Artificial Intelligence · Cross-listed from cs.SE
AgentCheck: A Reproduce-Intervene-Mitigate Workbench for LLM Agents over MCP
Aritra Mazumder and Nusrat jahan Lia
Tool-using LLM
agents are mostly evaluated assuming all tools work. When a tool times out, returns a week-stale value, or has its description poisoned in deployment, the developer needs a controlled way to reproduce the failure, test a fix, and confirm the fix worked before deployment. We present AgentCheck, an open-source web workbench that turns an MCP server into an intervention surface. AgentCheck runs an
agent against its real tools and records every tool response, then re-runs the
agent with the response perturbed by a fault (12 types) injector. Matching tool calls are replayed from cache, and later tool calls go live after the
agent diverges. This yields a reproduce-intervene-confirm loop: the developer toggles a mitigation, re-runs against the identical fault, and sees if the failure goes away. Scoring has two parts: deterministic pass/fail rules, plus an LLM judge for interpretive labels, validated against human annotations. Across five
agents, the best passes 105/120 scenarios and the weakest only 77. The failures are usually silent, confident use of incorrect tool outputs rather than crashes. On the weakest
agent, a retry mitigation raises success on timeout error faults from as few as 30% of cases to 100%, whereas stale-data faults remain near 3-4 of 10 regardless of the mitigation. AgentCheck makes these failure modes reproducible, comparable, and verifiable before deployment.
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Artificial Intelligence
OS-Pruner: Pruning Chains-of-Thought of Reasoning Models via Optimal Stopping
Mohammed Ehab, Aymane El Gadarri, Vivek F. Farias et al. (+2 more)
Large
Language Models (LLMs) have achieved remarkable success in complex
reasoning tasks through Chain-of-Thought (CoT) prompting. However, these models often exhibit "computational overthinking," generating redundant
reasoning steps that increase latency and cost without improving accuracy. Recent studies suggest that CoT trajectories can be significantly pruned, yet existing methods often rely on forcing a static thinking budget, heuristic filtering, sub-optimal early exit via classification, or expensive re-training. In this paper, we introduce OS-Pruner, a lightweight plug-in framework that formulates chain-of-thought
pruning as an optimal stopping problem. Given a
reasoning prefix, OS-Pruner learns whether further
reasoning is worth its
token cost by optimizing an explicit utility that trades off final-answer accuracy against generated length. Our novel formulation enables the model to dynamically assess the sufficient point of termination for a
reasoning chain. OS-Pruner is designed to be lightweight during both training and inference, and to provide users with fine-grained control over the
reasoning-effort vs. accuracy trade-off. On diverse
reasoning benchmarks and base models, OS-Pruner achieves 20-60% reduction in generation length with minimal accuracy sacrifice.
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All authors Mohammed Ehab, Aymane El Gadarri, Vivek F. Farias, Adam Jozefiak, Ciamac C. Moallemi
Artificial Intelligence
NVAITC AI Scientist: A Governed End-to-End Research System – A Hypertension GWAS Case Study
Eddie Huang, Ken Liao, Iven Fu et al. (+14 more)
Agentic research systems are emerging as a new paradigm for coordinating scientific workflows beyond isolated model inference, code generation, or statistical analysis. However, deployment in institutional biomedical environments requires governed mechanisms for research planning, data access, workflow orchestration, evidence tracking, reproducibility, and human oversight. We present NVAITC AI Scientist (NAIS), a governed end-to-end agentic research system designed to support domain-general scientific workflows while keeping protected data within institutional
privacy boundaries. NAIS integrates proposal review, execution planning, governed computational routing, reproducible workflow orchestration, evidence generation, and scientist-in-the-loop oversight. We validate NAIS in a real-world hypertension genome-wide association study (GWAS) using hospital-linked genotype and electronic health record (EHR) data from 286,422 individuals under an aggregate-only data policy. The
agent planned cohort extraction, orchestrated GWAS execution, generated quality-control summaries, and drafted publication-oriented outputs. Human-AI review identified phenotype discrepancies and enabled iterative refinement of the hypertension definition. After reconciliation, the
agent-orchestrated GWAS reproduced established hypertension loci, including FGF5, ATP2B1, CNNM2, FTO, and GRB14, with the strongest signal at FGF5 reaching $-\log_{10}(p) \sim 70$. As a secondary demonstration, NAIS also supported a drug-induced liver injury prediction workflow, achieving a
multimodal graph neural network AUC of 0.842. These results demonstrate that governed agentic research systems can support scalable AI-assisted biomedical discovery while producing outputs comparable to expert-led workflows.
