「Agent」の検索結果
307 件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。
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
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。
このリポジトリでは、高性能で大規模なベクトルデータベースとベクトル検索エンジンを提供しています。
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。
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。
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、トークナイザーの最適化を提供しています。
オープンソースのGPT/LLMエージェント作成ツールです。
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。
Feature Benchmarkは、複雑な特徴の開発を評価するための枠組みである。
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。
Building on the pioneering paper of Kearns, Roth, and Ryu (SODA'26), we study information aggregation in a net
Cooperative multi-agent reinforcement learning (MARL) systems rely on past experience for learning coordinated
LLM agents deployed for software engineering fail expensively: they act confidently wrong, and bad actions are
Diffusion probabilistic models can capture the multi-modal, interaction-rich distribution of joint future traj
Large language models become consequential agents when surrounding systems let outputs change external state.
Modern LLM agents operate in persistent workspaces whose accumulated history can exceed both GPU KV capacity a
Distillation is common in LLM post-training, where on-policy knowledge distillation (OPKD) uses student-genera
Prefix caching, in which a serving engine reuses the key and value tensors of a shared prompt prefix across re
A runtime gate for an LLM tool agent is usually cast as a filter. In a ReAct loop a rejected proposal is follo
Data-sovereignty regulations increasingly require public institutions to deploy open-source, on-premise LLM ag
LLM decision components that can operate within agent workflows often produce action-relevant recommendations
Computer-use agents have advanced on benchmarks like OSWorld and AndroidWorld, but still act mostly through th
Machine-learning performance modeling is a uniquely hostile terrain for long-lived software: the assumptions b
Model upgrades are routine; memory migrations are not. An agent can keep the same memory store and still forge
Public vulnerability databases collect rich information about known software flaws, including their weakness t
Vision-Language-Action (VLA) models have shown promising progress in language-conditioned robotic manipulation
Building automation systems generate rich sensor data yet remain insight-poor because heterogeneous point nami
On-policy distillation (OPD) provides dense, per-token supervision for language model post-training, but its e
Production multi-agent systems replace agents constantly, on the assumption that an agent filling a role is in
Artificial intelligence (AI) is transforming not only what information systems researchers design, but also ho
LLM agent systems increasingly combine provenance tracking, authorization, policy enforcement, protocol adapte
Large language model agents increasingly rely on execution traces to master complex interactive tasks. However
Large language models (LLMs) are increasingly used to formulate optimization models from natural-language prob
Autonomous AI agents increasingly select actions in environments whose memory, execution-time, runtime, comput
Recent reports during the AAAI-27 review cycle highlight the risk of reviewers coordinating bids for reciproca
In this paper we illustrate a novel architecture generating interpretable behavior and explanations. We refer
This paper addresses navigation by composite heterogeneous robots in a decentralized system when policy reason
Biological agents navigate familiar environments not by re-solving routes for each new goal, but by reusing a
Evaluation of medical artificial intelligence agents remains predominantly answer-centric, assessing only the
Autonomous coding agents are increasingly proposed as AI-scientist systems that conduct analyses and write res
Large language models (LLMs) show potential for medical tasks, but their single-turn question-answer format do
As LLMs take on roles requiring moral advice, understanding how they attribute moral agency becomes critical.
