https://techgazette.com.br/research/ This week's papers, each with what its authors did and found, in sentences from its own abstract. en 180 Sat, 17 Oct 2026 12:00:00 GMT https://dl.acm.org/doi/10.1145/3859287 https://dl.acm.org/doi/10.1145/3859287 Sat, 17 Oct 2026 12:00:00 GMT ACM TACO (Architecture and Code Optimization) Systems and databases ACM TACO (Architecture and Code Optimization) — Did: Serverless workflows are commonly modeled as directed acyclic graphs (DAGs) composed of fine-grained functions. https://www.anthropic.com/research/designing-efficient-quantum-circuits https://www.anthropic.com/research/designing-efficient-quantum-circuits Fri, 16 Oct 2026 17:29:00 GMT Anthropic Research Journals and labs Anthropic Research — Did: Working largely on its own, Claude designed quantum circuits that improve on the best published versions, in some cases by an order of magnitude or more. https://dl.acm.org/doi/10.1145/3859244 https://dl.acm.org/doi/10.1145/3859244 Fri, 16 Oct 2026 12:00:00 GMT ACM TKDD (Knowledge Discovery from Data) Machine learning ACM TKDD (Knowledge Discovery from Data) — Did: Numerous survey studies have covered various aspects of LLMs, such as learning strategies, applications, alignment, explainability, and evaluation. https://dl.acm.org/doi/10.1145/3849700 https://dl.acm.org/doi/10.1145/3849700 Fri, 16 Oct 2026 12:00:00 GMT ACM TKDD (Knowledge Discovery from Data) Machine learning ACM TKDD (Knowledge Discovery from Data) — Did: Adapting large language models (LLMs) for time series forecasting introduces unique challenges due to the mismatch between continuous time series tokens and the discrete word tokens LLMs are trained on. https://dl.acm.org/doi/10.1145/3859251 https://dl.acm.org/doi/10.1145/3859251 Fri, 16 Oct 2026 12:00:00 GMT ACM TIST (Intelligent Systems) AI and agents ACM TIST (Intelligent Systems) — Did: Large Language Models (LLMs) struggle with knowledge-intensive tasks due to hallucinations and fragmented reasoning over dispersed information. https://dl.acm.org/doi/10.1145/3858419 https://dl.acm.org/doi/10.1145/3858419 Fri, 16 Oct 2026 12:00:00 GMT ACM TOSEM Software engineering ACM TOSEM — Did: Missing security checks on critical variables are an important class of semantic bugs in the Linux kernel and can lead to severe vulnerabilities. https://dl.acm.org/doi/10.1145/3858619 https://dl.acm.org/doi/10.1145/3858619 Fri, 16 Oct 2026 12:00:00 GMT ACM TOIS (Information Systems) Search and retrieval ACM TOIS (Information Systems) — Did: Sequential recommendation methods are important in recommender systems, as they capture users’ dynamic interests from their historical behavior sequences. https://dl.acm.org/doi/10.1145/3850128 https://dl.acm.org/doi/10.1145/3850128 Fri, 16 Oct 2026 12:00:00 GMT Communications of the ACM Journals and labs Communications of the ACM https://dl.acm.org/doi/10.1145/3849084 https://dl.acm.org/doi/10.1145/3849084 Fri, 16 Oct 2026 12:00:00 GMT Communications of the ACM Journals and labs Communications of the ACM — Did: Frontier labs say their AI models now help build their own successors. https://research.nvidia.com/publication/2026-12_imitation-learning-avoidance-modes-efficient-multimodal-model-predictive https://research.nvidia.com/publication/2026-12_imitation-learning-avoidance-modes-efficient-multimodal-model-predictive Fri, 16 Oct 2026 06:50:00 GMT NVIDIA Research Journals and labs NVIDIA Research https://research.nvidia.com/publication/2026-12_edgewise-bringing-long-horizon-agents-edge-selective-cloud-delegation https://research.nvidia.com/publication/2026-12_edgewise-bringing-long-horizon-agents-edge-selective-cloud-delegation Fri, 16 Oct 2026 06:45:00 GMT NVIDIA Research Journals and labs NVIDIA Research — Did: EdgeWise: Bringing Long-Horizon Agents to the Edge