Artificial Intelligence · Cross-listed from cs.LG
ReCAST: Reward Credit Assignment across Timesteps for Online Diffusion Reinforcement
Yihang Chen, Yuanhao Ban, Kuei-Chun Kao et al. (+1 more)
Training
diffusion models with multiple rewards requires distinguishing user preference from reward informativeness. User preference determines how much each reward should contribute to the overall objective; reward informativeness determines when its feedback is useful during denoising. Some rewards can meaningfully evaluate a sample as soon as global structure emerges, but others become informative only when the sample is nearly clean. To address both questions jointly, we propose ReCAST (Reward Credit ASsignment across T}imesteps), the first method, to our knowledge, for per-reward, timestep-dependent credit assignment in
diffusion reward fine-tuning. ReCAST separates user preferences from temporal allocation through a reward-by-timestep weight
matrix $W$, whose row sums match the user-specified reward budgets $\lambda$, while its column sums are equal, assigning the same total weight to each denoising step. Under these marginal constraints, ReCAST allocates weight according to each reward's informativeness, quantified by its Rényi discriminability gain at each step. These gains
telescope to the total discriminability between the reward-induced positive policy and the current policy, providing a basis for temporal credit assignment. We evaluate ReCAST by training SD3.5-Medium under two distinct four-reward settings, each across five reward budgets $\lambda$. ReCAST improves the training rewards in one setting and matches them in the other, improves every held-out judge in both, and is preferred by an independent LLM-as-a-Judge. Together, these results show that ReCAST yields improvements that generalize beyond the training rewards and support its core principle: assigning each reward greater weight at the denoising timesteps where its feedback is most informative.
Read the full abstract ▾
All authors Yihang Chen, Yuanhao Ban, Kuei-Chun Kao, Cho-Jui Hsieh
Artificial Intelligence
Vibe Patenting: Evaluating LLM Judges for Professional Patent-Drafting Agents
Toshiaki Koike-Akino, Vladislav Blaykhman, Ye Wang et al. (+2 more)
LLM judges are increasingly used to evaluate and improve AI-generated outputs, yet their reliability for complex professional work remains unclear. We study this problem through Vibe Patenting, an end-to-end patent-drafting testbed for AI-
agent evaluation. A separately-invoked LLM judge evaluates generated patent drafts and provides structured feedback for iterative revision. Across multiple inventions and drafting-
agent configurations, judge-guided revision consistently improves judge-assessed quality, while unguided revision tends to saturate. Notably, iterative judge feedback enables a low-
reasoning agent to approach the performance of a substantially more expensive high-
reasoning agent. Stronger models and increased
reasoning generally improve judge-assessed drafting quality, while domain-specific agentic workflows provide further gains. We validate the judge against independent evaluation by a professional patent attorney and find meaningful but strongly metric-dependent agreement and systematic calibration differences. These results highlight both the utility and limitations of LLM judges as evaluators and optimization signals for complex professional workflows.
Read the full abstract ▾
All authors Toshiaki Koike-Akino, Vladislav Blaykhman, Ye Wang, Jing Liu, Gene V. Vinokur
Artificial Intelligence
Generalized Agent Iteration: One Formal Framework for Iterative Policy Improvement and Recursive Self-Improvement
Hongyao Tang, Yi Ma, Pengyi Li et al. (+1 more)
When we speak of recursive self-improvement (RSI), are we speaking of a phenomenon, a mechanism, or a prospect? Towards autonomous and evolving intelligence, RSI is being claimed at many scales, while no single framework that formally describes these emerging instances exists. Its counterpart in the classical realm, iterative policy improvement, is characterized by generalized policy iteration (GPI), a framework of broad applicability with well-understood theoretical properties, but only where the update principle and the evaluation base lie outside the
agent. In this paper, we propose Generalized
Agent Iteration (GAI), a formal framework that describes iterative policy improvement and RSI as two cases of a single learning paradigm. GAI defines the
agent as a configuration of modifiable components within a system and models the learning process as a cycle of
agent evaluation and
agent improvement. Two pivotal dials then distinguish the instances: whether the improving mechanism is part of the
agent and whether the standard it is measured against is grounded outside it. The former dial delineates the boundary between GPI and RSI, and the latter determines a system's polarity as anchored, goal drift, or fully self-referential. Moreover, we use these coordinates to place existing systems on the same two axes and make the defects of recursive self-improvement statable one condition at a time. We see this paper as a first step toward exploring a formal characterization of RSI that rests on the classical account, makes existing systems comparable, and provides a principled basis for analyzing and designing new ones.
