TodayNewsResearchFieldsStacksMarketAnalytics
⌘K

Research

New papers ranked by attention, with their code, models and discussion linked. Pick a field on the left.

WeekMonthQuarterAll
NewestMost relevant

Papers

Open problems

Rising research topics

Ask the research

Sample
ByteTrail · AI and developer-tools intelligence
NewsResearchStacksAPI
Fields · this week
All fields3193Multimodal513Agents492Frontier LLMs422Inference & serving256Image segmentation1723D scene154Learning verifiable rewards149Graph learning143Voice AI95Classifier & causal discovery85Forecasting time76Flow matching69Evals66Teacher policy distillation48Federated learning48All 32 fields

Open problems across fields

  • General in-context learning for embodied agents1 paper raises it · 0 address it

    Enables models to adapt to new tasks and environments without retraining, improving robustness in real-world deployment.

    • Has GPT-6-Astra Achieved Embodied Intelligence?raises
  • Cost and latency in embodied control loops1 paper raises it · 0 address it

    High computational and time costs limit real-time applicability of agentic systems in dynamic environments.

    • Has GPT-6-Astra Achieved Embodied Intelligence?raises
  • Physical-state verification and failure recovery1 paper raises it · 0 address it

    Agents must reliably detect and correct errors in physical interactions to avoid irreversible failures in real-world tasks.

    • Has GPT-6-Astra Achieved Embodied Intelligence?raises
  • Long-horizon task preservation in embodied agents1 paper raises it · 0 address it

    Maintaining progress over extended sequences is critical for complex, multi-step tasks in robotics and navigation.

    • Has GPT-6-Astra Achieved Embodied Intelligence?raises
  • Open-source stacks for generalist computer-use agents1 paper raises it · 1 addresses it

    Closed systems hinder reproducibility and systematic study of agent design for real-world computer tasks.

    • OpenGCUA: Toward a Fully Open Stack for Generalist Computer Useraises · addresses

Named by an AI from these papers' abstracts.

Rising research topics

  • Training & fine-tuning46+155.6%
  • Computer vision36+140.0%
  • AI safety33+135.7%
  • Robotics22+120.0%
  • Evals66+57.1%
  • Code models14+40.0%
  • Small LLMs7+40.0%
  • Federated learning48+29.7%

Papers

  • arXiv cs.CLOct 17

    Toward a Universal Local Speech Feature Extractor Through Distillation

    Jessica Yang, Marcelo Beramendi Caballero, Ju-Chieh Chou, Karen Livescu

    Self-supervised speech models commonly consist of a set of convolutional neural network (CNN) layers used as a local feature extractor, followed by transformer layers. The CNN layers are computationally demanding, and are also similar across models and resemble the much simpler mel spectral features. We hypothesize that the CNN layers can be replaced by a simple, universal model. To test this hypothesis, we learn a two-layer feature extractor via distillation from one or more teacher models (HuBERT, data2vec, and WavLM). Experiments on SUPERB(-SG) benchmark tasks show that our model largely retains the performance of the teacher models and can also be used in an unseen model (wav2vec 2.0). Our feature extractor requires only 3% of the MACs of the CNNs, resulting in a $>$20% overall inference runtime reduction.

    PDF
  • arXiv cs.LGOct 17

    Cross-Dimension and Cross-Modality Relational Tensor Representation Learning for Multimodal Health Data Modeling

    Jiawei Guo, Meng Zhao

    Multimodal health data integrate information from heterogeneous clinical sources, creating substantial opportunities for data-driven decision support. Realizing these opportunities, however, requires effectively modeling complex dependencies that arise both within individual modalities and across modalities. This paper proposes Relational Tensor Representation (RTR), a unified framework that jointly learns dimension-specific tensor representations and cross-dimensional and cross-modality relationships. RTR introduces learnable relational bridge matrices that maintain relational information while preserving the structural identities of tensor dimensions and modalities. A structured fusion mechanism further integrates factor-level and relationship-level information into compact representations for downstream prediction. Experiments on simulated data and two multimodal neurophysiological datasets, covering emotion recognition and brain activity classification, demonstrate that RTR consistently outperforms representative tensor learning, multimodal fusion, and Transformer-based baselines. These results highlight the value of explicitly modeling cross-dimensional and cross-modality…

