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PDDL

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3

RESEARCH · 1 SOURCE · arXiv cs.AI

M-Plan: closed-loop multi-agent framework integrating long- and short-term memory for LLM agents

M-Plan is a closed-loop multi-agent system (MAS) framework that combines long-term and short-term memory via global-local and past-current task-specific guidance. The paper (arXiv:2610.27032v1) describes guidance generation during execution, local hints when agents are confused, post-task memory optimization for future retrieval, and reports improved accuracy and efficiency on multi-turn benchmarks including ALFWorld, WebShop, and PDDL compared to several baselines.

6.0

RESEARCH · 1 SOURCE · arXiv cs.AI

Testing LLM-based agent planning against formal PDDL semantics

This arXiv paper evaluates claims that LLM-based agents can perform planning by grounding natural-language tasks in hidden PDDL models that formally determine action applicability and goal achievement. Across five domains, four models, six prompting strategies and three agent architectures, the best configuration solved 78% of solvable tasks offline but only 40% online; unsolvable tasks were recognised in under 10% of episodes, and heavier prompting used 5–22× more tokens without clear benefit.

7.0

RESEARCH · 1 SOURCE · arXiv cs.AI

DRAGO: deterministic diagnosis framework to repair LLM-generated formal specifications

The paper introduces DRAGO, a repair framework that replaces ungrounded LLM critiques with deterministic, formalism-specific diagnosis and only accepts rewrites when a reference-free acceptance predicate shows reduced diagnosed error weight. Evaluated on SMT-LIB2, MiniZinc and PDDL benchmarks, DRAGO cuts specification regressions from 11.8% to 1.3% and raises answer accuracy by 12.9 percentage points over zero-shot formalization and 8.7 percentage points over LLM-diagnostic repair; the authors also identify and correct rendering defects that made half of the Planetarium split inconsistent with references.

7.0