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An Exact 93-Move Solution to a Nonlocal Crystal-Exchange Puzzle
Abstract
We determine the minimum number of directional commands needed to solve alevel of Order of the Sinking Star, modeled as a crystal-exchangepuzzle on an 11*11 board. A command swapsthe player with the nearest crystal in the selected row or column, regardlessof distance; only an empty ray permits an ordinary unit walk. Two gates admitthe player to the exit exactly while all sixteen crystals occupy their targetcells. We give an explicit 93-command solution and exclude every shortersolution by an exact, cost-bounded breadth-first search. The search representsa position by its seventeen occupied cells and one active cell. Its admissibleheuristic partitions command costs between a compressed core abstraction andresidual motion, strengthened by a necessary activation detour and a cornervisit condition. The completed computation retained 605,349,215 occupied setsand 1,863,925,136 states. We prove the reductions and pruning conditions,describe the exact storage scheme, and provide source code, execution logs,and a reproduction driver. The result is a computer-assisted proof for thespecified instance, with an explicit implementation trust boundary.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29493
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AdaGuard: An Adaptive Guard Model with User-defined Policies
2026-10-21 09:34:41Yunhao Feng, Yifan Ding, Yuxiang Xie, Zheng Li, Mingrui Lao, Zeyuan Wang, Yanming GuoAbstract
Guard models support the safe deployment of language model agents, but fixed risk taxonomies limit their ability to accommodate requirements that vary across applications and tasks. Under user-defined policies, detecting violations requires interpreting both the applicable rules and the agent's behavior, since identical actions can receive different judgments under different policies. To support learning this capability, we introduce AdaptiveSafety, a dataset of 10,939 training examples and 1,000 test examples covering policies with 1--100 rules. The dataset combines trajectories from multiple sources with policy and behavioral counterfactuals, pairing each example with an explanation and the complete set of violated rules. These counterfactuals expose changes that alter compliance, while structural augmentations provide supervision for consistency under rule reordering and identifier remapping. Building on this supervision, we propose SafePO, a reinforcement learning algorithm for refining violation identification while balancing explanatory reasoning and final verdicts. SafePO uses structured rewards to assess prediction correctness, retains group-relative advantages at the response level, and employs a separately trained value model to modulate token weights within explanation and verdict regions. Separate normalization controls their relative contribution to training despite differences in length. Through supervised initialization followed by SafePO, we develop AdaGuard, a family of 0.6B, 4B, and 8B guard models that assess agent trajectories under policies supplied at inference time. Our 4B model achieves binary accuracies of 89.30\% on AdaptiveSafety and 71.82\% on DynaBench. The project repository is available at this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2609.34241
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Generative Modeling via Nonlinear Schr'odinger Bridges and Branching Diffusions
2026-10-21 09:30:53Mathias Grau, Pierre Henry-Labord\`ereAbstract
We propose a new generative modeling framework grounded in forward-backward partial differential equations (PDEs). These equations arise naturally in mean-field game (MFG) theory, and are closely related to nonlinear Schrödinger systems. Our main contribution is to reinterpret this connection within the setting of generative modeling and to develop an efficient numerical methodology for its practical implementation. Specifically, we derive and characterize a nonlinear extension of the classical Schrödinger bridge problem through a coupled system of PDEs, yielding a model we call SBNL (Schrödinger Bridge NonLinear). To compute its solution, we design a simulation algorithm based on branching diffusions and the probabilistic representation of semilinear PDEs. The resulting method is embarrassingly parallel, scalable to high dimensions, and applicable to a broad class of nonlinear interaction functionals. We validate the approach through numerical experiments, including recovery of the classical Schrödinger bridge, nonlinear extensions with convex penalties (Gross-Pitaevskii) as well as potential interaction terms, and high-dimensional generative tasks such as image synthesis on the MNIST and FFHQ human face dataset. Furthermore, the same framework embeds score-based generative modeling, diffusion models, and tractable gaussian Schrödinger bridges.