    PDF
  • arXiv cs.CVOct 17

    S3D-Mosaic: Subgradient-Guided Cross-Band Structure Transfer for Spectral Demosaicing

    Weiqiang Zhao, Xian Li, Yuzhe Gui, Tianzhu Liu, Yanfeng Gu

    Multispectral filter array (MSFA) imaging enables snapshot spectral acquisition, but its per-band sparse sampling causes severe spatial aliasing. Although missing spatial structures can be inferred across bands, wavelength-dependent contrast variations make direct cross-band transfer unreliable. Existing model-based methods lack explicit magnitude-invariant mechanisms, and learning-based methods require layout-specific training or per-image tuning, limiting generalization. To address these issues, we propose S3D-Mosaic, a model-based spectral demosaicing method built upon a subgradient-guided cross-band structure transfer framework. We first establish a subgradient-guided regularization framework with a generalized cosine-similarity interpretation, providing a convex mechanism for magnitude-invariant information transfer. Then we design a compact Directional Sparse Equivariant Difference Convolution (DSE-DConv) for band-level extraction of orientation-selective, high-frequency structure features at low computational cost. Instantiating the framework with DSE-DConv on spectrally adjacent bands yields a cross-band self-structure similarity regularizer for spectral demosaicing,…

    PDF
  • arXiv cs.CLOct 17

    Ask Less, Ask Better: Allocating User Attention in Proactive LLM Agents with Interruption Budgets

    Xuanming Zhang, Amir H. Rezaeian, Miguel Ballesteros, Weiyi Sun, Werner Geyer +4

    LLM agents can ask clarifying questions to complete tasks, but answering each question demands users' time and effort. Existing evaluations typically measure this burden by counting questions, treating necessary and avoidable questions equally. We propose an evaluation protocol that distinguishes how often agents ask from how well they ask. An interruption budget limits the number of questions, while a user-attention cost charges extra for questions that are hard to read or not worth asking, as rated by a human-validated LLM judge. Beyond question counts, this cost captures perceived effort: on conversation pairs with the same number of questions, human annotators agreed with the cost on which conversation required more effort 75% of the time. Across eight LLMs and four customer-service domains, limiting agents to one to three questions reduces user-attention cost by 36-73% while keeping 86-94% of the task reward they earn with unlimited questions. At the same budget, reviewing each question against the memory of users' earlier answers cuts attention cost by another 13% with little reward loss and yields more valuable questions. Models also differ sharply in how well they ask: at…

    PDF
  • arXiv cs.SDOct 17

    Long Listening Thoughts: Eliciting Open Auditory Reasoning with Deliberative Perception and Cognitive Refinement

    Jaeyeon Kim, Chao-Han Huck Yang, Luoyi Zhang, Chan-Jan Hsu, Fernando Ruiloba Portilla +3

    We introduce Long Listening Thoughts (LLT), a framework for constructing post-training datasets that improves audio reasoning by pairing deliberate perceptual grounding with explicit cognitive behaviors. On audio understanding and reasoning benchmarks, large audio-language models (LALMs) are limited primarily by perceptual errors, yet existing post-training datasets rely on audio descriptions of limited diversity. Through a four-stage pipeline, LLT (1) builds multi-aspect dense audio descriptions spanning semantic, acoustic, and temporal information, (2) generates more challenging questions, (3) synthesizes incorrect reasoning traces, and (4) extends short reasoning traces into long ones via a thought-continuation strategy that elicits cognitive behaviors such as verification and backtracking. Using LLT, we synthesize 1.6M reasoning traces and train Qwen2.5-Omni-7B with a two-stage pipeline of supervised fine-tuning (SFT) followed by direct preference optimization (DPO). This improves accuracy on MMAU Test-mini (71.5 to 78.2), MMAR (56.7 to 63.7), and MMSU (60.6 to 66.0).

