Artificial Intelligence
Chess on Ice: Curling Tactical Decision-Making via Backward Induction and Deep Reinforcement Learning
Patrick Oberlin, Matteo Cederle, Aren Karapetyan et al. (+3 more)
Curling is often referred to as "Chess on Ice", owing to the tactical complexity of its decision-making process. Yet unlike chess, curling remains largely underexplored from a machine learning perspective, with prior work confined mainly to statistical approaches. We propose a reinforcement learning framework capable of quantitatively evaluating and comparing tactical options in curling. The game poses several modeling challenges: continuous state and action spaces,
stochastic action outcomes reflecting player skill variability, and state transitions that are highly sensitive to small perturbations in the executed action. To address them, we employ the Deep Deterministic Policy Gradient actor-critic
algorithm, adapted to exploit the finite-horizon structure of the game. Our experiments show that effective curling strategies can be acquired in a fully self-supervised manner, without any human-annotated data: on a reduced four-rock variant, the learned
agent matches a hand-crafted expert heuristic in a regime where that heuristic is close to optimal, a parity we quantify against the intrinsic hammer advantage of the variant. Beyond the resulting policy, the learned critic provides a dense value estimate over the entire continuous action space, enabling the quantitative comparison of tactical alternatives for applications such as post-game performance analysis and decision support during athlete preparation.
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All authors Patrick Oberlin, Matteo Cederle, Aren Karapetyan, Saverio Bolognani, Gian Antonio Susto, Florian Dörfler
Artificial Intelligence · Cross-listed from cs.LG
GLAIM: Learning Global and Local Adaptive Inter-Variable Dependency for Multivariate Time Series Imputation
Mingyang Wang, Rongwen Li, Xiao Wang et al. (+1 more)
Multivariate time series imputation is fundamental to downstream analysis, yet modeling inter-variable dependencies with incomplete observations remains challenging. Existing methods learn global dependencies across samples or dynamic local dependencies per sample. Global dependencies are stable but adapt poorly to sample variations and temporal non-stationarity, whereas local dependencies are adaptive yet unreliable when observations are insufficient, causing erroneous information propagation. To address these limitations, we propose GLAIM, a Global-Local Adaptive Inter-variable Dependency Modeling framework for multivariate time series imputation. GLAIM comprises two complementary components. The Stable Global Dependency Constructor derives robust global inter-variable dependencies from complementary temporal representations, providing a stable backbone less affected by sample-specific missingness and noise. The Sample-Conditioned Dependency Refiner adapts this backbone to each sample and time step using its temporal state and available observations, enabling reliable local refinement under incomplete observations. Extensive experiments on nine real-world
datasets demonstrate that GLAIM achieves state-of-the-art performance under random and block missingness, remains robust to missing-rate shifts, and benefits from its complementary global and local components. Code is available at https://github.com/LuRenjias/GLAIM.
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All authors Mingyang Wang, Rongwen Li, Xiao Wang, Changjian Chen
Artificial Intelligence
Faster-WAM: Do World Action Models Need Deep Action Modules?
Liheng Ma, Rui Heng Yang, Amin Abyaneh et al. (+8 more)
World Action Models (WAMs) build on pretrained
video models, whose representations are grounded in physical dynamics and provide a natural basis for action prediction. Despite this natural foundation, many WAMs still rely on deep, parameter-heavy action-prediction modules that incur high inference latency and may overfit to limited
robot demonstrations, restricting their real-world applicability. In this paper, we advocate a world-model-centric principle that concentrates capacity and computation in the
video world model, while a lightweight action expert translates the backbone's representations into executable
robot actions. We realize this principle through three key choices: Dock of
Transformers (DoT) with Lite KV-Fusion to give the shallow, lightweight action expert access to representations from all
video layers; world-model-only conditioning of the action expert; and retracted 1D-RoPE for positional
alignment between
video keys and action queries. We test this principle using Faster-WAM, a world-model-centric WAM with only a single-layer action expert. Despite this restriction on action-specific computation, Faster-WAM achieves competitive control performance on LIBERO and RoboTwin~2.0 without additional
embodied pretraining. It provides approximately $3.7\times$ and $1.3\times$ inference speedups over Fast-WAM and $\pi_{0.5}$, respectively. Consistent with its world-model-centric design, Faster-WAM demonstrates stronger generalizability under distribution shifts: the same LIBERO-trained policy achieves $78.3\%$ success on LIBERO-Plus, exceeding Fast-WAM and LingBot-VA by $26.8$ and $8.8$ percentage points, respectively. Finally, real-
robot experiments demonstrate success rates comparable to Fast-WAM, with substantially lower inference latency and shorter task-completion times.
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All authors Liheng Ma, Rui Heng Yang, Amin Abyaneh, George Z. Xue, Behnam Rahmati, Mateo Clemente, Ziwen Hu, Anlin Chen, Tongtong Cao, Zhanguang Zhang, Yingxue Zhang
Artificial Intelligence
SkillTrace: Traversing a Query-Skill Graph for Composable LLM Agents
Yue Yao, Shengyuan Wang, Xin Chen et al. (+4 more)
Large
language model
agents increasingly solve complex tasks by composing reusable skills from a library. To address this, the key challenge is not merely to retrieve individually relevant skills, but to identify a complete and executable skill composition. In this paper, we argue that this problem can be solved in a graph with three levels: compositional relations among skill queries, similarity between queries and candidates in the skill library, and the dependencies among the selected candidates. We introduce SkillTrace, which organizes the user query into a semantic hierarchy, matches skill queries and candidates, and propagates over the skill dependencies. Experiments on SkillsBench and ALFWorld demonstrate that SkillTrace achieves state-of-the-art performance, reaching a success rate of 53.17% on SkillsBench and 91.43% on ALFWorld. SkillTrace also delivers consistent improvements across different backbone
language models, demonstrating the generality and robustness of graph-based skill
retrieval.
