Computational Complexity
NP-Hardness of Bounded Distance Decoding for Reed-Solomon Codes
Daqing Wan and Jun Zhang
For an $[n,K]$ Reed–Solomon code, the covering radius is $n-K$. Gandikota, Ghazi, and Grigorescu proved deterministic NP-hardness of bounded-distance decoding when the decoding radius is $d$ below the covering radius for every $1\le d\le c\log n/\log\log n$, where $c>0$ is an absolute constant. We prove that, for every fixed rational $0<\alpha<1/2$, bounded-distance decoding is NP-complete under deterministic
polynomial-time many-one reductions over explicitly represented finite extension fields for the additive gap $d=\lfloor n^\alpha\rfloor$ below the covering radius. The hard codes have odd block length~$n$, dimension $K=(n+1)/2-d$, decoding radius $(n-1)/2$, and rate tending to $1/2$. The alphabet size is subexponential in the evaluation set size: for a fixed $0<\eta<1$ depending only on $\alpha$, it is $2^{\Theta(n^\eta\log n)}=2^{o(n)}$. The proof passes through moments subset sum on $n-1$ nonzero field elements, with required subset size $(n-1)/2$ and $d$ prescribed moments. The arithmetic ingredient is a uniform positive-completion
theorem over
prime fields $\mathbb{F}_q$ with $q\ge d^{2+\rho}$, for any fixed $\rho>0$. A sharper form follows from a higher-dimensional point-count estimate based on Deligne's
theorem; the weaker form used in our reduction is proved more elementarily using additive-character orthogonality, the one-variable Weil bound, a moment identity of order $2d$, and Newton identities. A universal completion pool, an extension-field quotient construction, and a deterministic linear-size simultaneous power condenser complete the reduction.
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Numerical Analysis · Cross-listed from eess.SP
Exact Factorisation and Fast Computation of Invertible Constant-Q Transforms
Facundo Franchino, Eloi Moliner and Vesa Välimäki
The constant-Q transform (CQT) represents audio on a logarithmic frequency axis. Its nonstationary Gabor formulation is exactly invertible, but the unequal numbers of time coefficients in its bands complicate GPU computation. An exact factorisation combines spectral selection, conjugation, windowing, and reordering into a fixed map between one packed
Fourier transform and the shorter band inverse transforms. The factors give waveform reconstruction, real adjoints for backpropagation, and bounds on arithmetic depth and block width; overlapping slices permit streaming with bounded
memory. Tests on two GPU models show that Flash-CQT reduces analysis-synthesis round-trip time by factors of two to eight relative to a baseline computing the same CQT. The proposed implementation also uses over 30% less peak temporary workspace and reaches a negligible reconstruction error, with a signal-to-noise ratio of about 130 dB, in single-precision floating-point arithmetic. These advances make Flash-CQT a practical, computationally efficient front end for spectral analysis and modern audio machine-learning systems.
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Computer Vision
UpDown-SC: Gravity-Canonicalized Dual-Envelope Scan Context for Indoor LiDAR Place Recognition
Jie Xu, Yongxin Yang, Ziyi Jin et al. (+4 more)
LiDAR place recognition is a key front end for loop closure and global relocalization, yet indoor
retrieval remains difficult when attitude or sensor mounting height changes between mapping and query sessions. Scan Context stores the maximum height in each polar
cell; indoors, broad ceilings can suppress the lower and mid-level geometry that distinguishes adjacent rooms and corridors. We present UpDown-SC, a training-free polar descriptor that first canonicalizes
gravity and then represents two complementary surfaces: the upper envelope of lower/middle structures and the lower envelope of overhead structures. Their physical split is estimated once from a
cell-balanced map height distribution and reused by every query. A mask-aware, non-uniform two-channel distance retains discriminative lower-level evidence while limiting sensitivity to its cross-session variation, without treating unobserved
cells as zero-height measurements. Conventional Scan Context shortlisting and circular yaw
alignment are retained, so retrieved hypotheses directly initialize
geometric verification. Experiments across repeated indoor sessions, mounting-height changes, mixed outdoor-to-indoor trajectories, and an outdoor transfer sequence show more reliable first-choice
retrieval on the indoor and mounting-height-varied sessions. A paired test finds a significant gain over Scan Context on the in-house sessions. UpDown-SC also gives the best or second-best F1max and AUPR under threshold-based acceptance while retaining a lightweight CPU front end. Continuous replay confirms that the retrieved hypotheses support metric prior-map localization. Code and evaluation artifacts: https://github.com/jiejie567/updown-sc.
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All authors Jie Xu, Yongxin Yang, Ziyi Jin, Kangjin Yu, Hongjun Huang, Chao Han, Zhongpu Xia
Machine Learning
A Concentration Bound for Two-Timescale Actor-Critic Algorithm
Prashansa Panda and Shalabh Bhatnagar
Significant research effort has been directed in recent years towards establishing both asymptotic and non-asymptotic convergence guarantees for two-timescale actor–critic
algorithms, where the actor recursion is run on a slower timescale than the critic recursion. This work derives a uniform all-time concentration bound for the actor–critic
algorithm with function approximation in the long-run average-reward setting. This bound helps us analyze the behavior of the actor parameter with high probability. We show that, after some finite time, the actor parameter enters a safe region and remains within it thereafter with high probability. Specifically, with probability at least $1-\epsilon_1-\epsilon_2$, the actor error $\Vert \theta_k-\theta^{*}\Vert$ is $O\left(\frac{n_0^{3/4}}{k}\frac{1}{\sqrt{\epsilon_2}}+\left(\frac{1}{n_0}\right)^{1/4}\log^{1/4}\left(\frac{1}{\epsilon_1}\right)+\left(\frac{1}{n_0}\right)^{1/4}\right)$ for all $k\geq n_0$ and sufficiently large $n_0$. We also present experimental results demonstrating that the aforementioned actor error diminishes with the number of actor-parameter updates.
