- Grattafiori et al\. \(2024\)Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al\-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, and 1 others\. 2024\.The llama 3 herd of models\.*arXiv preprint arXiv:2407\.21783*\.
- Gupta and Pruthi \(2025\)Tarun Gupta and Danish Pruthi\. 2025\.All that glitters is not novel: Plagiarism in ai generated research\.In*Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics \(Volume 1: Long Papers\)*, pages 25721–25738\.
- Hendrycks et al\. \(2021a\)Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt\. 2021a\.Measuring massive multitask language understanding\.In*International Conference on Learning Representations*\.
- Hendrycks et al\. \(2021b\)Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt\. 2021b\.Measuring mathematical problem solving with the math dataset\.In*Thirty\-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track \(Round 2\)*\.
- Karpathy \(2026\)Andrej Karpathy\. 2026\.autoresearch: Ai agents running research on single\-gpu nanochat training automatically\.GitHub repository\.[https://github\.com/karpathy/autoresearch](https://github.com/karpathy/autoresearch)\.
- Khattab et al\. \(2023\)Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vardhamanan, Saiful Haq, Ashutosh Sharma, Thomas T Joshi, Hanna Moazam, and 1 others\. 2023\.Dspy: Compiling declarative language model calls into self\-improving pipelines\.*arXiv preprint arXiv:2310\.03714*\.
- Kovač et al\. \(2025\)Grgur Kovač, Jérémy Perez, Rémy Portelas, Peter Ford Dominey, and Pierre\-Yves Oudeyer\. 2025\.Recursive training loops in llms: How training data properties modulate distribution shift in generated data?In*Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*, pages 32278–32297\.
- Kozachenko and Leonenko \(1987\)Lyudmyla F Kozachenko and Nikolai N Leonenko\. 1987\.Sample estimate of the entropy of a random vector\.*Problemy Peredachi Informatsii*, 23\(2\):9–16\.
- Landis and Koch \(1977\)J Richard Landis and Gary G Koch\. 1977\.The measurement of observer agreement for categorical data\.*biometrics*, pages 159–174\.
- Li et al\. \(2026\)Gengsheng Li, Jinghan He, Shijie Wang, Dan Zhang, Ruiqi Liu, Renrui Zhang, Zijun Yao, Junfeng Fang, Haiyun Guo, and Jinqiao Wang\. 2026\.R\-diverse: Mitigating diversity illusion in self\-play llm training\.*arXiv preprint arXiv:2602\.13103*\.
- Li et al\. \(2023\)Xuechen Li, Tianyi Zhang, Yann Dubois, Rohan Taori, Ishaan Gulrajani, Carlos Guestrin, Percy Liang, and Tatsunori B Hashimoto\. 2023\.Alpacaeval: An automatic evaluator of instruction\-following models\.
- Lu et al\. \(2024\)Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob Foerster, Jeff Clune, and David Ha\. 2024\.The ai scientist: Towards fully automated open\-ended scientific discovery\.*arXiv preprint arXiv:2408\.06292*\.
- McInnes et al\. \(2018\)Leland McInnes, John Healy, and James Melville\. 2018\.Umap: Uniform manifold approximation and projection for dimension reduction\.*arXiv preprint arXiv:1802\.03426*\.
- Merity et al\. \(2017\)Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher\. 2017\.Pointer sentinel mixture models\.In*International Conference on Learning Representations*\.
- Mihaylov et al\. \(2018\)Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal\. 2018\.Can a suit of armor conduct electricity? a new dataset for open book question answering\.In*Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*, pages 2381–2391\.
- Mishra \(2026\)Vaibhav Mishra\. 2026\.Preventing curriculum collapse in self\-evolving reasoning systems\.*arXiv preprint arXiv:2603\.13309*\.
- Pan et al\. \(2022\)Alexander Pan, Kush Bhatia, and Jacob Steinhardt\. 2022\.The effects of reward misspecification: Mapping and mitigating misaligned models\.In*International Conference on Learning Representations*\.
- Paperno et al\. \(2016\)Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, Ngoc\-Quan Pham, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda, and Raquel Fernández\. 2016\.The lambada dataset: Word prediction requiring a broad discourse context\.In*Proceedings of the 54th annual meeting of the association for computational linguistics \(volume 1: Long papers\)*, pages 1525–1534\.
- Patel et al\. \(2021\)Arkil Patel, Satwik Bhattamishra, and Navin Goyal\. 2021\.Are nlp models really able to solve simple math word problems?In*Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*, pages 2080–2094\.
- Radford et al\. \(2019\)Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, and 1 others\. 2019\.Language models are unsupervised multitask learners\.*OpenAI blog*, 1\(8\):9\.
- Raffel et al\. \(2020\)Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu\. 2020\.Exploring the limits of transfer learning with a unified text\-to\-text transformer\.*Journal of machine learning research*, 21\(140\):1–67\.
- Reimers and Gurevych \(2019\)Nils Reimers and Iryna Gurevych\. 2019\.Sentence\-bert: Sentence embeddings using siamese bert\-networks\.In*Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing \(EMNLP\-IJCNLP\)*, pages 3982–3992\.
- Schmidgall et al\. \(2025\)Samuel Schmidgall, Yusheng Su, Ze Wang, Ximeng Sun, Jialian Wu, Xiaodong Yu, Jiang Liu, Michael Moor, Zicheng Liu, and Emad Barsoum\. 2025\.Agent laboratory: Using llm agents as research assistants\.*Findings of the Association for Computational Linguistics: EMNLP 2025*, pages 5977–6043\.
- Shannon \(1948\)Claude Elwood Shannon\. 1948\.A mathematical theory of communication\.*The Bell system technical journal*, 27\(3\):379–423\.
- Shinn et al\. \(2023\)Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao\. 2023\.Reflexion: Language agents with verbal reinforcement learning\.*Advances in neural information processing systems*, 36:8634–8652\.
- Shumailov et al\. \(2024\)Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Nicolas Papernot, Ross Anderson, and Yarin Gal\. 2024\.Ai models collapse when trained on recursively generated data\.*Nature*, 631\(8022\):755–759\.
- Skalse et al\. \(2022\)Joar Skalse, Nikolaus Howe, Dmitrii Krasheninnikov, and David Krueger\. 2022\.Defining and characterizing reward gaming\.*Advances in neural information processing systems*, 35:9460–9471\.
- Talmor et al\. \(2019\)Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant\. 2019\.Commonsenseqa: A question answering challenge targeting commonsense knowledge\.In*Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 \(Long and Short Papers\)*, pages 4149–4158\.
- Tang et al\. \(2026\)Jiabin Tang, Lianghao Xia, Zhonghang Li, and Chao Huang\. 2026\.Ai\-researcher: Autonomous scientific innovation\.*Advances in Neural Information Processing Systems*, 38:9481–9520\.
- Taori et al\. \(2023\)Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B Hashimoto\. 2023\.Stanford alpaca: An instruction\-following llama model\.
- Trehan and Chopra \(2026\)Dhruv Trehan and Paras Chopra\. 2026\.Why llms aren’t scientists yet: Lessons from four autonomous research attempts\.*arXiv preprint arXiv:2601\.03315*\.
- Walker \(2026\)Ry Walker\. 2026\.Autoresearch tools\.Online\.[https://rywalker\.com/research/autoresearch\-tools](https://rywalker.com/research/autoresearch-tools)\.
- Xiong et al\. \(2026\)Lei Xiong, Kun Luo, Ziyi Xia, Wenbo Zhang, Jin\-Ge Yao, Zheng Liu, Jingying Shao, Jianlyu Chen, Hongjin Qian, Xi Yang, and 1 others\. 2026\.Autoresearchbench: Benchmarking ai agents on complex scientific literature discovery\.*arXiv preprint arXiv:2604\.25256*\.
- Yang et al\. \(2024\)An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, and 1 others\. 2024\.Qwen2\.5 technical report\.*arXiv preprint arXiv:2412\.15115*\.
- Zhou et al\. \(2023\)Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, and Le Hou\. 2023\.Instruction\-following evaluation for large language models\.*arXiv preprint arXiv:2311\.07911*\.
- Zhu and Xie \(2026\)Bingze Zhu and Yubo Xie\. 2026\.Countering model collapse in iterative self\-training via dynamic center\-edge sampling\.*Electronics*, 15\(4\):869\.