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All authors Eddie Huang, Ken Liao, Iven Fu, Yang-Hsien Lin, Chao-Shun Zhan, Andy Liao, Virginia Chen, Johnson Sun, Pika Wang, Richard Huang, Jiun-Cheng Jiang, Ting-Yuan Liu, Hsing-Fang Lu, Ray Y. Lee, Chi-Chou Liao, Simon See, Fuu-Jen Tsai
Artificial Intelligence · Cross-listed from cs.CV
Sunyoung Jung, Jiwoo Park, Yoonseok Choi et al. (+3 more)
Diffusion Transformers (DiTs) have advanced
video generation with high-quality, temporally coherent results. However, extending them to motion transfer, which requires following reference motion while aligning with a target
prompt, remains challenging due to limited understanding of motion and structure representations within DiTs. We analyze
video DiTs at the
attention-head level and identify distinct heads specialized for motion and spatial structure. Based on this insight, we propose a head-aware controllable motion transfer framework that requires no parameter updates. Our method refines motion cues from motion-specialized heads via semantic correspondence guidance and preserves structure through selective feature injection. This head-level control not only enables accurate motion transfer but also provides an interpretable foundation for controllable
video generation with DiTs.
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All authors Sunyoung Jung, Jiwoo Park, Yoonseok Choi, Kyobin Choo, Ming-Hsuan Yang, Seong Jae Hwang
Artificial Intelligence
Are LLMs Ready for Scientific Discovery? A Capability-Oriented Benchmark for AI Scientists
Chuhan Shi, Xiaoquan Ren, Sicheng Song et al. (+3 more)
Existing
benchmarks for scientific data analysis evaluate LLMs primarily on code execution or workflow completion, overlooking that scientific analysis serves to support distinct types of scientific claims: hypothesis exploration, statistical inference, mechanistic explanation, each with different assumptions and validity criteria. We introduce SDABench, a
benchmark that reorganizes evaluation around six capabilities (descriptive, exploratory, inferential, predictive, causal, and mechanistic) across five domains (Biology, Chemistry, Environment, Geography, Physics). SDABench comprises 527 real-data instances (SDA-Real) and 6000 synthetic instances (SDA-Synth), each in both multiple-choice and open-ended formats, constructed through an automated pipeline. Evaluating 15 representative LLMs, we find that models handle descriptive analysis well but degrade sharply on tasks requiring assumption selection, latent-process modeling, or mechanistic
reasoning. SDABench further provides a five-stage error analysis framework that locates where LLMs fail: more advanced models more reliably identify the relevant scope and variables, but still struggle to select appropriate analytical procedures, model variable relationships, and draw valid conclusions.
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All authors Chuhan Shi, Xiaoquan Ren, Sicheng Song, Haobo Li, Rui Sheng, Yushi Sun
Artificial Intelligence · Cross-listed from cs.CV
Do Video-LLMs Actually Watch? Diagnosing Character-Tracking Failures in Long-Form Video
Mohammad Al-Ratrout, Shayla Sharmin, Aditya Raikwar et al. (+1 more)
Can a
Video Large
Language Model (
Video-LLM) follow one person through a long
video, keeping track of who they are well enough to report, in order, how their outfit changes across a full TV episode?