    PDF
  • arXiv cs.CVOct 17

    ZeroDSS: Semantic Segmentation from Class Names Alone

    Tian Ming, Marzi Heidari, Yuhong Guo

    Semantic segmentation depends heavily on real images with pixel-level annotations, making dense supervision a major obstacle to deployment in new domains. We study task-specific data-free semantic segmentation, where training uses generic pretrained generative priors but has access to no target-domain images, masks, or captions--only a predefined category set. We introduce Zero-Data Semantic Segmentation (ZeroDSS), a feedback-driven approach that treats synthetic supervision as an adaptively controllable training distribution rather than a direct surrogate for real data. ZeroDSS constructs a semantically structured prompt distribution through LLM-guided compositional generation and adaptive class rebalancing, then converts the prompts into image-mask pairs using diffusion attention. Its feedback-driven distribution refinement consists of a two-phase closed-loop training procedure with a single loop-back step. In Phase I, a segmenter is trained using confidence-weighted supervision and dual-scale consistency. In Phase II, model feedback is used to adaptively target underperforming categories and refine diffusion-derived masks using model predictions. After this refined initial…

    PDF
  • arXiv quant-phOct 17

    Prototype-Guided Optimization of Quantum Data Embeddings with Linear Cost

    Seokhoon Jeong, Daniel K. Park

    In supervised quantum machine learning with classical data, classification performance critically depends on the geometry induced by quantum data embedding. Existing data-adaptive approaches rely on pairwise fidelity evaluations between samples, which scale as $O(B^2)$ for $B$ samples and create a computational bottleneck in iterative embedding optimization. To address this issue, we propose a prototype-guided framework for efficient data-adaptive quantum embedding. Using class-prototype separation and sample-to-prototype compactness, we derive a measurable lower certificate for class-ensemble distinguishability and construct a prototype-based objective requiring only $O(B)$ fidelity evaluations. We integrate this objective into an end-to-end pipeline that combines task-specific quantum circuit architecture optimization with continuous refinement of the data-embedding parameters. Across five benchmark datasets with up to 20 qubits, our method retains competitive quantum kernel support vector machine (SVM) performance while reducing fidelity-evaluation cost, with particularly strong gains over budget-matched pairwise optimization on T-MNIST. Geometric analyses examine how the…

    PDF
  • arXiv cs.CVOct 17

    Spatons: Spatiotonally Localized Building Blocks for Efficient XR Predistortion

    Mohammadjavad Maheronnaghsh, Sushant Kondguli, Steve Mann, Andreas Moshovos

    We present Spatons, a family of compositional, spatially localized neural operators for computationally efficient XR (extended reality) predistortion and distortion compensation. XR displays (AR, VR, and MR) suffer from spatial (e.g., geometric) and tonal (e.g., colour) distortions. Frames can be pre-distorted so that the projected image looks as intended. Distortions vary with manufacturing tolerances and projection systems, motivating programmable compensation. Untethered, battery-operated XR systems also require low energy and latency. A Spaton is a hybrid patch-to-pixel block combining tiny Multi-Layer Perceptrons (MLPs) with fixed-function interpolation. Using Spatons, we build programmable, computationally efficient pipelines for varied distortion scenarios. On the full spatiotonal Scenario 5, Sequential-5' matches CompenNet++ in SSIM (0.944 vs. 0.944) and closely matches its PSNR (27.5 vs. 27.7 dB), with substantially lower complexity. We demonstrate a flexible Spaton solution with 6 times fewer FLOPs and 106 times fewer parameters than CompenNet++, and 4-10 times fewer memory transfers than UNet++. We characterize software implementations and analytical hardware…

    PDF
  • arXiv cs.AIOct 17

    MSCR: Jointly Balancing Utilization Across Modalities and Discovering Synergistic Information

    Xinyu Chen, Liangjian Wen, Jiang Duan, Dongkai Wang, Yong Dai +5

    In multimodal learning, modality imbalance often lets one modality dominate optimization and keeps the model from fully exploiting cross-modal interactions. Balance-oriented methods such as the Multimodal Competition Regularizer (MCR) perform well by alleviating this imbalance during training. These dynamic balancing methods, however, mainly adjust modality contributions and do not consider how imbalance affects the formation of synergistic information during fusion. Synergy is a major source of multimodal advantage, because it is predictive gain that cannot be recovered from unimodal evidence alone. We propose the Multimodal Synergistic Competition Regularizer (MSCR), a fusion method that jointly promotes balanced modality utilization and the discovery of synergistic information. MSCR has two complementary objectives. An anti-dominance objective suppresses excessive unimodal influence and leaves room for synergistic information, and a synergy objective encourages the fused predictor to outperform a non-synergistic unimodal reference. An adaptive gate further concentrates the regularization on samples that need stronger collaborative modeling. Experiments on five benchmarks show…

