MLinfo | 機械学習・AI論文まとめ

cs249r_book — Machine Learning Systems: Foundations, Scaling, Agentic AI, and Physical AI (Vols I–IV) • Harvard CS249r | https://mlsysbook.ai

マシンラーニングシステムの理論と実装に関する本。

深層学習テキスト

用途: 機械学習システム
難易度: Easy
コスト: Medium

自然言語処理大規模言語モデルテキスト音声マルチモーダル

screenpipe — YC (S26) | Open Computer History | Continuously record your company computer work, map your workflows, help you find work worth automating, and power your agents' context

ユーザーの行動を認識し、オートエージェントを構築するためのツール。

用途: オートエージェント構築
難易度: Easy
コスト: High

ART — Agent Reinforcement Trainer: train multi-step agents for real-world tasks using GRPO. Give your agents on-the-job training. Reinforcement learning for Qwen3.6, GPT-OSS, Llama, and more!

ARTは、多段強化学習トレーナーです。このトレーナーは、GRPOを使用して、現実世界のタスクに対して、多段強化学習を行うことができます。

自然言語処理大規模言語モデル強化学習

用途: 多段強化学習トレーナー
難易度: Easy
コスト: High

flyte — Type-safe, distributed orchestration of agents, ML pipelines, and real-time inference on your k8s — in pure Python with async/await, also other languages (rust, go and ts)

metaflowは、AI/MLシステムを構築・管理・ディプロイするために使用できるプラットフォームです。

MLOpsモデルデプロイ

用途: AIワークロードの実行管理
難易度: Easy
コスト: Medium

reef — Infrastructure for continually self‑improving agents

Infrastructure for continually self‑improving agents

用途: 実装・検証基盤
難易度: Easy
コスト: High

mlflow — The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data.

このリポジトリでは、AIワークロードを管理するためのシステムであるSkypilotを提供しています。

品質予測/異常検知自然言語処理大規模言語モデル

用途: AIワークロードを管理するためのシステム
難易度: Easy
コスト: High

zenml — ZenML 🙏: One AI Platform from Pipelines to Agents. https://zenml.io.

aimは、利用しやすく強力なオープンソースのエクスペリメントトラッカーです。

用途: AIプラットフォーム
難易度: Easy
コスト: High

ModelBlaster: Closing the Performance Gap through Learning-Augmented Agents Across Models, Kernels and Hardware Backends

Optimizing accelerator code across hardware architectures is difficult. ML compilers search predefined spaces

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

OSGR: Observation-Support Graph Reasoning for Earth Observation Question Answering

Answering geographic queries over Earth observation (EO) archives requires solving a joint retrieval-and-deriv

自然言語処理大規模言語モデルQA

用途: QA
難易度: Hard
コスト: High

When Debate Amplifies Risk: Social-Bias Vulnerabilities in Multi-Agent Safety Deliberation

Retrieval-augmented Web applications increasingly employ multi-agent debate (MAD) to aggregate conflicting, no

自然言語処理RAG生成

用途: 生成
難易度: Hard
コスト: High

Harness-S: Learning from Sibling Harnesses for Generalizable Agent Training

Harness evolution from a common base exhibits Locality, Complementarity, and Nonstationarity: variants repair

自然言語処理大規模言語モデル強化学習

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Neologism Inoculation Prompting

Inoculation prompting is a method to reduce undesired behaviours such as reward hacking in frontier models. It

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

MI向き品質予測/異常検知自然言語処理RAG画像テキスト動画

AceAction-5M: Hierarchical Goals for Generalist Action Learning Across 3D Game Worlds

Video games provide a practical setting for collecting vision-language-action (VLA) data, whose acquisition in

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

DualWorldBench: Can Agents Plan Deliveries across Symbolic and Grounded Worlds?

Recent multimodal agents have shown promising progress in embodied planning and web interaction. However, exis

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: Hard
コスト: High

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

DiMuRe: Diagnosis-Guided Multilevel Recovery for Robotic Task Execution

Disturbances can invalidate the conditions required by a robot's next action even when much of its task progra

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

IntentGEO: Intent-aware Generative Engine Optimization for Recommendation

Large language models (LLMs) increasingly shape recommendations across chatbots, e-commerce, and user-generate

用途: 生成
難易度: Hard
コスト: High

Toward User-Centered Agentic Recommendation: When Does Platform Competition Benefit Users?

Conventional recommender systems operate within platform-specific catalogs, limiting access to potentially val

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Low

DoGNAVY-Exploit: Harnessing Parallel Open-Weight Agents for Stage-Aware Vulnerability Exploitation

Frontier LLM agents can turn known vulnerabilities into working exploits, but are costly and often access-rest

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Agentic Path Maps: Exposing an Agent's Traversal Through the Garden of Forking Paths

When an AI agent executes an underspecified task, it embeds choices that may contradict unarticulated user con

深層学習Transformer生成画像テキスト

用途: 生成
難易度: Hard
コスト: Medium

When the Consumer Is an Agent: Execution-Grounded Service-Level Agreements

Large language model (LLM) agents increasingly act as service consumers. They select operations and report whe

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

品質予測/異常検知自然言語処理大規模言語モデル分類生成セグメンテーション

Validation-Gated ML Pipeline Synthesis with Expert-Grounded Self-Evolving Skills

Recent advances in large language models have enabled agentic systems that automate machine learning pipeline

用途: 分類
難易度: Hard
コスト: High

Interpretable Early Termination of Web Navigation Agents via Closed Sequential Pattern Mining

Web navigation agents built on open-weight language models fail often and fail late, with most of the tokens s

説明可能生成AIGAN検出テキスト

用途: 検出
難易度: Hard
コスト: Medium

センサ/時系列自然言語処理大規模言語モデル予測テキストマルチモーダル

GlucoAgent: Personalized Glucose Forecasting from Multimodal Personal Context

Continuous glucose monitors, smartwatches, meal logs, and health profiles form a rich multimodal personal cont

用途: 予測
難易度: Hard
コスト: High

The Agent Board: Kanban for Machines; Durable Coordination for Agents That Outlive the Conversation

Harnesses for large language models have converged on a single-threaded shape: one agent, one history, one mac

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

センサ/時系列自然言語処理大規模言語モデルテキスト

ActOR: Ambient AI-Based Agentic Orchestration for Humanoid Autonomy in Operating Rooms

Operating rooms (ORs) increasingly contain ambient sensing infrastructure to enable AI-based optimization of w

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

コンピュータビジョンセグメンテーション3Dマルチモーダル

Online Hamilton-Jacobi Reachability Onboard the Robot Control Loop: A Real-Time GPU Solver for Safety-Critical Autonomy

Hamilton-Jacobi (HJ) reachability provides certified backward reachable tubes (BRTs) for safety-critical robot

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Finance-LLMs — Comprehensive Compilation of Real-World LLM & AI Agent Use Cases in Financial Services

販売データを分析するために、機械学習モデルが使用されるリソースが提供されていました。

用途: 販売データを分析する
難易度: Easy
コスト: High

agent-lightning — The absolute trainer to light up AI agents.

最適なAIモデルを効率的に学習するためのオーサリングツール。Agent Lightningを使用して、トレーナーをセットアップし、データをトレーニングしてモデルを学習することができる。

用途: AI_AGENTのトレーナーを簡単にセットアップする
難易度: Easy
コスト: High

OpenRLHF — An Easy-to-use, Scalable and High-performance Agentic RL Framework based on Ray (PPO & DAPO & REINFORCE++ & VLM & TIS & vLLM & Ray & Async RL)

OpenRLHFは、Ray上に構築された強化学習フレームワークです。このフレームワークは、PPO、DAPO、REINFORCE++など、様々な強化学習アルゴリズムをサポートしています。

深層学習Transformer画像

用途: 強化学習フレームワーク
難易度: Easy
コスト: High

oumi — Easily fine-tune (SFT/RL), evaluate, and deploy Qwen, Gemma, or any open agentic LLM/VLM!

Easily fine-tune (SFT/RL), evaluate, and deploy Qwen, Gemma, or any open agentic LLM/VLM!

用途: 実装・検証基盤
難易度: Easy
コスト: High

DocsGPT — Private AI platform for agents, assistants and enterprise search. Built-in Agent Builder, Deep research, Document analysis, Multi-model support, and API connectivity for agents.

このリポジトリでは、トークナイザーの最適化を提供しています。

深層学習Transformerテキスト

用途: トークナイザーの最適化
難易度: Easy
コスト: Medium

botpress — The open-source hub to build & deploy GPT/LLM Agents ⚡️

オープンソースのGPT/LLMエージェント作成ツールです。

用途: GPT/LLMエージェントの構築
難易度: Easy
コスト: High

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

Computer-Use Agents Gain Little Benefit from a World Model When Conditioned on Tutorials

Recent work has improved computer-use agents (CUAs) by combining retrieved task-relevant tutorials and look-ah

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Finite-Sample Convergence in Networked Average Reward MARL: Decentralization Pitfalls and Entropy Remedies

We study the problem of average reward Multi-Agent Reinforcement Learning (MARL) where agents interact within

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: Hard
コスト: Low

WASP-Mem: Let Agent Organize Procedural Memory. Human Spend Effort on Diagnosis

LLM agents struggle to automate complex enterprise procedures because static, pre-built harness cannot capture

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Rapid Mean-Field Formation Transport with a Shared-Representation Neural Fokker-Planck Controller

This paper develops a mean-field formation transport method with low online computational cost for large-scale

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

WebHarbor: Docking Real Websites for Evolving GUI Agent Environments

GUI web agents require realistic and interactive environments that remain stable during both evaluation and tr

深層学習軽量化・量子化画像強化学習

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Should We Combine Our Data? Algorithms and Limits for Privately Assessing Data Sharing

In this paper, we study when two parties should pool their data to train a machine learning model. Two agents,

コンピュータビジョンセグメンテーション分類

用途: 分類
難易度: Hard
コスト: Low

Spacetime Emergence in the Memory of a Learning Agent

We show that the causal structure of spacetime can emerge within the memory of a learning agent performing a q

強化学習モデルベース

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Can LLM Agents Improve Imperfect Earth System Model Parameterizations?