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Computer Vision
Spectral Amplitude Purification in Distribution Matching for Diffusion Distillation
Zhenyu Zhou, Can Wang, Chun Chen et al. (+2 more)
Distribution Matching
Distillation (DMD) enables high-quality
diffusion sampling in only a few steps, but its optimization dynamics remain dominated by coarse, low-frequency signals, delaying the recovery of fine-grained details. We identify a pronounced concentration of spectral amplitudes at low frequencies in the DMD directional error, where dominant low-frequency components overwhelm weaker mid- and high-frequency signals. To address this issue, we propose Spectral Amplitude Purification for Distribution Matching
Distillation (SAP-DMD), a plug-and-play approach that adaptively modulates the amplitude spectrum of the DMD directional field. By suppressing the dominant tail of the amplitude spectrum, SAP-DMD reduces low-frequency dominance and promotes more effective recovery of fine structures and textures. Experiments on PixArt-$\alpha$, SD3, and SD3.5 demonstrate that SAP-DMD accelerates training convergence and improves generation quality under both 2-step and 4-step sampling.
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All authors Zhenyu Zhou, Can Wang, Chun Chen, Zeyu Zheng, Defang Chen
Robotics
Modeling Load-, Velocity-, and Temperature-Dependent Transmission Errors of Cycloidal Drives for Industrial Robots Using Fourier Series
Christian J. E. Bauer, Valentin Kamm, Marcel Dzubba et al. (+3 more)
Industrial
robots are rarely used for machining tasks due to their limited path accuracy. This accuracy is mainly limited by inaccuracies in the drive trains. Compliance and transmission errors occur in the joint gearboxes. While transmission errors have been extensively studied for strain wave gears, there is little research on these errors in cycloidal drives. This gearbox type is commonly used in industrial
robots for medium to heavy payloads. It is proposed to model the mainly periodic transmission errors using a
Fourier series where amplitude and phase are defined as a
polynomial function of the main influence factors load-torque, velocity, and temperature. Measurements of the transmission errors were conducted using an experimental setup representing a single
robot joint. In the evaluation of the measurement data, harmonic frequencies were related to mechanical properties of the cycloidal drive. These frequencies were used to identify the parameters of the
polynomial Fourier series model. Compared to validation measurements, the derived model shows an average root mean square error of 0.026 mrad. It is proposed to use the output of the resulting model in a feedforward control approach to compensate the transmission errors and to increase the path accuracy of industrial
robots.
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All authors Christian J. E. Bauer, Valentin Kamm, Marcel Dzubba, Lukas Steinle, Armin Lechler, Alexander Verl
Artificial Intelligence
CounterRoute: Self-Routed Reasoning via Hierarchical Counterfactual Credit Assignment
Ruochen Jiao, Besnik Fetahu, Zhenyu Shi et al. (+1 more)
Reasoning-capable
language models often produce long chains of thought when direct answers suffice, wasting inference compute. Dual-mode models offer both thinking and direct-answer modes, but typically leave mode selection to users. Automating this selection while improving responses under both modes is challenging because routing targets evolve with the policy, strong initial mode preferences can destabilize exploration, and sequence-level objectives entangle routing with response learning. We introduce CounterRoute, an online reinforcement-learning framework that jointly learns routing and mode-conditioned responses in one shared policy directly from a native dual-mode checkpoint, without method-specific SFT warm-up. Paired current-policy counterfactual rollouts estimate cross-mode routing credit, which is assigned only to the routing
token, while within-mode GRPO trains response
tokens. A paired-to-self-routed curriculum stabilizes early training with forced coverage of both modes before progressively transferring training to autonomous routing. Across nine
benchmarks, CounterRoute better balances accuracy and efficiency than heuristic and learned adaptive-routing methods, improving macro-average accuracy at both Qwen3 scales while cutting mean generated
tokens by 51% and 41%, respectively, versus always-thinking checkpoints. On instruction-following and commonsense
benchmarks where direct answering is strong, think rates fall as low as 1% while response quality improves. Despite training only on math and synthetic instruction-following data, its routing and response quality generalize to held-out tasks in coding, science, knowledge, and commonsense.
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All authors Ruochen Jiao, Besnik Fetahu, Zhenyu Shi, Priyanka Nigam
Artificial Intelligence
Functional Architecture of European Electricity Trading Markets: Requirements for AI Supported Trading Systems under Regulatory Constraints
Walter Kurz and Wojtek Stricker
European electricity trading in the EU operates as a constrained multi-layer system in which legal design, exchange microstructure, and network physics are executed jointly across forward, day-ahead, intraday, and balancing horizons. This paper develops a functional architecture for AI-supported trading that is aligned with
market-coupling mechanics, cross-zonal transfer constraints, and compliance obligations under REMIT, MiFID II, MiFIR, and EMIR. The contribution is a formal system specification composed of a decision-state vector, residual-exposure accounting, constrained optimization objective, executable-action permission gate, and fail-closed AI control logic with auditable records. The analysis maps major Nominated Electricity
Market Operator (NEMO) venues and related exchange operators into an operational venue
topology and identifies where cross-border coordination fails in practice: interface-level timing, permission heterogeneity, and balancing-layer coupling. The resulting framework proposes how AI can be deployed as a bounded decision component inside regulated
market operation with explicit governance, rather than as an unconstrained prediction layer.
