Data Structures and Algorithms
Linear-Query Deterministic Approximation for Non-monotone Submodular Maximization under a Knapsack Constraint
Zihui Liu and Zhijie Zhang
Submodular maximization under a knapsack constraint (SMK) is a fundamental combinatorial optimization problem with broad applications across machine learning and data mining. Motivated by large-scale applications where query efficiency is paramount, we study non-monotone SMK and focus on deterministic
algorithms with linear query complexity. Prior deterministic linear-query
algorithms achieve at best a $1/5-\varepsilon$ approximation, falling short of the $1/4-\varepsilon$ ratio attainable by randomized
algorithms. We close this gap by presenting a deterministic $(1/4-\varepsilon)$-approximation with $O(n\log^2(1/\varepsilon)/\varepsilon^2)$ queries. Our approach partitions the analysis based on the cost of the largest optimal element $r$: when the cost of $r$ is moderate, we refine the threshold-twin-greedy framework via residual-budget enumeration to tighten the analysis; when the cost of $r$ is large, we reduce the problem to bicriteria submodular maximization. As a secondary contribution, we obtain a $(1/2-\varepsilon, O(1/\varepsilon))$-bicriteria approximation with $O(n\log(1/\varepsilon)/\varepsilon^2)$ queries, improving over the previous $O(n^2/\varepsilon)$ query bound.
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Artificial Intelligence
Toolcompass: Guiding Tool Trialing, Not Suppressing It
Junlin Fang, Chong Zhang, Do Nguyen-Thanh et al. (+3 more)
Large
language model (LLM)
agents must generalize from tools seen during training to unseen tools at deployment. A key challenge is tool trialing, i.e., excessive trials waste the interaction budget, whereas selective trials enable exploration of unfamiliar tools. Existing outcome-based post-training leaves wasteful trials unguided, while turn-level supervision may suppress necessary exploration. We introduce ToolCompass, a post-training framework that guides tool trialing by organizing tool-call representations according to shared functions. Specifically, ToolCompass models each function class as a von Mises–Fisher distribution and jointly reduces intra-function variation across domains and increases inter-function separation. This structure transfers experience from seen tools to functionally similar unseen tools, directing exploration away from unrelated alternatives. ToolCompass requires no ground-truth call traces or unseen-tool access and incurs no inference overhead. Experiments on AppWorld and FTRL show consistent gains across GRPO, RFT, and DMPO. improves AppWorld OOD task success by up to 10.71 percentage points over vanilla post-training and performs best among competitive baselines on both
benchmarks.
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All authors Junlin Fang, Chong Zhang, Do Nguyen-Thanh, Xiaogang Xu, Zhen Fang, Sean Du
Artificial Intelligence
Seeing Is Not Perceiving: When Synthetic Consumers Can and Cannot Pretest Visual Marketing
Yi-Lin Tsai and Yung-Hsiu Lai
Marketers now deploy generative AI
agents as synthetic consumers to pretest visual assets such as logos, packaging, and advertising at a fraction of human-panel cost. However, this procedure assumes that a model seeing a visual cue can also perceive its consumer meaning, which is largely untested. We stress-test the assumption using six canonical visual marketing experiments, varying the two levers managers control: model generation (GPT-4o-mini vs. GPT-5.4-mini) and input format (plain text vs. JSON). Every resulting configuration passed the manipulation checks; however, none of the configurations reproduced more than two of the six human effects, and the remainder were nonsignificant. The one exception was a significant reversal of the human pattern. Providing conceptual or empirical evidence through in-context learning steers average responses toward the human effect. Yet steering has a limit: even when it succeeds, a configuration reproduces less than half of the natural spread of human responses and so understates consumer heterogeneity. We integrate these results into an AI governance protocol (Calibrate, Intervene, Deploy) that delineates when synthetic consumers can responsibly screen creatives and when human panels remain necessary.
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Machine Learning
Marginal Log-Likelihood Increments under Dirichlet-Smoothed Markov Estimation
Levin David Schwab
For a Dirichlet-smoothed transition model, the effect of adding one workflow trace to the training archive is an exact change in reference-weighted log likelihood. We derive that change and show that it is a weighted reduction of Kullback–Leibler divergence between the reference conditionals and the model. From this form we obtain an upper bound on the gain available to any acquisition, which expresses a millinat difference as a share of what is attainable, an exact covariance identity for the effect of the reference weighting, and a sign criterion for the interaction between two candidates, from which the batch objective is neither submodular nor supermodular. A case study on the BPI Challenge 2012 loan-application log measures all three and finds a positive selection result in one of the four combinations of reference weighting and budget unit. There, of two regressors fitted to identical descriptors and identical labels, the one that predicts individual increments far more accurately, median $R^2$ 0.87 against 0.62, realizes the smaller share of the attainable gain, 61 against 69 per cent, so ranking accuracy for individual traces is neither necessary nor sufficient for batch quality.
