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Hugging Face günlük masasında bugün en çok oy alan makaleler. Atıf sırası OpenAlex’ten: son bir yıl, yapay zeka alt alanı. Oy, yıldız ve atıf sayıları kaynağın kendi rakamı.

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Akış 1 Kasım 2026 · 19:48 · Hugging Face · OpenAlex

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101 oy13 yıldızAçıkLook Only When Needed: Certificate-Guided Visual Routing for Efficient Diffusion VLMsDiffusion vision-language models (DVLMs) process their entire input at every denoising step. Since visual tokens make up 89-97% of this input on the benchmarks we study, most of the decoding computation is spent repeatedly processing the same image. Visual token pruning methods partially relieve this burden by removing part of the visual tokens, but they lose the removed information and still process the remaining…Donghyeon Cho, Joowon Kim, Seungho Shin ve 1 kişi · kaist-ai · 27 Ekim 2026
xiaomi-research · 28 Ekim 2026A-RSI: Adaptive Task Refresh for Zero-Data Multimodal Self-Improvementgalilai-group · 29 Ekim 2026Proving and Improving the Platonic Representation Hypothesis with JEPAskaist-ai · 29 Ekim 2026L3G-World: Real-Time Infinite World Generation with Latent 3D Gaussian Memory

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37 oyGünlük masaIQuestLab · 27 Ekim 2026WorkFactory: Turning Professional Intents into Executable Work for Scalable Agent TrainingScaling supervised training for working agents requires diverse professional tasks that yield reliable expert demonstrations. However, missing or inconsistent task information can obstruct teacher execution and weaken the resulting supervision. We propose WorkFactory, a scalable framework for constructing coherent and executable professional tasks. Starting from professional intents, information-level planning bui…Makaleyi aç →
28 oyGünlük masatencent · 23 Ekim 2026A Broader Look into MLLM's Retrieval HeadsLocating relevant evidence in long inputs is a core capability of Multimodal Large Language Models. Prior studies attribute this capability to retrieval heads, a sparse set of attention heads that transfer information from the context to the output. However, these analyses are confined to a limited set of modalities and the corresponding models. Moreover, how retrieval heads can be leveraged to enhance the model c…Makaleyi aç →
24 oyGünlük masaHugging Face · 29 Ekim 2026Read, Allocate, Route: Role-Coupled Tokenization for Pixel-Space Generative TransformersPixel-space generative Transformers typically use one token per image patch, so capturing finer detail requires more tokens and makes global attention increasingly expensive. We observe that finer detail does not necessarily require more global communication: low-frequency structure benefits from global exchange, while high-frequency detail can be modeled locally. Based on this insight, we introduce SpecSlot, a pi…Makaleyi aç →
23 oyGünlük masaTsinghua · 29 Ekim 2026HairDeck: Animation-Ready Stylized Hair Cards from a Single ImageThe hair of stylized characters in games and avatars is built from hair cards: artist-placed ribbons whose geometry is the visible hair, animated by spring bones. Existing image-based methods produce realistic hair or a single fused mesh, while CHARM, the only generator of stylized hair cards, decodes a hairstyle autoregressively as a long sequence of control points. Observing that a card hairstyle is an unordered…Makaleyi aç →
22 oyGünlük masananjinguniv · 22 Ekim 2026VibeGame: Prompt-to-Game Development with AI-Native Engine and Self-Evolving Adversarial Agent TeamLarge language model agents have enabled software development through natural-language interaction, but extending this paradigm to end-to-end game development remains challenging. Building a playable game requires coordinating code, scenes, assets, animations, physics, and configurations, while allowing developers to validate their integrated behavior through interactive testing. We introduce VibeGame, a framework…Makaleyi aç →
21 oyGünlük masaUIUC-CS · 21 Ekim 2026Your Benchmark Is an Empirical Measure Over DifficultyA benchmark is not merely a collection of test questions; it naturally induces an empirical measure over difficulty. In this work, we develop this measure-based view of benchmark evaluation. This perspective allows us to identify and formalize two central failure modes: measure mismatch, where the benchmark difficulty distribution differs from the intended difficulty distribution, and coverage gaps, where uncovere…Makaleyi aç →
