huggingfaceHugging Faceあり2026-11-01
Advances in Agentic Computer Graphics
Computer graphics is built on code. Large language model (LLM) agents can now generate graphics code and itera
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
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「text」の検索結果
66 件huggingfaceHugging Faceあり2026-11-01
SWE-Trail: History-Guided Trajectory Collection for Coding Agent Training
Coding agents solve repository issues through long sequences of exploration, editing, and validation. These tr
自然言語処理ファインチューニングテキスト
- 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2026-10-31
Wave Forcing: Towards Speed-of-Light Streaming Video Generation
Real-time long-video generation requires continuous output with consistent appearance and natural motion. Exis
品質予測/異常検知深層学習軽量化・量子化生成テキスト動画
- 用途
- 生成
- 難易度
- Easy
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- High
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huggingfaceHugging Faceあり2026-10-30
DecisionRank: On-Policy Distillation for Decision-Model Rerankers
Decision models target structured tasks by directly predicting choices or scores, avoiding autoregressive deco
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2026-10-30
BYOD: Build Your Own DLM - Efficient Autoregressive-to-Diffusion Language Model Conversion
Unlike autoregressive (AR) models, diffusion language models (DLMs) permit parallel token updates, flexible ge
品質予測/異常検知深層学習軽量化・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2026-10-30
DeFT-RAG: Decomposing Multi-Hop Questions into Self-Contained Sub-Questions with Reusable Answers
Retrieval-augmented generation (RAG) lets a large language model (LLM) answer questions by retrieving relevant
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2026-10-30
DAMI: Dual-Axis Memory Initialization for Enhanced Long-Context Retrieval in Gated Linear Attention
Linear attention scales linearly with sequence length, but its finite-state memory can limit long-context retr
深層学習Transformer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2026-10-30
TinyAstral: Transferring Self-Evolving Harnesses to Tiny Generalist Manipulation Agents
Robot agents built on vision-language models depend on both the planner and its harness: the tools, feedback,
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マルチモーダル
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
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- High
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huggingfaceHugging Faceあり2026-10-30
Latent Reasoning in Continuous Space for Unified Multimodal Models
Unified Multimodal Models (UMMs) achieve remarkable success on diverse multimodal understanding and generation
品質予測/異常検知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2026-10-30
WeFair: Fidelity-Aware Alignment from Human Feedback for Personalized Query Rewriting in Short-Video Search
Short-video search needs compact query rewriters that recover users' personalized intent from ambiguous querie
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
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- High
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huggingfaceHugging Faceあり2026-10-30
TurnTurk-GPT: Text-Based Turn-Taking Prediction for Turkish
This paper presents TurnTurk-GPT, a text-based turn-taking predictor for Turkish obtained by fine-tuning a Tur
自然言語処理大規模言語モデル検出テキスト音声
- 用途
- 検出
- 難易度
- Easy
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- High
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huggingfaceHugging Faceあり2026-10-30
SketchDirector: Complex Scene Sketching via Agentic Layout Reasoning and Detail Refinement
Existing sketch generation methods often rely on image guidance and are largely limited to individual objects
品質予測/異常検知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2026-10-29
Video Generation Meets Agent Harness: Agentic Video Generation, Agentic World Modeling and Recursive Self-Improvement
Video generation is shifting from end-to-end synthesis toward agentic generation, where planning, tool use, an
品質予測/異常検知生成AI動画生成生成画像テキスト
- 用途
- 生成
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-10-29
Diagnosing the Reference Gap: What Visual Scaffolds Can and Cannot Fix in Multimodal Reasoning
Multimodal large language models can recognize visual content yet struggle to reason reliably over it across m
自然言語処理大規模言語モデル画像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2026-10-29
MAServeBench: Benchmarking Multi-Agent LLM Serving Systems with Hierarchical Collaboration Workloads
Large language model (LLM)-based multi-agent systems (MAS) coordinate specialized agents to solve complex task
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
→
huggingfaceGitHubありHugging Faceあり2026-10-29
Where Do Vision Language Models Fail at 3D Reasoning?
