Reinforcement learning from verifiable rewards (RLVR) has become the dominant training signal for frontier reasoning models, but existing verified environments are dominated by symbolic or code-centred tasks. Scientific inverse problems—CT, MRI, compressed sensing, phase retrieval—remain unmeasured despite their continuous, ill-posed, uncertainty-sensitive structure. We release ten RL environments spanning five scientific modalities with two design properties absent from current benchmarks: (i) every reward is split-conformal calibrated to a target 1−α coverage, so honest posterior width is rewarded alongside point-estimate quality; and (ii) every measurement is procedurally regenerated per query, making fixed-string contamination mathematically impossible at ∼10²2 effective instances per env. On 50 paired (env, model) comparisons across six frontier models, classical baselines significantly outperform every tested LLM on 32 at p<0. 05 (uncorrected and Bonferroni-corrected), pooled mean Δ=+0. 199 (10k paired bootstrap). Top LLMs (Haiku 4. 5, Opus 4. 7, Sonnet 4. 6) reach 0. 53−0. 56 cross-env mean, below classical 0. 630. Empirical conformal coverage across all ten envs lands at 0. 9013±0. 0166 against the 0. 90 target (N=200). Environments are MIT-licensed on the Prime Intellect Hub. AI Tool Disclosure: Author used Claude (Anthropic) and Codex (OpenAI) for code generation of benchmark infrastructure, figure generation scripts, and manuscript drafting assistance. All scientific claims, experimental design, baseline selection, statistical analysis, and conclusions were developed and verified by the author.
Stelios Zacharioudakis (Sun,) studied this question.