Artificial Intelligence
Embodied CAD: Solver-Grounded LLM Agents for Parametric B-Rep Assembly Modeling
Fumin Liu, Haoyu Zhou, Fei Hao et al. (+1 more)
Large
language models can write plausible CAD scripts, but reliable industrial CAD modeling requires more than syntactically valid code: every feature, placement, and assembly relation must be accepted by an exact
geometric kernel while remaining editable as parametric boundary representation geometry. We present
Embodied CAD, solver-grounded LLM
agents for parametric B-Rep assembly modeling. Instead of generating a complete script in one pass, the
agent iteratively selects actions from a stratified L0-L4 CAD skill library, resolves them into typed
geometric operations, executes them in a CAD backend, and uses solver feedback to plan, repair, and learn. The framework combines action grammar constraints, deterministic parameter resolution, and solver-derived rewards for supervised warm-up and GRPO-style refinement. We evaluate
Embodied CAD on multi-step mechanical, industrial equipment, and mold-oriented assembly tasks using solver-aligned metrics: executable rate, skill accuracy, operation-family accuracy, exact policy accuracy, and task completion success. The results show that solver-grounded planning executes all strong-planner workflows in the current
benchmark, while learned controllers reach high executable rates and expose the remaining gap between valid tool calls and exact long-horizon policy prediction.
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All authors Fumin Liu, Haoyu Zhou, Fei Hao, Lin Yang
Artificial Intelligence · Cross-listed from cs.CL
Probing Stylistic Appropriation using Large Language Models: An Evaluation Framework for Copyright Infringement under EU Law
Noah Scharrenberg and Chang Sun
Large
language models (LLM) trained on web-scale corpora generate output that may infringe copyright, yet existing technical safeguards focus narrowly on verbatim memorisation. EU copyright doctrine applies a broader standards: substantial similarity, which extends to stylistic choices, narrative structure, and creative elaboration. This mismatch between what current methods detect and what the law protects leaves a significant compliance gap. We introduce PSALM, an LLM-as-a-judge framework that operationalises EU copyright doctrine through ten evaluators assessing computational overlap, stylistic dimensions (writing style, narrative voice), content dimensions (character, plot, scene, world building), and statutory exceptions (parody, pastiche, quotation, scènes à faire). Applying PSALM to Llama~3.2 models fine-tuned on translated historical Dutch literary works, we find that: 1) instruction-tuned models exhibit non-trivial baseline stylistic similarity prior to corpus exposure; 2) fine-tuning induces systematic stylistic appropriation across all infringement-relevant dimensions, extending beyond verbatim memorisation to abstract narrative patterns; 3) Negative Preference Optimisation unlearning substantially reduces similarity but leaves detectable residual stylistic patterns. These findings indicate that safeguards targeting literal copying alone are insufficient to mitigate broader copyright risks. PSALM provides infrastructure for auditable, legally informed compliance evaluation, though the relationship between automated similarity scores and infringement determinations requires validation by legal experts. This work bridges qualitative legal standards and quantitative technical measurement, exposing fundamental tensions between generative AI and EU intellectual property law.
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Artificial Intelligence
Delta-JEPA: Learning Action-Sensitive World Models via Latent Difference Decoding
Zhenghao Zhang, Yuanxiang Wang, Zhenyu Guan et al. (+11 more)
Learning visual world models for planning requires compact latent dynamics that remain sensitive to actions, yet reconstruction-free joint-
embedding objectives can collapse to action-insensitive representations. We propose Delta-JEPA, an end-to-end reconstruction-free world model that augments latent forward prediction with a Latent Difference Action Decoder (LDAD). Unlike inverse decoders that infer actions from concatenated endpoint
embeddings, LDAD reconstructs the executed action from the latent displacement between consecutive observations. This displacement-level supervision directly regularizes transition geometry: adjacent
embeddings cannot collapse without losing action information, and different actions are encouraged to induce distinguishable latent changes for rollout-based planning. Delta-JEPA uses only latent prediction and action reconstruction, avoiding pixel reconstruction and distribution-matching regularizers. Across four visual continuous-control tasks, Delta-JEPA improves planning over JEPA-based and representation-learning world model baselines. Ablations show that displacement-based action decoding is consistently more effective than endpoint concatenation, and action-sensitivity analyses show clearer action-conditioned latent responses. These results indicate that supervising latent differences is a simple and effective mechanism for collapse-resistant and action-sensitive world model learning.
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All authors Zhenghao Zhang, Yuanxiang Wang, Zhenyu Guan, Yujia Yang, Bingkang Shi, Tianyu Zong, Hongzhu Yi, Guoqing Chao, Xingchen Chen, Tiankun Yang, Chenxi Bao, Tao Yu, Jingjing Zhou, Jungang Xu
Artificial Intelligence
Agentic-Ideation: Sample Efficient Agentic Trajectories Synthesis for Scientific Ideation Agents
Keyu Zhao, Lingyan Kong, Fengli Xu et al. (+1 more)
Ideation plays a pivotal role in scientific discovery. Recent LLM, especially AI Scientist systems, show promising potential for automated ideation. However, existing approaches predominantly rely on pre-defined agentic workflows. This constraint severely limits the flexibility required to navigate the vast search space of scientific literature and the complex action space of research
reasoning. Recently, training Agentic LLMs has emerged as a promising direction, offering flexible
reasoning frameworks and the capability for autonomous tool utilization. However, there remains a non-trivial challenge: applying previous agentic data synthesis methods to scientific ideation suffers from prohibitively high data synthesis cost. To bridge this gap, we propose Agentic-Ideation, a novel framework comprising an automated trajectory synthesis pipeline and a specialized agentic LLM trained for scientific ideation. Specifically, we first define a comprehensive tool space incorporating three external tools and three cognitive tools. Then we introduce an Oracle-Guided Data Synthesis strategy. By leveraging a reference idea as oracle guidance, this approach steers the multi-
agent system to efficiently reconstruct the logical
reasoning and tool invocation paths, transforming aimless trial-and-error into directed trajectory generation. Finally, we train the
agent on these synthesized trajectories, employing a masking strategy on tool execution results. This ensures the model focuses on decision-making logic without interference from external feedback. Experimental results demonstrate that our method outperforms the SOTA workflow-based baseline by 11.91% in overall quality. Furthermore, our approach improves the sample efficiency of high-quality data synthesis by \textbf{over 10$\times$}.