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Computer Vision
WildHSR: Metric Feed-Forward 4D People-Scene Reconstruction from a 3D Foundation Model
Jerrin Bright and John Zelek
3D foundation models recover
video cameras and geometry in one forward pass, but some of the strongest are up to scale. Joint people-scene reconstruction then requires two missing outputs: metric scale and persistent person identity. We ask whether one up-to-scale foundation representation can support both through lightweight adaptation. Exact metric labels are scarce, but unlabeled in-the-wild
video is abundant. We use people in curated web
video to initialise the solution: a posed metric body and 2D keypoints give an approximate, closed-form scale pseudo-label. These pseudo-labels pretrain a Scale Readout, which is then fine-tuned together with a lightweight adapter using exact metric supervision from standard real-
video training splits. At inference the head predicts metric scale from foundation-model
tokens, without the ruler or its teachers. For person identity, we probe the pretrained foundation model alone and find evidence that its intermediate query-key features encode person correspondence across frames. In most evaluated moving-person clips, a mid-layer
token prefers that person over the vacated location and other people. A tiny projection reads this correspondence; together with metric pelvis motion and proposal confidence, it drives dustbin-aware Sinkhorn association of per-frame bodies. WildHSR combines both readouts to reconstruct metric cameras, scene and people from
monocular video. Each window is predicted feed-forward; analytic association and Sim(3) composition connect windows. On EMDB-2, WildHSR is the first feed-forward method in the published comparison to beat the best optimization-based WA-MPJPE and RTE while leading feed-forward methods on all three world-frame metrics. On RICH, it leads feed-forward people-and-scene methods on WA-MPJPE and W-MPJPE. The complete pipeline runs at 10.1 fps on one GPU.
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Robotics
TRACE: Interactive Bi-Directional Tracing of Monochrome Cables Amid Clutter
Nidhya Shivakumar, Ethan Ransing, Josh Zhang et al. (+6 more)
Accurate state estimation (tracing) of Deformable Linear Objects (DLOs) such as cables is a critical challenge for data centers, manufacturing, construction, homes, and surgery, where precise cable management directly impacts operational safety and efficiency. However, resolving the state of multiple monochrome cables amid foreground and background clutter poses challenges due to occlusions, overlap, and ambiguous crossings. We present Two-way Routing And Cable Estimation (TRACE), which combines bi-directional cable tracing with interactive perception primitives-Divergence Push and Cluster Dilation-to actively resolve ambiguities. Evaluation with 110 physical experiments suggests that TRACE can increase the percentage of cable length correctly traced in complex scenarios (with up to 4 cables and 40 crossings) from ~60% with the strongest prior method, HANDLOOM 2.0, to ~90%, outperforming RT-DLO, Nano Banana Pro, and ChatGPT 5.2 as well. For a trial run on a workstation with an NVIDIA GeForce RTX 4090 GPU, the average computation time is 0.4 seconds per cable. Project website: https://trace-paper.github.io/.
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All authors Nidhya Shivakumar, Ethan Ransing, Josh Zhang, Shamak Gowda, Kevin Yang, Miles Hua, Anika Agrawal, Justin Yu, Ken Goldberg
Computation and Language
Zeyu Michael Li, William Xingxu Chen, Bingshuo Qian et al. (+2 more)
Fully continuous
diffusion language models (dLMs) denoise continuous representations without intermediate discretization, then decode all response
tokens in parallel at the final step. Their performance on challenging
reasoning tasks remains less established than that of autoregressive (AR) LLMs and masked dLMs. We scale Embedded
Language Flows (ELF) to mathematical
reasoning and code generation on GSM8K, MATH-500, HumanEval, and MBPP. We introduce ELF-REG, which improves learning with representation
alignment and
entanglement (REPA+REG), where a frozen AR teacher supervises intermediate denoiser features and supplies a global representation that is jointly denoised with the response. ELF-REG-L achieves 55.96% pass@1 on GSM8K at 64 network function evaluations (NFE), and 13.39% on MATH-500 and 22.56% on HumanEval at 128 NFE. It outperforms the evaluated comparable-scale dLMs in pass@1 on GSM8K and code, and improves MATH-500 pass@1 from 10.55% for the ELF-L baseline to 13.39% with ELF-REG-L. Without few-step training, the same task-specific checkpoints support strong low-NFE performance through early-stop, which decodes an intermediate clean prediction without completing the denoising trajectory. At 16 NFE, ELF-REG-L reaches 41.21% HumanEval pass@10, outperforming recent continuous dLMs of comparable scale.
