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版本更新 · v2arXiv:2610.29061

OccJEPA: Occlusion as Masking for Amodal Video Segmentation

Genpei Zhang, Runmin Jiang, Xingzu Zhan, Yibo Zhong, Ruien Li, Zhou Zhang

原文摘要

Amodal video segmentation asks for the complete mask of an object in every frame, including the parts hidden behind other objects. The best current methods fine-tune video diffusion models to generate these masks; they need a depth network and tens of denoising steps, and their outputs vary from sample to sample. Self-supervised video world models of the JEPA family are pre-trained on a closely related task, predicting the representations of masked space-time regions from the visible ones, yet their predictor is usually discarded after pre-training. We reuse it by treating occlusion as masking. Tokens not covered by the object's visible mask are dropped, the V-JEPA 2.1 predictor is fine-tuned to fill them in, and a lightweight decoder turns all features into amodal masks. The resulting model, OccJEPA (Occlusion-as-masking JEPA), is deterministic and 32x faster per frame than Diffusion-VAS, the diffusion-based state of the art. It is also more accurate on SAIL-VOS (+3.70 occluded-region mIoU over all occluded frames), on TAO-Amodal (+4.00 AP50 on a held-out test split, +4.32 with SAM2-tracked masks) and on Kubric. Controlled experiments trace most of the gain to the predictor's pre-trained weights rather than its capacity, and the predictor helps at every encoder size tested (87M to 1.85B parameters).

cs.CV · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.04072

Finite-Sample Convergence in Networked Average Reward MARL: Decentralization Pitfalls and Entropy Remedies

Yizhou Zhang, Yashaswini Murthy, Laixi Shi, Adam Wierman

原文摘要

We study the problem of average reward Multi-Agent Reinforcement Learning (MARL) where agents interact within a network. Each agent in the network conducts local updates with information within its $k$-hop neighborhood and collaboratively maximizes the overall reward of the entire network. We first provide impossibility results in achieving convergence to the global optimal joint policies in our setting. These impossibility results highlight the challenges of decentralized learning with local information in multi-agent systems, namely the issues of multiagency, where each agent independently optimizes its policy, and partial observability, where agents have limited visibility into the global state. Given these challenges which indicate that decentralized policy optimization can generally be certified only up to stationarity, we provide finite-sample convergence guarantees for a decentralized actor-critic algorithm with linear function approximation that converges to an approximate stationary point with small gradient norm. To overcome such impossibility results, we further show that adding fixed-state entropy regularization reshapes the objective landscape which leads to even stronger global convergence guarantees for our proposed actor-critic algorithm.

cs.LG · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06280

DyPSI: Dynamic Physics Sensing via Joint Field and Sensor-Trajectory Generation

Yizhou Zhang, Panqi Chen, Lei Cheng, Ting Zhang, Jianlong Li, Jianfeng Shi, Shikai Fang

原文摘要

Physics sensing in evolving environments requires reconstructing dense spatiotemporal fields from sparse observations while adapting where future observations should be collected. Existing reconstruction and sensor-placement methods have made substantial progress, but they typically rely on static sensing assumptions: they either use a single layout across all frames or optimize placements independently at each frame, which limits their ability to produce temporally coherent sensing trajectories. We introduce DyPSI, a dynamic physics sensing framework that formulates this problem as the joint generation of future fields and sensor trajectories conditioned on historical observations. DyPSI lifts discrete sensor coordinates into continuous Sensor Position Fields, encodes fields and sensing configurations with a shared Functional Tucker representation, and trains a joint diffusion model to generate future fields and sensing trajectories simultaneously in the resulting latent space. A Gaussian-process temporal kernel correlates the perturbation injected during diffusion training, biasing the denoiser toward temporally smooth recoveries, and we further provide an analysis linking the EDM training objective to a sequence-aggregated A-optimality criterion and a kernel-induced bound on discrete trajectory variation. Experiments on turbulent flow, GLORYS12 sea-surface temperature, and 3D car aerodynamics show that DyPSI consistently outperforms static and frame-wise placement strategies, with substantial error reduction under tight sensor budgets. The code is available at https://github.com/mengqi132/DyPSI.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v7arXiv:2606.03371

See, Foresee, Act: Intent-Aware World Modeling for Proactive Retail Assistance

Honghui Zhang, Anna Min, Chenmeinian Guo, Yujia Zhang, Zezhou Zhang, Yichen Yu, Guanyu Liu, Yongming Qin 等 12 位作者

