satisfiability modulo theories
PulseAugur coverage of satisfiability modulo theories — every cluster mentioning satisfiability modulo theories across labs, papers, and developer communities, ranked by signal.
6 day(s) with sentiment data
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New SM Trap method enables cost-effective DoS attacks on large reasoning models
Researchers have developed a new method called SM Trap to launch cost-effective denial-of-service (DoS) attacks against large reasoning models (LRMs). This technique bypasses the need for direct model feedback or traini…
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New neuro-symbolic frameworks boost hardware verification efficiency
Two new research papers introduce novel neuro-symbolic frameworks for accelerating hardware verification. The first, NeuroAssertion, uses LLMs and formal methods to generate more comprehensive and reliable RTL assertion…
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CPMpy library translates constraint models across solvers
Researchers have developed CPMpy, an open-source library designed to translate high-level constraint satisfaction and optimization models into various lower-level formalisms. This framework allows users to express probl…
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AI system learns network behavior autonomously for verification
Researchers have developed a novel approach to network verification by creating self-evolving verifiers that automatically learn and adapt to actual network behavior. This system uses a coding agent to propose extension…
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New research refines decision tree performance and sensitivity analysis · 2 sources tracked
Two new research papers explore advancements in decision tree algorithms. The first paper, "Optimal or Greedy Decision Trees? Revisiting their Objectives, Tuning, and Performance," investigates optimal decision trees (O…
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New STL-GO methods tackle complex multi-agent planning challenges
Researchers have developed two new methods, one based on mixed-integer programming (MIP) and another on satisfiability modulo theory (SMT), to address multi-agent planning problems with complex spatio-temporal and topol…
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AI tools sought for multi-objective optimization on study data
A user on Reddit's r/MachineLearning subreddit is seeking recommendations for tools to perform Multi-Objective Surrogate-Based Optimization (MOSBO) on heterogeneous study data. The project involves fitting a continuous …
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EZSMTV3 framework advances hybrid reasoning for complex problems · 1 source tracked
A new framework called EZSMTV3 has been developed for Constraint Answer Set Programming (CASP), a hybrid reasoning paradigm combining Answer Set Programming with Constraint Processing and Satisfiability Modulo Theories …
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New SMT-based method synthesizes paths for 2D and 3D maze construction
Researchers have developed a new pipeline for generating maze structures from input patterns like text or shapes. This process involves encoding the path-synthesis problem into Satisfiability Modulo Theories (SMT) as gl…
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New benchmark MINIF2F-DAFNY tests LLMs for mathematical theorem proving
Researchers have developed MINIF2F-DAFNY, a new benchmark for evaluating Large Language Models (LLMs) in mathematical theorem proving. This system translates the miniF2F benchmark to Dafny, an auto-active verifier, enab…
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New concolic testing method enhances Transformer robustness analysis
Researchers have developed a new concolic testing method for Transformer classifiers that uses SHAP estimates to prioritize path predicates based on their influence on the model's predictions. This approach, implemented…
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New tools enable formal verification of industrial PLC ladder diagram programs
Researchers have developed ESBMC-PLC and Graph-ESBMC-PLC, new tools for formally verifying industrial control programs written in the IEC 61131-3 Ladder Diagram (LD) format. These tools translate graphical LD programs i…
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New system uses AI and formal methods for better clinical trial matching
Researchers have developed SatIR, a novel retrieval system designed to improve the matching of patients to clinical trials. This system goes beyond simple semantic similarity by treating trial eligibility criteria as fo…
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LLM-Assisted System Enhances Industrial Planning with Natural Language Interaction
A new paper introduces a hybrid system that combines a Satisfiability Modulo Theories (SMT) planner with a Large Language Model (LLM) for industrial automation planning. This system aims to improve the interpretability …
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New k-NBCs enhance safety for unknown nonlinear systems
Researchers have developed k-inductive neural barrier certificates (k-NBCs) to enhance safety guarantees for nonlinear systems with unknown dynamics. This method relaxes traditional safety constraints by allowing tempor…
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New SMT-based algorithm learns weighted automata efficiently
Researchers have developed a new SMT-based active learning algorithm for nondeterministic weighted automata (WFAs). This method offers a practical and robust alternative to existing techniques, producing minimal WFAs an…