## Appendix AA Conceptual Model of Algorithmic Mode Collapse

We sketch a conceptual account that predicts when and why a code\-level autonomous research loop should undergo algorithmic mode collapse, framing the four diagnostic axes \(§[3\.2](https://arxiv.org/html/2609.00077#S3.SS2)\) and theDAPScomponents \(§[3\.4](https://arxiv.org/html/2609.00077#S3.SS4)\) as instruments for testing its predictions\.

Letπθ\(ρ,c∣𝒫,h\)\\pi\_\{\\theta\}\(\\rho,c\\mid\\mathcal\{P\},h\)denote the proposer’s joint distribution over patchesρ\\rhoand their mechanism categoriescc, conditioned on the current pipeline𝒫\\mathcal\{P\}and historyhh\. Three structural facts about this distribution drive the dynamics\.

\(F1\) Non\-uniform prior over mechanism categories\.Pretraining and instruction data over\-represent some ML edit types: learning\-rate adjustment, optimizer hyperparameter changes, simple regularization tweaks\. Meanwhile, they under\-represent others, such as custom kernels, packed\-sequence handling, or idiosyncratic loss formulations\. Marginalizingπθ\\pi\_\{\\theta\}over patches yields a heavily peaked categoricalπθ​\(c∣𝒫\)\\pi\_\{\\theta\}\(c\\mid\\mathcal\{P\}\)\.