原文摘要

World models allow agents to anticipate action consequences, but proactive assistance also requires interpreting incomplete observations and deciding whether to intervene before a user asks for help. We propose See-Foresee-Act and instantiate it as the Proactive Intent World Model (PIWM) for retail assistance. See preserves role-relevant observable evidence alongside AIDA-BDI customer states; Foresee predicts one-step consequences for candidate actions; Act explicitly compares those consequences, including non-intervention. GuidanceSalesBench supplies predominantly synthetic supervision linking observations, states, candidate outcomes, and decisions. On 509 Full test examples, consequence-based selection achieves 63.65% decision accuracy, compared with 45.38% for direct decision output, an improvement of 18.27 percentage points. These results support candidate-consequence comparison for decision reasoning in this retail task. Code and data will be released after consent verification and upon publication.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2511.09394

EyeAgent: An Agentic AI System for Multimodal Clinical Decision Support in Ophthalmology

Danli Shi, Xiaolan Chen, Bingjie Yan, Weiyi Zhang, Jiancheng Yang, Ruoyu Chen, Xinyuan Wu, Siyu Huang 等 26 位作者

原文摘要

Current medical artificial intelligence (AI) systems are often task-specific, poorly integrated, and difficult to interpret, limiting support for multimodal clinical workflows. We developed and evaluated EyeAgent for ophthalmic decision support. EyeAgent comprises three layers including 54 task-specific EyeTools, 32 clinical skills, and a modular large language model reasoning core with interchangeable backbones. A multi-level evaluation distinguished this scope from empirical system-level validation. Component-level evaluation assessed newly developed models on held-out splits of datasets totaling 871,862 samples. System-level evaluation included illustrative cases of workflow breadth, a 200-case expert-rating study of output quality, and an exploratory multicenter reader study comparing unassisted and EyeAgent-assisted performance among 27 ophthalmologists from 11 centers. Skill-level and backbone ablations further assessed orchestration and robustness. In illustrative cases, EyeAgent showed task-specific advantages over GPT-5.5 in lesion quantitation, cross-modality synthesis, and uncertainty-aware reporting. In the expert-rating study, EyeAgent achieved an F1 score of 0.84 for skill selection, with 82.7%-92.0% of outputs rated acceptable or better across six clinical dimensions. In the exploratory multicenter reader study, diagnostic accuracy increased from 72.8% unassisted to 91.9% assisted (difference, 19.1 percentage points (pp); 95% CI, 13.6-24.7; Holm-adjusted P<0.001). Report completeness increased from 55.8% to 75.6% (difference, 19.8 pp; 95% CI, 14.8-24.7; Holm-adjusted P<0.001). Across the evaluated settings, EyeAgent supported interpretable, evidence-guided reasoning and showed potential for human-AI collaboration. Broader real-world effectiveness and generalizability require prospective and cross-population validation.

cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01621

FinForesight: A Diagnostic Backtesting Benchmark for Deep Research Systems in Financial Forecasting

Fengyuan Liu, Mukai Li, Jieshun You, Yilin Zheng, Chenjia Min, Yizhou Zhang, Harper Chen, Qi Liu

原文摘要

Anticipating financial outcomes requires synthesizing evolving evidence and reasoning under uncertainty, making deep research systems promising tools for forecasting. Yet evaluating forecasting ability is challenging because historical backtests may reward memorized outcomes or information retrieved after the forecast date. Live forecasting avoids direct access to realized outcomes, but feedback arrives only after events resolve. To obtain timely feedback and assess historical scores, we introduce FinForesight, a diagnostic backtesting benchmark for financial deep research systems. Across the Chinese, U.S., and Hong Kong markets, the benchmark evaluates four forecasting tasks: valuation, return ranking, event direction, and future financial fundamentals. Two capability diagnostic tasks assess counterfactual reasoning and financial calculation, scored separately from forecasting performance. Our harness-compatible framework restricts retrieved evidence to information available at the forecast date. We compare pretraining-era, resolved post-cutoff, and live evaluations to test whether controlled backtests approximate future performance. Pretraining-era scores exceed post-cutoff scores in most temporal comparisons, and stronger retrieval controls narrow the gap between post-cutoff backtests and live results. Forecasting attribution and robustness analyses reveal how scores respond to retrieval policy and how predictions respond to prompt rewrites and numerical perturbations. The code and benchmark data are publicly available.

cs.CL · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.31066

Dual Exam: Controlled Evaluation of Multimodal Agents and World Models

Zhefan Rao, Meng Chu, Xuan Billy Zhang, Jingfan Chen, Guohao Cai, Runyi Li, Shiyi Du, Xu Huang 等 92 位作者

原文摘要

World Models (WMs) can support Multimodal Agents (MAs) through simulated experience, while Agent trajectories can inform World Model refinement. Evaluating this interaction requires separate evidence about action selection and state evolution. We introduce Dual Exam, which tests Agents under fixed environment response mechanisms and World Models under fixed action interventions across physical, digital, social, and scientific domains. The Agent track compares 10 configurations on 1,088 items in 15 reporting categories. The World Model track contains 400 cases across eight scenarios; 15 configurations have full coverage and one covers six scenarios. GPT 6 Astra leads the case-weighted Agent Index at 31.3, and GPT-Image-2 leads the full-coverage WM Overall at 21.7. These scores use different tasks and metrics and are not directly comparable. A 150-case pilot of one coupled pairing records zero task successes and motivates component-level diagnosis. Results reveal uneven task performance and failures to preserve task-relevant state. Their value for predicting coupled success or sustained reciprocal learning remains to be established.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.06126

