Category
LLM、RAG、埋め込み、ファインチューニングなど、AIアプリ実装に必要な自然言語処理技術を整理します。
prompts.chatは、コミュニティが共有したChatGPT用のプロンプットを発見・収集できる場所で、無料でオープンソースで提供されている。
Unsloth Studioは、オープンモデルのトレーニングと実行を支援するWebUIです。このライブラリは、Gemma4、Qwen3.5などのオープンモデルのテストとトレーニングを支援するために使われます。
このライブラリは、空間情報を扱うためのコンピュータビジョンライブラリです。
paperless-ngxは、コミュニティによってサポートされたスーパーチャージドのドキュメント管理システムで、ドキュメントのスキャン・インデックス・アーカイブが可能である。
rayは、core分布ランタイムとAIライブラリで構成されたAI計算エンジンで、スケーラブルなAI計算をサポートする。
Rustを使ってモジュラーLLMアプリケーションを構築することができるライブラリです。
paperless-ngxは、コミュニティによってサポートされたスーパーチャージドのドキュメント管理システムで、ドキュメントのスキャン・インデックス・アーカイブが可能である。
rayは、core分布ランタイムとAIライブラリで構成されたAI計算エンジンで、スケーラブルなAI計算をサポートする。
Rustを使ってモジュラーLLMアプリケーションを構築することができるライブラリです。
Apple Silicon上でLLM推論サービスをシステムエンジニアが作成するチュートリアル。
このリポジトリはChatGPT、GPT-3、FlanT5などのLLMsの在り方や、in-context learningとprompt engineeringのリソースをまとめたものです。
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。
Unsloth Studioは、オープンモデルのトレーニングと実行を支援するWebUIです。このライブラリは、Gemma4、Qwen3.5などのオープンモデルのテストとトレーニングを支援するために使われます。
Weights & Biasesは、AI開発を支援するプラットフォームです。このプラットフォームは、モデル開発から生産準備までを支援し、コストをコントロールし、モデルとデータへのアクセスを管理します。
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。
Infrastructure for continually self‑improving agents
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。
このリポジトリでは、AIアプリケーションをローカルに実行できるツールキットであるRunAnywhere-sdksを提供しています。
マルチモーダルAIに適したオープンレイクハウスフォーマットです。このフォーマットでは、パレットからデータを2行のコードで変換することができ、100倍速くなります。また、ベクトルインデックスやデータバージョニングが可能です
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。
このリポジトリでは、AIモデルの互換性を確保するためのオープンスタンダードであるONNXを提供しています。
画像やビデオやオーディオディフュージョンモデルのファインチューニングを行うための、汎用的なファインチューニングキット。
We study online learning with directed weakly observable feedback graphs and propose a Follow-the-Perturbed-Le
Data-driven algorithms are ubiquitous across industries, and there is an extensive literature on analyzing the
How much label noise has a network memorized, and when? We study a multilayer perceptron and what statistical
Deployed language models are updated continually, and regulators increasingly accept changes that were declare
We study the minimization of the average of $n$ convex $(L_0,L_1)$-smooth functions: the Hessian norm of each
Long-horizon video generation is increasingly moving toward autoregressive rollout, where each newly generated
We study nonparametric estimation and forecasting for GARCH-type processes and propose an iterated neural esti
Small students degrade on large reasoning models' (LRMs') long solutions, so recent work shortens them by inte
Model merging combines multiple fine-tuned models without additional training, and arithmetic methods have eme
Optimizing accelerator code across hardware architectures is difficult. ML compilers search predefined spaces
Routing benchmarks often judge a router against a hindsight oracle that counts a query as solved whenever any
In-image machine translation (IIMT) replaces text in an image with its translation while preserving the surrou
Answering geographic queries over Earth observation (EO) archives requires solving a joint retrieval-and-deriv
In groups, knowledge is distributed among individuals, and how it is brought together depends on social ties.
Retrieval-augmented Web applications increasingly employ multi-agent debate (MAD) to aggregate conflicting, no
Protein structure predictors reliably identify native conformations, but do not describe the continuum connect
Harness evolution from a common base exhibits Locality, Complementarity, and Nonstationarity: variants repair
External text can affect a retrieval-augmented generation (RAG) system only after the retriever admits it to t
Video games provide a practical setting for collecting vision-language-action (VLA) data, whose acquisition in
Disturbances can invalidate the conditions required by a robot's next action even when much of its task progra
Large language models (LLMs) increasingly shape recommendations across chatbots, e-commerce, and user-generate
Conventional recommender systems operate within platform-specific catalogs, limiting access to potentially val
Frontier LLM agents can turn known vulnerabilities into working exploits, but are costly and often access-rest
Large language model (LLM) agents increasingly act as service consumers. They select operations and report whe
Recent advances in large language models have enabled agentic systems that automate machine learning pipeline
Continuous glucose monitors, smartwatches, meal logs, and health profiles form a rich multimodal personal cont
How is it possible for perturbation-based methods like Evolution Strategies (ES) to optimize billion-parameter
Longitudinal clinical context is often distributed across noisy, repetitive forms and narrative records. We st
Harnesses for large language models have converged on a single-threaded shape: one agent, one history, one mac
Practitioners who compare systems with an LLM judge and have no human labels often check a conclusion by repea
Understanding what kind of prompts users typically ask and how well an LLM performs on these prompt categories
NLP evaluation reports routinely cite sliced performance differences, most prominently the intra-inter gap in
Services that operate in Korean often write their evaluation rubrics in Korean and hand them to an LLM judge.
Can an LLM directly edit the weights of a diffusion-based image-restoration model to \emph{improve} restoratio
Promptable perception, from GroundingDINO to Rex-Omni, puts one model behind one prompt interface and returns
Diffusion-based data augmentation has emerged as a powerful strategy for addressing data-scarce image classifi
Separating touching nuclei requires local boundary detail, while cell type classification requires broader mor
Dynamic driving scene reconstruction has made remarkable strides in recent years by leveraging generative prio
Capturing an image in a low-light environment produces a weak, noisy sensor signal. Low-light image enhancemen
Operating rooms (ORs) increasingly contain ambient sensing infrastructure to enable AI-based optimization of w
prompts.chatは、コミュニティが共有したChatGPT用のプロンプットを発見・収集できる場所で、無料でオープンソースで提供されている。
このリポジトリでは、データとAIアルゴリズムを製品化するためのプラットフォームであるTaipyを提供しています。
LLMを評価するプラットフォームであり、さまざまなモデルとデータセットをサポートする。
マルチモーダル理解技術のための新しいアプローチであるMIRRORを提案しました。MIRRORは、テキスト、図、テキストと図の組み合わせから等価な視点を提供することで、視覚的な推論や複雑な推論力を向上し、さまざまなモデルの
Open-source code for training and scaling up energy-based foundation models.
