「text」の検索結果
588 件🤗 Transformersは、テキスト・ビジョン・音声など複雑なモデル定義をサポートするフレームワークで、インフェレンスターやトレーニングに使用できる。
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。
マシンラーニングシステムの理論と実装に関する本。
データラベル化と注釈化を行うためのツールです。
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。
SGLangは、大規模言語モデルのサービングフレームワークです。このライブラリは、高性能なサービスフレームワークで、大規模言語モデルのサービングをサポートしています。
Under expert parallelism, mixture-of-experts (MoE) layers pay all-to-all token dispatch, grouped GEMMs, and a
Predicting stock returns requires modeling persistent company relations and time-varying market signals. Graph
Tokenization is a fundamental component of modern language models: before a model sees data, its tokenizer has
Deployed language models are updated continually, and regulators increasingly accept changes that were declare
Wearable sports sensors can recognize actions on the device itself, where lower-precision numbers reduce model
Reasoning vision-language-action (VLA) models pair physical control with natural-language rationales that supe
Long-horizon video generation is increasingly moving toward autoregressive rollout, where each newly generated
Diffusion models represent a powerful class of generative models known for their solid theoretical foundations
Vision-language models (VLMs) increasingly support construction hazard monitoring, yet their frequent false al
What makes a pretraining corpus transferable? We describe a corpus by three properties: its structure, its dim
Optimizing accelerator code across hardware architectures is difficult. ML compilers search predefined spaces
Individual-level trajectories are essential for applications that rely on user-level mobility behavior. Existi
Medicinal plants are usually identified using practitioners' experience, but reproducible, image based methods
In-image machine translation (IIMT) replaces text in an image with its translation while preserving the surrou
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
Power-grid customer service increasingly relies on community social media, where customers describe outages, b
Recent multimodal agents have shown promising progress in embodied planning and web interaction. However, exis
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
Frontier LLM agents can turn known vulnerabilities into working exploits, but are costly and often access-rest
When an AI agent executes an underspecified task, it embeds choices that may contradict unarticulated user con
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
Why do language models fail on long inputs? The common answer is length.
An AI inventory is a structured, often public record in which a government or organization lists and describes
Web navigation agents built on open-weight language models fail often and fail late, with most of the tokens s
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
AI-generated images have reached unprecedented realism, while instruction-driven AI editors re-synthesize imag
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
Repeated decisions over a shared document create two distinct opportunities: reusing document computation and
Parallel decoding speeds up masked diffusion language models by writing several tokens per forward pass. Publi
Large language model (LLM) services face rapidly rising serving costs. To sustain free or affordable access fo
Large language models (LLMs) often generate responses longer than necessary, increasing inference latency and
NLP evaluation reports routinely cite sliced performance differences, most prominently the intra-inter gap in
Chest radiography (CXR) diagnosis is critical for thoracic disease assessment, yet challenged by the need to i
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
Semi-supervised cardiac image segmentation aims to exploit abundant unlabeled images together with limited ann
Large 3D repositories make it easy to collect assets, but difficult to determine whether they can be reused re
Operating rooms (ORs) increasingly contain ambient sensing infrastructure to enable AI-based optimization of w
.diffusion モデルのライブラリ。画像・動画・音声生成に利用可能。
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。
ノードベースのビジュアルプログラミングツールです。
SANAは、高解像度画像生成モデルSANAを紹介する本研究であり、低計算コストで優れた高解像度画像を生成できる。
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。
Non-autoregressive System 1 decision engine. Typed choice, score and yes/no decisions over any text in a singl
電気生理信号から表現を学習し、脳コンピューターインターフェースの開発を支援する。
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。
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。
LLMを利用するために、セマンティック検索やLLMのオーケストレーションなどを行えるフレームワーク。
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。
Selecting small, effective instruction-tuning datasets requires identifying which aspects of the original data
Real-world problems span diverse domains, including program synthesis, symbolic reasoning, planning, and optim
Recent work has improved computer-use agents (CUAs) by combining retrieved task-relevant tutorials and look-ah
Offline multi-task reinforcement learning (MTRL) aims to learn a unified policy from heterogeneous offline dat
LLM agents struggle to automate complex enterprise procedures because static, pre-built harness cannot capture
Long-context inference is bound by the KV cache, which 2-bit codes could shrink about sixfold if their error s
Large language models (LLMs) read company news at scale, and backtests suggest that their readings predict sto
Large Language Bayes (LLB) transforms a natural language problem description into a collection of candidate pr
Large language models (LLMs) can memorize and reproduce fragments of their training data, raising privacy and
Generative AI tools for research are widely deployed, yet individual productivity gains do not guarantee bette
Large Language Models (LLMs) often generate fluent yet incorrect responses, making un- certainty estimation im
Modern high-performance computing (HPC) clusters integrate multi-generation CPUs and GPUs, which generate rich
Large foundation models often answer even when the available visual or textual evidence is insufficient to sup
Retaining predictive gains can require persistent errors in a model's response to interventions. For determini
Multimodal language models continue to improve on broad music understanding benchmarks, but which musical feat
Genomic foundation models are increasingly used to propose DNA sequences and edits. But successful design requ