Read the full abstract ▾
All authors Hongyao Tang, Yi Ma, Pengyi Li, Yifu Yuan
Artificial Intelligence
Converge Then Diversify: Decoupling Convergence and Diversity in Multi-Objective Bayesian Optimisation
Chao Jiang, Yueling Huang and Miqing Li
Multi-objective
Bayesian optimisation (MOBO) is a sample-efficient approach for optimising expensive black-box functions with multiple objectives. In MOBO, the goal is to adequately approximate the Pareto front; that is, to obtain a high-quality solution set with 1) good convergence (closeness to the Pareto front) and 2) good diversity (spread across the Pareto front). Existing MOBO methods typically aim to accomplish these two tasks simultaneously, i.e., driving the search towards the Pareto front while maintaining a diverse set of nondominated solutions, such that the solutions, ideally, can gradually approach the entire front. When sufficient search budgets are available, this approach is effective. However, considering both convergence and diversity throughout the search is not easy and requires careful design. Under very tight budgets, there may not be enough solutions generated to be able to simultaneously approach the entire Pareto front. To address this issue, this paper proposes a converge-then-diversify (CTD) approach that decouples convergence and diversity into two stages. In the first stage, CTD focuses on convergence, aiming to quickly drive the search toward a single point on the Pareto front. In the second stage, CTD focuses on diversity, aiming to spread solutions across the front. We present two simple instantiations of CTD by using widely adopted acquisition functions in the area. Experimental results show that, across all 446 pairwise comparisons, CTD statistically outperforms state-of-the-art methods in 72.9% of the cases, performs equivalently in 21.1%, and is statistically worse in only 6.1%, with the advantage being particularly evident in settings with very tight evaluation budgets or in high-dimensional problems.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.LG
Task-Aware Federated Fine-Tuning for MoE-based Large Language Models
Tingqi Wang, Hongyu Ke, Haoxin Wang et al. (+2 more)
Mixture-of-Experts (MoE) has become a widely adopted architecture for Large
Language Models (LLMs), as it improves model capacity while limiting computational overhead through sparse expert activation. This property makes MoE-based LLMs particularly attractive for resource-constrained distributed environments. However,
federated fine-tuning of MoE-based LLMs remains challenging under heterogeneous client data. Since clients often correspond to different task preferences, directly aggregating their local updates may weaken expert specialization and introduce conflicting update directions on shared experts. To address these challenges, we propose FedTAR, a task-aware
federated fine-tuning method for MoE-based LLMs. FedTAR establishes the association between local updates and task preference via routing outputs. Specifically, we apply Singular Value Decomposition (SVD) to both routing features and local updates to extract low-dimensional task coordinates and update directions. Based on the task coordinates, FedTAR performs intra-cluster aggregation among clients with similar task preferences and inter-cluster aggregation across different task groups. The aggregated update is then reconstructed through the learned task-to-update mapping, ensuring that the final update remains aligned with task-specific optimization directions. In this way, FedTAR preserves expert specialization and mitigates destructive interference among heterogeneous clients. We evaluate FedTAR on four
benchmark tasks under different non-IID settings. Experimental results demonstrate that FedTAR consistently outperforms strong
federated fine-tuning baselines and achieves state-of-the-art performance.
Read the full abstract ▾
All authors Tingqi Wang, Hongyu Ke, Haoxin Wang, Rafal Angryk, Zhipeng Cai
Artificial Intelligence
ZGCM-1: A Fully Open and Extremely Efficient Foundation Model for Math and Agentic Search
Jiyan He, Guang Liang, Hao Liu et al. (+19 more)
In this work, we present ZGCM-1, a fully open 7B dense foundation model trained from scratch with extreme data, system, and algorithmic efficiency. ZGCM-1 is founded on a core premise: compact models cannot passively memorize the open web, but can overcome parametric capacity limits by coupling deliberate internal thinking with active external tool use. To support this paradigm across a 256K context, we develop an end-to-end, high-efficiency open training recipe: Architecture & System Co-design: interleaved gated sliding-window and full
attention, and a stable FP8
Muon optimizer; Progressive Curriculum & MDP Mid-Training: context scaling across 16K, 64K, and 256K, and the reformulation of interaction traces into Markov Decision Processes. Furthermore, we establish an AI-native R&D workflow where
agent swarms autonomously manage cluster operations, data curation, and rapid diagnostic evaluation. Extensive evaluations show that ZGCM-1-7B is competitive across 7B model family on general
benchmarks. On several challenging mathematical
reasoning and agentic search suites, it remains competitive with frontier models orders of magnitude larger, such as Qwen3-235B-A22B and GLM-5.1. We also show that our pre-training design offers a ~4.2x efficiency improvement in 16K pre-training time-to-loss. Across the full development lifecycle, we distill eight actionable empirical findings-spanning architectural scaling, SFT quality
pruning, long-context generalization, and agentic co-training dynamics. To facilitate community research, we open-source model weights from the pre-training, mid-training, and post-training stages, intermediate checkpoints, training code, per-stage data and data recipes, and W&B logs.
Read the full abstract ▾
All authors Jiyan He, Guang Liang, Hao Liu, Haoxiang Guan, Jinbo Sun, Junyi Guo, Wenjun Feng, Yantai Xie, Yifei Shen, Bin Shao, Chuyang Wei, Kai Chen, Kexin Zhou, Minghang Zhu, Shuxin Zheng, Tie-Yan Liu, Taine Zhao, Wenhui Zhu, Xueyin Xu, Xiaoqing Zhang, Yatao Li, Yuxuan Ren
Artificial Intelligence · Cross-listed from cs.CR
SkillAtlas: An Attack Trace Library for Agent Skills
Yuxin Tian, Zenghao Duan, Liang Pang et al. (+2 more)
Agent skills are reusable units for
language-model
agents, but their risks emerge through model decisions, user context, tool calls, and execution feedback rather than through stable signatures or a single sandbox run. Existing static, dynamic, and
benchmark-style evaluations rarely preserve public evidence that can be inspected, searched, and reused. We present SkillAtlas, a hosted attack trace library that converts private
agent-skill security report bundles into reviewed, redacted, and searchable public cases. The library contains 3,014 cases, 6,589 traces, 151,131 steps, 233 affected skills, and 8 risk categories; 42.5% of successful cases first become successful after a non-success initial round, and trajectory-grounded labels improve pre-execution guard accuracy to 0.770.