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。
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。
Easily fine-tune (SFT/RL), evaluate, and deploy Qwen, Gemma, or any open agentic LLM/VLM!
この論文では、LLM を提供するために使用される Mooncake サービス プラットフォームについて説明しています。Mooncakeは、Kimi というリーディングのLLMサービスを提供するサービスです。Kimiは、M
TensorZeroは、LLMゲートウェイ、オブザーバビリティ、評価、最適化、実験を統一したオープンソースのLLMOpsプラットフォームです。
このリポジトリでは、高スループットと低メモリ消費のLLMインフェレンザエンジンであるVLLMを提供しています。
flyteは、高度に動的で堅牢なAIオーケストレーションプラットフォームであり、データ、モデル、コンピューティングを統合してAIワークフローを作成することができます。
CVV または CWE への分類を実現し、バグ修正のために重要な手順となるCVEへの CWE 分類を自動化する。
統計チャートの生成は、タブラーのデータから生成することが難しい。新しい作成フローでは、データのスクリーン、プロット提案、コード生成、レンダリング、検証による改良が含まれる。
LLMを使用して、自然言語処理における情報抽出を行うためのPythonライブラリです。
ドキュメントを構造化するために使えるオープンソースのETLソリューション。
オープンソースのGPT/LLMエージェント作成ツールです。
この研究では、自然言語処理の負担を減らすモジュラリティを目指しています。モジュラリティとは、システムを小さくて独立した部分に分割して、それぞれを簡素化することです。この研究では、文脈に応じてモジュラリティを変更できるメカ
テキスト分析、センチメント分析や単語分割などを行えるライブラリ。
trafilaturaはPythonとコマンドラインツールで、Webからテキストとメタデータを取得して、CSV、JSON、HTML、MD、TXT、XML形式で出力を提供します。
Real-world problems span diverse domains, including program synthesis, symbolic reasoning, planning, and optim
We ask how hard it is to build a neural network when its parameter values are given in advance. An instance su
Recent work has improved computer-use agents (CUAs) by combining retrieved task-relevant tutorials and look-ah
We study the problem of average reward Multi-Agent Reinforcement Learning (MARL) where agents interact within
Learn-then-Test (LTT) provides a general framework for certifying deployment policies against a prescribed ris
Prior-data fitted networks are typically trained using synthetic data generated by a single, manually construc
Prestress governs the stability, stiffness, and dynamic behavior of tensegrity structures, but it is not direc
LLM agents struggle to automate complex enterprise procedures because static, pre-built harness cannot capture
Large Language Bayes (LLB) transforms a natural language problem description into a collection of candidate pr
Low-rank adapters (LoRA) let one base model serve many fine-tuned variants: multi-tenant engines keep a single
Large Language Models (LLMs) often generate fluent yet incorrect responses, making un- certainty estimation im
Hyperparameter optimization (HPO) returns a single optimal configuration, systematically discarding the explor
AIRoA-0 (A-0) is a bimanual manipulation system learned from 16,151 hours of handheld demonstrations, with no
Genomic foundation models are increasingly used to propose DNA sequences and edits. But successful design requ
Sensorized human demonstrations offer scalable data for mobile manipulation, but embodiment differences compli
Reinforcement learning with verifiable rewards (RLVR) can enable generalizable improvement for large language
Group-based reinforcement learning with verifiable rewards (RLVR) learns only from prompts whose sampled compl
In the near future language model pretraining will become bound by total data rather than total compute. Such
We consider nonparametric maximum likelihood estimation of the intensity function of a Poisson process, allowi
Earth system models (ESMs) rely on parameterizations to represent unresolved processes such as convection, clo
LLM agents show strong potential in software engineering tasks such as competitive programming and code synthe
Contact-rich manipulation benefits from tactile feedback that captures both detailed contact geometry and inte
Organisations increasingly deploy large language model agents that must follow organisational values. We check
Discrete diffusion models remain far less understood than their continuous counterparts, despite recent breakt
Graph machine learning benchmarking results are scattered across papers that use quite different sets of datas
Actor--critic methods rely on critic gradients to improve continuous-action policies, yet finite-sample guaran
Sparse attention cuts the cost of long-context inference by making the attention matrix sparse: each query att
A frontier language model was recently reported to solve 1D NMR structural elucidation without tools. We ask w
Full Waveform Inversion (FWI) is a fundamental inverse problem in geophysics to estimate subsurface properties
Long-context LLM inference is costly because attention repeatedly reads a growing key-value (KV) history. We i
LaBraM, an early EEG foundation model, demonstrated that large-scale EEG pretraining can yield strong represen
Machine learning supports materials design, atomistic simulation and experimental characterization, yet models
LLM post-training via reinforcement learning has shown consistent improvements in accuracy at the cost of redu
Active-learning Kriging often replaces candidate pools by continuous metaheuristic search. A pre-specified com
Reasoning language models write a chain of thought before they answer, and many checks in it do not change the
Time-series segmentation, studied as either Change Point Detection (CPD) or State Detection (SD), underpins a
Two machines can each recognize an object well and still fail together. We study classifiers that see differen
Classical decompositions of predictive uncertainty, such as the information-theoretic one, are defined for a f
Financial workflows need fast, calibrated, auditable decisions, not just generated text. We train System-1 dec
Language model agents increasingly act on users' behalf by manipulating external environments through tools, o
Spatial multi-omics technologies jointly profile diverse molecular modalities with spatial context, providing
We compare three zero-shot time-series foundation models (Timestral-5M, Timestral-209M and Chronos-2, 8192-hou
Best subset selection is a central problem in statistical regression, drawing increasing attention from the br
Overall and group-level averages, and ranking metrics such as the AUC, can hide sets of observations for which
Scientific discovery builds on accurate observations interpreted through domain knowledge and reasoning. For s
Reinforcement Learning with Verifiable Reward (RLVR) has emerged as a promising approach for enhancing the rea
In mixed-vendor accelerator fleets, many training jobs can run on several hardware types, and the best choice
Large Language Models (LLMs) serve both as tools for aiding human decision-making in everyday life and as mode
A brain MRI scan can carry an unknown mix of artifacts, so restoration has to be blind, yet most deep-learning
Large language model (LLM) assistants increasingly advise backers on which crowdfunding campaigns to support.
