Dou, Shihan, Liu, Shaofan, Lu, Zhonghang, Lin, Jiahang, Liu, Shichun, Wang, Binghai, Jin, Jiajie, Dong, Guanting, Gui, Tao, Zhang, Qi, Huang, Xuanjing · Sep 28, 2026
EvoIn authors propose training agents to internalize decision procedures
Markdown · JSON · Original source
The authors of EvoIn propose a framework that develops an agent’s decision-making procedures and trains the model to use them without an evolved harness. The framework analyzes execution traces, tests new procedures in the harness, and uses validated procedures to produce . It then rewrites those traces to remove explicit references to harness instructions before fine-tuning the model. The authors report pass-rate gains of 10.9 points in-domain and 9.2 points out-of-domain, and say the procedures generalize to unseen tasks. Their example is an agent checking a document’s length to decide whether to read it all or search it. These are results; the supplied abstract does not establish how well the approach performs in practical deployments or what training costs it requires. Escape Meridian interpretation: whether procedures learned without the harness transfer reliably to real tasks beyond the evaluated benchmarks remains unknown.
Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.
Original source
cs.AI · Article
Source details
Original source date: Sep 28, 2026 (2026-09-28).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-14T05:17:44.969Z. First observed: 2026-10-28T06:34:34.102Z. Retrieved: 2026-10-30T01:33:15.131Z.
Referenced by
Recorded citations and public file attachments. A reference alone does not establish agreement or influence.
No public references recorded here yet.
Limits
Evidence is the paper abstract on ; the supplied text does not include evaluation details, names, or full results. The abstract does not establish how the reported results translate to practical deployments or what training costs the framework requires.
Open question
How reliably do EvoIn’s internalized procedures transfer to real tasks beyond the evaluated ?
Source-stated
EvoIn uses execution traces to develop and validate decision procedures in a harness, then fine-tunes the model on rewritten reasoning traces so the procedures can be used without the evolved harness.
Finally, EvoIn fine-tunes the model on the rewritten traces, internalizing these procedures so that the improved decision-making persists without the evolved harness at inference time.
Source-stated
The authors report pass-rate gains of 10.9 points in-domain and 9.2 points out-of-domain.
raising the pass rate by 10.9 points in-domain and by 9.2 points out-of-domain.
Source-stated
The authors say the internalized procedures generalize to unseen tasks and describe an example in which an agent checks document length before choosing how to read it.
Results further show that the internalized decision procedures generalize to unseen tasks. Case studies show that agents can learn to decide how to solve a task before solving it, for example by checking a document's length
Escape Meridian interpretation
The abstract reports benchmark results but does not establish performance in practical deployments or training costs.
We evaluate EvoIn on diverse benchmarks and find that it consistently enables agents to learn stronger decision-making procedures
Source and contributor context
Dou, Shihan, Liu, Shaofan, Lu, Zhonghang, Lin, Jiahang, Liu, Shichun, Wang, Binghai, Jin, Jiajie, Dong, Guanting, Gui, Tao, Zhang, Qi, Huang, Xuanjing · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Fine-tuning
Further training of an existing model to adapt it to a particular task or use.
Chain of thought
The step-by-step working a model writes out before giving its final answer.
Benchmark
A defined set of tests used to compare systems. Results depend on the tasks and measures selected.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: 1608a569-e80e-4905-87f9-4f2fffe98686.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-14T05:17:44.969Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 3671da0b304c062421e47f612492e3a682477cd31818d4e48024ee223cc6d84e. Source text hash: f067c20b81a874d5f864b40e0ad8757adc40d49e42c8f1d7ea422f64b105a4a2.
Revision and source-check history
Version 1 · 2026-11-14T05:17:44.969Z
EvoIn authors propose training agents to internalize decision procedures
The authors of EvoIn propose a framework that develops an agent’s decision-making procedures and trains the model to use them without an evolved harness. The framework analyzes execution traces, tests new procedures in the harness, and uses validated procedures to produce . It then rewrites those traces to remove explicit references to harness instructions before fine-tuning the model. The authors report pass-rate gains of 10.9 points in-domain and 9.2 points out-of-domain, and say the procedures generalize to unseen tasks. Their example is an agent checking a document’s length to decide whether to read it all or search it. These are results; the supplied abstract does not establish how well the approach performs in practical deployments or what training costs it requires. Escape Meridian interpretation: whether procedures learned without the harness transfer reliably to real tasks beyond the evaluated benchmarks remains unknown.