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All authors Zeyu Michael Li, William Xingxu Chen, Bingshuo Qian, Jiayin Liu, Xiang Cheng
Machine Learning
Language Specificity vs. Domain Diversity: Benchmarking Transformers for Bangla Medical NER
Rakib Abdullah and Md. Maruful Islam Maruf
Medical Named Entity Recognition (NER) for low-resource
languages remains a challenging task due to high linguistic variability and a scarcity of domain-specific annotated corpora. This work presents a comprehensive empirical
benchmark evaluating three fine-tuned
transformer encoders-BanglaBERT,
multilingual BERT (mBERT), and XLM-RoBERTa-against GPT-4o mini under zero-shot and few-shot prompting configurations for Bangla medical NER. In contrast to prior studies that evaluated large
language models on limited subsets of only 50 samples, we conduct a large-scale evaluation across the full test set of 3,179 samples, providing statistically robust and reproducible baselines. Our fine-tuned XLM-RoBERTa model achieves an F1- score of 0.5959, establishing a new state-of-the-art and surpassing the previously reported best result of 0.5848. Crucially, we demonstrate that the
language-specific BanglaBERT model consistently underperforms its
multilingual counterparts with an F1-score of 0.4937, indicating that pretraining domain diversity can outweigh
language specificity in highly specialized clinical settings. Furthermore, we present a detailed per-entity-type analysis for this task, revealing that Medicine and Specialist categories are recognized with high reliability, achieving F1- scores above 0.83, while the Symptom category remains the most challenging with an F1-score of 0.4367 despite being the most frequent training class. Finally, fine-tuned
transformer models outperform the optimal prompting configuration by a factor of 3.76, confirming that
prompt-only pipelines remain inadequate for structured clinical entity extraction in low-resource
language environments.
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Systems and Control
Variable-Horizon Model Predictive Control for Switched Systems
Rui Zhao, Zhiqiang Zuo, Yang Shi et al. (+3 more)
This paper investigates model predictive control (MPC) for switched systems subject to control and state constraints. A variable-horizon switched MPC approach is proposed. By steering the system state into a well-designed switching feasible set, the proposed method structurally decouples the dwell-time conditions from the MPC constraints, thereby relaxing the dwell-time requirements to match those of the unconstrained switched systems. Furthermore,
algorithms are developed to construct this switching feasible set and characterize the domain of attraction, ensuring both persistent feasibility and closed-loop asymptotic stability. To further decouple the prediction horizon length from strict dwell-time bounds, advanced short-horizon switched MPC schemes are designed, which expand the overall domain of attraction. Simulations illustrate the efficacy of the proposed methods.
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All authors Rui Zhao, Zhiqiang Zuo, Yang Shi, Yijing Wang, Zheng Li, Guanrong Chen
Cryptography and Security
TraceGuard: Adaptive Multimodal Poison Filtering through Cross-Feature Rank Agreement
Haoyang Li, Yaxin Xiao, Linyan Dai et al. (+5 more)
Multimodal training relies on image-text corpora collected from external sources, creating opportunities for attackers to poison the data. Stealthy attacks can preserve plausible image-text pairs while concealing the differences used by detectors, so apparently clean data can still redirect the trained model. We therefore ask which properties a poison set must preserve for the attack to remain effective. A small poison set must still exert enough collective influence during training to induce the attacker's target behavior. We analyze this influence in terms of how often an attack pattern occurs and how strongly the examples carrying it jointly affect the model. This analysis motivates six corpus-level features that examine cross-modal neighborhoods, recurring text, and changes after text-span erasure without training the victim model. We introduce TraceGuard, an adaptive rank-based filtering method that uses agreement among complementary feature rankings to identify suspicious examples. It refines the selected set through shared patterns and adapts the removal threshold to each corpus without knowing the attack or poison rate. Across 19 attack configurations spanning image-text learning, generative
vision-
language model fine-tuning, and encoder-transfer tests, TraceGuard removes an average of 98.4% of poisoned examples and 5.4% of clean examples. After training on the filtered corpora, the residual attack metric is at most 1% in 13 configurations. Matched-removal controls and ablations support the contributions of sample selection and adaptive removal. Stress tests also identify detection failures under adaptive attacks and unnecessary removal on poison-free corpora.
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All authors Haoyang Li, Yaxin Xiao, Linyan Dai, Jiawen Fu, Zi Liang, Jason Xue, Qingqing Ye, Haibo Hu
Machine Learning
Downside-Controlled Online Forecast Combination under Delayed and Revised Outcomes
Minkyoung Kim, Hyunjung Byun, Yohan Lee et al. (+1 more)
Post-hoc correction adjusts a forecaster that cannot be retrained, such as a foundation model, but a correction fitted where errors are stable can hurt where they shift. We aim for downside control: not much worse than the starting forecast. We combine the frozen forecaster, a static corrector and an online corrector on the simplex, using only losses that mature after the horizon. Across seven
benchmarks and four base models, two of them foundation models, the worst deterioration over 28 pairs at the main horizon is 0.15% and gains reach 11.5%. On day-ahead load for seven European bidding zones it lowers mean MSE in all seven zones, while single correctors raise mean MSE by up to 102% where the published forecast is most accurate. Three empirical conditions on expert speed, stream length and outcome
alignment, each fixed by a documented failure, delimit its scope. Learning from the provisional outcome improves four zones on the settled one; learning on the settled outcome restores all seven.
