huggingfaceGitHubありHugging Faceあり2026-06-07
Trajectory-Refined Distillation
On-policy distillation (OPD) has become a central post-training tool for large language models (LLMs), providi
深層学習軽量化・量子化テキスト
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
→
「RAG」の検索結果
51 件huggingfaceHugging Faceあり2026-06-05
SWE-Explore: Benchmarking How Coding Agents Explore Repositories
Repository-level coding benchmarks such as SWE-bench have driven a rapid surge in the capabilities of coding a
深層学習軽量化・量子化検出テキスト
- 用途
- 検出
- 難易度
- Easy
- コスト
- Low
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huggingfaceHugging Faceあり2026-06-05
SlimSearcher: Training Efficiency-Aware Web Agents via Adaptive Reward Gating
Deep research agents have demonstrated remarkable capabilities in complex information-seeking tasks, yet this
深層学習Transformer強化学習
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
→
huggingfaceGitHubありHugging Faceあり2026-06-05
Your UnEmbedding Matrix is Secretly a Feature Lens for Text Embeddings
Large language models exhibit impressive zero-shot capabilities across a wide range of downstream tasks. Howev
品質予測/異常検知自然言語処理大規模言語モデルテキスト
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
→
huggingfaceHugging Faceあり2026-06-05
MMAE: A Massive Multitask Audio Editing Benchmark
We introduce MMAE, a Massive Multitask Audio Editing benchmark, serving as the first comprehensive evaluation
MI向き自然言語処理大規模言語モデル生成画像テキスト
- 用途
- 生成
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-06-05
dots.tts Technical Report
We present dots.tts, a 2B-parameter continuous autoregressive text-to-speech (TTS) foundation model that model
センサ/時系列品質予測/異常検知深層学習軽量化・量子化生成テキスト音声
- 用途
- 生成
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-06-04
Answer Presence Drives RAG Rewriting Gains
Retrieval-augmented QA pipelines often route retrieved passages through an LLM rewriter before a smaller reade
品質予測/異常検知自然言語処理大規模言語モデルテキスト
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
→
huggingfaceHugging Faceあり2026-06-04
Physics in 2-Steps: Locking Motion Priors Before Visual Refinement Erases Them
Image-to-Video diffusion models leverage input images to generate visually stunning content, yet frequently pr
自然言語処理RAG生成画像動画
- 用途
- 生成
- 難易度
- Easy
- コスト
- High
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huggingfaceGitHubありHugging Faceあり2026-06-04
Almieyar-Oryx-BloomBench: A Bilingual Multimodal Benchmark for Cognitively Informed Evaluation of Vision-Language Models
Despite the rapid progress of Vision-Language Models (VLMs), the field lacks benchmarks that rigorously diagno
品質予測/異常検知深層学習Transformer生成画像テキスト
- 用途
- 生成
- 難易度
- Easy
- コスト
- High
→
huggingfaceHugging Faceあり2026-06-04
Compress-Distill: Reasoning Trace Compression for Efficient Knowledge Distillation
Reasoning models produce long chain-of-thought traces that are costly to distill and encourage verbose student
深層学習軽量化・量子化
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
→
huggingfaceHugging Faceあり2026-06-04
Code2LoRA: Hypernetwork-Generated Adapters for Code Language Models under Software Evolution
Code language models need repository-level context to resolve imports, APIs, and project conventions. Existing
深層学習RNN / LSTMテキスト
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
→
huggingfaceGitHubありHugging Faceあり2026-06-04
MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discovery
Large language model (LLM) agents are increasingly applied to long-horizon tasks such as scientific discovery
自然言語処理大規模言語モデル生成テキスト
- 用途
- 生成
- 難易度
- Easy
- コスト
- High
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huggingfaceGitHubありHugging Faceあり2026-06-04
Dream.exe: Can Video Generation Models Dream Executable Robot Manipulation?
