SciDialect: Symbolic Compression as an Intrinsic Reward for Scientific Discovery Agents

Autonomous scientific discovery often depends on expensive or delayed external validation, leaving intermediate reasoning decisions without a task reward. We ask whether abstraction itself can provide useful intrinsic guidance. Our principle, grounded symbolic compression, rewards shorter representations of task-visible scientific content while checking that their claims, conditions, and evidence remain recoverable through a designed decoder. We instantiate it in SciDialect, a black-box controller that calibrates reusable scientific primitives on development tasks, then uses independently validated, query-specific instantiations to condition the next solver step. Its online representation reward requires no external correctness score. Our analysis establishes when verified predictive compression improves expected log score after representation cost, and when the scientific-state representation preserves downstream decision information, with a qualified minimality result. Across PreScience, DiscoveryBench, and SciCode, our proposed SciDialect achieves the highest mean contribution token F1 on three model families, improves adapted discovery score over prompt compression, and improves mean observable code execution. Four equal-budget scientific optimization tasks, i.e., MBO, NHO, SPO, and TMC, provide complementary evidence of improved trajectory quality when the intrinsic controller augments an existing objective-based search. Together, these results support grounded abstraction as an intrinsic signal for scientific reasoning, without treating it as a substitute for scientific validation.