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All authors Keyu Zhao, Lingyan Kong, Fengli Xu, Yong Li
Artificial Intelligence
Thinking Before Retrieving: Robust Zero-Shot Composed Image Retrieval via Strategic Planning and Self-Criticism
Gunho Jung, Jeong-Woo Park, Seon Bin Kim et al. (+1 more)
Composed image
retrieval requires identifying a target image from a gallery by integrating a reference image with a textual modification instruction. In a training-free zero-shot setting, this task relies on constructing a
retrieval-oriented textual query within a frozen
vision–
language embedding space at inference time. Existing approaches predominantly rely on a single-pass generation strategy that fuses the reference context and modification text into a unified description. This strategy makes it difficult to detect or correct semantic distortions and omissions during generation. Consequently, the preservation of reference attributes and the integration of textual requirements interfere with each other, which degrades
retrieval precision. To address these challenges, we introduce PEC-CIR, a training-free framework that structures query construction as a multi-stage
reasoning pipeline. The framework operates through a Planner–Executor–Critic architecture where the Planner extracts explicit constraints, the Executor generates multiple candidate target descriptions, and the Critic evaluates these candidates based on constraint compliance. By reframing query construction as a staged inference process instead of a single-pass output, PEC-CIR reduces the propagation of generative errors by explicitly evaluating candidate queries before
retrieval, thereby improving
retrieval stability.
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All authors Gunho Jung, Jeong-Woo Park, Seon Bin Kim, Seong-Whan Lee
Artificial Intelligence · Cross-listed from cs.RO
Information-Aided DVL Calibration
Zeev Yampolsky and Itzik Klein
The Doppler velocity log (DVL) velocity measurements are critical to the accuracy of autonomous
underwater vehicle (AUV)
navigation solutions and, consequently, to mission success. To ensure accurate measurements, the DVL is commonly calibrated before mission start while the AUV sails on the water surface, receiving global
navigation satellite system (GNSS) signals that provide accurate reference measurements. Conventionally, Kalman filter-based approaches are employed during calibration to estimate the scale factor and misalignment errors. However, in certain environments, GNSS signals may be unavailable, rendering conventional calibration impossible and forcing the use of uncalibrated DVL measurements, which degrades
navigation performance. To address this limitation, this work proposes information-aided calibration (IAC) with two main contributions: first, improving the accuracy of conventional Kalman filter-based calibration in GNSS-enabled environments, and second, enabling GNSS-free DVL self-calibration. Using real-world AUV
datasets, the proposed IAC models achieve up to a 20% average improvement in GNSS-enabled environments and up to a 35% improvement in velocity vector estimation during GNSS-free DVL self-calibration. Overall, the proposed approach improves
navigation accuracy, reduces
navigation drift, and consequently enhances mission reliability.
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Artificial Intelligence · Cross-listed from cs.CL
Can LLMs Imagine Moral Alternatives Beyond Binary Dilemmas?
Jongchan Choi, Nari Yang, Sung Soo Park et al. (+4 more)
As LLMs increasingly serve as moral advisors and
agents, they must address conflicts between competing values. Yet prior work on moral dilemmas overlooks a central aspect of human moral cognition: imagining alternatives beyond the given options. We introduce MoralAltDataset, comprising 307 Advisor and AI-facing
Agent dilemmas augmented with compromise and reframed alternatives. We compare human and LLM judgments in binary and four-option settings. Across human participants and 15 LLMs, aggregate moral choice distributions differ substantially between the two settings, with compromise often preferred over either original binary option. Results show value shifts and stronger human-LLM agreement on alternatives. Source-stratified results reveal a descriptive gap: human alternative-selection rates are similar across authoring sources, whereas LLMs select GPT-5-authored alternatives substantially more often. We then compare human-authored alternatives with outputs from three representative LLMs through pairwise preference and expert-based evaluations. Alternatives from these LLMs are generally preferred and better satisfy fine-grained structural and ethical criteria, while revealing a trade-off between structural quality and practical feasibility. Our
dataset is available here: https://huggingface.co/
datasets/jongchanch/MoralAltDataset, and our project page is here: https://jongchanchoi.com/moral-imagination
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All authors Jongchan Choi, Nari Yang, Sung Soo Park, Jaemin Cho, Han Seoyoung, Haerin Shin, Jun-Hyung Park
Artificial Intelligence
Long-term Traffic Simulation via Structured Autoregressive Modeling
Lingyu Xiao, Zexin Feng and Xintao Yan
Interactive traffic simulation is a vital world model for autonomous driving. A central challenge in long-horizon simulation is modeling sustained multi-
agent interactions, which is further exacerbated by dynamic
token cardinality as
agents continuously enter and exit the scene. In this work, we propose that the solution lies in the synergy between the architectural inductive biases and statistical priors of large-scale sequence models, e.g., Large
Language Models (LLMs). Our probing experiments reveal that the transferability of
attention mechanisms and the distributional consistency between motion
tokens and natural
language enable small-scale, heavily frozen LLMs to rapidly adapt to traffic modeling. Building on this insight, we introduce RosettaSim, a unified framework that projects scene
topology,
agent states, and spawning intents into a structured autoregressive stream with variable length, achieving both strong short-term accuracy and stable long-horizon simulation fidelity. Furthermore, evaluating extended rollouts presents yet another hurdle, as one-to-one
agent correspondence inevitably fades over time. To address this, we introduce
Retrieval-based Traffic Evaluation (RTE), which retrieves semantically similar real-world scenarios as context-aware reference anchors. Experiments on the Waymo Open Sim
Agent Challenge (WOSAC) demonstrate that RosettaSim achieves state-of-the-art performance in both short- and long-term simulation. Furthermore, RTE exhibits a stronger correlation with standard metrics ($r=0.83$) than existing approaches ($r=0.74$), indicating improved
alignment with long-horizon simulation fidelity.