Muon has recently emerged as a promising optimizer for pretraining large language models, offering a speedup o
Reinforcement learning with verifiable rewards (RLVR) can enable generalizable improvement for large language
Communication is important for our well-being, but it can be impaired by disabilities, such as locked-in syndr
Vision-Language Models (VLMs) hold promise for zero-shot video task-progress estimation in robot learning, yet
In the near future language model pretraining will become bound by total data rather than total compute. Such
Earth system models (ESMs) rely on parameterizations to represent unresolved processes such as convection, clo
Computer programmers chain together the same reusable computational primitives, for example, conditional state
Organisations increasingly deploy large language model agents that must follow organisational values. We check
LLM agents for GPU kernel optimization sit at two ends of a structure-freedom tradeoff. Scripted agents fix wh
Discrete diffusion models remain far less understood than their continuous counterparts, despite recent breakt
Generative models increasingly simulate users at different skill levels in education and AI evaluation. Yet it
Graph machine learning benchmarking results are scattered across papers that use quite different sets of datas
Identifying ligands for a protein target requires searching a vast chemical space for a comparatively small se
Recent language model (LM) safety failures have largely been studied as disparate phenomena. In agentic misali
**Purpose:** Ultrasound biometry is widely used across various clinical applications, including prenatal and c
Suicide prevention remains a critical public health challenge, and electronic health records (EHRs) provide an
Attention remains a substantial computational cost in Transformer training, particularly at long context lengt
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
As general-purpose Large Language Models (LLMs), such as Gemini and DeepSeek, exhibit increasingly advanced re
Zeroth-order (ZO) optimization enables memory-efficient large language model (LLM) fine-tuning, and recent met
Large-scale annotation of environmental sound recordings presents significant challenges under time and resour
Zeroth-order (ZO) optimization has emerged as a promising approach for memory-efficient large language model (
Dynamic graphs capture evolving interactions in Web systems, including online communication, user engagement,
Long-context LLM inference is costly because attention repeatedly reads a growing key-value (KV) history. We i
Large language models (LLMs) have introduced a generative approach to recommendation, producing item lists fro
We classify real and complex state-space models (SSMs) with linear transitions, distinguishing fixed-basis dia
Claim verification is the task of deciding whether a source text supports a claim, contradicts it, or lacks su
Reasoning language models write a chain of thought before they answer, and many checks in it do not change the
We study regret minimization for time-homogeneous episodic tabular reinforcement learning with $S$ states and
Language models are typically trained with a single choice of architecture on diverse domains and data sources
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
Fusing independently normalized attention heads can increase the number of explicit key-value entries, or atom
Given a relational database (RDB) storing heterogeneous tabular information, how can we predict missing (or fu
Spatial multi-omics technologies jointly profile diverse molecular modalities with spatial context, providing
Recent works have observed implicit cross-modal alignment between independently trained unimodal models. Howev
We compare three zero-shot time-series foundation models (Timestral-5M, Timestral-209M and Chronos-2, 8192-hou
The Poll Schumann model requires airframe and engine parameters, but published tables cover only part of the f
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
As large language models are fine-tuned for increasingly specialized tasks, their performance depends more on
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
Increasingly complex neural models predict blood-oxygen-level-dependent (BOLD) responses to naturalistic stimu
Modern learning systems increasingly combine pretrained models with downstream heads. Despite the growing impo
Generative search engines increasingly shape how users discover products, services, and commercial entities by
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
Small language models (<=9B parameters) can support local medical question answering (QA) while reducing priva
A stored experience may answer one future question and offer little help with another. We measure this task-co
Students can use large language models to complete assignments, but a correct solution does not establish whet
Healthcare information systems are fragmented, and retrieving data from legacy web portals is a recurrent need
Reasoning substantially improves language models' ability to solve difficult problems, but long reasoning trac
Computational drug discovery offers a growing range of tools for proposing and evaluating molecules, but combi
Next-visit diagnosis prediction forecasts the diagnoses documented at a patient's next encounter, turning retr
Chain-of-Thought (CoT) generation has substantially improved the performance of Large Language Models (LLMs) o
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
Timely and accurate crop health diagnosis is essential for sustainable agricultural production, yet accurate d
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
Skill extraction benchmarks score models against a few human-annotated positives and treat every other skill i
Language models in customer-facing products must be continually updated, since the data they serve shifts over
Third-party Agent Skills extend what an autonomous agent can do, but a malicious Skill can hide data exposure
Next-generation open radio access network (Open RAN) and AI-RAN deployments turn the RAN into a disaggregated,
Financial research agents can generate and monitor trading rules faster than a human can document the resultin
Every multimodal-LLM multiple-choice benchmark ends with a scoring rule that converts a model's response, ofte
Artificial intelligence (AI) technologies and their capabilities have advanced rapidly over the past few years