Enterprises increasingly want LLM agents to automate standard operating procedures over their software stacks
Large language model (LLM) agents are increasingly used as synthetic participants in opinion-dynamics experime
Assistive systems typically tell users everything, often spending time and attention on what a user already kn
Small language models (<=9B parameters) can support local medical question answering (QA) while reducing priva
Students can use large language models to complete assignments, but a correct solution does not establish whet
Simulation benchmarks are the standard way to compare robot policies, but each benchmark brings its own evalua
Computational drug discovery offers a growing range of tools for proposing and evaluating molecules, but combi
Chain-of-Thought (CoT) generation has substantially improved the performance of Large Language Models (LLMs) o
A model is only half of an AI agent system. The other half is the inference strategy: the reasoning procedure
Large language models (LLMs) show growing promise for scientific discovery, yet basic questions remain about w
As pretraining scales, medical language models have advanced rapidly in clinical question answering. Their abi
Federated learning (FL) runtimes standardize aggregation and deployment, but each new study still requires fed
The Model Context Protocol (MCP) lets agentic AI frameworks connect language models to external tool providers
LLM coding agents can increasingly automate model post-training, but whether they can act as forward-deployed
Connecting new generation and demand to the grid depends on two scarce skills: checking applications against a
Can AI contribute to formal demography? We report an experiment in which an AI model, Claude, was asked to exp
Government intelligence teams need LLM agents that can analyze large, messy evidence collections, but security
LLM agents are judged by what they deliver, and in professional work what gets delivered is a project, not a t
Best-of-N decoding improves generation quality by sampling multiple candidates and selecting the best, but it
Every multimodal-LLM multiple-choice benchmark ends with a scoring rule that converts a model's response, ofte
Crime-reporting mobile applications can enhance community safety and engagement in urban areas, yet their adop
Artificial intelligence (AI) technologies and their capabilities have advanced rapidly over the past few years
Text-to-SQL translates natural language questions into SQL queries, enabling database access without writing S
Computer-use agents for scientific software require application-specific expertise. Much of this expertise is
LLM agents compact long histories to fit their context windows, but the compacted context must still carry the
When a language model answers a yes/no question, that answer changes how it answers a different question about
Protein-complex structure prediction with AlphaFold 3 depends on MSA content, depth, and cross-chain pairing.
Knowledge is how understanding accumulates across people and generations: durable records that outlive their p
Procedural modeling offers compact, editable, and reusable representations for architectural content, but buil
An experimental agent can succeed for the wrong reasons. Evaluations, however, often focus on the outcome, not
While Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced multimodal reasoning, e
Restricting what a model can say is often mistaken for restricting an attacker's influence over its decision.
Large language model (LLM) embeddings have been increasingly adopted in sequential recommendation to encode st
Large language model applications are increasingly being organized as tool-using and multi-agent workflows rat
Criminal verdicts contain descriptions of criminal behavior that can support quantitative legal research, but
With the popularization of livestreaming applications, automatic camera tuning has become indispensable for or
Long-horizon tasks require large language model (LLM) agents to coordinate interdependent actions, a capabilit
The multi-turn checker of the Berkeley Function Calling Leaderboard (BFCL) evaluates final state correctness b
PlagiaCorpus is a bilingual Arabic-English dataset for benchmarking plagiarism and text-reuse detection, inclu
Online political discussions increasingly involve AI-mediated participation, as users invoke LLMs to assess co
An agent that explores, whether it generates conjectures or research ideas, must decide which of them are wort
Remote sensing image fusion and semantic interpretation are usually optimized separately, so reconstruction qu
Users convey frustration in what they say and in how they say it, and voice-native models, which hear speech i
High-quality annotations are essential for training and evaluating artificial intelligence (AI) systems. Quali
Reclamation of saline-alkali land requires management plans that account for soil and water conditions, treatm
Sampling and selection spend test-time compute inside a candidate pool, but they cannot recover a correct answ
TLAPS-Bench is a benchmark and execution harness for constructing machine-checked proofs for real-world system
Mathematical formalization plays an important role in establishing rigorous and machine-verifiable foundations
AI scientists are expanding automated research across scientific domains, yet their ability to turn the scient
Governing equations provide an interpretable description of the mechanisms underlying complex network dynamics
Large language model agents increasingly take consequential actions in digital and physical environments, wher
Interactive narratives let users shape a story while an Experience Manager (EM) keeps it coherent, balancing p
Career-support assistants must distinguish user claims from evidence of current ability and withhold judgments
Reverse engineering obfuscated binaries is a core task in malware analysis and software security auditing. Whi
For over 50 years, the database community has developed a wide range of successful engines to answer two funda
With the rise of Group Agent such as Claude Tag, agents increasingly serve as digital workers for long-term te
Large language models provide a variety of responses at inference (generation) that conventionally depend on a
ufakzeka-kulak is an open Turkish speech-to-text model of 31.6M parameters, a FastConformer encoder with a tok
Large language model (LLM) coding agents increasingly operate with shell access, allowing them to write script
Research taste, the judgment of what to read, which problems to pursue, and which methods to try, increasingly
Previous research shows that vision-language (VL) models struggle to understand fine-grained image details and
