arXiv Machine Learning

The Oligarch Barely Steers Model Collapse in Multi-Model Ecosystems

The paper investigates whether an oligopolistic concentration of generative AI models accelerates or steers the phenomenon of model collapse when models are recursively trained on each other’s outputs. Using controlled ecosystems of 13 open‑source models and an injected probe that pushes one model’s market share to 90%, the authors find that varying market concentration has little effect on the speed or final state of collapse. Instead, the pace of collapse is largely determined by which models supply the training pool and how susceptible those models are to being carried along, with human‑written text in the pool roughly halving the drift.

arXiv Machine Learning
Sep 11

A Fragility Spectrum for Recursive Language-Model Training

The paper investigates how recursive contamination—retraining language models on their own generated text—affects output diversity across 13 publicly released checkpoints. Using a fixed contamination protocol over five generations, the authors find a wide spread in 4‑gram diversity (0.187 to 0.940), indicating that some models collapse into repetitive fragments while others remain largely unaffected. The study shows that a model’s susceptibility to collapse is an intrinsic property of the checkpoint, not predicted by parameter scale or static indicators, and that simple interventions such as tightening top‑p sampling can significantly slow or halt collapse.

By Yangze Liu, Zhongyi Han
arXiv AI
Sep 1

Moving the Mean Toward the Known Good, Not Beyond It: What Inference-Time Interventions and Weight Consolidation Buy in Open-Ended Generation

The study investigates how inference‑time interventions and weight consolidation affect open‑ended generation in an online bin‑packing task. By iteratively generating, verifying, selecting, and consolidating with LoRA, the model’s outputs shift toward higher value, reducing excess by 1.7 points and outperforming random consolidation by 3.1 points. Across three independent runs, the mean performance remained consistent, and the best candidates converged to the classic heuristic’s level without exceeding it, while consolidation also lowered the proportion of better‑than‑classic candidates but increased their absolute number.

By Roberto I. Ono Filho
arXiv AI
Jun 11

Search Discipline for Long-Horizon Research Agents

arXiv:2606. 11522v1 Announce Type: new Abstract: Autoresearch agents now propose, evaluate, and select scientific candidates against a metric, and that metric is usually an aggregate reduced over a heterogeneous space of regions, slices, or cohorts.

By Adithya Srinivasan, Devesh Paragiri
arXiv AI
Jun 4

BioBlue: Systematic runaway-optimiser-like LLM failure modes on biologically and economically aligned AI safety benchmarks for LLMs with simplified observation format

arXiv:2509. 02655v3 Announce Type: replace-cross Abstract: Many AI alignment discussions of "runaway optimisation" focus on RL agents: unbounded utility maximisers that over-optimise a proxy objective (e.

By Roland Pihlakas (for the Three Laws collaboration), Sruthi Susan Kuriakose (for the Three Laws collaboration)
arXiv AI
Aug 6

Beyond the Dirac Delta: Mitigating Diversity Collapse in Reinforcement Fine-Tuning for Versatile Image Generation

arXiv:2601. 12401v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has emerged as a powerful paradigm for fine-tuning large-scale generative models, such as diffusion and flow models, to align with complex human preferences and user-specified tasks.

By Jinmei Liu, Haoru Li, Zhenhong Sun, Chaofeng Chen, Yatao Bian, Bo Wang, Daoyi Dong, Chunlin Chen, Zhi Wang
arXiv AI
Sep 10

What LLM Trading Agents Actually Do in Production: A Six-Month, Population-Scale Record from Two Fleets

The paper reports a six‑month, population‑scale measurement of autonomous language‑model trading agents operating in two production fleets: DX Terminal Pro, with 3,505 user‑funded vaults trading real ETH in Base memecoin markets, and the DXAP live alpha fleet, with 500–599 user‑created agents trading Hyperliquid perpetuals. Across roughly 7.5 million single‑model invocations and 231,638 multi‑tool turns, the study finds that operating layer design, risk sliders, and leaderboard boundaries drive behavior more than strategy text; agents are volatility‑blind in sizing, capture little upside, and show no directional edge compared to a retail benchmark. The analysis includes regression discontinuity, permutation nulls, and a 17‑rule methodology canon to validate the findings.

By T. J. Barton, Chris Constantakis, Patti Hauseman, Annie Mous, Alaska Hoffman, Brian Bergeron, Hunter Goodreau
arXiv Machine Learning
Aug 6

The Price of Isolation: Estimating the Ecosystem Cost of Symmetric Two-Sided A/B Testing

arXiv:2608. 04432v1 Announce Type: cross Abstract: On two-sided content platforms, symmetric two-sided isolation (assigning matched fractions of creators and viewers to isolated treatment and control submarkets) is widely used for creator-side and cold-start experiments because it removes cross-arm marketplace interference.

By Yuanyuan Shen, Yiren Yan, Wenjie Li, Chunhui Zhu