AI alignment requires AI systems to adhere to human norms, values, or intentions. Under value pluralism there
Agents tend to optimize, select, or constrain execution structures before decisive runtime outcomes are observ
LLM-based chatbots are increasingly used as everyday confidants. Because they are designed to maximize user sa
Artificial intelligence (AI) now supports investment workflows from data and prediction through research, port
Many long-horizon LLM deployments face tight prompt budgets: latency, cost, and context limits make full-conte
Automotive infotainment validation still relies on manual testing, slow, costly, and incompatible with agile r
Repository-scale refactoring requires coding agents to propagate a single change across many interdependent fi
Designing effective and fiscally sustainable policies for solar photovoltaic (PV) adoption requires balancing
Long-running LLM agents are stateful: beyond the transcript they accrete compressed summaries, plaintext memor
Improving an industrial recommender is an iterative research-and-engineering process rather than a direct path
Computer-use agents can execute increasingly complex tasks in graphical interfaces, but their interaction expe
Recent advances in large language models (LLMs) create opportunities to enrich simulation-based energy policy
Skill libraries improve the sample efficiency of agentic reinforcement learning (RL) by enabling large languag
While autonomous mobile agents hold great potential for assisting older adults with smartphone usage, existing
Media bias in news articles operates through subtle linguistic cues---loaded language, selective framing, and
Ransomware detection and family attribution require analysis of different modalities because it can use packin
Embodied agents performing long-horizon tasks require a memory representation in which the state transitions o
Diffusion language models (DLMs) offer a non-autoregressive alternative for mobile edge agentic artificial int
Large language model (LLM)-based multi-agent systems have experienced rapid growth in recent years. Despite th
Striking a balance between helpfulness and safety remains a fundamental challenge in aligning large language m
Generative AI and coding agents can accelerate research software development, but they also increase the need
A merchant's payment processor, ledger, ERP and bank feed are updated by messages that get delayed, duplicated
Background. Large language models (LLMs) are being adopted in biomedical research at a rapid and accelerating
Training a competitive Text-to-SQL agent usually depends on human-annotated natural-language/SQL pairs, which
AI coding systems are moving from autocomplete and chat toward agents that can inspect repositories, edit mult
Existing post-training pipelines for coding and terminal agents suffer severe token and control fidelity error
Large language model (LLM) agents are increasingly proposed for enterprise workflows, yet existing evaluations
Natural-language-to-SQL systems have ad- vanced rapidly on academic benchmarks, yet production enterprise sche
LLM agents are rapidly becoming production software, deployed to handle customer service, adjudicate disputes,
This paper describes the participation of the BIT.UA team from the University of Aveiro in the 14th edition of
We present a system that uses a Vision-Language Model (VLM) as a diagnostic agent for adapting a detect-to-tra
Recent progress in multimodal large language models (MLLMs) has fueled significant enthusiasm in their potenti
Image generation and editing models have advanced rapidly, yet remain unreliable when prompts require external
Vision-Language-Action (VLA) or World Action (WAM) models have recently demonstrated remarkable performance in
Cooperative rehabilitation enhances engagement, task performance, and social-motor interaction, yet it demands
Personalized autonomous packing requires robots to account for resident preferences that cannot be inferred fr
We study the online fair division of indivisible items, where items arrive one at a time and must be allocated
Envy-free cake cutting is a central problem in fair division with a striking divide between existence and comp
The strategic facility location problem is defined as follows: $n$ agents report their location in a metric sp
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。
A conformal certificate can be valid when an LLM answers alone and invalid when the same LLM sees peers that u
Reinforcement Learning from Verifiable Rewards works well when a task has a programmatic checker, but most lon
Large Language Models (LLMs) owe much of their success to next-token prediction (NTP), but their autoregressiv
Multi-Task semantic communication (SemCom) prioritizes simultaneous execution of multiple tasks over bit-accur
Agent evaluations report a tool-call rate read off the serving stack. That number can be zero while the model
RL-based post-training for reasoning models is increasingly bottlenecked by repeated fresh rollout generation,
We study the allocation of indivisible goods among agents with identical additive valuations, focusing on envy
Knowledge graphs describe reality in crisp assertions, while the systems now consuming them, foundation models
Despite increasing reliance on LLMs that reason with external evidence supplied by tools, retrieval-augmented
Generalising to unseen tasks remains a fundamental challenge in offline multi-agent reinforcement learning (MA
The dominance of Neural Networks (NNs) in RL is partially due to their incremental learning capability, which
Nano self-assembly organizes molecular components into bioactive nanoscale structures. Self-assembled nanopart
LLM-based code generation fails when correctness depends on execution-dependent coupling: the meaning of one r
Self-driving laboratories (SDLs) combine automated experimentation with adaptive decision-making to accelerate
Prompt injection is widely recognized as a major security threat to AI agents that interact with untrusted ext
Designing and authoring high-performance custom kernels for accelerators is a complex task that requires deep
Agent reinforcement learning (RL) increasingly runs through full execution harnesses, and a multi-harness reci