Video generation models have made impressive strides in synthesizing visually compelling content, yet their ou
品質予測/異常検知自然言語処理RAG生成画像動画
- 用途
- 生成
- 難易度
- Easy
- コスト
- High
→
huggingfaceHugging Faceあり2026-06-04
Unsupervised Skill Discovery for Agentic Data Analysis
Inference-time skill augmentation provides a lightweight way to improve data-analytic agents by injecting reus
品質予測/異常検知深層学習軽量化・量子化生成教師なし
- 用途
- 生成
- 難易度
- Easy
- コスト
- Low
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huggingfaceHugging Faceあり2026-06-04
LLMs Can Leak Training Data But Do They Want To? A Propensity-Aware Evaluation of Memorization in LLMs
Large language models can reproduce training data, but existing memorization evaluations mostly measure whethe
深層学習軽量化・量子化生成テキスト
- 用途
- 生成
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-06-04
Towards One-to-Many Temporal Grounding
Temporal Grounding (TG) aims to localize video segments corresponding to a textual query. Prior research predo
品質予測/異常検知自然言語処理大規模言語モデルテキスト動画
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
→
huggingfaceHugging Faceあり2026-06-04
Imagine Before You Predict: Interleaved Latent Visual Reasoning for Video Event Prediction
Video event prediction (VEP) requires models to infer unobserved future states from partial video evidence. Ex
自然言語処理大規模言語モデル画像テキスト動画
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
→
huggingfaceHugging Faceあり2026-06-04
Revising Context, Shifting Simulated Stance: Auditing LLM-Based Stance Simulation in Online Discussions
Large language models are increasingly used to simulate social media users and infer how individuals may respo
深層学習Transformerテキストマルチモーダル
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
→
huggingfaceGitHubありHugging Faceあり2026-06-04
AURA: Intent-Directed Probing for Implicit-Need Surfacing in Situated LLM Agents
A situated query like "where is Lin Wei?" often encodes more than its literal content: the user may also want
自然言語処理大規模言語モデル
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
→
huggingfaceHugging Faceあり2026-06-04
ForeSci: Evaluating LLM Agents for Forward-Looking AI Research Judgment
AI research often requires decisions before future evidence exists: which bottleneck to attack, which directio
自然言語処理大規模言語モデル生成予測
- 用途
- 生成
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-06-03
Agents' Last Exam
Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not transla
自然言語処理RAG
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- Low
→
huggingfaceHugging Faceあり2026-06-03
Why Muon Outperforms Adam: A Curvature Perspective
Muon improves training efficiency over Adam in large language-model training by about two times, but the local
深層学習正規化・最適化手法テキスト
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-06-03
GENEB: Why Genomic Models Are Hard to Compare
Progress in genomic foundation models is difficult to assess due to fragmented benchmarks, incompatible evalua
少数データ向き自然言語処理RAG
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
→
huggingfaceHugging Faceあり2026-06-03
TIDE: Proactive Multi-Problem Discovery via Template-Guided Iteration
Agents are widely deployed as assistants over documents, tools, and code. However, they typically act only on
自然言語処理RAGテキスト
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- Low
→
huggingfaceHugging Faceあり2026-06-03
Flash-WAM: Modality-Aware Distillation for World Action Models
World-action models (WAMs) jointly generate future video and robot actions through iterative diffusion, achiev
深層学習軽量化・量子化動画
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
→
huggingfaceHugging Faceあり2026-06-03
SePO: Self-Evolving Prompt Agent for System Prompt Optimization
System prompt optimization improves agent behavior without modifying the underlying model, yielding human-read
自然言語処理RAG生成テキスト
- 用途
- 生成
- 難易度
- Easy
- コスト
- High
→
huggingfaceHugging Faceあり2026-06-03
M^3Eval: Multi-Modal Memory Evaluation through Cognitively-Grounded Video Tasks
As multi-modal models advance towards long-form video understanding, memory emerges as a critical capability.
自然言語処理RAG動画
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
→
huggingfaceGitHubありHugging Faceあり2026-06-03
SpeechEditBench: A Bilingual Multi-Attribute Benchmark for Instruction-Guided Speech Editing
Instruction-guided speech editing requires a model to modify specified speech attributes while preserving unre
自然言語処理大規模言語モデル生成テキスト音声
- 用途
- 生成
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-06-02
Lean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectory
Equipping Large Language Models (LLMs) to execute reliable multi-step workflows has become a central challenge
自然言語処理大規模言語モデル検出テキスト
- 用途
- 検出
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-06-02
A Cookbook of 3D Vision: Data, Learning Paradigms, and Application
3D vision has rapidly evolved, driven by increasingly diverse data representations, learning paradigms, and mo
自然言語処理RAG生成動画3D
- 用途
- 生成
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-06-02
MAOAM: Unified Object and Material Selection with Vision-Language Models
Selection is a core operation in interactive image editing. To be practical, a user should be able to specify
MI向き自然言語処理RAG生成セグメンテーション画像
- 用途
- 生成
- 難易度
- Easy
- コスト
- High
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huggingfaceGitHubありHugging Faceあり2026-06-02
EvoDS: Self-Evolving Autonomous Data Science Agent with Skill Learning and Context Management
Recent progress in Large Language Model (LLM) agents has enabled promising advances in automated data science.