Vision-language models can get individual judgments right while struggling to recover and consistently use the
自然言語処理ファインチューニング画像テキスト3D
- 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2026-10-29
Apriel Voice 27B Preview: An Open Model for Speech, Reasoning, and Tool Use
A capable voice assistant must do more than recognize words; it must understand requests, reason over context,
深層学習軽量化・量子化画像テキスト音声
- 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2026-10-29
CaPtain: A Pipeline for Post-Training Code-as-Policy Robot Agents
Across coding, web, and tool-use tasks, language-model agents are now routinely trained with the same recipe:
自然言語処理大規模言語モデルテキストマルチモーダル強化学習
- 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2026-10-29
HALO: Human-Grounded Granular Evaluation of Proactive Agents
Proactive agents must decide whether, when, and how far to assist without an explicit request. Existing evalua
表形式向き品質予測/異常検知深層学習Transformerテキスト表形式
- 用途
- 技術検証・論文読解補助
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- Easy
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- Low
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huggingfaceHugging Faceあり2026-10-29
On the Origin and Evolution of Training-Inference Mismatch in LLM Reinforcement Learning
On-policy reinforcement learning for large language models assumes that training responses are sampled from th
自然言語処理大規模言語モデルテキスト強化学習
- 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2026-10-29
MemSteer: Turning User Histories into Supervision for Agent Memory Optimization
An effective agent memory system should adapt to a user's history and task requirements. Human-annotated super
強化学習方策勾配 (PPO / A3C)生成テキスト
- 用途
- 生成
- 難易度
- Easy
- コスト
- Low
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huggingfaceHugging Faceあり2026-10-29
L3G-World: Real-Time Infinite World Generation with Latent 3D Gaussian Memory
Interactive world models generate visual content conditioned on user actions, yet maintaining spatial consiste
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
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huggingfaceGitHubありHugging Faceあり2026-10-29
Intrinsic Distribution Matching
One-step and few-step text-to-image generation must accurately render the details specified in the prompt desp
深層学習軽量化・量子化生成画像テキスト
- 用途
- 生成
- 難易度
- Easy
- コスト
- Low
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huggingfaceHugging Faceあり2026-10-29
Youtu-Parsing-Omni: One Encoder, One Schema, All Modalities
We present Youtu-Parsing-Omni, an omni-modal parser that converts documents, images, audio, and video into str
深層学習軽量化・量子化画像テキスト音声
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
→
huggingfaceHugging Faceあり2026-10-29
Glance: The Generator Is Its Own Reranker
Retrieval-augmented generation commonly serves separate models for reranking and answering, duplicating model
品質予測/異常検知深層学習軽量化・量子化生成テキスト
- 用途
- 生成
- 難易度
- Easy
- コスト
- Low
→
huggingfaceHugging Faceあり2026-10-29
Online Dense Reinforcement Learning with Planning-Grounding Agent for UI Navigation on Live IOS Apps
While frontier vision-language models demonstrate strong capabilities in user interface (UI) navigation, their
深層学習軽量化・量子化異常検知画像テキスト
- 用途
- 異常検知
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-10-29
PhysInsert: Physically Plausible Object Insertion with Geometric Priors and Bidirectional Object-Scene Interactions
We present PhysInsert, a framework for physically plausible reference-based object insertion that jointly adap
コンピュータビジョンセグメンテーション生成画像テキスト
- 用途
- 生成
- 難易度
- Easy
- コスト
- High
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huggingfaceGitHubありHugging Faceあり2026-10-29
OpenFlyV2: An onboard vision-language-action model for aerial perception and navigation
Human cognition flexibly draws on a shared perceptual foundation to understand environments, attend to referen
自然言語処理プロンプトエンジニアリング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2026-10-28
Cross-Primitive Consistency in Jev
Jev enables efficient decision-making with large language models (LLMs) through typed querying primitives, all
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2026-10-28
On-Policy Self-Distillation with Teacher Evolution
On-Policy Self-Distillation (OPSD), which aligns a student model's token-level distribution with that of its 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
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huggingfaceHugging Faceあり2026-10-28
A Tutorial on Generative Models for Music Audio
Music audio generation spans instrumental clips, full songs with vocals, edits of existing recordings, and sys
MI向きコンピュータビジョンセグメンテーション検出生成テキスト
- 用途
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- 難易度
- Easy
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- High
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huggingfaceHugging Faceあり2026-10-28
IntReal: A Real-World Dataset and Benchmark for Multimodal Interleaved Generation
Humans routinely consume multimodal content, where textual and visual information is naturally interleaved. Al
コンピュータビジョンセグメンテーション生成画像テキスト
- 用途
- 生成
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-10-28
Investor's Last Exam: Can AI Agents Do Equity Investment Without Seeing the Future?