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URL
https://arxiv.org/abs/2610.29476
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OmniSimulator: Aligning Small Language Models for Authentic Heterogeneous Behavior Modeling
2026-10-21 09:30:12Jiawei Chen, Ruoxi Xu, Boxi Cao, Zhuoqun Li, Ruotong Pan, Yunfei Zhang, Zhirui Yang, Jingyan Chen, Tingting Gao, Han Li, Yaojie Lu, Xianpei Han, Le Sun, Xiangyu Wu, Hongyu LinAbstract
Large language models (LLMs) have shown great potential as general-purpose user simulators for interactive systems. Prior studies have demonstrated that training on high-quality data can substantially improve model reasoning and downstream task performance. However, the upper bound of user simulation is still constrained by the model's reasoning paradigm: native chain-of-thought often degenerates into shallow pattern matching and fails to adequately model the behavioral factors underlying real user decisions. In this paper, we propose OmniSimulator, a novel training framework designed to improve the predictive ability of small LLMs for realistic user simulation on short-video platform environments. Specifically, OmniSimulator organizes behavior-oriented reasoning through a structured schema with five dimensions, carefully selects data from real user-behavior scenarios, and introduces a teacher model to generate high-quality reasoning traces under the proposed schema, helping small models learn behavior-relevant reasoning patterns rather than relying on superficial behavioral correlations. Based on this process, we construct 9,881 high-quality training instances from three months of historical interaction trajectories of 200 real users, averaging about 50 distilled examples per user. Experimental results show that OmniSimulator improves the average score of Qwen3-8B by 34.33%, achieves performance competitive with Claude-Sonnet-4.5, and supports smaller-scale, self-hosted deployment. These results suggest that learning structured behavior-oriented reasoning patterns is a promising direction towards scalable and high-fidelity LLM-based user simulation.
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URL
https://arxiv.org/abs/2610.29472
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Harness Scaling for Generalizable Agentic Reinforcement Learning
2026-10-21 09:15:50Fei Bai, Huatong Song, Chuan Hao, Ran Tao, Jian Yang, Bryan Dai, Wayne Xin Zhao, Ji-Rong WenAbstract
Agent harnesses have become a central component of modern agentic systems, shaping how models use tools, manage context, and coordinate long-horizon interaction. Realizing the benefits of these harnesses depends on how effectively the underlying model can make use of them, motivating reinforcement learning directly through the harness. However, existing approaches typically train through a single fixed harness, which can lead to specialization and limited transfer across harness configurations. To address this, we introduce \textbf{Harness Scaling}, a new scaling dimension for agentic RL that extends training from a single harness to diverse execution configurations. Concretely, we construct a factorized harness space, select a compact training subset with balanced factorial coverage, and organize cross-harness training to combine global coverage with controlled variation for the same task. Across multiple benchmarks, model scales, and RL algorithms, Harness Scaling consistently improves generalization to unseen white-box configurations. Moreover, models trained through structured white-box variation transfer effectively to unseen external black-box harnesses, outperforming models trained through a single complex black-box harness. These results establish harness diversity as a meaningful scaling dimension for agentic RL and highlight the importance of how this diversity is organized for cross-harness generalization.
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URL
https://arxiv.org/abs/2610.29458
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DynaWeave: Learning to Adapt through Dynamic Scenario Synthesis
2026-10-21 09:03:10Xuan Yang, Chunxiao Liu, Hongsheng Xin, Hao Xu, Ning MiaoAbstract
Large language model (LLM) agents increasingly use tools to complete tasks in stateful environments. However, many training environments remain largely static apart from changes caused by the agent's own actions, whereas external events introduced by other users, services, or sudden contingencies in real environments can invalidate ongoing plans. To address this gap, we introduce DynaWeave, an automated synthesis framework that transforms existing static tasks into verifiable dynamic scenarios. The framework generates exogenous events relevant to each task, covering changes in task requirements, environment state, and external information. It uses directed acyclic graphs (DAGs) to represent dependencies between events and an execution engine to activate them during agent interaction. A dynamic verifier revises only the evaluation checks affected by events that actually occur and retains all other checks. Using DynaWeave, we collect 2,095 verified interaction trajectories across 36 environments and use them for supervised fine-tuning of Qwen3.5 models. Experiments on Gaia2, CostBench, and ToolMaze show that DynaWeave achieves consistent improvements in overall performance across all three evaluated model sizes, increasing the task completion rate on CostBench by 67.34 percentage points and even exceeding the performance of Gemini-3.1-Pro-Preview on ToolMaze. These results support dynamic scenario synthesis as a practical approach to training agents to adapt to changing environments. Our code is available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29443