\(F2\) Category\-dependent acceptance rate\.The acceptance ruleRRretains an edit only when its measured gain exceeds a noise floorϵ\\epsilon\. Different categories have systematically different probabilities of producing edits with reliable signal aboveϵ\\epsilon: small\-step micro\-tunings \(scheduling, regularization\) movemoptm^\{\\text\{opt\}\}by small but consistent amounts, while structural edits \(architecture, loss reformulation\) move it more in expectation but with high variance and frequent regressions\. Writingα⁡\(c\)\\alpha\(c\)for the per\-category acceptance rate, the empirical distribution of*accepted*edits is proportional toπθ​\(c\)⋅α​\(c\)\\pi\_\{\\theta\}\(c\)\\cdot\\alpha\(c\), already a sharpening ofπθ​\(c\)\\pi\_\{\\theta\}\(c\)toward low\-variance categories\.

\(F3\) Self\-imitation through history conditioning\.Code\-level autonomous research loops typically include accepted edits inhh\. The conditionalπθ​\(c∣𝒫,ht\)\\pi\_\{\\theta\}\(c\\mid\\mathcal\{P\},h\_\{t\}\)then drifts toward the empirical distribution of past acceptances, a positive\-feedback loop that further concentrates probability mass on theπθ​\(c\)⋅α​\(c\)\\pi\_\{\\theta\}\(c\)\\cdot\\alpha\(c\)\-favoured corner\.

Consequence\.Iterating \(F1\) to \(F3\), the system converges on a narrow subset of mechanism categories, those jointly favoured by the proposer’s prior and the acceptance noise floor,*independently of whether those categories contain the largest true improvements*\. Inside the loop this manifests as continued growth ofmoptm^\{\\text\{opt\}\}through increasingly fine\-grained edits within a shrinking category set\. Outside the loop, the audit and blind metrics improve only insofar as the favoured categories happen to contain genuine capability\-affecting edits, a property the dynamics does not select for\. The faithfulness gapΔtfaith\\Delta^\{\\text\{faith\}\}\_\{t\}\(Eq\.[1](https://arxiv.org/html/2609.00077#S3.E1)\) therefore widens monotonically\.

Testable predictions\.The account yields six predictions that organize the empirical study:

- P1\.Surface diversitySt\\mathrm\{S\}\_\{t\}is largely orthogonal to collapse, sinceπθ​\(c\)\\pi\_\{\\theta\}\(c\)can sharpen without lexical repetition of diffs\.
- P2\.Proposers whose pretraining yields broader categorical priors should collapse later and less deeply\.
- P3\.Raising proposer sampling temperature redistributes mass within categories but does not alterπθ​\(c\)\\pi\_\{\\theta\}\(c\), and should therefore not arrest collapse\.
- P4\.Memory\-based deduplication of recently accepted edits prevents semantic\-cluster cycling but leavesπθ​\(c\)\\pi\_\{\\theta\}\(c\)untouched; it should reduceΔ​C\\Delta\\mathrm\{C\}only modestly\.
- P5\.Interventions that directly re\-weight the effectiveπθ​\(c\)\\pi\_\{\\theta\}\(c\), category\-coverage incentives, attack \(F1\) at its root and should yield larger reductions inΔ​C\\Delta\\mathrm\{C\}\.
- P6\.Held\-out audits are the only mechanism that can undo edits that exploit the gap between the in\-loop and the audited metric, since by construction the gap is invisible inside the loop\.

The four diagnostic axes operationalize the antecedent of each prediction:St\\mathrm\{S\}\_\{t\}for P1,Ht\\mathrm\{H\}\_\{t\}together withCt\\mathrm\{C\}\_\{t\}andVt\\mathrm\{V\}\_\{t\}for P2 to P5, andΔtfaith\\Delta^\{\\text\{faith\}\}\_\{t\}for P6\. The threeDAPScomponents correspond directly:pemaddresses \(P4\),ccraddresses \(P5\), andhvgaddresses \(P6\)\. The absence of an analogousDAPScomponent for \(P3\) is itself a prediction of the model\. That is, raising temperature is not a designed intervention because the account claims it would not help\. The longer\-horizon run of Appendix[L](https://arxiv.org/html/2609.00077#A12)is a further test: because \(F1\) to \(F3\) compound with iteration count, the account predicts deeper rather than self\-correcting collapse, which is what we observe\.

