Artificial Intelligence · Cross-listed from cs.CV
Human-in-the-Loop Atlas-Based 3D Asset Segmentation for Interactive Content Workflows
Paul Julius Kühn, Saptarshi Neil Sinha, Jakob Hansen et al. (+1 more)
Segmenting 3D assets into meaningful regions remains challenging, especially when
segmentation criteria are application-dependent and require user control. We present a human-in-the-loop pipeline for generating a segmented 2D parameterized atlas from a 3D model for interactive media, game, and XR content workflows. Our method first selects a compact set of rendered views using a greedy set cover strategy over sampled surface points, and then supports interactive
segmentation of these views with SAM~2 and Label Studio. The resulting masks are back-projected onto the model's UV parameterization to produce a unified segmented atlas that supports downstream production tasks such as segment-wise material assignment, style transfer, and semantic labeling. We assess the pipeline through a demonstration-based technical evaluation on eight cultural heritage objects. The results show that the approach can generate usable segmented atlases across diverse geometries while revealing recurring sources of manual correction, particularly fine structures, cavities, and weak appearance boundaries. The code is available at https://github.com/saptarshineil/ai_assisted_atlas_
segmentation
Read the full abstract ▾
All authors Paul Julius Kühn, Saptarshi Neil Sinha, Jakob Hansen, Robin Horst
Artificial Intelligence
DecoSearch: Complexity-Aware Routing and Plan-Level Repair for Text-to-SQL
Esteban Schafir, Xu Zheng, Hojat Allah Salehi et al. (+4 more)
Large
Language Models (LLMs) have demonstrated remarkable capabilities in translating natural
language to SQL, yet existing methods still falter on complex queries requiring multi-step, data-aware
reasoning. We introduce DecoSearch, a training-free framework that addresses this by routing each query to the appropriate level of
reasoning effort. A lightweight Schema Selector first prunes the full database schema to the relevant tables and columns. An LLM Judger then decides whether the question requires decomposition: straightforward questions follow a direct generation path and complex ones are escalated to a Directed Acyclic Graph (DAG) of atomic sub-questions, each solved by a targeted SQL generation step. A RAG component grounds the decomposer with semantically similar training examples, and a
Topology Refiner restructures the
reasoning plan when execution failures signal a flawed decomposition rather than a fixable SQL error. DecoSearch achieves 70.53% execution accuracy on BIRD and 88.31% on Spider with a DeepSeek backbone, surpassing all training-free baselines while consuming an order of magnitude fewer
tokens than competing methods. It also functions as a model-agnostic wrapper, consistently improving fine-tuned SQL generation backbones without any modification to the pipeline.
Read the full abstract ▾
All authors Esteban Schafir, Xu Zheng, Hojat Allah Salehi, Zhuomin Chen, Mo Sha, Wei Cheng, Dongsheng Luo
Artificial Intelligence · Cross-listed from cs.SE
A Framework for Evaluating Agentic Skills at Scale
Maksim Shaposhnikov, Nicolas Fortuin, Simon Stipcich et al. (+3 more)
Agent skills – structured, reusable knowledge artifacts that augment LLM
agent capabilities – have been rapidly adopted in industry, yet their cross-domain impact and use across commercial and open-source models remain under-studied, and no reusable methodology exists for evaluating an individual skill. In this work, we present an evaluation framework that lets a skill author construct realistic tasks to rigorously assess the aspects of a skill that matter most to them, and that estimates skill utility by solving those tasks. Further, we apply our evaluation approach at scale to 500 real-world skills, generating 1,000 tasks derived from the skills' content, along with instruction-following and goal-completion scoring rubrics. Using these metrics, we evaluate how 19
agent-model configurations, both proprietary and open-source, perform on the tasks. Our results show that models vary widely in how closely they adhere to the instructions encoded in skills, leading to substantial differences in their performance gains. Furthermore, we show that access to a skill significantly changes model behavior compared to the no-skill setup, providing an essential mechanism for encoding opinionated workflows into LLM
agents. We release our evaluation
dataset to support future work on
agent skills.
Read the full abstract ▾
All authors Maksim Shaposhnikov, Nicolas Fortuin, Simon Stipcich, Maria I. Gorinova, Amy Heineike, Rob Willoughby
Artificial Intelligence · Cross-listed from cs.LG
Conservation Laws for Modern Neural Architectures
Viet-Hoang Tran, Vinh Khanh Bui, Tan Lai Ngoc et al. (+3 more)
Understanding gradient descent dynamics is key to explaining the success of over-parameterized models, where implicit bias manifests through conservation laws in gradient flow. While such laws are well understood for linear and ReLU networks, they remain largely unexplored for modern architectures. This work develops a unified framework to characterize conservation laws for contemporary models, including feedforward networks with GELU, SiLU, and SwiGLU activations, multihead
attention with sinusoidal and rotary positional encodings, and Mixture-of-Experts architectures under diverse gating designs. Our theoretical findings are supported by experiments that validate the predicted invariants.