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Artificial Intelligence
Towards Inclusive Mobility Modeling: Characterizing and Evaluating Elderly Trajectory Patterns in Urban Systems
Zhengxuan Wang, Haohan He and Mengying Zhou
The rapid advance of smart cities increasingly depends on trajectory data mining, yet underrepresented demographic groups, particularly the elderly, are often sparsely represented in public mobility
datasets. This underrepresentation can introduce systematic bias into mobility modeling and downstream urban planning. Using the 2016-2020 Jersey City subset of the Citi Bike System Data, this study quantitatively examines how the absence of underrepresented subgroups' mobility signatures affects mobility modeling, using synthetic trajectory generation as a case study. The analysis reveals that elderly riders exhibit a structurally distinct mobility signature, including localized activity spaces (958 m vs. 1,189 m for young riders), lower mobility
entropy (1.82 vs. 4.15), and asymmetric off-peak temporal patterns. To demonstrate that relying on majority-dominated training data yields biased synthetic outcomes, we further evaluate both a first-order Markov chain and a Qwen3-4B model fine-tuned with QLoRA across three demographic training settings: the full population, young riders only, and elderly riders only. Results show that models trained on majority-dominated populations systematically misrepresent elderly mobility behavior, particularly for spatial mobility metrics. The Markov model trained on the full population overestimates elderly step length by 4.5% and dwell time by 8.9%, whereas the elderly-specific model achieves substantially lower errors across most metrics. Comparisons between the Markov and LLM-based frameworks further show that higher-capability models do not necessarily improve subgroup-level fidelity under limited demographic data. These findings underscore the importance of demographic representation in mobility modeling and its downstream applications for underrepresented populations.
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Artificial Intelligence
Agentic RAG-VLM: Affordance-Aware Retrieval-Augmented Generation with Self-Reflective Planning for Robotic Grasping
Tao Chen, Lizheng Liu, Jiaxu Wang et al. (+4 more)
Generalizable robotic grasping in cluttered environments is essential for deploying manipulators in unstructured human spaces, yet existing VLM-based methods rely on visual similarity for object matching, neglecting physical affordances such as handle graspability and material fragility, and operate open-loop without spatial
reasoning or failure recovery, limiting their effectiveness when objects are densely packed or physically diverse. We present Agentic RAG-VLM, a unified framework that bridges VLM-based semantic understanding and physically grounded grasp execution by integrating
retrieval-augmented generation (RAG) with
vision-
language models (VLMs) and agentic self-reflective planning. Agentic RAG-VLM introduces three tightly coupled components: (1) a Hierarchical Affordance-Aware RAG (HAA-RAG) that encodes four-dimensional affordance descriptors, including type, material, fragility, and graspable region, and retrieves strategies by functional affordance compatibility rather than visual appearance; (2) a Scene Graph Constraint Reasoner that constructs spatial relationship graphs from VLM perception and translates proximity, occlusion, and support constraints into concrete grasp parameter adjustments; and (3) an Agentic Self-Reflective Pipeline with a 14-type failure taxonomy and three-level adaptive retry for closed-loop grasp refinement. Evaluated on a 12-task
benchmark spanning single-grasp, interactive, and long-horizon scenarios with 360 trials per configuration, Agentic RAG-VLM achieves 78.3 percent overall success, a 53.3 percentage-point absolute gain over VLM-only baselines, demonstrating that affordance-aware
retrieval, scene graph
reasoning, and agentic recovery are jointly essential for robust manipulation.
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All authors Tao Chen, Lizheng Liu, Jiaxu Wang, Ziyue Jiang, Ruiqi Tian, JiGuang Huo, Zhongxue Gan
Artificial Intelligence · Cross-listed from cs.CV
Distilling Temporal Coherence into 2D Networks for Transrectal Ultrasound Prostate Video Segmentation
Dong Yeong Kim, JunGyu Lee, Jaewon Choi et al. (+5 more)
Real-time
video segmentation of the prostate in Transrectal Ultrasound (TRUS) is essential for image-guided interventions. While conventional 2D methods suffer from inter-frame inconsistencies by disregarding temporal context, 3D architectures incur prohibitive latency. To resolve this dilemma, we present a Temporally Consistent Learning Framework that distills temporal coherence into a 2D network during training, preserving single-frame inference efficiency. Our design is driven by a key clinical observation: the prostate exhibits
geometric stability, whereas the surrounding acoustic environment fluctuates due to physiological motion and transducer pressure. Because conventional temporal constraints propagate erroneous gradients from these unstable regions, we introduce a Confidence-Weighted Temporal Consistency objective derived from optical flow warping residuals, selectively attenuating contributions from unreliable regions. Complementing this pixel-wise constraint, a Dual-scale Prototype
Alignment Module enforces semantic coherence through contrastive optimization of local boundary and global semantic features. Furthermore, to eliminate the need for dense per-frame
video annotations, we employ
geometric equivariance-based pseudo-labeling with knowledge
distillation from a pretrained teacher. Extensive experiments on SUN-SEG and our newly introduced TRUS-V
benchmark (2,679 frames) demonstrate state-of-the-art accuracy and temporal consistency at real-time speed. Code and
dataset are available at https://github.com/DYDevelop/DTC-TRUS.