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Robotics
Teaching Reinforcement Learning and Humanoid Robotics to High-School Students: An Expert-Validated Curriculum Design on a Low-Cost Open Platform
Yuanzhe Dong, Jie Cao and Shuman Wang
Lower cost open source
robots and reinforcement learning (RL) simulation tools create new opportunities for precollege students to engage with contemporary robotics. However, translating a complete research workflow, spanning mechanical assembly, electrical setup, simulation, policy learning, system identification, and physical deployment, into a coherent course for novice learners remains challenging. We present an integrated robotics course framework that organizes these activities around a shared robotic artifact. The framework combines parallel disciplinary tracks, sequencing based on technical dependencies, progressive integration of simulation and hardware, layered performance checkpoints, and structures for balancing collaborative work with individual accountability. We illustrate the framework through a high school curriculum organized around a
robot project in which pairs of students assemble an open source
humanoid robot, train a walking policy in simulation, and deploy it on the physical platform. The framework was developed through an iterative design process that included formative review by five experts in robotics research, engineering, secondary STEM education, and curriculum design. Expert feedback highlighted three central design tensions: authenticity versus cognitive load, system integration versus timely visible progress, and team construction versus individual accountability. These tensions informed the final framework presented in this paper. This work offers a structured approach for adapting robotics research workflows into interdisciplinary precollege courses; future classroom studies are needed to examine implementation and student learning.
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Computation and Language
From Utterances to Networks: Modelling Slang Adoption and Diffusion Across Subreddits
Xiaoning Wang, Ted Underwood and Zhewei Sun
Adoption and
diffusion of neologisms in online communities have received renewed
attention in recent years. As internet slang terms such as APT, referring to a K-pop song, and phrases such as Canon Event meaning an embarrassing but pivotal event, go viral online, it becomes increasingly important to understand the mechanisms that contribute to their success. Prior studies have often explained slang
diffusion either from the perspective of social interaction or from the linguistic properties of the slang itself, but rarely from both perspectives together. One major obstacle has been the high cost of annotating slang usage in large-scale online communication. Recent advances in large
language models (LLMs), however, make it possible to use them as scalable annotators for such tasks. In this study, we first curate a human-annotated
benchmark to evaluate LLM performance in detecting slang usage in real Reddit communication. We then leverage LLM-based annotations to model slang adoption and
diffusion. Our results show that slang diffusers with higher bridging capital are associated with increased subsequent adoption, whereas diffusers with higher bonding capital are associated with reduced adoption. We also find that wider contextual usage of a slang term is associated with a longer time before new users officially adopt it. Together, these findings suggest that both social-network structure and linguistic context shape the
diffusion of neologisms in online communities.
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Robotics
CDKF-Track: Cluster-aware Data-Driven Kalman Filtering for Cooperative 3D Multi-Object Tracking
Maria Damanaki, Nikos Piperigkos, Alexandros Gkillas et al. (+1 more)
Multi-Object Tracking (MOT) is essential for EdgeAI perception systems, where accurate object localization and reliable identification enable safe decision-making. Singleagent MOT suffers from occlusions, sensor noise, and partial scene understanding in complex real-world scenarios. While multi-
agent systems improve robustness by exploiting shared information, they introduce redundant measurements that lead to false data associations, and still struggle to capture nonlinear object dynamics. To address these challenges, we propose CDKFTrack, a Cluster-aware Data-Driven Kalman Filtering framework for Cooperative 3D MOT. The proposed method first fuses multivehicle 3D
LiDAR detections through a Graph
Laplacian-based formulation. Then, a cluster-aware redundancy reduction scheme groups spatially related detections and selects representative observations to reduce duplicate inputs to the tracker. The resulting detections are processed by a data-driven Kalman filter that learns object motion dynamics from data, reducing dependence on predefined linear motion assumptions. Furthermore, a wavelet-based temporal refinement module leverages the multiresolution decomposition property of wavelets to attenuate shortterm positional fluctuations and improve trajectory continuity. To the best of our knowledge, CDKF-Track is the first framework to jointly address detection-level fusion redundancy and learnable motion modeling in cooperative 3D MOT. Experimental results on the real-world V2V4Real
dataset indicate that CDKF-Track achieves up to 27.99% improvements in tracking accuracy over state-of-the-art multi-
agent MOT methods.
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All authors Maria Damanaki, Nikos Piperigkos, Alexandros Gkillas, Aris S. Lalos
Robotics
Vishnu Sashank Dorbala and Dinesh Manocha
We present and tackle two problems associated with deploying Foundation Models (FMs) on
Embodied Agents performing
navigation: 1) Training bias in FMs leading to poor personalization in unseen environments, and 2) Limited FM context length hindering success, especially on long horizon tasks. Our solution for the former involves priming the FM with human-habit data mined from the scene and our solution for the latter involves active
memory management via a novel `
memory head' augmentation. We first present a taxonomy of existing literature on FM-based
Embodied Navigation, and highlight these limitations. We then present our approaches, Transit-Aware Planning (TAP) and MemCtrl to address the limitations. With TAP, we present real-world results in a lab environment with a Turtlebot for personalized target finding that shows an average improvement of 18% over a non-TAP baseline. On MemCtrl, we report a 6% average improvement across various
embodied tasks, with 20% on long instruction subsets, all while using nearly half the context used in the baseline model. Motivated by these result, we present our stance the deployability of FM-based
embodied agents in real-world environments, and highlight open research directions.
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Machine Learning
When Riemann flows with Wasserstein: Generative Modeling of Probability Distributions on Manifolds
Doron Haviv, Edward De Brouwer, Rishabh Anand et al. (+4 more)
Many scientific
datasets, such as molecular conformational ensembles or single-
cell tissue measurements, are naturally modeled as meta-distributions: distributions over probability measures on non-Euclidean domains. Existing generative methods largely assume Euclidean geometry and fail to capture this structure. We introduce Riemannian Wasserstein Entropic Flow Matching (RWEFM), a generative framework on the Wasserstein space $\mathcal{P}_2(\mathcal{M})$ of a Riemannian
manifold $(\mathcal{M},g)$. RWEFM is trained by regressing a neural vector field onto Riemannian optimal transport velocities, using McCann displacement interpolations as conditional paths. We confirm theoretically that this construction leads to a valid flow matching approach on $\mathcal{P}_2(\mathcal{M})$ and introduce the Riemannian Entropic Map, a GPU-efficient approximation of the optimal transport map on
manifolds. Our experiments show that by respecting the intrinsic geometry of the data, RWEFM can generate whole single-
cell samples in hyperspherical latent spaces and
protein conformational ensembles on the
torus. As RWEFM requires only a geodesic distance and a projection operator, it is not restricted to
manifolds with closed-form geometry, which we demonstrate by generating distributions on a general triangulated mesh.