## Appendix BEfficiency: Per\-Component Overhead and Cross\-Task Convergence

Table 7:Per\-iteration wall\-clock decomposition on T2 with Claude Opus 4\.7 as proposer and a single A100 executor\. Times averaged over a300300\-iteration run\.Table[7](https://arxiv.org/html/2609.00077#A2.T7)reports the per\-iteration timing decomposition on T2\.hvgdominates theDAPSoverhead at∼\\sim3\.53\.5seconds per iteration \(amortized3535\-second audit evaluations everyK=10K\{=\}10iterations\);ccrandpemare negligible\. The single blind evaluation per run adds3535seconds once, i\.e\.0\.02%0\.02\\%of a300300\-iteration budget\. On the other three tasks, the total overhead is similar in absolute terms but varies modestly as a percentage with audit evaluation length:1\.4%1\.4\\%on T1 \(validation perplexity over a0\.50\.5B\-token shard takes longest\),1\.2%1\.2\\%on T3, and0\.7%0\.7\\%on T4\.

Convergence trajectories on T1, T3, and T4 mirror Figure[4](https://arxiv.org/html/2609.00077#S4.F4):DAPSandVanilla ARare statistically indistinguishable through the first∼\\sim150150iterations and remain within one standard deviation of each other atT=300T\{=\}300\. Per\-task scalar summaries \(iterations to reach80%80\\%ofVanilla AR’s terminal gain\) are187±14187\_\{\\pm 14\}vs192±16192\_\{\\pm 16\}on T1,108±9108\_\{\\pm 9\}vs109±10109\_\{\\pm 10\}on T3, and96±796\_\{\\pm 7\}vs98±898\_\{\\pm 8\}on T4\.

## Appendix CHyperparameter Sensitivity

Table[8](https://arxiv.org/html/2609.00077#A3.T8)reports the sensitivity ofDAPSto each of its hyperparameters, with the remaining values fixed at the main\-experiment defaults and the proposer fixed to Claude Opus 4\.7\. All rows are averaged over33seeds on T2; per\-row standard deviations are below0\.050\.05onρTaudit\\rho^\{\\text\{audit\}\}\_\{T\}and below1\.51\.5ong¯Topt\\bar\{g\}^\{\\text\{opt\}\}\_\{T\}\. Defaults are highlighted in bold\.

Table 8:Hyperparameter sensitivity ofDAPSon T2\. Defaults used in the main experiments are bold\.Five qualitative observations on Table[8](https://arxiv.org/html/2609.00077#A3.T8)guide hyperparameter selection in practice\.*First*,τm\\tau\_\{m\}exhibits a sharp failure mode at0\.950\.95:pemrejects almost no candidates andρTaudit\\rho^\{\\text\{audit\}\}\_\{T\}collapses back toward theVanilla ARvalue \(0\.460\.46\), confirming that semantic deduplication carries most of the diversity recovery rather than the other two components alone\.*Second*,τm=0\.75\\tau\_\{m\}\{=\}0\.75damages in\-loop gain by starving the proposer \(too many candidates rejected before reaching the executor\) without commensurately improving faithfulness\.*Third*,MMis comparatively forgiving in the central range, butM=50M\{=\}50allows semantic cycling to re\-emerge within aW=20W\{=\}20window andM=500M\{=\}500begins to over\-suppress legitimate revisits of previously useful edit categories\.*Fourth*,τc\\tau\_\{c\}is essentially flat in the range tested, since the category\-coverage reweighting acts as a soft prior on rare categories whose exact temperature matters little\.*Fifth*,τh\\tau\_\{h\}andKKtrade in\-loop gain against faithfulness in opposite directions: aggressive auditing \(τh=70\\tau\_\{h\}\{=\}70th,K=5K\{=\}5\) slightly raisesρTaudit\\rho^\{\\text\{audit\}\}\_\{T\}at the cost ofg¯Topt\\bar\{g\}^\{\\text\{opt\}\}\_\{T\}, while loose auditing \(τh=90\\tau\_\{h\}\{=\}90th,K=20K\{=\}20\) preserves in\-loop gain but lets Goodhart edits accumulate between audits\. The chosen defaults sit near the knee of both trade\-offs\.

## Appendix DQualitative Analysis of Collapse

Inspecting the accepted\-edit logs reveals a recurring pattern underVanilla AR\. On T1, the first∼\\sim5050iterations show a broad mix of optimizer, architectural, and data\-mixing edits\. By iteration200200,74%74\\%of accepted edits modify the learning\-rate schedule or AdamW hyperparameters; many are micro\-tunings \(e\.g\., warmup steps200→220200\\to 220,β2\\beta\_\{2\}0\.95→0\.960\.95\\to 0\.96\) that nudge the in\-loop validation loss without affecting LAMBADA or C4 perplexity\. On T3, a striking61%61\\%of late\-stage edits are variants of “increase chain\-of\-thought temperature” or “add an arithmetic\-only loss term”; audited ARC\-Easy accuracy is flat under these\. UnderDAPS, the edit stream remains qualitatively heterogeneous through iteration300300: representative late\-stage edits on T1 include “swap RMSNorm for LayerNorm with re\-tunedϵ\\epsilon,” “insert a curriculum filter on token\-length,” and “modify attention\-mask handling for packed sequences\.” These are not necessarily better edits, but they sample the edit space rather than concentrating on a single mode\.