Instruction tuning for multi-task network traffic analysis requires data with clearly defined sources, supervi
Text-to-SQL translates natural language questions into SQL queries, enabling database access without writing S
Embodied agents are increasingly deployed in open-world environments, yet current robustness evaluations remai
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
Rapid advances in cowork agents are shifting human-AI collaboration toward real-world automation, delivering s
Repository-level AI coding benchmarks measure task success but do not directly capture the conditions required
Knowledge is how understanding accumulates across people and generations: durable records that outlive their p
Despite recent advances in CUAs, their development remains constrained by the limited diversity, controllabili
Large language models (LLMs) are driving autonomous agents toward real-world environments, with the Web emergi
Accurate preoperative prediction of lung adenocarcinoma invasiveness is important for surgical planning and in
Theoretical limitations of transformers on state tracking---the ability to track information about entities or
Personal AI agents are increasingly capable of handling long-horizon tasks across applications, yet users ofte
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
Self-supervised learning inherits the coverage of its source data. When an unlabeled corpus is long-tailed, th
PlagiaCorpus is a bilingual Arabic-English dataset for benchmarking plagiarism and text-reuse detection, inclu
Large Language Models (LLMs) have shown promise for empowering search agents to tackle complex information-see
An agent that explores, whether it generates conjectures or research ideas, must decide which of them are wort
Recent advancements in sign language processing can be attributed to developments in both large-scale dataset
Remote sensing image fusion and semantic interpretation are usually optimized separately, so reconstruction qu
Large Language Models (LLMs) are increasingly used in medical question answering (QA) systems. While recent wo
Reclamation of saline-alkali land requires management plans that account for soil and water conditions, treatm
Speculative decoding (SD) accelerates large language model inference by reducing the sequential cost of token-
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
Language model agents often struggle with complex tasks even when equipped with expert-written skills. Such sk
Reaction data underpin machine learning for reaction prediction, condition recommendation, and retrosynthetic
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
Reinforcement learning (RL) elevates joint audio-video generation, but optimizing heterogeneous, modality-spec
Career-support assistants must distinguish user claims from evidence of current ability and withhold judgments
Robust driver-state modelling requires synchronized physiological, behavioural, and contextual signals, yet ex
Reverse engineering obfuscated binaries is a core task in malware analysis and software security auditing. Whi
We introduce Dory1.0-4B, a 4-billion-parameter looped (latent-recurrent) language model with elastic depth: a
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
Law changes over time. A court decides a case under the law in force on that day, and legal QA benchmarks grad
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
The widespread adoption of Large Language Models (LLMs) demands reliable detection of human-authored versus sy
Dhārā Bench is a retrieval benchmark for Indian Knowledge Systems (IKS), with 325 questions in 12 categories a
Large language agents for literary role-playing are typically modeled around dialogue generation, leaving char
People often attach instructions to their search queries, asking for pages written for beginners, documents in
Scaling depth is a key route to stronger large language models, but Pre-LN architectures often suffer from dep
On-policy distillation (OPD) mitigates the student's exposure bias by optimizing on student-generated trajecto
Complex documents such as scientific papers contain rich information in tables, figures, and equations; howeve
We propose a simple, linguistically motivated approach to detecting AI-generated peer reviews based on the inh
Improving a sentiment classifier with limited resources involves more than simply deciding how much data to co
This paper studies retrieval-augmented generation (RAG), which leverages external knowledge to improve the per
As streaming generation becomes the dominant serving paradigm for both text-only and multimodal large language
Prompt compression is aimed at reducing input prompt lengths to enable cheaper, faster, and potentially higher
Large language models (LLMs) are increasingly used to evaluate candidates for professional roles, raising conc
When a masked diffusion language model is sampled in a few parallel steps in a random order, the model actuall
The author shows a methodology for constructing a network map of the artworld. The tweets posted by a manually
Large language models (LLMs) have achieved strong performance across diverse tasks, from question answering to
We present ICJ-Graph, a machine-readable resource connecting documents of the International Court of Justice (
Enterprise conversational systems require memory to personalize interactions, retain information across sessio
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
Automated liver ultrasound report generation improves the diagnosis of liver diseases by producing clinically
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
Scaled weight decay for Muon (Muon-SW) has been reported to train language models more efficiently than consta
Programs that use a language model as a classifier act on its most probable label. Text injected into the inpu
Large language models are increasingly used as measurement instruments for socially consequential constructs s
Dialectal Arabic speech recognition remains constrained by the scarcity of dialect-resolved transcribed speech
LLM planning often relies on reactive executors (e.g., ReAct) that generate a single action at each step to re
Language models with similar benchmark accuracy can succeed on different items. Overall scores do not show whe
Most benchmarks for real-time voice systems focus on one aspect, such as turn-taking, instruction following, p
In the LLM-as-formalizer paradigm, a large language model translates a problem into a formal program and leave