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All authors Yue Yao, Shengyuan Wang, Xin Chen, Minke Zhang, Jia He, Bingjun Luo, Tom Gedeon
Artificial Intelligence
KC-Agent: A Dual-Process Cognitive Architecture for Efficient ML Model Improvement
Gusseppe Bravo-Rocca, Jordi Guitart, Ajay Dholakia et al. (+2 more)
Data drift poses significant challenges for machine learning systems in production, requiring continuous model updates to maintain performance. We present KC-
Agent, a dual-process cognitive architecture for automated ML model improvement that combines fast pattern recognition (System 1) with deliberate incremental updates (System 2). Our approach implements structured
memory systems enabling System 1 to leverage successful solutions previously discovered by System 2, achieving efficient pattern-based responses without costly re-computation. KC-
Agent incorporates atomic change principles and rollback capabilities to ensure reliable, verifiable updates in production environments. We evaluate our method on five
datasets including real-world NASA turbofan data with authentic temporal degradation and synthetic
datasets with controlled drift scenarios. KC-
Agent achieves state-of-the-art performance (76.8% accuracy) while maintaining optimal efficiency (13.2s execution time), outperforming established cognitive architectures: CodeAct (+2.4%), Tree of Thoughts (+3.6%), ReAct (+8.0%), and Reflexion (+8.9%). Consensus evaluation by a panel of state-of-the-art LLMs confirms superior strategic efficacy (8.33/10 Smartness score), significantly outperforming baseline
agents. The knowledge consolidation mechanism delivers 91% speedup over the slow variant while maintaining higher accuracy. Our approach demonstrates both theoretical foundations and practical viability for cognitive-inspired automated ML improvement systems capable of handling complex real-world data drift scenarios.
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All authors Gusseppe Bravo-Rocca, Jordi Guitart, Ajay Dholakia, David Ellison, Puneet Jain
Artificial Intelligence
Mamba with Hierarchical Memory: Solving Representation Bottleneck in Long Sequence Modeling
Qinwen Wang, Jieping Luo, Aoxiang Qin et al. (+5 more)
Recurrent linear
attention models (RLAs) such as Mamba offer efficient linear-time sequence modeling as an alternative to
Transformers, yet their fixed-capacity recurrent states limit long-sequence modeling. Drawing inspiration from hierarchical human
memory, we propose Hierarchical
Memory Mamba (HMM) to address this limitation. Building upon a pre-trained Mamba backbone, HMM integrates a lightweight working
memory that extracts slow paragraph-level semantics (PLS) from the fast sensory
memory embedded in the backbone's hidden states. The PLS is subsequently compressed into persistent long-term
memory for task-relevant
retrieval. The hierarchical processing of semantic information overcomes the representation bottleneck of RLAs and endows HMM cross-task generalization through parametric learning, which is not observed in other long-context enhanced Mamba variants. Evaluations on Passkey
Retrieval and LongBench-E tasks demonstrate that HMM improves
retrieval success by 34.3–37.1% and
reasoning accuracy by 1.6–14.2% over strong Mamba-based models, while adding only 2% extra parameters and with minimal training overhead.
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All authors Qinwen Wang, Jieping Luo, Aoxiang Qin, Ruoyu Zhao, Jianxiong Tang, Wei Zhang, Zhichao Lu, Luziwei Leng
Artificial Intelligence · Cross-listed from cs.CL
Can AI Agents Simulate A/B Test Outcomes? A Validation Framework for Agentic Experimentation
Stefan Hut and Lorenzo Masoero
A/B testing remains the standard for rolling out new features in the technology industry. Each experiment, however, consumes real traffic, engineering effort, and weeks of wall-clock time. Can AI
agents—conditioned on behavioral profiles and contextual descriptions of the intervention—simulate outcomes accurately enough to vet candidate treatments before committing live traffic? We formalize this question as a Simulated Randomized Controlled Trial (S-RCT) and derive a two-layer error decomposition that separates
agent approximation error from subsampling error, enabling targeted improvements to each. The framework is
agent-agnostic: any behavioral model—from a fine-tuned specialist to a general-purpose foundation model—can serve as the simulation engine. Validated on 67 historical marketing A/B tests, a baseline S-RCT using an off-the-shelf foundation model captures directional signal (sign overlap 0.70) but systematically overshoots effect magnitudes. A two-phase pre-period calibration protocol reduces the squared prediction error (after removing irreducible measurement noise) by ${\sim}77\times$; a within-subject design—where each
agent is exposed to both arms—reduces standard errors by ${\sim}2.4\times$. We discuss limitations of the current approach and identify applications where experimenters stand to benefit from agentic signals.