via Selective Cloud Delegation Complex and long-horizon agent tasks often rely on frontier models deployed in the cloud to achieve strong performance. https://www.nature.com/articles/d41586-026-03338-y https://www.nature.com/articles/d41586-026-03338-y Fri, 16 Oct 2026 00:00:00 GMT Nature: Computer Science Journals and labs Nature: Computer Science https://arxiv.org/abs/2610.22371 https://arxiv.org/abs/2610.22371 Thu, 15 Oct 2026 17:59:00 GMT arXiv (vision, retrieval & HCI) cs.CV Vision arXiv (vision, retrieval & HCI) cs.CV — Did: We introduce Gen4P, a unified framework for recovering 4D geometry and motion from monocular video. Found: Together, these results demonstrate the potential of video generative pretraining for unified 4D perception. By Yitong Xia, Zeren Jiang, Chuanxia Zheng. https://arxiv.org/abs/2610.22370 https://arxiv.org/abs/2610.22370 Thu, 15 Oct 2026 17:59:00 GMT arXiv (vision, retrieval & HCI) cs.CV Vision arXiv (vision, retrieval & HCI) cs.CV — Did: In this work, we propose Dynamics-aware Token Merging (DaTMe), a training-free framework that grounds token merging in the underlying diffusion dynamics. Found: Experiments on PixArt-a and FLUX demonstrate that DaTMe maintains visual fidelity across the entire image, highlighting diffusion dynamics as an effective token-level merging decision in efficient generation. By SeungJu Cha, Ye-Chan Kim, HyunGee Kim, Sungho Koh, Dong-Jin Kim. https://arxiv.org/abs/2610.22369 https://arxiv.org/abs/2610.22369 Thu, 15 Oct 2026 17:59:00 GMT arXiv (vision, retrieval & HCI) cs.CV Vision arXiv (vision, retrieval & HCI) cs.CV — Did: We propose SoftCache, which performs soft caching: instead of a binary switch, the headroom sets how far each cached step goes. Found: SoftCache requires no training and adds no per-backbone settings: on FLUX for images and on HunyuanVideo and Wan2.1 for video, at speedups from $3.5\times$ to $6.9\times$, it is ahead of every baseline on all four fidelity metrics. By Shimon Malnick, Nadav Orenstein, Ohad Fried, Shai Avidan. https://arxiv.org/abs/2610.22363 https://arxiv.org/abs/2610.22363 Thu, 15 Oct 2026 17:59:00 GMT arXiv (vision, retrieval & HCI) cs.CV Vision arXiv (vision, retrieval & HCI) cs.CV — Did: We introduce P$^{3}$-WAM, a unified world action model for egocentric dexterous manipulation that learns 3D perception, action-conditioned prediction, and goal-directed planning through three complementary modes: Perceive, which reconstructs articulated hand motion and camera trajectories from video; Predict, which generates visual… Found: Through unified training, our model outperforms specialized baselines on 3D hand reconstruction and action-conditioned video prediction across four datasets, while producing goal-directed manipulation videos with stronger instruction adherence and action progression than video-generation baselines. By Zirui Wu, Chen Guo, Tianjian Jiang, Zeren Jiang, Zeming Li, Lingchen Yang. https://arxiv.org/abs/2610.22360 https://arxiv.org/abs/2610.22360 Thu, 15 Oct 2026 17:59:00 GMT arXiv (vision, retrieval & HCI) cs.CV Vision arXiv (vision, retrieval & HCI) cs.CV — Did: We argue that building reliable 3D systems requires not only stronger generation and reasoning, but also verification feedback that identifies unmet requirements and explains their violations, allowing systems to reflect on and refine their outputs. Found: In human-labeled sixteen-candidate pools, oracle selection improves complete factual success from 26.7% to 90.0%. By Shuanglin Li, Jiaying Lin, Shuquan Ye. https://arxiv.org/abs/2610.22372 https://arxiv.org/abs/2610.22372 Thu, 15 Oct 2026 17:59:00 GMT arXiv cs.RO Journals and labs arXiv cs.RO — Did: We introduce LIBERO-PeRM, a benchmark for preference learning in embodied agents. Found: Mixed training achieves better