Benchmarks increasingly score this kind of task, and the strongest open-source 7–8B models now reach 37–38% on InfiniBench's global appearance task, which asks exactly that. But does that score come from tracking the named character, or from something easier? We test this with a nine-condition diagnostic protocol applied to three architecturally distinct open-source
Video-LLMs, with Gemini~2.5~Flash as a frontier reference, and find the accuracy does not come from character tracking. When we change the character named in the question to a different cast member, leaving the
video and answer options untouched, the models change their answer only 4–31% of the time, so they are largely ignoring who the question asks about. Breaking that test down by the gender of the swapped name shows why: the models react more when the name is changed to a different-gender character than to a same-gender one (a 13–28 point gap), picking up coarse gender cues but unable to tell same-gender individuals apart. This shallow processing surfaces again when we drop the multiple-choice options and ask the same questions open-endedly: open-source accuracy drops 18–25 points, with none of 151 answers fully correct, versus a 12-point drop for Gemini. Further checks rule out the obvious innocent explanations, adding subtitles, using the most informative frames, or doubling the number of frames all leave character tracking unimproved, so the bottleneck is not how much
video the model sees but how it ties that
video to the person the question names. We release a diagnostic toolkit for
auditing what such
benchmark scores actually measure.
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All authors Mohammad Al-Ratrout, Shayla Sharmin, Aditya Raikwar, Roghayeh Leila Barmaki
Artificial Intelligence
Chenyang Li, Kaige Li, Zeyu Jiang et al. (+1 more)
Despite progress in
Embodied AI,
Vision-and-
Language Navigation systems remain vulnerable to
adversarial visual disturbances. Most existing methods rely on white-box access to target model gradients, which is often unrealistic for real-world deployed systems and computationally exhaustive due to recursive backpropagation for optimization, limiting their applicability. While previous black-box methods predominantly target single-step, instantaneous decision tasks, they struggle to handle the task complexities and temporal dependencies. This highlights the need for a gradient-free attack method that can effectively disrupt the multistep sequential perception-action loop using only observable inputs and outputs. Therefore, we propose AdvNav, a behavior-guided black-box
adversarial attack framework that disturbs an
agent's first-person views during
navigation. To construct an informative surrogate objective for effective optimization guidance in gradient-free search under the black-box setting, we design a dual-granularity behavior-based feedback, aggregating a trajectory-level performance score representing overall
navigation degradation, an action-level reward score considering the potential decision risk, and a deviation indicator, all of which are extracted from the
agent's self-output behaviors. This feedback guides a hybrid optimization strategy that heuristically tunes perturbation strength via adaptive updates and evolves noise spatial structure genetically, to iteratively discover the most disruptive noise configuration. Evaluated against
Transformer-based HAMT and LLM-based MapGPT with two types of backbones on R2R
dataset, AdvNav achieves 49.70/65.96/87.30% Attack Success Rate. The result demonstrates the effectiveness and generality of AdvNav, reveals critical perception vulnerabilities and offers insights for the design of future resilient VLN models.
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All authors Chenyang Li, Kaige Li, Zeyu Jiang, Changhao Chen
Artificial Intelligence · Cross-listed from cs.CL
Flout at Your Own Risk: LLMs Struggle with Pragmatic Cooperativity Under Epistemic Asymmetry
Hannah VanderHoeven, Abhijnan Nath and Nikhil Krishnaswamy
Fruitful collaborations require effective communication of privately-held information. The increasing use of LLMs as "collaborators" raises questions about the extent to which they exhibit these pragmatic capabilities. In this paper, we perform a novel investigation into the pragmatic
reasoning capabilities of LLMs in a multiparty collaborative task under partial information conditions. We first formalize a notion of collaborative epistemic asymmetry and prove that fully actionable instructions in this context requires adherence to Grice's cooperative principle. We then empirically assess various LLMs' abilities to act cooperatively as both speakers and listeners, including both prompting and post-training strategies. Our results show that while LLMs exhibit certain pragmatic capabilities in collaborative settings, they still face challenges in pragmatic communication with incomplete information, and that Grice's maxims predict distinct failure modes.
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Artificial Intelligence · Cross-listed from cs.SE
BackendForge: Benchmarking Agentic End-to-End Code Generation with Backend Services
Yuzhe Guo, Mengzhou Wu, Yuan Cao et al. (+4 more)
Large
language models (LLMs) are increasingly used in agentic coding settings, where they can inspect files, execute commands, run tests, observe failures, and iteratively revise code. This shift raises a central evaluation question: can an agentic LLM generate an end-to-end software artifact that is both deployable and behaviorally correct under execution? Backend services provide a controlled but realistic substrate for this evaluation. Their APIs expose application-level executable semantics, and deployed behavior can be checked deterministically against an OpenAPI contract through black-box HTTP interactions. We introduce BackendForge, a
benchmark of 56 contract-defined backend generation tasks rewritten from real open-source applications. Given a visible specification and an OpenAPI contract, an LLM must generate a Dockerized service that is built, deployed, and evaluated only through HTTP tests. To strengthen evaluation without introducing hidden requirements, BackendForge uses a test
agent and a code
agent to co-evolve the test oracle and reference service, where the test
agent proposes specification-grounded backend tests and the code
agent repairs the reference implementation. Although the best-performing model, GPT-5.5, succeeds on 55.4% of tasks under the base oracle, it succeeds on only 28.6% under the final oracle. This gap suggests that current LLMs can implement many local API behaviors, but still struggle to produce complete backend services.