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All authors Doron Haviv, Edward De Brouwer, Rishabh Anand, Rex Ying, Aïcha Bentaieb, Gabriele Scalia, Hector Corrada Bravo
Robotics
History-Conditioned Flow Matching for Probabilistic Dynamics of Tendon-Driven Continuum Robots
Hang Yang, Tingcong Liu, Junjie Xiong et al. (+2 more)
Deterministic dynamics modeling of tendon-driven continuum
robots remains challenging owing to uncertainties in material behavior, tendon transmission, friction, and contact. Measured joint configurations and nominal tendon commands do not fully characterize these internal mechanical factors, leaving uncertainty in the subsequent motion. We therefore develop a history-conditioned, physics-informed flow-matching framework for probabilistic dynamics prediction, using motion and actuation histories to predict the distribution of the next complete joint configuration. By recursively sampling next-step configurations under prescribed commands, the model predicts distributions of future whole-body motions. In simulation, scenario-specific models achieve five-second trajectory Energy Scores (lower is better) of 12.05 mm under internal friction variation and 9.29 mm under unobserved actuation disturbances. Relative to the conditional variational autoencoder and
diffusion baselines, Flow attains lower Energy Scores and coverage closer to the nominal level in both scenarios. Ablations support history and structural conditioning in both scenarios. On the physical
robot, predictions under two tendon-command profiles excluded from training capture the principal motion sequences, with five-second Energy Scores of 11.91 and 11.42 mm, lower than the compared baselines. The predicted-to-measured spread ratios are 1.65 and 1.22 (closer to 1 is better). These results support history-conditioned probabilistic dynamics prediction under incomplete mechanical observations.
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All authors Hang Yang, Tingcong Liu, Junjie Xiong, Fangju Yang, Ke Wu
Machine Learning
Targeted Review for AI-Assisted Biodiversity Surveys: Active Continuous-Score Occupancy Modeling
Timm Haucke, Lauren Harrell, Justin Kay et al. (+2 more)
We increasingly use machine learning to label scientific
datasets. The models we develop and deploy are improving all the time, but they are not and will likely never be perfect. Mistakes matter, as errors can propagate into our scientific understanding, particularly when systematically biased. Very reasonably, scientists thus review substantial proportions of ML-generated labels to verify or correct mistakes in pursuit of ensuring their scientific findings are not biased by ML. In this work, we focus on helping scientists optimally allocate this reviewing effort relative to their scientific goals. We focus on a specific class of scientists (ecologists) and a specific, widespread, and impactful modeling target (occupancy modeling, which estimates where species are likely to occur, conditioned on environmental factors). We introduce Active Continuous-Score Occupancy Modeling (ACORN), a method that incorporates ML predictions into occupancy models and strategically selects samples for expert review that are maximally informative for downstream ecological analysis. Across camera-trap and bioacoustic
datasets, our method recovers ecological conclusions close to those obtained from fully human-labeled data, while requiring substantially fewer expert reviews than non-targeted review policies. Our results suggest that ML-assisted scientific workflows should optimize expert effort for downstream inference, rather than for classifier accuracy alone, especially when human review budget is limited. Our code is available at https://github.com/timmh/acorn
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All authors Timm Haucke, Lauren Harrell, Justin Kay, Mary Clapp, Sara Beery
Machine Learning
From Experts to Sub-experts: Fine-grained Parameter-Efficient Fine-Tuning for MoE LLMs
Zhentao Tan, Chang Liu, Yao Liu et al. (+2 more)
As large
language models (LLMs) scale rapidly, dense full-parameter adaptation becomes increasingly expensive, motivating sparse and modular architectures such as Mixture-of-Experts (MoE) models. This shift raises a key question for parameter-efficient fine-tuning (PEFT): at what granularity should parameters be selected and updated? Existing PEFT methods such as LoRA operate on predefined weight
matrices, while expert-level sparse tuning methods update entire selected experts. However, we observe that activated experts are internally sparse, with only a small fraction of intermediate channels strongly responding to downstream tasks, indicating that expert-level adaptation is still too coarse. We propose NSFT (Neural Sub-expert Fine-Tuning), a fine-grained PEFT framework that refines MoE adaptation from experts to sub-experts. NSFT decomposes each expert along the intermediate dimension into structured channel groups and selects task-relevant sub-experts by combining routing importance with intra-expert activation saliency. To optimize sparse partial updates, NSFT further introduces learning-rate scaling and dynamic gradient scaling to compensate for the reduced effective update magnitude. Experiments on OLMoE and Ling-mini-2.0 across challenging domain-specific tasks and general
benchmarks show that NSFT consistently outperforms representative PEFT and expert-level sparse tuning baselines, while using substantially fewer trainable parameters and preserving competitive general capability. These results suggest that sub-expert-level adaptation is a more precise and efficient PEFT paradigm for MoE LLMs.
