← 研踪 PaperTrailarXiv:2611.14777 · v1
Mach2: Scientific Discovery Platform with Deterministic Execution Constraints
首次提交 2026-11-11 06:09
版本更新 2026-11-11 06:09
雷达观测 2026-11-13 10:09
摘要
Language-model agents can formulate workflows and invoke specialized tools, yet compliance with natural-language operating rules is not guaranteed by prompting alone. Mach2 addresses this limitation by coupling Eigen orchestration to deterministic, lint-like checks of tool requests, source changes, acceptance tests, and artifact contracts. Domain requirements are represented as executable predicates: mandatory failures prevent advancement of the checked operation, while diagnostic findings are retained for correction or review. User-specified test-driven development (TDD), CodeGrapher's abstract-syntax-tree analysis, and history-guided evolutionary search provide complementary verification and optimization mechanisms. Mach2 also generates task-specific agent packages for execution through independently selected endpoints. The originating harness validates returned artifacts against declared interfaces and evidence requirements without assuming continuous supervision of the remote run. Model-internal-knowledge (MIK) dependencies are recorded separately from source-grounded and test-supported findings. This paper formalizes the rule-gated execution model and the agent-package lifecycle, and proposes measurable criteria for evaluating rule compliance, engineering validity, and cross-endpoint task continuity.
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v1 · 2026-11-11 06:09 · 最新观测版本
Mach2: Scientific Discovery Platform with Deterministic Execution Constraints
Language-model agents can formulate workflows and invoke specialized tools, yet compliance with natural-language operating rules is not guaranteed by prompting alone. Mach2 addresses this limitation by coupling Eigen orchestration to deterministic, lint-like checks of tool requests, source changes, acceptance tests, and artifact contracts. Domain requirements are represented as executable predicates: mandatory failures prevent advancement of the checked operation, while diagnostic findings are retained for correction or review. User-specified test-driven development (TDD), CodeGrapher's abstract-syntax-tree analysis, and history-guided evolutionary search provide complementary verification and optimization mechanisms. Mach2 also generates task-specific agent packages for execution through independently selected endpoints. The originating harness validates returned artifacts against declared interfaces and evidence requirements without assuming continuous supervision of the remote run. Model-internal-knowledge (MIK) dependencies are recorded separately from source-grounded and test-supported findings. This paper formalizes the rule-gated execution model and the agent-package lifecycle, and proposes measurable criteria for evaluating rule compliance, engineering validity, and cross-endpoint task continuity.
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