Large language agents for literary role-playing are typically modeled around dialogue generation, leaving char
Improving a sentiment classifier with limited resources involves more than simply deciding how much data to co
Real-time omnimodal assistants are expected to support people in everyday life, but audiovisual understanding
This paper studies retrieval-augmented generation (RAG), which leverages external knowledge to improve the per
Even successful long CoTs may contain harmful supervision, motivating the need for methods that can automatica
The author shows a methodology for constructing a network map of the artworld. The tweets posted by a manually
We present ICJ-Graph, a machine-readable resource connecting documents of the International Court of Justice (
Large language models are increasingly consulted for religious knowledge, including the recall of specific Had
Large language models (LLMs) are increasingly considered for education, recruitment, workplace decision suppor
Attention-based rerankers score each candidate by the attention mass that retrieval heads route to it from the
Scientific discovery from the literature requires assessing connections among findings obtained under differen
Large language models (LLMs) can generate visual assets through text-based formats such as SVG and CAD, but th
Large language models are increasingly used as measurement instruments for socially consequential constructs s
Long-term conversational memory faces a timing mismatch: write-time summaries and graphs organize history befo
Dialectal Arabic speech recognition remains constrained by the scarcity of dialect-resolved transcribed speech
Most benchmarks for real-time voice systems focus on one aspect, such as turn-taking, instruction following, p
Rewards for diversity, coverage and distribution matching make the reward of each sampled response depend on t
In the LLM-as-formalizer paradigm, a large language model translates a problem into a formal program and leave
Whether an aligned language model refuses depends in part on which words a prompt contains, regardless of whet
AI assistants store values that a language model derived from a person's records under the person's own defini
We compare how often two released language models give outdated numeric values from Norwegian statutes and reg
Looped language models can stop iterating early, and recent work shows that post-hoc readouts of the loop traj
Medical artificial intelligence agents should retrieve patient identifiers only when a task requires them, the
Sycophancy in language models is well documented as a behaviour but poorly localised as a mechanism. We ask wh
Surgical scene understanding is pivotal for computer-assisted intervention, yet demands compositional reasonin
Humans imagine scene changes under hypothetical motions, an ability central to spatial reasoning and robotic p
Learning from human data offers a scalable path toward embodied intelligence, yet it requires models to accura
We present Isekai3D Synthetic Data Generation (SDG), a platform for simulating and capturing synthetic 3D game
Multimodal large language models (MLLMs) can generate detailed video captions, yet they may overemphasize mino
Electron microscopy (EM) serves as a cornerstone in virology enabling the visualization of viral particles and
Open-set recognition (OSR) requires neural networks to classify known classes while rejecting unknown samples,
Earth observation (EO) workflows require selecting among known outcomes, estimating confidence, and processing
Geospatial text spotting involves locating and transcribing geographic labels, such as road names, district na
Semantic change detection in remote sensing aims to identify and characterize land-cover transitions observed
Diffusion models are evolving from general tools into personalized systems that adapt to user needs through fi
An image that is hard to classify on its own may have a helpful neighbor in the same unlabeled batch. Zero-sho
Real-world driving requires context-aware decision-making. Existing end-to-end driving benchmarks primarily as
Cloth-changing person re-identification (CC-ReID) is still very challenging, and no method clearly dominates:
Misleading charts can lead vision-language models to conclusions that conflict with underlying data. Existing
Traditional image cropping assumes the photograph already contains the composition worth keeping, and asks onl
Interleaved multimodal chain-of-thought reasoning improves the visual reasoning capabilities of multimodal lar
Financial document recognition is commonly evaluated using character and word error rates, which do not adequa
Multi-task dense prediction seeks a single, coherent representation that supports simultaneously discriminativ
Understanding how objects and their spatial relationships evolve in 3D space over time remains challenging for
Precise localization of pathologies in endoscopy is a critical task in medical imaging, with deep learning (DL
Producing an executable CAD script is not sufficient to establish that an agent has satisfied an engineering d
Detecting defects in real-world industrial inspection often requires learning from only a few normal images pe
3-D road anomaly segmentation is essential for risk-aware perception in autonomous driving. Its central challe
Membership inference determines whether a candidate example was used to train a text-to-image diffusion model
Clinical evidence is inherently relational: medical images are interpreted with prior studies, cross-modal exa
Spatial proteomics measures tens of proteins in every cell of a tissue section, but its cost confines it to sm
Quantum-inspired computer vision often reshapes high-dimensional features into tensors and applies tensor-netw
Engineering drawings specify mechanical parts through orthographic projections. Recognizing which part a drawi
Underwater object detectors are typically evaluated on pre-transmission images, overlooking additional degrada
Open-set object detection (OSOD) suffers from a fundamental limitation under dense proposal mechanisms: highly
In virtual environments, choosing appropriate outfits requires contextual harmony with surrounding scenes, yet
Recent interactive video world models generate visually compelling videos from a single image, following user-
Computed tomography (CT) provides diverse patient information and various clinical tasks, for which numerous d
As robot learning moves beyond the tabletop, many tasks require mobility, adjustable reach, active perception,
Training robot policies for multi-task pick and place requires demonstrations on many objects, which handheld
Open-world robotic mobile manipulation requires acting in physical environments with only partial information.