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All authors Yuzhe Guo, Mengzhou Wu, Yuan Cao, Jialei Wei, Dezhi Ran, Wei Yang, Tao Xie
Artificial Intelligence · Cross-listed from cs.HC
Same Stories, Different Journeys: Exploring Persona-Grounded Conversational Agents for Supporting Career Exploration with Peers' Posts
Pengping Tan, Baoquan Zhao, Shuai Ma et al. (+1 more)
Young job seekers frequently explore their career possibilities by browsing peers' posts that share job-seeking experiences. However, static browsing requires them to reconstruct fragmented cases and privately judge what others' experiences mean for themselves, sometimes intensifying anxiety through upward social comparison. In this paper, we examine how transforming these posts into persona-grounded conversations reshapes this sensemaking process. We developed JobMate, a prototype featuring
agents that have personas built upon peers' posts and follow the self-determination theory to converse with users. In a between-subjects comparative study with 24 participants, RedNote browsing exposed diverse trajectories but left reconstruction and comparison largely to users, whereas JobMate supported case selection and continued questioning. The conversations further prompted users to articulate previously implicit constraints and accept, challenge, or revise the
agent's interpretations. We discuss design implications for combining authentic peer experiences with generative AI in career exploration.
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All authors Pengping Tan, Baoquan Zhao, Shuai Ma, Zhenhui Peng
Artificial Intelligence
QwenPaw-Data: Bridging Facts, Methodology, and Execution for Autonomous Enterprise Data Analytics
Tianjing Zeng, Yuntao Hong, Zhongjun Ding et al. (+17 more)
Enterprise data analysis is emerging as a distinct frontier for autonomous
agents. Compared with general-purpose interaction and software engineering, it operates in an open, ambiguous, and continuously evolving environment. These characteristics call for a data-
agent architecture that treats semantics, methodology, execution, and
evolution as first-class system concerns. To this end, we introduce QwenPaw-Data, an agentic data system designed for enterprise intelligent data analysis. QwenPaw-Data consolidates heterogeneous assets from warehouses, dashboards, documents, interaction logs, and historical tasks into reusable, governable, and evolvable analysis assets, then turns natural-
language requests into end-to-end analytical workflows spanning data understanding,
retrieval, analysis, report generation, and decision support. Its architecture decomposes the problem into three collaborative subsystems: DataBridge provides trustworthy semantic grounding through interconnected metadata, knowledge, and trace graphs; Skill-Hub codifies expert analytical methodology into reusable and verifiable skills; and Host materializes these evidence and method assets into controllable, artifact-centric runtime execution. Across these subsystems, semantics, methods, traces, and feedback are continuously deposited back into the system, forming a self-evolving asset flywheel. Experiments on public
benchmarks and real-world industrial BI workloads show that QwenPaw-Data improves both verifiable data access capability and higher-level analytical quality, offering a practical foundation for reliable, traceable, and continuously improving enterprise data
agents.