Read the full abstract ▾
All authors Viet-Hoang Tran, Vinh Khanh Bui, Tan Lai Ngoc, Nam Nguyen, Tuan Dam, Tan M. Nguyen
Artificial Intelligence · Cross-listed from cs.LG
No-Free-Fairness: Fundamental Limits and Trade-offs in Learning Systems
Khoat Than
In this paper, we establish a set of theoretical impossibility results, termed the No-Free-
Fairness theorems, that identify three fundamental sources of disparity in learning systems. First, we show that when a task exhibits irreducible cost on a subgroup, any decision rule must trade off overall performance with disparity, yielding an inherent
fairness–cost frontier. Second, we prove that even in ideal, noise-free settings where a perfectly fair and accurate solution exists, finite-sample learning alone induces nontrivial subgroup disparity, ruling out distribution-free
fairness guarantees. More seriously, enforcing strict relative
fairness creates a statistical bottleneck: achieving low cost may require exponentially many samples. Third, we show that limitations of the model class can independently induce disparity: if the model cannot represent accurate solutions for a subgroup,
fairness remains unattainable regardless of data or training procedure. Overall, these results demonstrate that unfairness is not solely a consequence of biased data or suboptimal optimization, but arises from the intrinsic structure of decision problems, the constraints of finite data, and the expressivity of models. Our framework applies broadly beyond standard supervised learning, and suggests that achieving
fairness requires explicit trade-offs and should be treated as a core design consideration.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.SE
Position: Coding Benchmarks Are Misaligned with Agentic Software Engineering
Maria I. Gorinova, Macey Baker, Amy Heineike et al. (+3 more)
Coding
agents have become a major mode of software engineering, but the
benchmarks we use to compare them were designed in a pre-
agent era: they collapse model, harness, and environment into a single end-to-end score, typically computed against one reference solution, with no component-level signal for iteration. We argue that current coding
benchmarks are misaligned with agentic software engineering. A coding
agent in practice is not a model: it is a system harness – a composite of models, harnesses, contexts, environments, and feedback signals, any one of which can move the
benchmark score by margins comparable to those between adjacent model generations. We discuss three symptoms: (i)
benchmark scores conflate the model with the rest of the harness; (ii) grading against a single reference solution penalises equally valid alternatives; and (iii) the absence of signal at the level of individual harness components makes the end-to-end system score difficult to iterate on.
Read the full abstract ▾
All authors Maria I. Gorinova, Macey Baker, Amy Heineike, Maksim Shaposhnikov, Rob Willoughby, Dru Knox
Artificial Intelligence · Cross-listed from cs.CV
LiveStarPro: Proactive Streaming Video Understanding with Hierarchical Memory for Long-Horizon Streams
Zhenyu Yang, Kairui Zhang, Bing Wang et al. (+2 more)
Despite the remarkable progress of
Video Large
Language Models (
Video-LLMs), current online architectures still struggle to simultaneously process continuous
video streams, decide autonomously when to respond, and preserve long-horizon contextual
memory. These obstacles undermine real-time responsiveness and cause severe forgetting throughout prolonged interactions. In this work, we introduce LiveStarPro, a live streaming assistant that is designed for proactive
video understanding over long-horizon streams. The design of LiveStarPro rests on three complementary components. The first component is Streaming Verification Decoding (SVeD), an inference framework that identifies the appropriate response timing through single-pass perplexity verification, thereby eliminating the dependency on explicit silence
tokens. The second component is Streaming Causal
Attention Masks (SCAM), a training strategy that enforces incremental
video-
language alignment over variable-length streams. The third component is Tree-Structured Hierarchical
Memory (TSHM), a recursive
memory architecture that organizes evicted historical information into event chains and consequently enables efficient
retrieval from effectively unbounded
video streams. To facilitate a comprehensive evaluation under realistic online conditions, we further present OmniStarPro, a large-scale
benchmark that spans 15 diverse real-world scenarios and that extends to hour-scale streams for the assessment of long-term recall. Extensive experiments demonstrate that LiveStarPro consistently surpasses existing methods, attaining a 28.9% improvement in semantic correctness and an 18.2% reduction in timing error, while its streaming key-value cache further yields a 1.58x inference speedup over the same model without caching. The model and the code are publicly available at https://github.com/sotayang/LiveStarPro.
Read the full abstract ▾
All authors Zhenyu Yang, Kairui Zhang, Bing Wang, Shengsheng Qian, Changsheng Xu
Artificial Intelligence · Cross-listed from cs.AR
MIVE: A Minimalist Integer Vector Engine for Softmax LayerNorm and RMSNorm Acceleration
Kosmas Alexandridis and Giorgos Dimitrakopoulos
The rapid growth of Large
Language Models (LLMs) has intensified the need for specialized hardware accelerators that can satisfy stringent inference latency and power constraints. Although
matrix multiplications dominate the overall computational workload, non-linear vector normalization operations, such as LayerNorm, RMSNorm and Softmax can become critical hardware bottlenecks. Existing accelerators typically implement these functions using dedicated hardware blocks, leading to duplicated resources and inefficient silicon utilization. To address this limitation, we propose a Minimalist Integer Vector Engine (MIVE), a programmable architecture capable of executing all three operations within a unified datapath. By exploiting common computational patterns across LayerNorm, RMSNorm and Softmax the proposed vector engine maximizes hardware sharing while reducing implementation overhead. Physical ASIC implementation results show that MIVE provides comprehensive multi-function support while achieving higher area and hardware efficiency than most state-of-the-art standalone accelerators.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.SD
A Neuromorphic Trigger for Efficient Audio Event Detection
Benjamin Hatton, Oliver Rhodes and Luca Peres
Efficient processing of continuous audio streams remains a key challenge for real-time and resource-constrained systems. This paper introduces a neuromorphic trigger for audio event detection, based on a spiking neural network (SNN) that selectively gates input to downstream models. The proposed neuromorphic trigger acts as a flexible low-cost front-end, identifying salient audio segments and enabling these to be processed by a more computationally intensive model for tasks such as classification. The trigger is implemented as a lightweight fully connected SNN using a close-open filter for postprocessing, and is evaluated on two representative tasks: Anomalous Sound Detection (ASD) and Sound Event Detection (SED). For ASD, the trigger achieves a one-second segment-based F1 score of 0.97 on a class-agnostic form of the URBAN-SED
dataset, demonstrating high reliability in identifying relevant audio regions. For SED, the trigger is combined with the Dang classifier on the DCASE 2017 Challenge Task 2
dataset, showing a potential $42.6\times$ reduction in FLOPs while reducing the lower bound of the event-based error rate from 0.41 to 0.25. These results highlight the potential of neuromorphic triggers as real-time, energy-efficient front-end filters, enabling substantial reductions in computational cost.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.HC
Talking to Your Data: Exploring Embodied Conversation as an Interface for Personal Health Reflection
Nikola Kovacevic, Bastien Husler, Di Zhuang et al. (+2 more)
Personal health data from wearables are typically presented through dashboards of charts and summary statistics, requiring users to actively interpret patterns and implications. We explore an alternative interaction paradigm: engaging with personal health data through an
embodied conversational
agent that facilitates objective data reflection in dialogue with the user. We present a system that combines lightweight preprocessing of wearable data with a Unity-based
embodied character. Internally, the system follows a dual-
agent design in which an Observer
agent extracts descriptive statistics and temporal trends, and a Presenter
agent communicates these findings through "spoken statistics," intentionally refraining from clinical advice to isolate the impact of the interaction modality. We evaluate this approach through a simulated-self user study (N=5) using a within-subject design. Participants adopted health personas and goals derived from the LifeSnaps
dataset to compare traditional dashboard exploration with
embodied conversational reflection. Our evaluation focuses on perceived understanding, the specificity of generated actions, and the cognitive shift from passive viewing to active sensemaking. The paper contributes a functional prototype, a design pattern for objective health data narrative generation, and early empirical insights into how embodiment affects the interpretation of personal health metrics.