Read the full abstract ▾
All authors Yuxin Tian, Zenghao Duan, Liang Pang, Zhiyi Yin, Xueqi Cheng
Artificial Intelligence · Cross-listed from cs.CV
ProtoCAM: Interpretable Few-Shot Mask-Guided Prototypical Learning for Breast Lesion Classification in Ultrasound Imaging
Ashkan Ebadi
Breast ultrasound imaging plays an important role in the early detection and diagnosis of breast cancer, particularly for patients with dense breast tissue. However, developing reliable deep learning models for ultrasound analysis is challenging due to limited annotated medical data and the need for interpretable predictions. To address these challenges, this paper proposes ProtoCAM, an explainable few-shot learning framework for breast lesion classification that integrates mask-guided feature encoding, prototypical metric learning, and gradient-based visual explanations. The proposed approach leverages lesion masks to guide feature extraction and constructs class prototypes within an
embedding space to enable robust classification under limited training samples. The framework was evaluated on the BUSI
dataset using a stratified group k-fold cross-validation protocol to prevent patient-level data leakage. Experimental results demonstrate ProtoCAM's high performance in low-data scenarios. In a 3-way 5-shot setting, the proposed method achieves a macro F1-score of 0.910, representing a substantial improvement over standard supervised CNN models. Among the evaluated backbone networks, ResNet18 achieved the best performance, reaching a macro F1-score of 91.65% under a 15-shot configuration, providing interpretable insights into the classification decisions. These results highlight the potential of explainable few-shot learning frameworks for reliable computer-aided breast cancer diagnosis in data-scarce medical imaging environments.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.RO
Bridging Thought and Action: Taming Long-Horizon Instability in Open-Source LLM Agents with a MetaTool-Enhanced ROS Framework
Kazi Abrar Mahmud, Nilotpaul Kundu Dhurubo, Tamal Kirttonia et al. (+2 more)
Large
Language Models (LLMs) have enabled more natural human-
robot interaction, but open-source models often exhibit unstable long-horizon
reasoning and inefficient action execution when deployed in agentic robotic frameworks. This paper presents an enhanced ROS-
Agent based architecture that improves task reliability and execution efficiency for agentic robotic systems using open-source LLMs. The proposed system introduces a novel intermediate mechanism, termed the MetaTool, which enforces structured planning prior to action execution. Given a natural-
language command, the MetaTool induces the LLM to generate a pseudo-code plan of intended tool invocations, which is stored in the ROS-
Agent's scratchpad and persists throughout execution. By explicitly separating planning from execution, the proposed approach reduces execution loops and improves deterministic behavior. The architecture is validated on a custom mobile robotic platform with
multimodal perception and motion control capabilities. Experimental results on real-world interactive tasks demonstrate improved task completion and contextual consistency, with up to ~24% gains on complex tasks compared to the baseline framework.
Read the full abstract ▾
All authors Kazi Abrar Mahmud, Nilotpaul Kundu Dhurubo, Tamal Kirttonia, Sabbir Hossain Ujjal, Mohammad Ariful Haque
Artificial Intelligence · Cross-listed from cs.SE
The Agentic Company OS: Substrate Inversion for Sustained Enterprise Agent Deployment
Oliver Aleksander Larsen and Mahyar T. Moghaddam
Enterprise AI
agents often succeed in a demonstration and then stall once they must operate day after day. An industry report estimates that most pilots never reach production and that deployed systems rarely retain feedback or improve over time, while
agent benchmarks show single-run successes masking unreliable repetition. We argue that these failure modes share a common architectural root:
agents reason over data structured for human operators and traditional applications, not for the
language models that power them. This position paper proposes that companies deploying
agents in sustained operation should rebuild their cognitive substrate, the shared environment
agents read as working context, around representations matched to that
reasoning surface, isolating schema translation to the action boundary. Markdown is the instantiation available today, not a proven
agent-native primitive. Two mechanisms ground the argument: context-bandwidth asymmetry, the gap between one-pass reading of connected prose and field-by-field typed access that strips relations; and cross-loop coupling, the claim that action, skill, and policy loops compound only if they share one substrate. A four-layer framework (Data, Knowledge, Intelligence, Governance) operationalizes the position, with a Sync
Agent enforcing the action boundary and a per-skill trust gradient, making governance and auditability structural properties of the substrate. The position revives the shared-substrate tradition of classical multi-
agent systems under LLM-era economics. We analyze the main objections and risks, including indirect
prompt injection on the compile path, and outline a research agenda for evaluating substrates directly.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CV
Pedestrian Crossing Intent Classification From Event-Based Vision Using Convolutional Spiking Neural Networks With Temporal Augmentation
Henok Teklu, Mustafa Sakhai, Maciej Wielgosz et al. (+1 more)
Anticipating whether a pedestrian will cross the road is safety-critical for autonomous vehicles, requiring real-time inference under challenging conditions including motion blur, high dynamic range, and class imbalance. Conventional frame-based deep networks process redundant RGB data at fixed frame rates, limiting their temporal resolution and energy efficiency. In this work we present an end-to-end pipeline that (i) converts real-world driving footage from the Joint
Attention in Autonomous Driving (JAAD)
dataset into synthetic dynamic
vision sensor (DVS) event streams using the v2e simulator, (ii) augments training with the CARLA-simulated DVS sequences of the DVS-PedX
dataset under both normal and adverse weather conditions, and (iii) trains a novel convolutional spiking neural network (Conv-SNN) with clip-consistent DVS augmentation to classify pedestrian crossing intent as binary: crossing or non-crossing. We detail all architectural decisions, the exact leaky-integrate-and-fire
neuron dynamics with surrogate-gradient learning, the class-balanced loss formulation, JAAD oversampling at 6x, and a 70/15/15 stratified splitting protocol. The trained model achieves 95.83% accuracy and F1 = 0.9695 on the JAAD DVS test set, 97.79% accuracy and F1 = 0.9478 on normal CARLA DVS, and 94.78% accuracy and F1 = 0.8369 on adverse-weather CARLA DVS, all from a 1.07M-parameter architecture trained on CPU. Compared to prior frame-based approaches on JAAD, our method closes or surpasses the reported accuracy while operating natively on sparse temporal representations. We include a thorough analysis of the convergence behaviour across all 15 training epochs, domain transfer characteristics, and a quantitative comparison with representative related work.