Earth system models (ESMs) rely on parameterizations to represent unresolved processes such as convection, clo

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

品質予測/異常検知自然言語処理大規模言語モデル生成

DistDebug-Bench: Can LLM Agents Diagnose Bugs in Distributed Systems?

LLM agents show strong potential in software engineering tasks such as competitive programming and code synthe

用途: 生成
難易度: Hard
コスト: High

VIAS: Value-Isolating Activation Steering for Multi-Value Alignment and Compliance for Organisations

Organisations increasingly deploy large language model agents that must follow organisational values. We check

用途: 生成
難易度: Hard
コスト: High

Ask, Don't Equip: Question-Driven Coding Agents for Deep Kernel Optimization

LLM agents for GPU kernel optimization sit at two ends of a structure-freedom tradeoff. Scripted agents fix wh

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Characterizing When Language Models Take Harmful Actions to Protect Their Assigned Goals

Recent language model (LM) safety failures have largely been studied as disparate phenomena. In agentic misali

コンピュータビジョンセグメンテーションテキスト

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

The SIMPLE Law for Energy Cost of LLM Inference

Generative AI is billed per token, yet tokens are not equally expensive. Each token enters the KV-cache and ra

深層学習軽量化・量子化生成

用途: 生成
難易度: Hard
コスト: High

LLMs Depend on Specialist Tools for Out-of-Distribution Generalization in MS/MS Structural Elucidation

A frontier language model was recently reported to solve 1D NMR structural elucidation without tools. We ask w

自然言語処理大規模言語モデル異常検知テキスト

用途: 異常検知
難易度: Hard
コスト: High

An Axiomatic Framework for Reinforcement Learning in Open Multi-Agent Systems

Open multi-agent systems, where only a subset of agents are controllable, are central to real-world domains su

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

CPUで試しやすいコンピュータビジョンセグメンテーション

What Structurally Breaks an Agent? Auditing Difficulty in Stateful Tool-Use Workflows

An aggregate task score does not distinguish failures of scheduling, state retention, and candidate selection.

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Bayesian Sequential Optimal Experimental Design for Single-Pixel Imaging using Reinforcement Learning

Adaptive compressed sensing (CS) aims to select the most informative measurements sequentially, which is cruci

センサ/時系列深層学習軽量化・量子化画像強化学習

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Action Verification through Conjecture and Refutation for Language Model Agents

Language model agents increasingly act on users' behalf by manipulating external environments through tools, o

自然言語処理プロンプトエンジニアリングテキスト

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

表形式向き説明可能深層学習Transformerテキスト表形式

Why Tabular Foundation Models Excel on Relational Data They Have Never Seen

Given a relational database (RDB) storing heterogeneous tabular information, how can we predict missing (or fu

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

ScienceVision: Can AI Observe and Reason Like a Scientist?

Scientific discovery builds on accurate observations interpreted through domain knowledge and reasoning. For s

品質予測/異常検知自然言語処理RAG生成QA画像

用途: 生成
難易度: Hard
コスト: High

Exploit or conform? How norm utility prompting changes the way LLMs use community beliefs

Large Language Models (LLMs) serve both as tools for aiding human decision-making in everyday life and as mode

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

ROCm InferaSim: Cut LLM Serving Trial Costs and Deploy the Best Configuration

Serving an LLM efficiently depends on the engine, how the model is split across GPUs, precision, scheduler set

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

品質予測/異常検知強化学習方策勾配 (PPO / A3C)生成テキスト

Characterizing Adversarial GEO via Implicit Brand Injection

Generative search engines increasingly shape how users discover products, services, and commercial entities by

用途: 生成
難易度: Hard
コスト: Low

説明可能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

Measure Before You Repair: An Explainable Agent for Blind Restoration of Multi-Artifact 3-D MRI

A brain MRI scan can carry an unknown mix of artifacts, so restoration has to be blind, yet most deep-learning

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

コンピュータビジョンセグメンテーション画像音声マルチモーダル

When Music Needs Meaning: Semantic Communication as a Transformative Enabler for the Musical Metaverse

The Musical Metaverse (MM) enables geographically distributed musicians, audiences, sound engineers, and AI ag

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

A Guideline-Grounded Multi-Agent Framework for Personalized Exercise Prescription from Clinical and Wearable Data

Translating population-level exercise guidance into individualized prescriptions requires integrating clinical

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

SLATE: A Large-Scale Benchmark for Long-Horizon API Selection in E-commerce Tool Use

Enterprises increasingly want LLM agents to automate standard operating procedures over their software stacks

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Measuring Model-Specific Alignment Drift in LLM-Based Opinion Dynamics

Large language model (LLM) agents are increasingly used as synthetic participants in opinion-dynamics experime

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

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テキスト

What Is This Memory Useful For? Measuring Task-Conditioned Utility in Long-Term Agent Memory

A stored experience may answer one future question and offer little help with another. We measure this task-co

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Low

ProofPaths: An AI Agent Framework for Assessing Student Reasoning

Students can use large language models to complete assignments, but a correct solution does not establish whet

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Performance, Scalability and Maintenance: An Experimental Study of RPA vs. Autonomous Agents in the Healthcare Domain

Healthcare information systems are fragmented, and retrieving data from legacy web portals is a recurrent need

深層学習RNN / LSTMテキスト音声

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

品質予測/異常検知深層学習軽量化・量子化生成テキスト

Why Pay for Long Reasoning When Rewrite Do Trick? Causal Effects of Reasoning History Compaction on Future Generation and Agent Cost

Reasoning substantially improves language models' ability to solve difficult problems, but long reasoning trac

用途: 生成
難易度: Hard
コスト: High

MI向き品質予測/異常検知自然言語処理大規模言語モデル生成テキスト

El Agente Fármaco: A Scalable AI Scientist for Drug Discovery

Computational drug discovery offers a growing range of tools for proposing and evaluating molecules, but combi

用途: 生成
難易度: Hard
コスト: High

Rethinking Next-Visit Diagnosis Prediction Beyond Aggregate Metrics: Retrieval--Memory Multi-Agent Adjudication

Next-visit diagnosis prediction forecasts the diagnoses documented at a patient's next encounter, turning retr

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: Hard
コスト: High

Strategist: Evolving Reasoning with Agents

A model is only half of an AI agent system. The other half is the inference strategy: the reasoning procedure

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Low

DiabAgent: Toward Agentic Physiological Control

As pretraining scales, medical language models have advanced rapidly in clinical question answering. Their abi

自然言語処理大規模言語モデルQAテキスト

用途: QA
難易度: Hard
コスト: High

Four Bridges: Measuring Deception Propensity in a Multi-Agent Game

AI agents are increasingly deployed in settings where they interact and coordinate with other agents, yet hone

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

FedReady: Bounded Agents for Federation Formation

Federated learning (FL) runtimes standardize aggregation and deployment, but each new study still requires fed

自然言語処理大規模言語モデル分類

用途: 分類
難易度: Hard
コスト: High

MCP Tool Poisoning in Agentic AI Frameworks: A Cross-Framework Measurement Study

The Model Context Protocol (MCP) lets agentic AI frameworks connect language models to external tool providers

自然言語処理大規模言語モデル分類テキスト

用途: 分類
難易度: Hard
コスト: High

Can LLM Agents Be Forward-Deployed Engineers? Requirement Discovery and Delivery for Enterprise Text-to-SQL

LLM coding agents can increasingly automate model post-training, but whether they can act as forward-deployed

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

An Agentic Framework for Automated Power System Studies and Grid Connection Assessment

Connecting new generation and demand to the grid depends on two scarce skills: checking applications against a

自然言語処理RAG生成

用途: 生成
難易度: Hard
コスト: Low

Harder Than the Task: From Technical to Organizational Difficulty in AI Benchmarking

AI benchmarks have made remarkable progress in measuring the ability of AI systems to perform increasingly dem

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Low

RuntimeTickets: Source-Grounded Security Obligations for Agent Skills

Third-party Agent Skills extend what an autonomous agent can do, but a malicious Skill can hide data exposure

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: Hard
コスト: Medium

Optimize the Harness, Not the Model: Fitting a Minimal Agent to a Self-Hostable LLM for Investigative Analysis

Government intelligence teams need LLM agents that can analyze large, messy evidence collections, but security

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

DeliverBench: Benchmarking AI Agents on Delivering Realistic Multi-Stage Professional Projects

LLM agents are judged by what they deliver, and in professional work what gets delivered is a project, not a t

MI向き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: Hard
コスト: High

From Reactive to Agentic Resilience: Multi-Timescale AI-RAN Orchestration Under Signaling Storms

Next-generation open radio access network (Open RAN) and AI-RAN deployments turn the RAN into a disaggregated,

機械学習特徴量エンジニアリング生成テキスト

用途: 生成
難易度: Hard
コスト: Medium

コンピュータビジョンセグメンテーション生成テキスト

Can We Stop the Backtest Yet? Anytime-Valid Promotion Tests for Adaptive Financial Research Agents

Financial research agents can generate and monitor trading rules faster than a human can document the resultin

用途: 生成
難易度: Hard
コスト: Medium

From FAIR to AI-FAIR Metadata-First Infrastructure for AI-Enabled Science

Artificial intelligence (AI) technologies and their capabilities have advanced rapidly over the past few years

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: Hard
コスト: Low

SQLoom: Set-Contrastive Learning of Syntactic, Semantic, and Logical Dependencies for Text-to-SQL

Text-to-SQL translates natural language questions into SQL queries, enabling database access without writing S

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Top-$k$ Retrieval Leaves Memory Value Undetermined

A memory-augmented agent must decide which memories to keep, merge and retrieve, and its only evidence of thei

機械学習特徴量エンジニアリング

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Low

VagueBench: Benchmarking the Robustness of Embodied Agents on Ambient Context Illusions

Embodied agents are increasingly deployed in open-world environments, yet current robustness evaluations remai

機械学習教師あり学習生成テキスト

用途: 生成
難易度: Hard
コスト: Medium

ScienceCUA: Resource-to-Skill Construction and Execution-Guided Skill Evolution for Scientific Computer Use Agents

Computer-use agents for scientific software require application-specific expertise. Much of this expertise is

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: Hard
コスト: Low

Axion 1.0: Open Agent Foundation Models, Simplified

We introduce Axion 1.0, a fully open agent foundation model that combines strong and broad agentic capabilitie

深層学習軽量化・量子化生成強化学習

用途: 生成
難易度: Hard
コスト: High

Do Agents Still Follow the Rules? Benchmarking Rule Compliance under Agent Context Compaction

LLM agents compact long histories to fit their context windows, but the compacted context must still carry the

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Can Coding Agents Sustain Continual Evolution without Forgetting?