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All authors Zhentao Tan, Chang Liu, Yao Liu, Yue Wu, Jieping Ye
Robotics
CODA: Depth-Aligned Scene Completion and Object Decomposition from a Single RGB-D Image
Dongwon Son, Junhyek Han, Yoontae Cho et al. (+5 more)
Robots operating safely in cluttered everyday environments often need to infer scene geometry from partial observations. Methods that detect objects in 2D and reconstruct them independently struggle in such scenes: a missed object is never reconstructed, a merged detection can fuse two objects, and separately reconstructed meshes may overlap or fail to touch their supporting surfaces. We introduce CODA (Complete Once, Decompose Afterward), a generative model that instead reconstructs the complete scene geometry from a single unsegmented RGB-D image, then separates the surface into the surrounding environment and movable objects. Still, generated scene geometry can drift from the observed partial point cloud. To reduce this drift, CODA uses two explicit 3D grounding mechanisms to keep reconstructed geometry consistent with observed surfaces while completing unseen regions. Experiments on HomebrewedDB and our custom cluttered-scene
dataset show more accurate reconstructions and a higher fraction of objects remaining in place under simulated
gravity than both object-first and scene-first baselines.
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All authors Dongwon Son, Junhyek Han, Yoontae Cho, Minseok Lee, Hong-seok Choi, Jiwook Choi, Hyungjin Kim, Beomjoon Kim
Robotics
PhyVisGen: Physically and Visually High-Fidelity Robotic Manipulation Data Generation
Yu Zheng, Qiyu Feng, Yixin Wu et al. (+6 more)
Large-scale manipulation demonstrations are essential for learning robust visuomotor policies, yet real-world data collection is expensive and difficult to scale. Simulation offers a promising alternative, but physical and visual discrepancies can limit the transferability of synthetic data, particularly for manipulation with soft grippers. We present PhyVisGen, a physically and visually high-fidelity framework for scalable robotic manipulation data generation. On the physical side, PhyVisGen introduces an arm-gripper coupling method based on the Incremental Potential Contact (IPC), enabling high-fidelity soft contact throughout complete manipulation trajectories. On the visual side, it combines real-scene reconstruction with real-time path tracing to generate visually realistic observations while preserving captured scene appearance. Quantitative evaluations demonstrate the physical and visual fidelity of PhyVisGen. Policies trained exclusively on synthetic manipulation demonstrations achieve 65-95% success across five real-
robot tasks, without real-
robot demonstration data or policy fine-tuning.
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All authors Yu Zheng, Qiyu Feng, Yixin Wu, Baoquan Yang, Yixuan Zhou, Bingyang Hu, Kemeng Huang, Guansheng Yang, Hesheng Wang
Computer Vision
GameDirector: Decoupling Gameplay Logic from Rendering for Player-Configurable Game World Models
Zijun Lin, Zhiyang Deng, Yuzhe Wu et al. (+2 more)
Recent game world models support realistic visual simulation and interactive gameplay based on player inputs. However, they typically learn environment dynamics from pixel-level supervision, jointly modeling perception,
memory, state transitions, and rendering within a single end-to-end framework. While this design enables open-ended, action-controllable generation, it still falls short of delivering a complete gameplay experience. Games are governed by explicit mechanics, such as health deduction, skill activation, combat rules, and termination conditions. These mechanics depend on precise and consistent state transitions that generative models alone cannot reliably enforce. In contrast, game engines can guarantee such mechanics through hard-coded rules, but provide limited flexibility for player-driven creation. To bridge these paradigms, we introduce GameDirector, the first agentic framework that decouples rule-based gameplay logic from visual rendering. Given player-defined configurations, the framework acts as an intelligent director that interprets visual observations, updates game states, tactically controls NPCs, and enforces gameplay rules. It then translates these decisions into text
prompts that guide the
video world model to render the resulting gameplay. This separation allows players to configure characters, states, and rules much like a game developer while preserving coherent game mechanics. Experiments on three games, using data collected by our automated gameplay
agent, show that GameDirector achieves accurate state tracking, reliable rule following, and improves boss action quality by more than 39.9% over various end-to-end game world model settings. Overall, by externalizing player-controllable game logic, GameDirector establishes a middle ground between hard-coded simulation and generative modeling, enabling more flexible and closed-loop gameplay experiences.
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All authors Zijun Lin, Zhiyang Deng, Yuzhe Wu, Bihan Wen, Yeying Jin
Human-Computer Interaction
SurgGraph: Quantitative Laparoscopic Video Understanding via Geometry-Grounded Scene Graphs
Jingying Wang, Rosiana Natalie, Marquise D Singleterry et al. (+7 more)
Surgical videos are a primary resource for teaching trainees anatomy, tool usage, and procedural skills. Yet learning from them at scale requires systems that understand
surgical scenes. Existing approaches fall short:
vision-
language models lack fine-grained domain
reasoning, task-specific models do not generalize, and prior scene graphs omit clinically meaningful detail. We present SurgGraph, a training-free pipeline that generates quantitative scene graphs from
surgical videos. Operating on
segmentation masks and depth maps, SurgGraph encodes each clinically meaningful relation (attachment, occlusion, separation, tool actions) as a <subject, verb, object, value> tuple whose numeric value quantifies the relation's extent over time. Technical evaluations show more precise scene understanding than state-of-the-art
surgical VLM baselines. We then build SurgGraphQA, a proof-of-concept learning application that retrieves meaningful and boundary-case exemplars and generates visual explanations and feedback. A study with 17 medical students and 2 resident surgeons shows significant learning gains, demonstrating its educational value.