no-preference retention than sequential adaptation and improves performance with contextual prompts compared with preference-only training, but can weaken motion-preference learning; preference generalization, decomposition, and conflict handling remain difficult. By Zhixu Li, Keqian Tang, Litian Gong, Jingyu Yao, Wenqian Zhang, Tianze Xu. https://arxiv.org/abs/2610.22367 https://arxiv.org/abs/2610.22367 Thu, 15 Oct 2026 17:59:00 GMT arXiv quant-ph Journals and labs arXiv quant-ph — Did: Here we introduce an evolutionary framework in which large language models (LLMs) propose circuit designs and an independent evaluator verifies and scores them. Found: By handling reversibility differently, the architecture makes divsteps -- a classical algorithm previously impractical for reversible integer arithmetic -- viable and substantially improves the Toffoli-qubit trade-off for modular inversion. By Andi Gu, Alek H. Kemeny, Susanne F. Yelin, Mikhail D. Lukin. https://arxiv.org/abs/2610.22364 https://arxiv.org/abs/2610.22364 Thu, 15 Oct 2026 17:59:00 GMT arXiv cs.RO Journals and labs arXiv cs.RO — Did: We introduce T2-Z for test-time optimization of environment latents, which augments a robotic world model with a learnable environment latent to condition world predictions. Found: Ablations further show that T2-Z can efficiently infer environment latents from as little as a single test-time interaction trajectory. By Chenyuan Zhou, Yining Hong, Huang Huang, Yunfan Jiang, Wenlong Huang, David Held. https://arxiv.org/abs/2610.22362 https://arxiv.org/abs/2610.22362 Thu, 15 Oct 2026 17:59:00 GMT arXiv cs.RO Journals and labs arXiv cs.RO — Did: We introduce CoDA, a framework in which a simulation data engine and an action policy co-evolve through a shared heterogeneous memory of environment construction, policy failures, and learning progress. Found: On LIBERO-Pro, 15 rounds of generation and training raise success from 25% to 84% under position perturbations and from 1% to 76% under instruction perturbations. By Tianjiao Yu, Zhilin Ou, Yifan Shen, Xinzhuo Li, Yuanzhe Liu, Onkar Kishor Susladkar. https://arxiv.org/abs/2610.22355 https://arxiv.org/abs/2610.22355 Thu, 15 Oct 2026 17:58:00 GMT arXiv cs.CL Language models arXiv cs.CL — Did: To enhance the robustness of visual verifiers in complex multimodal scenarios involving compositional requirements, we move beyond conventional binary verifiers that produce only True/False discrete judgments and introduce a verifier that automatically decomposes complex prompts or environmental rules into fine-grained components and… Found: Overall, our findings suggest a key insight: the transition from generative models to structured world-modeling agents critically hinges on the availability of accurate pairwise graded visual verifiers. By Bowei Liu, Xinchen Zhang, Xuhuan Li, Tianlin Zhang, Kaian Jiang, Jingwei Liu. https://arxiv.org/abs/2610.22358 https://arxiv.org/abs/2610.22358 Thu, 15 Oct 2026 17:58:00 GMT arXiv cs.LG Machine learning arXiv cs.LG — Did: To model this dependence, we introduce PathFM, a trajectory-based forecasting model that uses flow matching to generate complete future paths. Found: Experiments on chaotic systems further show that our flow model sustains long-run variability, while strong baselines rapidly collapse. By Jiachen Yao, Xiyuan Zhang, Annan Yu, Boran Han, Abdul Fatir Ansari, Shuai Zhang. https://arxiv.org/abs/2610.22353 https://arxiv.org/abs/2610.22353 Thu, 15 Oct 2026 17:58:00 GMT arXiv cs.LG Machine learning arXiv cs.LG — Did: We identify an exploration dilemma between these designs: shared experts can make routing less exploratory by reducing the variability of token representations. Found: MirrorMoE consistently improves performance across model scales. By Bingcong Li, Hao