Recently, harness self-evolution has emerged as an important paradigm for improving agent performance. Prior s

生成AIGAN

用途: 技術検証・論文読解補助
難易度: Easy
コスト: Medium

Auto-Research Agent on MSA Organization to Improve Protein-Protein Complex Structure Prediction

Protein-complex structure prediction with AlphaFold 3 depends on MSA content, depth, and cross-chain pairing.

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

品質予測/異常検知深層学習Transformerテキスト

CoworkOS:Scaling Agent Coworkers for Real-world Automation

Rapid advances in cowork agents are shifting human-AI collaboration toward real-world automation, delivering s

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Toward Knowledge-Centric AI: Discovering, Internalizing, and Using Explicit Knowledge Across Agents and Generations

Knowledge is how understanding accumulates across people and generations: durable records that outlive their p

用途: 生成
難易度: Hard
コスト: High

品質予測/異常検知深層学習軽量化・量子化生成画像テキスト

CUA-Master: Scaling Computer-use Agents through Open-World Recursive Improvements

Despite recent advances in CUAs, their development remains constrained by the limited diversity, controllabili

用途: 生成
難易度: Hard
コスト: High

ComplianceAtlas: A Taxonomy-Driven Agentic AI Framework for Automated AI Act Gap Analysis and Remediation

Assessing regulatory compliance for high-risk AI systems requires systematic identification of technical gaps

生成AIGAN

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Low

From Local Skills to Long-Horizon Web Tasks: Progressive Skill Exploration for LLM Web Agents

Large language models (LLMs) are driving autonomous agents toward real-world environments, with the Web emergi

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

LUCA: A Synthetic World for Experimental Reasoning

An experimental agent can succeed for the wrong reasons. Evaluations, however, often focus on the outcome, not

品質予測/異常検知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: Hard
コスト: High

Avatar Intelligence: Amplifying Human Agency Through Persistent Personal Agents

Personal AI agents are increasingly capable of handling long-horizon tasks across applications, yet users ofte

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: Hard
コスト: Medium

Towards a Trustworthy and Governable Internet of Agentics: An Architectural Perspective on Protocols, Systems, and Services

Large language model applications are increasingly being organized as tool-using and multi-agent workflows rat

MI向き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: Hard
コスト: High

Networked AI: Architecture, Theory, and Applications

Recent advances in Artificial Intelligence (AI), particularly large-scale deep learning, have been driven by m

説明可能数学・理論解釈可能性 (XAI)異常検知

用途: 異常検知
難易度: Hard
コスト: High

BLPO: Beyond-Local Policy Optimization for Long-Horizon LLM Agents

Long-horizon tasks require large language model (LLM) agents to coordinate interdependent actions, a capabilit

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

The Web Doesn't Sit Still: Adversarial Self-Evolving Attacks on Search Agents

Large Language Models (LLMs) have shown promise for empowering search agents to tackle complex information-see

深層学習Transformerテキスト

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

What makes a theorem interesting? Operationalizing interestingness for open-ended exploration

An agent that explores, whether it generates conjectures or research ideas, must decide which of them are wort

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: Hard
コスト: Medium

TLAPS-Bench: Formally Proving Correctness of Real-World System Specifications with TLA+ Proof System

TLAPS-Bench is a benchmark and execution harness for constructing machine-checked proofs for real-world system

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

SpecOPD: Branch-Parallel On-Policy Distillation for Speculative Decoding

Speculative decoding (SD) accelerates large language model inference by reducing the sequential cost of token-

説明可能深層学習軽量化・量子化生成テキスト

用途: 生成
難易度: Hard
コスト: High

Math Foundation Formalization Agent: A General-Purpose Approach to Formalizing Mathematical Foundations

Mathematical formalization plays an important role in establishing rigorous and machine-verifiable foundations

自然言語処理大規模言語モデル分類生成テキスト

用途: 分類
難易度: Hard
コスト: High

CARE: Grounded Recursive Self-Improvement for Production Customer-Service Agents

Recursive self-improvement (RSI) in production agents involves transforming accumulated experience into reliab

品質予測/異常検知深層学習軽量化・量子化

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Reimagining computational quantum research with AI scientists

AI scientists are expanding automated research across scientific domains, yet their ability to turn the scient

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Think with Skills: Pseudocode Planning Before Execution for Advanced Reasoners

Language model agents often struggle with complex tasks even when equipped with expert-written skills. Such sk

数学・理論最適化テキスト

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

コンピュータビジョンセグメンテーション分類テキストマルチモーダル

RxnMiner: A Multimodal Agent for Structured Reaction Data Extraction from Chemistry Figures

Reaction data underpin machine learning for reaction prediction, condition recommendation, and retrosynthetic

用途: 分類
難易度: Hard
コスト: High

Discovering hierarchical governing equations for community-structured complex network dynamics with LLM agents

Governing equations provide an interpretable description of the mechanisms underlying complex network dynamics

説明可能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: Hard
コスト: High

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

Predictive Safety Guardrails Do Not Always Make LLM Agents Safer

Large language model agents increasingly take consequential actions in digital and physical environments, wher

用途: 予測
難易度: Hard
コスト: High

SelfImprove-Bench: Evaluating Generalization in Self-Improving Agents

Recursively self-improving (RSI) systems are increasingly viewed as a promising means of accelerating progress

深層学習軽量化・量子化生成

用途: 生成
難易度: Hard
コスト: High

Stop Handcrafting Your Engine! GenDB-V: Towards AI-Generated and Formally Verified Database Systems

For over 50 years, the database community has developed a wide range of successful engines to answer two funda

用途: 生成
難易度: Hard
コスト: High

Dory1.0-4B: A Looped Normalized-GPT

We introduce Dory1.0-4B, a 4-billion-parameter looped (latent-recurrent) language model with elastic depth: a

深層学習Transformerテキスト強化学習

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

EvoGroup: A Self-Evolving Harness for Group Agents in Shared Contexts

With the rise of Group Agent such as Claude Tag, agents increasingly serve as digital workers for long-term te

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

コンピュータビジョンセグメンテーションQAテキスト

NitiBench+: A Temporal Thai Legal Question Answering Benchmark Across 17 Domains

Law changes over time. A court decides a case under the law in force on that day, and legal QA benchmarks grad

用途: QA
難易度: Hard
コスト: Low

VeriHarm-Bench: Measuring Harmful Success in LLM Coding Agents Verifiably

Large language model (LLM) coding agents increasingly operate with shell access, allowing them to write script

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

IdeaTaste: Do LLMs Share the Research Taste of Human Researchers?

Research taste, the judgment of what to read, which problems to pursue, and which methods to try, increasingly

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

ACTOR: Action-Centered Temporal Organization for Literary Role-Playing Agents

Large language agents for literary role-playing are typically modeled around dialogue generation, leaving char

自然言語処理RAG生成テキスト

用途: 生成
難易度: Easy
コスト: Low

DR Pixel: Page-Native Deep Research with Vision-Language Models

Complex documents such as scientific papers contain rich information in tables, figures, and equations; howeve

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: Hard
コスト: High

A State Change Is Local, Its Consequences Are Not: Dependency-Aware State Revision for Long-Term Agent Memory

Long-lived agents rely on persistent memory of users, tasks and decisions, and this memory must stay consisten

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

TRIO: Task Refinement with Individual Optimization for Retrieval-Augmented Generation

This paper studies retrieval-augmented generation (RAG), which leverages external knowledge to improve the per

品質予測/異常検知自然言語処理RAG生成テキスト

用途: 生成
難易度: Easy
コスト: Low

SMILE: A Structured Multi-Agent Clinical Reasoning for Liver Ultrasound Understanding

Automated liver ultrasound report generation improves the diagnosis of liver diseases by producing clinically

コンピュータビジョンマルチモーダル生成QA画像

用途: 生成
難易度: Hard
コスト: High

Beyond Accuracy: How Quantization Affects Coding Agents

Agents are increasingly used for coding tasks, but the inference cost of large language models (LLMs) limits w

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

LEANGUARD: Lean-Verified Guardrails for Tool-Using Agents

Tool-using language-model agents often execute actions that their domain policies forbid. We present LeanGuard

コンピュータビジョン動画認識

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Accessed, never disclosed: Silent access to protected health information by medical artificial intelligence agents