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Events, Not Pixels: Where World Models Get the Return Wrong
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World models are increasingly used for planning, where a planner ranks candidate actions by returns imagined by the model. However, these models are judged mainly by how well they render frames, even though a visually faithful rollout can still assign the wrong return. To identify where this mismatch arises, we introduce a transition-level diagnostic that replaces imagined transitions with real ones and measures the reduction in return error against a budget-matched random control. Our analysis yields two findings. (1) Event transitions, the few steps where a reward is earned or an episode ends, are rendered as well as other steps (median pixel-error ratio 0.85), yet restoring them removes a median of 70% of the return error. In contrast, restoring transitions with the largest MSE, SSIM or LPIPS error removes less than a random choice. Across the 44 Atari-100k model--collection pairs that pass our reliability checks, including three released families, event patches outperform random patches in 39 (sign test p=1.4 x 10^{-7}), with a median gain of +44 percentage points. (2) The remaining error is structured: a complementary patch separates errors at true events from those elsewhere, while an eight-step timing tolerance recovers few events missed by our deterministic models. This structure also guides repair: a rule recorded before four repair outcomes were known predicts when a diffusion model repairs these failures. Event patches improve plan ranking, and the same concentration appears on control tasks with rare reward events.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29435
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BIRD-RL: Scaling Agentic Reinforcement Learning over Stateful Data-Centric Environments
2026-10-21 08:50:39Ge Qu, Jinyang Li, Xiaohan Xu, Xiaolong Li, Bowen Qin, Nan Huo, Ziwei Tang, Chenhao Ma, Reynold ChengAbstract
While agentic reinforcement learning (RL) has emerged as a promising paradigm for training small language models (SLMs), existing frameworks are predominantly tool-centric and struggle with data-centric environments. Specifically, applying tool-centric frameworks to stateful tasks like SQL debugging introduces severe spatial inefficiencies since proper environment isolation typically requires a separate database container per rollout. Naively reusing databases without tracking evolving states across multi-turn interactions causes trajectory contamination and cross-trajectory interference, which severely destabilizes policy updates. To overcome these bottlenecks, we introduce BIRD-RL, a novel four-stage agentic RL curriculum that alternately enhances foundational agent reasoning and trajectory-adaptive on-policy exploration to circumvent the instability inherently associated with direct end-to-end learning. Crucially, to support this curriculum, BIRD-RL is driven by a Trajectory-scoped Persistent Execution Infrastructure, which enables safe database replica reuse while preserving trajectory isolation. This design improves spatial RL efficiency by 12.8x, successfully enabling scalable agentic RL for complex data-centric tasks without needs of Kubernettes. Empirical evaluations on the BIRD-CRITIC-SQLite benchmark demonstrate that our 7B and 14B models achieve Success Rates of 44.40% and 48.00%, respectively, showing competitive performance against strong proprietary model-based agents such as Claude-Opus-4.6 Agent (48.20%). Furthermore, BIRD-RL successfully trains unified SLMs capable of both advanced SQL debugging and generation, highlighting its broader potential for multi-objective optimization across heterogeneous, real-world data-centric tasks.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29426
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Solving Go under Positional Superko: Sound Proof Transfer and New Board Values
Abstract