We formulate indirect prompt injection as a test-time search over a task-dependent attack surface induced by t
Benchmarks for the side effects an agent causes on the way to a goal already exist, but HarvestBench is the fi
Large language models deployed in high-stakes settings frequently generate plausible but ungrounded claims. St
Large language models (LLMs) are being deployed at scale in consequential real-world systems, from financial m
Runtime traces are becoming a central substrate for understanding agentic systems, yet interpretation has focu
Early-onset colorectal cancer is increasing among younger adults, yet red-flag symptoms in this age group have
Medical visual question answering (Med-VQA) is often assumed to require medical fine-tuning, large models, or
Information abstraction, which groups strategically similar private states into a tractable number of buckets,
As terminal-based code agents become prevalent, agent trajectories have accumulated at scale, while realistic,
Evaluating agents on the growing number of agentic benchmarks is challenging because they often require comple
Neural machine translation (NMT) systems typically produce a single output per input, obscuring the alternativ
While diffusion base models such as GPT-Image-2 and Nano-Banana exhibit remarkable visual expressiveness, thei
Banks need conversational systems that can answer product questions, assist customers with account-related req
In online meeting delegation, LLM agents fail to recognize when to speak. With no structured way to track stan
Question answering agents in long-term conversations must reason over massive, temporally dispersed dialogue h
Parametric computer-aided design (CAD) modeling is difficult to evaluate with a single metric. Existing CAD be
Computer-aided engineering (CAE) simulation is among the largest and most demanding areas of engineering, wher
Multi-agent debate (MAD) improves the reasoning capabilities of large language models by having multiple agent
We investigate what makes synthetic OCR supervision transfer to real Thai documents and use the resulting insi
Large language models (LLMs) are increas- ingly deployed as long-horizon conversational agents, motivating gro
An agent that inherits six one-line memories may pull at most one archived source record before acting; a dire
Accountability means a decision can be examined, justified, and contested. LLMs make this hard: fluent output
Modern LLM-based agents operate through a harness of tools, reusable skills, and specialist agents that shapes
The Neural Finite State Machine (NFSM) framework offers a pragmatic path to full-duplex dialogue by serializin
Personalized assistants should not only comply with user requests but also assess whether those requests are a
Long-horizon multimodal agents should remember not only what happened but also who participated. This capabili
Camera-conditioned world models generate interactive videos in which commanded actions should induce the expec
Video generators build long videos by composing shorter parts, either by generating segments one after another
This technical report is a study of the use of differential game (DG) theory to solve the target-assignment an
This paper presents an energy-based controller for a multiagent robotic system designed to achieve and maintai
Vision-Language Navigation in Continuous Environments (VLN-CE) requires an agent to follow natural language in
Air-ground collaborative Vision-and-Language Navigation (VLN) pairs an unmanned aerial vehicle (UAV) with a gl
We present Iris-mini and Iris-pro, two search agents trained at the 35B-A3B and 397B-A17B scales, together wit
Scaling interactive and verifiable environments is critical for training terminal agents. As frontier models b
Gaussian-process Bayesian optimization (GP-BO) excels at black-box optimization of costly functions, e.g., hyp
Multiobjective evolutionary algorithms (MOEAs) naturally expose population-level parallelism, but many mature
Counterfactual audits are the standard tool for checking whether a clinical agent treats demographically disti
Using a zoom-in tool is an important foundational part of modern visual agents, because it allows to efficient
Expressive speech systems make a decision before any waveform is rendered: how an utterance is delivered. In d
Long-term LLM agents must preserve information across interactions while distinguishing repeated evidence, his
Multi-agent LLM pipelines increasingly assign roles, including execution and verification, to models of differ
Evaluating LLM agents is essential for guiding their development, yet it has grown prohibitively expensive: a
Autonomous agents are beginning to carry out machine-learning (ML) research end to end. These agents combine a
World models are an emerging paradigm in representation learning in which an agent jointly learns state-action
Humans can perform complex manipulations given a simple intent through an overall instruction, while continuou
Vision-Language Navigation (VLN) requires an embodied agent to follow natural-language instructions in unseen
This paper is the first in a series on Turn-Based Combat Arena, a configurable framework for turn-based strate
A central challenge in mechanism design is to develop truthful trade mechanisms that maximize the expected gai
We investigate the fair allocation of indivisible items among agents with asymmetric entitlements in mixed man
Multi-agent LLM systems commonly use an orchestrator to decompose a task for a team of workers and then improv
Visual fluency in generated video does not imply physical reliability, and a scalar quality score alone is inc
Traditional speaker-attributed ASR systems treated ASR and speaker diarization as two separate tasks. Recently
Faithfully translating research papers into repository-level implementations remains challenging because paper
LLMs are increasingly deployed as orchestrators that coordinate specialized subagents to solve complex tasks t
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。
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。
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。
Language model agents increasingly propose actions, observe external feedback, and explain their own behavior.