深層学習軽量化・量子化テキスト強化学習
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-06-02
ThoughtFold: Folding Reasoning Chains via Introspective Preference Learning
Large Reasoning Models (LRMs) have achieved remarkable progress thanks to Reinforcement Learning with Verifiab
深層学習Transformer強化学習
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- Low
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huggingfaceHugging Faceあり2026-06-02
KletterMix: Climbing Toward High-Quality German Pretraining Data
High-quality pretraining data is a central ingredient in modern language models, but German-language resources
MI向き品質予測/異常検知自然言語処理RAGテキスト
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-06-02
MemTrain: Self-Supervised Context Memory Training
Memory is an indispensable capability for long-horizon LLM agents, enabling them to preserve and utilize infor
品質予測/異常検知自然言語処理大規模言語モデルテキスト自己教師強化学習
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-06-02
WebRISE: Requirement-Induced State Evaluation for MLLM-Generated Web Artifacts
Existing benchmarks for MLLM-generated web artifacts assess interaction through local evidence and miss the re
品質予測/異常検知自然言語処理大規模言語モデル画像テキスト動画
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-06-02
BraveGuard: From Open-World Threats to Safer Computer-Use Agents
Computer-use agents extend language models from text generation to sustained interaction with files, terminals
自然言語処理大規模言語モデル検出生成テキスト
- 用途
- 検出
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-06-01
AdaCodec: A Predictive Visual Code for Video MLLMs
Video is temporally redundant: adjacent frames usually share most objects, background, and layout. Yet existin
自然言語処理大規模言語モデル画像テキスト動画
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
→
huggingfaceGitHubありHugging Faceあり2026-06-01
Filter, Then Reweight: Rethinking Optimization Granularity in On-Policy Distillation
On-Policy distillation (OPD) in large language models is shifting from full-trace KL supervision toward more s
品質予測/異常検知深層学習軽量化・量子化テキスト
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
→
huggingfaceHugging Faceあり2026-06-01
MMG2Skill: Can Agents Distill In-the-Wild Guides into Self-Evolving Skills?
Abundant procedural knowledge on the Web holds great potential for helping agents solve long-horizon tasks. Ho
自然言語処理RAG回帰テキストマルチモーダル
- 用途
- 回帰
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-05-31
BenchEvolver: Frontier Task Synthesis via Solution-Centric Evolution
The rapid progress of frontier large language models has led to widespread benchmark saturation, limiting the
品質予測/異常検知自然言語処理大規模言語モデル生成テキスト
- 用途
- 生成
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-05-31
Measuring the Symmetry--Data Exchange Rate
Equivariance theory predicts that an architectural symmetry prior reduces sample complexity by a factor of |G|
自然言語処理RAG
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- Low
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huggingfaceHugging Faceあり2026-05-30
SDR: Set-Distance Rewards for Radiology Report Generation
Reinforcement learning with verifiable rewards has rapidly advanced reasoning in vision--language models. Howe
品質予測/異常検知深層学習Transformer生成テキスト強化学習
- 用途
- 生成
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-05-30
Critic-R: Improving Agentic Search using Instruction-tuned Retrievers with Natural Language Introspective Feedback
Agentic search systems iteratively interact with retrieval models to answer complex queries. Despite substanti
品質予測/異常検知自然言語処理RAGテキスト
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
→
huggingfaceGitHubありHugging Faceあり2026-05-30
Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain
Transfer learning aims to facilitate the learning of a target domain by transferring knowledge from a source d
説明可能自然言語処理RAG画像半教師あり
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
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- Low
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huggingfaceGitHubありHugging Faceあり2026-05-29
The Shape of Addition: Geometric Structures of Arithmetic in Large Language Models
Large Language Models exhibit paradoxical fragility in fundamental arithmetic, implying a disconnect between i
深層学習軽量化・量子化テキスト
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-05-28
ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research
AI coding agents are increasingly used for scientific work, but their end-to-end autonomous research capabilit
深層学習軽量化・量子化マルチモーダル
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
→
huggingfaceHugging Faceあり2026-05-27
Pruning and Distilling Mixture-of-Experts into Dense Language Models
Mixture-of-Experts (MoE) is now the dominant architecture for frontier language models, yet it requires all ex
深層学習軽量化・量子化テキスト
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
→
huggingfaceHugging Faceあり2026-05-27
CORE: Contrastive Reflection Enables Rapid Improvements in Reasoning
Language models can use verifiable rewards to improve at a wide variety of reasoning tasks. However, both para
説明可能深層学習軽量化・量子化テキスト
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-05-26
DEI: Diversity in Evolutionary Inference for Quality-Diversity Search
We present DEI: Diversity in Evolutionary Inference, a distributed Quality-Diversity (QD) search framework tha
品質予測/異常検知自然言語処理大規模言語モデルテキスト
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
→
huggingfaceHugging Faceあり2026-05-26
Trust Region Q Adjoint Matching
Off-policy reinforcement learning of pretrained flow policies remains challenging due to the instability of op
自然言語処理RAG強化学習
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
→