Positional superko (PSK) forbids recreating any earlier whole-board position. It is part of the Chinese rules as written and of the rules used in computer Go. Yet almost all solved Go boards were computed under a simpler basic-ko rule set. PSK makes solving hard: legality depends on the whole game history, and this breaks the three tools that make solving tractable, namely transposition tables (the graph-history interaction problem), static life-and-death recognition, and safe move pruning. We present Sanko, a parallel depth-first proof-number solver that restores all three soundly. Its core is a cheap proof-transfer theorem: a stored proof stays valid on a new path unless a board that the proof's winner must create has already occurred. Sanko proves 5x5 Go in 16 minutes with six threads, where the previous state of the art needed 5.25 hours with six processes, and in about a minute and a half on one 192-thread machine. It also establishes the first PSK values of twenty-three further boards, eleven of which were unsolved under any rules. Superko changes game-theoretic values: 4x7, a full-board win under basic ko, is worth only 12 points under PSK, and we pinpoint the move at which the previously published optimal line fails. Unlike earlier solutions of empty Go boards, we release the solver source together with the command line and outcome of every run behind our values and measurements, so that all reported values can be reproduced.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29414
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LiveAct: Towards Real-Time Multi-Agent World Models with Heterogeneous Action Interfaces
2026-10-21 08:34:03Xinyu Wang, Huafeng Shi, Yue Ma, Zijie Meng, Teng Xi, Yuzhuo Chen, Yucheng Wang, Ruicheng Zhang, Guoxin Zhang, Pengfei Wan, Xiu Li, Fangneng ZhanAbstract
Interactive video world models predict how an environment evolves under user actions, but most existing systems assume one controlled agent and one task-specific action interface. We study multi-agent video world modeling, where one model generates a joint visual future for multiple controlled agents while interpreting heterogeneous controls ranging from binary game buttons to continuous robot commands. This task presents two complementary challenges. First, every agent-indexed action stream must control its intended visual entity independently. Second, the model must adapt the meaning of each control variable across different tasks, where the same tensor dimension may denote an attack, steering command, or robot command. We introduce LiveAct, centered on a Heterogeneous Action Interface (HAI) whose two components mirror this decomposition. To support independent control, Agent-Aware Spatial Modulation uses language-driven spatial gates and agent-specific attention to bind each action stream to its intended visual agent. To accommodate heterogeneous controls, Schema-Guided Action Adaptation describes each control variable in language and modulates its semantic token with the synchronized numerical value. We further transfer the complete interface to a blockwise causal generator through teacher forcing and rollout-aware distribution matching, enabling efficient streaming generation while retaining agent-specific control and action semantics. We instantiate the formulation on six multi-agent sources spanning games and multi-robot manipulation, and evaluate visual quality, action following, agent attribution, and latency. Across games and robot manipulation, LiveAct reaches 0.898 and 0.942 agent-attribution accuracy without agent locations or identity crops, and the causal generator attains 17.70 FPS at $512\times 448$.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29408
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Integrating Flow Matching and Model Predictive Control for Safe Robot Navigation
2026-10-21 08:03:15Philipp Holzmann, Maik Pfefferkorn, Joao Carvalho, Ali Younes, An Thai Le, Georgia Chalvatzaki, Jan Peters, Rolf FindeisenAbstract
Learning-based path planners based on diffusion and flow matching can generate diverse trajectories from demonstrations but typically lack mechanisms for constraint satisfaction and collision avoidance during deployment. This work proposes a framework that combines flow-matching-based path planning with model predictive path-following control to integrate data-driven path generation with real-time, constraint-aware execution. The flow-matching model captures multimodal path distributions, while the predictive controller adapts motion online to satisfy state and input constraints and avoid obstacles using updated local sensor information. An event-triggered re-planning scheme incorporates predictive control solutions into subsequent path generation, promoting collision-free navigation in environments with previously unseen obstacles. Simulation results for mobile robot navigation demonstrate robust performance across cluttered environments. The framework provides a systematic approach to combining flow matching with predictive control for adaptive, data-driven navigation.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29377
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Memory-Centric Continual Learning for AI Agents
2026-10-21 07:49:42Dongfang Li, Zhuoen Chen, Bonian Jia, Zixuan Liu, Zhengxuan Lu, Can Deng, Yukun Shi, Yuhang Meng, Xin Zhang, Jiahe Huang, Fuhao Li, Junmai Wang, Shunyao Wen, Jingjun Sun, Jiaxu Tian, Jiangling Zhang, Zihan Wang, Liangyou Li, Yitong Yao, Shuangyong Song, Beilun Wang, Pengxu Wei, Hanlin Xu, Chang Liu, Yongxiang Li, Baotian Hu, Xiangyang Ji, Min ZhangAbstract