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All authors Jingying Wang, Rosiana Natalie, Marquise D Singleterry, Filippos Bellos, Brian George, Gurjit Sandhu, Jason J Corso, Anhong Guo, Vitaliy Popov, Xu Wang
Computer Vision
Decoupling Disease, Covariates, and Individual Variability: A Unified Disentanglement Framework for Medical Image Classification
Shengjie Zhang, Jinglin Zhang, Zhuangzhuang Jiang et al. (+10 more)
Accurately isolating disease-related features from confounding
covariates (e.g., age, gender, site) and individual variations remains a fundamental challenge in medical image classification. Traditional regression-based approaches may ignore non-linear relations between image features and true
covariates. To overcome this issue, we present a generalized Medical Imaging Disentanglement Learning (MedIDL) framework. MedIDL maps image features into three mutually orthogonal latent spaces through specialized disentanglement heads: a disease classification head guided by a supervised loss, a
covariate-
alignment head constrained by cross-subject similarity matching, and a Gaussian head absorbing individual variations. We evaluated our framework across 7
datasets encompassing diverse imaging modalities. MedIDL outperforms state-of-the-art supervised and self-supervised classification methods in accuracy across all
datasets. Association analyses demonstrate that MedIDL successfully isolates target-specific latent representations. Gradient-based interpretability mappings localize pathognomonic patterns aligning with established clinical literature.
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All authors Shengjie Zhang, Jinglin Zhang, Zhuangzhuang Jiang, Ziqi Yu, Yipin Zhang, Qi Zhang, Xiang Chen, Haibo Yang, Fei Gao, Longbiao Cui, Yuan Zhou, Xiao-Yong Zhang, Alzheimer's Disease Neuroimaging Initiative
Robotics
Skill Sequence Planning for Collaborative Multi-Robot Construction
Xi Wang, Bo Fu, Carol C. Menassa et al. (+2 more)
Robots have significant potential to automate construction processes. However, their industry adoption remains limited, partly because of the programming effort required to adapt
robots to diverse tasks. This paper presents a skill sequence planning method that enables a heterogeneous team of multi-functional
robots to collaboratively perform construction assembly work using reusable, preprogrammed skills such as grasping, drilling, and fastening. A central controller transforms the digital representation of the building into a construction relationship graph that represents construction entities, their states, and their parent-child relationships. Based on this representation, the system selects the next construction target, generates a symbolic sequence of skills for capable members of the
robot team, and produces collision-free
geometric motion plans for skill execution. The symbolic planning problem is dynamically regenerated as the construction state changes. An interactive digital twin presents the planned skill sequence and
robot states to human co-workers for review and approval before execution. The method is evaluated through a construction assembly case study. By reducing the need to program
robots separately for each task variation, the proposed approach supports more flexible deployment of collaborative
robot teams in construction.
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All authors Xi Wang, Bo Fu, Carol C. Menassa, Vineet R. Kamat, Min Deng
Artificial Intelligence
Testing-Driven Reliability Audit of Trajectory-Based Early Outcome Prediction for LLM Agents: Target-Specific Calibration Transfer Persists Within a Single Benchmark
YanZe Cao
Predicting early outcomes based on trajectory can decrease the expenses associated with
agent evaluation by terminating a run once the outcome becomes sufficiently predictable, assuming that the predictor's confidence is properly calibrated. Calibration is at risk when a predictor is applied to an
agent on which it was never trained, but it is not known whether such transfer failures are broad across
agent systems or concentrated in specific target
agent/head combinations. Using public SWE-bench Verified trajectories and a frozen dual-head early-outcome prediction pipeline, we ran a leave-one-
agent-out calibration audit, a shared-predictor leave-two-
agents-out control, oracle prior correction, and a robustness battery over training cohorts, task resampling, task halves, jackknife, and thresholds. Fixed-scaffold TerminalBench analysis served as a pre-registered boundary test. Broad same-predictor pairwise heterogeneity was not supported; the median pairwise corrected-gap differences were 0.0180 (SUCCESS head, 45 pairs) and 0.0385 (FAILURE head, 35 pairs), and the pre-registered heterogeneity criterion was not met on either head. Two specific combinations, gpt-5-mini/SUCCESS and claude-opus-4.6/FAILURE, showed persistent calibration-transfer errors (median corrected gaps 0.1377 and 0.1107) without a sign reversal under any frozen control. TerminalBench did not establish cross-
benchmark replication: the success target produced zero decisions (INDETERMINATE), and the failure target did not satisfy the pre-registered persistence criterion. Therefore, a strong target-specific calibration-transfer error can exist within one frozen environment, but the evidence does not establish that the error is intrinsic to the model or general across
benchmarks.
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Networking
Key Reconciliation with RC-LDPC/Error Estimation for Satellite-based FSO/QKD Systems
Cuong T. Nguyen, Hoang D. Le, Anshul Jaiswal et al. (+2 more)
Satellite-based free-space optics (FSO)
quantum key distribution (QKD) systems have recently attracted significant research interest due to their potential to enable globally secured applications. However, the inherent uncertainty of FSO channels, caused by weather conditions and satellite mobility, induces severe fluctuations in
quantum bit-error rate (QBER) between legitimate users. This makes designing an efficient key reconciliation, an essential step in the QKD post-processing, particularly challenging. In this work, we propose a key reconciliation scheme that combines protograph rate-compatible (RC) low-density parity-check (LDPC) codes with a syndrome-based error estimation method. The proposed error estimation method reduces the number of communication rounds without requiring additional information disclosure. Furthermore, to our best knowledge, an analytical framework is first developed to evaluate end-to-end secret-key throughput (SKT), accounting for the impact of imperfect error estimation and dynamic FSO channel conditions. Numerical results demonstrate that the proposed scheme consistently outperforms conventional blind reconciliation under diverse FSO channel conditions and provide practical guidelines for system parameter selection. Finally, we validate the proposed framework through a case study that incorporates data from a Starlink low-Earth orbit (LEO) satellite and moving ground vehicles.