    PDF
  • arXiv cs.AIOct 17

    Trust the Edge, Doubt the Arrow: Auditing Validation Signals for Text-Mined Clinical Causal Knowledge Graphs

    Su Lan, Shicheng Zhou, Yiqi Wang, Yanming Zhu, Xuefei Yin +1

    Clinical causal retrieval-augmented generation systems increasingly use knowledge graphs (KGs) mined from notes, yet extracted assertions remain candidates that require audit for patient-level EHR support and direction. We construct a directed candidate graph from 328,839 MIMIC-IV discharge summaries using LLM extraction and UMLS normalization, and evaluate three signals against SemMedDB: within-admission co-occurrence lift, cross-admission diagnosis-coding temporal precedence, and literature corroboration. Lift filters unsupported co-mentions and provides useful EHR support, but not causal proof. Coding order is a weak direction signal: among the 686 candidates where it commits, it reaches 54.2% agreement with SemMedDB, below the extractor's 69.4% on the same subset. Literature corroboration supplies independent direction evidence but covers only 30.4% of candidate relations. A MIMIC-III replication yields the same pattern: extractor direction remains stable (70.5%), whereas coding-order direction is near chance (50.9%). Thus, text-mined clinical causal candidate KGs are best treated as EHR-supported but direction-uncertain. In a leakage-controlled causal QA benchmark, lift-only…

    PDF
  • arXiv cs.CEOct 17

    Learning volatility-adaptive forgetting via GARCH-modulated LSTM for stock price forecasting

    Xin-Rui Lu, Da-Qing Zhang

    Stock price forecasting is hindered by the heteroscedastic nature of financial time series, in which turbulent and tranquil regimes alternate. Although the Long Short-Term Memory (LSTM) network is widely used for this task, its gates are oblivious to such volatility variation, so memory retention remains fixed regardless of market conditions. A common remedy is to supply volatility estimates as an additional input feature, yet this leaves the internal memory dynamics untouched. Instead, volatility can serve as a direct regulatory signal over the gate activation itself. This paper presents the Gsigmoid-LSTM model, which embeds Generalized Autoregressive Conditional Heteroscedasticity (GARCH(1,1))-derived volatility estimates into the LSTM forgetting gate through the Gsigmoid activation function, a volatility-adaptive variant of the sigmoid that combines a volatility adjustment factor with slope regulation via a learnable parameter. The model thereby automatically prioritizes recent information during turbulent periods while preserving long-term dependencies in stable markets. In the engineering application of daily stock price forecasting, extensive experiments, including…

    PDF
  • arXiv cs.AIOct 17

    Bridging Exploration and Persona: Information-Directed Dueling for Test-Time Agent Personalization

    Jie Mao, Pingchen Lu, Xiangyi Wang, Hengquan Huang, Shuang Qiu +2

    Effective test-time personalization requires LLM agents to adapt to query-dependent user preferences from lightweight interaction. We study persona selection with binary comparisons between candidate responses as the online signal. We present ID-TAP, a scalable, IDS-inspired neural dueling-bandit framework for large discrete persona spaces. ID-TAP combines a query-conditioned neural utility model with a lightweight Bradley-Terry explorer, and transfers comparison evidence across related queries through similarity-weighted warm starts. The neural model shares statistical strength across queries while preserving query-specific scores, and efficient BT updates score hypothetical challengers. Across seven user-conditioned generation benchmarks under reference- and model-based preference simulators, ID-TAP achieves the strongest aggregate performance among the evaluated baselines within a fixed ten-round comparison budget. These results support information-directed challenger selection as an effective design for test-time persona adaptation.

    PDF
  • arXiv cs.CEOct 17

    A decomposition-integration framework coupling singular spectrum analysis with a volatility-adaptive LSTM for stock price forecasting

    Xin-Rui Lu, Da-Qing Zhang

    Financial time series exhibit multi-scale, high-noise, and non-stationary volatility dynamics. Decomposition--integration frameworks built on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) have advanced this field, yet existing CEEMDAN hybrids remain unstable: high-frequency mode mixing contaminates downstream predictors, and the heterogeneous volatility of decomposed components is not matched by any volatility-adaptive mechanism. To address these gaps, this paper proposes CEEMDAN-SE-SSA-VMGL. CEEMDAN adaptively decomposes the original series, and sample-entropy-driven K-means clustering groups the intrinsic mode functions into high-, mid-, and low-frequency and residual components. Singular spectrum analysis (SSA) is then applied to the high-frequency group, whose singular-value decomposition of the trajectory matrix and principal-component reconstruction decouple intertwined pseudo-oscillatory modes and alleviate mode mixing. We further design a plug-and-play robust LSTM variant, the volatility-modulated GARCH-LSTM (VMGL), which embeds a differentiable GARCH(1,1) layer inside the LSTM cell as a risk gate; the estimated conditional volatility…

