Computer Vision
Tracking the Unseen: An Occlusion-Robust Framework for Target Tracking Under Full and Long-Term Occlusion
Mais Mohammed, Sharifa Mohammed, Hanan Awadh et al. (+3 more)
Real-time multi-object tracking systems remain highly vulnerable to full and long-term occlusion, where targets temporarily or completely disappear from the camera's field of view. Conventional trackers may terminate trajectories prematurely, resulting in identity loss and reduced situational awareness in applications such as defense and surveillance. This work proposes an occlusion-robust target tracking framework that maintains target identity and trajectory continuity through the integration of YOLOv11n object detection, Kalman Filter motion prediction, and occlusion-aware appearance-based re-identification. The framework consists of three stages: object detection, position estimation during occlusion, and identity recovery after target reappearance. Six Re-Identification (Re-ID) architectures were evaluated within the same tracking framework under identical conditions, with the Occlusion-Aware Mask Network (OAMN) achieving the best overall performance and therefore selected for the final pipeline. The framework was benchmarked against OccluTrack on the public OVIS
dataset, achieving relative improvements of 18.1 percent in Multiple Object Tracking Accuracy (MOTA) and 25.1 percent in Identity F1 Score (IDF1), while reducing identity switches by 12.8 percent. On a custom military
dataset simulating surveillance and battlefield-like environments with long-term occlusion, the framework achieved a MOTA of 0.734 and an IDF1 of 0.729, corresponding to relative improvements of 14.2 percent and 5.8 percent over OccluTrack. The system demonstrated strong tracking continuity, robust identity preservation, and reliable trajectory estimation under challenging occlusion conditions, highlighting its effectiveness for defense-related surveillance applications requiring continuous target tracking during visibility loss.
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All authors Mais Mohammed, Sharifa Mohammed, Hanan Awadh, Haneen Bamaas, Raghad Bawazeer, Elham Alghamdi
Computer Vision
SlotDiT: Object-Centric Representations for Diffusion Transformers
Gjergj Plepi and Sven Behnke
Text-conditioned latent
diffusion models perform strongly in
video generation and are promising backbones for robotic applications. However, existing approaches rely on pixel-level or VAE-based latent representations that lack explicit semantic structure, leaving the impact of the representation space largely unexplored. Slot-based object-centric representations offer a structured alternative by decomposing scenes into object-level latents, or slots. While they have shown success in dynamics modeling and planning, they have not yet been explored for
diffusion-based generative modeling. We introduce SlotDiT, a text-guided
Diffusion Transformer (DiT) that operates in a slot-based latent space. Given a reference image and a
language instruction, SlotDiT decomposes the scene into object-centric slots representing individual entities. Conditioned on the instruction and observed scene context, the model autoregressively denoises future slot trajectories to predict scene dynamics. To systematically investigate latent-space design for
diffusion transformers, we compare slot-based representations against VAE-based and semantics-aligned alternatives within a unified DiT framework. Our experiments show that using slots as DiT latents yields competitive
video generation quality while consistently improving task-completion rates across four robotic
datasets. Furthermore, their compact representation provides a computationally efficient alternative to VAE-based and semantics-aligned latent spaces. Overall, our results demonstrate that object-centric structure is a powerful inductive bias for
diffusion-based generative modeling in robotic environments. The project page is available at https://slot-dit.github.io/.
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Computer Vision
SSC-Priors: Exploring Semantic and Visibility Priors to Boost Lidar Semantic Scene Completion
Tetiana Martyniuk, Jonathan Seele, Alexandre Boulch et al. (+3 more)
This paper investigates easy strategies to boost the performance of existing networks for
lidar semantic scene completion (SSC) without requiring complex architectural redesigns. The fact is that, over the last years, SSC methods have mostly pursued architectural innovations, making the models heavier and more complex, e.g., by jointly training a point cloud semantic
segmentation branch. In this work, we take a step back and explore two priors used as simple ingredients (possibly noisy) to improve existing approaches: semantic pseudo-labels and sensor visibility information. Concretely, we provide both kinds of information directly as additional inputs to a given SSC network, requiring only a minimal adaptation of the original architecture. We first demonstrate that endowing input point clouds with semantic pseudo-labels from off-the-shelf segmenters significantly improves the performance of existing SSC models. In fact, by evaluating these models against an oracle, we establish that high-quality semantic priors are a primary driver of semantic gains (mIoU), and that the SSC model can be trained just once with ground-truth semantics and then exploited without retraining using any segmenter. Furthermore, we equip the input
lidar point cloud with visibility information that distinguishes between empty spaces (between the
lidar and a scanned point) and unknown spaces (outside of lines of sight), providing a secondary performance boost across the tested architectures. We study the design space of data for representing visibility information and bound the remaining headroom with a ground-truth oracle on the free-space labels. On SemanticKITTI, these enhancements make older models competitive with state-of-the-art systems across four architectures, in one case even outperforming them. On the SSCBench-nuScenes
benchmark, both priors also transfer with the sparser 32-beam sensor.