Read the full abstract ▾
All authors Nikola Kovacevic, Bastien Husler, Di Zhuang, Rafael Wampfler, Barbara Solenthaler
Artificial Intelligence · Cross-listed from math.OC
Horizon-Uniform Sensitivity and Decay of Terminal Reward Perturbations in Discrete-Time Pontryagin Systems
Pyuyi Chufeng Huang and Zikang Song
We study local stationary solutions of finite-horizon discrete-time Pontryagin systems near a steady extremal. Suppose that the stationarity equation for the control is regular, the reduced state–costate map is
hyperbolic, and the endpoint conditions satisfy a scaled transversality condition with respect to the stable and unstable subspaces. Then the linearized boundary-value problem admits an inverse whose Green estimate is uniform in the horizon. The Green kernel separates interior decay from the two reflections induced by the endpoint conditions. For $x_0=x_{\rm in}$ and $p_T=r_x(x_T,y)$, a contraction argument in a weighted norm proves existence and uniqueness in a neighborhood independent of $T$, together with uniform Lipschitz estimates and a pointwise quadratic remainder. We also derive an explicit admissible data radius and an a posteriori criterion for existence and local uniqueness near an approximate trajectory. For these graph boundary conditions, a one-sided Green estimate shows that a perturbation of the terminal reward changes the initial control and the gradient with respect to the initial state of the stationary objective by $O(e^{-\alpha_{\rm ter} T})$ for every $\alpha_{\rm ter}$ below the dichotomy rate. For linear-quadratic systems with invertible $A$, stabilizable $(A,B)$, $Q\succ0$, $R\succ0$, and a nonpositive terminal Hessian, a
symplectic graph condition verifies the assumptions, and the finite-horizon Riccati
matrix and initial feedback gain converge at rate $O(e^{-2\gamma T})$. Numerical experiments verify the certificates and the predicted decay rates.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.RO
ED3R: Energy-Aware Distributed Disaster Detection via Cooperative Agents in Robotic Systems
Lina Magoula, Nikolaos Koursioumpas, Nancy Alonistioti et al. (+1 more)
Robotics are expected to support environmental monitoring and disaster detection, where decisions must be made under uncertainty, resource limitations, and strict operational constraints. In critical missions, such as wildfires,
robots must not only identify hazardous events with sufficient confidence, but also manage the energy cost and time until detection. This paper introduces ED3R, an energy-aware distributed framework for wildfire detection under uncertainty that enables hierarchical cooperative decision-making between a
robot and a remote controller. The remote controller decides upon the
robot's motion, while the
robot senses the environment and decides where to execute the wildfire detection (onboard or remotely) and how. The common goal is to detect wildfires with a required confidence while minimizing the energy consumed by any
robot operation. ED3R further integrates mechanisms to avoid nearby obstacles, prevent redundant exploration, enable adaptive early mission completion, and ensure feasibility through a custom penalty function. ED3R also introduces a forward-looking capability, enabled through distributed neural regression models that allow the
agents to anticipate the future by evaluating candidate strategies before execution. The framework is evaluated through realistic robotics simulations, ablation studies, and baseline comparisons. ED3R achieves a mission success rate of up to 97.18%, defined as the percentage of missions with true positive detections meeting the required confidence, excluding false positives and battery depletions. Especially in the most demanding missions, it reduces energy consumption by up to 36.4% and detects wildfires up to 41% faster than baselines.