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All authors Minkyoung Kim, Hyunjung Byun, Yohan Lee, Beakcheol Jang
Machine Learning
Where Does Exactly-Once Live? Model, Harness, and Tool-Contract Effects on Duplicate Side Effects in LLM Agents
Jiapeng Li
When a tool-using
agent's write times out or returns a server error, the action may already have taken effect. Retrying blindly duplicates it – a second charge, a second announcement, a second deployment – while giving up skips required work. We ask where exactly-once behaviour should be enforced: in the model, in the
agent harness, or in the tool contract. We introduce LIMBO, a deterministic sandbox of six services with realistic contracts (optional idempotency keys, eventually consistent and missing read paths) and twelve fault modes injected at the service boundary, including late commits, redelivery and partial batches; every episode is graded against a ledger of committed effects. Across 25,930 episodes spanning nine recent models, three production
agent harnesses, two contract variants and fifteen recovery conditions, the answer depends on the fault. When an immediate read-back can reveal what happened, the model decides: frontier models instructed to act exactly once almost never duplicate a write whose acknowledgement was lost (0.5%), weaker models often do, and the model explains 53% of the explained variance. When it cannot – the request is still in flight, or the transport delivered it twice – the same frontier models duplicate in 56% and 74% of episodes, and the contract explains 81%. We prove that no verification-only policy is exactly-once under late commits without a bound on in-flight time. Waiting works when such a bound is short and known, but with heavy-tailed in-flight delays even an hour of waiting per episode falls short of offering an idempotency key on every write, which lowers the duplicate rate from 28% to 4% because
agents use keys when they exist. The harness barely matters, a guard that attaches keys transfers across harnesses unchanged, and
agents reported success in 90% of the episodes in which they had duplicated an effect.
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Robotics
A Support-Enhanced Granular-Jamming Gripper for RL-based Grasping with Continuum Manipulators
Danyu Liu, Tianlin Zhang, Wei Chen et al. (+3 more)
Continuum manipulators provide dexterous motion in confined spaces, but structural compliance, hysteresis, and load-dependent deformation leave residual position and orientation errors that can undermine reliable contact with rigid grippers. To address this limitation, this paper presents a lightweight support-enhanced granular-jamming gripper tailored to a continuum manipulator. The gripper maintains compliance before jamming while establishing a direct load path to the continuum manipulator tip after jamming. To improve its grasping performance, we systematically designed membrane materials, particles, filling ratios, and the internal support structure, and further identify geometry-dependent grasp boundaries with respect to contact offset and object shape. Building on these results, we construct a physical manipulation system integrating the continuum manipulator, granular-jamming gripper, visual feedback, tendon actuation, and pneumatic control. We then train a reinforcement-learning-based reaching controller in a randomized simulation and deploy it on the physical system, demonstrating how positioning control and contact level mechanical adaptation can complement each other in a modular grasp-and-release task. By introducing an adaptive structure that relaxes the need for highly accurate modeling and positioning control, this work explores a design paradigm that integrates physical and
embodied intelligence.
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All authors Danyu Liu, Tianlin Zhang, Wei Chen, Wei Tang, Kecheng Qin, Zhongyu Li
Robotics
DAWN: Noise-Robust Quadruped Parkour via Depth-Denoising World Models
Yohan Choi, Min-Jun Kim, Jin-Sung Kim et al. (+2 more)
Vision-based legged locomotion methods assume clean depth at training time and rely on hand-tuned post-processing filters at deployment. However, filter parameters are rarely disclosed, hindering reproducibility, and performance degrades substantially when depth noise is left unaddressed. Building noise robustness directly into the learning pipeline would eliminate this dependency. While such robustness has been explored for proprioceptive inputs, analogous approaches for depth perception remain largely absent in legged locomotion. We propose DAWN (Denoising and
Alignment in World models for Noise-robustness), a noise-robust perception framework for legged locomotion, which builds noise robustness directly into a world model via two modifications: (1) feeding noisy depth to the encoder while keeping clean depth as the reconstruction target, forcing the model to implicitly denoise its input; and (2) applying contrastive learning to align the latent states of noisy and clean depth. Importantly, DAWN is not tied to a specific noise model, requiring no manual tuning to the noise distribution at deployment. Furthermore, it incurs no additional inference cost over existing world model-based methods. Without any manual filter calibration – relying solely on the learned noise-robust representation – DAWN achieves zero-shot quadruped parkour on a Unitree Go1: traversing stairs up to 18 cm, clearing gaps up to 70 cm, and mounting steps up to 45 cm from raw depth observations. Ablation studies show that denoising and contrastive
alignment contribute at complementary levels – reconstruction and representation, respectively – and yield additive gains when combined.
Videos and code are available at: https://dawn-parkour.github.io/
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All authors Yohan Choi, Min-Jun Kim, Jin-Sung Kim, Yong-Jae Kim, Youn-Hee Han
Robotics
From Passive Execution to Active Exploration: Agentic Embodied Manipulation in Realistic Environments
Shilin Ma, Chubin Zhang, Xulong Bai et al. (+3 more)
Recent advances in agentic systems have substantially enhanced the long-horizon capability of
embodied manipulation. However, many existing frameworks still follow a passive execution paradigm, which limits their applicability to real-world scenarios involving textual semantic cues, distractors, and initially invisible targets. To bridge this gap, we propose an
agent-based active exploration framework that enables
robots to dynamically interact with the environment rather than merely execute predefined instructions. Specifically, our framework consists of three collaborative modules: a planning module for high-level task
reasoning, a perception module for visual scene understanding, and an execution module for low-level manipulation. This design allows the
robot to actively acquire task-relevant information, adapt its behavior based on environmental feedback, and complete manipulation tasks under partial observability. Furthermore, we introduce a fine-grained perception-execution interleaving strategy, which tightly couples visual feedback with skill execution to improve exploration robustness. We evaluate our method on a realistic Find-and-Place task, demonstrating its effectiveness in challenging environments where target objects must be actively discovered before manipulation.