20 oyGünlük masaQwen · 26 Ekim 2026Co-Design Bench: Multimodal Collaborative Coding Evaluation for Agentic ModelsEvaluating human-AI co-design requires testing whether coding agents can translate real users' multimodal requests into working application revisions. We introduce Co-Design Bench, a reference-free benchmark of 273 tasks curated from 37,463 deduplicated queries across 26,314 real user coding sessions. Its two tracks, Multimodal Editing (ME) and Repository Editing (RE), cover localized edits with optional image, au…Makaleyi aç →
18 oyGünlük masainclusionAI · 29 Ekim 2026Intrinsic Distribution MatchingOne-step and few-step text-to-image generation must accurately render the details specified in the prompt despite limited opportunities for refinement. Distribution matching distillation (DMD) uses score differences to bring the generated distribution closer to a reference distribution. Following prior work, we estimate this difference with internal velocity branches, avoiding a separate fake-score network. We ask…Makaleyi aç →
18 oyGünlük masatencent · 28 Ekim 2026WeVisCut: The Whole Editing Room in One Agent From Narration to Long-Form VideoProducing long-form videos from narration requires interdependent decisions that remain coherent across shots. Existing agentic editors often use multi-stage workflows driven by general models, leaving the quality-cost trade-off of compact, task-adapted policies unclear. We address this gap with WeVisCut, a long-form script-to-video system that connects tool execution with policy learning. Its agent harness mainta…Makaleyi aç →
18 oyGünlük masaIQuestLab · 15 Ekim 2026RealCLI: Toward a Unified Evaluation of Human-Agent Collaboration for CLI AgentsCommand-line interface (CLI) agents increasingly execute longer-horizon tasks, yet existing benchmarks either assume that user instructions remain fixed throughout execution or evaluate task outcomes without capturing runtime collaboration quality. In this paper, we present REALCLI, a paired benchmark that unifies the evaluation of task execution and human-agent collaboration quality on realistic CLI tasks. To com…Makaleyi aç →
17 oyGünlük masaHugging Face · 16 Ekim 2026PersonaCUABench: Benchmarking Computer-Use Agents Serving Diverse PersonasComputer-use agents (CUAs) are commonly evaluated against fixed task specifications, but users may omit requirements, add constraints, or require approval before consequential actions. Completing the benchmark instruction does not guarantee serving the person who asked. We introduce PersonaCUABench, which holds 74 tasks across 11 domains fixed and varies only who asks. A persona agent, playing one of 44 personas i…Makaleyi aç →
15 oyGünlük masaMassachusetts-Institute-of-Technology · 28 Ekim 2026Multi-Token Prediction Done RightMulti-Token Prediction (MTP) is now a common ingredient in language-model pre-training, as it improves downstream performance and enables faster inference through speculative decoding. We identify a flaw in standard MTP training, in which a residual component of the MTP gradient interferes with both next-token and multi-token learning, increasingly so with scale. Specifically, the MTP cross-entropy gradient can be…Makaleyi aç →
14 oyGünlük masameituan-longcat · 28 Ekim 2026IntReal: A Real-World Dataset and Benchmark for Multimodal Interleaved GenerationHumans routinely consume multimodal content, where textual and visual information is naturally interleaved. Although Unified Multimodal Models (UMMs) can jointly generate text and images, multimodal interleaved generation remains largely underexplored. Progress in this area is hindered by two key limitations: (1) the lack of well-defined interleaved generation tasks and corresponding training data, and (2) the abs…Makaleyi aç →
13 oyGünlük masatencent · 29 Ekim 2026Youtu-Parsing-Omni: One Encoder, One Schema, All ModalitiesWe present Youtu-Parsing-Omni, an omni-modal parser that converts documents, images, audio, and video into structured records grounded in space and time, providing a perception layer for agents that read, locate, and cite evidence across modalities. Its design rests on one encoder for every modality and one schema for every task. Youtu-Omni-Encoder, initialized from a pre-trained language model to strengthen text…Makaleyi aç →