Read the full abstract ▾
All authors Henok Teklu, Mustafa Sakhai, Maciej Wielgosz, Matej Mertik
Artificial Intelligence
Rethinking Heterogeneous System Disaggregation for Subquadratic Attention
Arya Tschand, Yaosheng Fu, Vikram Sharma Mailthody et al. (+6 more)
Frontier
language models are more aggressively using subquadratic
attention to reduce the
memory footprint and compute requirements during inference while still delivering frontier accuracy. While existing systems make dense
attention-centric disaggregated serving decisions, we show that disaggregating inference around the unique arithmetic intensity and
memory footprint of subquadratic
attention LLMs can achieve significant throughput and energy efficiency gains on emerging DRAM-based and SRAM-only heterogeneous systems. We introduce SQD (SubQuadratic Disaggregation), a fine-grained heterogeneous disaggregation scheme that splits decode by quadratic and subquadratic
attention rather than by operator type, and that applies across subquadratic
attention variants. For sparse
attention LLMs, we disaggregate decode into top-k selection, which must index through the full KV, and top-k
attention plus FFN, which have static
memory footprints. For linear and sliding-window
attention LLMs, we disaggregate decode into dense
attention layers and subquadratic
attention layers plus FFN. In an adjusted 8xB200 heterogeneous system proxy, we observe average
tokens/J improvements of 53% on GLM 5.2, 31% on Nemotron 3 Ultra, and 56% on Gemma 4 31B over the strongest GPU-only baselines. In an analytical model of a Rubin plus LPX system with fixed power budgets, we observe 1.2x to 1.5x tighter achievable latencies and up to 3.6x higher throughput over the best baseline of
attention-FFN disaggregation. Our experiments also reveal architectural insights on chip and interconnect provisioning for next-generation heterogeneous systems serving subquadratic
attention.
Read the full abstract ▾
All authors Arya Tschand, Yaosheng Fu, Vikram Sharma Mailthody, Nicolai Oswald, Po-An Tsai, Ritchie Zhao, Oreste Villa, Vijay Janapa Reddi, Karu Sankaralingam
Artificial Intelligence
A Hybrid LSTM-XGBoost Framework for Multi-Horizon Stock Return Prediction Across Diversified Equity Portfolios
Seif ElDein Mostafa, Yahia Ahmed, Farah Datwish et al. (+1 more)
Accurate prediction of equity returns remains a major challenge in computational finance due to the non-stationary, nonlinear, and low signal-to-noise ratio nature of financial time series. This paper proposes a hybrid two-stage architecture that combines a long short-term
memory (LSTM) network with an XGBoost gradient-boosted regressor for multi-horizon stock return prediction across a diversified panel of 14 U.S. equities spanning six industry sectors. The LSTM component, comprising two stacked layers with 64 hidden units, processes 60-day sliding windows of five sequential
market features to produce 64-dimensional temporal
embeddings that encode learned sequential
market dynamics. These
embeddings are concatenated with 14 hand-crafted technical indicators to form a 78-dimensional hybrid feature vector, which is subsequently passed to an XGBoost regressor tuned via 3-fold cross-validation grid search. The framework is trained on a multi-stock pooled corpus using strict chronological splits and per-stock MinMaxScaling to prevent look-ahead bias, and evaluated across four prediction horizons of 30, 90, 252, and 365 trading days. Experimental results demonstrate that the hybrid model achieves a test RMSE of 0.0949 on the 30-day horizon, roughly one-third that of the standalone LSTM baseline, while marginally matching or surpassing the XGBoost-Only baseline across the majority of stocks. Directional accuracy rises with horizon length, reaching 97.6% at 365 days; we show, however, that this largely tracks the high base rate of positive long-horizon returns in the sample, and we therefore
benchmark directional accuracy against a naive always-positive predictor and treat the above-base-rate gap at short horizons as the more informative signal. A composite investment scoring framework derived from multi-horizon predictions is further proposed to support portfolio ranking and decision support.
Read the full abstract ▾
All authors Seif ElDein Mostafa, Yahia Ahmed, Farah Datwish, Marwa Solayman
Artificial Intelligence
CMA-OT: Hierarchical Expert Supervision for Dance-to-Music Generation
Jinting Wang, Chenxing Li, Dong Yu et al. (+1 more)
Dance-to-music (D2M) generation aims to synthesize music that is rhythmically and stylistically aligned with dance
videos. A key challenge arises from the semantic mismatch between sparse dance cues, such as rhythm and style, and the dense information required for music composition, including structure, instrumentation, and expressive dynamics. Existing methods typically rely on these sparse cues and supervise only the final audio output, resulting in poorly learned music representations and generated music with limited musicality and structural coherence. To address these issues, we propose Curriculum-guided Multi-scale representation
Alignment with scale-aware Optimal Transport (CMA-OT), a novel paradigm that leverages an external music expert to provide hierarchical supervision for the generator's latent features, bridging the semantic gap and enhancing representation learning. To effectively incorporate hierarchical supervision, we introduce a curriculum-guided multi-scale learning strategy that progressively transfers musical knowledge from the expert to the music generator, enabling stable and effective representation learning. Moreover, to accommodate the semantic and structural variations across different expert scales and achieve fine-grained
alignment under temporal mismatch, we propose a scale-aware optimal transport
alignment mechanism, which models soft correspondences between hierarchical expert representations and the generator's latent features. Extensive experiments on two
datasets demonstrate that CMA-OT achieves state-of-the-art performance in rhythmic synchronization, perceptual quality, and overall music generation.