Navigating crowded urban environments requires robots to avoid pedestrians while adhering to social norms such
We release all 3,718 episodes from the ArmnetBench v0.1 evaluation (arXiv:2607.24481), which contains 600 tele
Progress in robot learning, particularly physical multi-agent reinforcement learning (MARL), is currently chal
KlaskTron is an open-hardware physical platform for two-player adversarial reinforcement learning, with a lear
Generalization remains a central challenge in robotics, calling for policies that address domain variations fr
Recent robot systems combine wrist and fixed cameras, requiring camera-to-robot transforms for both. Most hand
Reliable separation mechanisms are critical for planetary micro-rovers, which must withstand launch vibrations
We reimplement the dynamics model of the Robotic World Model (RWM) and test the uncertainty estimate that its
Universal humanoid motion tracking is essential for enabling humanoid robots to perform diverse tasks in human
General-purpose robot policies must work across robots, tasks, and environments, yet the variation that makes
Treating morphology and control as separate design problems has constrained the efficiency and adaptability of
No algorithm is best on all problems, but a real warehouse is a narrow problem: its layout and processes rarel
Online large language model (LLM) services face a pricing cold start: providers initially lack the historical
We study a stylized verification-allocation model for role-structured pipelines with binary inherited corrupti
Alternating updates, that is, where each player is updated in turn, tend to typically perform better in regret
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。
This repository collects an extensive list of awesome papers about Story Generation / Storytelling, exclusivel
さまざまなLLMのゲートウェイとして使えるライブラリ。
telegramSummaryBotは、グループチャットをAIでサマライズすることができる。無料でデプロイして使用できる。
Ludwigは、LLM (Large Language Model) のカスタム化と構築のための低コストフレームワークです。このフレームワークは、ユーザーがカスタム LLM を構築し、トレーニングするのを容易にします。
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。
OpenAIに互換性があり、Cloud APIとして利用できるLLM。
データの期待値を把握するためのフレームワークです。
モデルをサービングするためのライブラリを紹介している。
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。
医学画像に対する疾患検出モデルを開発し、臨床現場で早期検出と迅速な介入を容易にすることを目的としたフレームワークを提案します。
このリポジトリは、Natural Language Toolkit（NLT）のソースコードを収録しています。
最新のマシンラーニング研究論文を紹介している。
ModelScopeは、モデルをサービス化するためのプラットフォームです。モデルを作成し、ホスティングし、管理し、配信することができます。
This paper presents a near-quadratic-time algorithm that correctly matches almost all nodes of two correlated
Training with chain-of-thought (CoT) supervision is an effective approach for teaching models multi-step reaso
Conformal prediction (CP) provides a distribution-free framework for uncertainty quantification with finite-sa
Organizations adopting AI face the challenge of deciding which outputs can be released without human review wh
Selective prediction with split conformal calibration promises a post-hoc fix for molecular property predictio
Uncertainty estimates from fuzzy regression are informative only when the implementation is correct and the co
Barrier coverage is a key paradigm for intrusion detection and surveillance in IoT-enabled heterogeneous wirel
Reasoning models answer better when they think longer, but a thinking trace keeps going well after its answer
Large language models (LLMs) are increasingly used to annotate text for social science research, content moder
This work presents an evaluation of large language models (LLMs) for English to dialectal Arabic machine trans
AI reviewers are increasingly used to assess scientific manuscripts, yet it remains unclear whether their judg
A vote over a model's samples can only help when the samples disagree. For open-ended generation the binding q
LLM agents are increasingly deployed in Multi-Agent Systems (MAS), and they introduce the risk of coordination
An input guard decides whether a request reaches the model, but never sees what the model says. Harm that emer
FOLLIE (for First-Order Logic for Language Inference and Entailment for French) connects French sentences, com
Harness optimization can substantially improve LLM agents without modifying model weights, but existing method
Locating political parties on environmental and climate policy is central to the study of party competition, y
Digital learning environments produce rich clickstream data that can reveal how students approach learning tas
Social-media comments are interactions, yet they are often translated and evaluated as isolated text. This mis
Social media translation requires preserving meaning while capturing the cultural nuances of user-generated co
Simulated students offer a scalable way to test learning platforms and AI tutors before deployment with real s
Memory systems for LLM agents are evaluated mostly on social dialogue, where facts accumulate and rarely chang
Traditional interactive narrative systems offer strong authorial control and structural coherence but often re
LLM-based agentic systems are increasingly combined with CoT reasoning to solve tasks that require accurate re
When user queries are incomplete, large language models should ask targeted questions before committing to an
Agentic workflows organize complex tasks into reusable procedures. However, invoking an LLM at every step incu
LLM agents learn by interacting with environments, yet existing environments are designed for specific tasks a
A rule written for people fixes its atomic conditions and connectives but leaves open how the connectives are
Multiple studies have shown that neural networks can be behaviorally systematic, producing correct outputs for
Large language models (LLMs) read knowledge-graph facts ranked by relevance, leaving open whether they suffice
Large language model (LLM)-based multi-agent systems (MAS) tackle challenging tasks by dividing work among age
Query-conditioned sparse KV selection reduces long-context inference cost by attending to a subset of historic
Accurate house price prediction is essential for real estate buyers, sellers, agents, and financial institutio
Goal pursuit in open-ended dialogue requires agents to adapt their actions to interaction feedback across succ
Concerns about LLM homogeneity are typically quantified with embedding similarity, which reduces two texts to
Calculating inter-annotator agreement is an established practice in NLP, and disagreement-aware work increasin
A language model that learns a new fact by fine-tuning can repeat it but cannot reason with it: taught that a
Activation steering controls large language models at inference time by adding a vector to their internal repr
Humans continuously acquire knowledge from carefully crafted materials such as books and tutorial videos, carr
The widespread adoption of large language models (LLMs) is reshaping how textual content is produced, complica
Attention kernels on Tensor Processing Units (TPUs) often underutilize their matrix units (MXUs) because of re
Harness engineering and self-evolving agents improve language-model agents quickly, yet their gains live outsi
Purpose: Retrieval-augmented generation (RAG) can introduce electrocardiogram (ECG) criteria that lack patient
Memory systems for production LLM agents mostly store semantic memory (facts about users and domains) or episo
Applications use Typed Decision Models to route and moderate text in many languages, and they often act only o
Robust segmentation of biomedical images remains challenging primarily due to ill-defined object boundaries. A
Scarce and unrepresentative training data limit the performance of prostate MRI prediction models. Synthetic T
Concept-based explanations of melanoma classifiers are rarely tested against controls with known targets. We b
Vision-language models for remote sensing answer questions about satellite images by generating text: every an
Embodied agents that answer questions about their route need a memory of what they saw, and scene graphs are a
Vision-language models (VLMs) suffer from visual hallucination, such as saying an object that is not present i
The spatial intelligence of large vision-language models (LVLMs) requires the ability to combine local visual
Recent advances in generative AI have enabled multiple pathways for high-fidelity video synthesis: text-to-vid
This paper proposes a directional bounded-confidence variant of the discrete-time Vicsek multiagent model, in
Retrieving objects from densely packed storage requires anticipating how contact disturbs surrounding objects
Navigation world models predict future observations conditioned on navigation actions to support planning, but
Humanoid controllers need the ability to yield to external forces while retaining the capacity to resume their
Robotic surface exploration benefits from tactile interfaces that accommodate contact and capture variations i
Robots manipulating in cluttered spaces must often plan from partial views where foreground objects occlude th
To reduce reliance on manual design and extensive parameter tuning, a framework for the autonomous evolution o
In modern robot learning, generative robot policies are first pretrained through supervised learning from demo
Robot-use Agents (RUAs) have shown promising zero-shot manipulation capabilities, but reliance on frontier, pr
Tactile sensing plays a crucial role in everyday human interaction. Consider inserting a plug into a tight soc
Agentic robots combine a vision-language brain, a tool-using harness, and a vision-language-action (VLA) polic
Computer graphics is built on code. Large language model (LLM) agents can now generate graphics code and itera
Coding agents solve repository issues through long sequences of exploration, editing, and validation. These tr
Scaling computer-use agents from individual actions to complex software work requires experience that connects
As coding agents achieve increasingly high success rates on software engineering benchmarks, recent efforts ha
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。
Assessing the reliability, accuracy, and safety of large language models, RAG pipelines, and AI agents.