13 oyGünlük masaHugging Face · 29 Ekim 2026PhysInsert: Physically Plausible Object Insertion with Geometric Priors and Bidirectional Object-Scene InteractionsWe present PhysInsert, a framework for physically plausible reference-based object insertion that jointly adapts objects and their surrounding scenes. Existing methods often rely on masked inputs, generation-based pairs, or training pairs with limited viewpoint variation, leaving pose and illumination adaptation and bidirectional object-scene interactions insufficiently constrained. We construct the Insert World d…Makaleyi aç →
12 oyGünlük masaCASIA · 15 Ekim 2026CoSTA: Compressed Speech Token Adaptation for Efficient Spoken Dialogue ModelsSpoken Dialogue Models (SDMs) typically share a speech-token space for generation and synthesis, leading to long autoregressive sequences and costly inference. Lower-rate speech tokenizers shorten sequences but may introduce compression-quality trade-offs and costly replacement without breaking this coupling. To address this limitation, we propose CoSTA, a compatibility-preserving post-hoc framework that decouples…Makaleyi aç →
12 oyGünlük masaHugging Face · 27 Ekim 2026WebAnything: From the Wild Web to Scalable Agent EnvironmentsWeb agents require diverse, executable, and resettable website environments for reproducible training and evaluation. Human-assisted mirrors demand per-site engineering, while generative construction can diverge from specified real websites. Capture-and-replay preserves observations, with interaction coverage constrained by captured content and replay capabilities. We present WebAnything, a fully automated framewo…Makaleyi aç →
11 oyGünlük masaHugging Face · 28 Ekim 2026Investor's Last Exam: Can AI Agents Do Equity Investment Without Seeing the Future?Financial agents must form forecasts and investment judgments from decision-time evidence, with predictions evaluated against subsequent outcomes. Existing benchmarks assess financial knowledge, research workflows, or forecasting, but evaluating the full research process requires a strictly frozen information set and professional evaluation criteria. We introduce Investor's Last Exam (ILE), an equity-research benc…Makaleyi aç →
11 oyGünlük masaNPCI · 30 Eylül 2026TauIndianBankBench: Procedure-Aware, Action-Oriented Evaluation of Tool-Using Conversational AgentsMany Indian banks serve customers through FAQ pages and scripted chatbots. Assistants based on language models could respond faster and carry out requests such as blocking a card, but only if they give correct information and follow the bank's rules. These rules prescribe a procedure, a set of required steps, such as obtaining a closure quote before closing a deposit. Adapting models to this domain requires measur…Makaleyi aç →
11 oyGünlük masaairun-ai · 29 Ekim 2026Glance: The Generator Is Its Own RerankerRetrieval-augmented generation commonly serves separate models for reranking and answering, duplicating model residency and passage processing. We introduce Glance, which equips the generator with a lightweight decision adapter and pointer head to score candidate passages from a shared prefill. Short relevance probes branch from the cached context, producing one score per passage without separately prefilling each…Makaleyi aç →
10 oyGünlük masaCASIA · 28 Ekim 2026AskStream: When and What to Ask Proactively under Uncertainty in Streaming Video UnderstandingRecent advances in multimodal large language models have increased interest in interaction paradigms for video understanding. The evolution from offline to streaming and proactive interaction has gradually shifted models from passive responders toward more proactive agents that can decide when to answer. However, existing proactive paradigms typically assume that the information required to answer a query is compl…Makaleyi aç →
10 oyGünlük masaadobe · 29 Ekim 2026EditAgent: Rethinking Agentic Image EditingAgentic image editors decompose complex requests into steps, but when a step fails, they mostly retry with the same narrow set of tools and a judge that scores each output by eye directly. We argue that such agents are bounded by two spaces: the action space of what the agent can do, and the observation space of what its judge can see. On our expert-annotated benchmark, a general-purpose editor yields no usable re…Makaleyi aç →