## Appendix EQualitative Edit Examples

To complement the quantitative collapse signals reported in §[4\.4](https://arxiv.org/html/2609.00077#S4.SS4), we present representative accepted\-edit diffs sampled from the T1 logs \(single\-GPUnanochat\-style pretraining on theautoresearchframework of[Karpathy \(2026\)](https://arxiv.org/html/2609.00077#bib.bib15), where the agent is permitted to modify onlytrain\.py\)\. Hunk headers report iteration index and the mechanism category assigned by the procedure of §[3\.2](https://arxiv.org/html/2609.00077#S3.SS2)\. Diffs are paraphrased and trimmed to a few lines of relevant context for legibility\. The full version of verbatim logs will be released\.

### E\.1Early\-Phase Diversity UnderVanilla AR

In the first∼\\sim6060iterations the proposer explores a broad set of mechanism categories\. The four examples below come from a single seed ofVanilla ARon T1 and span four distinct categories of the taxonomy in Table[1](https://arxiv.org/html/2609.00077#S3.T1)\.

optimizer=torch\.optim\.AdamW\(

model\.parameters\(\),

weight\_decay=0\.1,fused=True,

\)

classBlock\(nn\.Module\):

def\_\_init\_\_\(self,config\):

super\(\)\.\_\_init\_\_\(\)

self\.c\_proj=nn\.Linear\(4\*n\_embd,n\_embd,bias=False\)

### E\.2Late\-Phase Collapse UnderVanilla AR

The five edits below come from the same trajectory between iterations250250and290290\. All five fall into the*optimizer*or*scheduling*category despite editing different identifiers on different lines oftrain\.py\. This is the qualitative signature underlying the entropy collapseH300=1\.18\\mathrm\{H\}\_\{300\}\{=\}1\.18nats reported in Table[4](https://arxiv.org/html/2609.00077#S4.T4): surface diversity is preserved while mechanism diversity is not\.

lr\_decay\_iters=900

min\_lr=3e

weight\_decay=0\.1,fused=True,

optimizer=torch\.optim\.AdamW\(

model\.parameters\(\),

\)

### E\.3Late\-Phase Diversity UnderDAPS

The five edits below come from aDAPStrajectory on the same task, sampled in the same iteration window \(250250to290290\)\. They span five distinct mechanism categories\.CcrandPemdo not prohibit any category; they re\-weight rare categories upward and suppress near\-duplicates of recently accepted edits\. Scheduling edits still appear \(e\.g\., iteration290290\) but no longer dominate the window\.

defstream\_batches\(tokens,block\_size\):

yieldpack\_into\_blocks\(tokens,block\_size\)

classMLP\(nn\.Module\):

defforward\(self,x\):

self\.c\_proj=nn\.Linear\(4\*n\_embd,n\_embd,bias=False\)

### E\.4Connection to the Diagnostic Axes

The contrast between Appendices[E\.2](https://arxiv.org/html/2609.00077#A5.SS2)and[E\.3](https://arxiv.org/html/2609.00077#A5.SS3)illustrates each axis introduced in §[3\.2](https://arxiv.org/html/2609.00077#S3.SS2): surface diversitySt\\mathrm\{S\}\_\{t\}is similar in both panels \(lines edited, tokens touched, and structural shape of the hunks are comparable\); semantic cluster countCt\\mathrm\{C\}\_\{t\}and mechanism entropyHt\\mathrm\{H\}\_\{t\}are clearly lower underVanilla AR; and corpus similarityVt\\mathrm\{V\}\_\{t\}is visibly higher underVanilla ARsince LR\-schedule micro\-tunings closely match the most common edit pattern in publicly available ML training repositories\. We selected the windows above to be representative rather than extremal\. The complete logs containing the full trajectories from which these hunks were sampled will also be released\.

## Appendix FSummary of Validity, Robustness, and Scope Checks

Table[9](https://arxiv.org/html/2609.00077#A6.T9)indexes the checks on which the claims of §[4\.2](https://arxiv.org/html/2609.00077#S4.SS2)and §[4\.3](https://arxiv.org/html/2609.00077#S4.SS3)rest\. Each row names a threat to those claims, the check that addresses it, and the appendix that reports the outcome; the numbers appear only in the referenced appendix and are not restated here\. Across every check the direction of the collapse and the orderingVanilla AR<<diversity baselines<<DAPSare preserved\. On this basis we scope the headline claims to ARLs that edit ML pipelines at single\-GPU executor scale over horizons up to1,0001\{,\}000iterations, and treat evidence outside that setting as preliminary\.

Table 9:Index of the validity, robustness, and scope checks\. The right column gives the appendix in which each result is reported\.
## Appendix GThree\-Tier Evaluation Protocol: Roles, Per\-Task Results, and Absolute Values

This appendix specifies the information flow among the three evaluations of §[3\.3](https://arxiv.org/html/2609.00077#S3.SS3)and reports the results that Table[3](https://arxiv.org/html/2609.00077#S4.T3)summarizes\.