Read the full abstract ▾
All authors Jinting Wang, Chenxing Li, Dong Yu, Li Liu
Artificial Intelligence · Cross-listed from cs.RO
Language-Guided Terrain-Adaptive Neural MPC for Autonomous Traversal of Articulated Tracked Robots
Zhenfeng Gan, Yanbo Chen, Lirong Che et al. (+3 more)
In urban search and rescue, articulated tracked
robots (ATRs) must traverse structured but contact-rich environments such as stairwells and cluttered building interiors. Reliable autonomy remains challenging because
robot-terrain interaction (RTI) is hybrid and discontinuous, and effective flipper-track coordination is difficult to model analytically. We present ASTRIL-MPC, a
language-guided neural kinematics model predictive control (MPC) framework for autonomous traversal. A learned kinematics model predicts short-horizon task-state increments from a height sequence and recent trajectories; NMPC plans with multi-objective costs and strict feasibility constraints; and a large
language model (LLM) proposes bounded updates to selected weights and bounds through a safety-checked interface with range clipping, rate limiting, and consistency checks. The compiled predictor enables a full control cycle within 100 ms. Across three traversal tasks and a multi-height generalization setting, ASTRIL-MPC improves an aggregate traversal-quality score by up to 71% over a non-adaptive NMPC and by 67% over a PPO baseline, while eliminating measurable collision impacts during descent. These results indicate that combining terrain-conditioned neural kinematics, optimization-based planning, and
language-guided adaptation yields data-efficient and robust autonomy for articulated tracked
robots. Real-
robot trials over four indoor obstacles further demonstrate transfer to contact-rich physical traversal.
Read the full abstract ▾
All authors Zhenfeng Gan, Yanbo Chen, Lirong Che, Yongyi Ma, Rongkai Zhu, Xueqian Wang
Artificial Intelligence
Embodied-BenchForge: A Closed-Loop Agentic Workflow for Embodied Benchmark Construction
Baoyang Jiang, Fengchun Zhang, Leyuan Wang et al. (+9 more)
Agentic systems offer a promising way to automate
embodied benchmark construction, but existing approaches typically cover isolated stages or remain specialized to predefined environments and task families. More importantly, multi-step construction produces dependent intermediate artifacts that are often passed downstream without artifact-specific verification, allowing local defects to propagate into the final
benchmark. We present
Embodied-BenchForge, an agentic framework that transforms user-specified evaluation intents into complete
embodied benchmark artifacts. It formulates construction as Closed-Loop
Benchmark Synthesis, integrating forward artifact synthesis with backward verification and repair. Skill-Orchestrated Artifact Synthesis composes typed and reusable skills into executable workflows, while an artifact dependency graph records intermediate outputs and their dependencies. Requirement-Guided Verification and Repair applies artifact-specific contracts throughout construction and uses provenance to trigger local re-execution or upstream rollback when verification fails.
Embodied-BenchForge constructs six
benchmarks covering diverse
embodied scenarios in the Offline EQA Track, together with one interactive
benchmark containing 220 executable tasks in the Interactive
Embodied Track. Evaluations of representative MLLMs and
embodied agents show that the
benchmarks distinguish model capabilities in both observation-based understanding and closed-loop execution. Quality assessment and ablations validate
benchmark quality and the effectiveness of verification and repair, while repair and skill-reuse analyses demonstrate efficient localized recovery and cross-
benchmark reusability.
Read the full abstract ▾
All authors Baoyang Jiang, Fengchun Zhang, Leyuan Wang, Haotian Li, Yida Wang, Zhe Ji, Jinshan Lai, Xi Ren, Danyang Li, Zheng Yang, Jianwei Hu, Qiang Ma
Artificial Intelligence · Cross-listed from eess.AS
MP-Bench: Evaluating Voice Agents as a Multiparty Conversation Participant
Yi-Jen Shih, Shih-Yun Shan Kuan, Guan-Ting Lin et al. (+7 more)
Conversational voice
agents have advanced significantly, offering increasingly natural human-machine interactions through both cascaded and end-to-end architectures. However, while recent
benchmarks extensively evaluate dyadic interactions and passive audio comprehension, they largely overlook a prevalent real-world scenario: multi-party conversations. Evaluating
agents in these settings is fundamentally more challenging than in dyadic interactions due to the exponentially greater conversational complexity. For voice
agents to integrate seamlessly into human group dynamics, they must not only generate contextually appropriate responses but also demonstrate a nuanced understanding of open turn-taking. To address this gap, we introduce Multiparty Bench (MP-Bench), the first
benchmark specifically designed to objectively evaluate conversational
speech systems as active participants within multi-party contexts. MP-Bench assesses
agent behavior along two primary dimensions: turn-taking awareness and response appropriateness. Additionally, we incorporate comprehension-based question-answering tasks as a complementary evaluation. By benchmarking 12 voice
agents, we find that real-time voice
agents stay at or below 22% on multiparty comprehension and remain near chance on multiparty turn-taking, exposing an open challenge for real-time voice
agents under multiparty scenario.