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All authors Shilin Ma, Chubin Zhang, Xulong Bai, Zifeng Gao, Shiyi Zhang, Yansong Tang
Computation and Language
Can Classical Semantic-Extractive Summarization Be Evaluated in Hindi? A Replication Study
Showket Ahmad Khan, Mudasir Mohd, Nasrullah Sheikh et al. (+4 more)
We replicate the distributional-semantics extractive summarisation method of Mohd, Jan and Shah (2020) and adapt it to Hindi, substituting a Devanagari-appropriate component at every
language-specific step. The system is evaluated on two independent corpora — the Hindi portion of XL-Sum and FIRE ILSUM 2.0 Hindi — under a Devanagari-aware ROUGE implementation validated against the XL-Sum authors' own
multilingual scorer, with all comparisons drawn as 1000-resample paired bootstraps. In its published equal-weight configuration the replicated system is significantly worse than a three-sentence lead baseline on both corpora, trailing Lead-3 by 0.042 ROUGE-1 Fon XL-Sum and by 0.265 on ILSUM. A feature ablation shows that sentenceposition is the only feature that contributes: position alone reproduces the lead baseline exactly, removing position gives the weakest configuration,and a validation-tuned weighting can at best equal Lead-3 and never exceed it. TextRank fails identically, making this a class-level rather than an implementation-level result. A selection analysis shows the remaining features steer extraction towards long, entity-dense body sentences while the references reuse the article lead.Current Hindi
benchmarks therefore cannot reward non-lead content selection, motivating purpose-built evaluation resources.
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All authors Showket Ahmad Khan, Mudasir Mohd, Nasrullah Sheikh, Mohsin Altaf Wani, Abid Hussain Wani, Hilal Ahmad Khanday, Niyaz Ahmad Wani
Machine Learning
Physics and Data Driven Transformer-Mamba Framework for Flow Field
Zhuo Zhang, Shun Zou, Canqun Yang et al. (+1 more)
While deep learning accelerates expensive partial differential equation solving in computational fluid dynamics (CFD), existing methods like PINNs and FNOs often struggle with generalization, noise robustness, and physical consistency. We introduce the
Transformer-Mamba for Flow Field (TM4FF) framework, a physics-constrained operator learning model with three key innovations: a Residual Wavelet Mamba (RWM) layer for feature denoising, a
Transformer-based
attention mechanism for enhanced feature fusion, and a physics-informed loss using
Fourier derivatives to enforce the Navier-Stokes equations. Experiments on four CFD
datasets show TM4FF achieves high accuracy and robust generalization across varying flow conditions.
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All authors Zhuo Zhang, Shun Zou, Canqun Yang, Xi Yang
Artificial Intelligence
A Rapid Pipeline for Training and Deploying ML Models on WeBe Band
Ehsan Kourkchi, Asmita Asmita, Houman Homayoun et al. (+1 more)
Developing optimized machine-learning
algorithms for edge devices with limited computational and
memory resources is challenging, time-consuming, and highly dependent on device-specific constraints. In this work, we streamline an edge ML workflow to enable rapid development, optimization, and deployment of machine-learning (ML) models directly on the WeBe Band, a wrist-worn wearable device designed for
multimodal physiological data monitoring. The proposed system automatically generates hardware-efficient ML models that can be easily integrated into the WeBe core firmware, supporting AutoML, hardware-aware quantization, and performance profiling to build models that meet desired latency targets while remaining compatible with device
memory and power limitations. The proposed framework tightly integrates the open-source Piccolo AI ecosystem with an automated pipeline that generates deployable firmware artifacts, performs hardware-aware model compilation, and supports over-the-air (OTA) deployment. The system supports multiple lightweight model classes, including classical machine-learning
algorithms and neural networks, and provides built-in on-device profiling tools to evaluate inference latency and
memory footprint under realistic execution conditions. Experimental results demonstrate clear trade-offs between model complexity and deployability on a microcontroller, showing that classical models offer strong real-time performance while lightweight neural networks require careful resource management. Rather than proposing new learning architectures, the current work mainly focuses on system-level automation, deployability, and enabling researchers and developers to rapidly iterate on models and evaluate them directly on target hardware. Although demonstrated on the WeBe Band platform, the workflow is designed to be extensible to other ML-powered edge devices.
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All authors Ehsan Kourkchi, Asmita Asmita, Houman Homayoun, Mahdi Eslamimehr
Social and Information Networks
Advancing the Physical Internet with GraphRAG: A New Way to Review and Integrate Existing Research
Hisatoshi Naganawa, Enna Hirata, Russell G. Thompson et al. (+1 more)
Physical Internet (PI) is an emerging concept that applies the digital internet as a design metaphor for the development of sustainable, interoperable, and collaborative freight transportation. It is considered a way to bring logistics into the next generation of transformation. Effective tools for organizing and integrating knowledge are essential for navigating the emerging research in this area. In this study, we explore the application of Graph
Retrieval Augmented Generation (GraphRAG) in the context of PI, using GPT-4o mini and Neo4j to construct a knowledge graph and systematically analyze existing PI-related literature. Our approach synthesizes scattered research findings, highlights emerging trends, and identifies knowledge gaps. Furthermore, we demonstrate that GraphRAG improves accessibility by structuring complex information into interconnected graphs and provides a deeper understanding of underlying research dynamics. This research will contribute to future research and innovation by providing a new method of information analysis in the PI domain.