Ma, Michael Muehlebach, Niao He, Yi Ma. https://arxiv.org/abs/2610.22356 https://arxiv.org/abs/2610.22356 Thu, 15 Oct 2026 17:58:00 GMT arXiv (vision, retrieval & HCI) cs.CV Vision arXiv (vision, retrieval & HCI) cs.CV — Did: We propose DK-VLM with Dual-Kernel Attention, which computes attention from both a visual token's input position and its geometric position in the scene. Found: RC supervision further improves multi-step spatial reasoning, while the full model preserves general video understanding. By Han Su, Meng Tian, Tianyu Huang, Mingyu Gao, Lennox Wei, Dongxu Li. https://arxiv.org/abs/2610.22352 https://arxiv.org/abs/2610.22352 Thu, 15 Oct 2026 17:58:00 GMT arXiv (vision, retrieval & HCI) cs.CV Vision arXiv (vision, retrieval & HCI) cs.CV — Did: To resolve these challenges, we introduce SUPRA-Tex, built on the principle of decoupling visible-material estimation, surface alignment, and observation-guided 3D completion. Found: Trained solely on public datasets, SUPRA-Tex achieves SOTA performance in both geometry-conditioned texturing and end-to-end asset generation, matching or even exceeding leading commercial systems on in-the-wild images. By Chen Yang, Jiemin Fang, Fanpeng Meng, Zihao Huang, Jichen Pang, Guanjun Wu. https://arxiv.org/abs/2610.22350 https://arxiv.org/abs/2610.22350 Thu, 15 Oct 2026 17:57:00 GMT arXiv cs.AI AI and agents arXiv cs.AI — Did: We introduce FDE-Gym, a benchmark of 95 end-to-end delivery tasks from 20 customer scenarios across 18 sectors, each adapted by industry experts from real-world delivery projects. Found: These results reveal a substantial gap between solving well-specified tasks and delivering effective AI systems for real customers. By Dongxu Yang, Hongda Yan, Renxin Wang, Baoli Zhang, Haojia Zuo, Ying Zhao. https://arxiv.org/abs/2610.22347 https://arxiv.org/abs/2610.22347 Thu, 15 Oct 2026 17:57:00 GMT arXiv (vision, retrieval & HCI) cs.CV Vision arXiv (vision, retrieval & HCI) cs.CV — Did: We introduce Speculative Forcing, a framework that treats intermediate clean estimates as drafts and predicts when individual tokens can be safely accepted early. Found: On Lingbot-World 2.0, where camera motion continually changes the visible content, it maintains quality with ~2.5 denoising steps per token and runs 1.35x faster at 24.5 fps. By Hyunwook Choi, Seungryong Kim, Dongyoon Han, Sangdoo Yun. https://arxiv.org/abs/2610.22344 https://arxiv.org/abs/2610.22344 Thu, 15 Oct 2026 17:56:00 GMT arXiv cs.AI AI and agents arXiv cs.AI — Did: We propose a post-training method for autonomous long-horizon MLE optimization and apply it to train MLExpert, a 35B Mixture-of-Experts agentic model. Found: Further experiments show that MLExpert improves performance on NatureBench Lite, agent scaffold optimization, and algorithm optimization, suggesting that the learned optimization capability generalizes to other tasks. By Shangheng Du, Jinxin Shi, Shuaiyu Zhang, Yiming Wu, Junpeng Zhao, Shiyang Feng. https://arxiv.org/abs/2610.22343 https://arxiv.org/abs/2610.22343 Thu, 15 Oct 2026 17:56:00 GMT arXiv cs.CV Vision arXiv cs.CV — Did: We propose GeoState, a view-decoupled geometry representation for predictive 3D world modeling that separates scene-state evolution from camera-dependent observation. Found: Experiments show that GeoState outperforms prior 3D world models on Waymo, supports geometrically consistent cross-view querying, and generalizes zero-shot to KITTI and nuScenes. By Hongchang Liu, Jian Liu, Yujuan Ding, Liang Peng, Xun Jiang, Fei Ma. https://arxiv.org/abs/2610.22341 https://arxiv.org/abs/2610.22341 Thu, 15 Oct 2026 17:56:00 GMT arXiv cs.CV Vision arXiv cs.CV — Did: We introduce 3DCodeVerse, a research ecosystem that enables coding agents to create editable and verifiable 3D content at scale. 