Multi-agent combinatorial optimization problems are notoriously challenging due to their NP-hard nature. Recen
Multimodal Large Language Models (MLLMs) are strong perceivers of images and video. We ask how far that reach
We describe SCARAB--Swarm-Capable Autonomous Robotic Aquatic Bridging. Using distributed swarm control and mul
Vision-language-action (VLA) models map visual observations and language instructions directly to robot action
Robotic systems are deeply embedded in both industry and everyday life, where they are expected to act with sp
Executing long-term tasks in dynamic environments requires embodied agents to maintain robust and adaptive 3D
Vision-and-Language Navigation (VLN) requires an agent to navigate through unseen 3D environments according to
Exploring unknown environments with multiple UAVs requires coordination under intermittent communication, maki
In indoor environments, object positions frequently change due to human activities or embodied-agent interacti
Information sharing can improve a pooled estimate while eliminating independent rescue actions. This paper sep
We develop a framework for mechanism design with AI agents whose alignment (preferences) and capabilities (fea
We study weighted fair division of indivisible mixed manna under additive valuations. First, we resolve the ge
In this work, we study radically uncoupled learning in discounted general-sum Markov games. Assuming ``$\maths
As agents move from research prototypes to deployed tools, their capability increasingly depends on model-exte
Embedding-based code retrieval is a core component of coding agents and retrieval-augmented code generation, w
We study federated online reinforcement learning with linear function approximation. While recent multi-agent
Agent evaluations often use one benchmark to choose a workflow and then search for task types where its advant
Chain-of-thought (CoT) reasoning powers generative models by eliciting intermediate steps before producing an
World models have made remarkable progress in action-conditioned future prediction for embodied agents, yet st
This paper presents FAIRY, a full-stack smart-agriculture agent system developed for and deployed to an operat
Embodied navigation requires agents to translate heterogeneous goals and visual observations into actions acro
This paper considers decentralized contingency MPC for multi-agent control under a state-only information patt
Source term estimation (STE), which aims to estimate key properties of the gas source, is essential for identi
Long-horizon physical-world agents must reason over distant goals while grounding decisions in reliable closed
General embodied agents should perceive, predict, act, evaluate, and improve within a unified system. World mo
We study fair allocation of indivisible goods under additive valuations and matroid constraints. A challenging
Bidding games are graph games in which a token is placed on a vertex, each player starts with an initial budge
We study the problem of locating a new homogeneous facility under a prelocated facility. Here, a set of $n$ ag
Envy-freeness up to any good (EFX) and pairwise maximin share (PMMS) are standard local fairness criteria for
Residual maximin share (RMMS) is the largest share threshold that remains guaranteeable throughout dynamic all
We study complete allocations under nonnegative additive valuations through the positive supports of goods, fo
Command-line coding agents (e.g., Claude Code, Gemini CLI) can already read and write files and sustain long s
Coding agents are now commonly evaluated on the SWE-bench family of benchmarks, whose tasks are built from cur
Understanding agent behavior requires methods that scale to thousands of trajectories and surface new patterns
Many important forms of human learning begin with a vague goal, such as "become a better physicist" or "improv
Large language models (LLMs) increasingly interact with external environments and accumulate substantial behav
Agent performance depends jointly on the model parameters and the executable harness code that manages context
Professional agent tasks often depend on conventions that are absent from public corpora, yet benchmarks rarel
We present FoldingAgent, an agentic framework for inferring explicit parametric folding programs directly from
In this paper, we study fair division problems in which resources are structured as graphs and agents must rec
We study fair division of indivisible items when agents have arbitrary two-level preferences: the value of eac
Recent machine learning research has increasingly focused on equilibrium analysis in non-cooperative games rat
Decision-time equilibrium search carried poker to superhuman play, but it has so far relied on tractable subga
This paper studies the problem of proportionally fair clustering, where the goal is to select $k$ ``centers''
We study active elicitation of agent preferences for collectively choosing among $m$ alternatives using promin
Privacy-preserving learning is often motivated by the idea that protecting users' data can preserve trust and
We study risk-averse decision making, in which an agent selects actions while being uncertain about the true s