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All authors Hisatoshi Naganawa, Enna Hirata, Russell G. Thompson, Akira Yamada
Numerical Analysis
A compact high-order and positivity-preserving active flux method for compressible Navier–Stokes equations on Cartesian meshes
Junming Duan, Wasilij Barsukow and Christian Klingenberg
This paper develops a compact fourth-order positivity-preserving active flux (AF) method for the one- and two-dimensional compressible Navier–Stokes equations on Cartesian meshes. The method retains the
cell averages and shared point values of the standard third-order AF method as its degrees of freedom. To avoid the order reduction that can arise when
diffusion is discretized using operators from the standard third-order AF method, while maintaining compactness, the divergence of the viscous flux is discretized directly using compact fourth-order operators. For the inviscid part, incorporating a downwind point value into the biased stencil yields fourth-order accuracy. A monolithic flux limiting blends high-order total numerical fluxes with low-order positivity-preserving counterparts, treating the inviscid and viscous fluxes jointly while maintaining local conservation. Together with a scaling limiter for point values, this procedure preserves density and pressure positivity for both
cell averages and point values. Numerical experiments demonstrate fourth-order convergence, positivity preservation, and accurate resolution of shocks and viscous flow structures. For the two-dimensional viscous shock tube, a comparison with a discontinuous Galerkin method shows improved computational efficiency in resolving complex interaction of shock waves and boundary layers.
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Machine Learning · Cross-listed from stat.ML
Feature Space Selection and Heterogeneous Effect Estimation for Blood-Brain Barrier Permeability: A Random Forest to the Generalized Random Forest Pipeline
Tshemollo Rapolai, Seite Makgai and Mohammad Arashi
Predicting blood-
brain barrier (BBB) permeability is critical for central nervous system drug discovery. Using the MoleculeNet BBBP
dataset (n = 2039), this study systematically ablates molecular feature spaces to isolate featurisation from model architecture. We evaluate three feature families (Morgan fingerprints, RDKit physicochemical descriptors, SMILES bigrams) across four learning
algorithms. Results demonstrate that predictive performance depends jointly on feature representation and
algorithm. Dynamic Random Forest using combined features achieved the highest mean AUC (0.970, 95% CI: 0.963-0.977). Second, this optimal representation enables exploratory estimation of heterogeneous associations between molecular structure and BBB permeability using Generalized Random Forests. Constructing a pseudo-treatment from a LogP median split, we applied double/debiased machine learning to account for confounding. Orthogonalization substantially attenuates the heterogeneity detected by naive causal forests; no conditional effects remained significant after false discovery rate correction (smallest adjusted p = 0.082). Furthermore, orthogonalized feature importance shifted toward residual structural information in SMILES bigrams. Ultimately, once observed confounding is properly accounted for, evidence that LogP-BBB associations vary systematically across chemical space is insufficient. This underscores that feature representation and model architecture are coupled design choices, and that unorthogonalized causal forests risk overstating genuine treatment effect heterogeneity.
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Artificial Intelligence
CRISS: A Retrieval-Augmented AI Chatbot for Assisting Cancer Registrars
Vani Seth, Mohammad Beheshti, Anirudh Kambhampati et al. (+4 more)
Cancer registrars, including Oncology Data Specialists (ODSs), must interpret complex and frequently updated coding and staging standards. We developed CRISS (Cancer Registry Intelligent Support System), a
retrieval-augmented generation (RAG) conversational assistant that provides rapid, citation-supported access to registry guidance. This study evaluated whether CRISS could (1) support accurate and citation-supported responses, (2) improve access to and interpretation of relevant guidance, and (3) support training/helpdesk use while preserving human oversight of final abstraction decisions. We built a domain-specific knowledge base from national cancer registry standards, segmented into metadata-tagged passages and indexed as dense
embeddings. Retrieved passages were used to generate citation-grounded responses through a large
language model (LLM). Open-weight, proprietary, and non-RAG baseline models across Gemini and GPT families were evaluated on easy, medium, and hard registry questions using an LLM-as-a-Judge protocols. RAG configurations consistently outperformed non-RAG approaches, especially as question difficulty increased. Mean grounding scores for RAG were 0.62/0.56/0.59 across easy/medium/hard tiers versus 0.29/0.26/0.29 for non-RAG. RAG models also achieved higher semantic-similarity scores overall. Proprietary RAG models performed strongest on easy and medium questions, while local RAG models ranked highest on hard questions and proprietary models were generally more cautious. Domain-specific RAG improved evidence grounding and response quality for cancer registry questions while enabling citation-supported assistance across complexity levels. CRISS demonstrates the potential of human-centered, citation-grounded AI to support cancer registrars while preserving human oversight for final coding decisions.