Retrieval-augmented generation (RAG) extends large language models with external evidence, yet relevant eviden
Agents are increasingly used for coding tasks, but the inference cost of large language models (LLMs) limits w
Generating visualisations from natural language requests (text-to-vis) enables users to explore data more effi
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
Text-to-SQL systems answer natural language questions over structured data, but can fail before SQL generation
Cross-Domain Few-Shot Segmentation (CD-FSS) aims to segment objects from unseen domains using only a few annot
Transfer learning from pretrained backbones has become standard practice in multi-sensor computer vision---yet
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
Feature matching underpins many robotic perception tasks, including visual localization, mapping, SLAM, and au
Multimodal large language models (MLLMs) can generate detailed video captions, yet they may overemphasize mino
Text-to-CAD generation aims to make CAD modeling more accessible by generating executable CAD programs from na
Video dubbing aims to synthesize lip movements that match target speech while preserving the speaker identity,
Streaming digital humans produce detailed video while continuing speech as new text arrives. High-resolution d
AI-generated videos often depict people within detailed scenes while leaving their small facial regions poorly
Source-only domain generalization (DG) for remote sensing semantic segmentation seeks reliable predictions in
Earth observation (EO) workflows require selecting among known outcomes, estimating confidence, and processing
Multimodal retrieval must integrate complementary evidence from views that may be missing, noisy, or unequally
Geospatial text spotting involves locating and transcribing geographic labels, such as road names, district na
Omni-modal large language models (Omni-LLMs) typically process large numbers of multimodal tokens from audio-v
Estimating the metric 3D position of people from a monocular camera is important for robot navigation and moti
Diffusion models are evolving from general tools into personalized systems that adapt to user needs through fi
Fingerspelling - the sequential signing of letters that compose proper names, technical terms, and out-of-voca
An image that is hard to classify on its own may have a helpful neighbor in the same unlabeled batch. Zero-sho
Multimodal large language models (MLLMs) have shown strong potential in robotic surgery for question answering
Image-text corpora contain copies of one image, captions that describe one image in different ways, captions r
Multi-source remote sensing data classification has received growing attention recently. However, existing met
Real-world driving requires context-aware decision-making. Existing end-to-end driving benchmarks primarily as
We present a GPU-native volumetric-video system for real-time reconstruction of graphics-compatible textured t
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
Existing RAW-based detectors typically introduce learnable ISP modules to convert RAW inputs into sRGB-like fo
Classical structure-from-motion reconstruction proceeds iteratively, refining camera poses and scene geometry
Urban photogrammetric point clouds are commonly processed as local 3D patches, limiting the context available
Autoregressive video diffusion models (AR-VDMs) enable real-time, long-horizon video generation but degrade ra
Few-step autoregressive video diffusion enables efficient generation, yet maintaining consistency over long ro
Interleaved multimodal chain-of-thought reasoning improves the visual reasoning capabilities of multimodal lar
Lip reading plays a crucial role in applications such as speech recognition, assistive technologies for the he
Financial document recognition is commonly evaluated using character and word error rates, which do not adequa
Understanding how objects and their spatial relationships evolve in 3D space over time remains challenging for
Long-form multi-shot video editing requires an intended edit to remain coherent across abrupt changes in scene
Precise localization of pathologies in endoscopy is a critical task in medical imaging, with deep learning (DL
Supporting a person over repeated interactions requires interpreting their current state in context and antici
Joint nuclei instance segmentation and classification requires precise boundaries and neighborhood information
Widespread image sharing on social media has increased the need for visual privacy recognition to identify ina
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
Reconstructing detailed geometry and realistic appearance from a single RGB image is essential yet fundamental
Diffusion models are powerful generative frameworks for producing high-quality images by denoising latent vari
Effective polyp segmentation in colonoscopy images is crucial for early detection of colorectal cancer, a lead
Membership inference determines whether a candidate example was used to train a text-to-image diffusion model
3D generation, editing, and completion are typically treated as regenerating the complete 3D asset under task-
Prompt engineering is commonly used to encourage text-to-image models to generate more detailed images. Howeve
Safe autonomous driving requires more than recognizing the current scene: the ego vehicle must anticipate how
Clinical evidence is inherently relational: medical images are interpreted with prior studies, cross-modal exa
Engineering drawings specify mechanical parts through orthographic projections. Recognizing which part a drawi
Cooperative perception (CP) requires substantial communication bandwidth to exchange sensing information among
Inertial sensors are moving into clothing, from sensorized sportswear to garments instrumented with hundreds o
Reinforcement learning (RL) post-training improves the single-sample quality of visual generative models, but
Hyperspectral image (HSI) classification requires the representation of contiguous spectral signatures and spa
In virtual environments, choosing appropriate outfits requires contextual harmony with surrounding scenes, yet
Cross-view object correspondence (CVOC) aims to establish associations of the same target between query and se
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
Open-world robotic mobile manipulation requires acting in physical environments with only partial information.