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All authors Tetiana Martyniuk, Jonathan Seele, Alexandre Boulch, Gilles Puy, Renaud Marlet, Raoul de Charette
Data Structures and Algorithms
Determinant maximization subject to a partition matroid constraint via stable distributions
Yihang Sun and Jan Vondrak
Given vectors $v_i \in {\mathbb R}^d$, we consider the problem of choosing a set $I$ independent in a partition matroid in order to maximize the determinant $\det (\sum_{i \in I} v_i v_i^T)$. Our main result is a
polynomial-time approximation
algorithm that finds a solution of value $det ( \sum_{i \in I} v_{i} v_{i}^T) \geq e^{-O(d)} OPT$, where $OPT = \max_{I^*} det ( \sum_{i \in I^*} v_{i} v_{i}^T)$. For partition matroids of rank $m \leq d$, we give a similar result for approximating the $m$-dimensional volume spanned by the chosen vectors, within a factor of $e^{O(m)}$. This matches earlier known
algorithms that estimate the optimal value but do not find the corresponding solution, up to a constant in the exponent. Similar to these estimation
algorithms, our
algorithm is based on the saddle-point relaxation proposed by Nikolov and Singh. A new ingredient is a randomized transformation based on $1/2$-stable distributions, which converts the saddle-point relaxation into a more convenient multilinear relaxation.
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Game Theory
On testing the incentive compatibility of single-parameter allocation mechanisms
Jason Milionis and William Pires
This paper is the first work at the intersection of game theory and property testing, giving
algorithms and lower bounds for efficiently testing whether an allocation mechanism is incentive compatible (IC). We propose distinguishing whether a mechanism is $\epsilon$-far from being IC, i.e., when it observes many monotonicity "violations." Conceptually, inspired by the literature on Boolean function monotonicity testing, we construct a tester for discrete single-parameter allocation rules. Technically, our work is the first to consider monotonicity testing of vector-valued functions on the hypergrid. We give a $\tilde{O}(n/\epsilon)$-query
algorithm to test whether a function (representing n-player allocation mechanisms) is coordinate-wise monotone versus $\epsilon$-far from it. We also show a matching lower bound: the class of coordinate-wise monotone vector-valued functions on a Boolean hypercube or hypergrid requires $\tilde{\Omega}(n/\epsilon)$ queries to test whether it is $\epsilon$-far from monotonicity, and this holds even if the tester is two-sided and allowed to make adaptive queries. Finally, we extend our upper bound to and give a tester of the same query complexity for pricing functions of allocation mechanisms. This requires overcoming the technical challenge that the path in function space to the closest IC mechanism may involve interdependent changes to both the price and the allocation rule.
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Robotics
Optimized Wrench Polytope Analysis for Real-Time Stability Control of Legged Robots in Complex Multi-Contact Configurations
Friedrich Graaf, Elias Birkefeld, Christian Eichmann et al. (+5 more)
Legged
robots offer a variety of automation applications in real-world scenarios. But areas that are difficult to traverse, like slopes, caves, or scaffolding, still pose a great challenge for traversal. To tackle this problem, we propose an optimized
algorithm for evaluating the full actuatable wrench polytope for arbitrary contact scenarios. With our improved analysis
algorithm, the torques for each joint of the
robot can be calculated within a control frequency of 49 Hz. The achieved speedup allows for deployment within a regular control loop for actuating
robot poses for different contact scenarios. We evaluated our stability controller extensively in simulation scenarios and validated its applicability by deploying it on actual walking
robot hardware. The proposed controller achieved stability in very complex scenarios that are currently not achievable by any other controller.