3DCodeVerse comprises large-scale, high-quality datasets of executable 3D code, a coding harness for spatial reasoning and automated verification, and practical guidance for 3D coding. Found: Notably, our 3DCodeVerse harness achieves a 90% win rate against Claude Design (3D Object) in pairwise human evaluations, when both are utilized by Claude Opus 5.5. By Yipeng Gao, Ziyao Zeng, Jinfa Huang, Yijiang Li, Haolin Xiong, Wang Qin. https://arxiv.org/abs/2610.22339 https://arxiv.org/abs/2610.22339 Thu, 15 Oct 2026 17:55:00 GMT arXiv cs.LG Machine learning arXiv cs.LG — Did: To this end, we propose LeanGraphSearch, a graph-based retrieval method for Lean 4 that constructs a graph from Mathlib's forward dependencies and traverses it to uncover supporting lemmas through multi-hop dependency chains. Found: Across three theorem proving benchmarks, LeanGraphSearch improves pass rate at {8, 16, 32} reflection turns by as much as 40% compared to vanilla prover and at most 10% compared to LeanSearch v2, showing higher accuracy-cost efficiency. By Yihan Xu, Jia-Chen Gu, Junyi Zhang, Di Wu, Haikang Deng, Po-Nien Kung. https://arxiv.org/abs/2610.22340 https://arxiv.org/abs/2610.22340 Thu, 15 Oct 2026 17:55:00 GMT arXiv cs.RO Journals and labs arXiv cs.RO — Did: We present DSP: Dexterity from supervised play, a framework in which a robot learns a play policy from minutes of human play through behavior cloning, then deploys it autonomously to collect hours of interaction data to train a world model. Found: These results demonstrate that supervised play can autonomously collect interaction data, guide model-based planning, and enable policy learning without any task-specific demonstrations or sim-to-real transfer. By Botao He, Yiming Dou, Yiannis Aloimonos, Jitendra Malik, Akash Sharma, Ruoshi Liu. https://arxiv.org/abs/2610.22336 https://arxiv.org/abs/2610.22336 Thu, 15 Oct 2026 17:54:00 GMT arXiv cs.LG Machine learning arXiv cs.LG — Did: We develop a robust reinforcement learning (RL) framework for learning adaptive treatment policies that generalize across patients and remain reliable under errors in the measured biomarker and shifts in the disease dynamics. Found: Our findings suggest that robust RL holds great promise as a principled methodology for learning reliable adaptive treatment policies. By Chengze Xie, Xuan Wang, Ying Ding, Hairong Wang, Yan Li. https://arxiv.org/abs/2610.22333 https://arxiv.org/abs/2610.22333 Thu, 15 Oct 2026 17:53:00 GMT arXiv cs.AI AI and agents arXiv cs.AI — Did: We release all 47,880 judged responses to support further work. Found: These results suggest that safety evaluations in Indic languages may not reflect how models treat their everyday users. By Trizal Garg, Sibayan Mitra, Arya Karanjkar, Advait Singh, Jagat Sesh Challa, Yash Sinha. https://arxiv.org/abs/2610.22324 https://arxiv.org/abs/2610.22324 Thu, 15 Oct 2026 17:52:00 GMT arXiv cs.CL Language models arXiv cs.CL — Did: In our work, we study document-grounded ideation, focusing on increasing the diversity of LLM generated ideas and ensuring they are high quality. Found: We show that our trained 8B model achieves higher creative utility (a measure combining quality and diversity) than Claude Opus 4.8 and GPT-6 Astra on our contamination-free benchmark. By Manya Wadhwa, Pranav Narayanan Venkit, Hiroaki Hayashi, Yu Li, Tuhin Chakrabarty, Chien-Sheng Wu. https://arxiv.org/abs/2610.22327 https://arxiv.org/abs/2610.22327 Thu, 15 Oct 2026 17:52:00 GMT arXiv cs.LG Machine learning arXiv cs.LG — Did: We instead introduce complementary representations as a design principle for time-series reasoning. Found: Together, these results demonstrate that complementary representations provide an effective design principle for general-purpose TSLMs, enabling models to exploit