「RAG」の検索結果
345 件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。
Trade-up recommendation identifies higher-quality alternatives that preserve a customer's purchase intent whil
We present a Hessian-based approach to numerically continue periodic orbits in dynamical systems. A loop (peri
Hyper-Connections and their manifold-constrained variant mHC widen a residual pathway from one stream to n, ye
We introduce GLASS, a framework for graph-level anomaly detection (GLAD) that achieves robust cross-domain tra
Artificial intelligence models are promising for medical diagnosis, but they require large numbers of unbiased
Dynamical symbolic regression methods identify governing differential equations from noisy data, balancing int
This paper introduces Regime-aware Constraint-Based and Noise-Based causal discovery with Markov Blankets (RCB
Evaluating the calibration of Large Language Models (LLMs) is critical for their safe deployment as zero-shot
Counterfactual explanations for graph-structured data seek to determine minimal and realistic modifications re
AI-driven de novo molecular design offers a promising route to accelerate early-stage drug discovery by genera
Conformal counterfactual inference enables network operators to use logged telemetry to reliably answer 'what-
Diffusion probabilistic models can capture the multi-modal, interaction-rich distribution of joint future traj
Detecting orchestrated cyberattack campaigns that span multiple organizations traditionally requires sharing s
Inferring cellular dynamics from unpaired single-cell snapshots requires modeling both state transitions and p
We introduce WEECFP, a parameter-free 1024-dimensional continuous molecular fingerprint that scatters each Mor
We study stochastic gradient descent with random reshuffling for finite sums \[ F(x)=\frac1n\sum_{i=1}^n f_i(x
Multimodal Graph Neural Networks have become standard for recommendation by augmenting sparse interaction data
We study regression under bounded responses in terms of excess mean squared error. When the comparator class i
We propose Ref-GeNVS, a training-free, reflection-aware method for generative novel view synthesis (NVS) in mi
Model upgrades are routine; memory migrations are not. An agent can keep the same memory store and still forge
Building automation systems generate rich sensor data yet remain insight-poor because heterogeneous point nami
Human knowledge is inherently structured and interdependent: mastery of a concept requires prior mastery of it
A rapidly expanding ecosystem of actors is removing built-in safety guardrails from open-weight AI models. We
Mixture-of-Experts (MoE) architectures provide an efficient paradigm for scaling large language models (LLMs),
Recent reports during the AAAI-27 review cycle highlight the risk of reviewers coordinating bids for reciproca
Biological agents navigate familiar environments not by re-solving routes for each new goal, but by reusing a
Electroencephalogram (EEG) visual decoding aims to recover visual semantics from non-invasive neural time-seri
As the scale of video surveillance data outpaces manual annotation capacities, weakly supervised video anomaly
Quantum software development is iterative and error-prone. Noisy hardware and repeated re-execution make exper
Hallucination-where a language model generates outputs that are factually incorrect or unsupported by the sour
Agents tend to optimize, select, or constrain execution structures before decisive runtime outcomes are observ
Recent structured RAG methods leverage tree- or graph-based reasoning structures to improve multi-hop QA. Howe
Multi-expert models have become the dominant paradigm for long-tailed learning, largely attributed to their pr
Many long-horizon LLM deployments face tight prompt budgets: latency, cost, and context limits make full-conte
Reliability evaluation of deep neural networks under hardware faults commonly relies on fault injection, but e
Large language models (LLMs) have significantly advanced automated program repair (APR), yet existing evaluati
Repository-scale refactoring requires coding agents to propagate a single change across many interdependent fi
Accurate station-level precipitation nowcasting is critical for agriculture, water resource management, and di
As vision-language models (VLMs) rapidly advance in image understanding, cross-modal reasoning, and complex in
While autonomous mobile agents hold great potential for assisting older adults with smartphone usage, existing
Ransomware detection and family attribution require analysis of different modalities because it can use packin