Medical artificial intelligence agents should retrieve patient identifiers only when a task requires them, the

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

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

SurgEye: Instruction Tuning-Free Perception-Reasoning Framework for Surgical Scene Understanding

Surgical scene understanding is pivotal for computer-assisted intervention, yet demands compositional reasonin

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Isekai3D SDG: A Platform for Diverse Synthetic Gameplay Visual Data

We present Isekai3D Synthetic Data Generation (SDG), a platform for simulating and capturing synthetic 3D game

自然言語処理RAG生成画像動画

用途: 生成
難易度: Hard
コスト: High

品質予測/異常検知自然言語処理大規模言語モデルQA画像テキスト

Reviso: Not All Visual Evidence Deserves Equal Attention in Video Captioning

Multimodal large language models (MLLMs) can generate detailed video captions, yet they may overemphasize mino

用途: QA
難易度: Hard
コスト: High

Parametric CAD Bench v3: Evaluating Agentic Parametric CAD Authoring from Text and Engineering Drawings

Producing an executable CAD script is not sufficient to establish that an agent has satisfied an engineering d

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: Hard
コスト: High

SeMo3D: Unifying 3D Generation, Editing, and Completion via Selective Modeling

3D generation, editing, and completion are typically treated as regenerating the complete 3D asset under task-

深層学習Transformer生成画像テキスト

用途: 生成
難易度: Hard
コスト: High

センサ/時系列深層学習軽量化・量子化検出セグメンテーションテキスト

MirrorComm: A Communication-Efficient Cooperative Perception Framework via Mirrored World Models

Cooperative perception (CP) requires substantial communication bandwidth to exchange sensing information among

用途: 検出
難易度: Hard
コスト: Medium

AgiWorld: An Agentic Video World Model for Building Persistent Worlds

Recent interactive video world models generate visually compelling videos from a single image, following user-

自然言語処理ファインチューニング画像テキスト動画

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

FEIJOA: Task-Driven Grounding and Execution for Mobile Manipulation

A mobile manipulator asked to "put the toys away in the toy box" must decide which objects are toys and which

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Physical Experience Builds Continual Spatial World Models

World models are increasingly used to support prediction and planning in embodied agents, but the knowledge is

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

TAMPAgent: Agentic Belief-Space Planning for Mobile Manipulation

Open-world robotic mobile manipulation requires acting in physical environments with only partial information.

自然言語処理RAG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: Hard
コスト: High

自然言語処理プロンプトエンジニアリングテキスト強化学習

KlaskTron: An Open-Source Platform for Physical Adversarial Multi-Agent RL

Progress in robot learning, particularly physical multi-agent reinforcement learning (MARL), is currently chal

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Swarm Choreography Made Simple: Superposed Guiding Vector Fields for Rigid Formation Path Following

This paper presents a decentralized guiding vector field framework for rigid formation path following in multi

コンピュータビジョン3D・点群3D

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

An intelligent satellite must do nothing well

Autonomous inspection of a resident space object is usually posed as a contest between classical next-best-vie

品質予測/異常検知強化学習モデルベース

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Closing the Dynamics Gap through Self-Directed Robotic Interaction

Agentic Real2Sim2Real systems can reconstruct a scene in simulation from camera observations, but contact-rich

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Touching Projected Agents through the DAT Screen -System Design and Questions of Interaction Boundaries-

Touching a virtual agent involves more than detecting a finger on a display: the contact surface can deform an

センサ/時系列強化学習

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

RoboColosseum: Evaluating Physical Intelligence at Scale In-The-Wild

General-purpose robot policies must work across robots, tasks, and environments, yet the variation that makes

自然言語処理RAGマルチモーダル

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Late Autumn: LLM Agents Specialize a General Multi-Robot Planner for One Warehouse

No algorithm is best on all problems, but a real warehouse is a narrow problem: its layout and processes rarel

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Verification Allocation in Stylized LLM Agent Pipelines under Hidden Tool Degradation: A Discrete-Time Mean-Field Analysis

We study a stylized verification-allocation model for role-structured pipelines with binary inherited corrupti

用途: 生成
難易度: Hard
コスト: High

huggingfaceHugging Faceあり2026-11-02

Measuring AI Agents as Teammates with Collaborative Intelligence

Progress in AI is often measured by what a single model can do on its own. Yet teams of AI agents are starting

用途: 技術検証・論文読解補助
難易度: Easy
コスト: Medium

OpenBB — Open Data Platform for analysts, quants and AI agents.

OpenBBは、分析家・量算家・AIエージェント用の金融データプラットフォームを提供している。

機械学習教師あり学習

用途: 金融分析用データ
難易度: Easy
コスト: Medium

DATAGEN — DATAGEN: AI-driven multi-agent research assistant automating hypothesis generation, data analysis, and report writing.

AIドライブのマルチエージェント研究アシスタント。仮説の生成、データ分析、およびレポートの生成を自動化する。

用途: AI研究アシスタント
難易度: Easy
コスト: High

agents-towards-production — End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.

AIエージェントの開発と実装を行うためのエンドツーマンド、コードファーストのチュートリアル。

用途: AIエージェントの開発と実装
難易度: Easy
コスト: High

giskard-oss — 🐢 Open-Source Evaluation & Testing library for LLM Agents

giskard-ossは、LLMエージェントの評価とテストライブラリを提供します。

用途: LLMエージェントの評価とテストライブラリ
難易度: Easy
コスト: High

Principled Human-AI Collaboration in Agentic Workflows

Organizations adopting AI face the challenge of deciding which outputs can be released without human review wh

品質予測/異常検知自然言語処理RAG

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Low

コンピュータビジョンセグメンテーション分類検出テキスト

Decision Models as Policy Gates for Coding Agents: Explicit Policy Boundaries Improve Detection and Reduce False Alarms

Coding agents can destroy data, leak secrets and message the wrong person faster than a human can review. Coul

用途: 分類
難易度: Hard
コスト: Low

Multi-Agent Collusion and the J-Space

LLM agents are increasingly deployed in Multi-Agent Systems (MAS), and they introduce the risk of coordination

説明可能自然言語処理大規模言語モデル教師なし

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Consolidating Reasoning with Test-Time Training

Effective multi-agent collaboration requires finalizing solutions from reasoning that is distributed across th

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

ParetoSaddler: Pareto-Guided Optimization of Agent Harnesses for Quality and Cost

Harness optimization can substantially improve LLM agents without modifying model weights, but existing method

品質予測/異常検知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: Hard
コスト: High

Switch-SWE: Do Coding Agents Hold Focus When Switching Between Unfinished Tasks?

Software development sessions often involve several unfinished tasks rather than one issue carried through to

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: Hard
コスト: Low

Retrieval-Enhanced Cultural Evaluation for Social Media Translation Training

Social media translation requires preserving meaning while capturing the cultural nuances of user-generated co

自然言語処理大規模言語モデル分類強化学習

用途: 分類
難易度: Hard
コスト: High

TrackMem: Tracking Evolving Beliefs in Long-Horizon Financial Dialogue

Memory systems for LLM agents are evaluated mostly on social dialogue, where facts accumulate and rarely chang

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

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

TAC-CoT: Tool-Empowered Implicit Chain-of-Thought via Interleaving Explicit Tokens

LLM-based agentic systems are increasingly combined with CoT reasoning to solve tasks that require accurate re

用途: 生成
難易度: Hard
コスト: High

arxivGitHubあり2026-11-01

Self-Evolving Agentic Workflows via Semantic Decomposition of Complex Tasks

Agentic workflows organize complex tasks into reusable procedures. However, invoking an LLM at every step incu

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

LegoEnv: Self-Evolving Agentic Environments via Modular Recombination

LLM agents learn by interacting with environments, yet existing environments are designed for specific tasks a

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

MemRole: From Memories to Decisions for Long-Horizon Reasoning in Role-Playing Agents

LLM-based role-playing agents are fluent at imitating a character's style, yet struggle to make role-consisten

深層学習軽量化・量子化分類テキスト

用途: 分類
難易度: Hard
コスト: High

Evolving Memory, Learning to Meta-Control: Test-Time Adaptation for Experience-augmented LLM Agents

Self-evolving agents accumulate experience from task streams, but existing frameworks typically rely on fixed

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

MAC-Bench: Benchmarking Multi-Agent Collaboration via Interconnected Tasks

Large language model (LLM)-based multi-agent systems (MAS) tackle challenging tasks by dividing work among age

MI向き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: Hard
コスト: High

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QAテキスト

CREDO: Evidence-Conditioned Credit Assignment for Temporal Reasoning Agents

Temporal Knowledge Graph Question Answering (TKGQA) requires language model agents to retrieve evidence, selec

用途: QA
難易度: Hard
コスト: High

表形式向き説明可能自然言語処理大規模言語モデル画像テキスト表形式

LLM-Enhanced Multimodal House Price Prediction using Structured, Textual, and Visual Features

Accurate house price prediction is essential for real estate buyers, sellers, agents, and financial institutio

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Can Large Language Models Extend Their Agentic Capabilities to Goal Pursuit in Open-Ended Multi-Turn Interaction?

Goal pursuit in open-ended dialogue requires agents to adapt their actions to interaction feedback across succ

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

品質予測/異常検知深層学習Transformer生成テキスト

Accounting for Irregular State Transitions through Belief Estimation in Online Medical Consultation

While online medical consultation broadens access, unprompted patient disclosures and topic shifts frequently

用途: 生成
難易度: Hard
コスト: High

MI向き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

Can Large Language Model Agents Learn by Watching Tutorial Videos?