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Artificial Intelligence
Hard Constraints, Smooth Gradients: Learning Feasible Inventory Policies via Differentiable Projection
Patrick Helm, Jan-Niklas Doerr, Joren Gijsbrechts et al. (+1 more)
Many operational problems are constrained sequential decision processes with combinatorial action spaces and interdependent constraints. Mixed-integer linear programs (MILPs) handle such constraints flexibly but scale poorly in
stochastic environments. Deep reinforcement learning (DRL) promises scalable decision rules, but existing methods either penalize constraints rather than enforce them, or rely on feasibility mechanisms that break down once constraints interact. We bridge this gap by
embedding a differentiable optimization module inside the policy: a neural network proposes continuous action targets, a quadratic program projects them onto the relaxed feasible set, and a dual-informed integer mapping restores integrality while preserving feasibility. Given a differentiable simulator, the policy trains end-to-end from sampled trajectories using pathwise gradients, while handling hard constraints with similar flexibility to MILPs. We show that our approach has bounded error relative to an exact integer projection and ensures the entire feasible action space is reachable. We apply the method to multi-echelon production-inventory planning under shared resource and material constraints. Our policy attains an average optimality gap of 1% on 3-item instances and consistently outperforms a rolling-horizon multi-stage
stochastic program by at least 5.6% in 10-item networks. Compared to state-of-the-art echelon base-stock policies, savings reach 15.3% on 40-item instances. On an industry-scale case study with nonstationary demand from ASML, we reduce average cost by up to 1.8% relative to the best-known
benchmark policy. The savings are largest where planning is hardest: in tightly capacitated systems with high demand variability. More broadly, we show that DRL can improve upon state-of-the-art baselines in sequential decision problems with hard constraints, which are widespread in practice.
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All authors Patrick Helm, Jan-Niklas Doerr, Joren Gijsbrechts, Stefan Minner
Artificial Intelligence · Cross-listed from cs.LG
Diffusion Policy with Behavioral Advantage Correction for Offline Reinforcement Learning
Botao Dong, Longyang Huang, Ning Pang et al. (+1 more)
In offline reinforcement learning (RL), the distribution shift between behavioral data and the learned policy can lead to erroneous Q-value estimation, thereby misguiding the direction of policy optimization. To address this issue, we develop a behavioral advantage corrected policy evaluation (BAC-PE) approach, which utilizes the Q-function of the behavior policy to correct the learned policy's Q-function, thus mitigating pessimistic conservatism and overestimation bias. Furthermore, the convergence of BAC-PE is analyzed theoretically, and an upper bound on the difference between the learned Q-function and the true Q-function is derived. To alleviate distribution shift, this work employs
diffusion models to represent both the behavior policy and the learned policy, performing distribution matching for accurate policy regularization. Additionally, Q-value guidance is incorporated into the training process to achieve effective policy improvement. By combining BAC-PE with
diffusion policy modeling, we propose the
diffusion policy with behavioral advantage correction (DPBAC)
algorithm. Compared to existing offline methods, DPBAC demonstrates stronger policy representation capabilities and effectively mitigates the bias in Q-value estimation. Experimental results on multiple domains of D4RL tasks show that DPBAC achieves superior performance, with notable advantages over state-of-the-art (SOTA)
algorithms.
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All authors Botao Dong, Longyang Huang, Ning Pang, Hongtian Chen
Artificial Intelligence · Cross-listed from cs.CV
Context-Aware Mixture of Domain Experts for Bodily Expression of Emotion in the Wild
Mohammad Mahdi Dehshibi and David Masip
The same body posture can convey entirely different
emotions depending on its surrounding context, yet most methods for recognising bodily
emotions treat scene and object cues as auxiliary feature augmentations rather than as structured priors over the plausibility of
emotions. We introduce the Context-Aware Mixture of Domain Experts (CA-MoDE) for bodily
emotion recognition. CA-MoDE incorporates dedicated scene and object experts to generate soft distributions over
emotion categories conditioned on their respective domains. These domain-conditioned soft predictions serve as structured contextual priors that modulate the body expert's predictions at the distributional level rather than at the feature level. To fuse these multi-domain signals, we propose a task-tailored max-endorsement gating strategy that selects the strongest contextual signal across experts for each
emotion dimension. Our gating strategy mitigates the signal dilution that typically occurs when conflicting or uninformative context distributions are averaged. CA-MoDE achieves an
Emotion Recognition Score of 0.3269 on the Body
Language Database. By outperforming existing temporal models using only single still images, our framework demonstrates that explicitly modelling structured spatial context can serve as a complementary discriminative proxy for the behavioural dynamics typically captured by
video.
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Artificial Intelligence · Cross-listed from cs.DB
FastGFDs: Efficient Validation of Graph Functional Dependencies with Desbordante
Anton Chernikov, Yurii Litvinov, Kirill Smirnov et al. (+1 more)
Graph functional dependencies (GFD) are a recently-developed concept aimed at capturing both topological structures in graphs and functional dependencies between attributes. The process of verifying whether a given GFD holds over a particular graph is referred to as GFD validation. In this very computationally expensive problem, locating suitable subgraphs accounts for about 99% of the total run time. The concept's authors originally proposed a parallel scheme (
algorithm), targeting specifically clusters of high-performance servers. The goal of this study is to open GFD validation to a broader public by making it possible to run it on a consumer class PC. Our initial experiments demonstrated that the existing
algorithm may not be optimal for these purposes. Therefore, we propose FastGFDs - a GFD validation
algorithm that employs a recently developed graph matching technique. In contrast to the parallel scheme, it is sequential and operates on the entire graph. Its novelty lies in the use of Core-First Decomposition and the Compact Path Index (CPI). We compare it with the naive sequential
algorithm and the parallel scheme, evaluating run times and
memory consumption. The current study is the first step towards designing an efficient
algorithm for GFD validation in low-end single-node environments. We also provide an open-source implementation of GFD validation over large data graphs. To the best of our knowledge, this is the only publicly available implementation of an
algorithm for this problem. It is developed in Desbordante - an open-source high-performance data profiler aimed at science-intensive tasks. Finally, our experiments on a real-life graph demonstrated up to three times performance (2.6x on average) improvement over the parallel scheme. Employing the new subgraph matching
algorithm also reduced
memory consumption by five times.