Read the full abstract ▾
All authors Lina Magoula, Nikolaos Koursioumpas, Nancy Alonistioti, Ramin Khalili
Artificial Intelligence
Shattering the Autoregressive Curse: Dynamic Epistemic Entropy Orchestrated Erasable Reinforcement Learning for LLMs
Ziliang Wang, Kang An, Faqiang Qian et al. (+5 more)
Although reinforcement learning (RL) has expanded the cognitive boundaries of large
language models (LLMs), it often remains vulnerable to the autoregressive curse in long-horizon logical
reasoning: small epistemic perturbations introduced early in generation can propagate irreversibly along the Markov decision process flow, triggering cascading failures that drive the
reasoning trajectory toward collapse. To overcome this autoregressive cascade, in which a single early mistake can compromise all subsequent
reasoning steps, we propose dynamic epistemic
entropy orchestrated erasable reinforcement learning ($\text{E}^3\text{RL}$). $\text{E}^3\text{RL}$ eliminates reliance on external signals by grounding the model's endogenous local autoregressive cross-
entropy as an intrinsic coordinate of epistemic uncertainty. By introducing segment-level adaptive dynamic thresholds and advantage allocation, $\text{E}^3\text{RL}$ enables the model to precisely excise localized logical defects while reusing historical key-value (KV) cache streams, thereby endowing the
reasoning process with a self-healing capability. We train $\text{E}^3\text{RL}$ on the DeepMath-103k
dataset. Experimental results show that $\text{E}^3\text{RL}$ reshapes the exploration efficiency of long-sequence
reasoning and improves sample efficiency while maintaining linear
memory overhead. On mathematical
reasoning benchmarks such as AIME, $\text{E}^3\text{RL}$ achieves substantial performance gains, with the 4B and 8B parameter models surpassing previous state-of-the-art (SOTA) results by 5.349% and 6.514%, respectively. These findings suggest that $\text{E}^3\text{RL}$ shatters the autoregressive curse in long-sequence
reasoning and establishes a theoretical and systems-level foundation for the next generation of self-healing artificial general intelligence (AGI).
Read the full abstract ▾
All authors Ziliang Wang, Kang An, Faqiang Qian, Jialu Cai, Cijun Ouyang, Yuhang Wang, Qibing Ren, Yichao Wu
Artificial Intelligence
LongWebBench: Evaluating Structural and Functional Webpage Generation in Long-Horizon Settings
Yi Zhao, Zhen Yang, Mengpan Chen et al. (+5 more)
Recent
vision-
language models (VLMs) have shown promising progress in generating webpages from visual inputs, yet existing evaluations mainly focus on short, single-screen, and largely static webpages. We introduce LongWebBench, a
benchmark for evaluating long-horizon webpage generation from both structural and functional perspectives. LongWebBench contains 490 real-world long webpages for structural fidelity evaluation and 507 goal-oriented interaction tasks over 129 webpages for functional evaluation. It employs two complementary protocols: a multi-dimensional VLM-based metric for assessing long-range structural coherence, and a DOM-augmented
agent-based pipeline for end-to-end functional verification. We further examine the automatic evaluation protocols through human agreement analysis. Experiments with state-of-the-art open-source and proprietary VLMs under single-image and multi-image settings reveal that structural fidelity degrades as webpage length increases, while visually plausible generations often fail to support executable multi-step interactions. These results highlight the need to evaluate long webpage generation beyond visual similarity, with executable interaction as a core criterion. Our code and data are available at https://github.com/zheny2751-dotcom/LongWebBench.
Read the full abstract ▾
All authors Yi Zhao, Zhen Yang, Mengpan Chen, Mingde Xu, Shanghui Gong, Xijun Liu, Jibing Gong, Jie Tang
Artificial Intelligence · Cross-listed from cs.CV
Structured Adversarial Camouflage via Voronoi Diagrams
Jens Bayer, Stefan Becker, David Münch et al. (+2 more)
Pixel-wise
adversarial patches are computationally heavy and often visually detectable, limiting utility in security-critical systems. We present
adversarial Voronoi camouflage that optimizes only seed-point locations under fixed, printable palettes using a soft assignment, producing structured, splinter camouflage-like patterns without additional regularization. Evaluated on person detection with COCO-style AP@[.5:.95], naive placement (Inria -> COCO) performs comparably bad, while garment-level application via
segmentation mask (3DPeople) results in a significant AP drop. The attack transfers to out-of-domain backgrounds and across detector families (YOLOv9/10/11/12), indicating robustness in black-box settings. Repainting with different palettes largely nullifies the effect, and single-color tweaks show limited tolerance (<=0.17), highlighting a structure-palette coupling. The parameter-efficient, palette-constrained design improves visual plausibility while degrading real-time detector performance. Physical validation and color calibration are left for future work. Code: https://github.com/JensBayer/Voronoi This paper was originally presented at the International Conference on Military Communication and Information Systems (ICMCIS), organized by the Information Systems Technology (IST) Scientific and Technical Committee, IST-224-RSY - the ICMCIS, held in Bath, United Kingdom, 12-13 May 2026.
Read the full abstract ▾
All authors Jens Bayer, Stefan Becker, David Münch, Michael Arens, Jürgen Beyerer
Artificial Intelligence · Cross-listed from cs.CV
Vision-language models for chest radiography do not always need the image
Mahshad Lotfinia, Sebastian Ziegelmayer, Lisa Adams et al. (+4 more)
Vision-
language models that answer questions about chest radiographs are evaluated by their accuracy on labels derived from radiology reports. High
benchmark accuracy is often interpreted as evidence that the model uses the image. A model that answers from the finding named in the question can score as well as a model that uses the radiograph. Keeping the question fixed, we audit eight open-weight systems by swapping in another patient's radiograph with the same or the opposite label, occluding the radiologist-marked region or an equal region elsewhere, and removing the radiograph or replacing it with noise or a photograph. On 2,548 yes-or-no questions from MIMIC-CXR, one
multimodal model answers Yes regardless of the image, another
multimodal model changes its answers without following the label, and four systems use the image but keep about half of their correct answers when the radiograph is swapped for an opposite-label radiograph. A medical model that receives only the question text scores 55.3% on the pooled questions, higher than two
multimodal systems. It scores 91.8% where every finding is present, and answering Yes to every question scores 100% there. Where the image is necessary, the best
multimodal system exceeds this model by 10.4% in balanced accuracy. The categories are unchanged on CheXpert. Confidence is not higher when a correct answer depends on the marked region. In a reader study with three radiologists, the two radiologists who read a balanced set of 200 cases score 86.0% and 82.0%, and the systems score 50.0% to 73.0%. Accuracy does not establish image use, but an intervention on the image can test it.