Many manipulation goals are easier to visualize than to specify numerically through explicit geometric constra
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
Autonomous robots are gaining traction in off-road environments, driven by their rising demand in agriculture,
Robust multimodal perception for soft robotic grasping is challenged by modality-dependent noise, semantic amb
The Generalized Singular Value Decomposition (GSVD) is a central tool for comparing two linear operators that
Long-horizon robotic assembly requires reliable perception and action across grasping, alignment, and insertio
Recent advances in Vision-Language-Action (VLA) models have enabled fine manipulation capabilities, yet perfor
Online large language model (LLM) services face a pricing cold start: providers initially lack the historical
Classical Bayesian persuasion assumes that an information designer can condition signals on the entire state.
Faster Whisper transcription with CTranslate2
This repository collects an extensive list of awesome papers about Story Generation / Storytelling, exclusivel
Official repository for LTX-Video
Matcha-TTSは、高速で条件付き流のマッチングを実現するTTSアーキテクチャであり、話者の特徴を考慮する。
A collection of resources on controllable generation with text-to-image diffusion models.
presidioは、テキスト、画像、構造化データを含む敏感データを検出、削除、マスク、アノニマイズするオープンソースフレームワークです。自然言語処理、パターンマッチング、カスタマイズ可能なパイプラインをサポートします。
Accurate forecasting and temporal downscaling of solar irradiance is critical for reliable renewable energy in
Spectral shrinkage encompasses a broad family of estimators for high-dimensional linear regression that regula
Training with chain-of-thought (CoT) supervision is an effective approach for teaching models multi-step reaso
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
Machine learning systems are challenging to test using traditional software testing methods because their beha
Key-value (KV) cache eviction methods rank tokens by attention scores, and each layer scores its own cache. To
A vote over a model's samples can only help when the samples disagree. For open-ended generation the binding q
Coding agents can destroy data, leak secrets and message the wrong person faster than a human can review. Coul
Standard attention forms query (Q), key (K), and value (V) matrices through learned linear projections of inpu
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
Effective multi-agent collaboration requires finalizing solutions from reasoning that is distributed across th
Locating political parties on environmental and climate policy is central to the study of party competition, y
Social-media comments are interactions, yet they are often translated and evaluated as isolated text. This mis
Software development sessions often involve several unfinished tasks rather than one issue carried through to
While language models (LMs) excel in natural language tasks, their ability to learn impossible languages--thos
Medical language models promise expert clinical support, but the strongest open-source LLMs remain too large f
Simulated students offer a scalable way to test learning platforms and AI tutors before deployment with real s
On-policy distillation (OPD) can improve answer accuracy while weakening responsiveness to supporting evidence
Traditional interactive narrative systems offer strong authorial control and structural coherence but often re
Factual recall in language models is typically framed as retrieving a factual association: a subject--relation
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
Linear recurrences are becoming an increasingly common building block in large language models, replacing the
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
How does a relational representation shared across queries drive a query-specific choice? Controlled entity-bi
Language models can analyze and assess student texts using predefined rubrics and deliver structured feedback,
LLM-based role-playing agents are fluent at imitating a character's style, yet struggle to make role-consisten
Self-evolving agents accumulate experience from task streams, but existing frameworks typically rely on fixed
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
Web communities rely on moderators to catch harmful messages such as insults, hate speech, and scams, but no o
Electroencephalography (EEG) provides a non-invasive means for neurological and mental health assessment, yet
Temporal Knowledge Graph Question Answering (TKGQA) requires language model agents to retrieve evidence, selec
Depression affects millions of Bengali speakers across South Asia, yet automated detection systems have relied
Accurate house price prediction is essential for real estate buyers, sellers, agents, and financial institutio
Verifying that a packaged food is Halal requires reading ingredient declarations and E-number additives before
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
A language model that learns a new fact by fine-tuning can repeat it but cannot reason with it: taught that a
Visual-text compression renders long contexts as images and is engineered around how much of the text a vision
Activation steering controls large language models at inference time by adding a vector to their internal repr
While online medical consultation broadens access, unprompted patient disclosures and topic shifts frequently
We present See, Think, Use, a protocol that traces each answer of a vision-language model to perception, reaso
Humans continuously acquire knowledge from carefully crafted materials such as books and tutorial videos, carr
Language agents read code, tool output, and dialogue in contexts that grow at every step. Dynamic sparse atten
The widespread adoption of large language models (LLMs) is reshaping how textual content is produced, complica
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
Sparse autoencoder (SAE)-based activation steering is a lightweight approach for modifying the behavior of lar
Applications use Typed Decision Models to route and moderate text in many languages, and they often act only o
Multimodal Large Language Models (MLLMs) have made substantial progress in 3D scene understanding from multi-v
Vision-Language Models (VLMs) and Vision-Language-Action (VLA) models process hundreds of visual tokens, makin
Automated classification of retinal pathologies from Optical Coherence Tomography (OCT) images remains challen
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
Studies of neural network generalization and robustness often summarize smoothness with a single number, such
The spatial intelligence of large vision-language models (LVLMs) requires the ability to combine local visual
Reinforcement learning with verifiable rewards (RLVR) has become an effective post-training paradigm for visio
Recent advances in generative AI have enabled multiple pathways for high-fidelity video synthesis: text-to-vid
Embodied question answering asks an agent to navigate, observe, and then answer questions about what it saw, y
This report presents GatedVLA, our entry for the Physical AI AV (PAI-AV) track. Our learned vision-language-ac
Robotic surface exploration benefits from tactile interfaces that accommodate contact and capture variations i
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
Assessing the reliability, accuracy, and safety of large language models, RAG pipelines, and AI agents.