9 oyGünlük masaCambUni · 14 Ekim 2026Amortizing Sequential Experimental Design in Reasoning LLMsDespite their extensive pretraining knowledge and strong reasoning capabilities, large language models (LLMs) struggle to adaptively gather information in experimental design tasks. We study how to amortize sequential Bayesian experimental design (BED) into an LLM by explicitly training the reasoning that guides action selection using information-gain rewards. Our approach specifies the experimental generative mod…Makaleyi aç →
9 oyGünlük masaapple · 29 Ekim 2026Online Dense Reinforcement Learning with Planning-Grounding Agent for UI Navigation on Live IOS AppsWhile frontier vision-language models demonstrate strong capabilities in user interface (UI) navigation, their high inference costs prohibit everyday use. Conversely, smaller models trained via supervised fine-tuning (SFT) or offline reinforcement learning often memorize static trajectories, leading to brittle execution and an inability to recover from errors. To address these limitations, we propose a novel onlin…Makaleyi aç →
8 oyGünlük masaHugging Face · 27 Ekim 2026SO-101 Bench: Accessible Spatial ManipulationLow-cost arms such as the SO-101 have become the most common entry point to robot learning, yet no released policy performs spatially constrained, language-conditioned tasks on them out of the box, and existing SO-101 benchmarks run only on physical hardware with simple tasks and few held-out objects. We introduce SO-101 Bench, a simulated benchmark for language-conditioned spatial manipulation built on an Isaac L…Makaleyi aç →
8 oyGünlük masaCarnegieMellonU · 29 Ekim 2026Training Foundation Models with a Single AlgorithmFoundation models are typically trained in stages: pretraining, midtraining, supervised fine-tuning (SFT), and reinforcement learning (RL), with changes in both data and learning algorithms. We show that this algorithmic separation is not fundamental. By deriving suitable RL objectives as maximum-likelihood fitting to model-generated target distributions, we place all of these stages under a single likelihood-base…Makaleyi aç →
8 oyGünlük masaTsinghua · 21 Ekim 2026LongZero: Enhancing Long-Context Reasoning in LLMs from Zero DataReinforcement learning (RL) is an effective way to strengthen the long-context reasoning of large language models (LLMs), but it needs training data that are long, diverse, challenging, and verifiable. Human annotation of such data is expensive, naturally occurring data are too scarce to sustain RL, and existing synthesis methods depend on external corpora or stronger teacher models, which become harder to obtain…Makaleyi aç →
8 oyGünlük masaFangcunAI · 26 Ekim 2026Evaluating DeepSeek V4.1 Flash-1017 for loss of control and harmful manipulation risksAs frontier language models grow increasingly capable and autonomous, independent third-party evaluations are critical for characterizing their systemic safety risks. While closed-source releases are routinely accompanied by comprehensive system cards, open-weight models receive less scrutiny regarding frontier risks. To address this gap, we present an independent safety evaluation of DeepSeek V4.1 Flash-1017, the…Makaleyi aç →
7 oyGünlük masaByteDance-Seed · 29 Ekim 2026Your LR Schedule May Not Be Principled: Effective Learning Rate Scheduling in Weight-Normalized TrainingWeight-normalized (WN) training is emerging as a compelling alternative to conventional weight decay (WD), with recent studies reporting substantial pretraining loss gains. We show that these gains are driven primarily by the induced effective learning rate (ELR) trajectory rather than by normalization alone. Matching ELR trajectories largely collapses the training loss dynamics of WN and WD. Schedule design shoul…Makaleyi aç →
6 oyGünlük masaIPEC-COMMUNITY · 29 Ekim 2026OpenFlyV2: An onboard vision-language-action model for aerial perception and navigationHuman cognition flexibly draws on a shared perceptual foundation to understand environments, attend to referenced entities, reason about spatial relationships, and act. Endowing aerial robots with an analogous functional organization remains challenging, as existing systems are often specialized for individual capabilities, dependent on offboard computation, or unable to sustain closed-loop operation on resource-c…Makaleyi aç →