Synthetic medical time-series generation can alleviate data scarcity and support the development of reliable c
Autoregressive (AR) large language models formulate reasoning as token-level probabilistic sampling, which ind
Large language models (LLMs) increasingly rely on information retrieval (IR) systems, such as Retrieval-Augmen
Generative AI research has increasingly evaluated factuality, citation, coverage, and report structure. Yet pa
Large language model (LLM)-based multi-agent systems have experienced rapid growth in recent years. Despite th
In this paper, we study the problem of personalized survey response prediction using fine-tuned large language
Personalizing large language models (LLMs) is essential for delivering AI assistance that aligns with individu
Wireless foundation models (WFMs) have emerged as a promising approach for learning reusable representations f
Existing post-training pipelines for coding and terminal agents suffer severe token and control fidelity error
Self-evolving language models improve by proposing candidate updates and keeping whatever raises a visible sco
This paper studies adaptive recommendation under intent drift, where feedback from each recommendation outcome
The abundance of online data is at risk of unauthorized usage in training deep learning models. To counter thi
We introduce PetQA, a Korean long-form question-answering (QA) benchmark for evaluating veterinary knowledge a
Leveraging their inherent sparse event-driven computation, spiking neural networks (SNNs) offer a promising pa
Vision-language models are increasingly used as reward functions for robotic learning, but this role requires
Computational phylogenetics has become an essential tool in historical linguistics, yet its application at a g
Assessing the impacts of social policy changes is a widely acknowledged challenge for policymakers. Econometri
Retrieval-Augmented Generation (RAG) enhances language models with external knowledge, but the lengthy retriev
Vision-Language Models are commonly evaluated through their final predictions, but understanding whether these
Long-form literary narratives pose a distinctive information-processing challenge for retrieval-augmented gene
This paper describes the participation of the BIT.UA team from the University of Aveiro in the 14th edition of
Mixture-of-Experts (MoE) models activate only a small subset of experts per token, but the full expert set oft
Reinforcement learning (RL) has become one of the primary paradigms for reasoning enhancement of large languag
Traditional Retrieval-Augmented Generation (RAG) systems score each passage independently against the query, a
Reliable 3D understanding of the surrounding environment is a core requirement for autonomous driving. Multi-v
Recent advances in Earth Observation representation learning accommodate heterogeneous sensors and missing obs
Explicit primitive-based radiance fields such as 3D Gaussian Splatting typically model view-dependent appearan
Continuous sliders are useful only when coefficient changes produce predictable image changes. Yet most diffus
Recent hybrid Structure-from-Motion (SfM) systems combine the robustness of feed-forward 3D reconstruction wit
Industrial anomaly detection must handle two distinct defect families: structural anomalies, which manifest as
Cross-view localization (CVL) estimates the pose of a ground image by matching it to a geo-referenced satellit
Weakly supervised 3D occupancy prediction reduces the reliance on costly 3D annotations by learning from 2D ps
Monocular depth estimation foundation models, such as the Depth Anything series, have achieved remarkable perf
Multi-vehicle cooperative autonomous driving enhances the safety and reliability of autonomous driving systems
To enable accurate and rapid photon control point and proton beamlet dose calculation in the DoseRAD2026 chall
Recent zero-shot 3D visual grounding methods leverage vision-language models (VLMs) to localize objects in 3D
Human-robot interaction (HRI) enables intuitive and intelligent collaboration between humans and robots in rea
Achieving robust SLAM in large-scale underground coal mines with complex structures and severe degeneracies re
World-action models guide action generation with predicted future observations, but vision-centric predictions
Autonomous robots continuously encounter objects, changes, and situations, and every event admitted into cogni
Three-dimensional scene graphs (3DSGs) have emerged as a promising approach for building geometrically grounde
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。