Read the full abstract ▾
All authors Yi-Jen Shih, Shih-Yun Shan Kuan, Guan-Ting Lin, Kai-Wei Chang, Siddhant Arora, Shu-wen Yang, Abdelrahman Mohamed, Shinji Watanabe, Hung-yi Lee, David Harwath
Artificial Intelligence
Autonomous Research for Open-Ended Problems: A Case Study on Telecom Ticket Retrieval
Junghyun Min, Huseyin Uzunalioglu and Mohamed Trabelsi
Recent breakthroughs in LLM-based systems and their abilities in problem solving and coding have allowed progress in the AI for Science paradigm, potentially replacing human roles in machine learning (ML) research. However, while several frameworks of fully autonomous end-to-end ML research have been proposed, successful implementations of them are often limited to problems with narrow search spaces, like
language modeling or biomedical ML
benchmarks. In this paper, we explore how autonomous research can be adapted to solve open-ended, industry-grade ML problems, by considering a case study: telecom ticket
retrieval, an open-ended task with degrees of freedom in representation, architecture, and training data generation. We discover that autonomous research for open-ended problems with commercial and open-source
agents shows both promise and limitations: while autonomous research can excel in narrow hyperparameter optimization, it lacks human-like intuition and creativity and requires operational overhead. Even with minimal human supervision, autonomous research can reach $90\%$ of state-of-the-art performance (0.34 vs. 0.38 Recall@1) in a much shorter time period (10 weeks vs. 10 months of human work) at a modest cost (up to \$200 per Cursor campaign). Our empirical evidence recommends that human researchers and autonomous research frameworks work together for best results in ML research.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.LG
MAxBench: A Multinomial Concept Recovery Benchmark
Divya Appapogu, Freya Behrens, Yonatan Belinkov et al. (+1 more)
Fine-grained control of
language model behaviors (e.g., steering) is among the more actionable outcomes of interpretability research. For binary concepts such as refusal, a single direction in activation space often suffices for steering. However, many concepts are not binary: Animals and Countries contain many subcategories, each with multiple instances. For these concepts, the search space over possible representation geometries is far larger than for binary concepts; it is thus not clear what geometries are most appropriate, nor what methods are most effective at recovering them. In this work, we introduce MAxBench, a geometry-agnostic evaluation framework for multinomial concept representations based on sampling from the recovered concept representation. We use MAxBench to compare 10 localization methods (covering 5 geometry types) across 6 concepts and 4 models. Using this framework, we find that (i) affine subspaces steer more reliably and have greater recall than rank-one or linear subspaces; (ii) much of this advantage is due to better non-zero offsets rather than the choice of bases; (iii)
manifold steering is competitive with the best methods when applicable; and (iv) no method consistently outperforms prompting, in
alignment with prior findings on binary concepts. These findings underscore the importance of expanding the scope of interpretability research and meta-evaluation to concepts with more varied structure.
Read the full abstract ▾
All authors Divya Appapogu, Freya Behrens, Yonatan Belinkov, Aaron Mueller
Artificial Intelligence · Cross-listed from cs.SE
Involving before Evolving: A Vision for Trustworthy Enterprise Digital Twin Engineering
Kérian Fiter, Adil Lagrou, Franck Dervault et al. (+1 more)
Enterprise Digital Twins (EDTs) promise data-driven decision support at organizational scale, but realizing them requires navigating siloed departments, tacit knowledge, and high-stakes decisions with long-horizon consequences. Existing approaches involve domain experts during model development but focus less on early organizational buy-in in EDTs. We present a
vision for trustworthy EDT engineering grounded in an `involving before evolving' paradigm: rapidly involving stakeholders through a working prototype before evolving toward federation and full interoperability. Our three-stage approach combines foundation models for rapid prototyping, an ontological backbone for
federated interoperability, and observability tooling for stakeholder trust. We ground our
vision in an ongoing collaboration with Michelin, a multinational manufacturer, where an initial prototype has helped support stakeholder buy-in.
Read the full abstract ▾
All authors Kérian Fiter, Adil Lagrou, Franck Dervault, Bentley Oakes
Artificial Intelligence
Anchoring Clinical Events in Time: UID-Preserving Multimodal Reconstruction and Source-Grounded Adjudication
Sayantan Kumar, Nicolas Grimaldi, Jack Cummins et al. (+1 more)
Clinical timelines support treatment-window analysis and leakage-free modeling, but discharge summaries often obscure chronology and structured EHR tables describe only part of the patient course. We present a UID-preserving framework that links each narrative event occurrence to its source span and retains that identity through text-only estimation, structured-evidence
retrieval, timestamped source-row grounding, and joint revision. We also present GAVEL, an LLM judge that compares two UID-aligned timelines against the narrative and structured record, to augment prior matching and temporal assessments. Across six open-weight models and 40 mixed-critical-care summaries, the GLM 5.2
multimodal revision, as compared to its text-only variant, improved temporal agreement without reducing event recovery and performed competitively with clinician annotations, while other model revisions showed smaller gains and lower overall performance. Ablations showed that UIDs primarily preserve event retention, whereas source-row linkage supports temporal placement. Blinded human review upheld most GAVEL findings, and controlled adjudication favored
multimodal over text-only GLM 5.2 but did not for DeepSeek V3.2. In developing the UID and judge pipeline, we are able to demonstrate 43% increased event recovery, a framework competitive with clinician annotations, and a system with occurrence-level provenance for both reconstruction and evaluation.
Read the full abstract ▾
All authors Sayantan Kumar, Nicolas Grimaldi, Jack Cummins, Jeremy C. Weiss
Artificial Intelligence · Cross-listed from cs.RO
Dynin-Robotics: Omnimodal Unified Diffusion Vision-Language-Action Model
Hoeun Lee, Jaeik Kim, Jusang Oh et al. (+4 more)
Visual goal and dynamics prediction can provide
language-conditioned
robot policies with both a target outcome and a representation of action-dependent scene changes. We bring these predictions into action generation and selection through a shared trajectory model. Dynin-Robotics implements this formulation on Dynin-Omni, an omnimodal masked-
diffusion backbone, representing
language, visual observations, goals, and actions as discrete
tokens. By varying conditioning and target spans, the same model learns action prediction, action-conditioned next-observation prediction, terminal goal-state prediction, and trajectory-to-instruction reconstruction. These interfaces support test-time scaling through goal prediction, action-candidate evaluation, and joint refinement of action and future-state predictions. We continually pretrain the model on approximately 1.33 million trajectories from 48 Open X-Embodiment
datasets and adapt it separately to downstream domains. On two VLABench tasks,
robot pretraining improves adaptation within a fixed Stage-2 step budget, and the full objective mixture improves shifted-instruction success over Policy-only post-training under the same coupled decoder. Combining goal guidance with joint action-next-state denoising further improves shifted-instruction success over action-only decoding; the benefit depends on how the predictions are composed. Dynin-Robotics achieves competitive performance on LIBERO and zero-shot LIBERO-Plus, together with a 78.4% average success rate across four manipulation conditions on a Franka Research 3
robot. An optimized block-parallel implementation accelerates model-side action decoding by up to 29.2x relative to the base implementation under the reported profiling setup. These results support shared trajectory modeling as a common interface for learning complementary
robot objectives and composing their predictions during control.