temporal structure without sacrificing access to the original measurement frame. By Hedi Zisling, Liran Nochumsohn, Ilan Naiman, Mihir Parmar, Omri Azencot. https://arxiv.org/abs/2610.22331 https://arxiv.org/abs/2610.22331 Thu, 15 Oct 2026 17:52:00 GMT arXiv cs.AI AI and agents arXiv cs.AI — Did: We propose KnowGPO, a reinforcement learning framework that aligns trajectories by the task-relevant knowledge they have acquired rather than by their surface observations. Found: Experiments on Xbench, BrowseComp-ZH, and GAIA show KnowGPO consistently outperforms outcome-only (GRPO) and process-supervision baselines (GiGPO, IGPO) by 3.8 to 4.2 points, with our 8B model rivaling or exceeding most 32B open-source systems. By Siqi Chen, Zhengxi Lu, Yuchen Yan, Xingyu Wu, Qipeng Chen, Xin Zhang. https://arxiv.org/abs/2610.22330 https://arxiv.org/abs/2610.22330 Thu, 15 Oct 2026 17:52:00 GMT arXiv cs.AI AI and agents arXiv cs.AI — Did: In this paper, we conduct a systematic study on Transformer architectures for CDLMs and introduce Language Diffusion Transformer (LangDiT), combining feature and query-key normalization, wider noise conditioning, likelihood-shortcut gradient routing and calibration, and confidence-gated training self-conditioning. Found: Our study identifies architectural design as an important source of improvement in continuous diffusion language models. By Weijian Luo, Yujian Chen, Hao Jiang, Guang Cheng, Tianyang Hu. https://arxiv.org/abs/2610.22328 https://arxiv.org/abs/2610.22328 Thu, 15 Oct 2026 17:52:00 GMT arXiv cs.CV Vision arXiv cs.CV — Did: We introduce a training scheme that makes multi-turn image editing stable under this accumulating distribution shift. Found: Experiments show substantially reduced degradation over long multi-turn sequences while maintaining editing quality and instruction adherence. By Sara Dorfman, Nir Goren, Gal Barak, Daniel Cohen-Or, Or Patashnik. https://arxiv.org/abs/2610.22325 https://arxiv.org/abs/2610.22325 Thu, 15 Oct 2026 17:52:00 GMT arXiv (vision, retrieval & HCI) cs.CV Vision arXiv (vision, retrieval & HCI) cs.CV — Did: We introduce Dense4D, to our knowledge the first feed-forward model dedicated to dense 4D correspondence across the body, clothing and hair from a set of monocular RGB images, without a body template. Found: Experiments on real people demonstrate substantially higher tracking accuracy and closer agreement with scanned surfaces than the evaluated baselines. By Rıza Alp Güler. https://arxiv.org/abs/2610.22314 https://arxiv.org/abs/2610.22314 Thu, 15 Oct 2026 17:51:00 GMT arXiv cs.CL Language models arXiv cs.CL — Did: To bridge these gaps, we introduce MindEval2, an evaluation framework that consists of two components: Mental Health Quality Rubrics (MHQR), for evaluation, and MindSim, for patient simulation. Found: We evaluate 18 proprietary and open-weight models on MindEval2, finding that while frontier models have largely improved on issues related to surface level quality and boundaries, guidance calibration remains the dominant failure mode across all models, accounting for most of the quality gap. By John Mendonça, Daniel Tschernutter, José Pombal, Areti Vassilopoulos, Catarina Botelho, Ignacio Sarasua. https://arxiv.org/abs/2610.22316 https://arxiv.org/abs/2610.22316 Thu, 15 Oct 2026 17:51:00 GMT arXiv cs.LG Machine learning arXiv cs.LG — Did: In this work, we propose a steering agent that supervises execution on the fly and steers the system back on track when drift occurs. Found: Experiments on information-seeking search tasks show that the steering agent provides effective supervision for both single-agent and multi-agent systems, improving their performance and efficiency. By Leyang Shen, Yaxin Gong, Yang Zhang, Chenxiao Fan, Jiakai Wang, Dong