The Imandra Automated Reasoning System (system description)

• URL: http://arxiv.org/abs/2004.10263v1
• Date: Tue, 21 Apr 2020 19:57:34 GMT
• Title: The Imandra Automated Reasoning System (system description)
• Abstract summary: Imandra is a modern computational logic theorem prover. It bridges the gap between decision procedures like SMT and inductive provers like ACL2. Imandra has novel features supporting large-scale industrial applications.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We describe Imandra, a modern computational logic theorem prover designed to bridge the gap between decision procedures such as SMT, semi-automatic inductive provers of the Boyer-Moore family like ACL2, and interactive proof assistants for typed higher-order logics. Imandra's logic is computational, based on a pure subset of OCaml in which all functions are terminating, with restrictions on types and higher-order functions that allow conjectures to be translated into multi-sorted first-order logic with theories, including arithmetic and datatypes. Imandra has novel features supporting large-scale industrial applications, including a seamless integration of bounded and unbounded verification, first-class computable counterexamples, efficiently executable models and a cloud-native architecture supporting live multiuser collaboration. The core reasoning mechanisms of Imandra are (i) a semi-complete procedure for finding models of formulas in the logic mentioned above, centered around the lazy expansion of recursive functions, and (ii) an inductive waterfall and simplifier which "lifts" many Boyer-Moore ideas to our typed higher-order setting. These mechanisms are tightly integrated and subject to many forms of user control. Imandra's user interfaces include an interactive toplevel, Jupyter notebooks and asynchronous document-based verification (in the spirit of Isabelle's Prover IDE) with VS Code.