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All authors Friedrich Graaf, Elias Birkefeld, Christian Eichmann, Elias Hofele, Tristan Schnell, Georg Heppner, Arne Roennau, Rüdiger Dillmann
Robotics
Residual Fault Adaptation for Dexterous In-Hand Manipulation Under Runtime Joint Faults
Linan Deng, Xing Liu, Lin Hong et al. (+4 more)
Dexterous in-hand manipulation requires coordinated control of multiple actuated joints, and a runtime joint fault can abruptly disrupt the contact configuration required for successful manipulation. In this work, we propose residual fault adaptation (RFA), a teacher-anchored framework for compensating for hidden command-channel faults. RFA retains a frozen healthy teacher to provide nominal behavior and trains a recurrent residual policy to infer corrective actions from proprioceptive and command-response history. During training, fault-injection domain randomization (FIDR) varies the fault mode, affected joint, severity, and onset time, while adaptive sampling increases the frequency of fault modes associated with lower recent performance. A frozen Direct FIDR policy provides a distributional reference only on fault-active training samples and is absent from deployment. The deployed controller receives neither fault labels nor controller-switching signals. Simulation experiments on the dexterous hand indicate that RFA can improve manipulation performance relative to the healthy policy under a fixed mixed-fault protocol. Real-
robot experiments with software-injected faults further demonstrate zero-shot deployment of the learned adaptation policy.
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All authors Linan Deng, Xing Liu, Lin Hong, Feng Hua, Guijun Ma, Zuogong Yue, Fumin Zhang
Data Structures and Algorithms
Pseudometric-Weighted Correlation Clustering via Spectral Preclustering
Chenglin Fan, Dahoon Lee and Euiwoong Lee
We study pseudometric-weighted correlation clustering, in which pairs have arbitrary positive or negative labels and nonnegative disagreement weights satisfying the triangle inequality. For every fixed $\varepsilon>0$, we give a randomized
polynomial-time $(2+\varepsilon)$-approximation, improving the previous factor of $10/3$. The
algorithm constructs an explicit feasible solution to the cluster LP with
polynomial support and value at most $(1+\varepsilon)\mathrm{OPT}$, where $\mathrm{OPT}$ is the minimum integral clustering cost. Our main contribution is a weighted preclustering construction that witnesses a near-optimal clustering while bounding the total weight of admissible edges by $\operatorname{poly}(1/\varepsilon)\mathrm{OPT}$. We obtain admissibility certificates from random walks, where the factor-two Ptolemy's inequality for pseudometrics reduces the required length of walks from $O(\log n)$ to a constant. We then bound the rounding error separately at each vertex and use the triangle inequality to control its weighted cost. Sampling and coverage corrections produce an exactly feasible solution, and the factor-two rounding completes the approximation
algorithm.
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Cryptography and Security
SCHERI: Provably Secure Speculation Under the Constant-Time Policy for CHERI (Extended Version)
Shixin Song, Davide Davoli, Elias Storme et al. (+4 more)
Capability-based architectures such as CHERI provide strong support for the architectural isolation of software components. To additionally protect against microarchitectural leakage, software can be written in a constant-time fashion. Modern processors, however, rely heavily on speculative execution, which can invalidate the constant-time guarantees and leak isolated secrets transiently. In this work, we show that providing secure speculation for CHERI is non-trivial, and that existing proposals fail to preserve the confidentiality guarantees. We develop a formal framework for
reasoning jointly about capability safety, speculative execution, and information-flow security, and use it to demonstrate potential leaks. We then present SCHERI, a new processor design within this framework, and formally prove that it provides end-to-end secure speculation guarantees for the constant-time policy. Our results provide formal foundations and practical guidance for building future capability-based processors, which are resilient to Spectre attacks for constant-time programs.