Wang. https://arxiv.org/abs/2610.22322 https://arxiv.org/abs/2610.22322 Thu, 15 Oct 2026 17:51:00 GMT arXiv cs.MA AI and agents arXiv cs.MA — Did: We introduce market-based post-training (MPT), a framework for jointly learning action policies and self-orchestration in decentralized agent swarms. Found: Ablations show that both action and bidding benefit from learning, and that learned bids provide more effective action-training signals. By Guowei Xu, Zhenting Qi, Amani Kiruga, Himabindu Lakkaraju, Yilun Du. https://arxiv.org/abs/2610.22319 https://arxiv.org/abs/2610.22319 Thu, 15 Oct 2026 17:51:00 GMT arXiv cs.AI AI and agents arXiv cs.AI — Did: We describe the setup, how the run unfolded, the team's strategies (dividing the work, sharing what they learned, recovering each other's items), and the limits of the result. By Maxence Faldor, Satinder Singh, Roberta Raileanu. https://arxiv.org/abs/2610.22321 https://arxiv.org/abs/2610.22321 Thu, 15 Oct 2026 17:51:00 GMT arXiv cs.CV Vision arXiv cs.CV — Did: We propose an event-centric evaluation framework that disentangles audio and visual events at the event level, analyzes each event independently, cross-validates relations across modalities, and aggregates the judgments into a coarse-to-fine, three-level consistency metric. Found: AVC-Reward achieves state-of-the-art alignment with human judgment among automatic methods, provides interpretable rationales that localize failures, and improves the audio-video consistency of a joint generation model when used as an online reinforcement-learning signal. By Yuruo Wu, Kaihang Pan, Kai Shen, Jiacheng Li, Shuyuan Tu, Jisheng Dang. https://arxiv.org/abs/2610.22320 https://arxiv.org/abs/2610.22320 Thu, 15 Oct 2026 17:51:00 GMT arXiv (vision, retrieval & HCI) cs.CV Vision arXiv (vision, retrieval & HCI) cs.CV — Did: We propose EOS-Story, a training-free framework that harmonizes identity consistency with scene-specific adaptation across text and vision spaces. Found: Extensive experiments show that EOS-Story achieves competitive or superior performance on both benchmarks. By SeungJu Cha, Ye-Chan Kim, Kwanyoung Lee, Dong-Jin Kim. https://arxiv.org/abs/2610.22312 https://arxiv.org/abs/2610.22312 Thu, 15 Oct 2026 17:50:00 GMT arXiv cs.CL Language models arXiv cs.CL — Did: We introduce Grid-Bench, an executable benchmark in which eight families of operations transform discrete worlds represented as two-, three-, and four-dimensional grids. By Zixiang Xu, Zirui Song, Wanrong Zheng, Chiyu Zhang, Yue Huang, Yuyang Peng. https://arxiv.org/abs/2610.22310 https://arxiv.org/abs/2610.22310 Thu, 15 Oct 2026 17:50:00 GMT arXiv (systems & security) cs.DB Systems and databases arXiv (systems & security) cs.DB — Did: To this end, we propose TraHist, a histogram-based estimator approximating density-aligned partitioning via efficient grid-based propagation. Found: Experiments on large-scale real-world datasets show that TraHist reduces estimation error by over 20x relative to the strongest competing estimator in query regimes where trajectory fragmentation is a dominant source of estimation error, while maintaining sub-millisecond query latency and competitive construction cost. By Yujeong Jo, Incheol Baek, Changbeom Shim, Yon Dohn Chung. https://arxiv.org/abs/2610.22311 https://arxiv.org/abs/2610.22311 Thu, 15 Oct 2026 17:50:00 GMT arXiv (vision, retrieval & HCI) cs.CV Vision arXiv (vision, retrieval & HCI) cs.CV — Did: To address this bottleneck, we introduce Velox, a training-free attention router for region-level context selection using frustum slab correspondences from streaming geometry. Found: The fixed token-block granularity matches FlashAttention-style tiled execution and reduces the attention term that grows with rollout length. By Qiuhong Shen, Shizun Wang, Xinchao Wang.