Financial agents must form forecasts and investment judgments from decision-time evidence, with predictions ev
品質予測/異常検知自然言語処理大規模言語モデル予測テキスト
- 用途
- 予測
- 難易度
- Easy
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- High
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huggingfaceHugging Faceあり2026-10-28
AskStream: When and What to Ask Proactively under Uncertainty in Streaming Video Understanding
Recent advances in multimodal large language models have increased interest in interaction paradigms for video
自然言語処理大規模言語モデルテキスト動画マルチモーダル
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-10-28
Scaling Lean Theorem Proving for Real-World Code Verification
We present EasyProver, a Lean proving system built from recursive agents with isolated contexts, asynchronous
コンピュータビジョンセグメンテーションテキスト強化学習
- 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2026-10-27
FrameForge: Harnessing Priors of Video Foundation Models for Unified Image Generation
Unified conditional image generation requires a single model to preserve different aspects of a visual input w
自然言語処理RAG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2026-10-27
Follow the Gradient: Tracing Safety Degradation Back to Training Data
Fine-tuning Large Language Models (LLMs) on instruction data can quietly erode their safety, even when no trai
深層学習Transformer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2026-10-27
Venus-DefakerOne2: A Vision-Forensics Foundation Model with Broader Coverage
Real-world forgery is inherently heterogeneous: it manifests not only as digital manipulation but also as phys
自然言語処理RAG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2026-10-27
GEPA-Curator: Curated Learning for Self Improving Agents
Large Language Models (LLMs) are increasingly used in multi-turn agentic workloads that require sustained inte
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
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huggingfaceHugging Faceあり2026-10-27
Think Twice: The Metis-1.6 Technical Report
Metis is a bet that better data, better training methods and new architectures can buy what a bigger parameter
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
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- High
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huggingfaceHugging Faceあり2026-10-27
SciGame: Let Agents Play the Scientific Discovery Games in the Real and Counterfactual World
Large language model agents are increasingly used for scientific discovery. However, existing benchmarks mix l
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2026-10-27
Look Only When Needed: Certificate-Guided Visual Routing for Efficient Diffusion VLMs
Diffusion vision-language models (DVLMs) process their entire input at every denoising step. Since visual toke
深層学習軽量化・量子化画像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
→
huggingfaceHugging Faceあり2026-10-27
SO-101 Bench: Accessible Spatial Manipulation
Low-cost arms such as the SO-101 have become the most common entry point to robot learning, yet no released po
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
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huggingfaceHugging Faceあり2026-10-27
Know, then Draw: A New Learning Paradigm for Visual Generation
Visual generation requires both high-level semantic knowledge and fine-grained pixel details. We propose a lea
品質予測/異常検知生成AI拡散モデル生成回帰画像
- 用途
- 生成
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-10-26
CEPO: A General Framework for Improving Prompt Optimization in Multi-Agent Systems
Multi-agent systems (MAS) require both effective coordination and specialized task execution. Existing prompt
強化学習方策勾配 (PPO / A3C)テキスト
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- Medium
→
huggingfaceHugging Faceあり2026-10-26
SOMA: A Benchmark for Fine-Grained Egocentric Behavior and Body-Motion Understanding
Perceiving human body motion is critical for multimodal systems that understand and assist people. Yet current
自然言語処理大規模言語モデルテキスト動画マルチモーダル
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
→
huggingfaceHugging Faceあり2026-10-26
Co-Design Bench: Multimodal Collaborative Coding Evaluation for Agentic Models
Evaluating human-AI co-design requires testing whether coding agents can translate real users' multimodal requ
自然言語処理大規模言語モデル画像テキスト音声
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
→
huggingfaceHugging Faceあり2026-10-26
OmniShowcase: Omnidirectional Subject-Consistent Video Generation from Multi-View Images
Generating videos of a specific subject from a few reference images remains challenging when the camera reveal
品質予測/異常検知コンピュータビジョンセグメンテーション生成画像テキスト
- 用途
- 生成
- 難易度
- Easy
- コスト
- High
→
huggingfaceHugging Faceあり2026-10-26
Evaluating DeepSeek V4.1 Flash-1017 for loss of control and harmful manipulation risks
As frontier language models grow increasingly capable and autonomous, independent third-party evaluations are
