arXiv AI
Aug 6
Improving Auto-Design of Neural PDE Solvers with a Domain-Specific Language
arXiv:2608. 04384v1 Announce Type: new Abstract: Neural PDE solver auto-design is fundamentally a search-space representation problem.
AutoPDE: Reliable Agentic PDE Solving via Explicitly Represented Solver Strategies
arXiv:2606. 10752v1 Announce Type: new Abstract: Numerical solvers for partial differential equations (PDEs) are core computational tools in science and engineering.
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arXiv AI
Aug 5
Large language models for partial differential equation workflows
arXiv:2608. 03600v1 Announce Type: new Abstract: Partial differential equations (PDEs) become actionable in science and engineering not as isolated formulae, but as executable workflows that connect modelling assumptions, governing equations, numerical solvers, diagnostics, and decisions.
arXiv AI
Jul 14
Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards
arXiv:2607. 10474v1 Announce Type: cross Abstract: Partial differential equations (PDEs) are foundational to modeling in science and engineering, but constructing reliable numerical solvers remains labor-intensive, demanding expert knowledge of discretization schemes, stability conditions, and boundary treatments.
arXiv AI
Jun 29
Auto-Configuring Scientific Simulators with Lightweight Coding-Agent Adapters
arXiv:2606. 09774v2 Announce Type: replace Abstract: Configuring an advanced scientific simulator, translating a modeling goal into a valid, runnable input deck, is a persistent bottleneck that costs domain scientists hours to days.
arXiv Machine Learning
Jun 10
Compile Once, Differentiate Everywhere: A Differentiable Meta-Circular Interpreter
arXiv:2606. 09930v1 Announce Type: cross Abstract: The boundary between program execution and gradient-based optimization has long limited the use of code itself as a learnable scientific model.
arXiv AI
Jul 7
SkillOpt-Lite: Better and Faster Agent Self-evolution via One Line of Vibe
arXiv:2607. 03451v1 Announce Type: cross Abstract: While skill optimization for autonomous agents has gained traction, existing methods rely on complex pipelines.
arXiv AI
Jun 6
AutoDFT: A Closed-Loop Multi-Agent Framework for Autonomous DFT Calculations
arXiv:2605. 26179v2 Announce Type: replace-cross Abstract: Density functional theory (DFT) serves as the basis for computational discovery in materials science and chemistry, yet each calculation demands extensive human effort: adjusting algorithms when convergence stalls, revising plans when unexpected physics emerges, and inserting steps as intermediate results reshape the problem.
arXiv AI
Jul 21
TRIM: Reducing AI-Generated CodeSlop via Agent Trajectory Minimization
arXiv:2607. 18161v1 Announce Type: cross Abstract: Coding agents are increasingly used to accelerate code generation in many downstream tasks, such as fixing bugs, building applications, and prototyping.
arXiv AI
Jun 9
SIGA: Self-Evolving Coding-Agent Adapters for Scientific Simulation
arXiv:2606. 09774v1 Announce Type: new Abstract: Advanced scientific simulators expose specialized input languages that turn simulation goals into executable configurations, but learning them can cost domain scientists hours to days.
arXiv Machine Learning
Jun 30
SWE-INTERACT: Reimagining SWE Benchmarks as User-Driven Long-Horizon Coding Sessions
arXiv:2606. 30573v1 Announce Type: new Abstract: We introduce SWE-Interact, a new testbed for evaluating coding agents on multi-turn, interactive, user-driven software engineering tasks.
arXiv AI
Aug 3
AMTFV: Agentic Mathematical Tool-Flow Verification for LLM Self-Correction
arXiv:2607. 29549v1 Announce Type: new Abstract: Large language models have demonstrated strong mathematical problem-solving capabilities, yet reliably verifying their candidate answers remains challenging.
arXiv AI
Jul 24
ExecuGraph: A Multi-Agent, Execution-Grounded Framework for Reliable Backend Code Synthesis with Large Language Models
arXiv:2607. 20499v1 Announce Type: new Abstract: Large Language Models generate plausible backend code, but a single-pass paradigm provides no guarantee of correctness or runtime reliability.