Humans continuously acquire knowledge from carefully crafted materials such as books and tutorial videos, carr

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

When Should an Agent Refresh? Adaptive Index Reuse for Efficient Long-Horizon Reasoning

Language agents read code, tool output, and dialogue in contexts that grow at every step. Dynamic sparse atten

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Latent Harness: Compiling Agent Behaviors into Post-Block Activation Shifts

Harness engineering and self-evolving agents improve language-model agents quickly, yet their gains live outsi

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: Hard
コスト: Medium

Learning by Doing: Autonomously Compiling and Curating Agent Skills from Verified Traces

Memory systems for production LLM agents mostly store semantic memory (facts about users and domains) or episo

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

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

Do Embodied VLMs Actually Use Their Scene-Graph Memory?

Embodied agents that answer questions about their route need a memory of what they saw, and scene graphs are a

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

UniGS-SLAM: Monocular Gaussian SLAM with Enhanced Geometry and Open-Vocabulary Semantics

Monocular semantic SLAM aims to reconstruct scene geometry and build semantic maps from RGB observations. Exis

コンピュータビジョン3D・点群3D

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

When to Probe and When to Act: Clarity-Aware Risk-Adaptive Control for Interactive Driving

Safe navigation in interactive traffic requires autonomous vehicles to reason about unobservable human charact

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Directional Bounded-Confidence Matrix Alignment in Vicsek Flocking

This paper proposes a directional bounded-confidence variant of the discrete-time Vicsek multiagent model, in

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Low

Local Solution Repair for Multi-Agent Pickup and Delivery

Multi-robot systems in automated warehouses must efficiently attend to a constant stream of pickup-and-deliver

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Answering Without Arriving: An Evidence Audit for Embodied Question Answering

Embodied question answering asks an agent to navigate, observe, and then answer questions about what it saw, y

深層学習TransformerQA画像テキスト

用途: QA
難易度: Hard
コスト: High

SCOUT: Planning under Occlusion via Object-Centric World Model Rollouts

Robots manipulating in cluttered spaces must often plan from partial views where foreground objects occlude th

自然言語処理大規模言語モデル3Dマルチモーダル

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Recursive Self-Improvement of Exoskeleton Assistance Policies through Human-in-the-Loop Feedback

To reduce reliance on manual design and extensive parameter tuning, a framework for the autonomous evolution o

用途: 生成
難易度: Hard
コスト: High

Democratizing Robot-Use Agents: A Unified Harness with Open Models

Robot-use Agents (RUAs) have shown promising zero-shot manipulation capabilities, but reliance on frontier, pr

用途: 生成
難易度: Hard
コスト: High

自然言語処理大規模言語モデル生成テキストマルチモーダル

Serving Agentic Robot Fleets: Workload Characterization, KV-Cache Thrashing, and Admission Control

Agentic robots combine a vision-language brain, a tool-using harness, and a vision-language-action (VLA) polic

用途: 生成
難易度: Hard
コスト: High

Optimal Online Learning in Bilateral Trade Through Interest

We consider online learning in bilateral trade on platforms such as Uber, Airbnb, and eBay. In each round, a p

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

The Stability of Data Exchange in Competitive Markets

We study the stability of data exchange among competing firms. Unlike tangible goods, data is non-rival: shari

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Advances in Agentic Computer Graphics

Computer graphics is built on code. Large language model (LLM) agents can now generate graphics code and itera

用途: 技術検証・論文読解補助
難易度: Easy
コスト: High

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

品質予測/異常検知自然言語処理ファインチューニング生成強化学習

NorthCUA Technical Report: Turbocharging Computer-Use Agents through Experience at Scale

Scaling computer-use agents from individual actions to complex software work requires experience that connects

用途: 生成
難易度: Easy
コスト: High

CrossSWE: Benchmarking Cross-Repository Joint Development for Coding Agents

As coding agents achieve increasingly high success rates on software engineering benchmarks, recent efforts ha

用途: 技術検証・論文読解補助
難易度: Easy
コスト: High

githubGitHubあり2026-11-01

ai-engineering-hub — In-depth tutorials on LLMs, RAGs and real-world AI agent applications.

このリポジトリには、LLM、RAG、およびオーソリティの認識を含む、AIエンジニアリングのための深いドキュメントがあります。

用途: 記事を理解するためのテキスト分析ツール
難易度: Easy
コスト: High

githubGitHubあり2026-11-01

awesome-ai-eval — Assessing the reliability, accuracy, and safety of large language models, RAG pipelines, and AI agents.

Assessing the reliability, accuracy, and safety of large language models, RAG pipelines, and AI agents.

用途: 実装・検証基盤
難易度: Easy
コスト: High

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

CuriousTogether: Implications of Curiosity and Cultural Grounding in Multi-Agent LLM Interactions

Large Language Models (LLMs) are increasingly deployed in multi-agent settings, exhibiting emergent collective

用途: 生成
難易度: Hard
コスト: High

arxivGitHubあり2026-10-31

Auriga Technical Report: A Task-Agnostic Agent Harness for Zero-Shot Embodied Manipulation

Vision-language models can interpret instructions and plan manipulation tasks, yet struggle when acting direct

自然言語処理大規模言語モデル画像テキスト3D

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Robust Safety Assurance for Robotic Swarms Using Decentralized Collision Verification

In this paper we present the methodologies, results, and analysis used to deploy a decentralized safety verifi

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Low

MOST: Memory of Object States and Task Progress for Long-Horizon Mobile Manipulation

Long-horizon embodied manipulation requires policies to act on information that is no longer visible. Modified

深層学習RNN / LSTM生成画像テキスト

用途: 生成
難易度: Hard
コスト: High

自然言語処理プロンプトエンジニアリング画像テキストマルチモーダル

WorldSketch: Sketching World Models for Zero-Shot Mobile Manipulation

Most agentic robotic systems use vision-language models (VLMs) either to orchestrate predefined skills or to d

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

huggingfaceHugging Faceあり2026-10-31

StreamDuo: A Live Commentator and a Research Agent for Unseen Games

Streaming video LLMs can now commentate live gameplay, but they are trained and tested on the same games. We s

品質予測/異常検知深層学習軽量化・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

表形式向き深層学習Transformer生成表形式強化学習

Statistical Foundations of Reinforcement Learning: A Non-Asymptotic Perspective

Reinforcement learning (RL) embodies a foundational paradigm for sequential decision-making in the face of unc

用途: 生成
難易度: Hard
コスト: Low

In-Model Context Compression with Agentic Soft Tokens for Long-Horizon LLM Agents

Long-horizon LLM agents play an important role in solving complex and sustained tasks, with ever-growing inter

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: Hard
コスト: High

Local Budget Benefits, Population Commitment, and Solver Performance: A DGO Case Study with Controlled DE Transfer

An evaluated candidate can improve a local search step, alter the population used by later steps, and affect a

品質予測/異常検知強化学習マルチエージェント

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Hierarchical World Models with Implicit Dynamics

Planning with world models enables test-time adaptation for embodied agents, offering superior generalization

自然言語処理RAG動画

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

RoboArchitect: Automating the Design of Robotic Agents with Persistent Memory

Recent work shows that a frozen robot policy performs far better inside an agent harness, where control code d

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

REA: A Routed Embodied Agent Runtime for Asynchronous Brain-Cerebellum Control

Foundation models are becoming remarkably reliable at high-level reasoning, yet they remain too slow and costl

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Adaptive Coverage Algorithms for Mobile Ad Hoc Networks in Dynamic Worlds

This paper extends an existing framework for the coordination of mobile agents in unknown worlds, by consideri

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Low

Learning to Live: A Step Toward Lifelong Embodied Intelligence

Embodied agents are usually organized around individual tasks, yet the physical world keeps changing between t

深層学習軽量化・量子化QA画像動画

用途: QA
難易度: Hard
コスト: High

META-Robot: Self-Cognition and Capability Scaling in Agentic Robots

LLM-based robotic agent systems can generate programs for robot control, yet their generalization to new tasks

自然言語処理大規模言語モデル画像

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

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

SketchMate: Agentic Geometric Reasoning for Zero-Shot Precision Insertion

Inserting an unseen part requires sub-millimetre precision and the correct orientation of keyed features. Curr

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

When the Past Shapes Action: Understanding Embodied Memory

Recent advances in generative foundation models have expanded the capabilities of embodied AI, enabling increa

深層学習RNN / LSTM生成画像

用途: 生成
難易度: Hard
コスト: Medium

Online Fair Division of Chores with Upgrade Recourse

We study the online fair division of indivisible chores among $n$ agents under upgrade recourse, where earlier

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Strategic Tanking in Stable Matching Markets

Motivated by a broad literature in psychology, organizational behavior, and matching markets, we introduce str

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

When Is a Game a Markov Decision Process? Correspondences Between Game Theory and Reinforcement Learning

Reinforcement learning and game theory describe the same object from two sides. Game theory says what a ration

自然言語処理大規模言語モデル音声強化学習

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Truthful and Admissible Principal--Agent Testing with Nuisance Parameters

Statistical contracts reward experimenters according to the evidence produced by a costly study, so the reward

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

The Competitive Ratio of Online Budgeted Allocation is Strictly Smaller than $1-1/e$

A well-known conjecture in online algorithms is that the (randomized) competitive ratio of online budgeted all

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

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
コスト: High

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

githubGitHubあり2026-10-30

qlib — Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.

クエンティング投資プラットフォームを実現するためにAI技術を活用します。

強化学習方策勾配 (PPO / A3C)教師あり

用途: クエンティング投資プラットフォーム
難易度: Easy
コスト: Medium

githubGitHubあり2026-10-30

Awesome-Embodied-Robotics-and-Agent — This is a curated list of "Embodied AI or robot with Large Language Models" research. Watch this repository for the latest updates! 🔥

Embodied AIやロボットとLarge Language Modelを組み合わせた研究のリポジトリ。

用途: Embodied AIやロボット研究
難易度: Easy
コスト: High

githubGitHubあり2026-10-30

ml-agents — The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.