Original source
cs.AI · Article
Source details
Original source date: Sep 28, 2026 (2026-09-28).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-14T05:17:44.969Z. First observed: 2026-10-28T06:34:34.102Z. Retrieved: 2026-10-30T01:33:15.131Z.
Limits
Evidence is the paper abstract on ; the supplied text does not include evaluation details, names, or full results. The abstract does not establish how the reported results translate to practical deployments or what training costs the framework requires.
Open question
How reliably do EvoIn’s internalized procedures transfer to real tasks beyond the evaluated ?
Source-stated
EvoIn uses execution traces to develop and validate decision procedures in a harness, then fine-tunes the model on rewritten reasoning traces so the procedures can be used without the evolved harness.
Finally, EvoIn fine-tunes the model on the rewritten traces, internalizing these procedures so that the improved decision-making persists without the evolved harness at inference time.
Source-stated
The authors report pass-rate gains of 10.9 points in-domain and 9.2 points out-of-domain.
raising the pass rate by 10.9 points in-domain and by 9.2 points out-of-domain.
Source-stated
The authors say the internalized procedures generalize to unseen tasks and describe an example in which an agent checks document length before choosing how to read it.
Results further show that the internalized decision procedures generalize to unseen tasks. Case studies show that agents can learn to decide how to solve a task before solving it, for example by checking a document's length
Escape Meridian interpretation
The abstract reports benchmark results but does not establish performance in practical deployments or training costs.
We evaluate EvoIn on diverse benchmarks and find that it consistently enables agents to learn stronger decision-making procedures
Source and contributor context
Dou, Shihan, Liu, Shaofan, Lu, Zhonghang, Lin, Jiahang, Liu, Shichun, Wang, Binghai, Jin, Jiajie, Dong, Guanting, Gui, Tao, Zhang, Qi, Huang, Xuanjing · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Fine-tuning
Further training of an existing model to adapt it to a particular task or use.
Chain of thought
The step-by-step working a model writes out before giving its final answer.
Benchmark
A defined set of tests used to compare systems. Results depend on the tasks and measures selected.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: 1608a569-e80e-4905-87f9-4f2fffe98686.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-14T05:17:44.969Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 3671da0b304c062421e47f612492e3a682477cd31818d4e48024ee223cc6d84e. Source text hash: f067c20b81a874d5f864b40e0ad8757adc40d49e42c8f1d7ea422f64b105a4a2.
uncertain · 2026-11-14T05:17:44.969Z · anthropic/claude-opus-5.5
Publication: 1608a569-e80e-4905-87f9-4f2fffe98686
attribution — pass: The draft attributes the framework, results, generalization claim and document-length example to the EvoIn authors. Benchmark gains are framed as the authors' report, not as established fact.
Authors:Shihan Dou, Shaofan Liu, Zhonghang Lu, Jiahang Lin, Shichun Liu, Binghai Wang
summary — uncertain: The pipeline is described faithfully, but its central term 'harness' is never explained. The draft also does not plainly say that the gain persists at inference time, when the model runs without the extra instructions. A non-specialist may not grasp the main point. Smallest repair: add a term-library tooltip for 'harness' and keep 'at inference time'. Optionally note the authors' contrast with the 'potpourri' approach and their claim of gains on another model family. The draft paraphrases 'evolve' as 'develops'; restore the source term.
internalizing these procedures so that the improved decision-making persists without the evolved harness at inference time
date — pass: The originalPublished date of 2026-09-28 matches the v1 submission date. It is not the 27 Oct v2 revision or the feed date.
Submitted on 28 Sep 2026 (v1), last revised 27 Oct 2026 (this version, v2)
support — pass: All factual claims match the abstract. The draft separates the source's benchmark gains from real-world reliability. Its note that deployment performance and training costs are unestablished is a fair statement of a gap, and the Escape Meridian interpretation is labeled and conditional. No benefits or costs are invented.
raising the pass rate by 10.9 points in-domain and by 9.2 points out-of-domain
media — pass: The selected media is the verified direct arXiv abstract page for this paper, the most direct access to the source.
Challenge or correct this reading
Send a correction or an opposing source with this reading’s URL. Disagreement and uncertainty can remain unresolved.
Further training of an existing model to adapt it to a particular task or use.
Definition referenceThe step-by-step working a model writes out before giving its final answer.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivarXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceFurther training of an existing model to adapt it to a particular task or use.
Definition referenceThe step-by-step working a model writes out before giving its final answer.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivarXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition reference