Read the full abstract ▾
All authors Mahshad Lotfinia, Sebastian Ziegelmayer, Lisa Adams, Tri-Thien Nguyen, Daniel Truhn, Andreas Maier, Soroosh Tayebi Arasteh
Artificial Intelligence · Cross-listed from cs.LG
Confusion-Aware Transfer Teacher Curriculum Learning Framework: Disentangling Scoring and Pacing Effects
Savini Kommalage, Sanka Mohottala, Asiri Gawesha et al. (+5 more)
Curriculum learning couples two design choices, how samples are scored by difficulty and how harder samples are paced into training, making it difficult to attribute observed gains to either component. We disentangle these factors with two evaluation protocols: stage-wise test subsets that validate scoring functions independently of curriculum training, and a baseline that applies the same pacing schedule to randomly ordered data. Within the Transfer Teacher framework (TTF), we use these protocols to evaluate a confusion-aware difficulty score that considers both correct-class confidence and the probability distribution over incorrect classes. On CIFAR-10 with ResNet-18 and VGG-16, the proposed score produces model-interpretable difficulty rankings that align with human intuition. However, at full data, neither curriculum nor anti-curriculum ordering improves accuracy over standard training, indicating that improving the scoring function alone is insufficient to overcome the known failure modes of curriculum learning in TTF. In contrast, We find that confusion-aware curriculum ordering result in consistent data-efficiency benefits, outperforming random ordering by up to 8.7% points at the 20% data regime, suggesting the potential of TTF as a data-efficient training method.
Read the full abstract ▾
All authors Savini Kommalage, Sanka Mohottala, Asiri Gawesha, Dulara Madhusanka, Menan Velayuthan, Dharshana Kasthurirathna, Mahima Milinda Alwis Weerasinghe, Charith Abhayaratne
Artificial Intelligence · Cross-listed from cs.CV
SegTME-UNI2: A Foundation Model-Based Framework for Generalisable Multiclass Cell Segmentation and LLM-Driven Tumour Microenvironment Characterisation in Histopathology
Wan Siti Halimatul Munirah Wan Ahmad, Faris Syahmi Samidi, Mohammad Badal Ahmmed et al. (+3 more)
Characterising the TME from routine H&E-stained histology images requires simultaneous
cell segmentation, biological feature extraction, and interpretable clinical reporting. We present SegTME-UNI2, a unified framework addressing all three requirements end-to-end: a
segmentation backbone that converts raw H&E patches into per-
nucleus class labels, a structured feature-extraction pipeline that turns those labels into quantitative TME descriptors, and a
language-model narrative generator that turns those descriptors into clinician-readable text. At its core is UNI2-UperHoVer, a dual-head multiscale
segmentation model that pairs UNI2 with two parallel UperNet decoders: one for six-class semantic
segmentation and one for HV gradient regression enabling watershed-based
nuclear instance separation. It is trained via a three-stage progressive pseudo-label curriculum, scaling from PanNuke (Stage 1, 0.25um/pixel) to TCGA-UT Scale-0 (Stage 2, 0.5um/pixel) and full 1.6M-patch, six-scale TCGA-UT (Stage 3, 0.5 to 1.0um/pixel). TCGA-UT's coarser, broader per-patch context than PanNuke's also permits a larger tile stride during whole-slide inference. This pipeline computes 22 per-patch compositional, morphological, spatial-
entropy, and intercellular-distance metrics and translates them into six categorical phenotype labels and a standardised biological-
token vocabulary, fine-tuned via NVIDIA BioNeMo that converts into clinically grounded narratives whose individual claims can be spot-checked directly against the underlying features. Qualitative validation on IGNITE NSCLC tiles shows the pipeline produces biologically coherent phenotype classifications and narratives despite inter-institutional stain variability and imperfect
segmentation. The pseudo-labelled TCGA-UT
dataset and UNI2-UperHoVer checkpoints are publicly released to support large-scale TME profiling and spatial biology research.
Read the full abstract ▾
All authors Wan Siti Halimatul Munirah Wan Ahmad, Faris Syahmi Samidi, Mohammad Badal Ahmmed, Vimal Angela Thiviyanathan, Selvam Thavaraj, Anwar P.P. Abdul Majeed
Artificial Intelligence
EComAgentBench: Benchmarking Shopping Agents on Long-Horizon Tasks with Distributed Hidden Intent
Zeyao Du, Tong Li and Haibo Zhang
As LLM-based shopping
agents enter production, existing
benchmarks fail to capture how a shopper's requirements arrive: stated implicitly in the query, recorded in a profile, or revealed only when the right question is asked.
Benchmarks that expose full intent upfront and grade only the final choice can neither pose this long-horizon challenge nor explain which requirement an
agent missed. To address this gap, we introduce EComAgentBench, a
benchmark of 662 tasks grounded in real Amazon products and reviews. Each task scatters these requirements across a visible query, a tool-gated profile, and scripted clarification; an
agent must uncover hidden intent, verify candidates against attributes and review evidence, and commit to a single product within 100 tool calls. Moreover, typed, source-tagged rubrics grade every task, attributing each failure to a requirement and its source. Construction is automated yet reliable, with every answer fixed in code before any text is generated and every sample validated. Our evaluation of seven models reveals that even the strongest attains only 57.1% overall accuracy, and rubric satisfaction degrades from visible to hidden sources. Overall, we believe EComAgentBench will serve as a reproducible foundation for moving shopping
agents from single-query search toward dependable assistance over long horizons.