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All authors Shixin Song, Davide Davoli, Elias Storme, Marton Bognar, Dominique Devriese, Frank Piessens, Tamara Rezk
Computation and Language
Enhancing Accessibility of Medical Texts through Large Language Model-Driven Plain Language Adaptation
Ting-Wei Chang, Hen-Hsen Huang and Hsin-Hsi Chen
This paper addresses the challenge of making complex healthcare information more accessible through automated Plain
Language Adaptation (PLA). PLA aims to simplify technical medical
language, bridging a critical gap between the complexity of healthcare texts and patients' reading comprehension. Recent advances in Large
Language Models (LLMs), such as GPT and BART, have opened new possibilities for PLA, especially in zero-shot and few-shot learning contexts where task-specific data is limited. In this work, we leverage the capabilities of LLMs such as GPT-4o-mini, Gemini-1.5-pro, and LLaMA for text simplification. Additionally, we incorporate Mixture-of-
Agents (MoA) techniques to enhance adaptability and robustness in PLA tasks. Key contributions include a comparative analysis of prompting strategies, finetuning with QLoRA on different LLMs, and the integration of MoA technique. Our findings demonstrate the effectiveness of LLM-driven PLA, showcasing its potential in making healthcare information more comprehensible while preserving essential content.
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Cryptography and Security
Closing the Loop: Bidirectional Fully Encrypted Protocols
Baigang Chen and Nicholas Hopper
Fully encrypted protocols (FEPs) provide encrypted channels that make all protocol-generated bytes computationally indistinguishable from uniform random strings. Several previous works have explored security definitions and constructions of unidirectional FEPs: protocols in which one party acts only as a sender, and the other acts only as a receiver. However, most applications require two-way information exchange, and a network adversary can observe communication in both directions and their shared lifetime. Because the semantics of bidirectional channels involve more complex shared state, it is possible that the “naïve” composition of two unidirectional channels can result in a two-way protocol that can be detected based on dependencies between the two directions, such as traffic imbalance, channel closure, failures, or connection tear-down. To address this issue, we introduce new formal security definitions for bidirectional FEPs that capture exact shaping, delivery, protocol-state integrity, private half-close, and cross-direction isolation, while revealing a public “sending schedule” and “closing epoch” that may be randomized. We show that the trivial composition fails to meet these definitions, leading to practical detection attacks. We then construct provably secure bidirectional FEPs (BiFEPs) for both the datastream and datagram settings. For datastream, we combine two direction-separated FEPs with a “wrapper” layer that prevents detection based on the mismatch between uni- and bi-directional connection states. For datagram, we add encrypted DATA/FIN/ACK with replay protection and loss-tolerant close. We validate the design through a Rust implementation and show that none of the surveyed deployed protocols provides the full set of BiFEP security properties.
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Game Theory
The price of anarchy in the max-distance network creation game is not constant
Christoph Schlegel
At edge price $\alpha=1$, we construct an infinite family of pure Nash
equilibria of the unilateral max-distance network creation game with $\PoA\ge2^{\sqrt{\log_2 n}-O(\log\log n)}$. Together with the known upper bound, this gives $2^{\Theta(\sqrt{\log n})}$ along the constructed sequence of population sizes. We subdivide every edge of the bipartite double cover of a distance-uniform graph with large diameter constructed by Lavrov, Loh and Messegué, and let each subdivision vertex buy its two incident edges. A distance calculation rules out every profitable unilateral deviation. The
equilibria are not strict. We also give a short proof that the price of anarchy is constant for every polynomially vanishing edge price.
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Software Engineering
Coding Agents Have Converged: Why the SWE-bench Leaderboard Can No Longer Order Its Top Entries, and What to Measure Instead
Fengshuo Liu, Ying Liu, Ruize Sun et al. (+2 more)
Small differences on coding-
agent leaderboards are often read as an ordering of systems. We audit whether the published verdicts support this reading, using 254 SWE-bench submissions across four splits without running models. On Verified, the leading two entries each resolve 396 of 500 instances. The top ten share 285 successes and 51 failures, leaving 164 instances that distinguish their outcomes. Frontier solution sets have median nesting 0.935 against a score-implied baseline of 0.774, indicating strongly shared successes. Scores also depend on the evaluated model-scaffold pair: observed within-model scaffold ranges reach 29.8 percentage points, compared with the 8.8-point spread of the top thirty. Six of nine
cell-mean interaction tests remain significant after Holm correction, although this observational design does not identify causal scaffold effects. Exact paired McNemar tests separate none of the 29 adjacent Verified top-thirty pairs at alpha=0.05, while the larger Test split separates 14 of 23. A stated leader-based rule yields three descriptive tiers, or two after Holm correction; non-rejection does not establish equivalence. We release the partition and a five-step audit protocol that profiles shared outcomes, tests paired differences, reports grouping sensitivity, and estimates the instance budget needed for resolution. The results motivate reporting comparison-set-specific resolution and model-scaffold provenance instead of interpreting small aggregate gaps as established rank differences.