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All authors Cuong T. Nguyen, Hoang D. Le, Anshul Jaiswal, Swaminathan R., Anh T. Pham
Machine Learning
Efficient Cost-Aware LLM Evaluation via Bayesian Bandit Gittins Indices
Qian Xie, Yueli He and Nairen Cao
Exhaustively evaluating every candidate LLM configuration on every
benchmark item to identify a high-performing one is costly. We formulate configuration selection as a cost-aware
Bayesian bandit problem and propose GittinsEval, which draws on the
Bayesian-optimal Gittins policy to determine which configuration to evaluate next and when to stop. We extend the policy with an anytime recommendation rule over both fully and partially evaluated configurations, using an LCB-style score to account for posterior uncertainty. GittinsEval is computationally efficient, requiring only lightweight online updates after offline precomputation. Across GSM8K, PIQA, AlpacaEval, and MMLU response
matrices, GittinsEval is consistently competitive, with particularly strong gains over configuration-level
Bayesian optimization on large-example
benchmarks and over cost-unaware bandit baselines on large-candidate tasks. Crucially, GittinsEval often attains near-zero simple regret using only 1% to 2% of the exhaustive-evaluation cost; it also offers an adaptive stopping rule that typically triggers at 1% to 10%.
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Artificial Intelligence
Ladders of Thought: A Self-Evolving Curriculum of Progressively Simplified Reasoning Traces
Minghui Liu, Thomas Magelinski, Dehao Yuan et al. (+2 more)
Large
language models (LLMs) excel at
reasoning when scaled to hundreds of billions of parameters, but small- and mid-scale models remain brittle reasoners even with knowledge
distillation (KD). We present Ladders-of-Thought (LoT), a framework that improves
reasoning by combining progressive question rewrites with a self-evolving curriculum. LoT automatically generates semantically faithful but easier variants of
reasoning problems, organizes them into difficulty buckets using step-based measures, and employs a self-evolving bandit scheduler to allocate training adaptively. Evaluated on two
reasoning domains, math and multi-hop
reasoning, across 1-8B models from different families, LoT consistently improves over KD. It delivers large gains on arithmetic tasks (e.g., +32 percentage points on AddSub, +25pp on SVAMP), +2-8pp improvements on in-domain test splits, and strong though
dataset-dependent benefits on multi-hop
reasoning (e.g., +16pp on QASC, +25pp on StrategyQA). LoT also converges faster than staged curricula, highlighting the value of adaptive progression. These results show that progressive rewrites coupled with adaptive curricula provide a simple yet effective recipe for strengthening
reasoning in smaller LLMs.
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All authors Minghui Liu, Thomas Magelinski, Dehao Yuan, Qi Yu, Furong Huang
Robotics
Contact-Stable Deformable Tissue Simulation Using Implicit Integration and Live-Pose Grasp Constraints for Laparoscopic Surgery Robot Policy Evaluation
Juahn Oh, Dongho Yee, Jinseok Lee et al. (+9 more)
Closed-loop evaluation of
surgical robots requires tissue that deforms, can be grasped and lifted, and reproduces the anatomy in which the
robot will operate. We present a simulator in which this tissue is reconstructed from a fixed-view RGB-D recording of the
surgical field, composited to remove the instruments, closed into watertight volumes and tetrahedralised; the pipeline was applied unchanged to three specimens of two species (thirteen organs, 146,061 tetrahedra, no inverted elements). For one specimen, the organs are placed in a bimanual
cell in which two Franka FR3 arms operate motorised instruments through 6 mm trocars. The core contribution is the numerical and contact design that keeps this
cell stable: implicit integration, simulation meshes separate from collision meshes, numerical guards, and a grasp constraint captured at the live tissue pose. In 45 repeated grasp-lifts, a friction grasp held the tissue in 0 of 15 trials and each constraint grasp in 13 of 15; on displaced tissue, a rest-pose constraint produced one-step snaps of up to 17.8 mm, which live-pose capture eliminates. Against the recording, front-surface depth error is 1.33 to 1.41 mm, organ silhouette IoU is 0.80, and in five grasp-lifts reproduced from
video the landmark displacement RMSE is 11.8 mm against 14.2 mm for a static prediction. Biofidelity is not claimed; the environment is intended for closed-loop feasibility, safety, contact and policy screening.