Read the full abstract ▾
Artificial Intelligence
FllumaOne: A Code-Native Multimodal CAD Dataset with Executable Programs and Kernel-Validated Feature Histories
Jizong Zhan
Parametric computer-aided design records both final geometry and the ordered construction history that determines how a part can be edited.
Datasets for editable CAD research should therefore expose modeling operations, parameters, and feature dependencies together with validated geometry. We introduce FllumaOne, a code-native
multimodal CAD
dataset whose models are generated by executable Python programs in Flluma, a Qt/C++ OpenCASCADE-based CAD system. Each sample aligns its program with a structured feature tree, a training-oriented intermediate representation, STEP geometry, a surface point cloud, natural-
language descriptions, metadata, and eight canonical visible-edge renderings. The primary release, FllumaOne-100K, contains 100,000 accepted samples across four template-level complexity regimes. Programs are executed and retained only after kernel geometry, solid validity, and export checks; release reports also record modality completeness and split-level duplicate tests. A Qwen2.5-Coder-1.5B LoRA baseline trained on 80,000 samples achieves 99.98% Python syntax validity, 99.97% Flluma build success, and 99.14% STEP-export validity on the held-out 10,000-sample test split. For the 9,909 predictions converted to surface point clouds, the mean normalized Chamfer Distance is 0.002124. The
dataset supports conditioned CAD reconstruction, executable program synthesis, feature-tree prediction, B-Rep analysis,
retrieval, design completion, and editable reverse engineering.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CL
SuCo: Sufficiency-guided Continuous Adaptive Reasoning
Jiahao Wang, Bingyu Liang, Chenhao Hu et al. (+5 more)
Despite remarkable performance on complex tasks, Large
Reasoning Models (LRMs) often generate excessively long Chain-of-Thoughts (CoT), inflating computational costs even for simple queries. Existing efforts to mitigate this inefficiency typically rely on discrete
reasoning modes or fixed budget tiers, lacking a principled criterion of when
reasoning is sufficient. In this work, we introduce Minimal Sufficient CoT (MSC), defined as the shortest prefix of a CoT trajectory which is adequate for producing the correct answer. We empirically show that MSC not only reduces
reasoning tokens, but also improves accuracy across difficulty levels. Building on MSC, we propose Sufficiency-guided Continuous Adaptive
Reasoning (SuCo), a two-stage training framework for autonomous
reasoning control along a continuous spectrum. In stage 1, MSC-Aligned Fine-Tuning (MFT) constructs MSC data using problem-adaptive sufficiency thresholds that naturally scale with question difficulty, then fine-tunes the model to internalize concise yet sufficient
reasoning patterns. In stage 2, Sufficiency-Aware Policy Optimization (SAPO) further optimizes the model through reinforcement learning with dynamic complexity tracking and sufficiency-aware rewards that penalize both over- and under-thinking. Extensive experiments across mathematics, code, and science
benchmarks show that SuCo consistently achieves improvements in both accuracy and
reasoning efficiency.
Read the full abstract ▾
All authors Jiahao Wang, Bingyu Liang, Chenhao Hu, Longhui Zhang, Xuebo Liu, Min zhang, Jing Li, Xuelong Li
Artificial Intelligence · Cross-listed from cs.CV
See First, Answer Later: Visual Evidence Pre-Alignment via Sufficiency-Driven RL
Yilian Liu, Sicong Leng, Guoshun Nan et al. (+7 more)
Multimodal large
language models (MLLMs) integrate strong text
reasoning with visual inputs, yet their responses can be inconsistent with the underlying images, indicating ineffective utilization of visual evidence during inference. The prevailing training paradigm relies on large-scale caption-based pretraining for general
alignment, followed by supervised fine-tuning and reinforcement learning to enable instruction following and complex
reasoning. However, such pretraining provides only weak visual grounding: short, coarse captions bias models toward salient objects while neglecting fine-grained visual evidence. In this paper, we introduce Visual Evidence Pre-
Alignment (VEPA), an intermediate stage between pretraining and post-training that explores a novel sufficiency-driven objective with Group Relative Policy Optimization (GRPO) to optimize question-conditioned visual evidence descriptions. Extensive experiments across diverse
benchmarks show that our VEPA consistently enhances performance on visually demanding evaluations and complements standard supervised post-training. Further analyses show that the income stems from strengthened, transferable visual grounding, rather than from additional task-specific training.
