SPL: Orchestrating Workflows with Declarative Deterministic-Probabilistic Composition

• URL: http://arxiv.org/abs/2607.07727v1
• Date: Mon, 06 Jul 2026 03:53:10 GMT
• Title: SPL: Orchestrating Workflows with Declarative Deterministic-Probabilistic Composition
• Abstract summary: SPL is a declarative language that composes deterministic computation and probabilistic computation modes in a single specification.<n>A.spl specification runs unchanged across local nodes, cloud APIs, and distributed grids.<n>We validate SPL through an extensive 78-recipe cookbook and a controlled 1,200-run experiment.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present SPL (Structured Prompt Language), a declarative language that composes deterministic and probabilistic computation modes in a single specification. While existing frameworks separate these -- orchestration systems (AutoGen, CrewAI, LangGraph) for LLM calls, symbolic tools (SymPy, SageMath, Lean) for computation -- SPL unifies them. It provides GENERATE/EVALUATE for probabilistic computation and SOLVE/ASSERT for deterministic computation, sharing syntax, variable bindings, and runtime routing. A .spl specification runs unchanged across local nodes (Ollama), cloud APIs (OpenRouter, Anthropic), and distributed grids (Momagrid), with model and verifier selection deferred to invocation time. We validate SPL through an extensive 78-recipe cookbook and a controlled 1,200-run experiment (10 models x 20 problems x 2 arms x 3 repetitions; the 20 problems span 6 difficulty tiers). The solver arm achieves 82-93% machine-verified correctness (sonnet-4-6: 85%, gemma4:e2b: 93%) while the LLM-only arm measures output production without mathematical verification, making the comparison one of verified correctness against unverified fluency. A backend difficulty gradient emerges (SymPy 78%, Sage 54%), and the dominant failure mode is solver_error (kernel-rejected expressions), not format non-compliance.

Related papers

• Selective Left-Shift: Turning Test-Time Compute and Difficulty-based Curation into Training Data for Low-Resource Code Generation [0.8602553195689513]
  Large Language Models achieve strong code generation for high resource languages like Python and Java but suffer sharp performance drops on Low-Resource Programming Languages(LRPLs) such as Julia.<n>We propose a three-phase pipeline that resolves this trilemma by decoupling syntax acquisition from algorithmic reasoning.<n>Applying to Qwen3-8B, our pipeline improves pass@1 by up to +7.6 points on MultiPL-E and +14.2 points on the Agnostics LiveCodeBench for Julia compared to SOTA results.
  arXiv  Detail & Related papers  (2026-07-08T09:18:56Z)
• Constrained Decoding for Diffusion Language Models via Efficient Inference over Finite Automata [57.27430779838529]
  We present an exact and tractable algorithm for sampling from the constrained mean-field posterior under any constraint expressible as a finite automaton.<n>The approach guarantees constraint satisfaction by construction, supports both greedy and sampling-based decoding, and is compatible with parallel and block-wise decoding.<n> Empirical evaluations on Dream-7B and LLaDA-8 show substantial accuracy gains across various tasks.
  arXiv  Detail & Related papers  (2026-07-08T05:48:57Z)
• LLM-as-a-Verifier: A General-Purpose Verification Framework [74.40111651545979]
  We introduce LLM-as-a-Verifier, a general-purpose verification framework.<n>It provides fine-grained feedback for agentic tasks without requiring additional training.<n>It achieves state-of-the-art performance on several benchmarks.
  arXiv  Detail & Related papers  (2026-07-06T17:59:35Z)
• Automated Proving of Shannon-Type Entropy Inequalities via Fine-Tuned Language Models and Guided Tree Search [50.16356451328644]
  Proving Shannon-type entropy inequalities is a fundamental task in information theory.<n>We investigate whether small-scale large language models, fine-tuned on atomic proof steps, can automate this process.<n>We show that GPT-5.5 solves 1.7% samples under zero-shot prompting while Psitip solves 33.3% samples.
  arXiv  Detail & Related papers  (2026-06-04T05:43:12Z)
• Testing LLM Arithmetic Reasoning Generalization with Automatic Numeric-Remapping Attacks [2.6659012768342145]
  We introduce an automatic algorithm for generating numeric-remapping attacks on arithmetic word problems.<n>We evaluate DeepSeek-R1 (70B), Gemma4 (31B), and GPT-OSS (120B) on GSM8K, MAWPS, and MultiArith.
  arXiv  Detail & Related papers  (2026-06-02T13:09:44Z)
• Code-Guided Reasoning for Small Language Models: Evaluating Executable MCQA Scaffolds [14.64394876057282]
  We introduce Code-Guided Reasoning (CGR), an evaluation protocol and generated-program resource.<n>CGR standardizes six components: a normalized item interface, a direct solver prompt, a generator prompt, a Python scaffold, solver-call and extraction helpers, and a three-channel result record.<n>On 20,498 retained result rows from a locally prepared MCQA bundle and six metadata-registered solver models, the observed non-zero-baseline partition shows 66.21% macro assisted accuracy versus 38.11% direct accuracy.
  arXiv  Detail & Related papers  (2026-05-12T20:20:06Z)
• SwiftSolve: A Self-Iterative, Complexity-Aware Multi-Agent Framework for Competitive Programming [4.611794255231338]
  We present Swift, a complexity-aware multi-agent system for competitive programming.<n>We show that profiling and complexity-guided replanning reduce inefficiency while preserving accuracy.
  arXiv  Detail & Related papers  (2025-10-26T11:05:27Z)
• Self-Steering Language Models [113.96916935955842]
  DisCIPL is a method for "self-steering" language models (LMs)<n>DisCIPL generates a task-specific inference program that is executed by a population of Follower models.<n>Our work opens up a design space of highly-parallelized Monte Carlo inference strategies.
  arXiv  Detail & Related papers  (2025-04-09T17:54:22Z)
• Sample, Don't Search: Rethinking Test-Time Alignment for Language Models [55.2480439325792]
  We introduce QAlign, a new test-time alignment approach.<n>As we scale test-time compute, QAlign converges to sampling from the optimal aligned distribution for each individual prompt.<n>By adopting recent advances in Markov chain Monte Carlo for text generation, our method enables better-aligned outputs without modifying the underlying model or even requiring logit access.
  arXiv  Detail & Related papers  (2025-04-04T00:41:40Z)
• Not All Votes Count! Programs as Verifiers Improve Self-Consistency of Language Models for Math Reasoning [24.386388107656334]
  We introduce Prove, a framework that leverages translated programs derived from natural language solutions as a verification mechanism.<n>Unlike vanilla majority voting, our approach filters out solutions whose corresponding program output is inconsistent with the generated solution, aggregating only those that pass verification.<n>Our results show that Prove consistently outperforms vanilla majority voting for solving mathematical reasoning tasks across all model sizes and datasets.
  arXiv  Detail & Related papers  (2024-10-16T14:24:55Z)
• PAL: Program-aided Language Models [112.94785609781503]
  We present Program-Aided Language models (PaL) to understand natural language problems. PaL offloads the solution step to a programmatic runtime such as a Python interpreter. We set new state-of-the-art results in all 12 benchmarks.
  arXiv  Detail & Related papers  (2022-11-18T18:56:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.