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All authors Juahn Oh, Dongho Yee, Jinseok Lee, Jiyul Lee, Yechan Seo, Seong Jeong, Minsung Kim, Seonho Shim, Younghoon Noh, Hyuk Choi, Youngbin Kong, Hyoun-Joong Kong
Artificial Intelligence · Cross-listed from quant-ph
When Quantum Meets AI: Quantum Methods for Machine Learning and Machine Learning Methods for Quantum Systems
Tak Hur
This thesis studies the intersection of
quantum computing and artificial intelligence in two directions:
quantum methods for machine learning and machine learning methods for
quantum systems. For
quantum machine learning, Neural
Quantum Embedding learns data representations that increase the trace distance between embedded class ensembles, lowering an
embedding-dependent bound on empirical risk and improving classification on noisy
quantum hardware. A training objective based on the
Hilbert-Schmidt inner product extends this approach to deterministic
quantum computation with one
qubit (DQC1) and is demonstrated on an NMR
quantum processor. A margin-based generalization analysis then connects
quantum neural network performance to
quantum state discrimination. In the studied
benchmarks, margin distributions predict generalization more reliably than parameter-count metrics. For
quantum systems, a Mamba-based neural decoder for surface codes matches a reproduced
Transformer baseline in
memory experiments while reducing inference-cost scaling from quartic to quadratic in code distance. Under an explicit decoder-induced-noise model, it achieves lower logical error rates and a higher effective threshold. For neural
quantum states,
stochastic reconfiguration is interpreted as tangent-space ridge regression, with its diagonal shift controlling the bias-variance trade-off under finite Monte Carlo sampling. Multi-shift
stochastic reconfiguration reduces checkpoint-local validation residuals and update variance relative to fixed-shift SR, at additional computational cost. Together, these contributions show how learned representations, statistical control, and hardware constraints shape the exchange between
quantum computing and machine learning.
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Robotics
A Reconfigurable Bidirectional Cable-Driven Hip Exoskeleton with Swappable Bench/Backpack Dual-configuration Actuation
YuanLong Ji, Shuhan Xiang, Qihan Ye et al. (+1 more)
Hip exoskeletons provide an important hardware basis for lower-limb rehabilitation and locomotor assistance. Laboratory rehabilitation assessment and system development require substantial actuation and computing resources, whereas mobile assistance requires untethered portability. Integrating both capabilities within one reusable platform remains a central design challenge. This paper presents a reconfigurable bidirectional cable-driven hip exoskeleton platform that rapidly switches between bench-mounted and backpack-mounted actuation while sharing one cable-free wearable hip interface. The platform modularly adapts the actuation configuration, end-effector sensing path, and low-level control interface. Each cable-driven end-effector weighs 0.405 kg, excluding the cable and actuation unit, and integrates an encoder and a torque sensor; experiments validated bench-mounted admittance-based motion tracking capability and backpack-mounted open-loop torque tracking. Human-worn experiments with three healthy participants used myoMOTION to evaluate the platform's wearable-side hip-motion sensing capability, verified bench-to-backpack and backpack-to-bench motion-ready switching across 30 trials in $30.1\pm16.3$ s, and formed a small-scale
multimodal wearable-exoskeleton gait
dataset for sensing validation and data-driven
algorithm development, comprising 8 min bench-mounted treadmill records and 11 min backpack-mounted outdoor walking records. These results show that, by unifying the wearable structure, actuation interface, and sensing path, the proposed platform enables validation of the same hip exoskeleton in both bench-mounted and backpack-mounted configurations, providing reusable hardware for iterative development and applications across scenarios.
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All authors YuanLong Ji, Shuhan Xiang, Qihan Ye, Xingbang Yang
Computer Vision
What Drives Hierarchy-Aware Image Retrieval? Taxonomy Alignment, Objective Choice, and Geometry
Ling Shi
Foundation
vision models provide strong generic representations, yet high class-level
retrieval accuracy does not necessarily imply that an
embedding respects a target semantic taxonomy. We study strict explicit-taxonomy image
retrieval on frozen DINOv2 features and ask: when hierarchical
retrieval improves, how much of the change is associated with the organization of taxonomy-aware supervision, and how much with the Euclidean-
hyperbolic geometry choice? We evaluate higher levels with strict cross-class criteria that exclude finer-grained matches, and compare Euclidean and
hyperbolic projections trained with taxonomy-distance regression or a taxonomy-aware supervised contrastive objective. A compute-matched 2 x 2 Geometry x Loss factorial uses the same 768-256-32 projector capacity, optimization schedule, batch order, and fixed 100-epoch budget; the Loss axis denotes the Regression-to-Taxonomy-SupCon objective-family contrast. On CUB, the objective-family contrasts in mean hierarchy mAP (strict middle/high average, excluding Class/Leaf) are +0.0487 in Euclidean space and +0.0414 in
hyperbolic space, compared with geometry contrasts of +0.0102 and +0.0030. On NABirds Parent-disjoint
retrieval, the corresponding objective-family contrasts are +0.0467 and +0.0440, whereas geometry contrasts are +0.0017 and -0.0009. A semantic-
alignment control shows that the true taxonomy substantially outperforms a structure-preserving shuffled hierarchy, while a NABirds
curvature/radius control does not support stronger negative
curvature as the explanation for the observed hierarchy gains. Across the two taxonomies, the Regression-to-Taxonomy-SupCon contrasts are larger in aggregate than the evaluated geometry contrasts; semantic
alignment also matters separately, while geometry remains hierarchy-dependent.
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Cryptography and Security
SLED-IFV: Solver-Validated LLM-Guided Decomposition for Scalable Hardware Information-Flow Verification
Liangtao Dai, Yimin Gao, Melika Morsali et al. (+1 more)
Formal hardware information-flow verification (IFV) provides strong guarantees against secret-dependent timing and control behavior, but often scales poorly on realistic RTL. We identify two recurring proof barriers in self-composed IFV: implementation complexity, where proof-hard datapath logic dominates even though the property needs only a compact boundary relation, and relational inductive complexity, where the proof depends on cross-copy public-control facts that the backend prover does not infer efficiently. To address them, we introduce two semantic proof decomposition forms: functional simplification, which replaces a proof-hard RTL region with a validated over-approximate summary, and relational strengthening, which exposes and proves the cross-copy relations needed for induction. We further present SLED-IFV, a solver-validated LLM-guided flow that automates the selection of these forms and their concrete targets. Given a self-composed miter and an oracle-free decision sheet, the LLM proposes a decomposition, then materializes it into proof artifacts under controller checks. The controller compiles the checked artifacts into proof obligations, and the formal verification backend remains the sole authority for acceptance. Across nine nontrivial
benchmarks constructed from real RTL, SLED-IFV achieves up to 603x solver-only speedup and converts two 12-hour timeouts into completed proofs. The closed-loop flow produces verifier-accepted decompositions for all cases.