Unityを使用してマシンラーニングエージェントを訓練して訓練できるツールです。

コンピュータビジョン3D・点群3D強化学習

用途: Unityでマシンラーニングエージェント
難易度: Easy
コスト: High

Density-Guided Bootstrap Boundaries for Latent World-Model Reinforcement Learning

Latent world models allow reinforcement learning agents to learn policies from imagined trajectories, yet dete

自然言語処理大規模言語モデル強化学習

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Deep Reinforcement Learning for Blind Passive Beamforming in RIS-Assisted MIMO Systems

Reconfigurable Intelligent Surfaces (RIS) are a promising solution for improving millimeter-wave (mmWave) comm

強化学習モデルフリー (DQN / SAC)音声

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Renegotiable Options Fail to Prevent Hold-Up Between Two Profit-Maximizing Frontier LLM Agents

Do contract terms that protect investment between people also protect it between profit-maximizing language-mo

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Finding Competitive Equilibria in Online Marketplaces with Platform Fees, Taxes, and Subsidies

We study equilibrium computation in multi-unit trading networks with valuations satisfying generalized full su

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

品質予測/異常検知生成AI動画生成生成画像テキスト

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

用途: 生成
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

用途: 技術検証・論文読解補助
難易度: Easy
コスト: High

Does Scaling Toward AGI Depend on What System-1 Lets System-2 Execute?

Progress toward artificial general intelligence (AGI) has largely relied on scaling a resource-intensive execu

用途: 技術検証・論文読解補助
難易度: Easy
コスト: High

品質予測/異常検知深層学習Transformer生成画像動画

Wizard: Manipulate Any World in Real Time via Agentic Control

Recent advances in video world models have enabled the generation of increasingly realistic and interactive vi

用途: 生成
難易度: Easy
コスト: High

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

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:

用途: 技術検証・論文読解補助
難易度: Easy
コスト: High

When Requirements Interact: Scaling Long-Horizon Coding via Interdependent Specifications

To succeed in real-world long-horizon software development, coding agents must satisfy multi-dimensional task

MLOpsパイプライン構築生成強化学習

用途: 生成
難易度: Easy
コスト: High

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

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

用途: 技術検証・論文読解補助
難易度: Easy
コスト: Low

強化学習方策勾配 (PPO / A3C)生成テキスト

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

用途: 生成
難易度: Easy
コスト: Low

CPUで試しやすい自然言語処理大規模言語モデル音声

VoxLock: Target-Speaker Extraction for Voice Agents Without an Enrollment Step

A voice agent's speech recognizer transcribes everyone near the microphone, including people other than the ca

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

rTaste: Do Research Agents Have Taste? Evaluating Early Stage Research Judgment

Do research agents have taste, the judgment of which experiments are worth running before their outcomes are k

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音声

用途: 技術検証・論文読解補助
難易度: Easy
コスト: Medium

センサ/時系列自然言語処理大規模言語モデル予測時系列

TSFLab: A Self-Improving Agent-Agnostic Workbench for Time Series Forecasting Research

As publications and products on time series forecasting (TSF) proliferate, researchers face a growing burden o

用途: 予測
難易度: Easy
コスト: High

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

EditAgent: Rethinking Agentic Image Editing

Agentic image editors decompose complex requests into steps, but when a step fails, they mostly retry with the

コンピュータビジョンセグメンテーション画像

用途: 技術検証・論文読解補助
難易度: Easy
コスト: High

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

githubGitHubあり2026-10-29

atomic-agents — Building AI agents, atomically

AIエージェントを組み立てるためのライブラリ。

用途: AIエージェント建設
難易度: Easy
コスト: High

githubGitHubあり2026-10-29

Gymnasium — A standard API for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)

Gymnasiumは、シングルエージェントRLの疑似環境を提供するAPIです。

用途: 疑似環境を提供する
難易度: Easy
コスト: Medium

arxivPaper only2026-10-28

Pinpointing Pivot Points: The DECIDE Framework for Data Analysis Decision Importances

Human judgment calls made throughout the data science life cycle can change the conclusions drawn from data. I

センサ/時系列深層学習軽量化・量子化テキスト

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

arxivPaper only2026-10-28

Principal-agent hypothesis testing under adaptive experimentation

Principal-agent hypothesis testing studies statistical contracts in which a principal rewards an agent accordi

説明可能強化学習

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

arxivPaper only2026-10-28

Simultaneous Gradient Learning in First-Price Auctions

We study equilibrium learning in discretized Bayesian games, focusing on first-price auctions as a testbed for

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

センサ/時系列品質予測/異常検知深層学習Transformer予測テキスト時系列

arxivGitHubあり2026-10-28

Vendster: An Evaluation Arena to Investigate How Forecasting Tool Quality Affects LLM Agent Behaviors in Buyer-Vendor Negotiation

Time-series forecasters are ranked almost entirely by accuracy metrics, but a forecast's decision value, its e

用途: 予測
難易度: Hard
コスト: High

品質予測/異常検知自然言語処理大規模言語モデル動画

WeVisCut: The Whole Editing Room in One Agent From Narration to Long-Form Video

Producing long-form videos from narration requires interdependent decisions that remain coherent across shots.

用途: 技術検証・論文読解補助
難易度: Easy
コスト: High

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

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

用途: 予測
難易度: Easy
コスト: High

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

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

用途: 技術検証・論文読解補助
難易度: Easy
コスト: High

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

Scaling Lean Theorem Proving for Real-World Code Verification

We present EasyProver, a Lean proving system built from recursive agents with isolated contexts, asynchronous

用途: 技術検証・論文読解補助
難易度: Easy
コスト: High

EPSBench: Benchmarking LLM Agents for Earnings Forecasting

Quarterly earnings forecasting requires an agent to connect financial evidence to the income-statement line it

自然言語処理大規模言語モデル予測

用途: 予測
難易度: Easy
コスト: High

Agentic Distributional Regression: Conditional Density Estimation through Agent-Written Programs

Artificial intelligence offers statisticians a new paradigm for modelling and analysing complex datasets. Agen

自然言語処理大規模言語モデル回帰テキスト

用途: 回帰
難易度: Hard
コスト: High

Where Should the Evaluation Budget Go in LLM-Driven Agent Scaffold Evolution?

Large language model (LLM)-driven agent scaffold evolution improves agent workflows through iterative candidat

用途: 生成
難易度: Hard
コスト: High

Toward Understanding the Value of Anarchy: Empirical Studies on Best Response Dynamics

We study experimental results of best response dynamics (BRD) in randomly generated weighted action networked

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Low

Multi-Agent Reinforcement Learning for Global Climate Policy Cooperation

International climate policy requires coordination among countries whose mitigation decisions impose local cos

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: Hard
コスト: Low

Strategyproof Deep Reinforcement Learning for Online Resource Allocation

Online resource allocation faces two coupled challenges: (1) allocating scarce resources efficiently under unc

深層学習軽量化・量子化強化学習

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

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テキスト

Game-Theoretic Solvers with Bayesian Opponent Modeling and Moral Rhetoric Framing for GLEE

We describe the agent we submitted to the GLEE Competition, which plays bargaining, negotiation, and persuasio

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

When do token systems stabilize? Compatibility constraints and community structure

Token systems support repeated service exchange without monetary transfers, but their viability depends on the

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Impossibility of No-Regret in Multi-Agent Bidding

We consider $T$ repeated second-price auctions across $n$ pacing bidders with i.i.d. item arrivals, and show t

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Optimality and Impossibility in Multidimensional Strategyproof Facility Location

We study facility location in multidimensional normed spaces with the objective of minimizing social cost, the

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

huggingfaceGitHubありHugging Faceあり2026-10-27

Multi-Agent Relay Thinking

Existing multi-agent collaboration (MAC) methods have primarily explored a group thinking collaboration topolo

用途: 技術検証・論文読解補助
難易度: Easy
コスト: Medium

GEPA-Curator: Curated Learning for Self Improving Agents

Large Language Models (LLMs) are increasingly used in multi-turn agentic workloads that require sustained inte

用途: 技術検証・論文読解補助
難易度: Easy
コスト: High

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

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

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2026-10-27

WorkFactory: Turning Professional Intents into Executable Work for Scalable Agent Training

Scaling supervised training for working agents requires diverse professional tasks that yield reliable expert

品質予測/異常検知自然言語処理ファインチューニング

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2026-10-27

WebAnything: From the Wild Web to Scalable Agent Environments

Web agents require diverse, executable, and resettable website environments for reproducible training and eval

自然言語処理RAG生成

用途: 生成
難易度: Easy
コスト: High

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

用途: 技術検証・論文読解補助
難易度: Easy
コスト: High

TAO: Tool-Aware Joint Scheduling for LLM-Based Agentic Serving

Agentic services alternate between model inference on clustered GPUs and external tools on distributed CPUs. I

用途: 技術検証・論文読解補助
難易度: Easy
コスト: High

Agentic Algorithm Design: LLMs that Propose, Formalise, and Prove Runtime Bounds for Evolutionary Algorithms in Lean

LLMs now prove hard theorems in Lean. Can they also design algorithms and prove their correctness and efficien

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Bayesian Replicator Dynamics and the Inversion of Strict Dominance in Bimatrix Games

Replicator dynamics, the central model of evolutionary game theory, describe populations evolving under select

強化学習方策勾配 (PPO / A3C)分類

用途: 分類
難易度: Hard
コスト: Low

Formation of international environmental agreements and payoff allocation

Dealing with climate change requires that all countries engage in costly efforts to reduce their emissions. Re

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

品質予測/異常検知強化学習方策勾配 (PPO / A3C)

BROCADE: Broker-based Contract Design for the Multi-Agency Model

We study welfare maximization in a contract model with $m$ principals who privately value a public project's s

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Fair Division of Lotteries

We study fair division of uncertain items, where the utility of each item is a random variable whose realizati

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

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

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

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

用途: 生成
難易度: Easy
コスト: High

arxivPaper only2026-10-25

Online Fair Division with Frequency Predictions

We study online fair division of indivisible goods among $n$ agents with frequency predictions: before any goo

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

arxivPaper only2026-10-25

Envy-Free Allocations for Random Non-Additive Valuations

Benade et al. (EC 2024) introduced a semi-random model for non-additive fair division in which each agent star

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

arxivPaper only2026-10-25

Pricing Information in Search with Nonobligatory Inspection

As AI agents reshape Web search and advertising, platforms can monetize information that improves decisions. W

品質予測/異常検知機械学習特徴量エンジニアリング

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

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

githubGitHubあり2026-10-25

dafeiyu-cleaner — DeepSeek 蓝色大肥鱼消除计划：识别并清理 B 站（Bilibili）评论区中由 DeepSeek 大模型、AI Agent 驱动的机器人账号（大肥鱼）的 Chrome / Edge / Firefox 浏览器插件。离线规则 + DeepSeek API 精判，AI 评论检测、空间主页鉴定、鲸鱼娘一键清场。DeepSeek AI bot detector for Bilibili comments.