Autonomous agents powered by foundation models can reason, plan, and use tools, but their fixed weights and context limits hinder sustained learning from experience over long-term deployment. While external memory systems attempt to bridge this gap, they mostly act as passive retrieval caches, evaluated primarily by whether they can recall stored facts or past interactions. Whether memory can drive genuine continual learning remains an open question. In this paper, we introduce Memory-Centric Continual Learning (MCCL), arguing that memory should be judged by whether it improves future actions, not merely what it retrieves. MCCL has three main parts. First, a three-axis taxonomy organizes memory by where state is stored (substrates), what form it takes (assets), and how it is managed (policies). This defines a two-loop lifecycle that updates memory state in an inner loop while refining management policies in an outer loop based on task feedback. Second, an evaluation protocol measures retention under interference, adaptation to change, and task transfer to isolate genuine behavioral gains from simple context expansion. Third, a memory scaling framework models how agent performance scales with experience, storage capacity, and compute budgets across non-parametric and parametric memory. Together, these components reframe agent memory from passive lookup to continuous learning, offering a principled route toward self-improving autonomous agents.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29357
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Payload generalization on feedforward control actions for trajectory tracking in robots with rigid and elastic joints
2026-10-21 07:49:11Giorgia Pazzi, Giorgio Simonini, Michele Pierallini, Paolo Salaris, Giorgio Grioli, Manolo Garabini, Franco AngeliniAbstract
Industries employ robots for repetitive tasks due to their high precision and performance, typically achieved through feedforward control actions based on an accurate robot description. However, an exact dynamic model can be difficult to obtain, especially for robots with elastic elements or when the payload changes. Learning-based feedforward approaches are promising for trajectory tracking under model uncertainties, but typically face generalization issues when task parameters vary. This paper proposes a novel method to generalize the feedforward control action with respect to payload parameters, independently of the underlying controller. Firstly, the minimal number of example objects needed for payload generalization is derived. Then, an algorithm is proposed to execute the same trajectory with a new payload, given only its parameters, without knowledge of the robot's dynamic model and even in the presence of elastic joints. The effectiveness of the proposed algorithm is validated in simulations and experiments on tracking tasks with different robots and controllers. Two sets of experiments are performed: on a standard Franka Emika Panda robot, and on a Franka Emika Panda robot equipped with an elastic wrist.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29356
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Decodability Is Not Controllability: Local and Anisotropic Speaker Control in Continuous-Latent TTS
2026-10-21 07:38:25Yuanzhong Zheng, Wei Huang, Ming Ma, Kailun Jin, Youhai JiangAbstract
High linear decodability does not entail causal controllability in continuous-latent text-to-speech (TTS). In VoxCPM2, position-matched probes and equal-norm interventions across 30 speakers and 60 directed dyads expose this separation: reference-audio states at the final residual acoustic language model layer (RALM L7) retain 0.970 probe accuracy, yet aligned edits yield 0/60 target-dominant dyads. Edits at early reference-audio positions produce the opposite pattern. At L0, pair-aligned updates increase target similarity by 0.326 and achieve target dominance in all 60 dyads, against at most 1/60 under any equal-norm direction or position control. Independent speaker encoders, retrieval against held-out speaker prototypes, and double-blind listening confirm that the shifted voice is heard as the intended target. Cross-architecture interventions in this http URL distinguish backbone leverage from direct acoustic control: backbone edits displace the source without target attraction, while x-vector ablation reduces source similarity from 0.849 to 0.006. These findings identify two requirements for training-free speaker control: geometric specificity and a computational route that carries the intended speaker condition into acoustic synthesis.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29345
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Plan First, Act Later: Streaming Reasoning for Executable Memory in Robot Control
2026-10-21 07:27:58Qinghang Liu, Conglin Wang, Zongsheng Li, Boyong He, Han Gao, Pengcheng Wang, Tao Sun, Lige Liu, Chang LiuAbstract