    PDF
  • arXiv cs.AIOct 17

    IF-MAP: Influence-Function-Inspired Agent Prioritization in Multi-Agent Systems

    Jie Mao, Pingchen Lu, Zikun Qu, Jiahang Sun, Xiangyi Wang +4

    Improving every agent in an LLM-based multi-agent system can be prohibitively costly, making it important to identify which agent should be prioritized to achieve the greatest system-level gain with limited resources. Failure attribution can inform this decision, but an agent's responsibility for failures does not directly reveal the system-level gains achievable by improving it. We therefore introduce IF-MAP, an influence-function-inspired method for identifying agents with high potential for system-level improvement. For each agent, IF-MAP measures end-to-end loss changes under controlled semantic prompt perturbations, fits a regularized local response model in a low-dimensional prompt-embedding subspace, and uses the estimated response norm as a priority score. This black-box procedure requires no access to model parameters or gradients and produces agent priorities that can guide subsequent improvement effort. We evaluate IF-MAP across six multi-agent workflows spanning code generation, question answering, and mathematical reasoning, using the same priorities for prompt optimization and model upgrades. Optimizing the selected agents' prompts achieves a macro-average gain of…

    PDF
  • arXiv cs.AIOct 17

    Is This Your Agent? Identity Watermarking for Agent Systems in Dynamic Environments

    Yuwei Zhong, Yu Cheng, Jiuan Zhou, Weizhi Liu, Yi Xie +3

    Agent systems deployed in dynamic environments continuously update their memories, skills, and runtime configurations, leading to evolving trajectories and challenges for persistent identity authentication. Existing text and agent watermarking methods struggle to achieve stable watermark embedding under dynamic environments, while embedded watermarks may become difficult to recover due to environmental changes. This paper proposes DASMark, an identity watermarking framework for dynamic agent systems. DASMark introduces Action-Preserving Embedding (APE), which learns semantically similar but distinguishable decision-expression channels from diverse interaction trajectories to enable stable watermark embedding in dynamic systems. It further introduces Shift-Robust Recovery (SRR) to mitigate context-induced shifts and improve watermark recovery stability under dynamic environments. In dynamic settings, DASMark achieves 98.5% bit-decoding accuracy and maintains 95.2% accuracy under attack scenarios, while preserving task utility comparable to unwatermarked agents.

    PDF
  • arXiv cs.CVOct 17

    Learning from the Right Regions: Class-Conditioned Explanation Guidance for Medical Imaging

    Zubair Faruqui, Rahul Dubey

    Deep neural networks can achieve strong predictive performance in medical imaging while relying on regions unrelated to the predicted finding. Explanation-guided learning can address this problem, but multi-label classification introduces a key challenge: multiple abnormalities may coexist in an image, requiring each prediction to align with its corresponding spatial evidence. We propose a class-conditioned explanation-guided learning framework that constructs a separate explanation for each positive class and penalizes evidence outside its class-specific annotated regions. We instantiate the framework using Gradient-weighted Class Activation Mapping (Grad-CAM) and evaluate it on 14 abnormalities using EfficientNet-B1, ViT-B/16, ViT-B/32, and Swin-B. Explanation guidance substantially improves annotation alignment across all architectures: All-Saliency Precision increases from 0.140 to 0.625, 0.052 to 0.551, 0.060 to 0.403, and 0.095 to 0.570, respectively. These improvements are accompanied by smaller, architecture-dependent changes in classification performance. Our results show that class-conditioned explanation guidance can substantially improve where multi-label classifiers…