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All authors Anton Chernikov, Yurii Litvinov, Kirill Smirnov, George Chernishev
Artificial Intelligence · Cross-listed from cs.LG
BRiG-AFA: Bellman Risk-to-Go Learning for Non-Myopic Active Feature Acquisition
Jiaorong Feng, Qian Li and Ying Li
Active feature acquisition (AFA) asks which unobserved feature to measure next for each test instance under a budget. Greedy rules are easy to train but can overlook context features whose value is realized only through later acquisitions, while reinforcement-learning and generative approaches introduce difficult optimization or conditional-density estimation. We introduce \method, a deployable, supervised alternative that learns a separate candidate-conditioned risk-to-go function for every remaining budget. Starting from the one-step terminal classification risk, the functions are fitted backward with Bellman targets; inference greedily minimizes the learned terminal risk using only observed values, the mask, candidate identity, and remaining budget. A controlled non-myopic
benchmark shows the expected mechanism: at budgets two and three, \method improves accuracy over its one-step ablation by $4.84\pm2.17$ and $4.39\pm1.10$ percentage points (mean $\pm$ standard error over five seeds). On Fashion-MNIST with 20 candidate pixels, it improves accuracy at every nontrivial reported budget on average, including $10.20\pm0.74$ points at four acquisitions; its mean paired gain across budgets $\{2,4,8,12,16\}$ is $3.50\pm0.37$ points. A three-seed MiniBooNE study is mixed at small budgets but positive at 8 and 16 acquisitions, identifying a current boundary rather than supporting a universal claim. These results establish a reproducible mechanism-level case for direct Bellman risk regression and delimit the experiments still needed for state-of-the-art comparison.
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Artificial Intelligence
Trajectories That Segment Themselves: Agent-Declared Boundaries as a Training Unit
Jingxi Wei
Long-horizon coding-
agent trajectories are poorly matched to the credit units available to train on: a single action has no stable value, an episode label merges productive exploration with abandoned directions, and a fixed window cuts where the logging mechanics fall. We introduce collection-time semantic self-
segmentation, in which a declarative contract has the acting
agent expose its own boundaries while the trajectory is generated. Instantiated with falsifiable causal hypotheses, successive adoptions expose variable-length semantic phases, and no milestone vocabulary, gold patch, environment replay, teacher logits, or retrospective segmenter places a boundary. Because the
agent names its
conjecture, a reviewer can negate it by name, which lets our protocol manufacture wrong-cause-then-correction transitions that recorded work rarely contains; one collection then yields four supervised targets, including audit supervision from exactly the failed regions an episode label discards. We then ask what survives deleting the declaration. Given the cut points but not the hypothesis, a model attributes action blocks to their governing hypothesis at over twice chance, beating equal-length blocks over the same trajectories (paired sign test $p = 0.0002$), surviving a lexical control and collapsing under label permutation. Asked instead to place boundaries, a code-blind annotator matches 24 of 40 where random placement matches 11.5, while a mechanical test-event rule beats chance at neither end of a strict-to-permissive sweep. The segments are therefore coherent and not cheaply reproducible. Downstream, DPO on 2,551 phase-boundary pairs changes no decision on 91
adversarial held-out items, while four of 60 change on matched-construction items, all wrong to right, where two controls change none: with 1,825 pairs from one generator, the variable to vary next is corpus diversity, not the boundary.
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Artificial Intelligence
MechGeo: Autoformalizing and Proving Euclidean Geometry in Lean 4
Hao Shen, Junyu Guo, Tian Cui et al. (+2 more)
We present MechGeo, a Mathlib native agentic framework that jointly addresses faithful autoformalization and certified proof construction for Euclidean geometry. In this framework, GeoFormalizer represents informal problems in GeoIR, deterministically translates them into Lean 4, and iteratively repairs candidate statements using structural diagnostics and semantic evaluation. GeoProver constructs
geometric proof plans, derives intermediate lemmas, and selectively algebraizes suitable subgoals through a library verified in Lean. Singular or SymPy may generate algebraic certificates, but all resulting proofs and counterexamples are checked by Lean's kernel. Experiments across seven LLM backbones show substantial improvements in autoformalization, particularly for models with weaker direct translation performance. On 43 historical IMO geometry problems, GeoFormalizer generates formal statements that GeoProver proves in 29 cases; for the remaining 14, it constructs counterexamples verified in Lean and proves all repaired statements after expert correction. Together with IMO 2026 Problem 2, this yields, to the best of our knowledge, the largest reported collection of automated, kernel-checked Lean proofs for IMO geometry problems. On the 14 geometry statements in LEAP's Lean-IMO-Bench, MechGeo proves 12 for the first time, formally refutes the remaining two, and proves both repaired statements. These results establish counterexample guided diagnosis,
geometric reasoning, and certified symbolic computation as a practical foundation for trustworthy formal geometry.
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All authors Hao Shen, Junyu Guo, Tian Cui, Yuxuan Xiao, Lihong Zhi
Artificial Intelligence
Shared Prefixes, Better Credit: Adaptive Routing for Multi-Agent Reasoning
Yiqing Liu, Zihao Wang, Hantao Yao et al. (+2 more)
Multi-
agent reasoning (MAR) improves
reasoning reliability through iterative solution exchange and refinement. Existing adaptive MAR methods typically learn routing decisions from query-level labels or trajectory-level returns, but such coarse supervision cannot accurately estimate the state-conditioned utility of individual operators in multi-step collaboration. We propose TreeCredit, a shared-prefix credit assignment framework for efficient adaptive MAR. Its core insight is to estimate operator utility through state-matched downstream comparisons, rather than directly attributing trajectory-level outcomes to preceding decisions. TreeCredit constructs shared-prefix collaboration trees by expanding candidate operators from the same intermediate state and assigns each state–operator pair a correctness-prioritized suffix credit based on the terminal correctness and cumulative additional cost of its complete continuation. These structured credits are converted into state-local operator preferences to train a lightweight pairwise state router, which dynamically selects the next admissible operator during inference. Experiments on six
reasoning benchmarks show that TreeCredit modestly improves accuracy while substantially reducing inference cost, achieving a better accuracy–cost trade-off than representative MAR methods.
