Formal Logic
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Benchmarks
BIG-bench
Most implemented
Learning Deductive Reasoning from Synthetic Corpus based on Formal Logic
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Learning Symbolic Rules for Reasoning in Quasi-Natural Language
Are LLMs Reliable Translators of Logical Reasoning Across Lexically Diversified Contexts?
Training Step-Level Reasoning Verifiers with Formal Verification Tools
Papers
Recover, Decode, Reguard: Guard-Agnostic Defense Amplification againstEncoded VLM Jailbreaks
Safety classifiers ("guards") are the dominant black-box defense for vision-language models, yet they judge an input's surface form, not its meaning: a harmful request re-encoded as set theory, formal logic, a rare langu…
Formal LogicLet AI Agents Translate Networks, Not Reason About Them
A formal model enables verifying reachability, localizing an outage, or anticipating the blast radius of a change. Yet, virtually no production network has one, since writing a model by hand demands rare expertise and is…
Formal LogicInvariant Discovery for Networked Systems
Invariants, the relations expected to hold among measured signals of a network, underpin applications from verification to traffic generation, telemetry imputation, and input validation, yet writing them by hand demands …
Formal LogicHABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective Optimization
While Large Language Models (LLMs) excel in many general NLP tasks, their formal reasoning capabilities are often compromised by content effects, demonstrating a measurable bias towards real-world plausibility. In this p…
Logical ReasoningFormal LogicReM-MoA: Reasoning Memory Sustains Mixture-of-Agents Scaling
Mixture-of-Agents (MoA) architectures improve inference-time scaling by organizing multiple LLM agents into layered reasoning pipelines. However, existing MoA variants fail to sustain gains as depth increases, exhibiting…
Formal LogicUncertainty Reasoning with Large Language Models for Explainable Disease Diagnosis
Clinical decision-making requires reasoning over incomplete, imprecise, and linguistically expressed patient narratives. While large language models (LLMs) excel at extracting latent information from natural language, th…
Medical DiagnosisFormal Logic