Pytorchの教科書としては非常に詳細で、実践的な方法を提示しています。
学習中のアイデアや知識を整理するための日記。
We study simultaneous quantile inference under local differential privacy for a number of target probabilities
Riemannian stochastic gradient paths estimate constrained parameters, but confidence regions usually require e
We develop online statistical inference for Polyak-Ruppert averages of inexact Newton iterates on Riemannian m
We develop an online multiplier bootstrap for manifold-valued parameters estimated by averaging the iterates o
We estimate the integral curve of an unknown vector field from noisy tangent vector field on a known compact R
Visualizing and interpreting the dependence structure in relational data often benefits from geometric represe
Across diverse markets, it is important for firms to balance attracting customers with managing operational co
Hidden Markov models (HMMs) offer an important capability to interpret fitted latent states and transitions. W
The Lane-Emden Equation is a class of nonlinear differential equations commonly used to model phenomena in ast
User simulators generate responses in place of human participants, allowing researchers to estimate the result
LLM judges often check, against an amendment record, whether the documents an answer cites are still in force.
What happens when language models are given tools to iteratively modify their own system prompts? We present a
Large Language Models (LLMs) are increasingly deployed in multi-agent settings, exhibiting emergent collective
Faced with an unanswerable question, small language models can either refuse or confabulate: confidently produ
Voice conversion (VC) models can be used to anonymize speech data while preserving naturalness. Existing model
End-to-end planners decode the ego trajectory either by coordinate regression, which is fast but blends plausi
As learned manipulation policies become increasingly capable of solving practical manipulation tasks, assessin
Garment flattening and folding require robots to identify garment structure during manipulation and adapt thei
Vision-language models can interpret instructions and plan manipulation tasks, yet struggle when acting direct
When a World Action Model fails at control, is the bottleneck proposal generation or trajectory scoring? We in
Tactile sensing plays an important role in imitation learning for contact-rich manipulation by providing direc
Fine-grained robotic manipulation requires instruction grounding, robust perception, and precise contact contr
This work addresses the challenge of controlling multi-quadrotor systems subject to tight uncertain couplings.
In this paper, we present a reconstruction pipeline that empirically quantifies the photorealistic scene recon
The MetaFine Open Competition (RoboFineMani workshop, CoRL 2026) evaluates fine-grained manipulation beyond bi
Reinforcement learning (RL) is increasingly used to adapt pretrained vision-language-action models (VLAs). We
Most agentic robotic systems use vision-language models (VLMs) either to orchestrate predefined skills or to d
When searching for an object, humans naturally combine semantic intuition - reasoning about where the object i
Freeze-thaw Bayesian optimization (BO) evaluates candidates in resumable increments but usually stops at a bud
We study the stochastic second-order oracle complexity of solving smooth convex finite-sum optimization with $
We study high-dimensional graph combinatorial hypothesis testing problems in which a signal is supported on th
Direct Preference Optimization (DPO) treats every reported comparison as correct, yet preference data contain
We study the non-asymptotic convergence of Stochastic Mirror Descent (SMD) algorithm when the noisy first-orde
Simplicial neural networks (SNNs) are powerful tools for prediction tasks on higher-order relational datasets
Can a vision-language-action (VLA) policy signal likely task failure from its own action probabilities? We int
Before foraging, desert ants perform learning walks (LWs) to acquire visual memories around their nest. With e
Retrieval-augmented generation (RAG) systems have seen growing adoption in embodied settings as language-query
Collaborative robots (cobots) are designed to be safer and more interactive than traditional industrial robots
Uncontrolled hemorrhage remains a leading cause of preventable trauma mortality. Timely intervention is not al
Autonomous systems that navigate their environment need methods to quickly assess the feasibility and safety o
Planning with world models enables test-time adaptation for embodied agents, offering superior generalization
Recent work shows that a frozen robot policy performs far better inside an agent harness, where control code d
Large-scale egocentric human videos provide rich and scalable manipulation experience for robot policy learnin
This paper extends an existing framework for the coordination of mobile agents in unknown worlds, by consideri
LLM-based robotic agent systems can generate programs for robot control, yet their generalization to new tasks
Inserting an unseen part requires sub-millimetre precision and the correct orientation of keyed features. Curr
We show that, for a broad class of gradient-based learning rules, scalar zero-sum games form a complete test f
Reinforcement learning and game theory describe the same object from two sides. Game theory says what a ration
Answer-level rewards cannot distinguish reasoning traces with equally correct outcomes. We study whether auxil
Robot agents built on vision-language models depend on both the planner and its harness: the tools, feedback,
Unified Multimodal Models (UMMs) achieve remarkable success on diverse multimodal understanding and generation
Short-video search needs compact query rewriters that recover users' personalized intent from ambiguous querie
This paper presents TurnTurk-GPT, a text-based turn-taking predictor for Turkish obtained by fine-tuning a Tur
Existing sketch generation methods often rely on image guidance and are largely limited to individual objects
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。
分析システムの性能を向上するための学習モデル開発を行う。
Latent world models allow reinforcement learning agents to learn policies from imagined trajectories, yet dete
We study first-order regret guarantees for adversarial bandits with a fixed, known directed feedback graph. Fo
Synthetic data are widely used for training modern AI systems. However, their statistical understanding remain
Artificial intelligence (AI) models can accelerate cell-type annotation in digital pathology, but safely scali
In this paper, we study the regret minimization problem in episodic tabular MDPs. Our algorithm achieves the f
Self-supervised contrastive learning (SSCL) learns representations by contrasting augmented views of the same
Tabular foundation models use in-context learning to estimate conditional expected outcomes and average treatm
This paper studies robust uncertainty quantification for separable nonlinear least squares (SNLLS) problems. W
Automatic operator design (AOD) has increasingly adopted large language models (LLMs) to generate problem-spec
Do contract terms that protect investment between people also protect it between profit-maximizing language-mo