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All authors Yiqing Liu, Zihao Wang, Hantao Yao, Wu Liu, Yongdong Zhang
Artificial Intelligence
SKT: Skill-Use Training at Scale via Verified Synthetic Data Generation
Zelin Tan, Yiqun Zhang, Hao Li et al. (+11 more)
Agent skills have become an important mechanism for equipping
language-model
agents with reusable procedural knowledge. However, providing skills alone does not guarantee that current models can effectively identify, apply, and coordinate them. To improve skill-use capabilities, we introduce SKT, a verified data synthesis pipeline that constructs skill-grounded tasks and executable trajectories from large collections of
agent skills. SKT selects suitable single-skill and multi-skill configurations, synthesizes tasks through rule-based and
agent-based verification with feedback-guided repair, and retains only successful trajectories that substantially use every required skill. Using 2,000 public skills, SKT produces 4,000 task packages and 27,164 verified trajectories. Based on the same pipeline and a disjoint test pool, we further construct SkillEval, a held-out executable
benchmark for evaluating skill use. Experiments across diverse models,
benchmarks, and
agent harnesses show that supervised fine-tuning on SKT-generated trajectories consistently improves skill-use performance. Verification ablations, cross-harness evaluation, and scaling experiments further demonstrate that these gains depend on high-quality supervision, extend beyond a single
agent interface, and increase with broader skill coverage. Together, these results establish verified data synthesis as an effective and scalable approach for skill-use training.
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All authors Zelin Tan, Yiqun Zhang, Hao Li, Zhiyao Cui, Hejia Geng, Shao Zhang, Hangfan Zhang, Yang Chen, Xiaosong Wang, Lilong Wang, Zhenfei Yin, Shuyue Hu, Chen Zhang, Lei Bai
Artificial Intelligence
Harness-R1: Learning to Edit Executable Runtime Harnesses from Agent Failure Trajectories
Shuai Shao, Kangning Zhang, Qingyao Li et al. (+7 more)
Agents built around large
language models continually accumulate interaction trajectories during deployment, yet their behavior typically remains fixed. Beyond updating model weights, these trajectories can improve the
agent harness that constructs context, mediates tools, validates actions, and recovers execution. We introduce Harness-R1, the first method, to our knowledge, that makes failure-conditioned, lifecycle-wide editing of an existing executable runtime a learned capability. It post-trains a dedicated harness engineer with online reinforcement learning so that its edits are optimized for the realized task success they produce, rather than proposed by a fixed editor. A separate 9B engineer converts batches of target-
agent failures into validated executable patches; fresh same-batch reruns of the frozen target provide outcome rewards, so training updates only the engineer. Cold-start supervised fine-tuning initializes this editing policy, which is then trained online with group-relative policy optimization. Across WebShop, ALFWorld, and DBBench, Harness-R1 raises vanilla Qwen3.5-9B success from 44.3% to 53.6% (+9.3 percentage points). After direct target-
agent fine-tuning, a target-specific engineer raises the average further from 59.2% to 64.2% (+5.0 points); because these gains hold both before and after fine-tuning the target, Harness-R1 points toward co-evolving the harness engineer and the target
agent.
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All authors Shuai Shao, Kangning Zhang, Qingyao Li, Shijian Wang, Hao Wang, Wenxiang Jiao, Yuan Lu, Yi Guo, Weiwen Liu, Weinan Zhang
Artificial Intelligence · Cross-listed from cs.RO
TS-MAMP: A Remanufactured Agricultural Robot with Second-Life EV Components and NMS-Free On-Device Weed Detection
Weijie Shi, Zicheng Xu, Zhenbang Cheng et al. (+3 more)
Agriculture 4.0 robotic systems improve field efficiency yet remain too capital-intensive for the fragmented smallholdings that dominate global agriculture. Meanwhile, a growing number of retired low-speed electric-vehicle (LSEV) powertrains retain functional electromechanical value but are destructively recycled. This paper presents TS-MAMP (Telescopic-Sleeve Modular Agricultural Mobile Platform), a remanufactured
robot built under 3R (reduce, reuse, recycle) circular-economy principles. Retired 48 V brushless-DC (BLDC) hub motors are paired via back-EMF matching, and lead-acid battery modules screened at 60%-80% state of health are actively balanced within a 100 mV inter-module voltage deviation. Together, these reused components reduce the powertrain-and-chassis BOM cost by approximately 60%, to below USD 450 (perception and weeding modules excluded). The truss chassis provides at least 200 kg static load, continuously adjustable track width from 1200 mm to 2000 mm, and no more than 5-minute module changeover. An NMS-free (non-maximum-suppression-free) YOLOv10n detector with consistent dual-assignment training and negative-sample learning achieves 80.87% mean average precision (mAP)@0.5 (58.41% mAP@0.5:0.95) on the Wanxi Crop-Weed
dataset, and is deployed via FP16 TensorRT on a Jetson Nano, confirming on-device inference feasibility. TS-MAMP demonstrates that retired EV components, under modest screening, can be re-engineered into affordable, AI-enabled agricultural
robots–opening a remanufacturing pathway for the smallholder fields that commercial automation leaves unserved.