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All authors Tianjing Zeng, Yuntao Hong, Zhongjun Ding, Dandan Liu, Yinan Mei, Yunxiang Su, Yiming Wang, Xiaojian Zhang, Jingyu Zhu, Junhao Zhu, Zhuowen Liang, Jiazhen Peng, Lianggui Weng, Zhihao Ding, Kerui Yi, Qifeng Wang, Rong Zhu, Bolin Ding, Liyu Mou, Jingren Zhou
Artificial Intelligence · Cross-listed from cs.CV
SynCLIP: Synonym-Coherent Language-Image Pretraining for Robust Open-Vocabulary Dense Perception
Mingjie Xie, Guangjun He, Dongli Xu et al. (+5 more)
Open-vocabulary dense perception (OVDP) aims to localize objects unseen during training by leveraging textual knowledge. Despite the remarkable progress of recent CLIP-based approaches, we identify a critical limitation: synonym-induced grounding inconsistency, where semantically equivalent expressions yield disparate spatial
attention patterns. This inconsistency undermines the robustness and performance of existing methods in real-world OVDP applications. To address this issue, we propose SynCLIP, a Synonym-Coherent
Language-Image Pretraining framework that enhances synonym-robust grounding for OVDP. SynCLIP introduces a Semantic-consistent Spatial
Attention alignment (SSA) module to enhance spatial
attention consistency by minimizing discrepancies between
attention maps of original and synonymous expressions. Furthermore, a Spatial
Attention Refinement (SAR) module selectively strengthens the most semantically relevant spatial regions within aligned maps for more precise and stable grounding. To support synonym-coherent pretraining, we also construct a Synonym-Enriched Visual Corpus (SEViC), which augments each category with multiple synonyms and textual definitions. Extensive experiments on multiple
benchmarks demonstrate that SynCLIP substantially improves grounding consistency under diverse linguistic variants and achieves state-of-the-art performance among CLIP-based OVDP methods. Code is available at https://github.com/Justlovesmile/SynCLIP.
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All authors Mingjie Xie, Guangjun He, Dongli Xu, Youtian Lin, Hongjue Li, Pengming Feng, Jian Guan, Yue Deng
Artificial Intelligence · Cross-listed from math.OC
Actor-Critic Learning for Extended Mean Field Control with Deterministic Policies
Ziheng Cheng, Xin Guo, Huyên Pham et al. (+1 more)
This paper develops a model-free reinforcement learning framework for continuous–time extended mean field control problems, where both the dynamics and reward may depend on the joint distribution of states and controls. We adopt deterministic feedback policies, under which the state–action distribution is induced directly as a push–forward of the state law. This avoids optimization over
stochastic kernels and bypasses key limitations of existing approaches in extended mean field settings. We first establish a model–free sensitivity formula for parameterized McKean–Vlasov dynamics and use it to derive a deterministic policy gradient formula expressed through an advantage–rate function on the Wasserstein space. We then refine this formula by introducing local value and advantage–rate representations that depend on the state, action, and joint state–action distribution, yielding a policy gradient that includes both action derivatives and measure–derivative terms with respect to the control distribution. These characterizations lead to a martingale–based learning principle and motivate a continuous–time deep deterministic policy gradient
algorithm combining particle approximations, measure–dependent neural networks, temporal–difference learning, and exploration in either action or parameter space. Numerical experiments on
stochastic Cucker–Smale consensus control and optimal liquidation with trade crowding demonstrate the efficiency, stability, and robustness of the proposed method, including problems with explicit dependence on the control distribution.
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All authors Ziheng Cheng, Xin Guo, Huyên Pham, Yufei Zhang
Artificial Intelligence · Cross-listed from cs.RO
Affordance-Based Manipulation Planning with Text Goals and Sim-to-Real Generalisation via Real-to-Sim Image Conversion
Solvi Arnold, Rin Karashima, Tadashi Adachi et al. (+2 more)
We present a manipulation planning system based on affordance recognition and action effect prediction. The system reasons through possible futures in visual form, and evaluates candidate plans by agreement of predicted outcomes with text-based goals set at run-time, using a multi-modal goal-matching module. Positions of objects named in the goal text are tracked through predictions even when occluded, making it possible to generate action plans even when objects become occluded, or when their initial descriptors cease to identify them in future states. We further expand the system with an image conversion module for translating real-world state images with objects of varied shapes and visual appearances into a consistent visual appearance, to facilitate manipulation planning in a physical
robot setup. We evaluate performance of the system's modules in isolation and demonstrate the integrated system's manipulation planning capabilities on a set of challenging tasks in both simulation and on hardware.