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All authors Fengshuo Liu, Ying Liu, Ruize Sun, Lie Luo, Siyuan Guo
Logic in Computer Science
Predictable Modelling and Analysis of Software-defined Vehicle Implementations
Pavlo Tokariev, Yosri Ayari and Julien Deantoni
Software-Defined Vehicles (SDVs) rely on middleware-based communication and hardware abstraction mechanisms that introduce temporal uncertainty affecting end-to-end timing guarantees. Previous work proposed probabilistic architectural models for early timing analysis, but the representativeness of these abstractions with respect to SDV implementations remained unclear. This paper presents an experimental framework combining probabilistic design-time timing analysis with a monitored Kuksa-based implementation. The same reaction-time analysis is applied both to simulation and implementation traces, enabling direct comparison between predicted and observed timing behaviour. We additionally introduce a comparison methodology separating conservative coverage from predictive fidelity of timing distributions. The results show that the proposed abstractions remain representative under different middleware load conditions while preserving conservative timing guarantees, supporting incremental timing verification approaches for SDV platforms.
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Artificial Intelligence
FlashVector: Agent for Hierarchical Model Serving Stack Optimization
Qi Wu, Lohan Lemire, Kai Meng et al. (+8 more)
Model serving is one of the largest cost drivers in production recommender systems. Maximizing its throughput requires navigating a deeply layered hierarchy: GPU kernels, the ML framework computation graph, the model server, and on-demand feature processing – each demanding specialized domain expertise. Such cross-layer expertise is inherently difficult to acquire, and does not scale with a workload that continuously grows and evolves, leaving significant cost efficiency gains unrealized. While recent AI
agents have demonstrated human expert level efficiency in standalone GPU kernel optimization, automated tuning and optimization for the rest of the serving stack remain largely unexplored. We present FlashVector, an agentic system that optimizes performance across all layers of the model serving stack. The key contribution is an extensible framework to generalize the single kernel optimization
agent paradigm to heterogeneous technical stacks, and to deliver performance improvements holistically. After deployment in Unity's Vector advertising platform, FlashVector achieved up to 2x throughput increase and up to 1.98x latency speedup on model server, and up to 1.6x throughput increase on feature store. These optimizations were discovered not only at the GPU kernel and computation graph levels, but also across the other components of the model serving stack, such as the model server (NVIDIA Triton's C++ codebase) and the on-demand feature transformation service (Python codebase), demonstrating the extensibility of the framework to more complex system architectures.
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All authors Qi Wu, Lohan Lemire, Kai Meng, Zhongmou Cai, Raphael Bargues, Petr Zhitnikov, Zeyuan Cao, Yao Wang, Shujun Bian, Wei Chen, Sean Sheng
Logic in Computer Science
Refining Timing Uncertainty from Logical Time Specification to Operation
Pavlo Tokariev and Julien Deantoni
Real-time and cyber-physical systems are developed through successive refinements from abstract requirements to platform deployments. While timing knowledge evolves throughout this process, existing
stochastic real-time formalisms typically require uncertainty to be embedded from the outset or necessitate model reconstruction when new timing information becomes available, hindering iterative timing engineering. This paper presents a refinement-oriented timing specification framework built upon the Clock Constraint Specification
Language (CCSL). It supports the progressive introduction of timing knowledge across three levels: logical timing relations, quantitative real-time constraints, and
stochastic timing models. Instead of altering behavioural semantics,
stochastic information is attached directly to timing quantities as a refinement. This enables implementation measurements and operational observations to be incorporated incrementally within a unified declarative framework. We implement our approach in an OCaml-based simulation tool and evaluate it on a simplified but representative Software-Defined Vehicle use case.