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All authors Weijie Shi, Zicheng Xu, Zhenbang Cheng, Haoran Xuan, Mingbo Duan, Gan Ge
Artificial Intelligence
Self-Certification of Representation Adequacy: Sequential Certification at Minimum Task Loss
Zijie Huang
Agents that act on a compressed representation of their history face a structural risk: if the representation aliases histories with different optimal actions, no rule measurable with respect to the representation can avoid an irreducible per-round loss, and the
agent may be unable to detect this from its own transcript. This paper develops a four-layer theory of self-certification of representation adequacy. The static layer defines decision-theoretic adequacy through a Bayes-risk grouping identity and prices a one-shot external verification by an exact total-variation threshold. The sequential layer poses certification as an optimal-stopping problem in the currency of task loss: we define an environment-wise certification complexity constant through a covering linear program, prove an information-task-loss lower bound for every delta-correct strategy, and give a Certification Track-and-Stop policy whose cost matches the bound asymptotically. A final boundary layer gives an explicit kernel-switching example and identifies the open
theorem needed to cover policy switching or representation repair; it does not claim that the fixed-kernel guarantees extend to representation revision. The proofs of the two main
theorems are given in full in the appendices.
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Artificial Intelligence · Cross-listed from cs.CV
Open-Set Visual Text Forensics via Sparse-Constraint Rectified Flow
Jiangling Zhang, Shuxuan Gao, Zeyu Chen et al. (+2 more)
Rapidly evolving Generative AI enables sophisticated visual text manipulations that increasingly evade current forensic detectors. Existing discriminative models often overfit specific forgery patterns, limiting their generalization to unseen, open-set attacks. To address this challenge, we propose a generative detector that localizes tampering by estimating the local restoration cost required to align a query image with authentic visual-text statistics, rather than by learning forgery-specific decision boundaries. Specifically, we introduce Sparse-Constraint Rectified Flow (SC-RF), a detector-oriented adaptation of Flow Matching for spatially sparse anomaly localization. We further mitigate data scarcity via self-supervised Artifact Injection and preserve high-frequency forensic traces using a pixel-space Forensic-DiT. Extensive experiments on three
benchmarks show that our method achieves state-of-the-art performance, surpassing the runner-up by 3.2 and 4.8 percentage points in F1 and IoU, respectively. In particular, the proposed detector demonstrates strong zero-shot performance on challenging unseen text editing patterns. We further provide an auxiliary stress-test analysis showing that local harmonization produced by our model can weaken the statistical cues relied upon by existing detectors, offering a complementary vulnerability-analysis perspective.
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All authors Jiangling Zhang, Shuxuan Gao, Zeyu Chen, Yichao Liu, Yu Zhou
Artificial Intelligence
Homebot: A Personal AI Agent for Conversational Home Assistance and Automation
Shengyuan Ye, Yixin Zhang, Han Liang et al. (+3 more)
Homebot is a locally deployable AI
agent for conversational household assistance and automation. It accepts voice and instant-messaging requests through a shared runtime that combines
language-model responses with registered tools and task-specific skills. The design separates common request processing from session ownership: messaging history remains scoped to a channel and chat, whereas voice interaction is bounded by wake-word activation. For hands-free use, Homebot combines local wake-word detection, streaming
speech recognition and synthesis, and an explicit dialogue-state protocol for ending, following up, or continuing a conversation. Clear channel, tool, and skill contracts support practical customization for household use.
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All authors Shengyuan Ye, Yixin Zhang, Han Liang, Liekang Zeng, Jiangsu Du, Mu Yuan
Artificial Intelligence · Cross-listed from cs.CV
HarMoE: Multi-Source Chest Radiograph Pretraining with Dataset-Disentangled Experts
Haozhe Luo, Ziyu Zhou, Shelley Zixin Shu et al. (+1 more)
Recent
vision-
language models for chest X-ray understanding are largely built on image-report
alignment and therefore rely heavily on MIMIC-CXR as the dominant pretraining source. While effective at scale, this paradigm underexplores an important alternative source of supervision: a range of existing multi-label classification
datasets, which provide cleaner and more explicit disease signals than free-text reports, and can offer broader pathology coverage when combined across sources. However, learning from such heterogeneous
datasets is nontrivial, as differences in label ontologies, annotation protocols, acquisition pipelines, and report styles can cause models to entangle clinical semantics with
dataset identity, leading to poor transfer despite increased scale. In this work, we revisit radiology VLM construction from the perspective of harmonized multi-source learning. We propose HarMoE, a
dataset-aware mixture-of-experts framework that learns shared cross-
dataset medical semantics while confining source-specific variation to lightweight residual experts in deeper decoder layers. To further exploit clean supervision from labeled
datasets, we train in a unified disease vocabulary with masked multi-
dataset supervision, enabling the model to leverage complementary annotations without introducing false negatives. Experiments on large-scale chest X-ray
benchmarks show that HarMoE consistently improves zero-shot classification, out-of-distribution transfer, and grounding over strong baselines. Our results suggest that building robust radiology VLMs requires moving beyond single-source image-report
alignment toward structured knowledge construction from heterogeneous
datasets with cleaner supervision and broader coverage. Code and the 873k harmonized
dataset will be released at https://github.com/Roypic/harmoe.
