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ARXIV:2602.09447 · AGENTS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2602.09447AGENTSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Develop SWE-AGI as a benchmark for evaluating LLMs in specification-driven software construction using MoonBit.
Opportunity summary
Pain Develop SWE-AGI as a benchmark for evaluating LLMs in specification-driven software construction using MoonBit.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Develop SWE-AGI as a benchmark for evaluating LLMs in specification-driven software construction using MoonBit. We introduce SWE-AGI, an open-source benchmark for evaluating end-to-end, specification-driven construction of software systems written in MoonBit.
Although large language models (LLMs) have demonstrated impressive coding capabilities, their ability to autonomously build production-scale software from explicit specifications remains an open question. We introduce SWE-AGI, an open-source benchmark for evaluating end-to-end, specification-driven…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Across frontier models, gpt-5.3-codex achieves the best overall performance (solving 19/22 tasks, 86.4%), outperforming claude-opus-4.6 (15/22, 68.2%), and kimi-2.5 exhibits the strongest performance among…
Agents moved forward this cycle; last verified April 2026. Public score 5.0/10.
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Develop SWE-AGI as a benchmark for evaluating LLMs in specification-driven software construction using MoonBit.
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10.48550/arXiv.2602.09447Develop SWE-AGI as a benchmark for evaluating LLMs in specification-driven software construction using MoonBit.
Abstract
Although large language models (LLMs) have demonstrated impressive coding capabilities, their ability to autonomously build production-scale software from explicit specifications remains an open question. We introduce SWE-AGI, an open-source benchmark for evaluating end-to-end, specification-driven construction of software systems written in MoonBit. SWE-AGI tasks require LLM-based agents to implement parsers, interpreters, binary decoders, and SAT solvers strictly from authoritative standards and RFCs under a fixed API scaffold. Each task involves implementing 1,000-10,000 lines of core logic, corresponding to weeks or months of engineering effort for an experienced human developer. By leveraging the nascent MoonBit ecosystem, SWE-AGI minimizes data leakage, forcing agents to rely on long-horizon architectural reasoning rather than code retrieval. Across frontier models, gpt-5.3-codex achieves the best overall performance (solving 19/22 tasks, 86.4%), outperforming claude-opus-4.6 (15/22, 68.2%), and kimi-2.5 exhibits the strongest performance among open-source models. Performance degrades sharply with increasing task difficulty, particularly on hard, specification-intensive systems. Behavioral analysis further reveals that as codebases scale, code reading, rather than writing, becomes the dominant bottleneck in AI-assisted development. Overall, while specification-driven autonomous software engineering is increasingly viable, substantial challenges remain before it can reliably support production-scale development.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Dimensions overall score 5.0
PROBLEM
Develop SWE-AGI as a benchmark for evaluating LLMs in specification-driven software construction using MoonBit. We introduce SWE-AGI, an open-source benchmark for evaluating end-to-end, specification-driven construction of software systems written in MoonBit.
METHOD
Although large language models (LLMs) have demonstrated impressive coding capabilities, their ability to autonomously build production-scale software from explicit specifications remains an open question. We introduce SWE-AGI, an open-source benchmark for evaluating end-to-end,...
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Across frontier models, gpt-5.3-codex achieves the best overall performance (solving 19/22 tasks, 86.4%), outperforming claude-opus-4.6 (15/22, 68.2%), and kimi-2.5 exhibits the strongest performance amon...
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Develop SWE-AGI as a benchmark for evaluating LLMs in specification-driven software construction using MoonBit. We introduce SWE-AGI, an open-source benchmark for evaluating end-to-end, specification-driven construction of software systems written in MoonBit.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Although large language models (LLMs) have demonstrated impressive coding capabilities, their ability to autonomously build production-scale software from explicit specifications remains an open question. We introduce SWE-AGI, an open-source benchmark for evaluating end-to-end, specification-driven construction of software systems written in MoonBit.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Across frontier models, gpt-5.3-codex achieves the best overall performance (solving 19/22 tasks, 86.4%), outperforming claude-opus-4.6 (15/22, 68.2%), and kimi-2.5 exhibits the strongest performance among open-source models.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Agents moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Develop SWE-AGI as a benchmark for evaluating LLMs in specification-driven software construction using MoonBit.
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reason
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stale
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passport absent
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
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missing
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Write integration checklist from prototype path and target workflow.
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ARTIFACTS
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DEFENSIBILITY
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