    PDF
  • arXiv cs.AIOct 17

    Train Locally, Learn Together: Distributed Multi-Agent Training via ADMM

    Yiwei Chen, Changsheng Wang, Yicheng Lang, Qing Qu, Alfred O. Hero +1

    Multi-agent systems (MAS) can outperform individual language-model agents by combining specialized roles, diverse reasoning processes, and collaborative decision-making. However, collaborative MAS training is typically centralized, requiring a common trainer to aggregate role-local trajectories and learning signals and maintain model and optimization states. This motivates a distributed multi-agent learning paradigm in which agents improve system-level capabilities through role-local learning and decentralized communication, paving the way toward scalable, resilient, and self-organizing MAS. To this end, we introduce, to the best of our knowledge, the first optimization-grounded framework for distributed MAS training based on consensus alternating direction method of multipliers (ADMM). We formulate collaborative MAS learning as a consensus-constrained finite-sum optimization problem, naturally lending itself to distributed optimization via ADMM. The proposed approach alternates role-local learning with inter-agent consensus using distributed communication protocols such as ring all-reduce, enabling joint optimization while keeping each agent's data and training state local. We…

    PDF
  • arXiv cs.CROct 17

    Defending Against Dormant Poisoning Attacks Across Language and Multimodal Agents

    Wonjun Lee, Vaidehi Patil, Zaid Khan, Yue Zhang, Hyunji Lee +2

    Recent work shows that attackers can implant dormant malicious behaviors that remain hidden at release but emerge after benign fine-tuning. As foundation models are used as agents, whether dormant poisoning extends to harmful agent actions and multimodal settings remains unclear. We show two agentic risks: (1) harmful behaviors activated by benign fine-tuning can extend to unseen actions; (2) multiple harmful behaviors can coexist and be jointly activated by benign downstream fine-tuning. Existing defenses often rely on external safe responses, which are hard to obtain for agent actions, or a trusted clean reference model. We exploit a key property of dormant poisoning: the attacker must preserve safe behavior at release to conceal poisoning, and this behavior can serve as a safety reference. Leveraging this property, we automatically construct a Safety Buffer pairing harmful inputs with the released model's responses and benign versions generated by the same model to remove harmful intent. The Safety Buffer can replace external safe responses in existing defenses, but these defenses do not distinguish updates that weaken safety from those that preserve it, often increasing…

    PDF
  • arXiv cs.CVOct 17

    PACE-Mem: Prefix-Aware Complementary Evidence for Multimodal Agents

    Shidu Ren, Rong-cheng Tu, Hang Zhou, Xiao Luo

    As multimodal agents accumulate experience, their long-term memories contain many similar observations of recurring scenes, actions, and entities. Answer quality depends on the evidence retrieved from these memories into the model's context. However, query-relevant memories may contain overlapping information, and a fixed retrieval depth may return too little or too much evidence for a given question. To address these challenges, we propose PACE-Mem, Prefix-Aware Complementary Evidence, which constructs a complementary evidence sequence and determines its length from the same selection trajectory. For structured memories, query-guided pooling combines records within each clip to obtain relevance scores and representations. Using these relevance scores and representations, maximal marginal relevance (MMR) selection balances query relevance against overlap with previously selected evidence. From the resulting score trajectory, a prefix-length selection algorithm determines retrieval depth by comparing length-normalized adjacent declines, accounting for both score changes and the amount of evidence accumulated. PACE-Mem is a training-free, plug-in retrieval method for clip-based…

    PDF
  • arXiv cs.AIOct 17

    Beyond Next-Screen Prediction: The Decision Value of GUI World Models

    Sizhe Tang, Hanhan Zhou, Jeffrey Svajlenko, Rashmi Gangadharaiah

    GUI world models are increasingly used to improve action selection by predicting the consequences of candidate actions, but it is unclear how much of the resulting gain comes from prediction itself rather than from the evaluator that interprets those predictions. We study this attribution problem through controlled interventions on GUI action selection. With candidates and predictions held fixed, lookahead gains can change with how the prediction is presented to the evaluator, so end-to-end gains cannot be attributed to prediction alone. We then introduce Direct Action Ranking (DAR), which learns from reference actions to score candidates without generating future screens, and compare it with a prediction-augmented variant under matched supervision. Predicted successors add no detectable ranking signal once evaluation is learned directly. Without retraining, DAR improves AndroidWorld task success by 5.8--16.4 points across three proposers, outperforms world-model lookahead, and transfers zero-shot to unseen apps, where every prediction-conditioned configuration falls below proposer confidence. These results suggest that GUI world models should be evaluated by their decision…

    PDF
Load more