Credo: Reusable Declarative Primitives for Agentic Workflows
Duo LuAndrew CrottyU\u{g}ur \c{C}etintemel
Aug 2026
Artificial Intelligence
Abstract
An LLM application depends on both a model and a harness: the program that determines what each call sees, how many calls to make, and which answers to trust. Coding agents can now discover strong harnesses by searching over candidate programs, but the resulting artifact is an opaque block of imperative code whose logical steps, runtime signals, physical execution decisions, and prompt strategies remain implicit and task-specific, forcing subsequent tasks to start the harness search process from scratch. The potential for reuse, however, is substantial. A searched harness encodes significant knowledge, such as the logical steps that work, the signals that matter, the physical operator decisions that adapt execution, and the prompt strategies that are effective, yet this knowledge is buried in imperative code with no inspectable or reusable structure, nor does it carry any provenance or metadata. Credo addresses this problem by recovering a structured declarative description of a searched harness, tagging each extracted primitive with relevant metadata, and cataloguing all of it with provenance. A compiler can then bind stored primitives to generate harnesses for new tasks without having to start the search over from scratch. This paper provides preliminary results demonstrating the potential of our approach and lays out a related research agenda that the database community is well-positioned to tackle, including cost-based compilation over declarative catalogs and catalog maintenance under model and workload drift.
Investigating how experienced developers use agents in building software, including their motivations, strategies, task suitability, and sentiments finds that while experienced developers value agents as a productivity boost, they retain their agency in software design and implementation out of insistence on fundamental software quality attributes.
An adaptive surrogate modeling method for problems with very high-dimensional spatio-temporal outputs is developed that combines exploration and exploitation to improve the surrogate model accuracy with the fewest possible runs of the expensive physics-based model.
B. Kapusuzoglu, S. Mahadevan, Shunsaku Matsumoto et al.· Structural And Multidiscipli...· 17 citations
An adaptive jailbreak attack framework for systematic evaluation of both cascaded pipelines and end-to-end large audio-language models under a unified experimental setting that achieves consistently higher attack success rates across diverse audio-based LLM systems.
Linghan Huang, Bo Li, Huaming Chen et al.· 12 citations· ⚡2
This review provides a systematic literature review of LLM-based Verilog code generation, analyzing 102 papers (70 published and 32 high-quality preprints) from SE, AI, and EDA venues and outlines a roadmap highlighting potential opportunities in LLM-assisted hardware design.
This work introduces Behavior-Outcome Freedom (F), a pre-synthesis diagnostic of signed behavior-outcome rank mismatch, and formalizes its candidate-conditional role through Signed Anchor-Rank Transfer, which preserves validated capability resources, removes runtime orchestration, and conditionally inherits pipeline guidance using a calibrated rule over F.
Binyan Xu, Dong Fang, Haitao Li et al.· arXiv.org· 10 citations
Simulation results confirm the effectiveness and benefits of DMs in generating neighbor velocity estimates in a four-UAV swarm coordination task using Deep Reinforcement Learning (DRL), and explore the integration of DMs with RL and DT.