Roles and partitions\.Table[10](https://arxiv.org/html/2609.00077#A7.T10)lists the exact dataset or partition used for each role and task\. Onlymoptm^\{\\text\{opt\}\}is visible to the proposer\.mauditm^\{\\text\{audit\}\}is read byhvgonce everyK=10K\{=\}10iterations and by the calibration ofτh\\tau\_\{h\}over the first3030iterations, in both cases through a scalar comparison; the proposer never observes audit values, per\-example outcomes, or the identity of audit examples, and the failure notice appended tohhis templated and value\-free\.mblindm^\{\\text\{blind\}\}is evaluated once, after the run terminates, by a separate offline script that has no channel back into the loop\. Baselines B1 to B6 consume no audit signal, so for them bothρTaudit\\rho^\{\\text\{audit\}\}\_\{T\}andρTblind\\rho^\{\\text\{blind\}\}\_\{T\}are pure post\-hoc evaluations; B7, B8, andDAPSconsume the audit signal at the same cadence, cost, and feedback format\.

Table 10:Datasets and partitions used for the three evaluation roles\. All three are disjoint within a task\.Per\-task faithfulness\.Table[11](https://arxiv.org/html/2609.00077#A7.T11)givesρTaudit\\rho^\{\\text\{audit\}\}\_\{T\}andρTblind\\rho^\{\\text\{blind\}\}\_\{T\}per task atT=300T\{=\}300over33seeds\.DAPSretains its advantage on sets it has never influenced, andVanilla AR, which consumes no audit signal, shows an audit\-to\-blind drop of comparable size, so the drop is attributable to benchmark idiosyncrasy rather than to leakage through the gate\. The gate fires2\.7±0\.92\.7\\pm 0\.9times per300300\-iteration run\.

Table 11:Audited and blind faithfulness ratios atT=300T\{=\}300, mean over33seeds\. The audit columns reproduce Table[2](https://arxiv.org/html/2609.00077#S4.T2); the blind columns are new\.Absolute values\.Table[12](https://arxiv.org/html/2609.00077#A7.T12)reports absolute audit\-metric values so that the practical magnitude of the movements can be judged directly\. For T1 these correspond to log\-perplexity reductions of0\.0580\.058nats \(Vanilla AR\) and0\.1080\.108nats \(DAPS\) against in\-loop loss reductions of0\.1420\.142and0\.1390\.139, reproducing the T1 ratios of Table[2](https://arxiv.org/html/2609.00077#S4.T2)\. In\-loop absolutes follow the same pattern: T2 dev win\-rate rises from38\.438\.4to47\.347\.3\(Vanilla AR\) and47\.247\.2\(DAPS\), and T3 GSM8K dev exact match from31\.231\.2to42\.942\.9and42\.742\.7\.

Table 12:Absolute audit\-metric values\. Thet=0t\{=\}0column is a single evaluation of the shared initial pipeline; the other two columns are means over33seeds atT=300T\{=\}300\.
## Appendix HConstruct\-Validity Controls

To checkρT\\rho\_\{T\}andΔfaith\\Delta^\{\\text\{faith\}\}measure Goodharting rather than benchmark mismatch, we ran two control sets whose ground truth is known by construction\.

Positive controls\.We curated1212known\-good edits from published recipes, including the nanoGPT speedrun lineage and open post\-training changelogs: rotary position embeddings, a SwiGLU MLP, fused optimizer kernels that raise tokens processed per fixed budget, sequence\-length filtering, and corrected initialization scaling, among others\. Each was applied in isolation to the T1 and T3 starting pipelines\.

Negative controls\.We constructed88deliberately proxy\-only edits: dev\-shard\-specific example ordering on T1, judge\-phrasing tweaks on T2, and dev\-tuned answer\-format templates on T4\.

Table 13:Per\-edit faithfulness ratio for the two control sets, measured on the corresponding audit metrics\.Table[13](https://arxiv.org/html/2609.00077#A8.T13)shows that under our task pairsρ\\rhoseparates genuine improvement \(ρ≈1\\rho\\approx 1\) from proxy\-only improvement \(ρ≈0\\rho\\approx 0\), which is exactly the construct the main claims require\. Two further observations argue against benign distribution shift as an explanation ofρT<1\\rho\_\{T\}<1inside the loop\. First,RandSearchshares the identical task pairs but selects edits non\-adaptively and attains0\.740\.74to0\.810\.81\(Table[2](https://arxiv.org/html/2609.00077#S4.T2)\), so the pairs themselves support near\-full transfer and the lowρT\\rho\_\{T\}underVanilla ARis induced by adaptive optimization\. Second, a static mismatch would produce a constant offset, whereasΔtfaith\\Delta^\{\\text\{faith\}\}\_\{t\}widens monotonically within a fixed task pair \(Figure[2](https://arxiv.org/html/2609.00077#S4.F2)\)\. Finally, each task carries two independent audit components; reportingρ\\rhoper component, the orderingVanilla AR<<diversity baselines<<DAPSholds on all88components individually \(Vanilla ARrange0\.330\.33to0\.520\.52;DAPS0\.690\.69to0\.850\.85\) and on the44blind sets of Appendix[G](https://arxiv.org/html/2609.00077#A7), i\.e\. on1212independent evaluations in total\.