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All authors Liangtao Dai, Yimin Gao, Melika Morsali, Mircea Stan
Robotics
RoboFollow: Unveiling the Instruction Following Mirage in Embodied Agents
Chang Guo, Yukun Xie, Bohan Tan et al. (+6 more)
Modern
embodied agents achieve impressive success rates, yet their actual instruction-following ability is far weaker than these numbers suggest. We trace this illusion to a structural property we term low scene
entropy: when a visual scene admits only one valid task,
language becomes redundant and a policy can score highly while barely using it. We introduce RoboFollow, a diagnostic
benchmark with three principles: (1) High Scene
Entropy: each training scene supports multiple kinematically distinct task branches, making
vision alone insufficient and forcing reliance on
language. (2) Hierarchical Diagnostic Protocol: a four-level protocol (L0–L3) progressively perturbs visual layout and semantics, probing whether equivalent instructions yield consistent behavior and distinct ones yield discriminable behavior across spatial relations, attributes, trajectory constraints, and logic. (3) Confound-Controlled Diagnosis: we simplify interaction objects, restrict actions to the trained repertoire and report stage-wise Intent and Execution scores, isolating comprehension from motor execution. Evaluation of nine VLA and WAM policies shows that strong L0 performance, where attained, does not reliably transfer to L1–L3 under our fine-tuning setup. Representative mitigations, including stronger VLM backbones, QA co-training, LangForce, and Classifier-Free Guidance, all fail to close this gap. RoboFollow exposes genuine instruction following as a critical, overlooked bottleneck. Code and
dataset are available at https://github.com/AutoLab-SAI-SJTU/RoboFollow and https://huggingface.co/
datasets/AutoLab-SJTU/robofollow-data.
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All authors Chang Guo, Yukun Xie, Bohan Tan, Zheng Chang, Zhaokai Yin, Qianli Ma, Yingqiao Wang, Chao Liang, Zhipeng Zhang
Computer Vision
Shallow to Deep: Aligning Token Pruning with Stage-wise Roles in LVLMs
Shuo Zhang, Jintao Tong, Yixiong Zou et al. (+2 more)
Large
Vision-
Language Models (LVLMs) incur high computational costs from redundant visual
tokens. Although training-free
attention-based multi-layer
pruning in the
vision encoder stage has been explored as an effective strategy, we find that
pruning in shallow layers consistently degrades performance. In this paper, we aim to understand this problem and seek a solution. By analyzing
attention patterns across network depth, we find that shallow layers primarily function as edge detectors with chaotic
attention maps, while deeper layers transition through local subject recognition and unstable semantic aggregation. To address the misalignment between
pruning strategies and network stages, we propose STD, a hierarchical
token pruning framework that adapts
token selection mechanisms to the functional role of each network stage. STD employs High-Frequency Spectral Analysis in shallow layers to deterministically preserve structural edges, uses Gaussian-Smoothed
Attention in intermediate layers to maintain spatial coherence, and introduces a Stability-Adaptive Trigger in deep layers to execute
pruning only during semantically stable phases. Extensive experiments show that STD outperforms state-of-the-art
pruning methods by 1.1% on LLaVA-1.5-7B with 88.9%
token reduction, while also being plug-and-play and highly effective when combined with other methods, and by 2.1% on LLaVA-NeXT-7B with 94.4% reduction, delivering a 3.9x speed-up in the prefilling stage. Our code will be released at https://github.com/Twilight03/STD.
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All authors Shuo Zhang, Jintao Tong, Yixiong Zou, Yuhua Li, Ruixuan Li
Machine Learning
An Exploratory Replica-Overlap Probe of the Grokking Transition
A. C. Opus and J. Q. Lu
We trained 64 independently seeded networks in four configurations, continuing each to sustained convergence or a 40,000-epoch ceiling. We then asked whether an RSB-inspired distribution of pairwise weight overlaps changes across the grokking transition. It is the
alignment step, not the overlap statistic, that determines what this registered probe can report. The registered implementation permutes hidden units without the corresponding bias and head-internal permutations and therefore does not preserve the network function. Every q_wt value computed through this
alignment inherits the defect; q_fn does not, because it is computed from predictions of the unpermuted models. The numerical-precision requirement also failed, and an audit found protocol deviations. Consequently, the pre-registered rule gives no verdict: registered outcome UNDETERMINED (reason code C0_INSTRUMENT_INVALID). These data provide neither a confirmatory null nor a validated reading of the Parisi order parameter. Only frac40 cleared the 12/16 checkpoint-completeness requirement. For this configuration, a post-hoc criterion applied to the same data gave a Hartigan-dip interval containing zero (95% CI for Delta dip = [-0.017, 0.034]), whereas the overlap standard deviation increased by a factor of about 5.6. A post-hoc calibration assigns the dip test zero power at the simulated separations; the interval is therefore uninformative, not evidence of no change. The standard-deviation ratio is the only statistic here with power at the observed effect. Ensemble loss was near-flat only under the pre-specified 1% threshold. Finally, grokking rates of 0/16, 11/16 and 16/16 remain descriptive because train fraction is confounded with split identity.
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