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All authors Solvi Arnold, Rin Karashima, Tadashi Adachi, Takafumi Mochizuki, Kimitoshi Yamazaki
Artificial Intelligence · Cross-listed from cs.CV
LoSA-Net: A Localized and Scale-Adaptive Network for Boundary-Sensitive Prediction of Perineural Invasion in 3D MRI
Youngung Han, Hyunsu Go, Kyeonghun Kim et al. (+9 more)
Perineural invasion (PNI) is a clinically relevant indicator of tumor aggressiveness and can influence
surgical decision-making, motivating interest in reliable preoperative assessment. The subtle MRI features of PNI, however, often resemble nearby anatomy, complicating noninvasive prediction. These fine perineural cues are easily attenuated by routine downsampling or overly global feature aggregation, reducing the effectiveness of conventional volumetric models. We present LoSA-Net, a localized and scale-adaptive architecture for boundary-sensitive PNI prediction in 3D MRI. Talking Neighborhood
Attention (TNA) preserves nerve-aligned detail through localized self-
attention with head-wise mixing, and Scale-Adaptive Feature Mixing (SAFM) modulates the receptive field using multi-scale depthwise processing. Cross-Scale Refinement and
Alignment (CSRA) maintains consistency between semantic context and high-resolution boundaries across stages. In contrast-enhanced MRI scans from 168 patients with cholangiocarcinoma, LoSA-Net achieves an AUC of 0.7567 and outperforms representative convolutional and
transformer baselines under matched preprocessing and optimization settings.
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All authors Youngung Han, Hyunsu Go, Kyeonghun Kim, Induk Um, Junga Kim, Jaewon Jung, Woo Kyoung Jeong, Won Jae Lee, Pa Hong, Ken Ying-Kai Liao, Hyuk-Jae Lee, Nam-Joon Kim
Artificial Intelligence · Cross-listed from cs.RO
Think When It Matters: Conditional VLM Reasoning for Social Navigation with RL Policies
Ali Ahmadi, Hamed Rahimi, Adrien Jacquet Cretides et al. (+3 more)
As mobile
robots become more integrated into everyday human environments, social
robot navigation is becoming essential for ensuring human comfort, safety, and trust. While reinforcement learning (RL)
navigation policies provide the fast inference and reactive behavior necessary for real-time deployment, they still lack flexible semantic
reasoning capabilities and often fail to generalize to complex social scenarios. Recent approaches have increasingly turned to
vision-
language models (VLMs) in place of RL policies to improve semantic and social
reasoning in
robot navigation. Nevertheless, their high computational cost and slow inference remain major barriers to real-time deployment. To overcome these limitations, we introduce HUMA (Hybrid Understanding for Multi-modal social
Navigation), a hybrid architecture that dynamically balances the computational efficiency of RL policies with the deep semantic understanding of VLMs. Our approach uses a reactive RL policy to handle low-density, routine
navigation tasks, while conditioning it on a post-trained high-level VLM when a human enters sensitive situations, such as the
robot's proximity zone. We evaluate HUMA on the Social-MP3D and Social-HM3D
benchmarks, where it achieves task success improvements of 20% and 3%, respectively, while significantly reducing personal space violations and human collisions against state-of-the-art baselines. Extensive ablation studies validate each architectural component, and real-world deployment on the Mirokaï mobile
robot further demonstrates the practical viability of our approach.
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All authors Ali Ahmadi, Hamed Rahimi, Adrien Jacquet Cretides, Marie Samson, Mahdi Khoramshahi, Mohamed Chetouani
Artificial Intelligence · Cross-listed from cs.CV
MMA-Former: Multi-Window Mixture-of-Head Attention Transformer for Adaptive PNI Prediction in 3D MRI
Youngung Han, Induk Um, Kyeonghun Kim et al. (+9 more)
Perineural invasion (PNI) is a critical prognostic factor in cholangiocarcinoma. Non-invasive prediction from 3D MRI is challenging, demanding models that efficiently capture both fine-grained details and global context. We propose the Multi-window Mixture-of-Head
Attention Transformer (MMA-Former), a novel end-to-end 3D architecture featuring a Coarse-Fine
Transformer (CFT) structure for parallel multi-scale feature extraction. We advance this structure by integrating a novel Window-Specific Mixture-of-Head
attention (WS-MoH) mechanism. Unlike standard Multi-Head Self
Attention (MSA), WS-MoH generates a representation for each 3D window and dynamically routes the entire window to specialized or common
attention heads. This enables spatially adaptive feature extraction tailored to the local context of each window, enhancing specialization and reducing redundancy without increasing parameters. Evaluated on a retrospective
dataset of 168 T1-weighted MRI scans, MMA-Former achieved an AUC of 0.752, outperforming other 3D architectures, including the best CNN (AUC of 0.708) and
Transformer baselines (AUC of 0.681).