## Appendix IRobustness of the Diagnostic Pipeline

The collapse signal should not hinge on any single measurement choice\. We therefore recomputed cluster decayΔ​C\\Delta\\mathrm\{C\}on T2 under systematic variations of the summarizer, the embedding model, and the clustering algorithm \(Table[14](https://arxiv.org/html/2609.00077#A9.T14)\)\. Per\-windowCt\\mathrm\{C\}\_\{t\}trajectories correlate at Spearman\>0\.93\>0\.93across all variants, and theVanilla ARagainstDAPSgap is preserved in every row\.

Table 14:Cluster decay on T2 forVanilla AR, withDAPSin parentheses\.Human labels\.Three annotators labelled300300sampled edits with the categories of Table[1](https://arxiv.org/html/2609.00077#S3.T1)\(majority vote;κ=0\.81\\kappa=0\.81against our rule\-based parser, in line with the parser\-against\-LLMκ=0\.84\\kappa=0\.84of §[3\.2](https://arxiv.org/html/2609.00077#S3.SS2)\)\. Mechanism entropy computed from the human labels drops from1\.721\.72to1\.211\.21nats underVanilla AR, closely tracking the parser\-based1\.761\.76to1\.181\.18of Figure[2](https://arxiv.org/html/2609.00077#S4.F2)\.

A measurement with no LLM in the path\.An AST\-based rule detector applied directly to the raw code diffs reproduces the Appendix[D](https://arxiv.org/html/2609.00077#A4)statistic:71%71\\%of late\-stage T1 edits touch optimizer or learning\-rate\-schedule code, against74%74\\%for the parser\-based count\. Independently,Δtfaith\\Delta^\{\\text\{faith\}\}\_\{t\}uses only executed metric values, with no summarizer, embedding, clustering, or taxonomy anywhere in its computation, and it widens exactly whenCt\\mathrm\{C\}\_\{t\}andHt\\mathrm\{H\}\_\{t\}collapse \(Figure[2](https://arxiv.org/html/2609.00077#S4.F2), Table[4](https://arxiv.org/html/2609.00077#S4.T4)\)\.

## Appendix JTaxonomy Variants and a Taxonomy\-Free Estimate

A hand\-designed taxonomy could in principle place category boundaries so as to manufacture the entropy drop, even though ours was frozen before our runs \(§[3\.2](https://arxiv.org/html/2609.00077#S3.SS2)\)\. We therefore recomputedHt\\mathrm\{H\}\_\{t\}on T2 under four alternative taxonomies\.*Coarse*: four super\-categories merging Table[1](https://arxiv.org/html/2609.00077#S3.T1)\(Optimization==Optimizer\+\+Scheduling; Model==Architecture\+\+Numerical\+\+Decoding; Data and Objective==Data\+\+Loss\+\+Regularization; Other\)\.*Fine*:1717subcategories obtained by re\-clustering the pilot with a stricter merge criterion\.*Random*:1010taxonomies of nine categories each, obtained by randomly re\-partitioning the1717subcategories\.*Human*: the300300human\-labelled edits of Appendix[I](https://arxiv.org/html/2609.00077#A9)\.

Table 15:Mechanism\-entropy drop fromt=30t\{=\}30tot=300t\{=\}300on T2, in nats\.Table[15](https://arxiv.org/html/2609.00077#A10.T15)shows that the collapse and its mitigation are invariant to granularity and to random boundary placement\. Two taxonomy\-free measurements corroborate this: the semantic cluster countCt\\mathrm\{C\}\_\{t\}already reported in the main paper, and a Kozachenko\-Leonenkokk\-nearest\-neighbour differential\-entropy estimate\([Kozachenko and Leonenko, 1987](https://arxiv.org/html/2609.00077#bib.bib18)\)computed directly on the description embeddings, which drops by0\.440\.44nats underVanilla ARand0\.070\.07nats underDAPS\.

