Jul 2026· Proceedings of the TwentyThird International Conference on Principles of Knowledge Representation and Reasoning· pp. 1098-1108· 0 citations
TL;DR
An empirical evaluation shows that ALM–ASP significantly improves both syntactic validity and end-to-end correctness over general-purpose LLM baselines, and also achieves improved instance coverage compared to the closest agentic alternative, CP–Agent.
Abstract
Answer Set Programming (ASP) is a declarative formalism widely used in knowledge representation and reasoning for modeling and solving combinatorial problems, yet current Large Language Models (LLMs) often struggle to generate correct programs from natural language specifications. This difficulty stems both from the limited presence of ASP in training corpora and from the strict syntactic and semantic constraints imposed by stable model semantics. We introduce ALM–ASP (Agentic Loop for Modeling in ASP), a multi-agent architecture for automatic ASP modeling grounded in a functional model of language agents equipped with tools and persistent state. ALM–ASP instantiates this model via two interacting agents: a Modeler, which incrementally constructs candidate ASP programs, and a Validator, which assesses their alignment with the original specification and provides feedback for refinement. The agents interact through a shared ASP execution environment backed by the CLINGO engine, yielding an iterative construct–validate loop. An empirical evaluation on a challenging subset of CP–Bench and on problems from recent LP/CP Programming Contests shows that ALM–ASP significantly improves both syntactic validity and end-to-end correctness over general-purpose LLM baselines, and also achieves improved instance coverage compared to the closest agentic alternative, CP–Agent.
Reducing bug-triggering programs to their minimal essential form is a fundamental task in debugging language processors such as compilers and interpreters. Existing reduction techniques are limited by their reliance on predefined, syntax-driven transformations that lack semantic understanding of the target program, and by their inability to learn from past reduction experiences. We present a new approach that recasts program reduction as an autonomous reasoning task powered by agentic Large Language Models (LLMs). Instead of applying fixed transformation rules, our method enables an LLM to analyze program semantics, formulate reduction hypotheses, and iteratively refine its approach based on execution outcomes. Successful reduction experiences are further distilled into reusable strategies, allowing the system to continuously improve over time. We realize this approach in PROJ, a framework built around two collaborative components: a reducer agent that performs semantic-aware, case-specific program reduction, and a reflector agent that extracts and accumulates transferable reduction knowledge. Extensive experiments on 90 benchmarks spanning three programming languages show that PROJ consistently produces smaller reduced programs than all existing state-of-the-art reducers while maintaining high efficiency.
Xintong Zhou, Hongxu Xu, Chun-Feng Liao et al.· 0 citations
These results show that compiling procedural structure improves the reliability and efficiency of skill execution while retaining model judgment where it is needed, and shows that compiling procedural structure improves the reliability and efficiency of skill execution while retaining model judgment where it is needed.
Jayanaka L. Dantanarayana, Savini Kashmira, Lingjia Tang et al.· 0 citations
Large Language Models (LLMs) excel at natural language understanding and generation but remain unreliable for multi-step logical reasoning, especially in safety-critical or compliance-sensitive domains. Recent neuro-symbolic approaches address this gap by coupling neural models with external symbolic engines, yet most integrations are bespoke and lack a standardized interface for tool-augmented agents. This paper presents Euclid-MCP, an open-source MCP server that provides deterministic logical reasoning via SWI-Prolog. Euclid-MCP introduces Euclid-IR, an engine-agnostic intermediate representation for Horn-clause logic that is human-readable, easy for LLMs to generate, and straightforward to compile into Prolog or alternative backends. The server exposes a compact tool interface that supports a translate-run-inspect-repair loop, enabling LLM clients to delegate inference while retaining full access to proof traces and derivation logs. We evaluate Euclid-MCP on a realistic IT security and compliance use case. Results show that while LLMs alone are sufficient on small knowledge bases, they hallucinate systematically on larger problems, whereas Euclid-MCP delivers exact answers with lower latency and more compact outputs. We argue that semantic RAG is fundamentally unsuited for rule enforcement, and that Euclid-MCP can serve as a stable, shared reasoning substrate for both RAG-based assistants and agentic systems.
Multi-agent systems built on large language models (LLMs) are increasingly deployed for complex tasks requiring autonomous planning, tool use, and inter-agent coordination. However, the non-deterministic nature of LLM outputs and the emergent behavior arising from agent interactions render traditional test oracles ineffective, creating a critical gap in quality assurance for agentic AI. This work introduces MORPHAGENT, a framework designed to address the oracle problem in multi-agent LLM systems through trace-based behavioral analysis. Our contributions are threefold: (1) goal-preservation relations that verify consistent goal achievement under input perturbations, (2) coordination-consistency relations that validate inter-agent delegation and communication patterns under agent substitution and reordering, and (3) tool-use integrity relations that ensure semantic equivalence of tool invocation sequences under prompt paraphrasing. MorphAgent instruments agent execution to capture structured traces comprising planning steps, tool calls, message exchanges, and final outputs, then systematically applies metamorphic transformations and checks behavioral invariants without requiring ground-truth oracles. We evaluate the framework on four multi-agent benchmarks spanning code generation, research synthesis, customer service, and data analysis tasks, encompassing 2,840 source-followup execution pairs across three LLM backends. Results show that MORPHAGENT detects 82.0% of seeded behavioral faults, including 90.3% of coordination failures and 81.7% of goal-deviation faults, while maintaining a false positive rate of 6.1%. The framework uncovers 14 previously unreported behavioral anomalies in established multi-agent frameworks, demonstrating its practical utility for assuring agentic AI reliability. These results suggest that trace-based metamorphic testing can serve as a practical foundation for reliable validation of emerging agentic AI systems.
Gopalakrishnan Marimuthu· International Conference on...· 0 citations
This note argues that a skill is a software artefact and that its construction should follow software-engineering principles, with qualifications: single responsibility, separation of interface from implementation, low coupling, and economy in a shared token budget, together with behavioural evaluation in place of deterministic testing.
CEDAR is presented, a counterexample-guided framework that grounds instructions as regular languages over environment event traces and represents both skills and specifications as deterministic finite automata, suggesting that regular languages offer a practical verification layer between natural-language instructions and embodied-agent policies.
Le Chen, Alvaro Velasquez, Ashutosh Trivedi· 0 citations