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All authors Dong Yeong Kim, JunGyu Lee, Jaewon Choi, June Young Seo, Myeongseop Kim, Jinwook Choi, Taek Min Kim, Young-Gon Kim
Artificial Intelligence · Cross-listed from cs.CL
Gated Multi-Graph Fusion via Graph Attention Networks for Alzheimer's Disease Detection
Jinyu Li, Xiao Wei, Bin Wen et al. (+5 more)
Spontaneous
speech is a vital non-invasive biomarker for Alzheimer's Disease (AD), yet many systems overlook non-linear structural disruptions and clinical heterogeneity in pathological
language. We propose a Multi-View Gated Graph
Attention Network that transcribes audio via Automatic
Speech Recognition (ASR) to construct semantic, dependency, and co-occurrence graphs, characterizing
speech through a "content-structure-flow" framework. Notably, the co-occurrence graph leverages Pointwise Mutual Information (PMI) from a normative corpus to quantify narrative logic and linguistic deviation. To address symptomatic diversity, an adaptive gated fusion mechanism dynamically integrates these views. Evaluated on the ADReSSo
dataset, our model achieves 90.00% accuracy. Ablation results confirm that the PMI-based graph and heterogeneity-aware gating are essential for robust classification across diverse clinical populations. Our source code is publicly available at https://github.com/opeacc/AD.
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All authors Jinyu Li, Xiao Wei, Bin Wen, Kai Li, Yuqin Lin, Xiaobao Wang, Longbiao Wang, Jianwu Dang
Artificial Intelligence · Cross-listed from cs.LG
Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
Jiachun Li and David Simchi-Levi
Adaptive experiments for average treatment effects (ATE) require randomized allocations balancing valid inference with statistical efficiency. The oracle design is a
covariate-dependent Neyman rule governed by unknown arm-conditional outcome variances. We investigate whether this sequential variance-estimation and allocation process can be amortized via in-context learning. We introduce
Bayesian in-context experimenters:
transformer policies trained to imitate a
Bayesian posterior Neyman teacher. The teacher updates nonparametric beliefs over potential outcomes using experimental history to assign posterior Neyman treatment probabilities. This design converges to the oracle rule, supporting efficient ATE inference.
Transformers constructively implement this mapping through
attention-based sufficient statistics and projected gradient descent, imitating
Bayesian updating for Gaussian-series priors. To address unknown outcome smoothness, we combine smoothness-indexed experimenters using a mixture-of-experts
transformer. The gate acts as a hierarchical posterior over smoothness classes, concentrating on near-oracle experts. By bounding the complexity of the
transformer class, we prove this amortized policy can be learned via empirical risk minimization using supervised pretraining. Experiments confirm accurate teacher imitation, adaptive allocation, and improved ATE precision over baselines.
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Artificial Intelligence
AI-Assisted Discovery of Convex Relaxations via Dual Agents
Sungyoon Kim and Mert Pilanci
Recent work shows that LLM
agents can improve sharp-constant inequalities by searching for extremal constructions, which yield upper bounds. We address the complementary side: a lower bound holds for every admissible function and follows from a convex relaxation of the nonconvex problem, with tighter relaxations giving stronger bounds. We instantiate the autoresearch paradigm to discover such relaxations: a coding
agent proposes valid tightening constraints, a theory
agent verifies each one and searches for counterexamples, and every reported bound is certified by an explicit dual-feasible point checked in rigorous interval arithmetic. On two optimization constants studied by \citet{tao2025alphaevolve} - the first autocorrelation inequality ($C_{6.2}$) and the Erdős minimum-overlap constant ($C_{6.5}$) - we improve the certified lower bounds from $1.28$ to $1.2937$ and from $0.379005$ to $0.37912$, respectively.
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Artificial Intelligence
HealthAgentBench: A Unified Benchmark Suite of Realistic Agentic Healthcare Environments for Challenging Frontier AI Agents
Qianchu Liu, Sheng Zhang, Guanghui Qin et al. (+18 more)
As AI
agents become increasingly capable of complex, long-horizon
reasoning, rigorous evaluation is essential for measuring progress toward real-world healthcare applications. However, existing healthcare
benchmarks are often static or narrow in clinical scope, motivating broader evaluation of
agents acting on raw patient data. We introduce HealthAgentBench, a suite of 75 agentic healthcare tasks across 9 categories each with its unique environment. The
benchmark suite spans diagnosis, specimen assessment, research, and data management workflows and a broad range of modalities. Each task is designed to replicate an end-to-end clinical workflow: given task instructions and supporting resources, an
agent must explore raw healthcare data, operate within a complex environment, and execute multi-step solutions that go beyond naive prompting, such as querying large clinical databases or interpreting gigapixel pathology images. All tasks are scored using binary success criteria derived from reference labels, executable rules, or computational baselines. We evaluate six
agents (three GPT models through Codex and three Claude models through Claude Code) with one scored attempt per task (450 evaluations). Overall success ranges from 32.0% to 57.3%; Claude Code Opus-5.5 leads with a 57.3% success rate, followed by GPT-6-astra and Sonnet-5.5 at 50.7% each. Performance varies substantially by category with specialized imaging modalities such as CT and MRI being especially difficult. HealthAgentBench provides a challenging and realistic
benchmark with substantial room for future progress. We release our
benchmark athttps://github.com/microsoft/HealthAgentBench.
