arXiv subjects

Yangfan Wu

Publications and source records attributed to Yangfan Wu.

5 recordsLinked to original sources

arXiv2026

SpecPath: Testing Coding Agents Across Contract-Equivalent Specification Histories

Modern coding agents increasingly appear capable of following complex software requirements, yet their success leaves a critical ambiguity: do they resolve the active specification, or merely follow the most salient path by which it was stated? We identify specification-path sensitivity, a failure mode in which requirement histories that are equivalent in their final meaning lead the same agent system to produce behaviorally different programs. This reframes evolving-requirement evaluation as active-contract resolution: before writing code, an agent must determine which requirements still count. Building on this view, we introduce SpecPath, a diagnostic evaluation that holds the repository, final contract, verifier, agent system, and execution budget fixed while changing only the revision path that leads to the contract. Rather than treating each patch as an isolated pass or failure, SpecPath uses paired executable outcomes to reveal whether an agent realizes the same tested behavior across contract-equivalent histories. Across five calibrated software tasks and fourteen coding-agent configurations, aggregate direct and revision-history accuracy is nearly unchanged; nevertheless, 35 of 100 complete blocks that succeed on the direct specification fail on at least one equivalent history. These results show that implementation success on a consolidated request does not guarantee specification-path invariance. Evaluating evolving requirements therefore calls for controlled tests of whether agents are robust to the path by which a specification becomes final.

cs.SE↗

arXiv2026

CLMASP: Coupling Large Language Models with Answer Set Programming for Robotic Task Planning

Large Language Models (LLMs) possess extensive foundational knowledge and moderate reasoning abilities, making them suitable for general task planning in open-world scenarios. However, it is challenging to ground a LLM-generated plan to be executable for the specified robot with certain restrictions. This paper introduces CLMASP, an approach that couples LLMs with Answer Set Programming (ASP) to overcome the limitations, where ASP is a non-monotonic logic programming formalism renowned for its capacity to represent and reason about a robot's action knowledge. CLMASP initiates with a LLM generating a basic skeleton plan, which is subsequently tailored to the specific scenario using a vector database. This plan is then refined by an ASP program with a robot's action knowledge, which integrates implementation details into the skeleton, grounding the LLM's abstract outputs in practical robot contexts. Our experiments conducted on the VirtualHome platform demonstrate CLMASP's efficacy. Compared to the baseline executable rate of under 2% with LLM approaches, CLMASP significantly improves this to over 90%.

cs.AI↗

arXiv2026

A Four-Valued Normative Intermediate Representation for ASP-Oriented Compliance Reasoning

Technical-standard compliance reasoning may involve incomplete evidence, inconsistent observations, exceptions, and derived normative outputs. This paper presents \textsc{Monir}, a four-valued normative intermediate representation for ASP-oriented compliance workflows. \textsc{Monir} separates factual support from normative outputs: facts are represented by positive and negative support bits, while deontic labels are rule-generated output constructors interpreted by aggregation policies. Its core semantics is a staged transition system over support configurations. We define the language, rule-state interface, diagnostics, and policy-parametric reporting; characterize admissible staged evaluation by a dependency graph; prove deterministic polynomial-time evaluation for fixed evidence; and give NP/coNP upper bounds, with matching hardness for representative completion-based verdict queries.

cs.AI↗

arXiv2026

Synthesis of single-layered fluorographdiyne nanosheets via selective on-surface 2D covalent polymerization

Two-dimensional conjugated polymers (2DCPs) are significant macromolecular materials with intriguing and tunable physicochemical properties that depend on their geometries. Graphdiyne and its derivatives are exemplary 2DCPs featuring sp-sp2 hybridized skeletons. However, achieving single-layered, large-domain/regular graphdiyne and its derivatives on surfaces remains a formidable challenge due to the lack of selective 2D covalent polymerization methods. Here, we report a selective on-surface 2D covalent polymerization method via the combination of cobalt catalysis and coronene templating, achieving the synthesis of single-layered fluorographdiyne nanosheets up to 60*60 nm2 on Au(111) surface. Using scanning probe techniques, we visualize the sequential polymerization process and characterize cobalt-activated coupling intermediates at the atomic level. Experimental and theoretical analyses suggest that strong d-π coupling between cobalt and alkynyl transforms a robust Csp-Au bond into a weaker Csp2-Au bond, thereby facilitating the demetallization C-C coupling. Besides, the templating effect of coronene suppresses kinetically trapped defects and improves the selectivity of hexagonal-ring formation in the complex 2D covalent polymerization process.

cond-mat.mtrl-sci↗

arXiv2024

Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts

Multimodal learning has gained increasing importance across various fields, offering the ability to integrate data from diverse sources such as images, text, and personalized records, which are frequently observed in medical domains. However, in scenarios where some modalities are missing, many existing frameworks struggle to accommodate arbitrary modality combinations, often relying heavily on a single modality or complete data. This oversight of potential modality combinations limits their applicability in real-world situations. To address this challenge, we propose Flex-MoE (Flexible Mixture-of-Experts), a new framework designed to flexibly incorporate arbitrary modality combinations while maintaining robustness to missing data. The core idea of Flex-MoE is to first address missing modalities using a new missing modality bank that integrates observed modality combinations with the corresponding missing ones. This is followed by a uniquely designed Sparse MoE framework. Specifically, Flex-MoE first trains experts using samples with all modalities to inject generalized knowledge through the generalized router ($\mathcal{G}$-Router). The $\mathcal{S}$-Router then specializes in handling fewer modality combinations by assigning the top-1 gate to the expert corresponding to the observed modality combination. We evaluate Flex-MoE on the ADNI dataset, which encompasses four modalities in the Alzheimer's Disease domain, as well as on the MIMIC-IV dataset. The results demonstrate the effectiveness of Flex-MoE highlighting its ability to model arbitrary modality combinations in diverse missing modality scenarios. Code is available at https://github.com/UNITES-Lab/flex-moe.

cs.LG↗