Long-horizon robot manipulation requires a robot to remember task-relevant information beyond its short observation window. Existing memory-augmented policies typically retrieve relevant information from history and reason over it at execution time, placing a heavy reasoning burden on the high-level model. To address this challenge, we propose PAL (\textbf{P}lan first, \textbf{A}ct \textbf{L}ater), a hierarchical memory framework for vision-language robot manipulation. It maintains a short-term visual buffer, a semantic-event memory, and an executable \texttt{subtask\_list}. A high-level vision-language model (VLM) converts streaming visual observations into the \texttt{subtask\_list}. At execution time, the VLM selects the currently relevant subtask from the \texttt{subtask\_list} and passes it to a low-level vision-language-action (VLA) policy. To teach the model what to write to memory and when to update it, we introduce a two-stage data generation procedure that first expands sparse update supervision and then uses self-labeling with task-grounded verification to generate training data that better match the state distribution encountered during execution. We evaluate our approach on memory-dependent benchmarks and real-world manipulation tasks. On RoboMME, PAL achieves a \textbf{56.05\%} success rate, outperforming the strongest baseline, FrameSamp-Modul, at 44.51\%. In real-world experiments across four manipulation tasks, \pal{} reaches \textbf{60.83\%} overall success, surpassing MemER (45.00\%) and FrameSamp-Modul (28.33\%). These results show that proactively writing future-relevant subtasks into hierarchical memory improves long-horizon visual reasoning and robot control. Code and videos can be found on our website: this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2610.29329
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Asking What a Director Sees Can Change What Vision-Language Models Choose
2026-10-21 07:21:08Jiaxuan Guo, Xingyu Ren, Kejia Zhang, Jingxin Yang, Youran Sun, Haizhao YangAbstract
Vision-language models are now routinely tested on theory of mind: whether they can work out what another person sees, knows, and means. A common design asks the model to report that perspective and then to act for the person, and reads a correct report beside a wrong action as a failure to use the perspective. That reading rests on a quiet assumption: that asking for the report leaves the action unchanged. We show that it does not. In a rendered Director Task, a partition hides the largest block from a director who asks for "the largest block". Asking first which blocks the director can see raises the share of replies that choose this hidden block by 12 to 88 percentage points on six GPT models and by 17 on Qwen3.8-27B, even though the models mostly report correctly which blocks the director sees. Reasoning traces and prompt rewordings suggest why: the question shifts how the request is read, from the director's view toward the model's own. When the prompt states what the director knows and refers to, fewer than 1% of replies pair a correct report with a hidden-block choice. Any evaluation that elicits a perspective report before the action it scores needs a baseline without the question, and we deliver one: paired prompts that differ only in that question, a design any perspective-taking benchmark can adopt.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29318
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YingLan: A Multimodal Ocean Foundation Model for Unified Representation Learning from Heterogeneous Observations
2026-10-21 07:09:14Sen Jia, Siqi Wei, Junyu Wang, Zijun Lv, Pan Liu, Meng Xu, Xiyou Fu, Yuxi Sun, Qiang Liu, Shuyu Zhang, Lin MuAbstract
Multimodal ocean modeling remains challenging due to substantial heterogeneity in data structures, physical semantics, and spatiotemporal characteristics across satellite imagery, gridded oceanographic fields, and buoy observations. Existing approaches largely focus on task-specific ocean modeling, while learning transferable representations from heterogeneous observations remains an open problem. To address this challenge, we propose YingLan, a multimodal ocean foundation model that integrates physics-attribute-conditioned dynamic encoding, Spatiotemporal Increment Coupling (STIC), and ocean-state evolution-aware pretraining. Specifically, the dynamic encoding mechanism generates adaptive input projections conditioned on physical semantics and observation characteristics, enabling flexible representation of heterogeneous ocean variables. STIC models multiscale spatiotemporal dependencies through cross-scale interactions of latent temporal increments. The pretraining strategy further combines masked observation reconstruction with temporal evolution objectives to facilitate the learning of coherent ocean-state representations across different prediction horizons. YingLan is implemented using MindSpore and trained on Huawei Ascend 910B NPUs, demonstrating its compatibility with domestic AI computing infrastructure. In a seven-day sea surface salinity (SSS) forecasting task conducted in the Shenzhen coastal waters, YingLan achieves an average RMSE of 0.687508 PSU, corresponding to relative error reductions of 0.58%, 5.31%, and 12.42% compared with LangYa, XiHe, and GloNet2, respectively. These results provide initial evidence of the model's effectiveness in regional ocean forecasting, while broader cross-task and cross-regional evaluations remain necessary to assess its generalization capabilities.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29313
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HORIA: 4D Human and Object Reconstruction from a Monocular Video with Interaction-Aware Re-Rendering
