https://github.com/xfgao/dialfred
공식 코드DialFRED: Dialogue-Enabled Agents for Embodied Instruction Following
Papers with Code 아카이브
Logical Reasoning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 22
- 평가표
- 10
- 보존된 평가 기록
- 65
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/xiangchensong/ctrlns
공식 코드Causal Temporal Representation Learning with Nonstationary Sparse Transition
https://github.com/xiangyangli20/kerm
공식 코드KERM: Knowledge Enhanced Reasoning for Vision-and-Language Navigation
https://github.com/xiaohanlei/ieve
공식 코드Instance-aware Exploration-Verification-Exploitation for Instance ImageGoal Navigation
https://github.com/xiaomi/determlr
공식 코드DetermLR: Augmenting LLM-based Logical Reasoning from Indeterminacy to Determinacy
https://github.com/xiaoqian-shen/StoryGPT-V
공식 코드StoryGPT-V: Large Language Models as Consistent Story Visualizers
https://github.com/xid32/naacl_2025_twm
공식 코드Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal Understanding
https://github.com/xid32/soundmind
공식 코드SoundMind: RL-Incentivized Logic Reasoning for Audio-Language Models
https://github.com/xingjian-zhang/massw
공식 코드MASSW: A New Dataset and Benchmark Tasks for AI-Assisted Scientific Workflows
https://github.com/xingyi-li/icontrol3d
공식 코드iControl3D: An Interactive System for Controllable 3D Scene Generation
https://github.com/xinyuanwangcs/promptagent
공식 코드PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization
https://github.com/xiyuan-zhou/elecbench-a-power-dispatch-evaluation-benchmark-for-large-language-models
공식 코드ElecBench: a Power Dispatch Evaluation Benchmark for Large Language Models
https://github.com/xl-sr/CAL
공식 코드Conditional Affordance Learning for Driving in Urban Environments
https://github.com/xmindflow/awesome_mamba
공식 코드Computation-Efficient Era: A Comprehensive Survey of State Space Models in Medical Image Analysis
https://github.com/xqh19970407/InvDesFlow
공식 코드https://github.com/xuanxuanxuan-git/facelift
공식 코드Navigating Explanatory Multiverse Through Counterfactual Path Geometry
https://github.com/xufangzhi/logiformer
공식 코드Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning
https://github.com/xufangzhi/taco
공식 코드https://github.com/xwjim/DRN
공식 코드Discriminative Reasoning for Document-level Relation Extraction
https://github.com/xxxiaol/casa
공식 코드https://github.com/xyfrieder/science-ghosts
공식 코드https://github.com/xyz9911/FLAME
공식 코드FLAME: Learning to Navigate with Multimodal LLM in Urban Environments
https://github.com/xzascc/probingreflection
공식 코드From Emergence to Control: Probing and Modulating Self-Reflection in Language Models
https://github.com/xzhe-vision/personamagic
공식 코드PersonaMagic: Stage-Regulated High-Fidelity Face Customization with Tandem Equilibrium
https://github.com/yabozkurt/gmvae
공식 코드Interpretable Embeddings From Molecular Simulations Using Gaussian Mixture Variational Autoencoders
https://github.com/yale-lily/folio
공식 코드https://github.com/yanci87/lr-xfl
공식 코드LR-XFL: Logical Reasoning-based Explainable Federated Learning
https://github.com/yanweiw/noise2ptz
공식 코드Visual Pre-training for Navigation: What Can We Learn from Noise?
https://github.com/yasirbarlas/RL-BOED
공식 코드Performance Comparisons of Reinforcement Learning Algorithms for Sequential Experimental Design
https://github.com/ycpnotfound/geogen
공식 코드Enhancing the Geometric Problem-Solving Ability of Multimodal LLMs via Symbolic-Neural Integration
https://github.com/yichengduan/oobvlm
공식 코드https://github.com/yijia-xiao/logicvista
공식 코드LogicVista: Multimodal LLM Logical Reasoning Benchmark in Visual Contexts
https://github.com/yinghao-li/minesweeper-for-llm
공식 코드Assessing Logical Puzzle Solving in Large Language Models: Insights from a Minesweeper Case Study
https://github.com/yiranwu0/mathchat
공식 코드MathChat: Converse to Tackle Challenging Math Problems with LLM Agents
https://github.com/yiwei98/esc
공식 코드Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning
https://github.com/yixxin/xcon
공식 코드XCon: Learning with Experts for Fine-grained Category Discovery
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.