Papers Common Sense Reasoning
“Common Sense Reasoning” 태그가 달린 논문 954편 · 필터 해제
The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes
Large Language Models (LLMs) have achieved strong performance across natural language processing tasks, yet reliable reasoning remains an open challenge. Although modern LLMs show progress in structured inference, multi-…
Mathematical ReasoningCommon Sense ReasoningReinforcement LearningDomain GeneralizationMAGNIFIED: RL Fine-tuning of Multimodal Large Language Models for Motion Planning
Multi-modal Large Language Models (MLLMs) have demonstrated remarkable capabilities in semantic understanding and common sense reasoning, making them promising candidates for solving planning problems in autonomous drivi…
Common Sense ReasoningReinforcement LearningAutonomous VehiclesAutonomous DrivingERGeoBench:A Comprehensive Benchmark for Embodied Reasoning and Geo-localization in Multimodal Large Language Models
Multimodal large language models (MLLMs) have shown strong potential as embodied agents, yet embodied geo-localization remains underexplored due to the lack of fine-grained evaluation. We introduce ERGeoBench, a diagnost…
Common Sense ReasoningSpatial ReasoningConQuR: Corner Aligned Activation Quantization via Optimized Rotations for LLMs
Large language models (LLMs) are costly to deploy due to their large memory footprint and high inference cost. Weight-activation quantization can reduce these costs, but low-bit activation quantization remains difficult …
Common Sense ReasoningLeveraging Pretrained Language Models as Energy Functions for Glauber Dynamics Text Diffusion
We present a discrete diffusion-based language model using Glauber dynamics from statistical physics. Our main insight is that instead of trying to train a discrete state space diffusion model using Glauber dynamics with…
Common Sense ReasoningLong-Context Aware Upcycling: A New Frontier for Hybrid LLM Scaling
Hybrid sequence models that combine efficient Transformer components with linear sequence modeling blocks are a promising alternative to pure Transformers, but most are still pretrained from scratch and therefore fail to…
Common Sense ReasoningThe Equivalence Theorem: First-Class Relationships for Structurally Complete Database Systems
We prove The Equivalence Theorem: structurally complete knowledge representation requires exactly four mutually entailing capabilities -- n-ary relationships with attributes, temporal validity, uncertainty quantification…
Common Sense ReasoningNovelty Adaptation Through Hybrid Large Language Model (LLM)-Symbolic Planning and LLM-guided Reinforcement Learning
In dynamic open-world environments, autonomous agents often encounter novelties that hinder their ability to find plans to achieve their goals. Specifically, traditional symbolic planners fail to generate plans when the …
Reinforcement LearningCommon Sense ReasoningSinhala Physical Common Sense Reasoning Dataset for Global PIQA
This paper presents the first-ever Sinhala physical common sense reasoning dataset created as part of Global PIQA. It contains 110 human-created and verified data samples, where each sample consists of a prompt, the corr…
Common Sense ReasoningVOST-SGG: VLM-Aided One-Stage Spatio-Temporal Scene Graph Generation
Spatio-temporal scene graph generation (ST-SGG) aims to model objects and their evolving relationships across video frames, enabling interpretable representations for downstream reasoning tasks such as video captioning a…
Visual Question AnsweringScene Graph GenerationCommon Sense ReasoningVideo CaptioningAudioScene: Integrating Object-Event Audio into 3D Scenes
The rapid advances in audio analysis underscore its vast potential for humancomputer interaction, environmental monitoring, and public safety; yet, existing audioonly datasets often lack spatial context. To address this …
Common Sense ReasoningVisual GroundingPredicting Task Performance with Context-aware Scaling Laws
Scaling laws have transformed our understanding of large language models by linking upstream metrics like cross-entropy loss to design factors such as model size, training data, and compute. However, these conventional l…
Common Sense ReasoningArithmetic ReasoningMachine TranslationBreaking Language Barriers: Equitable Performance in Multilingual Language Models
Cutting-edge LLMs have emerged as powerful tools for multilingual communication and understanding. However, LLMs perform worse in Common Sense Reasoning (CSR) tasks when prompted in low-resource languages (LRLs) like Hin…
Common Sense ReasoningCoherent Multimodal Reasoning with Iterative Self-Evaluation for Vision-Language Models
Despite significant advancements, current large language models (LLMs) and vision-language models (LVLMs) continue to struggle with complex, multi-step, cross-modal common sense reasoning tasks, often exhibiting a lack o…
Common Sense ReasoningMultimodal ReasoningComparing Apples to Oranges: A Dataset & Analysis of LLM Humour Understanding from Traditional Puns to Topical Jokes
Humour, as a complex language form, is derived from myriad aspects of life, whilst existing work on computational humour has focussed almost exclusively on short pun-based jokes. In this work, we investigate whether the …
Common Sense ReasoningWorld KnowledgeSingLoRA: Low Rank Adaptation Using a Single Matrix
Low-Rank Adaptation (LoRA) has significantly advanced parameter-efficient fine-tuning of large pretrained models. LoRA augments the pre-trained weights of a model by adding the product of two smaller matrices that togeth…
parameter-efficient fine-tuningCommon Sense ReasoningImage GenerationLoSiA: Efficient High-Rank Fine-Tuning via Subnet Localization and Optimization
Parameter-Efficient Fine-Tuning (PEFT) methods, such as LoRA, significantly reduce the number of trainable parameters by introducing low-rank decomposition matrices. However, existing methods perform extensive matrix mul…
Common Sense Reasoningparameter-efficient fine-tuningEditInspector: A Benchmark for Evaluation of Text-Guided Image Edits
Text-guided image editing, fueled by recent advancements in generative AI, is becoming increasingly widespread. This trend highlights the need for a comprehensive framework to verify text-guided edits and assess their qu…
Artifact DetectionCaption GenerationCommon Sense Reasoningtext-guided-image-editingCheckManual: A New Challenge and Benchmark for Manual-based Appliance Manipulation
Correct use of electrical appliances has significantly improved human life quality. Unlike simple tools that can be manipulated with common sense, different parts of electrical appliances have specific functions defined …
Common Sense ReasoningQuestion AnsweringPrime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search
The problem of relocating a set of objects to designated areas amidst movable obstacles can be framed as a Geometric Task and Motion Planning (G-TAMP) problem, a subclass of task and motion planning (TAMP). Traditional a…
Common Sense ReasoningMotion PlanningTask and Motion PlanningTask Planning