Related papers

• A Compositional Theory of Causally Masked Transformers [50.88598486616582]
  We develop a formalization that derives expressivity directly from the model's implemented dynamics.<n>Each attention head updates its own state independently within a layer.<n>A modified form of soft attention supports irreversible, checklist-like state.
  arXiv  Detail & Related papers  (2026-07-29T14:47:19Z)
• CRAFTQA: A Code-Driven Adaptive Framework for Complex Structured Data Reasoning [17.012985962958194]
  Real-world scenarios involve massive heterogeneous structured data (e.g., tables, knowledge graphs), making effective reasoning over such diverse data increasingly important.<n>We propose CRAFTQA, a novel adaptive code-driven framework comprising two core modules, CodeSTEP and CRAFT.<n>The CodeSTEP module generates a complete executable Python code sequence, which contains step-by-step code-based reasoning operations based on the question.<n>The CRAFT module dynamically generates custom code functions for operations beyond the predefined function set, and seamlessly integrates with CodeSTEP to significantly enhance flexibility in handling complex reasoning.
  arXiv  Detail & Related papers  (2026-06-01T12:29:20Z)
• Stacked from One: Multi-Scale Self-Injection for Context Window Extension [69.24689919827817]
  modelname is a novel framework based on multi-grained context compression and query-aware information acquisition.<n>modelnameachieves performance superior or comparable to strong baselines.
  arXiv  Detail & Related papers  (2026-03-05T03:16:16Z)
• Do It for HER: First-Order Temporal Logic Reward Specification in Reinforcement Learning (Extended Version) [49.462399222747024]
  We propose a novel framework for the logical specification of non-Markovian rewards in Decision Processes (MDPs) with large state spaces.<n>Our approach leverages Linear Temporal Logic Modulo Theories over finite traces (LTLfMT)<n>We introduce a method based on reward machines and Hindsight Experience Replay (HER) to translate first-order logic specifications and address reward sparsity.
  arXiv  Detail & Related papers  (2026-02-05T22:11:28Z)
• Learning Multi-Order Block Structure in Higher-Order Networks [4.867153093815104]
  Higher-order networks are essential for modeling real-world systems involving interactions among three or more entities.<n>A recent simplification, a single-order model, mitigates this complexity by assuming a single affinity pattern governs interactions of all orders.<n>Here, we propose a framework that relaxes this assumption by introducing a multi-order block structure.
  arXiv  Detail & Related papers  (2025-11-26T12:56:37Z)
• Scaling Code-Assisted Chain-of-Thoughts and Instructions for Model Reasoning [65.20602712957725]
  Caco is a novel framework that automates the synthesis of high-quality, verifiable, and diverse instruction-CoT reasoning data.<n>Our work establishes a paradigm for building self-sustaining, trustworthy reasoning systems without human intervention.
  arXiv  Detail & Related papers  (2025-10-05T07:59:24Z)
• Generative Logic: A New Computer Architecture for Deterministic Reasoning and Knowledge Generation [0.0]
  Generative Logic (GL) is a deterministic architecture that begins from user-supplied axiomatic definitions.<n>GL enumerates candidate implications, applies normalization and type filters, and automatically reconstructs machine-checkable proofs.<n>The Python and MPL code to reproduce the Peano experiments, along with the full HTML proof graphs, are available in the project's GitHub repository.
  arXiv  Detail & Related papers  (2025-07-25T17:29:19Z)
• Sequential-Parallel Duality in Prefix Scannable Models [68.39855814099997]
  Recent developments have given rise to various models, such as Gated Linear Attention (GLA) and Mamba.<n>This raises a natural question: can we characterize the full class of neural sequence models that support near-constant-time parallel evaluation and linear-time, constant-space sequential inference?
  arXiv  Detail & Related papers  (2025-06-12T17:32:02Z)
• Transformers for Complex Query Answering over Knowledge Hypergraphs [48.55646194244594]
  Triple KGs, as the classic KGs composed of entities and relations of arity 2, have limited representation of real-world facts. We propose a two-stage transformer model, the Logical Knowledge Hypergraph Transformer (LKHGT), which consists of a Projection for atomic projection and a Logical for complex logical operations. Experimental results on CQA datasets show that LKHGT is a state-of-the-art CQA method over KHG and is able to generalize to out-of-distribution query types.
  arXiv  Detail & Related papers  (2025-04-23T09:07:21Z)
• Syzygy of Thoughts: Improving LLM CoT with the Minimal Free Resolution [59.39066657300045]
  Chain-of-Thought (CoT) prompting enhances the reasoning of large language models (LLMs) by decomposing problems into sequential steps. We propose Syzygy of Thoughts (SoT)-a novel framework that extends CoT by introducing auxiliary, interrelated reasoning paths. SoT captures deeper logical dependencies, enabling more robust and structured problem-solving.
  arXiv  Detail & Related papers  (2025-04-13T13:35:41Z)
• A Semantic Parsing Algorithm to Solve Linear Ordering Problems [2.23890712706409]
  We develop an algorithm to semantically parse linear ordering problems. Our method takes as input a number of premises and candidate statements. We then utilize constraint logic programming to infer the truth of proposed statements about the ordering.
  arXiv  Detail & Related papers  (2025-02-12T13:58:42Z)
• Effective Instruction Parsing Plugin for Complex Logical Query Answering on Knowledge Graphs [51.33342412699939]
  Knowledge Graph Query Embedding (KGQE) aims to embed First-Order Logic (FOL) queries in a low-dimensional KG space for complex reasoning over incomplete KGs. Recent studies integrate various external information (such as entity types and relation context) to better capture the logical semantics of FOL queries. We propose an effective Query Instruction Parsing (QIPP) that captures latent query patterns from code-like query instructions.
  arXiv  Detail & Related papers  (2024-10-27T03:18:52Z)
• Improving Complex Reasoning over Knowledge Graph with Logic-Aware Curriculum Tuning [89.89857766491475]
  We propose a complex reasoning schema over KG upon large language models (LLMs) We augment the arbitrary first-order logical queries via binary tree decomposition to stimulate the reasoning capability of LLMs. Experiments across widely used datasets demonstrate that LACT has substantial improvements(brings an average +5.5% MRR score) over advanced methods.
  arXiv  Detail & Related papers  (2024-05-02T18:12:08Z)
• Assessing Logical Reasoning Capabilities of Encoder-Only Transformer Models [0.13194391758295113]
  We investigate the extent to which encoder-only transformer language models (LMs) can reason according to logical rules. We show for several encoder-only LMs that they can be trained, to a reasonable degree, to determine logical validity on various datasets. By cross-probing fine-tuned models on these datasets, we show that LMs have difficulty in transferring their putative logical reasoning ability.
  arXiv  Detail & Related papers  (2023-12-18T21:42:34Z)
• Sorted LLaMA: Unlocking the Potential of Intermediate Layers of Large Language Models for Dynamic Inference [32.62084449979531]
  We extend SortedNet to generative NLP tasks by replacing Standard Fine-Tuning (SFT) with Sorted Fine-Tuning (SoFT) Our approach boosts model efficiency, eliminating the need for multiple models for various scenarios during inference. Our results show the superior performance of sub-models in comparison to Standard Fine-Tuning and SFT+ICT (Early-Exit)
  arXiv  Detail & Related papers  (2023-09-16T11:58:34Z)
• Query Structure Modeling for Inductive Logical Reasoning Over Knowledge Graphs [67.043747188954]
  We propose a structure-modeled textual encoding framework for inductive logical reasoning over KGs. It encodes linearized query structures and entities using pre-trained language models to find answers. We conduct experiments on two inductive logical reasoning datasets and three transductive datasets.
  arXiv  Detail & Related papers  (2023-05-23T01:25:29Z)
• Join-Chain Network: A Logical Reasoning View of the Multi-head Attention in Transformer [59.73454783958702]
  We propose a symbolic reasoning architecture that chains many join operators together to model output logical expressions. In particular, we demonstrate that such an ensemble of join-chains can express a broad subset of ''tree-structured'' first-order logical expressions, named FOET. We find that the widely used multi-head self-attention module in transformer can be understood as a special neural operator that implements the union bound of the join operator in probabilistic predicate space.
  arXiv  Detail & Related papers  (2022-10-06T07:39:58Z)
• On the Complexity of Rational Verification [5.230352342979224]
  Rational verification refers to the problem of checking which temporal logic properties hold of a concurrent multiagent system. We show that the complexity of rational verification can be greatly reduced by specifications. We provide improved results for rational verification when considering players' goals given by mean-payoff utility functions.
  arXiv  Detail & Related papers  (2022-07-06T12:56:22Z)
• ORCHARD: A Benchmark For Measuring Systematic Generalization of Multi-Hierarchical Reasoning [8.004425059996963]
  We show that Transformer and LSTM models surprisingly fail in systematic generalization. We also show that with increased references between hierarchies, Transformer performs no better than random.
  arXiv  Detail & Related papers  (2021-11-28T03:11:37Z)
• Transformer-based Machine Learning for Fast SAT Solvers and Logic Synthesis [63.53283025435107]
  CNF-based SAT and MaxSAT solvers are central to logic synthesis and verification systems. In this work, we propose a one-shot model derived from the Transformer architecture to solve the MaxSAT problem.
  arXiv  Detail & Related papers  (2021-07-15T04:47:35Z)
• Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models [61.480085460269514]
  We propose a framework for building interpretable systems that learn to solve complex tasks by decomposing them into simpler ones solvable by existing models. We use this framework to build ModularQA, a system that can answer multi-hop reasoning questions by decomposing them into sub-questions answerable by a neural factoid single-span QA model and a symbolic calculator.
  arXiv  Detail & Related papers  (2020-09-01T23:45:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.