Recommendation systems that optimize for the average user can fail to capture the preferences of diverse demog
We study routing games in which competing mediators recommend routes to fixed nonatomic populations. Each medi
Multimodal large language models can recognize visual content yet struggle to reason reliably over it across m
Large language model (LLM)-based multi-agent systems (MAS) coordinate specialized agents to solve complex task
Vision-language models can get individual judgments right while struggling to recover and consistently use the
Across coding, web, and tool-use tasks, language-model agents are now routinely trained with the same recipe:
Achieving the broad task generalization of embodied models in physical environments remains a central challeng
On-policy reinforcement learning for large language models assumes that training responses are sampled from th
A voice agent's speech recognizer transcribes everyone near the microphone, including people other than the ca
As publications and products on time series forecasting (TSF) proliferate, researchers face a growing burden o
Foundation models are typically trained in stages: pretraining, midtraining, supervised fine-tuning (SFT), and
Human cognition flexibly draws on a shared perceptual foundation to understand environments, attend to referen
Autoregressive (AR) image generators are powerful in principle but inflexible in practice: training with a fix
AIエージェントを組み立てるためのライブラリ。
NestJSベースのAIチャットボット開発ツールです。
この論文では、予測性維护と健康管理のための大型言語モデルが使用されます。この modelは、故障診断や寿命予測などの問題を解決するように設計されています。
CLIPという画像認識モデルをオープンソースとして実装したライブラリ。
Estimating conditional parameters, such as heterogeneous treatment effects, often requires rich covariate adju
Graphical models are widely used to represent conditional independence structures, but learning them is challe
Synapse-resolution fruit-fly (Drosophila) connectomes enable neuron-by-neuron simulation of entire insect nerv
We study a pricing game in markets with budget-constrained buyers, where each seller offers a single good at a
Bisimulation metrics offer a principled notion of task-relevant state similarity in reinforcement learning, bu
Recursive self-improvement requires models not only to acquire useful training experience, but also to decide
Producing long-form videos from narration requires interdependent decisions that remain coherent across shots.
Financial agents must form forecasts and investment judgments from decision-time evidence, with predictions ev
Recent advances in multimodal large language models have increased interest in interaction paradigms for video
Quarterly earnings forecasting requires an agent to connect financial evidence to the income-statement line it
Feature extraction from unstructured data typically relies on opaque embeddings unsuited to interpretable deci
Ship-to-home fulfillment requires a retailer to select a fulfillment location and carrier before the delivery
We study conformal prediction under covariate shift with a known covariate density ratio, seeking prediction s
Artificial intelligence offers statisticians a new paradigm for modelling and analysing complex datasets. Agen
We propose a test of whether two interventions induce the same outcome distribution when outcomes are structur
Variational inference with normalizing flows provides a flexible posterior approximation for Bayesian model ca
Relay and reseller APIs have become a common way to access large language models (LLMs), yet users cannot dire
Computationally expensive simulations and real-world observations often provide data at a lower temporal resol
Contextual dueling bandits learn from pairwise comparisons, the same feedback used to fine-tune language model
Large language model (LLM)-driven agent scaffold evolution improves agent workflows through iterative candidat
We study experimental results of best response dynamics (BRD) in randomly generated weighted action networked
International climate policy requires coordination among countries whose mitigation decisions impose local cos
Occupancy-state reductions make dynamic programming possible for zero-sum partially observable stochastic game
Unified conditional image generation requires a single model to preserve different aspects of a visual input w
Real-world forgery is inherently heterogeneous: it manifests not only as digital manipulation but also as phys
Large Language Models (LLMs) are increasingly used in multi-turn agentic workloads that require sustained inte
Robot policy evaluation often relies on a binary success signal for task completion. In real-world deployment,
Large language model agents are increasingly used for scientific discovery. However, existing benchmarks mix l
Scaling supervised training for working agents requires diverse professional tasks that yield reliable expert
Web agents require diverse, executable, and resettable website environments for reproducible training and eval
Low-cost arms such as the SO-101 have become the most common entry point to robot learning, yet no released po
Agentic services alternate between model inference on clustered GPUs and external tools on distributed CPUs. I
このライブラリは、空間情報を扱うためのコンピュータビジョンライブラリです。
Shapley-value-based explanations are widely used for feature attribution in machine learning. However, in comp
Dynamic creative optimization decides which creative of an ad to render for each request. Recent work places t
In token-priced language model services, users can see the prompts they submit and the answers they receive, b
Organisms must constantly weigh exploiting the resources they know against exploring for better ones. When for
Perceiving human body motion is critical for multimodal systems that understand and assist people. Yet current
Evaluating human-AI co-design requires testing whether coding agents can translate real users' multimodal requ
As frontier language models grow increasingly capable and autonomous, independent third-party evaluations are
The quality with which language models follow instructions falls as the load grows, yet there is still no quan
ゼネレーティブAIに関連するリソースの一覧。
Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training mode
As constellations of satellite grow in size, and become more costly, they are increasingly funded by multiple
Extending Vision-Language-Action models (VLAs) to humanoid robots requires coordinated whole-body control and
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
このリポジトリでは、Lecture Learning Modelsに対してReinforcement Learningを実行するライブラリを提供しています。
The neighborhood topology of particle swarm optimization (PSO) determines information propagation and regulate
In large language model (LLM)-based multi-agent systems, reported confidence increasingly determines the alloc
Do preferences Large Language Models (LLMs) express in standard elicitation tasks predict their strategic beha
In September 2026, a new large language model was released. Soon after, a new way of making explainer videos b
Neural architecture search (NAS) is expensive, but cross-task transfer can reduce costs by reusing knowledge f
Low-rank SVD-based compression offers a powerful strategy to reduce the computational costs of LLMs. However,