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Computer Vision
PanoGS-SLAM: Panoramic 3D Gaussian Splatting SLAM
Yongqi Mao, Hao Shi, Yufan Zhang et al. (+3 more)
Real-time dense SLAM is a core capability for robotics applications that require robust localization and high- quality mapping in dynamic or fast-changing environments. Recent 3D Gaussian Splatting (3DGS)-based SLAM methods have shown promising performance, but most are designed for narrow-FoV pinhole cameras, where limited angular coverage weakens pose observability and often leads to unstable photo- metric optimization under rapid motion and large viewpoint changes. We present PanoGS-SLAM, the first panoramic dense SLAM system built on 3D Gaussian Splatting. Our method per- forms differentiable rendering and pose optimization directly in the spherical domain, enabling omnidirectional photometric constraints for more stable tracking. To improve
geometric consistency and robustness, we introduce (1) a sphere-consistent photometric loss that compensates for the area distortion of equirectangular projection, and (2) a depth-guided Gaussian initialization strategy that stabilizes incremental mapping in newly observed regions. Extensive experiments on both real and synthetic panoramic
benchmarks (PALVIO and SynPano) show that PanoGS-SLAM consistently outperforms
geometric and GS-based baselines in tracking accuracy and rendering quality, while achieving fast front-end convergence and real-time perfor- mance. In addition, controlled field-of-view experiments reveal a clear monotonic improvement in optimization conditioning and convergence stability as angular coverage increases, high- lighting the fundamental role of sensing geometry in shaping the optimization landscape of differentiable Gaussian-based SLAM. The source code will be made publicly available.
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All authors Yongqi Mao, Hao Shi, Yufan Zhang, Zhonghua Yi, Xiangfei Guo, Kaiwei Wang
Machine Learning
Bridging the Confidence Gap: Temperature Scaling for Calibrating Test-Time Prompt Tuning
Yuwei Liang, Jian Liang, Dapeng Hu et al. (+2 more)
Test-time
prompt tuning (TPT) enables adaptation on a single test instance, achieving improved accuracy but often sacrificing calibration performance. Most existing calibration methods introduce additional regularization terms to promote dispersion across text
embeddings and reduce calibration error, yet these methods often suffer from a drop in accuracy. Motivated by the well-calibrated nature of zero-shot predictions, we propose CoTS, a simple yet effective post-hoc calibration method that preserves accuracy. Specifically, CoTS applies temperature scaling to minimize the confidence gap between adapted and zero-shot predictions. To fully exploit the potential of multiple augmentations during adaptation, we introduce a weak-strong ensemble strategy that further boosts accuracy. We then apply CoTS to this ensemble, termed E-CoTS, to maintain its well-calibrated property. Extensive experiments on diverse
datasets and backbones show that our approaches effectively mitigate miscalibration without compromising primary accuracy. For instance, E-CoTS reduces the average expected calibration error of TPT from 11.90% to 5.38% on ImageNet variants, while even increasing accuracy from 60.74% to 62.95%. Moreover, when integrated with existing calibration methods, E-CoTS usually enhances both accuracy and calibration simultaneously.
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All authors Yuwei Liang, Jian Liang, Dapeng Hu, Yinuo Xu, Ran He
Robotics
Exact Fusion and Coordinated Exploration in Multi-Robot Active Inference
Peng Wu, Mohsen Imani, Amidu Kamara et al. (+3 more)
Robot teams that learn a common environment model exchange belief summaries and plan by the expected information gain of their actions. Under conjugate exponential-family beliefs the shared belief is counted once per
robot at two points: at fusion, the product of local posteriors counts the common prior $n$ times, and at planning, every
robot scores its plan under the same belief and the team converges on the same unknown. Both errors are removed by adding evidence increments to the shared natural parameter, realized increments at fusion and expected increments at planning. The expected increment of a committed teammate gives the next
robot its conditional gain; corrected gains sum to the joint gain, the redundancy removed equals the total correlation of the planned observation streams, and sequential commitment keeps the $1/2$ greedy guarantee. The expected increment is exact for Gaussian beliefs with fixed sampling paths and for Dirichlet beliefs under the novelty approximation of discrete active inference, whose team objective has a closed concave form within an explicit bound of the exact mutual information, and fails for finite hypothesis classes, where a short exact enumeration replaces it. Experiments on cooperative RockSample, foraging, and field monitoring show that fusion correction leaves exploration redundancy unchanged, anticipated evidence removes it, and sequential commitment recovers most of the value of centralized joint planning at cost linear in the team size.
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All authors Peng Wu, Mohsen Imani, Amidu Kamara, Md Tamzeed Islam, Seyede Fatemeh Ghoreishi, Mahdi Imani
Information Theory
Multi-sequences with large linear and error linear complexity from function fields
Xubin Hu, Shu Liu, Liming Ma et al. (+1 more)
The linear complexity and the error linear complexity of multi-sequences are measures for security in stream ciphers. In this manuscript, we present a general framework for constructing periodic multi-sequences via function fields. We prove that the constructed multi-sequences possess both large linear complexity and large error linear complexity. We apply this framework of constructing multi-sequences to various maximal function fields and we obtain many new multi-sequences with various lengths and dimensions. As a byproduct, we adopt this idea and produce many new quasi-cyclic algebraic
geometric codes as well.
