Papers Logical Reasoning
“Logical Reasoning” 태그가 달린 논문 1,058편 · 필터 해제
Rewarding Novel Deductions: Solver-guided Process Supervision for Logical Reasoning
Logical reasoning remains a major challenge for large language models (LLMs), particularly on structured problems that require precise constraint tracking, consistency preservation, and multi-step deduction. This challen…
Logical ReasoningDODR: Deterministic Operator-Driven Reasoning in Latent Space
Autoregressive (AR) large language models formulate reasoning as token-level probabilistic sampling, which induces three fundamental defects in complex logical reasoning: error accumulation, probability substituting nece…
Logical ReasoningBeyond Surface Forms: Symbolic Edits as a Test for Logical Reasoning with LLMs
Logical reasoning with large language models (LLMs) is a critical capability, as it reflects a system's ability to correctly deduce hypotheses from a given context using faithful deductive processes. However, LLM reasoni…
Logical ReasoningSymbolLKG: Towards Verifiable Logical Reasoning via Logical Knowledge Graph and Symbolic Solvers
Large Language Models (LLMs) have demonstrated remarkable proficiency in natural language understanding, yet they struggle with strict multi-step reasoning, frequently suffering from hallucinations and inconsistency. Exi…
Natural Language UnderstandingLogical ReasoningNo Judgment Without a Reason: Counterfactual Receipts for Versioned AI Evaluators
Evaluators often produce correct labels via flawed reasoning, a critical failure for agentic systems gating actions, routing reviews, or supplying training feedback. Standard evaluation only verifies final label correctn…
Logical ReasoningTowards general embodied intelligence: integrating large language models, knowledge bases, and reasoning capabilities to build the next generation of AI agents
The convergence of large language models (LLMs), structured knowledge bases (KBs), and reasoning ability (RA) presents a promising trajectory toward general embodied intelligence (GEI). This paper reviews the evolution o…
Continual LearningLogical ReasoningCoAL-RAG: A Complexity-Aware Legal Retrieval-Augmented Generation Method
Legal consultation questions exhibit multi-level complexity. A single retrieval strategy often leads to over-reasoning for simple questions and poor interpretability for complex ones, making it difficult to meet the requ…
Semantic RetrievalLogical ReasoningClosureBench: A Constructive Benchmark for Compositional Graph Reasoning
Large language models fail on multi-step compositional reasoning, but measuring that failure is hard, because new models are trained on the benchmarks used to evaluate them. A fixed test set becomes a memorisation check …
Logical ReasoningCURV: Enhancing Chart Understanding Through Curriculum Visual Grounded Reasoning
Chart question answering (CQA) requires multimodal large language models (MLLMs) to integrate visual comprehension with logical reasoning, yet current models struggle with accurate visual grounding and coherent reasoning…
Chart Question AnsweringMultimodal ReasoningLogical ReasoningVisual ReasoningNative Multilingual Chain-of-Thought Reasoning in Low-Resource Southeast Asian Languages
Large Language Models have achieved substantial progress in reasoning capabilities. Yet in low-resource native settings, many suffer from cross-lingual collapse, reverting to English during intermediate steps that requir…
Mathematical ReasoningLogical ReasoningDo Diagrams Help Large Language Models Reason? Evidence from Syllogistic Reasoning
Diagrams are widely used to support logical reasoning, and prior studies suggest that representations such as Euler diagrams can improve human reasoning performance. Recent work has also explored their effects on large l…
Logical ReasoningSymStep: Symbolic Step Verification for Logical Reasoning
Chain-of-thought (CoT) prompting can fail severely on constraint-dense logical reasoning tasks, where unverified errors accumulate silently across steps. We introduce SymStep: an LLM makes one atomic claim at a time (DED…
Logical ReasoningX$^3$-OPD: Distilling Reasoning into Large Audio-Language Models via On-Policy Alignment
While large audio-language models have achieved remarkable progress in auditory perception, they still lag behind text-based large language models in deep logical reasoning, primarily due to the scarcity of high-quality …
Logical ReasoningAn Evaluation Framework for Structured Audio Captions Validated by Controlled Perturbations
Recent advancements in automated audio captioning (AAC) have shifted from monolithic sentence generation toward structured formats that explicitly disentangle distinct acoustic and semantic properties. However, evaluatin…
Logical ReasoningAudio captioningEuclid-MCP: A Model Context Protocol Server for Deterministic Logical Reasoning via Prolog
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-symb…
Natural Language UnderstandingLogical ReasoningDifferentiable Logic Programming to Mitigate Reasoning Shortcuts in Neurosymbolic Systems
Neurosymbolic (NeSy) systems integrate neural networks with logical reasoning to achieve both generalization and interpretability, but recent work has shown they are susceptible to shortcut reasoning behaviors. We propos…
Logical ReasoningNotes to Self: Can LLMs Benefit from Experiential Abstractions?
Humans distill experience into reusable abstractions, e.g., strategies and cautionary reminders, and apply them to gradually solve problems more effectively. We study whether Large Language Models (LLMs) can similarly be…
Reinforcement LearningLogical ReasoningCoTu at EXACT 2026: Neuro-Symbolic Reasoning for Transparent Educational QA
Transparent educational question answering asks for answers that are not only correct but explainable, and doing so with small models rules out the reasoning power of the largest proprietary systems. The EXACT 2026 compe…
Question AnsweringLogical ReasoningControlled Reformulation Testing for Logical Consistency in Large Language Models
Large language models (LLMs) frequently contradict themselves when the surface form of a logically equivalent question changes. We present a benchmark of 350 question families (1,750 total questions) for Controlled Refor…
Logical ReasoningHABIB_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 Logic