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
→
huggingfaceHugging Faceあり2026-10-26
Instruction Capacity: Behavior and Degradation of Language Models under Controlled Load
The quality with which language models follow instructions falls as the load grows, yet there is still no quan
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2026-10-25
Broken on Arrival: Non-Functional Quantized LLM Artifacts in Public Model Registries and How to Catch Them
Developers download quantized large language models (LLMs) from public registries to run them locally. None of
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2026-10-25
Matched Capabilities, Different Behaviors: A Controlled Study of MCP and CLI Interfaces for Language Agents
Language agents increasingly act through external tools and interactive environments, making the interface bet
深層学習Transformerテキスト
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- Medium
→
huggingfaceHugging Faceあり2026-10-24
After One-Click Video: The Inflow of Service-Type Content, the Failure of the Recommendation Pool Mechanism, and the BoLe System
In September 2026, a new large language model was released. Soon after, a new way of making explainer videos b
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2026-10-24
PreView: Enhancing GUI Grounding with Preprocessed Views and Cues
GUI grounding requires locating small controls in cluttered screenshots and identifying which control a natura
深層学習軽量化・量子化検出画像テキスト
- 用途
- 検出
- 難易度
- Easy
- コスト
- High
→
huggingfaceHugging Faceあり2026-10-23
A Broader Look into MLLM's Retrieval Heads
Locating relevant evidence in long inputs is a core capability of Multimodal Large Language Models. Prior stud
自然言語処理大規模言語モデル検索画像テキスト
- 用途
- 検索
- 難易度
- Easy
- コスト
- High
→
huggingfaceHugging Faceあり2026-10-23
Sensorimotor Alignment: A Platonic Proxy for Embodied Policy Success
Robot foundation models are typically evaluated through costly closed-loop rollouts, motivating low-cost offli
自然言語処理ファインチューニング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
→
huggingfaceHugging Faceあり2026-10-22
VibeGame: Prompt-to-Game Development with AI-Native Engine and Self-Evolving Adversarial Agent Team
Large language model agents have enabled software development through natural-language interaction, but extend
自然言語処理大規模言語モデル画像テキスト
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
→
huggingfaceHugging Faceあり2026-10-21
TernScope: Model-Hardware Co-Design for Efficient Ternary Endoscopic Segmentation
Edge medical segmentation must reconcile small model size, high accuracy, and fast inference. TernScope addres
深層学習軽量化・量子化セグメンテーションテキスト
- 用途
- セグメンテーション
- 難易度
- Easy
- コスト
- High
→
huggingfaceHugging Faceあり2026-10-21
LongZero: Enhancing Long-Context Reasoning in LLMs from Zero Data
Reinforcement learning (RL) is an effective way to strengthen the long-context reasoning of large language mod
自然言語処理大規模言語モデル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2026-10-21
CausGround: Causal Interaction Grounding for Adaptive Embodied Planning
Multimodal large language models (MLLMs) show promising reasoning capabilities, but reliable physical interact
自然言語処理大規模言語モデル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2026-10-20
Lost in Recovery: Demystifying Recovery Behavior of Computer-Use Agents via Goal-Oriented Action Replay
Recent advances in large (vision) language models have enabled computer-use agents that automate digital workf
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
- Low
→
huggingfaceHugging Faceあり2026-10-20
ProVe: Proposer-Verifier Decoding
Speculative decoding accelerates autoregressive (AR) language models by letting a small model draft tokens ser
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
→
huggingfaceHugging Faceあり2026-10-19
LOB-Impact Benchmark: Emergent Market Impact in Generative Limit Order Book Models
Realistic market impact is essential for generative limit order book (LOB) models to serve as world models for
深層学習Transformer生成テキスト
- 用途
- 生成
- 難易度
- Easy
- コスト
- Medium
→
huggingfaceHugging Faceあり2026-10-14
Amortizing Sequential Experimental Design in Reasoning LLMs
Despite their extensive pretraining knowledge and strong reasoning capabilities, large language models (LLMs)
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
→
huggingfaceHugging Faceあり2026-10-11
SIMIT: Self-Improving Vision-Language Models via Imagination at Test-Time
We introduce SIMIT, a test-time self-improvement framework in which a vision-language model creates its own qu
自然言語処理プロンプトエンジニアリング生成画像テキスト
- 用途
- 生成
- 難易度
- Easy
- コスト
- High
→
huggingfaceHugging Faceあり2026-09-30
TauIndianBankBench: Procedure-Aware, Action-Oriented Evaluation of Tool-Using Conversational Agents
Many Indian banks serve customers through FAQ pages and scripted chatbots. Assistants based on language models
機械学習教師あり学習テキスト
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
→