Forecast combination is a reliable way to improve predictive performance when several forecasting models are a
This article introduces peer $k$-oversight, a property of sequential collective decision mechanisms requiring
The VCG family and the AGV mechanism are two classical approaches to efficient implementation in the static so
We consider two-player GR(1) games on graphs, where the system player Eve must satisfy \[ \Box\Diamond A_1\lan
As large language models and increasingly capable AI agents are deployed in high-risk settings, aligning them
Coordination is a desirable feature in multi-agent systems, ranging from robotic swarms to socioeconomic netwo
エージェントRRLに関連するアワーショットリスト。
We study contraction properties of non-stationary continuous-time mean-field games (MFGs) under discounting an
In the study of Nash equilibria of finite-player games, one often seeks equilibria that are compatible with pr
As electricity market participants increasingly adopt learning-based agents for their bidding strategies, elec
In the subjective divisibility allocation model, all goods are divisible, every agent $i$ has a non-negative a
People's trust in AI advice diverges as they use it, deepening for some and eroding for others. We study this
Modern multi-agent systems are increasingly deployed at scale over large populations of agents in settings suc
Paradigmatic interaction models explain how collective behaviors can emerge in complex systems from interactio
Recent work in fair division has focused on either simultaneously satisfying closely related fairness notions
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。
Current LLM agent systems decide delegation before reasoning begins (a router picks a model) or after a respon
Social interaction can improve collective learning but also amplify early mistakes. We study this tension when
We consider fair allocation of indivisible goods in a setting in which agents have subjective valuation functi
Maximizing Nash welfare over indivisible goods is a central problem in resource allocation. For additive valua
We study multilevel fair resource allocation with tree-structured hierarchical relations among agents. At each
In truthful interval covering, each agent has a private interval of unit length, and the goal is to decide whe
This paper studies a class of multi-cluster aggregative games characterized by the coexistence of cooperation
In a deliberative poll, once submissions outnumber what anyone will read, some mechanism chooses which argumen
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。
While contemporary Evolution Strategies handle integer optimization problems effectively, their adaptation mec
Reinforcement learning (RL) algorithms have made strides over the past decade applying them to a wide range of
Standard solution concepts for stochastic games, such as Markov perfect equilibrium and Markov coarse correlat
AIエージェントを組み立てるためのライブラリ。
We design and analyze randomized strategyproof mechanisms for multi-facility location under the utilitarian so
The house allocation problem is a classical one-sided matching problem that concerns the assignment of a set o
Rust言語でCandleライブラリを利用して、PythonやPyTorchを使用せずにDecoder-only LLMを自作した。
We study temporal fair division of indivisible mixed manna. Items arrive over time and must be allocated irrev
GUI操作自動化に伴う停止判定、復讐、再検索に関する問題を解決し、 GUI操作自動化を実現するためのフレームワークを開発します。
Python is widely used in scientific research because it enables rapid development and provides rich ecosystems
We study the classical and parameterized complexity of efficient connected allocation problems on graphs, wher
As large language models evolve into decision-making agents, the ability to reason over preferences becomes fu
Classical seismic data reconstruction relies on manually designed structural priors and iterative operators, w
We introduce and study an online variant of the multi-agent contract model. In our model, agents arrive one-by
Learning algorithms are often used to make decisions in repeated multi-agent environments. When another player
We investigate whether agentic artificial intelligence can automate parts of the process of designing genetic
Peer prediction seeks to incentivize agents to truthfully report an observed signal by rewarding joint sets of
We study the strategyproof placement of \(k\) facilities on the real line for \(n\) agents who privately repor
Shared autonomy requires principled mechanisms for allocating and transferring control between a human and an
Shared autonomy requires principled mechanisms for allocating and transferring control between a human and an
We study the group-fair distortion of metric facility assignment problems, where a set of agents, partitioned
Proportionality (PROP) is one of the simplest fairness criteria for allocating items among agents with additiv
We study fair division of indivisible goods when agents' valuations are accessed only through ordinal comparis
We consider strategic facility location in Euclidean space $\mathbb R^d$, where a mechanism selects a single f
The maximin share (MMS) is a central fairness benchmark for allocating indivisible goods and chores. We study