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All authors Haozhe Luo, Ziyu Zhou, Shelley Zixin Shu, Mauricio Reyes
Artificial Intelligence · Cross-listed from cs.LG
Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learning
Yuan-Heng Tsai, Li-Hsing Yen and Yan-Wei Chen
Federated learning (FL) is a popular distributed learning framework where multiple clients perform local training and a server aggregates the locally updated models. FL enables decentralized training while preserving the
privacy of clients'
datasets. However, non-independent and identically distributed (non-IID) or noisy
datasets can lead to low model accuracy or high convergence latency. Precluding these clients through client selection may mitigate the problem, but heavily biased client selections may also degrade the learning performance. In this study, we first experimentally measure the impact of non-IID data (including skews in data quantity and label distribution), noisy data, and
fairness in client selection on model accuracy and convergence. We then propose a
privacy-preserving scoring method to assess each client's contribution in FL, with experiments conducted to demonstrate the effectiveness of the proposed assessment.
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Artificial Intelligence
Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability
Abdullah Mamun, Shovito Barua Soumma and Hassan Ghasemzadeh
Ensuring trust in AI systems is essential for the safe and ethical integration of machine learning systems into high-stakes domains such as digital health. Key dimensions, including robustness, explainability,
fairness, accountability, and
privacy, need to be addressed throughout the AI lifecycle, from problem formulation and data collection to model deployment and human interaction. While various contributions address different aspects of trustworthy AI, a focused synthesis on robustness and explainability, especially tailored to the healthcare context, remains limited. This review addresses that need by organizing recent advancements into an accessible framework, highlighting both technical and practical considerations. We present a structured overview of methods, challenges, and solutions, aiming to support researchers and practitioners in developing reliable and explainable AI solutions for digital health. This review article is organized into three main parts. First, we introduce the pillars of trustworthy AI and discuss the technical and ethical challenges, particularly in the context of digital health. Second, we explore application-specific trust considerations across domains such as intensive care, neonatal health, and metabolic health, highlighting how robustness and explainability support trust. Lastly, we present recent advancements in techniques aimed at improving robustness under data scarcity and distributional shifts, as well as explainable AI methods ranging from feature attribution to gradient-based interpretations and counterfactual explanations. This paper is further enriched with detailed discussions of the contributions toward robustness and explainability in digital health, the development of trustworthy AI systems in the era of LLMs, and various evaluation metrics for measuring trust and related parameters such as validity, fidelity, and diversity.
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Artificial Intelligence
PosterMELD: Multi-Agent Paper-to-Poster Generation for Controllable Design Diversity with Editable Print-Ready Outputs
Haojie Hu, Chenhao Dang, Yaojia Liu et al. (+3 more)
Scientific poster construction compresses a long
multimodal paper into a readable, editable canvas. Existing systems hide request-level failures by scoring only completed outputs; direct image generation is not element-editable, while coding-
agent workflows are costly. PosterMELD is a template-conditioned multi-
agent pipeline: capacity-aware slots guide writing before rendering, and deterministic gates plus
vision-
language model (VLM) review route failures to bounded repair. Each accepted request exports editable PowerPoint (PPTX) and Portable Network Graphics (PNG) artifacts; explicit design controls yield same-paper variants. Across 621 papers, Print-Ready Rate (PRR) counts requests passing
geometric, readability, asset-integrity, and obvious-factual-error checks, with native editability reported separately. A frozen VLM assigns conditional Craftsmanship-Harmony-Expressiveness (CHE) scores to print-ready outputs. PosterMELD attains 81.3% PRR, 3.4 times P2P's rate and 5.2 times PosterGen's, and the highest conditional CHE among generated methods with multiple print-ready outputs. Native editability and explicit design controls are retained at a mean cost of USD 0.38 per request, 3.5% of Codex+Skill's. Code and resources are available at https://github.com/Shannon4Science/PosterMELD.
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All authors Haojie Hu, Chenhao Dang, Yaojia Liu, Hengrui Kang, Conghui He, Weijia Li
Artificial Intelligence · Cross-listed from cs.DB
Fast Discovery of Inclusion Dependencies with Desbordante
Alexander Smirnov, Anton Chizhov, Ilya Shchuckin et al. (+2 more)
Inclusion dependency is a relation between attributes of tables that indicates possible Primary Key-Foreign Key references. Automatic discovery of inclusion dependencies is a relevant problem for both academic and industrial communities. The core concern for this problem is the efficiency of discovery process, since it is a computationally expensive task. However, existing studies only address the algorithmic side, while leaving out the implementation aspect. At the same time, engineering details are at least as important as the algorithmic ones for achieving good performance. In this paper, we describe techniques for efficient implementation of two
algorithms for discovery of inclusion dependencies - Spider and Faida. The first one is a classic
algorithm whose ideas lie in the foundation of many other inclusion dependency discovery
algorithms. We propose an efficient parallelization technique, which greatly speeds up the
algorithm while simultaneously reducing its
memory consumption. The second one is the state-of-the-art approximate
algorithm, which we approach by applying four types of optimizations: data buffering, SIMD-enabled execution, careful hash-table selection and parallelization. In order to experimentally evaluate our techniques, we have implemented these
algorithms in Desbordante - an open-source science-intensive data profiler written in C++. For Spider, we have evaluated several different options, and in case of Faida we have demonstrated that all our optimization techniques yield results. We also compared our implementations with Metanome - a Java-based data profiler. Overall, we report up to 5x improvement in terms of run time reduction for Spider and up to 8x for Faida.