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All authors Xubin Hu, Shu Liu, Liming Ma, Chaoping Xing
Machine Learning
OPEN-1B: A Fully Auditable Training Run
John Donaghy, Brian Wilcox, Oğuzhan Ersoy et al. (+6 more)
Open-source
language models have a reproducibility problem. Despite releasing weights, training data, and recipes, none of them are provably reproducible due to the non-associativity of floating-point arithmetic. Deep learning frameworks often offer a deterministic execution mode, allowing reproducible operations on the same machines. Unfortunately, this determinism does not carry across hardware such that a user can verify that a released checkpoint was actually produced using the declared training recipe. This leaves room for undisclosed data, injected biases, or
backdoors that existing techniques such as proof-of-learning or proof-of-training-data cannot rule out. We introduce a new tier of model transparency, fully auditable, in which every operation on every data sample during training is independently reproducible on heterogeneous commodity hardware with bitwise certainty. By imposing a definite order on the sources of training nondeterminism, GPU kernel reductions, data batch ordering across a data-parallel cluster, and inter/intra-node collective communication, we make it possible to replay any individual step of a large, distributed training run on a single piece of commodity hardware and check it against the published trajectory. Because replaying an entire run on one machine is infeasible, we support this with a collective verification scheme in which many independent auditors each certify individual steps, together covering the whole run. We release Open-1B, a model trained under this regime, together with its full pretraining
dataset, every intermediate checkpoint, the training codebase, and the audit harness needed to reproduce and verify any step of its training.
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All authors John Donaghy, Brian Wilcox, Oğuzhan Ersoy, Shikhar Rastogi, Adam St Arnaud, Alexey Titov, Jordan Greenberg, Ben Fielding, Harry Grieve
Machine Learning
Large Language Models Develop Belief State Geometry In-Context
Daniel Balcells, Andrew Jun Lee, Chirag Rastogi et al. (+3 more)
Large
language models (LLMs) trained on next-
token prediction exhibit remarkable in-context learning (ICL) abilities, yet the representations that support ICL remain poorly understood. We consider such representations in a controlled setting: prompting LLMs with data emitted from hidden Markov models (HMMs) and probing for the corresponding belief state – the posterior distribution over the HMM's hidden states given the observed
token history. Across six open-source LLMs prompted with data from 40 HMMs selected for non-trivial belief structure, we find that belief states are linearly decodable from residual stream activations, with peak probe $R^2$-values from 0.83-0.99 across HMM and LLM combinations, ranging from early to late layers. To establish functional relevance, we intervene directly on the probe-identified subspace via patching and steering, resulting in downstream prediction quality on the order of the untampered model, while controls degrade performance substantially. Together, these results provide representation-level evidence that ICL in open-source LLMs approximates optimal
Bayesian prediction over a context-inferred generative model. More broadly, our findings extend prior results linking input-distribution structure to activation geometry: from toy networks trained explicitly on HMM data to production-scale LLMs.
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All authors Daniel Balcells, Andrew Jun Lee, Chirag Rastogi, Paul M. Riechers, Adam Shai, Xavier Poncini
Robotics
XPACE: Joint World and Action Modeling from Heterogeneous Experience
Jiacheng Wei, Jerry Bai, Xiaoyu Yue et al. (+13 more)
A general-purpose
robot needs to draw on diverse experience, choose actions, and anticipate how those actions will change the world. We introduce XPACE, a unified
embodied world model that serves as both a world action model, jointly predicting executable
robot actions and future
video, and a world simulator, predicting the visual consequences of prescribed actions. Our key insight is that
video prediction can both connect heterogeneous experience to action learning and generate new experience for policy improvement. With a shared
video backbone between the policy and simulator, we use action-unlabeled
video to learn visual dynamics and action-labeled human and
robot demonstrations to jointly learn
video and action prediction. Building on this architecture, a coarse-to-fine training curriculum progressively emphasizes
robot control while retaining human experience, allowing the policy to learn behaviors beyond those covered by
robot demonstrations. Beyond learning from recorded experience, XPACE uses its simulator to create additional recovery supervision for the policy. Specifically, we adapt the simulator to its own generated context, synthesize deviation-recovery trajectories around expert demonstrations, and fine-tune the policy on filtered recovery examples. Experiments on XPENG's IRON
humanoid robot show that heterogeneous training improves robustness and enables transfer of human-observed skills to tasks absent from
robot demonstrations, while recovery data generated by the model's own simulator further improves real-world task completion. Together, these results demonstrate how joint world and action modeling connects learning from heterogeneous experience with simulation-driven policy self-improvement.