Read the full abstract ▾
All authors Yilian Liu, Sicong Leng, Guoshun Nan, Junyi Zhu, Jiayu Huang, Minghao Sun, Xuancheng Zhu, Yisong Chen, Zexian Wei, Xiaofeng Tao
Artificial Intelligence · Cross-listed from cs.LG
ASTEROID: A Spatiotemporal Information Transformer for Forecasting Multi-Step Time Series of Molecular Dynamics
Kexin Wu, Luonan Chen and Renxiao Wang
Molecular dynamics (MD) simulation is computationally demanding, particularly for large-scale systems requiring long-term analysis. Accurate forecast of the outcomes of a MD simulation is not only an attractive scientific challenge but also has substantial practical value. In this work, we developed a data-driven framework, termed ASTEROID (Advanced Spatiotemporal
TransformER fOr Inferring Dynamics), that can directly predict multi-step atomic coordinates, avoiding conventional iterative integration. For this purpose, our ASTEROID reformulates MD trajectories as high-dimensional spatiotemporal sequences and integrates the Spatiotemporal Information (STI) Transformation equation into a
Transformer architecture. The core innovation of ASTEROID lies in its ability to model multiscale spatiotemporal dependencies. In particular, for spatial dependencies, a local-global self-
attention mechanism captures both short- and long-range interactions. For temporal dependencies, an encoder-decoder structure integrates global context with autoregressive
forecasting. ASTEROID was evaluated on several
quantum-mechanics derived molecular
datasets. Our results indicate that ASTEROID achieved not only a higher level of accuracy in multi-step prediction than existing methods on various
benchmarks, but also significantly reduced computational cost of conventional MD simulation. Moreover, the model supports iterative multi-step
forecasting over an extended time scale. This work establishes a robust and generalizable data-driven paradigm for accelerating MD simulations.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.LG
Handling Feature Heterogeneity with Learnable Graph Patches
Yifei Sun, Yang Yang, Xiao Feng et al. (+4 more)
In recent years, the rapid development of foundation models and graph pre-training technologies has spurred increasing interest in constructing a universal pre-trained graph model or Graph Foundation Model (GFM). However, a significant challenge is that existing models are unable to address feature heterogeneity in graph data without textual information, which hinders the transferability of graph models across different
datasets. To bridge this gap, we propose the concept of learnable graph patches, which we regard as the smallest semantic units of any graph data. We decompose the graph into learnable graph patches by unfolding the node features and constructing corresponding patch structures separately. We then design a framework that mines transferable information from graph data across domains. Specifically, after extracting graph patches, we propose a patch encoder to extract knowledge from each unit and a patch aggregator to learn how the units are combined into a whole. Due to its domain-agnostic nature, the model can be applied to downstream data across different domains. Furthermore, we analyze the connection between our method and existing graph models, as well as the transferability of the node
embeddings it generates. Empirically, our method not only achieves the capability to use multi-domain graphs for pre-training, but also shows enhanced performance across various downstream
datasets and tasks. Moreover, we observe consistent improvement in downstream performance as the volume of pre-training data increases.
Read the full abstract ▾
All authors Yifei Sun, Yang Yang, Xiao Feng, Zijun Wang, Haoyang Zhong, Chunping Wang, Lei Chen
Artificial Intelligence · Cross-listed from cs.SE
FacProcessTwin: An LLM-Based System for Process Twin Development
Yash Pulse, Yong-Bin Kang, Abhik Banerjee et al. (+1 more)
Process twins provide real-time representations of entire production processes. By capturing how process steps interact, rather than monitoring a single machine in isolation as an asset-based digital twin does, they have the potential to drive efficiency gains across the whole process. However, developing a process twin is costly. It requires accurately modelling the entire production process: its process steps, the equipment and product-specific settings each step uses, and its process variations. The resulting model must then be bound to live operational data. We present FacProcessTwin, a system that leverages a large
language model (LLM) to reduce this development time, building a process twin from a plant's process documentation and natural-
language input from an operator. FacProcessTwin generates this complete process model and then automatically binds its process steps to live operational data. The generated model and its data bindings are rendered as an interactive process diagram through which manufacturing personnel can monitor and correct the system's autonomous decisions, such as resolving uncertainty at safety-critical binding steps. We evaluate FacProcessTwin through a real-world case study of an Australian food manufacturer, covering 16 production process flows that span chilled, frozen, and aseptic shelf-stable product categories and include process variations within the same product. The results show that FacProcessTwin generates these process models accurately (a mean F1 of 95.2% against ground truth) and builds each twin in roughly a sixth of the manual time. Its human-in-the-loop governance then keeps the safety-critical bindings correct: at ambiguous tags where a single-pass baseline silently mis-binds 75.0% of the time, FacProcessTwin defers to the operator and mis-binds none.
Read the full abstract ▾
All authors Yash Pulse, Yong-Bin Kang, Abhik Banerjee, Prem Prakash Jayaraman
Artificial Intelligence · Cross-listed from cs.IR
Temporal Preference Optimization for Unsupervised Retrieval
HyunJin Kim, Jaejun Shim, Young Jin Kim et al. (+1 more)
Unsupervised dense retrievers offer scalability by learning semantic similarity from unlabeled documents via contrastive learning, but they struggle to capture the temporal relevance, retrieving semantically related but temporally misaligned documents-an important aspect when a document collection spans multiple time periods (e.g., retrieving documents from 2018-2025 for "Who is the president in 2019?" introduces temporal ambiguity). Existing methods rely on supervised training with explicit timestamps, which are not always feasible. We propose TPOUR (Temporal Preference Optimization for Unsupervised Retriever), which uses our novel training method Temporal
Retrieval Preference Optimization (TRPO). TRPO reinterprets preference learning in the temporal dimension, guiding the retriever to favor temporally aligned documents. TPOUR further generalizes to unseen time periods via interpolation in a learned time
embedding, enabling continuous temporal
alignment. Experiments on temporal information
retrieval (T-IR), TPOUR outperforms both unsupervised and supervised baselines. Compared to Qwen-
Embedding-8B, despite being about 72.7x smaller, TPOUR Contriever improves average nDCG@5 by +4.04 (+12.15%) on explicit and +4.98 (+15.21%) on implicit queries. We provide our code at https://github.com/agwaBom/TPOUR.
