@inproceedings{wang-etal-2026-oasis,
title = "{OASIS}: Online Anchor-calibrated Self-Improvement System",
author = {Wang, Wenyi and
Laakom, Firas and
Nanbo, Li and
Dai, Yanning and
Wang, Yuhui and
Xu, Yifan and
Khizbullin, Dmitrii and
Pi{\k{e}}kos, Piotr and
Zhang, Yilan and
Chen, Yimeng and
Amayuelas, Alfonso and
Zhuge, Mingchen and
Schmidhuber, J{\"u}rgen},
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2150/",
pages = "45950--45963",
ISBN = "979-8-89176-462-0",
abstract = "Many recursively self-improving systems improve by editing their codebases based on observed rollout behavior. However, recent methods largely treat self-improvement as offline search: variants are optimized and evaluated on fixed benchmarks. This misaligns with real-world deployment, where tasks arrive sequentially and may have different distributions. We study recursive self-improvement as online learning over non-stationary task streams. In this setting, reliable execution and fallback are particularly difficult with limited evidence: different branches of self-improvement see different tasks, so raw rewards conflate capability with task difficulty, leading to overly conservative or optimistic reversion. We propose Online Anchor-calibrated Self-Improvement System (OASIS) that runs an adaptive reference agent on incoming tasks, converts raw rewards into paired comparisons, and uses confidence-calibrated rules for branch continuation and fallback without evaluating every agent on all tasks. Across SWE-Bench Pro and DeepResearch Bench, OASIS consistently improves over both the initial agent and prior RSI baseline SSA, raising the SWE-Bench Pro solve rate from 51.6{\%} to 56.9{\%} using Qwen3.6-35B-A3B and DeepResearch Bench from 35.18 to 39.31 using Gemini 3 Flash. This enables \textsc{OASIS} to outperform larger open weights and proprietary systems, including Qwen3.5-397B-A17B, Minimax 2.7, Qwen3.6-Plus, and GPT-5.3-Codex, with sustained deep self-improvements rather than shallow cycles."
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<abstract>Many recursively self-improving systems improve by editing their codebases based on observed rollout behavior. However, recent methods largely treat self-improvement as offline search: variants are optimized and evaluated on fixed benchmarks. This misaligns with real-world deployment, where tasks arrive sequentially and may have different distributions. We study recursive self-improvement as online learning over non-stationary task streams. In this setting, reliable execution and fallback are particularly difficult with limited evidence: different branches of self-improvement see different tasks, so raw rewards conflate capability with task difficulty, leading to overly conservative or optimistic reversion. We propose Online Anchor-calibrated Self-Improvement System (OASIS) that runs an adaptive reference agent on incoming tasks, converts raw rewards into paired comparisons, and uses confidence-calibrated rules for branch continuation and fallback without evaluating every agent on all tasks. Across SWE-Bench Pro and DeepResearch Bench, OASIS consistently improves over both the initial agent and prior RSI baseline SSA, raising the SWE-Bench Pro solve rate from 51.6% to 56.9% using Qwen3.6-35B-A3B and DeepResearch Bench from 35.18 to 39.31 using Gemini 3 Flash. This enables OASIS to outperform larger open weights and proprietary systems, including Qwen3.5-397B-A17B, Minimax 2.7, Qwen3.6-Plus, and GPT-5.3-Codex, with sustained deep self-improvements rather than shallow cycles.</abstract>
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%0 Conference Proceedings
%T OASIS: Online Anchor-calibrated Self-Improvement System
%A Wang, Wenyi
%A Laakom, Firas
%A Nanbo, Li
%A Dai, Yanning
%A Wang, Yuhui
%A Xu, Yifan
%A Khizbullin, Dmitrii
%A Piękos, Piotr
%A Zhang, Yilan
%A Chen, Yimeng
%A Amayuelas, Alfonso
%A Zhuge, Mingchen
%A Schmidhuber, Jürgen
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wang-etal-2026-oasis
%X Many recursively self-improving systems improve by editing their codebases based on observed rollout behavior. However, recent methods largely treat self-improvement as offline search: variants are optimized and evaluated on fixed benchmarks. This misaligns with real-world deployment, where tasks arrive sequentially and may have different distributions. We study recursive self-improvement as online learning over non-stationary task streams. In this setting, reliable execution and fallback are particularly difficult with limited evidence: different branches of self-improvement see different tasks, so raw rewards conflate capability with task difficulty, leading to overly conservative or optimistic reversion. We propose Online Anchor-calibrated Self-Improvement System (OASIS) that runs an adaptive reference agent on incoming tasks, converts raw rewards into paired comparisons, and uses confidence-calibrated rules for branch continuation and fallback without evaluating every agent on all tasks. Across SWE-Bench Pro and DeepResearch Bench, OASIS consistently improves over both the initial agent and prior RSI baseline SSA, raising the SWE-Bench Pro solve rate from 51.6% to 56.9% using Qwen3.6-35B-A3B and DeepResearch Bench from 35.18 to 39.31 using Gemini 3 Flash. This enables OASIS to outperform larger open weights and proprietary systems, including Qwen3.5-397B-A17B, Minimax 2.7, Qwen3.6-Plus, and GPT-5.3-Codex, with sustained deep self-improvements rather than shallow cycles.
%U https://aclanthology.org/2026.findings-emnlp.2150/
%P 45950-45963
Markdown (Informal)
[OASIS: Online Anchor-calibrated Self-Improvement System](https://aclanthology.org/2026.findings-emnlp.2150/) (Wang et al., Findings 2026)
ACL
- Wenyi Wang, Firas Laakom, Li Nanbo, Yanning Dai, Yuhui Wang, Yifan Xu, Dmitrii Khizbullin, Piotr Piękos, Yilan Zhang, Yimeng Chen, Alfonso Amayuelas, Mingchen Zhuge, and Jürgen Schmidhuber. 2026. OASIS: Online Anchor-calibrated Self-Improvement System. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 45950–45963, Budapest, Hungary. Association for Computational Linguistics.