長時間のビデオ生成を実現するためのモデルのサポートを紹介している。
Learning from extensive longitudinal clinical records--spanning repeated encounters, evolving conditions, and
Mitigating unfair disparities in sequential decision-making is a growing concern in machine learning. We study
Across diverse markets, it is important for firms to balance attracting customers with managing operational co
Calling a tool through generated text adds decoding steps, even when the computation itself is short. We study
Surprisal correlates closely with reading time and is widely used in the cognitive science of reading as a mea
Diffusion large language models (dLLMs) generate tokens in parallel via iterative denoising, but their bidirec
Post-trained language models fabricate citations at high rates. We ask whether that is reward hacking: scoring
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
Character names in large language model (LLM) fiction concentrate on a handful of recurrent names such as 'Ela
Faced with an unanswerable question, small language models can either refuse or confabulate: confidently produ
Diffusion-based vision-language-action (VLA) models cascade pretrained vision-language models (VLMs) with diff
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
Long-horizon embodied manipulation requires policies to act on information that is no longer visible. Modified
A robot's next action can depend on recent motion or earlier visual evidence absent from its current observati
Most agentic robotic systems use vision-language models (VLMs) either to orchestrate predefined skills or to d
Deploying deep learning on microcontrollers (MCUs) requires fitting both the model weights and runtime activat
Real-time long-video generation requires continuous output with consistent appearance and natural motion. Exis
Performativity arises when prediction-informed decisions change the data-generating process, creating feedback
Concept erasure is a prominent approach to achieving fairness in machine learning through the removal of sensi
Accurate forecasting with uncertainty quantification is critical in a variety of scientific applications rangi
Direct Preference Optimization (DPO) treats every reported comparison as correct, yet preference data contain
Conditional generation of time-series paths is a central problem in probabilistic modeling, where the goal is
Self-distillation methods for large language models with an exponential moving average (EMA) teacher already r
We study whether a language model can help design controllers whose behavior can be formally checked. In proof
Long-horizon LLM agents play an important role in solving complex and sustained tasks, with ever-growing inter
Collaborative robots (cobots) are designed to be safer and more interactive than traditional industrial robots
Automated endoscope guidance reduces the need for continuous manual camera control during minimally invasive s
Scaling humanoid motion foundation models is constrained by the scarcity of diverse, robot-executable data and
Inserting an unseen part requires sub-millimetre precision and the correct orientation of keyed features. Curr
Camera localization methods are mostly multi-stage pipelines relying on dedicated and complex maps that are co
Decision models target structured tasks by directly predicting choices or scores, avoiding autoregressive deco
Unlike autoregressive (AR) models, diffusion language models (DLMs) permit parallel token updates, flexible ge
Retrieval-augmented generation (RAG) lets a large language model (LLM) answer questions by retrieving relevant
Linear attention scales linearly with sequence length, but its finite-state memory can limit long-context retr
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。
Omniprediction concerns the task of making predictions that are simultaneously accurate with respect to differ
The marginals of a continuous diffusion model can be reproduced by a deterministic ordinary differential equat
Tabular foundation models use in-context learning to estimate conditional expected outcomes and average treatm
We study adaptive UAV sampling for radio environment map (REM) construction from non-Gaussian IoT observations
Whether models trained on different modalities converge to the same representation of the world is central to
Reinforcement learning with verifiable rewards (RLVR) makes a language model solve more problems on the first
Parameter estimation and classification in binary logistic regression deteriorate severely when the number of
Pixel-space generative Transformers typically use one token per image patch, so capturing finer detail require
Automatic operator design (AOD) has increasingly adopted large language models (LLMs) to generate problem-spec
The tension between the established technologies of connectionist AI and their neuromorphic challengers is att
Video generation is shifting from end-to-end synthesis toward agentic generation, where planning, tool use, an
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
A capable voice assistant must do more than recognize words; it must understand requests, reason over context,
Across coding, web, and tool-use tasks, language-model agents are now routinely trained with the same recipe:
Proactive agents must decide whether, when, and how far to assist without an explicit request. Existing evalua
On-policy reinforcement learning for large language models assumes that training responses are sampled from th
An effective agent memory system should adapt to a user's history and task requirements. Human-annotated super
Interactive world models generate visual content conditioned on user actions, yet maintaining spatial consiste