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All authors Qianchu Liu, Sheng Zhang, Guanghui Qin, Jeya Maria Jose Valanarasu, Xiaoke Huang, Peniel Argaw, Maximilian Rokuss, Mingyu Lu, Timothy Ossowski, Juan Manuel Zambrano Chaves, Cliff Wong, Naisargi Patel, Yashna Hasija, Mu Wei, Wen-wai Yim, Qin Liu, Zilin Jing, Jason Entenmann, Naoto Usuyama, Tristan Naumann, Hoifung Poon
Artificial Intelligence · Cross-listed from cs.LG
AETDICE: Unified Framework and Offline Optimization for Nonlinear Multi-Objective RL
Woosung Kim, Youngjun Suh, Jinho Lee et al. (+2 more)
Optimizing nonlinear preferences in multi-objective reinforcement learning (MORL) is essential for capturing complex trade-offs like risk aversion or
fairness. However, such non-linearity has historically bifurcated nonlinear MORL objectives into two distinct paradigms: Scalarized Expected Return (SER) and Expected Scalarized Return (ESR). While SER requires global-level optimization and ESR requires non-Markovian policies, leading to fragmented optimization strategies, we bridge this divide through the Aggregation-Expectation-Transformation (AET) framework. By unifying both criteria through a tripartite decomposition of scalarization, AET provides a principled foundation for general nonlinear MORL. Building on this framework, we propose AETDICE, a tractable offline RL
algorithm for AET objectives. By utilizing DICE-style density-ratio estimation in an augmented state space, AETDICE enables sample-based optimization from static
datasets. Our framework resolves long-standing barriers and captures respective trade-offs induced by AET framework, which existing methods fail to address.
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All authors Woosung Kim, Youngjun Suh, Jinho Lee, Jongmin Lee, Byung-Jun Lee
Artificial Intelligence
ClawArena-Team: Benchmarking Subagent Orchestration and Dynamic Workflows in Language-Model Agents
Kaiwen Xiong, Haonian Ji, Shi Qiu et al. (+4 more)
Production large
language-model (LLM)
agents are increasingly deployed not as lone problem-solvers but as managers: a main model creates specialized subagents, delegates work, and orchestrates their parallel, asynchronous returns through dynamic workflows. Whether one model can actually run such a team is largely unmeasured: existing
benchmarks score a policy's own task-solving or a fixed multi-
agent system's emergent behavior, but none isolate the management ability of the single LLM acting as leader. We introduce ClawArena-Team, a
benchmark of 41 multi-turn,
multimodal, multi-directory scenarios spanning 258 evaluation rounds and 72 staged updates that measures this management ability. The main
agent is deliberately constrained: it natively perceives only text and directly accesses only part of the workspace. It commands a fixed, locally served subagent pool, so score differences reflect management skill, not raw capability. All scoring is execution-based with no LLM judge: an overall score – the Subagent-Management Score (SMS) – multiplies task correctness by a least-privilege and modality-routing factor. Across twelve proprietary, community-hosted, and self-hosted models, experiments show that the management bottleneck is privilege granting rather than perception (no model exceeds 50% workspace-permission precision); that cost and management quality are decoupled (API cost spans over 100 times while the overall score spans under 4 times, with the cheapest open models on the Pareto frontier); and that most leaderboard scores cluster within a 9.9-point band while orchestration behaviors diverge by more than an order of magnitude. Code is available at https://github.com/aiming-lab/ClawArena.
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All authors Kaiwen Xiong, Haonian Ji, Shi Qiu, Zeyu Zheng, Cihang Xie, Xinyu Ye, Huaxiu Yao
Artificial Intelligence
Cross-Domain Feature Expansion for Tabular Medical Data via Knowledge Graphs Injection
Mengying Zhou, Yongjie Yin, Haoyan Xin et al. (+2 more)
Acquiring comprehensive cross-domain biomedical profiles is often costly and time-consuming, resulting in severe data scarcity in medical research. To address this challenge, we propose MedKGTab, a knowledge-injected framework specifically engineered for cross-domain feature expansion in tabular medical data. MedKGTab seeks to infer uncollected biomedical features from available ones by exploiting their inherent statistical dependencies and established medical correlations. By employing a row-column dual-
attention mechanism, MedKGTab operates directly on raw structured tabular data, inherently capturing exact numerical distributions without the structural loss caused by tokenization. Crucially, MedKGTab integrates data-driven statistical priors with the SPOKE biomedical knowledge graph, achieving an optimal synergy between the data and knowledge channels. Within this synergy, the representations derived from the data channel are modulated by the injected biomedical knowledge, ensuring the final generated data are grounded in empirical medical research. Experimental results demonstrate that MedKGTab achieves high data fidelity and realistic data representation in cross-domain feature expansion. It outperforms both SOTA medical large models (e.g., Baichuan M3-plus) and specialized tabular models designed for medical data generation. Furthermore, MedKGTab consistently delivers superior performance across various data generation scenarios, whether inferring missing features within the same
dataset or generalizing across different medical cohorts.