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All authors Vani Seth, Mohammad Beheshti, Anirudh Kambhampati, Vishwa Bhayani, Lucinda Ham, Prasad Calyam, Iris Zachary
Computer Vision
Seeing Is Not Measuring: Tool-Augmented Metric Spatial Reasoning for Vision-Language Models
Kai Glantz and Clemens Grange
Vision-
Language Models (VLMs) describe scenes well but reason poorly about metric 3D structure such as absolute distances, physical sizes, or
egocentric directions. We present a modular, predictor agnostic, tool-augmented framework that equips a small VLM (Qwen3.5-4B) with
geometric tools: 3D object detection, metric depth estimation, and deterministic solvers for distance, size and bearing. Each object is detected in the camera frame of its own best view, and the tools use that frame's pose to lift every detection into one shared world frame. Moving metric computation out of the model's weights and into explicit solvers yields large gains on three of four ReVSI-Bench tasks: with a strong
monocular detector (WildDet3D), absolute distance rises from 0.46 to 0.74 Mean Relative Accuracy (MRA), relative distance from 39.1% to 67.4%, and relative direction from a below-chance 25.9% to 73.4%. Because any detector can be swapped in behind the tool interface, comparing real detectors against ground-truth boxes separates perception error from
reasoning error: orchestration costs only 0.03 MRA. Object size is bounded by the detector: the tools are near-exact on groundtruth boxes (0.97) yet the best real detector barely beats the no-tool baseline (0.61 vs. 0.58), because size reads straight off a box extent
monocular detectors get wrong. Without a predefined recipe, the model already sequences the tools correctly on its own, matching a scripted pipeline on three of four tasks.
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Sound
On a Separate Note: Robust Score-Informed Note Separation with a Two-Stream TFC-TDF U-Net and Adaptive Set Ownership
Benjamin Shiue-Hal Chou, Purvish Jajal, Nicholas John Eliopoulos et al. (+5 more)
Score-informed note separation seeks to extract the performed waveform of all individual notes, often from a polyphonic recording. Existing deep learning systems generally only target instrument-level stems. We present, to our knowledge, the first deep learning approach to score-informed note separation, NoteSep. NoteSep extracts the queried notes by applying an extraction stage model, NoteGrab, once per note. Conditioned on pitch, onset, and offset, NoteGrab separates harmonic and percussive components in two U-Nets linked by bidirectional cross-
attention; selective harmonic gating suppresses lower-octave interference while preserving percussive attacks. Finally, a joint separation stage applies Adaptive Set Ownership (ASO) to compare concurrent NoteGrab estimates and reallocate mixture energy. We curate SCNS-Train (25,729 mixtures and 743,920 targets) for training and SCNS-Eval (16 instruments, disjoint scores and libraries) for evaluation. On SCNS-Eval, NoteSep reaches a median SI-SDR of 7.39~dB, compared with 2.49~dB for our strongest baseline. See the demo page at https://benschou.com/notesep.
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All authors Benjamin Shiue-Hal Chou, Purvish Jajal, Nicholas John Eliopoulos, James C. Davis, George K. Thiruvathukal, Kristen Yeon-Ji Yun, Hao-Wen Dong, Yung-Hsiang Lu
Machine Learning
BranchShine-CR: Compact Multilingual IPA Transcription with Self-Conditioned CTC and Consistency Regularization
Nikhil Navas, Sergio Chevtchenko, Talisson Damiao et al. (+1 more)
We introduce BranchShine-CR, a 25M-parameter model for
multilingual transcription into the International Phonetic Alphabet (IPA). It combines log-mel features, a rotary-position E-Branchformer encoder, intermediate self-conditioned connectionist temporal classification (CTC), and consistency regularization across augmented views. On 16,646 shared IPApack++ test utterances, it achieves 4.47% IPA character error rate, a 22.3% relative reduction from ZIPA-CTC-NS, with approximately one-twelfth as many parameters while being trained from scratch. BranchShine-CR also outperforms a similarly sized NeMo Conformer baseline across all 41
dataset language labels. Ablation studies indicate the individual components synergetically acting in model performance contribution. These findings support compact IPA recognition capabilities under limited compute budget, for applications in low-resource on-device pronunciation assessment.
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All authors Nikhil Navas, Sergio Chevtchenko, Talisson Damiao, Saeed Afshar
Software Engineering
Design, development, and preliminary validity and reliability evidence of the Software Engineering Self-Efficacy Scale (SESES)
Albert D. Ritzhaupt, Jhonathan Sora-Cardenas, Priya Ganapathy Prasad et al. (+3 more)
The purpose of this research is to design, develop, implement, and provide preliminary validity and reliability evidence of the Software Engineering Self-Efficacy Scale (SESES). Framed by a conceptual framework using guidance in software engineering curriculum and concepts along with the notion of self-efficacy, we generated an initial item pool of n = 87 items to operationalize and measure software engineering self-efficacy among undergraduate computing students. The conceptual framework traces five dimensions: 1) Requirements Engineering, 2) Teamwork and Collaboration, 3) Software Quality Management, 4) Software Design and Architecture, and 5) Software Agile Methodologies. We pilot tested the SESES with n = 527 undergraduate computing students who had completed a software engineering course in the current semester or a previous academic semester. We employed Exploratory Factor Analysis (EFA) with the Principal Axis Factoring method and an oblique (Promax) rotation to examine the underlying structure of the SESES, resulting in the same five internally consistent latent constructs in the conceptual framework with minimal cross-loading and a simple structure in the pattern
matrix, explaining approximately 57% of the variability in these data. Our findings suggest that software engineering self-efficacy is a multidimensional construct of five theorized and correlated, yet distinct latent factors. We unpack the limitations and delimitations of the research while exploring undergraduate computing students' software engineering self-efficacy using necessary domain-specific measurements.
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All authors Albert D. Ritzhaupt, Jhonathan Sora-Cardenas, Priya Ganapathy Prasad, Kai A. Hackney, Neha Rani, Pedro Guillermo Feijóo-García