One-step and few-step text-to-image generation must accurately render the details specified in the prompt desp
We present Youtu-Parsing-Omni, an omni-modal parser that converts documents, images, audio, and video into str
Retrieval-augmented generation commonly serves separate models for reranking and answering, duplicating model
While frontier vision-language models demonstrate strong capabilities in user interface (UI) navigation, their
We present PhysInsert, a framework for physically plausible reference-based object insertion that jointly adap
Human cognition flexibly draws on a shared perceptual foundation to understand environments, attend to referen
Human judgment calls made throughout the data science life cycle can change the conclusions drawn from data. I
Streaming federated learning (SFL) allows clients to train a shared model from continuously arriving data with
Time-series forecasters are ranked almost entirely by accuracy metrics, but a forecast's decision value, its e
Model distillation has become a major concern in the rapidly growing generative AI discourse. In its simplest
Jev enables efficient decision-making with large language models (LLMs) through typed querying primitives, all
On-Policy Self-Distillation (OPSD), which aligns a student model's token-level distribution with that of its s
Music audio generation spans instrumental clips, full songs with vocals, edits of existing recordings, and sys
Humans routinely consume multimodal content, where textual and visual information is naturally interleaved. Al
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
We present EasyProver, a Lean proving system built from recursive agents with isolated contexts, asynchronous
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
Artificial intelligence offers statisticians a new paradigm for modelling and analysing complex datasets. Agen
Language model performance depends strongly on the mixture of domains in the pretraining data corpus. Popular
We propose a test of whether two interventions induce the same outcome distribution when outcomes are structur
Model discrepancy may prevent a single calibration parameter from reproducing a physical response across the c
Relay and reseller APIs have become a common way to access large language models (LLMs), yet users cannot dire
Prediction models degrade when the deployment population differs from the one used to train them, and training
Contextual dueling bandits learn from pairwise comparisons, the same feedback used to fine-tune language model
We introduce the Axiomatic Learning Structures (ALS) and Axiomatic Probabilistic Structure (APS) in order to s
Traffic forecasting requires modeling complex temporal dynamics and the spatial dependencies imposed by road t
Large language model (LLM)-driven agent scaffold evolution improves agent workflows through iterative candidat
We describe the agent we submitted to the GLEE Competition, which plays bargaining, negotiation, and persuasio
Miner extractable value (MEV) poses a persistent challenge for blockchain ecosystems. It arises from the fact
Unified conditional image generation requires a single model to preserve different aspects of a visual input w
Fine-tuning Large Language Models (LLMs) on instruction data can quietly erode their safety, even when no trai
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
Metis is a bet that better data, better training methods and new architectures can buy what a bigger parameter
Large language model agents are increasingly used for scientific discovery. However, existing benchmarks mix l
Diffusion vision-language models (DVLMs) process their entire input at every denoising step. Since visual toke
Low-cost arms such as the SO-101 have become the most common entry point to robot learning, yet no released po
Visual generation requires both high-level semantic knowledge and fine-grained pixel details. We propose a lea
spaCyはPythonで動くIndustrial-strength Natural Language Processing(Language理解の高度なライブラリです。文脈理解、構文解析、名詞の抽出など、複雑なNLPタ
Shapley-value-based explanations are widely used for feature attribution in machine learning. However, in comp
In token-priced language model services, users can see the prompts they submit and the answers they receive, b
Multi-agent systems (MAS) require both effective coordination and specialized task execution. Existing prompt
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
Generating videos of a specific subject from a few reference images remains challenging when the camera reveal
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。
Text injection trains speech recognition models on unpaired text alongside paired audio. We feed the speech en
Developers download quantized large language models (LLMs) from public registries to run them locally. None of
Language agents increasingly act through external tools and interactive environments, making the interface bet
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。
Emotion-driven Style Controlを使用してテキストから声の変換が実行され、感情のあるテキストをエモタイザブルな声に変換することが可能になります。
中文分词 词性标注 命名实体识别 依存句法分析 成分句法分析 语义依存分析 语义角色标注 指代消解 风格转换 语义相似度 新词发现 关键词短语提取 自动摘要 文本分类聚类 拼音简繁转换 自然语言处理
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
GUI grounding requires locating small controls in cluttered screenshots and identifying which control a natura
この論文では、Causal-Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive
Hardware-aware neural architecture search (NAS) requires jointly optimizing predictive accuracy and device-spe
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
Evolutionary program search with large language models keeps the structure of an evolutionary algorithm and us