Locating relevant evidence in long inputs is a core capability of Multimodal Large Language Models. Prior stud
Robot foundation models are typically evaluated through costly closed-loop rollouts, motivating low-cost offli
Standard sequence models encode position by token rank rather than physical coordinate, typically forcing prac
Evolutionary program search with large language models keeps the structure of an evolutionary algorithm and us
We study an intermediary that buys nonperishing items from arriving sellers and sells them to arriving buyers,
We propose a market-making game played among $N \ge 2$ strategic market makers and a single non-strategic repr
Large language model agents have enabled software development through natural-language interaction, but extend
Heuristic solvers for general-sum extensive-form games produce a stream of iterates, and practice reports the
A benchmark is not merely a collection of test questions; it naturally induces an empirical measure over diffi
Reinforcement learning (RL) is an effective way to strengthen the long-context reasoning of large language mod
Multimodal large language models (MLLMs) show promising reasoning capabilities, but reliable physical interact
エージェントRRLに関連するアワーショットリスト。
Evaluations of large language model (LLM) agents report that pricing agents collude at others' expense. Yet co
Recent advances in large (vision) language models have enabled computer-use agents that automate digital workf
Speculative decoding accelerates autoregressive (AR) language models by letting a small model draft tokens ser
SNNs are attracting interest due to their potential for energy efficiency and event-driven processing. However
As neuromorphic systems scale to thousands of near-memory cores, aging-induced permanent faults in neuron-upda
We study turn-based deterministic zero-sum games with discounted and long-run average costs. For discounted co
Phone scams impose substantial and growing costs on individuals and financial systems worldwide, yet existing
(この項は、MiniOneRec — Minimal reproduction of OneRecのリポジトリで説明しているため、このリポジトリは削除しました)
A very simple framework for state-of-the-art Natural Language Processing (NLP)
Neural simulation is an essential methodology for studying biological nervous systems, but it remains limited
Mean-Payoff Games (MPGs) form an interesting family of deterministic, infinite-duration, zero-sum, turn-based
Scientific discovery does not follow a single fixed methodology: research methods vary across domains and evol
How does post-training change cooperation during repeated interaction? Within each of seven released model lin
Language-model agents are increasingly deployed to act on behalf of people in settings where individual decisi
このリポジトリでは、AIエンジニアリングのためのリソースを提供しています。
An awesome & curated list of best LLMOps tools for developers
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。
Weight perturbation (WP) estimates learning directions from random parameter perturbations and a scalar change
The Wave Optics Optimizer (WOO) incorporates optical diffraction and interference into numerical optimization
We present a scalable framework for solving multi-step tasks through a decentralized market of self-interested
Understanding the objectives that drive agents' behavior is crucial in strategic environments, where multiple
Despite their practical significance, modern AI systems pose significant societal risks, including toxicity, m
Command-line interface (CLI) agents increasingly execute longer-horizon tasks, yet existing benchmarks either
An agentic market is a decentralized serving-time market where AI agents bid to claim each user prompt. An age
Despite their extensive pretraining knowledge and strong reasoning capabilities, large language models (LLMs)
LLM evolutionary search is currently evaluated by counting proposals (candidate programs). We demonstrate this
We study packet routing in queuing systems where queues act as selfish, myopic agents that use no-regret learn
We prove that the last iterate of Optimistic Follow-The-Regularized-Leader (OFTRL) converges at a linear rate
Freshness shapes an LLM decision at the moment it is made; the action it commits to is latent, carried out lat
Conflict-graph search can stagnate despite effective operators when interventions fail to address the conflict
We argue that tacit knowing is better modelled as an operating regime of a memory than as a kind of content. O
このリポジトリは自然言語処理(NLP)に関するリソースをまとめたものです。
Safety-aligned large language models (LLMs) remain vulnerable to adversarial prompts that bypass their safety
We introduce SIMIT, a test-time self-improvement framework in which a vision-language model creates its own qu
home-llmは、ローカルLIMを使ってスマートホームの制御を可能にするHome Assistantの統合モデルです。
Many machine learning approaches are two-level: an expert selects a class of methods, within this class the ch
Metaheuristic comparisons are often reported at evaluation budgets far larger than an expensive engineering si
Black-box optimization (BBO) arises in many scientific and engineering problems where objective evaluations ar
EliteTable is a self-improving program-search algorithm guided by large language models (LLMs) that uses a fix
Solving high-dimensional structural model updating problems requires an algorithm capable of navigating comple
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
Small tabular datasets with expert-designed spectral features are the norm in operational Earth observation, a
Since their introduction, modern evolution strategies, such as the Covariance Matrix Adaptation Evolution Stra
Dasgupta, Stevens and Navlakha (2017) showed that the Drosophila olfactory circuit, modelled as a random spars
Several discrete design tasks, such as molecular discovery, require diverse collections of useful candidates a
AIを使ったwebスクレイピングツールです。
このリポジトリは大規モデルの無学習に関するリソースをまとめたものです。
The electric autonomous dial-a-ride problem (EADARP) extends the classical dial-a-ride problem by incorporatin
Large Language Models (LLMs) are increasingly used for automated algorithm design. However the computational c
Intrinsic explainability remains a challenging problem, particularly in contexts where multilayer perceptrons
Large language model (LLM)-driven evolutionary search is a powerful paradigm for automated program and algorit
Dynamic resource assignment, the real-time allocation of task streams to heterogeneous processing nodes, is th
Large language model (LLM)-based automatic heuristic design (AHD) iteratively proposes and refines heuristics,
How search strategies evolve in finite, depletable landscapes remains a question in foraging theory. We study
Neuromorphic spiking neural networks (SNNs) offer a promising alternative to conventional deep neural networks
As the input dimension $n$ grows, rule-based machine learning, such as Learning Classifier Systems (LCSs), fac
Evolutionary program search driven by large language models (LLMs) has produced record-breaking constructions
Recent advances in agentic heuristic design use AI agents and execution feedback to automate algorithm discove
Population-based optimization methods often use previous search information through successful solutions, para