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All authors Youngung Han, Induk Um, Kyeonghun Kim, Junga Kim, Hyunsu Go, Jaewon Jung, Woo Kyoung Jeong, Won Jae Lee, Pa Hong, Ken Ying-Kai Liao, Hyuk-Jae Lee, Nam-Joon Kim
Artificial Intelligence · Cross-listed from cs.CV
EquiFusion: Kinematics-Agnostic Human Motion Prediction via Equivariant Latent Diffusion
Cecilia Curreli, Florian Hofherr, Dominik Muhle et al. (+3 more)
Existing
Stochastic 3D Human Motion Prediction models are fundamentally constrained by hard-coding the skeleton kinematics, severely limiting generalization, preventing cross-
dataset training, and requiring complex data retargeting. We introduce EquiFusion, the first kinematics-agnostic model to solve this bottleneck, implementing a latent
diffusion model with a permutation equivariant architecture. EquiFusion treats the kinematics' connectivity as an explicit input parameter, ensuring its internal computations are inherently agnostic to joint ordering and graph structure. This novel design enables truly cross-
dataset generalization to unseen kinematics and unlocks novel zero-shot directions, such as motion prediction from partial or occluded observations and targeted limb generation. EquiFusion achieves state-of-the-art results on major
benchmarks, being up to 75% more compact than previous kinematics-specific methods, while achieving faster training and inference. EquiFusion thus establishes a new, flexible standard for robust human motion prediction. Model and training code are available at https://ceveloper.github.io/publications/equifusion/.
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All authors Cecilia Curreli, Florian Hofherr, Dominik Muhle, Abhishek Saroha, Riccardo Marin, Daniel Cremers
Artificial Intelligence
From Checker to Forecaster: Code-Owned Evaluation of Model-Generated Strategic Routes Under Delayed Ground Truth
Aleh Manchuliantsau
Many evaluations of model outputs rely either on contracts checkable at evaluation time or on feedback that arrives within the operating loop. We study the complementary setting in which ground truth is delayed, censored, or private, so deterministic code cannot check correctness at scoring time and must instead issue a code-owned provisional forecast. RouteCast instantiates this regime for model-generated typed strategic routes: models propose candidate routes and structured factors; point-in-time evidence, reference classes, and deterministic transformations produce a provisional forecast-ranking; later outcomes evaluate the forecast. In a retrospective venture pilot on 21 binary-outcome cases (6 positive, 15 negative), the whole-packet RouteCast score showed preliminary retrospective discrimination (AUC 0.756, 95% CI [0.471,0.980]), while a blind LLM judge reached AUC 0.678 [0.419,0.897] and an identity-exposed LLM judge reached AUC 0.761 [0.515,0.944], consistent with recognition- or outcome-related leakage risk. A preregistered decomposition ablation on the same binary subset found that converting the identical inputs into typed staged routes was indistinguishable from the whole-packet score (Delta AUC = -0.144, 95% CI [-0.471,0.176]) and from a deterministic heuristic (Delta AUC = -0.089, 95% CI [-0.412,0.278]). The pilot establishes an auditable feasibility result and exposes failure modes; it does not establish prospective calibration, causal decision improvement, route-decomposition advantage, or cross-domain validity.