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All authors Mengying Zhou, Yongjie Yin, Haoyan Xin, Guoping Liu, Yang Chen
Artificial Intelligence · Cross-listed from cs.RO
MIRTH: Mutual-Information Reasoning with Temporal Hubs for Vision-Language-Action Agents
Hao Sun, Yu Song, Shiyu Teng et al. (+2 more)
VLA models have emerged as a powerful paradigm for transferring semantic knowledge from web-scale data to physical robotic control. However, current single-frame architectures suffer from intrinsic limitations: temporal myopia that discards historical dynamics,
reasoning gaps between high-level instructions and low-level motor commands, and inference inefficiency due to autoregressive scalar decoding. In this work, we propose MIRTH, a unified framework designed to address these challenges. MIRTH augments a pretrained VLA backbone with three key innovations: (1) dual-scale temporal
memory hubs that compress long-term scene
evolution and short-term motion trends into compact
embeddings; (2) latent
reasoning tokens optimized via a mutual-information objective carving out a semantic plan space to align
multimodal context with action trajectories; and (3) a parallel action decoding scheme that replaces autoregressive generation with vector-wise prediction to maximize control throughput. Extensive evaluations on the LIBERO simulation
benchmark and a real-world LeRobot platform demonstrate that MIRTH achieves state-of-the-art performance and exhibiting emergent error recovery capabilities. The codes and collected
datasets are released at http://github.com/kiva12138/mirth.
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All authors Hao Sun, Yu Song, Shiyu Teng, Ziwei Niu, Yen-Wei Chen
Artificial Intelligence · Cross-listed from cs.LG
ComplianceGate: Classifier-Gated Multi-Tier LLM Routing for Inference in Regulated Industries
Abhishek Dey
Large
language models deployed in regulated industries operate under two constraints: compliance enforcement and cost efficiency. Personally identifiable information (PII) in user queries can reach model endpoints before the system determines whether that data should leave its jurisdictional boundary. Serving all queries through a single large model consumes full GPU capacity regardless of query complexity while offering no mechanism for geographic routing. Mixture-of-Experts architectures do not address this routing occurs between expert layers within the model after data has already arrived at the endpoint, with all experts loaded in
memory regardless of query complexity. We propose a classifier-gated routing architecture that enforces compliance by design. A trained encoder classifier sits before any decoder inference, evaluating each query for complexity and data sensitivity, then routing it to an appropriately sized dense model in the appropriate geographic location. PII-containing queries route to local endpoints before any LLM computation begins, making data residency violations structurally impossible. Simple queries reach small, fast models at a fraction of the cost. Our evaluation on 600 queries demonstrates 39% median latency reduction, 33-52% cost savings depending on query distribution, and generation throughput of 122-200
tokens/second versus 50-64 for the baseline. The encoder classifier achieves 99.2% accuracy with near-perfect PII recall at 7ms inference overhead, establishing pre-inference classification as a practical path to compliance-by-design LLM deployment.
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Artificial Intelligence · Cross-listed from cs.RO
LLM-Powered Interactive Robotic Action Synthesis from Multimodal Speech, Gestures, and Music
Snehasis Banerjee and Ranjan Dasgupta
The quest for intuitive and natural human-
robot interaction (HRI) remains a significant challenge in robotics. Traditional methods often rely on rigid, pre-programmed commands that limit the
robot's expressiveness and adaptability. This paper introduces a novel framework that leverages the
reasoning capabilities of Large
Language Models (LLMs) to synthesize complex robotic actions from a rich tapestry of
multimodal human inputs: natural
speech, hand gestures, and music/sound beats. Our system architecture integrates a
speech transcription model, a gesture recognition module, and a signal processing pipeline for beat detection. These processed inputs are contextualized using
prompt templates and fed into a LLM. The LLM, informed by a predefined
robot action space, reasons over the combined inputs to generate a coherent sequence of actions. This sequence is dispatched to an action queue for execution on a quadruped
robot over ROS. The framework has ability to interpret and fuse semantic commands from
speech, deictic information from gestures, and rhythmic cues from music. This work represents a step towards creating
robots that can interact with humans in a more fluid, creative, and context-aware manner.
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Artificial Intelligence · Cross-listed from cs.IR
One Retrieval to Cover Them All: Co-occurrence-Aware Knowledge Base Reorganization for Session-Level RAG
Shivam Ratnakar, Yixuan Zhu, Cecilia Cheng et al. (+1 more)
RAG systems retrieve documents optimized for answering one query at a time. Yet enterprise users arrive with sessions, that is, coherent episodes of related questions that span semantically distant parts of the knowledge base. We show that a single
retrieval call over a standard knowledge base covers only 41% of a user's session-level information need. To close this gap, we reorganize the KB offline using co-occurrence-aware clustering and expand
retrieval candidates through cluster neighborhoods at query time. On WixQA (6,221 enterprise support articles), our method raises single-query session coverage to 58% (+17% absolute; 95% CI: [14.1, 20.4]), reduces
retrieval calls to 70% coverage by 34%, and compresses the KB to 20% of its original size, all consistently across four
embedding models and six functional domains. We argue that session-level coverage, not single-query recall, should be the primary metric for enterprise RAG evaluation.