DeepSeek 蓝色大肥鱼消除计划：识别并清理 B 站（Bilibili）评论区中由 DeepSeek 大模型、AI Agent 驱动的机器人账号（大肥鱼）的 Chrome / Edge / Firefox 浏览器插件

自然言語処理大規模言語モデル検出

用途: 検出
難易度: Easy
コスト: High

arxivPaper only2026-10-24

The Conditional Value of Costly Information in Collective-Risk Dilemmas

Many high-stakes collective problems, e.g., climate mitigation and pandemic response, share a structure in whi

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

arxivPaper only2026-10-24

Strategic Calibration in LLM Multi-Agent Systems: Audited Pivot Scoring for Truthful Elicitation

In large language model (LLM)-based multi-agent systems, reported confidence increasingly determines the alloc

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

arxivPaper only2026-10-24

Jointly Modeling Preferences and Imperfect Decision-Making in Large Language Models

Do preferences Large Language Models (LLMs) express in standard elicitation tasks predict their strategic beha

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

arxivPaper only2026-10-23

Joint Service Depth and Priority Design in Queueing Systems

In AI-assisted service systems, the server can choose how much processing effort to spend on each job, which w

品質予測/異常検知深層学習Transformer

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

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

AgenticEvolve: Evolutionary Program Search with Persistent Language Model Agents that Design and Run Experiments

Evolutionary program search with large language models keeps the structure of an evolutionary algorithm and us

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Randomization to Balance Performance under Nominal and Compromised Communication

It is well-known that under nominal communication, where agents are able to share information with one another

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

A Resource Allocation Tournament

We introduce and analyze a round-robin simultaneous resource allocation contest, motivated by sports leagues a

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Matching Tasks to Agents: Mechanism Design for Multi-Agent Contracts

We study task assignment under adverse selection and moral hazard. In the single-task setting, agents privatel

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

A Weakly Group Strategy-Proof and Pareto-Stable Matching Mechanism with Two-Sided Indifferences under Gross Substitutes

We study finite many-to-one matching markets with indifferences on both sides and publicly known firm preferen

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

huggingfaceGitHubありHugging Faceあり2026-10-22

R^2Orch: Recursive Self-Improvement for Agent Orchestration via Self-Play within a Skill-Internalization Loop

In recent years, agentic orchestration has been widely applied to solve complex human tasks. However, existing

コンピュータビジョンセグメンテーションQA

用途: QA
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

HGM-Dual: Lineage-Aware Guided Agent Harness Optimization for Long-Horizon Planning

Harness optimization, which improves an agent's prompts, tools, and workflows, is an increasingly effective wa

深層学習軽量化・量子化生成テキスト

用途: 生成
難易度: Hard
コスト: High

Cutting a Cake For Human Consumption: An Elementary $n$-agent Cake Cutting Protocol

We give a bounded envy-free cake-cutting protocol for any number $n$ of agents in the Robertson--Webb model, w

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Pareto Inefficiency of EFX

We consider the problem of allocating indivisible goods to agents with additive valuations, under two classic

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Correlated Quantal Response Equilibrium: A New Equilibrium Notion for Human-AI Strategic Interactions

The modeling of the behavior of agents in strategic environments and the prediction of the resulting outcomes

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

githubGitHubあり2026-10-21

AgentsMeetRL — Awesome List for Agentic RL

エージェントRRLに関連するアワーショットリスト。

用途: エージェントRRL
難易度: Easy
コスト: High

Is Anyone Enforcing the Cartel? What Reinforcement Learning Teaches LLM Agents in Markets

Evaluations of large language model (LLM) agents report that pricing agents collude at others' expense. Yet co

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Exact Envy-Free Cake Cutting with Polynomial Expected Queries

Envy-free cake cutting asks how to divide a resource so that no agent prefers another agent's share. Finding s

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Subjective Valuations Make It Hard to be Fair: The Complexity of Resource Allocation to Groups of Agents

We study the parameterized complexity of finding an EF1 allocation of indivisible items in the group-sharing s

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Splitting Planar Necklaces with Few Straight Cuts

In the classical necklace splitting problem, beads of different colors lie on a line, and the goal is to divid

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Extending Mechanisms

Given an incentive-compatible mechanism for a single agent on a restricted domain of valuations, we consider i

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

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

arxivPaper only2026-10-19

Sector-Ring Linear Reservoirs: Sector-Wise Decay-Timescale Allocation With State Reuse Across Candidate Settings

In reservoir computing, performance depends on whether the fixed dynamics of the reservoir suit the task, and

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

arxivGitHubあり2026-10-19

Game of Phones: Harnessing Game Theory and LLMs in 'Bot Wars' to Counteract Phone Scams

Phone scams impose substantial and growing costs on individuals and financial systems worldwide, yet existing

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

arxivPaper only2026-10-19

Sustainable and Envy-free Multi-Period Allocation

Allocating renewable resources fairly requires accounting for how present decisions affect future availability

品質予測/異常検知強化学習

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

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

Let Them Sleep: Recursive Self-Pruning Memory for Long-Horizon LLM Agents

Agents that run for a long time have a memory problem, and it is not the one most systems optimize for. Storin

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

How Far Can an Agent Push? Trust-Safety Margins in Live Language-Based Economic Games

Game theory identifies when a strategic action should be accepted, but not how long a live, language-based cou

数学・理論確率・統計

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

MMS Allocation for Chores with Item Disposals

Web platforms organize collective work, including content review and community maintenance, that imposes heter

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Non-Monetary Mechanisms for Stochastic Scheduling via Sampled Pairwise Externality Compensation

How can a shared resource be allocated without money when agents have private, stochastic demands that can be

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

arxivGitHubあり2026-10-17

Organon : From Evolutionary Search to Recursive Self-Improving Agents for Scientific Discovery

Scientific discovery does not follow a single fixed methodology: research methods vary across domains and evol

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

arxivPaper only2026-10-17

Cooperation Without Shared Rewards via Reciprocal Q-Augmentation

General-sum games are more challenging than fully cooperative or adversarial settings because agents may decei

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: Hard
コスト: Medium

QD-Rec: Open-Ended Evolution of Recommendations via Quality-Diversity

Open-ended search combined with foundation models has produced advances such as faster algorithms, new results

品質予測/異常検知深層学習テキスト

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Coopshift: Post-Training Amplifies The Memory Curse In LLM Cooperation

How does post-training change cooperation during repeated interaction? Within each of seven released model lin

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Revisiting the Fairness of Pure Nash Equilibria of Round-Robin

We prove that for additive valuations, not every pure Nash equilibrium of the Round-Robin mechanism induces an

センサ/時系列機械学習時系列

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Contracting with an Agent under Model Mismatch

In the classical principal-agent setting, the principal delegates some task to the agent, who receives a payme

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

On the Convergence of Iterated Round Robin

Round Robin (RR) is a simple and widely used picking sequence mechanism for allocating indivisible goods, know

センサ/時系列機械学習時系列

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Cooperation Without Adaptation: LLM Agents in a Collective-Risk Dilemma

Language-model agents are increasingly deployed to act on behalf of people in settings where individual decisi

MI向き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: Hard
コスト: High

Optimally Curating an Event

We study the optimal design of a self-financing event, a problem that requires balancing the recruitment of co

品質予測/異常検知数学・理論確率・統計

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Anytime Contracts for AI Services

AI services are prone to moral hazard, as model providers can silently route requests to cheaper, lower-qualit

品質予測/異常検知深層学習軽量化・量子化

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

huggingfaceHugging Faceあり2026-10-16

ABF-Bench: A Verifiable Benchmark for Agentic Blockchain Forensics

Blockchain forensics is adversarial and open-ended: money launderers split, swap, and bridge funds, cut their

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2026-10-16

PersonaCUABench: Benchmarking Computer-Use Agents Serving Diverse Personas

Computer-use agents (CUAs) are commonly evaluated against fixed task specifications, but users may omit requir

MLOpsモデルデプロイ

用途: 技術検証・論文読解補助
難易度: Easy
コスト: Medium

githubGitHubあり2026-10-16

BettaFish — 微舆：人人可用的多Agent舆情分析助手，打破信息茧房，还原舆情原貌，预测未来走向，辅助决策！从0实现，不依赖任何框架。

微舆は人人可用的多Agent舆情分析助手であり、情報茧房を打破して舆情の原貌を還元し、未来の走向を予測し、決策を助けることができます。

用途: 舆情分析助手の問題を解決する
難易度: Easy
コスト: High

Dynamic Auction Mechanisms in a Closed-Credit Economy: Sequential Slot Reallocation in Air Traffic Management