2026-10-21 07:08:30Hyeongjin Nam, Taeryung Lee, Daniel Sungho Jung, Kyoung Mu LeeAbstract
Joint 4D reconstruction of a human and object from a monocular video is essential for understanding how people interact with their environment. While recent methods have made noticeable progress, capturing such human-object interactions remains difficult due to two main challenges. First, a video typically contains composite semantic context spanning multiple interaction phases, making it difficult to track the temporal changes of the interaction in the reconstruction. Second, under human-object occlusion, a single-view observation contains incomplete visual information about the interaction, resulting in geometrically inconsistent reconstructions. To address these challenges, we introduce HORIA, built upon two core designs. First, we decompose the entire input video into temporally grounded and semantically distinct interaction phases with motion-aware captioning, obtaining a specific text description for each phase. Second, conditioned on these text descriptions of the interaction, we propose an interaction-aware re-rendering that compensates for the limited single-view observation. Specifically, we render the initially reconstructed 4D human and object from novel viewpoints and refine the renderings with a text-to-video diffusion prior, which provides auxiliary novel-view supervision beyond single-view cues. As a result, our framework produces temporally coherent and accurate 4D reconstructions under diverse interaction scenarios, achieving state-of-the-art performance.
Abstract (translated)
URL
https://arxiv.org/abs/2610.25960
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ExpEvolve: Structuring and Reusing Experience for Open-Ended Discovery
2026-10-21 07:00:23Yongqiang Chen, Chenxi Liu, Deyu Zou, Zhenhao Chen, Tongliang Liu, Bo Han, Kun ZhangAbstract
Evolutionary coding agents such as AlphaEvolve are one of the notable successes in using Large Language Models (LLMs) to build AI Scientists. These agents tackle open-ended scientific problems by iteratively improving and evolving programs, leveraging the prior knowledge and reasoning capabilities of LLMs. Despite their success, mutations over programs are mostly low-level upon surface heuristics, without a persistent unit of control. In contrast, science compounds through ideas. In this work, we propose ExpEvolve, which injects idea-level extraction and control into evolution: it discovers and leverages the high-level factors from programs that explain scoring differences among different programs. We characterize theoretical conditions on when discovery with idea factors improves sample efficiency. We also build a practical framework to show that the extracted high-level idea factors are helpful for guiding exploitation from experience and exploration when discovery comes to a plateau. Across 11 open-ended mathematical tasks, ExpEvolve significantly improves the final performance and sample efficiency up to 5.0x. The improvements also transfer to 4 other LLMs and to 6 machine learning tasks from MLS-Bench. Analysis shows that ExpEvolve can discover idea-level factors to provide better exploration and exploitation for evolutionary discovery.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29303
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Computer Vision and Knowledge-Grounded Language Models for Intelligent Beekeeper Assistance
2026-10-21 06:36:55Asaduz Zaman, Mohaimenul Azam Khan Raiaan, Malika Nisal RatnayakeAbstract
Managed and wild bees sustain roughly a third of the global food products, yet colonies face mounting stress from climate-change including extreme weather patterns, altered parasite and disease patterns. Timely assessment of colony condition such as understanding colony strength, brood area, honey and pollen storage, comb usage, is essential to adaptive management. However, manual hive frame inspection is slow, costly, and quality depends on scarce expertise. We present an artificial targeted intelligent system framework focusing on edge deployable pipeline that turns hive frame photographs into interpretable and action-oriented inspection report. The pipeline includes an object detector to detect biologically-meaningful comb-cell classes; detections are distilled into structured fact schema; facts are converted into constrained factual text; the text triggers a grounded knowledge-base for interpretations and recommendations. Knowledge-base entries are required to be triggerable by evidence expressed in the fact schema. We built our system based on open source FAIRHiveFrames-1K dataset and favour reproducible, edge-deployable models over large general purpose frontier models. We additionally release FAIRHiveText-1K dataset that turns FAIRHiveFrames-1K dataset into interchangeable facts and text interpretation of the facts, making it easy for further LLM-based research into this area. We also propose a pathway to develop the grounded knowledge-base that will be helpful in providing interpretable and actionable reports from the facts.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29283
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