Merging a LoRA adapter into its base model is standard deployment practice: it removes the runtime adapter's p
Quantization is widely used to reduce the computational and memory demands of neural-network inference. In rec
Expert pruning reduces the memory and serving cost of Mixture-of-Experts (MoE) models by removing low-importan
A conformal certificate can be valid when an LLM answers alone and invalid when the same LLM sees peers that u
Adaptive network intrusion detection systems retrain classifiers after drift alarms, but an alarm detects chan
We explore the use of simulated data for training a model for protein annotation in crowded cryo-electron tomo
We introduce the first Probably Approximately Correct (PAC) learning framework for general-sum concurrent stoc
As edge-based deep learning applications become more complex, optimizing performance on heterogeneous System-o
We introduce an uncoupled learning algorithm which, when employed by all players of an arbitrary $N$-player no
Reinforcement learning with verifiable rewards (RLVR) and on-policy distillation (OPD) have emerged as two dom
Quantile treatment effects (QTEs) measure the effect of a treatment on the distribution of an outcome, and the
Label removal occurs frequently in classification systems with evolving taxonomies, where categories must be d
Retrieval-augmented generation (RAG) complements parametric models with retrieved external evidence. The same
Continual knowledge-updating methods are often declared superior from one final checkpoint and one conventiona
Graphs are a fundamental data structure underlying many problems in the natural and social sciences. Over the
Energy consumption is one of the largest operational expenditure items for mobile network operators, yet site-
Experimental design is commonly framed as choosing the experiment expected to provide the most information. Un
As integrated circuit technology scales into the nanometer regime, the traditional disconnect between logic sy
Action-conditioned JEPA world models enable planning toward visually specified goals without reconstructing fu
Existing theories derive neural scaling from data geometry or a specified data-model spectrum, but systems tra
Anomaly detection in Internet of Things (IoT) networks presents unique challenges due to the diversity of devi
Logit-based knowledge distillation for autoregressive language models usually aligns teacher and student next-
Whisper is a widely used foundation model for automatic speech recognition (ASR), but its generative decoder c
Accurate city-level IP Geolocation is an important enabler for the modern digital ecosystem, underpinning serv
Designing and authoring high-performance custom kernels for accelerators is a complex task that requires deep
Performance modeling is central to hardware design and software optimization, yet constructing these models re
Large language models deployed in high-stakes settings frequently generate plausible but ungrounded claims. St
Multilingual language models often produce inconsistent answers to semantically equivalent questions across la
Chinese online comments often convey social meaning through indirect and playful language that is hard to inte
Enterprise AI deployments fail not from model inadequacy, but because organizations lack a structured substrat
Decompensation represents a critical transition in the course of cirrhosis, yet clinicians have limited non-in
Large language models (LLMs) are increasingly used for consequential decisions, making their explanations an i
Evolutionary prompt optimizers such as GEPA suffer from prompt bloat: each iteration appends rules and caveats
Weakly-Supervised Dense Video Captioning aims to localize and describe multiple events in untrimmed videos giv
Latent entity extraction (LEE) tackles the challenge of identifying implicit, contextually inferred entities w
Safety alignment is usually posed as a topic-level question: is this subject harmful? Deployments ask a narrow
Early-onset colorectal cancer is increasing among younger adults, yet red-flag symptoms in this age group have
Music audio-language models are evaluated almost entirely by accuracy on multiple-choice questions. This proto
On-policy distillation (OPD) combines student-generated rollouts with dense token-level supervision from a tea
MLLM-based embedding models remain limited in compositional retrieval, often failing to distinguish scenes con
Large language models (LLMs) are increasingly used to edit existing code, but correctness alone is not enough:
While diffusion base models such as GPT-Image-2 and Nano-Banana exhibit remarkable visual expressiveness, thei