5 oyGünlük masaHugging Face · 29 Ekim 2026AB-Attention: Accurate Block Attention for Accelerating Video Diffusion TransformersVideo diffusion transformers generate high-quality videos, but the quadratic cost of 3D full attention dominates their inference. Dynamic block-sparse attention reduces this cost by computing exact attention only on key/value blocks selected from current activations. However, its fidelity degrades under aggressive sparsity: blocks are scored from coarse summaries or formed by costly global clustering, pruned block…Makaleyi aç →
5 oyGünlük masaHugging Face · 25 Ekim 2026Broken on Arrival: Non-Functional Quantized LLM Artifacts in Public Model Registries and How to Catch ThemDevelopers download quantized large language models (LLMs) from public registries to run them locally. None of the registries we examined documented functional testing of their files. We tested all 305 eligible code-capable GGUF tags (the llama.cpp format) across 15 model lines in the official Ollama library, plus 22 popular Hugging Face community files. A 15-task code screen identified suspects, which we retested…Makaleyi aç →
5 oyGünlük masaamazonwebservices · 28 Ekim 2026Scaling Lean Theorem Proving for Real-World Code VerificationWe present EasyProver, a Lean proving system built from recursive agents with isolated contexts, asynchronous search, parallel tactic probing, and an elastic elaboration service. Using an off-the-shelf frontier model with no additional training, it proves 669/672 PutnamBench problems and all six IMO 2026 formalizations and resolves 597/600 NTP4VC verification conditions and all 189 VERINA tasks by proof or refutat…Makaleyi aç →
4 oyGünlük masaoxford-lob · 19 Ekim 2026LOB-Impact Benchmark: Emergent Market Impact in Generative Limit Order Book ModelsRealistic market impact is essential for generative limit order book (LOB) models to serve as world models for trading agents. If a simulator misprices this mechanism, agents learn policies that are optimal in the simulator but wrong in the real market. Existing metrics assess generated streams purely through distributional comparison with historical data, leaving counterfactual response to an agent's own orders l…Makaleyi aç →
4 oyGünlük masaHugging Face · 21 Ekim 2026CausGround: Causal Interaction Grounding for Adaptive Embodied PlanningMultimodal large language models (MLLMs) show promising reasoning capabilities, but reliable physical interaction requires planning that accounts for when actions are executable and how they change the world. These preconditions and effects are environment-dependent and often not captured by MLLMs, making adaptation challenging. Yet existing approaches either require costly environment-specific training or rely on…Makaleyi aç →
4 oyGünlük masaModalityDance · 25 Ekim 2026Matched Capabilities, Different Behaviors: A Controlled Study of MCP and CLI Interfaces for Language AgentsLanguage agents increasingly act through external tools and interactive environments, making the interface between the model and its executable capabilities part of the decision process. Model Context Protocol (MCP) and command-line interfaces (CLI) can expose the same semantic operations through markedly different model-facing representations. We study whether these representational differences change agent behav…Makaleyi aç →
4 oyGünlük masaHugging Face · 24 Ekim 2026PreView: Enhancing GUI Grounding with Preprocessed Views and CuesGUI grounding requires locating small controls in cluttered screenshots and identifying which control a natural-language instruction refers to. Cropping and enlargement can expose local detail, but the resulting image may still leave ambiguity about the intended target. We propose PreView, an auxiliary preprocessor that improves the inputs to an existing grounding model without further training that model. PreView…Makaleyi aç →
4 oyGünlük masaHugging Face · 26 Ekim 2026Instruction Capacity: Behavior and Degradation of Language Models under Controlled LoadThe quality with which language models follow instructions falls as the load grows, yet there is still no quantity that expresses the limit of a model in units of load with an uncertainty. We propose an instrument in which the load consists of rules that replace single-token words in a text without meaning: such an instruction is atomic, the rules are independent by the construction of the task, and the outcome of…Makaleyi aç →