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All authors Shivam Ratnakar, Yixuan Zhu, Cecilia Cheng, Chaya Vijayakumar
Artificial Intelligence · Cross-listed from cs.LG
PPT-Eval: A Benchmark for Computer-Use Agents on PowerPoint Tasks
Apurva Gandhi, Vishwas Suryanarayanan, Raja Hasnain Anwar et al. (+6 more)
Creating and editing slides is a rich,
multimodal activity that is ubiquitous in professional and educational settings, making it an ideal testbed for real-world computer-use
agents. Microsoft PowerPoint is among the most widely adopted and feature-rich environments for presentation creation. We introduce PPT-Eval, a
benchmark of 120 PowerPoint tasks across 12 files that cover both content creation and presentation editing scenarios, organized by difficulty. A central challenge in this domain is evaluation: tasks are complex,
multimodal, and often admit many valid solutions. Moreover, today's
agents frequently make only partial progress, which binary success metrics fail to capture. To address this, we design a robust evaluation framework to help create task-specific rubrics for PowerPoint tasks, taking inspiration from and building on past works for rubric-based evaluation. These rubrics award partial credit for intermediate steps, penalize unnecessary changes and poor aesthetics, and provide natural
language feedback. This nuanced approach proves highly effective, achieving a Kendall's {\tau}-b correlation of 0.77 with human judgments. We find that existing frontier
agents still struggle with solving PowerPoint tasks, with strong models like Claude-4.5-Opus achieving only a 45% success rate and an average partial score of 57%. The
benchmark is located at: https://microsoft.github.io/ppteval.
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All authors Apurva Gandhi, Vishwas Suryanarayanan, Raja Hasnain Anwar, Firoz Shaik, Shubhang Desai, Thong Q. Nguyen, Muhammad Taqi Raza, Vishal Chowdhary, Graham Neubig
Artificial Intelligence · Cross-listed from cs.CV
PruneGround: Plug-and-play Spatial Pruning for 3D Visual Grounding
Duc Cao Dinh, Khai Le-Duc, Florent Draye et al. (+4 more)
3D Visual Grounding (3DVG) aims to localize target objects in 3D scenes given natural
language descriptions. Existing approaches typically perform
reasoning over the entire scene, leading to ambiguous predictions and high computational cost, especially in cluttered environments. We observe that many referential expressions rely on local spatial context and often correspond to restricted spatial regions rather than the full scene. Motivated by this insight, we propose PruneGround, an effective plug-and-play framework for 3DVG built upon three key components. First, we introduce
Language-Guided Spatial
Pruning (LGSP), which leverages a frozen
Vision Language Model (VLM) to identify
language-relevant regions, thereby reducing spatial computation and grounding candidates in the narrower search space. Second, we propose MultiView-Conditioned Description Reformulation (MCDR), which decomposes complex expressions into simplified target–anchor relations and augments missing spatial cues through multi-view
reasoning. Finally, we propose LLM-Grounder, which repurposes a detection-pretrained spatial LLM into a
language-conditioned grounding model by aligning point cloud and linguistic representations within the pruned region. Extensive experiments on the three most popular point cloud
benchmarks demonstrate that our method achieves state-of-the-art results on all three ScanRefer settings and on 9 out of 10 Nr3D/Sr3D settings. Code and models are publicly available: https://github.com/leduckhai/PruneGround
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All authors Duc Cao Dinh, Khai Le-Duc, Florent Draye, Chris Ngo, Terry Jingchen Zhang, Bernhard Schölkopf, Zhijing Jin
Artificial Intelligence · Cross-listed from cs.RO
A Modular Vision-Language-Action Robotics Framework for Indoor Environments
Anindya Jana, Snehasis Banerjee, Arup Sadhu et al. (+1 more)
This paper presents an integrated system for the CMU
Vision-
Language-Action (VLA) Challenge, designed to enable an autonomous
agent to perform complex tasks based on natural
language instructions. Our framework employs a modular architecture that orchestrates environment mapping, question processing, and
navigation. The system operates in two parallel streams: a perception pipeline that constructs a semantic voxel map from real-time camera feeds using OwlViT
embeddings, and a
language pipeline that classifies user commands with a
Vision-
Language Model. The mapping is time-constrained; the system proceeds with a partial map if a 500-second exploration limit is reached. The classified query is then grounded in the
geometric and semantic context of the map to generate a detailed
prompt for the VLM. This yields an actionable output, demonstrating a capable solution for bridging the gap between human
language and robotic action.
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All authors Anindya Jana, Snehasis Banerjee, Arup Sadhu, Ranjan Dasgupta
Artificial Intelligence
Beyond the Library: An Agentic Framework for Autoformalizing Research Mathematics
Arshia Soltani Moakhar, Iman Gholami, Max Springer et al. (+2 more)
While Large
Language Models (LLMs) have demonstrated exceptional capabilities in mathematical
reasoning, they frequently produce subtle errors that evade human detection. Formal mathematical
languages like Lean 4 offer mechanical proof checking, strongly motivating the need for autoformalization: the automatic translation of natural
language mathematics into verifiable code. Recent trends indicate that general-purpose LLMs, heavily optimized for standard programming, now outperform smaller models explicitly fine-tuned for Lean. Leveraging this shift, we introduce *Theo*, an agentic autoformalization framework powered by general coding LLMs. At the core of our system is an orchestrator that manages a multi-
agent pipeline tailored for research-level mathematics. Because cutting-edge research frequently relies on concepts outside the scope of existing libraries like Mathlib, our system dynamically extends necessary type definitions and validates them via a novel Auxiliary Lemma technique before formalizing the primary
theorems. We applied our approach to PutnamBench, producing machine-checked Lean proofs for a random sample of 32 problems. Furthermore, we evaluate our system on seven research papers—five from the ACM Symposium on Theory of Computing (STOC) and two recent OpenAI manuscripts—spanning combinatorics, communication complexity, mechanism design, learning theory, number theory, discrete geometry, and graph theory. We successfully formalize their main
theorems and proofs and validate the generated formalizations with human experts; notably, two developments require no axioms beyond Lean's kernel. All of our formalizations are available at https://beyondthelibrary.github.io/formal_arxiv/.