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All authors Alexander Smirnov, Anton Chizhov, Ilya Shchuckin, Nikita Bobrov, George Chernishev
Artificial Intelligence
MEGRAG: Multi-Granular Evidence Graphs for Answer-Aware Multi-Hop RAG
Weidong Bao, Yingying Sun, Jun Yang et al. (+7 more)
Multi-hop question answering is a fundamental challenge in
retrieval-augmented generation (RAG), because deriving an answer requires integrating dispersed evidence. Iterative RAG (iRAG) is widely used for this challenge, but existing methods have two limitations. First, most methods still support each
reasoning step with single-granularity evidence, making it difficult to balance information density and contextual noise. Second, existing methods often answer the original question only after aggregating evidence retrieved across intermediate steps, so redundant evidence and intermediate
retrieval errors may accumulate and degrade the final answer. To address these limitations, we propose MEGRAG, an answer-aware framework that represents multi-hop
reasoning as a path-structured multi-granular evidence graph. Offline, MEGRAG links passages to their sentences and extracted triples through a cross-granularity index. Online, it retrieves passages for the current query and selects aligned evidence, starting with compact triples and adding sentence or passage context as needed. MEGRAG uses the resulting intermediate answer and prior
reasoning to decide whether the Initial Query has been resolved. If not, it identifies the missing information and formulates a focused next query; otherwise, it stops
retrieval and returns the answer. Extensive experiments demonstrate consistent gains over a diverse set of RAG baselines.
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All authors Weidong Bao, Yingying Sun, Jun Yang, Yilin Wang, Zili Wei, Yubin Bao, Fangling Leng, Minghe Yu, Tiancheng Zhang, Ge Yu
Artificial Intelligence
PAC Approximation and DIRECT Optimization for Parametric Markov Models
Zhiming Chi, Ying Liu, Andrea Turrini et al. (+2 more)
In this paper, we consider the parameter synthesis and optimization problem for parametric Markov decision processes (pMDPs), the extension of classical MDPs where exact probability values are replaced by parametric expressions. Computing the rational function $f_{\lsf}$ that maps parameter valuations to the satisfaction value of a PRCTL property $\lsf$ is a computationally expensive task, particularly for pMDPs where the optimal policy may vary across the parameter space. We adopt the scenario approach to efficiently synthesize a probably approximately correct (PAC) approximation $\ApproxFunOfProperty{f}$ of $f_{\lsf}$: by sampling parameter configurations and solving a linear program, we obtain a
polynomial approximation whose error margin $\margin$ is guaranteed, with prescribed confidence, for all but an $\errorRate$-fraction of the parameter domain under the sampling distribution. We further show how this PAC framework can be combined with statistical model checking (SMC), enabling the analysis of black-box parametric models. Building on the PAC approximation, we integrate the DIRECT (DIviding RECTangles)
algorithm for derivative-free global optimization over the parameter space. We establish conditional optimality-gap guarantees: under explicit Lipschitz and PAC-good-set assumptions, the difference between the true optimum $f_{\lsf}(\parameters^{*})$ and the value found by DIRECT is bounded by a partition-diameter term and, in the PAC case, an additional approximation-error term. An empirical evaluation on 2997
benchmarks focuses on the new DIRECT-based optimization component. The results show that DIRECT variants solve fewer instances than the scenario optimizer, but on their common successful instances they often return slightly better objective values and usually run faster, while remaining close to the scenario values within the PAC margin.
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All authors Zhiming Chi, Ying Liu, Andrea Turrini, Lijun Zhang, David N. Jansen
Artificial Intelligence
From Profiling to Synthesis: Benchmarking Implicit Behavioral Alignment in Personalized LLM Agents
Jiajia Song, Bobo Li, Haiwen Yi et al. (+6 more)
Large
Language Models have enabled increasingly capable autonomous
agents, yet personalization remains critical for making such
agents practically useful. Recent
benchmarks have begun evaluating personalization in
agents, but they largely rely on static preference snapshots, fixed interaction logs, or question answering over predefined user profiles. Such designs fail to capture the complexity of evolving user preferences and neglect preference-conditioned task execution-a discrepancy we term as the knowledge-to-action gap. To address this challenge, we introduce IBA-Bench, a
benchmark for implicit behavioral
alignment constructed from longitudinal interaction histories that contain noise, implicit cues, and temporal inconsistencies. Unlike prior work, IBA-Bench evaluates whether an
agent can execute tasks while satisfying implicit user constraints inferred from historical interactions. We further propose IBA-
Agent, an
agent framework that reconciles conflicting priorities through broad
retrieval and trajectory-level
alignment. Experiment results on IBA-Bench show that effective personalization remains a significant challenge for state-of-the-art LLM
agents, and the proposed IBA-
Agent substantially improves behavioral
alignment in complex scenarios across nine application domains.
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All authors Jiajia Song, Bobo Li, Haiwen Yi, Zibo Ji, Meishan Zhang, Hao Fei, Min Zhang, Mong-Li Lee, Wynne Hsu
Artificial Intelligence
From Simple QA to Deep Research: A Verifiable Benchmark Constructed through Iterative Task Evolution
Can Wang, Haoran Chen, Haowen Gao et al. (+3 more)
Deep research
benchmarks require expert-level tasks and reliable evaluation grounded in task-specific knowledge. Existing
benchmarks rely heavily on expert authoring or pre-existing human-authored materials, while fully automatic construction struggles to ensure consistent and traceable verification. To address this gap, we introduce a verifiable
benchmark of 500 deep research tasks spanning 31 topics and 10 major categories, with three query forms designed to probe complementary capabilities required for deep research. The
benchmark is constructed automatically using an iterative Explorer-Formalizer-Challenger pipeline that progressively transforms simple questions into deep research tasks. Each task is represented as a directed acyclic graph (DAG) of atomic steps and associated checkpoints, enabling the query, DAG, and rubrics to evolve together in a controlled manner. Experiments demonstrate that the
benchmark clearly discriminates among models and query types, while its fact-grounded pointwise rubrics enable fine-grained, human-aligned, and stable evaluation. Our data, implementation, and results are publicly available.
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All authors Can Wang, Haoran Chen, Haowen Gao, Hao Ding, Zhaoyang Liu, Zhiying Tu