Several studies have evaluated the ability of Large Language Models (LLMs) for meal planning, yielding positiv
Understanding the frequency of factual errors in chatbot-generated text and evaluating systems that detect the
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
Large language models (LLMs) have become ubiquitous tools for code generation and editing. However, developmen
How do the methods used to train language models to refuse harmful requests shape how that refusal actually wo
AI-assisted computer-aided design (CAD) for industrial products involves two challenging phases. Part-level ge
Third-party retrieval-augmented generation (RAG) marketplaces create a new auditing problem: data providers ma
The comparative analysis of banks' financial statements poses significant challenges for automated question an
Synthetic data augmentation has become a common strategy for addressing class imbalance in NLP, but most appro
Land ownership in Bangladesh is recorded in Ana-Ganda-Kora-Kranti-Til, a base-16 positional fraction system wi
In low-resource settings, deploying TTS typically requires choosing between a large voice-cloning model with c
Retrieval-augmented generation (RAG) can improve the specificity and grounding of large language model respons
Reasoning traces of large language models are widely read as containing "breakthrough" moments and early-legib
Automated evaluation of creativity tasks remains challenging for LLM-as-a-Judge, as LLM is susceptible to bias
Large language models achieve superior performance on tasks that require extended reasoning, but long chains o
Idiomatic expressions are an integral part of natural language, reflecting cultural nuances and posing unique
The Neural Finite State Machine (NFSM) framework offers a pragmatic path to full-duplex dialogue by serializin
Large language models (LLMs) face severe memory bottlenecks in long-context inference due to the linearly grow
This paper proposes a spatio-temporal edge-movement direction pixel (STEMPix) for generating compact direction
Online 3D reconstruction models perform poorly on long videos. This happens because regressing poses relative
Streaming video understanding requires multimodal large language models (MLLMs) to process continuous visual i
Video virtual try-on (VVT) aims to generate realistic videos of a person wearing a target garment. Recent meth
Video diffusion models (VDMs) have achieved impressive progress in text-to-video generation, but their high me
We present OctWorld, a video diffusion framework with persistent 3D memory for generating explorable, world-co
We present ENEAS, a unified, text-promptable method for instance tracking and semantic discovery. Text-prompta
Histopathological subtyping relies on the recognition of characteristic histological patterns. These patterns
Accurate segmentation of corneal layers in optical coherence tomography (OCT) is essential for quantitative as
Background. Remote photoplethysmography estimates the cardiovascular pulse from facial video, and its explanat
3D scene representations like NeRF and 3D Gaussian Splatting (3DGS) suffer severe artifacts in sparse-view set
Recent advances in text-to-video (T2V) diffusion models have demonstrated remarkable generative capabilities,
While deep generative models offer new opportunities for medical image synthesis and data sharing, their abili
World models (WMs) have demonstrated strong potential for end-to-end autonomous driving by learning predictive
World models offer a promising paradigm for autonomous driving by predicting how traffic scenes may evolve and
Industrial inspection pipelines often restore a measured image before a detector acts on it, yet restoration c
Automated pig monitoring is essential for assessing their health, behaviour, and welfare. To date, most pig mo
Large-scale 3D surface reconstruction from aerial imagery is fundamental to geospatial mapping and urban model
The continuous emergence of high-quality video deepfakes requires detectors that continually adapt to new forg
Automatic generation of hand gestures is essential for the transmission of Indian classical dance and critical
In-Context Segmentation (ICS) aims to precisely segment arbitrary semantic concepts, such as objects or parts,
Aligning video generative models to human preferences heavily relies on Reinforcement Learning (RL), which suf
A key bottleneck in 3D Gaussian Splatting training is the continual growth of Gaussian primitives, which incre
Vision-language-action (VLA) policies have shown strong potential for general-purpose robotic manipulation, bu