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All authors Arshia Soltani Moakhar, Iman Gholami, Max Springer, Mahdi JafariRaviz, MohammadTaghi Hajiaghayi
Artificial Intelligence
Scenario Generation for Testing of Autonomous Driving Systems Using Real-World Failure Records
Anjali Parashar and Chuchu Fan
To ensure safe on-road behavior, pre-deployment testing and failure discovery of Autonomous Driving Systems (ADS) is crucial. Present day simulation based testing methods focus largely on mathematical models for efficient search of optimal scenarios, assuming a fixed scenario representation. On the other hand, real-world testing involves substantial manual effort to design scenario templates for testing. These templates represent distinct failure scenarios consisting of pre-deployment vehicle movements, map types, etc. Historical failure records for ADS are a reliable source of real-world failure conditions, which can be used for scenario generation. In this work, we propose a scenario generation pipeline using categorical and contextual information available from historical records in natural
language format. Our approach consists of modular LLM based synthetic scenario generation, compatible with the testing constraints of a given system. We successfully apply our method to generate a diverse set of scenarios for testing autonomous
navigation on Metadrive simulator using the NHTSA ADS crash records. Our approach results in accurate and diverse scenario generation with a combination of 4 road types, 3 non ego vehicle movement types, including on road anomalies in the form of working zones. Generated scenarios align with the provided testing conditions, and reveals interesting failures of the system within a limited testing budget of 20 scenarios. Code is available at https://github.com/anjaliParashar/crash2scenario.
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Artificial Intelligence · Cross-listed from cs.SD
UniSAE: Unified Speech Attribute Editing on Speaker, Emotion and Low-Level Content via Discrete Phonetic Posteriorgram Modelling
Chuanbo Zhu, Wuyou Zhou, Rongxiu Zhong et al. (+4 more)
Speech editing aims to modify specific portions of an utterance while preserving the remaining
speech. Existing approaches primarily focus on word-level content modification and typically treat content, speaker, and
emotion editing as separate tasks, limiting both editing granularity and flexibility. We propose UniSAE, a unified
speech attribute editing framework which supports composable speaker,
emotion and content editing from sub-phoneme to word level within a single architecture. UniSAE introduces a Discrete Phonetic PosteriorGram (DPPG) representation that factorizes
speech content into discrete
tokens encoding phoneme identity, pronunciation variants, and duration, enabling direct phoneme- and sub-phoneme-level editing. For higher-level modifications, an autoregressive content
transformer predicts edited DPPG sequences for word-level content editing. The edited sequences are rendered into
speech by a
diffusion-based acoustic decoder, conditioned on disentangled speaker and
emotion representations. Experimental results demonstrate that the proposed unified framework supports precise speaker and
emotion control, content editing at multiple granularities, and joint modification of all three attributes within a single framework.
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All authors Chuanbo Zhu, Wuyou Zhou, Rongxiu Zhong, Shilei Zhang, Kun Qian, Yike Guo, Wei Xue
Artificial Intelligence · Cross-listed from cs.CV
SkillSpotter: Pose-Aware Multi-View Skilled Action Detection and Grading in Ego-Exo Videos
Björn Braun and Christian Holz
To enable personalized, real-time coaching using Augmented Reality glasses or fixed camera setups in domains such as sports, cooking, or music, a system must understand not just what a person does, but how well they execute an activity. In an ego-exo
video setting, this requires simultaneously detecting individual skilled actions and classifying each as correct or needing improvement, which Ego-Exo4D's proficiency demonstration
benchmark formalized. We first adapt seven state-of-the-art temporal action detection architectures to this task, extend the evaluation protocol to disentangle detection from grading, and show that existing methods grade near-randomly. We then introduce SkillSpotter, a pose-aware multi-view architecture that jointly detects and grades skilled actions through three task-specific modules: (1) adaptive temporal suppression to handle the varying density of skilled actions across diverse activities, (2) gated 3D body pose fusion to leverage body kinematics as a complementary signal to visual features, and (3) bidirectional cross-view
attention to combine ego and exo views effectively. SkillSpotter improves class-specific mAP from 12.40 to 21.82 (+76%) and balanced accuracy from 55.99% to 60.40% over the best baseline. SkillSpotter's modules transfer to other temporal action detection models with consistent gains, and our method generalizes beyond Ego-Exo4D to HoloAssist. Code: https://github.com/eth-siplab/SkillSpotter
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Artificial Intelligence
The Past Is Prologue: A Plug-in Controller for Selective Updates in Sequentially Evolving LLM Memory
Zihan Chen, Songwei Dong, Chengshuai Shi et al. (+4 more)
Sequentially evolving LLM
memory enables
agents to reuse past experience, but existing systems usually deploy each locally generated
memory update without checking whether it improves future behavior. As a result, updates that help the current task may overwrite useful knowledge, introduce over-specific rules, or bias the final
memory toward recent examples. We propose Janus, a plug-in
memory controller that decides whether to accept a candidate
memory update or retain the previous
memory. To make this decision efficient, Janus uses a
Memory Momentum Trigger to identify suspicious deviations in the
memory-update trajectory, and compares old and new
memories on a compact hybrid evaluation set of coverage, boundary, and fresh tasks instead of replaying the full history. Janus is method-agnostic and wraps existing updaters without changing their update rules. Across six
datasets, two backbone LLMs, and two
memory updaters, Janus improves average accuracy by +2.7 to +4.6 points over the corresponding base updaters.
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All authors Zihan Chen, Songwei Dong, Chengshuai Shi, Peng Wang, Song Wang, Cong Shen, Jundong Li