In Shapley-Scarf housing markets, Ma (1994) shows that top trading cycles (TTC) is the unique mechanism satisf
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。
We study interval scheduling from the perspective of fair allocation. There are $m$ identical machines and a s
Many real-world interactions among self-interested parties can be modeled by game theory, and the rapid advanc
We introduce the Open-Strategy Dictator Game (OSDG), a variant of the classic dictator game in which each play
We introduce the problem of designing mechanisms that incentivize strategic agents to form self-funded marketp
We study the facility location mechanism design problem where $n$ strategic agents report locations in Euclide
We study equilibrium pricing in oligopolistic data markets with budget-constrained buyers (e.g., machine learn
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。
We examine the interplay between ordinal, preference-based solution concepts in games and the long-run behavio
Liquid民主主義の選出問題において、選出プロセスを効率化するための手法を提案。各意思決定者が他者に信頼する方法を考慮し、意思決定者間の協力と意思決定の正当性を考慮する。
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。
本論文では、LLMエージェント間の相互尊重を確立するために、「類似性シグナル」という新しいアプローチを提案します。このアプローチは、エージェント間の類似性を分析することで、相互尊重を促進するという考え方に基づいています。
この研究では、半推測状態の状況でアダプティブ行動を示すために、反射的な組織がどのように機能するかを調査する。これでは、現時点の観察だけを基に、内部状態が情報を保持できる計算プロパティを開発します。
この研究では、LLMへの促進の強化学習を効率化する目的で、検索コストを削減するためのコスト意識のあるクロスタイア転送を提案します。検索コストは、促進の評価に伴うLLMの回答によって大きく異なるためです。このアプローチでは
この研究では、ソフトウェアの開発が複数のエージェントによって長期間にわたって進行する場合の持続可能性を考慮した新しいアプローチであるEvoX Genesisを提案します。
We introduce the study of \emph{multilateral trade}: a mechanism-design problem in which a single potential tr
We study the existence of envy-free up to any item (EFX) allocations of indivisible chores when agents have mo
The emergence of language-based AI agents promises to transform the scope of machine economic activity. Instea
We study the agent-wise disjunction of two central fairness notions for indivisible items, where every agent m
We introduce the Dark Souls Learning Environment (DSLE), a containerized platform that presents all 22 boss en
We study envy elimination by adding goods (EEAG) when the additional pool has bounded supply and no separate b
Inspired by possible future markets of autonomous routing and driving (ARAD), we introduce competitive mediato
Learning dynamics in zero-sum games are typically analyzed under algorithmic symmetry: both agents use the sam
理論オブミンドの評価基準「Avalon-ToM-Bench」を提案。社会的認識を評価するための基準を提供する。
We study fair allocations of indivisible items under general set valuations. We prove that every instance with
This note aims to serve as an entry point to the literature on learning in games, a topic with significant the
We prove that no randomized integral or fractional algorithm for online vertex cover under general vertex arri
We study strategyproof mechanism design without transfers for the two-facility location problem in metric spac
We consider fair allocation of indivisible items among agents with non-negative and additive valuations. The g
The leximin++ proof of Plaut and Roughgarden for agents with identical monotone valuations gives a natural EFX
We prove that every fair-division instance with four agents, additive valuations over the non-negative reals,
Generative AI is shifting digital commerce from browsing toward agentic search, in which consumers delegate pr
As firms increasingly deploy machine learning for strategic decision-making, understanding algorithmic interac
This paper explores the idea of promoting well-being and safety in human-AI interactions by forcing AI agents
Sequential allocation mechanisms contain a class of widely studied mechanisms (e.g., round-robin) in the fair
A common assumption when designing an agent in a multi-agent system is that the other agents behave adversaria
Consider a revenue-maximizing seller who can access a binary signal about two bidders` joint values. We explor
エージェント評価のプロセスを迅速化するために、AV-AIVATは、イレギュラー情報ゲームにおいて、停止条件を確実に確立し、結果が明らかとなって停止できる手法を提案します。
この研究では、既存のAIエージェントをコンプライアンス管理に対応させるためのメカニズムを提案します。この手法は、リソース割り当てを用いて、AIエージェントの行動を管理することを目的としています。
固定価格の物を分配する問題は重要な問題である。Fair and Efficient Balanced Allocationsは、固定価格の物を分配する方法を提供し、利益を最大化する。
複合商品の販売では、商品を組み合わせることができないことがある。Large-Market Disciplineは、この問題を解決するためのフレームワークを提供し、価格と商品の組み合わせを最適化する。
Can cooperation among large language model (LLM) agents be evolutionarily stable against free-rider invasion?
The El Farol Bar game is a classical model of coordination under uncertainty that traditionally treats the ven
通信と計算のコスト
We study Evolution Strategies (ES) for continual control, where agents must adapt to changing tasks without fo
Automated formulaic alpha discovery aims to generate predictive and interpretable trading signals from large s
An LLM agent's capability depends not only on model weights but on its harness: prompts, tools, skills, and co
物理的システムの分離方程式を解くためには、ニュラルネットワークの設計、損失関数の定義、および最適化ダイナミクスの手動調整が必要である。研究者は、自動設計のためにLarge Language Models (LLMs)を利
Artificial life systems are typically defined by a set of dynamical rules over an environment, an agent, or bo
Can evolutionary dynamics characteristic of biological development arise without a designer-specified fitness