This paper presents a new method for headland coverage path planning for arable fields. Several earlier approa
For robots to operate reliably in real-world environments, they need to perceive their surroundings, act, and
Vision-language-action (VLA) models with billions of parameters now dominate the LIBERO manipulation benchmark
Autonomous unmanned aerial vehicles (UAVs) increasingly operate in cluttered environments where global planner
Developing humanoid robots capable of leveraging human behavioral data is essential for general-purpose embodi
An approval committee is Hare-core stable if no coalition meeting the Hare quota can strictly improve by movin
A reasoning model can improve from its own on-policy experience, but this inner loop is fragile: terminal veri
Audio-video diffusion models rely on cross-modal attention to coordinate text, sound, and visual content, yet
Hybrid LLMs pair softmax attention with linear-attention layers such as Gated DeltaNet (GDN), whose recurrent
BERTopicでは、BERTとc-TF-IDFを用いて、トピックを簡単に分析して解釈できるようにしたモデルを紹介しています。
The Gromov-Wasserstein (GW) distance provides a principled framework for aligning metric measure (mm) spaces b
Many NLP tasks, such as summarization and extractive question answering, reduce to retrieving relevant content
Causal inference is the practice of estimating the effect of a treatment or intervention from data. It traditi
Reinforcement learning typically optimizes average reward. For generative policies, the average can hide an im
Neural Architecture Search (NAS) has emerged as a powerful paradigm for automatically designing deep neural ne
This chapter reconstructs the Hopfield network as a physical theory of memory rather than merely an early neur
This chapter reconstructs the McCulloch-Pitts program as a physics of neural computation rather than the famil
Using a zoom-in tool is an important foundational part of modern visual agents, because it allows to efficient
Visual token pruning reduces the inference cost of vision-language models (VLMs), but most methods only ask wh
Most prior works focused on conflicts between an LLM's internal parametric knowledge and externally provided c
On-policy distillation (OPD) accelerates post-training by providing dense token-level supervision from a froze
Harnessing naturally occurring feedback from user interactions offers a promising learning signal for Large La
People increasingly use language models to support life decisions. Many such decisions involve a probabilistic
Evaluating LLM agents is essential for guiding their development, yet it has grown prohibitively expensive: a
Streaming video understanding is a critical capability for real-world applications, including embodied intelli
Low-resource authorship style transfer (LAST) aims to rewrite text into the style of an arbitrary target autho
Training data attribution (TDA) aims to identify training examples that shape model behavior, but its interven
Selecting a retrieval model for a production RAG system requires reliable comparative evaluation, but obtainin
Vision-language models (VLMs), such as CLIP, have achieved strong performance across multimodal tasks by align
Vision-Language-Action (VLA) policies fuse multimodal sensory inputs, but training on limited and homogeneous
Large-scale training and refined optimization techniques have greatly improved sparse multi-view 3D reconstruc
This paper investigates how a production transitional autonomous vehicle (tAV) develops and executes mandatory
Zero-shot generalization to unseen embodiments is important for generalizable vision-language-action (VLA) mod
Foothold-constrained terrain is characterized by sparse, discontinuous, or geometrically restricted feasible f
World Action Models (WAMs) leverage the capabilities of large-scale pretrained video diffusion models to joint
Accurate scene understanding in confined, cluttered spaces such as shelves is essential for service robots, as
In trajectory planning for autonomous driving, hybrid planning architectures are often realized as a collectio
Scaling offline goal-conditioned reinforcement learning (GCRL) to long-horizon tasks is difficult because (1)
Foot forward kinematics (FK) is widely used to improve proprioceptive legged odometry by providing reliable ve
This study proposes a novel Model Predictive Path Integral (MPPI)-based task-space control framework. The prop
Augmenting the dexterity of human surgeons has the potential to free them from tedious subtasks. We consider d
This paper is the first in a series on Turn-Based Combat Arena, a configurable framework for turn-based strate