Read the full abstract ▾
All authors HyunJin Kim, Jaejun Shim, Young Jin Kim, JinYeong Bak
Artificial Intelligence · Cross-listed from cs.LG
TuneAhead: Predicting Fine-tuning Performance Before Full Training Begins
Yuxiang Luo, Haonan Long, Chen Wang et al. (+6 more)
Fine-tuning large
language models (LLMs) is compute-intensive and error-prone: model performance depends sensitively on data quality and hyperparameter choices, and naïve runs can even degrade model performance. This raises a practical question:can we predict fine-tuning performance before committing to a full training run? We present TUNEAHEAD, a lightweight framework for pre-hoc prediction of fine-tuning performance. TUNEAHEAD encodes each candidate run as a meta-feature vector that combines static
dataset descriptors with dynamic probe features from a short standardized probe. A predictor maps these features to performance estimates, while SHAP-based attributions provide interpretable diagnostics that reveal which specific features drive the prediction. Across 1,300+ fine-tuning runs on Qwen2.5-7B-Instruct, TUNEAHEAD consistently outperforms strong baselines such as Early-Stop Extrapolation and ProxyLM. On a held-out test set of 370 runs, TUNEAHEAD achieves an RMSE of 1.47 percentage points and places 95.1% of predictions within +3/-3 percentage points of the true score. These accurate continuous predictions support practical go/no-go screening policies that can reduce unnecessary full fine-tuning while retaining most promising runs.
Read the full abstract ▾
All authors Yuxiang Luo, Haonan Long, Chen Wang, Qiqi Duan, Xiaotian Lin, Yanwei Xu, Yuyu Luo, Weikai Yang, Nan Tang
Artificial Intelligence
Using Cognitive Models to Improve Language Model Simulation of Human Persuasion Games
Zirui Cheng, Zeyu Shen, Thomas L. Griffiths et al. (+1 more)
People make decisions differently in strategic interactions. Some update beliefs like a
Bayesian; others exhibit biases like motivated
reasoning. Although creators of large
language models use simulated humans for safety evaluations and training, they often fail to cover this breadth of human behavior. We argue that cognitive science and economics provide a convenient tool for doing so, making use of mathematical models of human decision-making. We propose an approach that we call Equation-to-Behavior Prompting for guiding large
language models to match cognitive models, and evaluate this approach on persuasion games based on legal decision-making. We find that large models can approximate equation-based specifications –
Bayesian updating, affine distortion, motivated updating, and Grether's $\alpha$-$\beta$ model – using prompting, but small models fail to do so. However, training small models with reinforcement learning to adhere to mathematical rules, Equation-to-Behavior RL, reduces belief error by 26.5% in out-of-distribution parameterizations. We show that these simulations can help create diverse training environments; training small models to consider different kinds of decision-makers improves average belief change by 2.5%–12% over
Bayesian-only training, even when persuading GPT-5-mini. Our work could improve human simulations for training and evaluation in increasingly realistic settings, and could also enable novel research into more complicated mathematical models of human decision-making.
Read the full abstract ▾
All authors Zirui Cheng, Zeyu Shen, Thomas L. Griffiths, Peter Henderson
Artificial Intelligence · Cross-listed from cs.LG
A Risk Decomposition Framework for Pre-Hoc Fine-Tuning Prediction
Yuxiang Luo, Chen Wang and Nan Tang
The high cost of fine-tuning LLMs poses a significant economic barrier; pre-hoc performance prediction offers a critical solution to substantially reduce this expense. However, the theoretical limits of pre-hoc performance prediction remain unexplored. We formulate it as a
stochastic estimation problem under information constraints, decomposing prediction risk into two components: an intrinsic limit (static data-model compatibility) and a reducible optimization variance. We prove that optimization variance admits a necessary lower bound on its decay rate, implying fundamental constraints on how quickly uncertainty dissipates, regardless of the predictor used. Based on these dynamics, we derive a budget-optimal probing principle and introduce a predictability phase diagram that organizes tasks into three distinct regimes: Static-Sufficient, Dynamic-Critical, and Noise-Dominant. Extensive experiments on synthetic and real-world
benchmarks validate these theoretical regimes and demonstrate the efficiency of our probing strategy.
Read the full abstract ▾
Artificial Intelligence
From Brewing to Resolution: Tracing the Internal Lifecycle of Code Reasoning in LLMs
Siyue Chen, Yifu Guo, Yuquan Lu et al. (+9 more)
Standard accuracy metrics cannot explain why LLMs handle variable tracking but fail on semantically equivalent loops. We study an internal lifecycle of code
reasoning in which models first brew the answer, making it linearly recoverable many layers before it becomes self-decodable, and then diverge into one of four resolution outcomes: Resolved, Overprocessed, Misresolved, or Unresolved. Understanding this lifecycle matters because similar task accuracies can mask fundamentally different failure modes that surface-level evaluation cannot detect. We introduce a dual diagnostic framework pairing layer-wise linear probing with Context-Stripped Decoding (CSD) and apply it to six code-
reasoning task families across 16 models spanning Qwen, Llama, and DeepSeek architectures. All four outcomes carry substantial mass in every task family: overall Resolved is only 41.5%, with multiple tasks below 30%. Controlled sweeps over structure, depth, and operators expose task-specific failure bottlenecks: Function Call Resolved plunges from 61.1% to 2.5% as call depth increases from one to three. Across architectures and scales, the brewing scaffold remains stable, with normalized brewing duration 24-42% across all 16 models, while resolution success varies with capability. This indicates that the scaffold is a stable empirical regularity across the tested decoder-only
Transformer families, whereas resolution success covaries with capability, scale, and training. Code: https://github.com/euyis1019/llm-brewing
Read the full abstract ▾
All authors Siyue Chen, Yifu Guo, Yuquan Lu, Zishan Xu, Jiaye Lin, Jianbo Lin, Siyu Zhang, Cheng Yang, Junxin Li, Yujia Li, Yu Huo, Ruixuan Wang