Read the full abstract ▾
All authors Hoeun Lee, Jaeik Kim, Jusang Oh, Jinhyeok Kim, Geon Choi, Hyeonggeun Kim, Jaeyoung Do
Artificial Intelligence
Diffusion Models and Concept Formation
Zekun Wang, Karthik Singaravadivelan and Christopher J. MacLellan
Humans organize knowledge into a taxonomy of concepts with nested levels of abstraction and a basic level at which people recognize and name objects with the least cognitive effort. Cobweb is a classic cognitive account of this ability, an incremental learner that builds a probabilistic concept hierarchy by maximizing category utility. We argue that
diffusion models, although designed for image synthesis, implicitly perform the same computation. The noisy marginals of a
diffusion model are Gaussian smoothings of the data distribution, and the modes of these marginals form a hierarchy that corresponds to a Cobweb tree of probabilistic prototypes in four respects. Both are hierarchical density models, both are hierarchical-
Bayesian models with Gaussian prototypes, both treat categorization as score-following that reduces uncertainty, and in both a basic level emerges. We locate this basic level for a
diffusion model at an intermediate noise level, where recent analyses show that the reverse process commits to the class identity of a sample. The two models differ mainly in how they represent and learn the taxonomy. Cobweb learns a discrete tree incrementally, whereas a
diffusion model encodes a continuous, interpolable hierarchy in a single learned score field fit to the data distribution. We test the correspondence on MNIST and Fashion-MNIST by recovering the
diffusion hierarchy through mode-finding and comparing the basic levels of the two models. This reframes
diffusion as a cognitive model of concept formation and offers Cobweb a continuous, scalable instantiation.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CV
Unified CT and MRI Pancreas Segmentation for Label-Efficient Cross-Modality Subregion Transfer
Ziliang Hong, Hongyi Pan, Halil Ertugrul Aktas et al. (+7 more)
Robust medical image
segmentation across imaging modalities is challenging because of large differences in appearance and intensity distributions. Models trained on a single modality often show substantial performance drops when applied to unseen domains. In this work, we develop a unified 3D pancreas
segmentation framework that applies domain-
adversarial learning to 4,604 heterogeneous CT and MRI scans to learn anatomical representations. A shared nnU-Net encoder-decoder is trained for whole-pancreas
segmentation, with a latent domain discriminator encouraging CT-MRI feature
alignment. The learned encoder is subsequently transferred to pancreatic head-body-tail
segmentation using limited MRI-only subregion annotations. An average Dice score of 87.31% on the in-distribution test set and Dice scores ranging from 84.20% to 88.09% across external OOD
datasets were achieved in whole pancreas
segmentation. Dice scores of 80.53% on MRI and 83.05% on CT were achieved for downstream subregion
segmentation, without using CT subregion annotations. These results demonstrate that a unified anatomical representation can support both cross-modality pancreas
segmentation and label-efficient downstream transfer.
Read the full abstract ▾
All authors Ziliang Hong, Hongyi Pan, Halil Ertugrul Aktas, Andrea Bejar, Elif Keles, Frank H. Miller, Michael B. Wallace, Rajesh N. Keswani, Gorkem Durak, Ulas Bagci
Artificial Intelligence · Cross-listed from cs.LG
DynSHAP: Towards Explainable Dynamic Survival Analysis
Nastasya Anokhina, Jonas Jürß and Pietro Liò
Deep learning models for dynamic survival analysis (DSA) achieve strong predictive performance by incorporating longitudinal patient data, but their black box nature limits clinical trust and adoption. Existing explainability methods cannot handle longitudinal, irregular inputs and functional survival outputs simultaneously, which limits their usability in DSA. We propose DynSHAP, a SHAP framework suited specifically for dynamic survival analysis. It extends common marginal SHAP
estimators to this setting by treating time–feature pairs as players in the Shapley game. We further introduce Temporal DynSHAP, which learns linear dependencies in features over time and uses conditional sampling to address them in explanations. When applied to synthetic data with known ground-truth attributions, Temporal DynSHAP recovers temporally dependent features more accurately than marginal
estimators for a given state-of-the-art model. Applied to two real-world clinical
datasets and two DSA architectures, DynSHAP produces attributions faithful to model learning, allowing medical experts to see which patient information drove the prediction and when.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.LG
Groupoid-Based Internal State Representations for Reinforcement Learning with Local Symmetries
Ben Opperman, Eduardo Alonso and Esther Mondragón
Symmetries play a central role in reducing the complexity of reinforcement learning problems, yet most existing approaches rely on fixed group actions or predefined state abstractions. Classical reinforcement learning
algorithms typically assume a globally structured Markov decision process with uniformly applicable actions and transitions, an assumption that limits their ability to exploit modularity and local, context-dependent regularities present in many realistic environments. We propose a reinforcement learning framework using groupoids to capture local, state-dependent
symmetries and support the dy- namic discovery of equivalence structures during interaction. The
agent maintains orbit representatives together with transporters that map raw states to canonical forms, enabling learning and decision-making to be performed in a
symmetry-reduced space while preserving local distinctions. Empirical results demonstrate that the proposed groupoid-based approach improves sample efficiency and convergence in dense and large-scale environments exhibiting strong partial
symmetries, yielding substantial performance gains over standard Q-learning. These findings show that dynamically exploiting local
symmetry provides a practical and mathematically principled route to scalable and generalisable reinforcement learning.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.LG
Attention Quantization for Tabular Foundation Models
Jonas M. Kübler, Benjamin Jäger, Klemens Flöge et al. (+2 more)
With the recent rise and adoption of tabular foundation models, optimizing their inference performance becomes an emerging field for efficiency research. While the models are architecturally similar to
transformer-based large
language models (LLMs), the size and serving patterns differ significantly. We show that the focus should be on the
attention calculation and less on weight or KV cache quantization, which are more popular in LLMs. We develop a quantization strategy for queries, keys, and values to FP8 and use explicit FP8
matrix multiplication instructions to speed up the
attention calculation. We find that it is crucial to align the quantization error in the test rows with the quantization error in the training rows, as otherwise the accuracy drops drastically. Our Triton kernel achieves a speedup up to 1.7x over regular 16-bit kernels, and we show that on TabPFN-v3 and TabICLv2 there is no relevant accuracy loss across TabArena and BeyondArena.