AffectGPT-RL: Revealing Roles of Reinforcement Learning in Open-Vocabulary Emotion Recognition

Zheng Lian, Fan Zhang, Lan Chen, Yazhou Zhang, Rui Liu, Jinyang Wu, Haoyu Chen, Xiaobai Li 等 11 位作者

原文摘要

Open-Vocabulary Multimodal Emotion Recognition (OV-MER) aims to predict emotions without being constrained by predefined label spaces, thereby enabling fine-grained emotion understanding. Unlike traditional discriminative methods, OV-MER leverages generative models to capture the full spectrum of emotions and employs emotion wheels (EWs) for metric calculation. Previous approaches primarily rely on token-level loss during training. However, this objective is misaligned with the metrics used in OV-MER, and these metrics cannot be directly optimized via gradient backpropagation. To address this limitation, we turn our attention to reinforcement learning, as this strategy can optimize non-differentiable objectives. We term this framework AffectGPT-RL. Furthermore, we conduct extensive experiments to elucidate the role of reinforcement learning in this task, revealing the necessity of the reasoning process, the impact of different rewards, and the generalizability to other emotion tasks such as sentiment analysis and basic emotion recognition. Experimental results demonstrate that AffectGPT-RL yields significant performance improvements on OV-MER. Beyond this task, we also achieve remarkable performance gains on basic emotion recognition, attaining state-of-the-art results on MER-UniBench. To the best of our knowledge, this is the pioneering work exploring the role of reinforcement learning in OV-MER, providing valuable guidance for subsequent researchers. Our code is provided at: https://github.com/zeroQiaoba/AffectGPT/tree/master/AffectGPT-RL.

cs.HC

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.123ACL Anthology · ACL 2026

OneRec-Think: In-Text Reasoning for Generative Recommendation

Zhanyu Liu, Shiyao Wang, Xingmei Wang, Rongzhou Zhang, Jiaxin Deng, Honghui Bao, Jinghao Zhang Kuaishou- 快手科技, Wuchao Li 等 26 位作者

原文摘要

The powerful generative capacity of Large Language Models (LLMs) has instigated a paradigm shift in recommendation. However, existing generative models (e.g., OneRec) operate as implicit predictors, critically lacking the capacity for explicit and controllable reasoning—a key advantage of LLMs. To bridge this gap, we propose OneRec-Think, a unified framework that seamlessly integrates dialogue, reasoning, and personalized recommendation. OneRec-Think incorporates: (1) Itemic Alignment: cross-modal Item-Textual Alignment for semantic grounding; (2) Reasoning Activation: Reasoning Scaffolding to activate LLM reasoning within the recommendation context; and (3) Reasoning Enhancement, where we design a recommendation-specific reward function that accounts for the multi-validity nature of user preferences. Experiments across public benchmarks show state-of-the-art performance. Moreover, our proposed “Think-Ahead” architecture enables effective industrial deployment, achieving a 0.159% gain in APP Stay Time and validating the practical efficacy of the model’s explicit reasoning capability.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.353ACL Anthology · ACL 2026

HCSpec: Two-Tier Horizontal Cascade Speculative Decoding for High-Efficiency Large Language Model Inference

Yizhou Zhang Lanzhou University, Siming Chen, Hao Ye, Erhu Feng Shanghai Jiaotong University

原文摘要

Speculative decoding accelerates large language model (LLM) inference by using a draft model to propose token candidates for parallel verification by the target model. However, current state-of-the-art self-distilled draft models adopt a homogeneous architecture across all drafting positions, failing to account for a critical empirical observation: the expected utility of drafting decays rapidly after the initial positions. To exploit this imbalance, we propose Two-tier Horizontal Cascade Speculative Decoding (HCSpec), a novel framework that organizes heterogeneous, position-specialized draft modules into a horizontal cascade. The first tier employs a dual-layer, dual-path transformer that enhances early-step fidelity by decoupling token-logit prediction from recurrent feature propagation, while the second tier adopts a lightweight single-layer transformer that deliberately trades marginal accuracy for improved efficiency at later drafting steps. Extensive experiments on Qwen series models and Llama3.1-8B-Instruct, across multiple tasks and diverse inference configurations, demonstrate that HCSpec consistently outperforms the previous state-of-the-art (EAGLE-3). It delivers 15–30% higher end-to-end speedup over EAGLE-3 and achieves up to 3.72x acceleration over vanilla autoregressive decoding. Our code is provided in the supplementary materials.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表