3 oyGünlük masaOxford · 18 Ekim 2026Holo4D: Holistic 4D Reconstruction as Geometric Control for Video DiffusionSynthesizing a monocular video from a new camera trajectory requires preserving the observed scene geometry and motion while completing regions that become newly visible. Feed-forward 4D reconstructors recover the structure of the observed scene, whereas video diffusion models provide a generative prior for completing unseen content. We introduce Holo4D, a video reshooting framework that uses reconstructed 3D traj…Makaleyi aç →
3 oyGünlük masaImperialCollegeLondon · 16 Ekim 2026ABF-Bench: A Verifiable Benchmark for Agentic Blockchain ForensicsBlockchain forensics is adversarial and open-ended: money launderers split, swap, and bridge funds, cut their trail at mixers or custodial services, and change tactics from one incident to the next. Investigators must keep adapting their tracing in response. Yet the immutability of public ledgers preserves every recorded movement in public view, making much of the investigation independently verifiable. We introdu…Makaleyi aç →
2 oyGünlük masaGeorgiaTech · 28 Ekim 2026EPSBench: Benchmarking LLM Agents for Earnings ForecastingQuarterly earnings forecasting requires an agent to connect financial evidence to the income-statement line items that produce a company's reported earnings. We introduce EPSBench, a benchmark for studying this ability across 149 firm-quarters from 86 US companies. Agents read a fixed collection of filings, earnings-call transcripts, and news, then estimate company-specific income-statement drivers with cited evid…Makaleyi aç →
2 oyGünlük masajet-ai · 28 Ekim 2026Weaken to Strengthen: Weakening-Induced On-Policy Self-Distillation for Large Reasoning ModelsSelf-distillation offers a way to improve reasoning models when a suitable external teacher is unavailable. However, common approaches rely on privileged information to construct a stronger self-teacher, limiting their use when only unlabeled prompts are available. In this paper, we introduce Weakening-Induced On-Policy Self-Distillation (WISD), which instead constructs a weaker counterpart of the original model a…Makaleyi aç →
2 oyGünlük masaHugging Face · 29 Ekim 2026Unlocking Any-Order Generation in Pretrained Autoregressive Image ModelsAutoregressive (AR) image generators are powerful in principle but inflexible in practice: training with a fixed raster-scan order hardcodes a single factorization of the image distribution into the model, leaving it unable to inpaint, outpaint, or edit at inference time. Existing solutions, including the modern discrete-diffusion family, train from scratch on a mixture of token orderings, paying roughly 3x the co…Makaleyi aç →
2 oyGünlük masaBreadStudio · 24 Ekim 2026After One-Click Video: The Inflow of Service-Type Content, the Failure of the Recommendation Pool Mechanism, and the BoLe SystemIn September 2026, a new large language model was released. Soon after, a new way of making explainer videos became very popular: people asked the model to write a program, and the program produced the video. "One-click video" content then appeared in large amounts on many platforms. On the surface, this is a rise in creative power at the level of the individual. We argue that, at a deeper level, it changes the st…Makaleyi aç →
2 oyGünlük masaHugging Face · 27 Ekim 2026Know, then Draw: A New Learning Paradigm for Visual GenerationVisual generation requires both high-level semantic knowledge and fine-grained pixel details. We propose a learning paradigm that assigns these roles to autoregressive pretraining for knowledge acquisition and diffusion post-training for high-fidelity rendering. Within this framework, we study the objective, representation, and scaling of knowledge acquisition and rendering. Controlled comparisons identify autoreg…Makaleyi aç →
1 oyGünlük masaHugging Face · 29 Ekim 2026FDSML: Formula-Driven Supervised Metric Learning with Parameter-Aware Triplet LossFormula-Driven Supervised Learning (FDSL) enables vision pre-training without real images, but existing methods cast pre-training as classification on discretized labels, discarding continuity in the generative process. We propose Formula-Driven Supervised Metric Learning (FDSML), which derives relational supervision from image-generation parameters beyond discrete class labels. FDSML combines FineFractalDB, our r…Makaleyi aç →
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