Large language model agents have enabled software development through natural-language interaction, but extend
Harness optimization, which improves an agent's prompts, tools, and workflows, is an increasingly effective wa
Spiking Neural Networks (SNNs) promise energy-efficient inference through sparse, event-driven computation, ye
We present an AI-assisted mathematical addendum to Strategic Trip Planning: Striking a Balance Between Competi
Edge medical segmentation must reconcile small model size, high accuracy, and fast inference. TernScope addres
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
Wunjo Make: Face Swap, Lip Sync, Control Remove Objects & Text & Background, Restyling, Audio Separator, Clone
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
医療画像分析で、深層學習モデルが実装されている問題に対する解決策を提示します。治療を導くために、批判的結果に影響を与える変化について特に重点が置かれています。
As neuromorphic systems scale to thousands of near-memory cores, aging-induced permanent faults in neuron-upda
Phone scams impose substantial and growing costs on individuals and financial systems worldwide, yet existing
Realistic market impact is essential for generative limit order book (LOB) models to serve as world models for
A very simple framework for state-of-the-art Natural Language Processing (NLP)
Agents that run for a long time have a memory problem, and it is not the one most systems optimize for. Storin
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
General-sum games are more challenging than fully cooperative or adversarial settings because agents may decei
Open-ended search combined with foundation models has produced advances such as faster algorithms, new results
Can algorithms be learned and evolved as models, rather than repeatedly redesigned as programs? We explore thi
Repeated Bertrand competition is a stringent benchmark for algorithmic pricing: in homogeneous-goods markets,
Vowpal Wabbitは、機械学習を進歩させるためのオンライン学習、ハッシュ、reduceなどの強力なアルゴリズムを含むシステムです。その結果、さまざまな問題に応じて、高品質な解決策を提供できます。
Engineering design tools increasingly embed optimizers as ready-made components that users select by algorithm
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)
We show that two Transformers representing the same function can follow different training trajectories. For e
Modern Transformers rely on many scale-control mechanisms, including normalization, residual scaling, attentio
Assigning routine tasks to cheaper large language model (LLM) providers can reduce generation cost, but switch
Plurality-voting ensembles aggregate the predictions of multiple base classifiers and output the most voted cl
We present an overview of R package called \texttt{naspaclust}, a tool for spatial fuzzy clustering improvemen
Conflict-graph search can stagnate despite effective operators when interventions fail to address the conflict
We study the unit-demand pricing problem under \textit{min-buying} behavior, where customers purchase the lowe
🔥 On-Policy Self-Distillation in Diffusion Models
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
Spiking neural networks target energy-efficient language modeling through sparse, binary activations, but the
Clinical sleep medicine repeatedly documents profound divergences between objective sleep duration and subject
Many machine learning approaches are two-level: an expert selects a class of methods, within this class the ch
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
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
マルチラギングスピーチ生成やクリエイティブボイスデザイン、ルートライフクライミングなど、テクスチャファリーTTSの最新技術を実現するためのフレームワークです。
Where generalization capacity--the ability to extract context-invariant relational structures--first emerges r
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。
Living neuronal networks transform inputs through recurrent cellular and population dynamics, yet it is unknow
Frameworks that use large language models for scientific discovery typically rely on a fixed, human-designed a
Segment-based explanation methods such as LIME return a single explanation for each prediction, computed from
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
As Artificial Intelligence (AI) has evolved from task-specific models to foundation models and agents, the sco
Large language model (LLM)-driven evolutionary search is a powerful paradigm for automated program and algorit
End-to-end backpropagation has been the dominant mode of training in deep learning, allowing for the coordinat
Spiking neural networks (SNNs) offer low-energy sequence modeling through sparse, event-driven computation. Ho
Artificial intelligence increasingly serves as a natural-language interface to complex technical systems, lett
Large language model (LLM)-based automatic heuristic design (AHD) iteratively proposes and refines heuristics,
Parameter-efficient reinforcement learning aims to improve reasoning with a compact trainable interface to a p
Large language models are increasingly participating in complex real-world tasks in the form of algorithm-desi
Many Indian banks serve customers through FAQ pages and scripted chatbots. Assistants based on language models
Every day, people continuously infer situational context and adjust the way they understand and remember the w
Evolutionary program search driven by large language models (LLMs) has produced record-breaking constructions
Population-based optimization methods often use previous search information through successful solutions, para
Adam and its variants dominate neural network training, but a single run only reveals whether a configuration
Sensor networks and radars can deliver signals as organized sets, e.g. ordered signals, signals describing ran