Traditional speaker-attributed ASR systems treated ASR and speaker diarization as two separate tasks. Recently
Many statistical models involve parameter-dependent normalizing constants that are computationally intractable
We extend recent work establishing an equivalence between one-layer transformers and nearest-neighbor classifi
Reconstructing a damaged musical fragment is an inverse problem: the observed sequence contains partial inform
Black-Box Optimization (BBO) has broad applications, while traditional algorithms such as evolutionary algorit
Price extraction from websites is a key task for market monitoring, price comparison, and business analytics i
Dexterous manipulation with multi-fingered robot hands promises human-level dexterity, but collecting large-sc
Long-duration outdoor coverage with autonomous platforms remains challenging beyond classical planning: deploy
Online planning under uncertainty remains a fundamental challenge for robotic systems operating in partially o
Road traffic noise remains a major environmental challenge, yet most speed management strategies are static an
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
Most vision-language-action (VLA) models -- OpenVLA, $π_0$, RT-2, RDT-1B -- are monolithic: they emit raw moto
Combining learned policies with model predictive control can leverage learned task priors while retaining onli
Vision-and-Language Navigation (VLN) requires an agent to navigate through unseen 3D environments according to
Action chunking is a standard execution strategy in modern Vision-Language-Action (VLA) frameworks, but fixed
Robotic throwing enables fast object transport and extends a robot's reachable workspace beyond traditional pi
Conventional soft robot actuators excel in compliance, but their uncontrolled deformations compromise accuracy
Accurate tilt angle estimation is important in many engineering applications, such as robotics, motion trackin
Cooperative perception allows a drone fleet to combine observations from multiple viewpoints. However, existin
Animals' body morphology shapes the gait patterns they can perform, where mechanical resonance reduces the nee
Retrieval is the first stage of modern search and advertising systems, selecting a candidate set from a large
The attention prefilling phase of long-context LLM inference scales quadratically, making self-attention a sev
Embedding-based code retrieval is a core component of coding agents and retrieval-augmented code generation, w
Understanding animal motion is fundamental to modeling animal behavior and biomechanics, yet progress in this
Modern Text-to-SQL systems often follow generate-execute-select pipelines, generating multiple candidate queri
Neural network mixed-effects models (NMMs) have gained traction by combining the strong representation and pre
Agent evaluations often use one benchmark to choose a workflow and then search for task types where its advant
For embodied systems, predictive agreement alone does not determine whether evidence warrants action; evidenti
Extracting scenarios from unlabelled real-world sensor data streams is a critical but challenging task in the
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
Quality-diversity (QD) algorithms have been gaining traction in robot learning, where diverse motion primitive
Embodied navigation requires agents to translate heterogeneous goals and visual observations into actions acro
To improve torque capacity and energy efficiency of humanoid ankles, this paper proposes a 2-DoF parallel elas
While visual navigation has advanced through imitation learning from cross-platform demonstrations, fully leve
Uncoupled no-regret dynamics provide a decentralized route to equilibrium, but prior guarantees for individual
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
Information retrieval (IR) increasingly targets open-ended queries that admit diverse perspectives. Existing I
We present FoldingAgent, an agentic framework for inferring explicit parametric folding programs directly from
In resin transfer moulding, complete saturation of the fibre preform is necessary before the resin front reach
Large language models are increasingly required to generate responses that satisfy multiple competing objectiv
Structured pruning uses surrogate objectives because direct task evaluation over every feasible mask is too ex
Mobile AI acts as a visual oracle, empowering users to snap a picture of something and ask for information. Sn
While modern generative models excel at modeling complex data, precise inference-time control in conditional g