Read the full abstract ▾
All authors Jonas M. Kübler, Benjamin Jäger, Klemens Flöge, Noah Hollmann, Frank Hutter
Artificial Intelligence · Cross-listed from cs.CV
Label-Guided Knowledge Distillation for 3D-CNNs in Action Recognition
Yanjiang Shi, Peng Zhao, Nan Qi et al. (+1 more)
As a key model compression technique, knowledge
distillation aims to transfer knowledge from a high-capacity teacher model to a lightweight student model for enhancing the latter's performance. In this work, we reviewed the feature knowledge
distillation for 3D-CNNs and observed that most feature
distillation methods in
video analysis are simple adaptations of those used in image analysis, often neglecting the differences of
video features in the temporal dimension. To address this issue, we proposed Label-Guided Knowledge
Distillation (LGKD) to guide the
distillation of student model features using ground truth labels. Our method entails two components: sample-wise
distillation and class-wise
distillation, enabling the student model to learn feature representation of the teacher model at two levels. Sample-wise
distillation utilizes label information and the teacher's probability distribution to guide the learning of features that significantly impact temporal accuracy while mitigating noise. Meanwhile, class-wise feature
distillation employs a prototype network to further capture the relational knowledge among samples within the same category, enhancing the student's ability to learn higher-dimensional semantic information and improving model generalization. To demonstrate the effectiveness and superiority of our method, we conducted comprehensive experiments on two
benchmark action recognition
datasets, UCF101 and HMDB51, achieving competitive results.
Read the full abstract ▾
All authors Yanjiang Shi, Peng Zhao, Nan Qi, Guiqin Wang
Artificial Intelligence · Cross-listed from cs.CV
TileNet: Tile-Based CNN-SVM Architecture for Autonomous Unmanned Aerial Systems Inspection of Flat Roofs
Samuel Dunthorne and Hashim A. Hashim
Flat roofs are among the most influential components of the building envelope, governing both structural performance and thermal efficiency, and thereby contributing directly to household energy consumption, carbon emissions, and long-term environmental sustainability. Timely detection of roof defects is essential for reducing heating and cooling losses, preventing moisture-driven degradation such as mold growth, and supporting national
climate-change mitigation goals. This paper presents a real-time, Unmanned Aerial System (UAS)-based deep learning framework that autonomously detects defects using live imagery captured during dual-altitude aerial passes. The multi-resolution flight strategy is designed to aid the identification of both small, fine-scale defects and larger structural issues, enabling more comprehensive assessments. To meet the strict computational and power constraints of embedded UAS hardware, the proposed framework integrates a tile-based architecture with a lightweight Convolution Neural Network-Support Vector Machine (CNN-SVM) classifier designed for low-latency onboard inference. The final model-comprising five convolutional layers and four dense layers, the last a linear SVM head, achieved a mean test accuracy of $94.4\%$ ($95\%$ confidence interval $\pm0.4\%$ over three seeds) on a photo-level split ($43,383$ training, $3,869$ validation, and $2,540$ test tiled and augmented images), outperforming GoogLeNet ($89.2\%$) and AlexNet ($79.8\%$). Experimental evaluations using real UAS imagery collected by onsite visits with DJI Matrice 350 RTK drone demonstrate that the system supports rapid, repeatable, and safe roof inspections while reducing human risk, lowering operational costs, and enabling more sustainable building maintenance.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.RO
Comfort by Construction: Adaptive, Comfort-Bounded Action Spaces for Learned Driving Policies
Anna Rothenhäusler, Daniel Jost, Raghu Rajan et al. (+4 more)
Data-driven driving simulators command accelerations and steering rates from a fixed grid without constraining the realized accelerations and jerks. As a result, reinforcement-learning policies inflate safety metrics through abrupt, last-second maneuvers that lie far outside the range of human driving and would be unacceptable to occupants of a real vehicle, so the metrics measure simulator permissiveness rather than policy quality. Enforcing comfort bounds naively is not enough: lateral limits shrink quadratically with speed, so clamping a static grid saturates it and destroys fine-grained control ("grid collapse"). We propose an adaptive action parameterization that rediscretizes the grid at every step to span exactly the per-step feasible control set, via closed-form inversion of the lateral-jerk constraint. We further present PufferDrive-Editor, a browser-based tool to audit realized kinematics and author kinematically challenging scenes. On the Waymo Open Motion
Dataset and a hand-authored slalom, our adaptive model holds comfort violations below 1% while outperforming clipped-grid and direct-jerk baselines in navigability.
Read the full abstract ▾
All authors Anna Rothenhäusler, Daniel Jost, Raghu Rajan, Faris Janjos, Oliver Scheel, Andreas Look, Joschka Boedecker