## Appendix KHuman Validation of Corpus Similarity

Corpus similarityVt\\mathrm\{V\}\_\{t\}is a descriptive statistic \(§[3\.2](https://arxiv.org/html/2609.00077#S3.SS2)\), and this appendix quantifies how well it tracks human judgement\. We sampled120120edit descriptions stratified byVt\\mathrm\{V\}\_\{t\}quartile across tasks and asked three NLP researchers, blind toVt\\mathrm\{V\}\_\{t\}and to the generating method, to rate each on a11to55scale for novelty relative to common ML practice \(inter\-rater Krippendorffα=0\.61\\alpha=0\.61;[Ford, 2004](https://arxiv.org/html/2609.00077#bib.bib6)\)\. The Spearman correlation betweenVt\\mathrm\{V\}\_\{t\}and mean human novelty is−0\.47\-0\.47\(bootstrap95%95\\%CI−0\.60\-0\.60to−0\.31\-0\.31\), i\.e\. moderate validity in the expected direction\. We additionally tested wording sensitivity by re\-summarizing every description with a different LLM, which changesVt\\mathrm\{V\}\_\{t\}by only0\.0210\.021on average, so the statistic is not dominated by one summarizer’s phrasing\. Together these results support keepingVt\\mathrm\{V\}\_\{t\}as a secondary axis reported with the caveats stated in §[3\.2](https://arxiv.org/html/2609.00077#S3.SS2), and not as evidence of retrieval\.

## Appendix LHorizon and Target\-Scale Extensions

Table[16](https://arxiv.org/html/2609.00077#A12.T16)extends T2 along the two axes flagged in the Limitations section, with one seed per new configuration\. Over the longest horizon our budget allows, collapse deepens rather than self\-corrects, which is what the conceptual model of Appendix[A](https://arxiv.org/html/2609.00077#A1)predicts, since the feedback mechanisms \(F1\) to \(F3\) compound with iteration count;DAPSremains stable over the same horizon\. A first step up in target\-model scale reproduces the11B pattern\. Note that the main study already includes frontier\-scale proposers \(§[4\.6](https://arxiv.org/html/2609.00077#S4.SS6)\), so the axes that remain open are executor and target scale, and horizons beyond1,0001\{,\}000iterations\.

Table 16:Horizon and target\-scale extensions on T2\. The first row is reproduced from Tables[2](https://arxiv.org/html/2609.00077#S4.T2)and[6](https://arxiv.org/html/2609.00077#S4.T6); the other rows use one seed each\.
## Appendix MA Preliminary Non\-ML Autonomous Research Loop

Our headline claims concern ARLs that edit ML pipelines\. As a first probe outside that scope, we built a performance\-engineering loop in which the agent edits a Python log\-analytics pipeline to minimize wall\-clock runtime, subject to exact\-output correctness checks\. Heremoptm^\{\\text\{opt\}\}is runtime on2020development inputs, andmauditm^\{\\text\{audit\}\}is runtime on4040inputs drawn from a different data snapshot together with the correctness suite\. A new seven\-category taxonomy \(algorithmic complexity, data structures, batching and I/O, caching, parallelism, memory layout, micro\-optimization\) was derived with the same pilot protocol as §[3\.2](https://arxiv.org/html/2609.00077#S3.SS2), from public performance\-tuning commits rather than ML logs\.

With150150iterations,22seeds, and the same proposer, we observe the same signature: surface diversity is flat \(0\.710\.71to0\.700\.70\) while semantic clusters fall from1111to55\(Δ​C=0\.55\\Delta\\mathrm\{C\}=0\.55\),66%66\\%of late\-stage accepted edits are caching or micro\-optimizations, andρT\\rho\_\{T\}is0\.580\.58underVanilla ARagainst0\.810\.81underDAPS\. This suggests that neither the phenomenon nor the mitigation is specific to ML\-pipeline editing, but the study is small and single\-domain, so we report it as preliminary and keep the headline claims scoped as stated in §[1](https://arxiv.org/html/2609.00077#S1)\.

## Appendix NBaseline Tuning Grids

Every baseline received the same tuning budget asDAPS’s own calibration: a three\-point grid per key hyperparameter, evaluated on the first5050iterations of T2, with the best setting then fixed for all tasks and seeds\. The grids are:HiTempproposer temperature\{1\.0,1\.2,1\.5\}\\\{1\.0,1\.2,1\.5\\\};R\-Diverse\-Asimilarity threshold\{0\.80,0\.85,0\.90\}\\\{0\.80,0\.85,0\.90\\\}and memory size\{100,200,300\}\\\{100,200,300\\\};Prism\-Acluster\-coverage reward weight\{0\.1,0\.5,1\.0\}\\\{0\.1,0\.5,1\.0\\\};Reflexionreflective\-summary length\{64,128,256\}\\\{64,128,256\\\}tokens;HO\-EarlyStopandHO\-Revertaudit intervalK∈\{5,10,20\}K\\in\\\{5,10,20\\\}\.RandSearchhas no tunable hyperparameter beyond the size of its harvested edit corpus, which we fixed at200200modifications\. ForDAPSthe corresponding sweep is reported in Appendix[C](https://arxiv.org/html/2609.00077#A3)\.