Indivisible resources allocated in advance may need to be reallocated when capacity reductions make existing a

コンピュータビジョン動画認識

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Auditable and Deployable Multi-Agent Motion Planning via Posetal Games from Legal Guidelines

When robots are deployed in the real-world, they must interact with other agents and accomplish their own goal

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Decentralized LLM Workflows via Sequential Auctions

We present a scalable framework for solving multi-step tasks through a decentralized market of self-interested

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Learning Rewards in Strategic Games

Understanding the objectives that drive agents' behavior is crucial in strategic environments, where multiple

自然言語処理ファインチューニング強化学習

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

How language model agents reshape price competition

Repeated Bertrand competition is a stringent benchmark for algorithmic pricing: in homogeneous-goods markets,

説明可能深層学習Transformerテキスト

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

huggingfaceHugging Faceあり2026-10-15

RealCLI: Toward a Unified Evaluation of Human-Agent Collaboration for CLI Agents

Command-line interface (CLI) agents increasingly execute longer-horizon tasks, yet existing benchmarks either

品質予測/異常検知自然言語処理RAG

用途: 技術検証・論文読解補助
難易度: Easy
コスト: Low

Price of Anarchy of First-Price Auctions in Agentic Markets

An agentic market is a decentralized serving-time market where AI agents bid to claim each user prompt. An age

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Exact Cut Complexity of Equal-Length Proportional Cake Cutting

We study proportional cake cutting under an equal-length constraint, where each of $n$ agents must receive a b

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Effective-Balance Design in Ethereum Proof-of-Stake

Proof-of-Stake (PoS) systems offer a scalable and energy-efficient alternative to Proof-of-Work (PoW). The eco

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

PPAD-Completeness of Contiguous Envy-Free Cake Cutting with Additive Valuations

We prove that computing a complete contiguous $\varepsilon$-envy-free cake allocation is PPAD-complete for nor

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Truthful and Expected-Payment-Optimal Model Routing in Graph-Based Agentic Workflows

Assigning routine tasks to cheaper large language model (LLM) providers can reduce generation cost, but switch

深層学習Transformer生成テキスト

用途: 生成
難易度: Hard
コスト: High

Learning in Strategic Queuing with Geometric Recharge: From Bandits to Systems

We study packet routing in queuing systems where queues act as selfish, myopic agents that use no-regret learn

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Low

Maximin share allocations for two types of items

We study the fair allocation of indivisible items among $n$ agents with additive valuations (or costs) using t

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Falsification-proof non-market allocation mechanisms

Relying on manipulable characteristics to target the allocation of resources is fraught with difficulties. Nai

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

説明可能強化学習モデルフリー (DQN / SAC)

Strategic Multi-Agent Resource Allocation via Statistical Contracts

We study a class of strategic hypothesis testing problems in which a principal aims to allocate resources amon

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

When Does Opponent Modeling Pay Off? Spatial Inference Stabilises Reinforcement Learning under Partial Observability

Deep reinforcement learning agents in partially observable competitive games must act on beliefs about an unse

深層学習Transformer強化学習

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

Truthful Two-Facility Location with Candidate Locations in Metric Spaces

We study a truthful facility location problem in which agents have private positions in a metric space and kno

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

Certifying Capacity and Gaps of the Rank-Dependent Gain-Sharing Framework for Ranking

The randomized primal-dual framework of Devanur, Jain, and Kleinberg (SODA 2013) splits each unit matching gai

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

arxivPaper only2026-10-12

An Introduction to Contract Theory for Computer Scientists

This survey introduces contract design to computer scientists: using outcome-based payments to incentivize eff

強化学習方策勾配 (PPO / A3C)分類

用途: 分類
難易度: Hard
コスト: Low

arxivPaper only2026-10-12

Online Fair Division with Upgrade Recourse

We study the online fair division of indivisible goods. In the standard model, where assignments are irrevocab

センサ/時系列機械学習時系列

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

arxivPaper only2026-10-12

The Voter Model with Opinion Graph

We study the \emph{voter model with opinion graph}, a generalization of the standard (pull) voter model that a

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

arxivPaper only2026-10-09

A Multi-Agent LLM System for Automated Tuning of Specialized Neural Network Models

Many machine learning approaches are two-level: an expert selects a class of methods, within this class the ch

自然言語処理大規模言語モデル分類テキスト

用途: 分類
難易度: Hard
コスト: High

arxivPaper only2026-10-09

The value of information and the emergence of communication in evolved e-puck robots

We study when communication emerges between autonomous agents and what role an innate action selection archite

深層学習RNN / LSTM

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Medium

arxivGitHubあり2026-10-08

A Closer Look at Agentic BBO: Benchmarking LLM Agents for Black-Box Optimization

Black-box optimization (BBO) arises in many scientific and engineering problems where objective evaluations ar

条件最適化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: Hard
コスト: High

arxivPaper only2026-10-08

Learning to Orchestrate Evolutionary Search: Progression-Aware Deep Reinforcement Learning for Dynamic DE-CMA-ES Coordination in Optimization and Structural Model Updating

Solving high-dimensional structural model updating problems requires an algorithm capable of navigating comple

自然言語処理ファインチューニング生成強化学習

用途: 生成
難易度: Hard
コスト: High

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

githubGitHubあり2026-10-08

ai-agent-book — 《深入理解 AI Agent：设计原理与工程实践》（李博杰著）开源主仓库：全书正文、编译版 PDF 与按章配套代码

この論文では、現在のVision-Language-Benchmark（VLB）を超える、MLLMがアクティブな観察を実演できるようにするためのバenchmark、ActiveVisionを提案する。このActiveVi

用途: 弁論の実際的な対象を形成するためにAIが活用される
難易度: Easy
コスト: High

arxivPaper only2026-10-07

Evaluating the Transfer of Co-Evolved Communication from 2D to 3D Simulation

This work examines the transfer of a co-evolved communication mechanism between two robotic agents from a disc

コンピュータビジョン3D・点群3D

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

arxivPaper only2026-10-05

Visual Swarm Navigation via Deep Reinforcement Learning and Evolutionary Hybrid Design

Swarm robotics presents a robust and cost-effective paradigm for advanced automation in complex, dynamic envir

深層学習軽量化・量子化生成画像強化学習

用途: 生成
難易度: Hard
コスト: Low

arxivGitHubあり2026-10-04

AgentDiscover: Autonomous Discovery with Minimal Search Scaffolding

Frameworks that use large language models for scientific discovery typically rely on a fixed, human-designed a

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

arxivPaper only2026-10-02

FrugalEvo: Towards Cost-Aware LLM-Guided Program Evolution

LLM-guided evolutionary methods, such as AlphaEvolve, have emerged as powerful approaches for challenging comp

品質予測/異常検知深層学習軽量化・量子化

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

arxivPaper only2026-10-02

Evolutionary Computation for Trustworthy AI: From Attacks and Defenses to Self-Evolving Era

As Artificial Intelligence (AI) has evolved from task-specific models to foundation models and agents, the sco

機械学習特徴量エンジニアリング生成テキスト

用途: 生成
難易度: Hard
コスト: Medium

arxivPaper only2026-10-01

SEDIMA: Cross-Run Hierarchical Insight Memory for Evolutionary Search Agents

Large language model (LLM)-driven evolutionary search is a powerful paradigm for automated program and algorit

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

arxivPaper only2026-10-01

TRACE: Tackling Real-World Resource Assignment Problems via Agentic Heuristic Design

Dynamic resource assignment, the real-time allocation of task streams to heterogeneous processing nodes, is th

表形式向き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: Hard
コスト: High

arxivPaper only2026-10-01

Continual Reinforcement Learning with Neuroevolution

Despite many studies about causes and remedies of plasticity loss in Reinforcement Learning (RL) under continu

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

From DNA Design to DNA Slimming: Auditable Agentic Discovery of a Deletion-Only Designer

Compact regulatory DNA can free up space in vector payloads, reduce synthesis and assay burden, and expose whi

深層学習Transformer生成

用途: 生成
難易度: Hard
コスト: Medium

Null-model treatment of the sensory-motor boundary changes an evolutionary connectome comparison

Randomised copies of a connectome are the usual baseline for asking whether measured wiring matters, and the a

深層学習Transformer生成

用途: 生成
難易度: Hard
コスト: Low

Evolutionary foraging in grids: Intermittent search dynamics emerge in finite, depletable landscapes

How search strategies evolve in finite, depletable landscapes remains a question in foraging theory. We study

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Low

Self-Evolving Algorithm-Design Agents: Escaping In-Context Evolutionary Stagnation via Population-Curated Policy Optimization

Large language models are increasingly participating in complex real-world tasks in the form of algorithm-desi

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: Hard
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

arxivPaper only2026-09-29

Behavioral Persistence and Incomplete Functional Transfer of Co-evolved Communication in Evolutionary Robotics

This work evaluates the direct transfer of a co-evolved communication protocol from a 2D simulation to a 3D ph

深層学習RNN / LSTM3D

用途: 技術検証・論文読解補助
難易度: Hard
コスト: High

arxivPaper only2026-09-28

HeurEvo: Agentic Evolution of Hybrid Solver-Augmented Heuristics for Time-Critical Mathematical Optimization

Recent advances in agentic heuristic design use AI agents and execution feedback to automate algorithm discove

品質予測/異常検知自然言語処理RAG

用途: 技術検証・論文読解補助
難易度: Hard
コスト: Low