Causal representation learning (CRL) aims to recover latent causal variables and their structural relations fr
In metric social choice, each voter ranks a set of $m$ candidates by her distance to them in an unknown metric
Decision-time equilibrium search carried poker to superhuman play, but it has so far relied on tractable subga
Text-driven 3D generation has advanced rapidly in creating large-scale outdoor environments and detailed indoo
Reinforcement learning with verifiable rewards (RLVR) substantially improves single-sample accuracy (pass@1) b
Representation learning begins when training changes the features that define similarity between data. A froze
Synthetic data can improve statistical inference when real data are scarce, but naively treating synthetic sam
In the framework of network dynamics, learning models, and neural tangent kernels (NTK), we show that the corr
Reliable prediction of time-varying channel state information (CSI) is essential for efficient wireless commun
Prior analyses by Derezinski and Warmuth established all-size sampling identities, selected-OLS unbiasedness,
In the study of Nash equilibria of finite-player games, one often seeks equilibria that are compatible with pr
Kolmogorov-Arnold Networks (KANs) replace fixed activations in deep architectures with learnable univariate ed
We address the simultaneous prediction of multiple high-dimensional physical fields governed by linear equalit
The simple exclusion process (SEP) is a paradigmatic model for nonequilibrium transport, yet the rich dynamics
Stochastic gradient descent (SGD) is typically analyzed at a deterministic horizon chosen before the algorithm
We analyze exact-metric, Metropolis-adjusted Dikin walks by keeping the proposal determinant and reverse quadr
The functional annotation of genes in non-model organisms remains a significant challenge in computational bio
In this paper, we introduce ES-AHD, a novel framework that fundamentally integrates Evolution Strategy (ES) in
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。
Optimizing expensive, high-dimensional black-box functions remains a central challenge in modern machine learn
This note studies the geometry of full conformal prediction (FullCP) regions generated by an empirical energy-
Low-earth-orbit (LEO) satellites enable high-resolution, large-scale Earth observation for applications such a
Developments in high-performance computing (HPC) technology continue to drastically increase quantities of ava
Reversible computing is a novel paradigm that has recently emerged and extends traditional forwards-only compu
In a deliberative poll, once submissions outnumber what anyone will read, some mechanism chooses which argumen
Change data synthesis provides a cost-effective solution for expanding training data and improving the perform
Many common data dependencies can be characterized by graphs: time series data are sequential (chain graph), i
Highly overparameterized models often predict well despite interpolating training data in complex domains, cha
We establish a strong averaging principle for fast-slow stochastic differential equations with a time-dependen
Interpretability is critical for machine learning models deployed in scientific space missions such as ESA's A
Large language models (LLMs) often produce fluent but incorrect answers with unwarranted confidence. A central
We analyze a variant of stochastic gradient descent with initial regularization (SGDIR) and derive dimension-f
We develop a finite-width geometric framework describing how learned feature geometries are organized, transpo
Analog circuit topology synthesis remains challenging because useful designs occupy a tiny fraction of a combi
Symbolic protocol verification models the network attacker as a Dolev--Yao (DY) intruder, which does everythin
We give a randomized voting rule with expected metric distortion $5/2$, improving the previous best upper boun
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。
Knowledge-Based Visual Question Answering (KB-VQA) relies on retrieving external information to answer queries
Classical seismic data reconstruction relies on manually designed structural priors and iterative operators, w
We reconstruct the mentor--student network through which documented scholarly training passed across roughly n
We present an optical random access memory (ORAM) based on warm cesium (Cs) atomic vapor and demonstrate its o
We propose a randomized social choice rule called Mixed Integrated Veto (MIV) with metric distortion of $5/2$,