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Artificial Intelligence · Cross-listed from cs.DS
CGS: Configurable Graph Summarization with Bounded Neighborhood Loss and Query Support
Shubhadip Mitra, Sona Elza Simon, C Oswald et al. (+2 more)
Given a large graph, how to generate a compact summary graph that is configurable by the user and supports multiple graph queries with either no loss or with high accuracy? The ever growing size of graph
datasets makes the above question on graph summarization very pertinent. Although, there are several approaches, there does not exist a configurable graph summarization method that offers high compression along with support for multiple graph queries on the summary graph with high accuracy, and allows the user to configure the summarization based on: (1) lossless or lossy summarization, (2) amount of tolerable neighborhood loss, (3) the type of loss it can tolerate, in terms of false positive edges (i.e., extra edges), false negative edges (i.e., missing edges), or neither, in both the (a) reconstructed graph and the (b) query answers. To overcome these limitations, we propose a novel graph summarization framework CGS (Configurable Graph Summarizer) that builds upon the idea of aggregating nodes with common neighborhoods. The CGS framework consists of three summarization variants, CGS-E, CGS-I and CGS-U. While CGS-E is a lossless scheme, CGS-I and CGS-U are lossy schemes that allow reconstruction of the input graph with no false positive edges and no false negative edges, respectively. To bound the graph reconstruction loss, we introduce a user-specified parameter neighborhood loss tolerance threshold, that limits the maximum loss allowed in the neighborhood of each node. This allows graph reconstruction and neighborhood query evaluation with either no loss or with bounded loss guarantees. Empirical evaluation on several synthetic and real-world graphs shows that CGS offers superior summarization than the state-of-the-art methods, and can answer graph queries with fairly high accuracy and efficiency.
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All authors Shubhadip Mitra, Sona Elza Simon, C Oswald, Arnab Bhattacharya, Arindam Pal
Artificial Intelligence
SVR-R1: Bootstrapping Multi-modal Reasoning with Self-verification in Reinforcement Learning
Mingyuan Wu, Jingcheng Yang, Shengyi Qian et al. (+11 more)
We introduce Self-Verified Reasoner (SVR-R1), a multi-turn RL framework that turns a model's own verification into a learning signal for
multimodal reasoning. For each query, the model proposes an answer using the same weights, and issues a binary self-verdict (Yes/No). A 'No' triggers a second-chance rethink; a 'Yes,' or a turn cap, finalizes the output for computing the outcome-based reward. SVR-R1 is implemented with GRPO and an asynchronous multi-turn rollout framework and needs no external supervision or auxiliary critics. We evaluate SVR-R1 on
vision-
language reasoning benchmarks and show that it improves accuracy by a large margin over strong standard GRPO baselines. Training dynamics show decreasing reliance on verification-fewer verification turns, yet higher test accuracy-indicating that the gap between verification and generation narrows as the policy internalizes self-correction and chooses the most confident answer via our framework. SVR-R1 bridges the less explored intersection of inference-time self-refinement and RL training for VLMs, offering a simple yet effective recipe for bootstrapping
multimodal reasoning. We will open-source SVR-R1 to facilitate future research in VLMs.
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All authors Mingyuan Wu, Jingcheng Yang, Shengyi Qian, Xudong Wang, Jize Jiang, Qifan Wang, Aashu Singh, Khoi Pham, Fei Liu, Zhaolun Su, Zhuokai Zhao, Klara Nahrstedt, Jianyu Wang, Hanchao Yu
Artificial Intelligence · Cross-listed from cs.LG
Efficient Online Proportional Sampling with Applications to Smoothed Online Learning
Amirmahdi Mirfakhar, Maria-Florina Balcan and Hedyeh Beyhaghi
We study the problem of efficient online proportional sampling from a high-dimensional domain under a $\sigma$-smoothed adversary, where the sampling distribution is induced by a dynamically evolving weight function defined over a sequence of piecewise-structured partitions. This setting captures a broad range of applications, including principal-
agent games (e.g., pricing and contract design), and
algorithm configuration and parameter tuning. The central challenge is maintaining an efficient data structure as the induced partition grows increasingly complex over time – naively, the number of subregions can grow as $O(t^d)$ by round $t$ in $d$ dimensions. We design a data structure that supports efficient updates and proportional sampling while avoiding the cost of explicitly maintaining this exponential growth, where the discontinuities are structured from axis-parallel hyperplanes. Under a $\sigma$-smoothed adaptive adversary, we prove a tight $O(\sqrt{\sigma T})$ bound on the depth of our data structure, and an $O(\log T)$ bound under a random-order adversary – to our knowledge, the first such results for this class of problems. We apply this framework to online learning with piecewise-structured rewards, obtaining efficient no-regret
algorithms under both full-information and bandit feedback, with provable sublinear regret guarantees.
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