Automated machine learning (AutoML) systems search for pipelines within a space of preprocessing operators, le
Liquid democracy permits voters to vote directly or delegate their votes to others. Existing algorithmic analy
We find no evidence of critical scaling in the Schelling segregation model, in either the Moore neighborhood o
We study the group-fair distortion of metric facility assignment problems, where a set of agents, partitioned
Deciding whether a Sokoban puzzle is solvable is PSPACE-complete (Culberson, 1997): solutions can be exponenti
Generative search engines (GSEs) answer user queries directly from crawled web content. The capture of value f
An invariant behavioral profile is the defining vulnerability of traditional honeypot installations: a skilled
Many real-world interactions among self-interested parties can be modeled by game theory, and the rapid advanc
Evolutionary feature construction has shown strong promise in symbolic regression by automatically discovering
At a finite public-chance cut, counterfactual regret minimization (CFR) must choose how many outcomes to evalu
解決策候補の多様化を向上させる進化戦略を開発するために、LLMを用いて解決策候補を生成するアプローチを提案している。
Spiking Neural Networks (SNNs) encode information through binary spikes and compute in an event-driven manner,
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。
成功した突然変異戦略の中には、単一の実行で利用可能な知識が存在し、その知識は複数のタスク間で移行することができる。しかし、既存のLLM-ベースの進化的フレームワークでは、再利用可能な知識は捨てられ、同じアイディアの再発見
この研究では、モンゴメリーカット形式のサブモデル問題の最適化に関して研究が行われている。特に、与えられた制約で問題を解くことができる方法論が提案されている。研究では、多タスク問題を同時に解くことで、問題を解く時間が短縮さ
Full justified representation (FJR) is among the strongest known satisfiable proportionality axioms for approv
As the adoption of satellite-enabled Internet of Things (IoT) continues to rise, its intricate multidomain arc
Competitive artificial-life systems can rank trained controllers differently under training and ecological eva
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。
ポーカーの対局戦略を最適化する研究です。この研究では、独立チップモデル(ICM)を超える戦略的継続性最適化(SCO)アルゴリズムを提案しました。
This note aims to serve as an entry point to the literature on learning in games, a topic with significant the
We prove that every fair-division instance with four agents, additive valuations over the non-negative reals,
The peer-review process, the bedrock of scientific advancement, is increasingly undermined by sophisticated co
Uplink power control in dense, heterogeneous Internet-of-Things (IoT) tiers is fundamentally limited by incomp
A common assumption when designing an agent in a multi-agent system is that the other agents behave adversaria
Social media recommendation systems create conflict: content producers, content consumers, platform operators,
Audio-visual understanding remains challenging because models must jointly interpret spoken content, visual ev
Training controllers that are safe and robust in simulation, and systematically assessing their readiness for
この論文では、自律的な生成モデルの内部表現を分析し、未知のデータ分類に基づいて順序性が生じていることを示します。
回帰ニューラルネットワークの接続を簡素化する方法がいくつか提案されてきたが、生物学的背景に基づいた方法は少ない。研究者たちは、これまでノイズから切断するノイズ-プルーンという方法を提案しており、これは再起動後の機能を最も
Particle swarm optimization (PSO) has been widely applied to solve complex optimization problems from real-wor
均等可能な価値（Maximal Extractable Value：MEV）市場における中央集中化は、ブロックチェーンシステムにとって大きな懸念事項であり、経済力の集中化は、競争を抑制し、ブロックチェーンの目標であるデコ
We study the expected average rank achieved by the Efficiency-Adjusted Deferred Acceptance (EADA) mechanism in
この研究では、記憶を維持し更新する能力を強化するため、繰り返し神経ネットワークを用いて記憶の特性を分析しました。記憶を維持するための神経計算の一種として、ダイショビュールノーマリゼーション（Divisive Normal
Spiking Neural Networks (SNNs) enable event-driven computation with sparse activations, but building multimoda
The rapid development of Large Language Models (LLMs) has opened new avenues for Automated Heuristic Design (A
この論文では、スワームと進化アルゴリズムを分析するための新しいアプローチを提案しています。このアプローチでは、候補生成を分離し、目標によらず変化する部分と、目標に関係する部分を同定します。
An LLM agent's capability depends not only on model weights but on its harness: prompts, tools, skills, and co
Automated heuristic design (AHD) with large language models (LLMs) has produced strong heuristics for combinat
ニュロモーティックコンピューティングの研究を目的としたフレームワーク
Symbolic regression provides analytical expressions, but it is usually applied one output at a time. This is l
Particle swarm optimization (PSO) is a widely used metaheuristic, prized for its simplicity and small paramete
Gene regulatory network modeling often requires balancing predictive accuracy and mechanistic interpretability
Direct feedback alignment (DFA) trains hidden layers with fixed random projections of the output error, avoidi
Hydrogen infrastructure in enclosed environments, such as parking facilities for fuel cell vehicles, presents