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All authors Jiacheng Wei, Jerry Bai, Xiaoyu Yue, Zidong Wang, Xiaoyang Guo, Cheng Chen, Fanqi Pu, Fan Wu, Zhixu Yue, Yizhuo Li, Feng Qiu, Bo Liu, Yuying Ge, Hui Zhou, Chenyi Chen, Yixiao Ge
Data Structures and Algorithms
PrecPack: An Efficient Open-Source Exact Solver for Bin Packing with Generalized Precedence Constraints
Sunkanghong Wang, Zhengzhong Ricky You, Roberto Baldacci et al. (+4 more)
Efficient resource use in packing and assembly-line applications requires decisions that jointly account for capacity and precedence constraints. The strongly NP-hard bin packing problem with generalized precedence constraints (BPP-GP) models such decisions by minimizing the number of ordered, capacitated bins required to pack weighted items, even when precedence requirements span multiple bins. Existing exact
algorithms primarily focus on classical special cases, whereas general BPP-GP has been addressed only via compact integer models and heuristics, with no efficient open-source exact solver. We present PrecPack, a unified exact solver that extends branch-bound-and-remember (BBR) to arbitrary nonnegative precedence weights and naturally specializes to the classical cases. Generalized states capture restrictions that remain active across future bins, which are addressed through branching, dominance, and conflict-aware lower bounds. Root column generation uses fixed-point arithmetic to compute numerically valid dual bounds for
pruning or to prove optimality. To support reuse and verification, we provide common programming and command-line interfaces, independent assignment checking, explicit termination statuses, and reproducible batch execution; the core procedures require no commercial software. In same-machine, single-threaded comparisons on classic assembly-line
benchmarks, more instances are proven optimal, and average computing times are substantially reduced relative to leading source-available BBR implementations. Further comparisons with published
benchmark results for bin packing with precedence constraints and BPP-GP also show that more instances were proved optimal and that reported average gaps were smaller on most
benchmark sets. PrecPack is released under the MIT License at https://github.com/Sunkanghong-Wang/PrecPack.
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All authors Sunkanghong Wang, Zhengzhong Ricky You, Roberto Baldacci, Baichuan Mo, Hu Qin, Lijun Wei, Zhou Xu
Human-Computer Interaction
Lexplorer: Navigating the Complexity of Legal Document Landscapes
Daniel Fürst, Titus Pünder, Maximilian T. Fischer et al. (+1 more)
As technological and social innovations create novel regulatory challenges, legal systems grow in complexity - increasing the need for interfaces that enable effective interactions with legal document collections. Through interviews with legal scholars (n=15), we find that supporting legal work requires going beyond
retrieval-centered legal-information-system paradigms. Hence, we propose Lexplorer, a flexible interface for exploring, navigating, and analyzing legal documents, based on a taxonomy capturing user intents. Distinguishing text and data views for one, few, and many documents, Lexplorer enables context-sensitive interactions with evolving collections of interconnected legal texts, facilitating Adaptive Meaning Construction in law. We evaluate Lexplorer with legal scholars (n=20) in the context of European Union law, validating our elicited requirements, intent taxonomy, and prototype design. Resulting from a close collaboration between visual-analytics researchers and legal scholars, our work also provides nuanced insights into the process required to design interactive systems for expert domains driven by implicit methodological knowledge.
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All authors Daniel Fürst, Titus Pünder, Maximilian T. Fischer, Corinna Coupette
Data Structures and Algorithms
The Classical Weisfeiler-Leman Algorithm Stabilizes in $O(n)$ Rounds
Simon Döring and Daniel Neuen
The classical Weisfeiler-Leman
algorithm (also known as the $2$-dimensional Weisfeiler-Leman
algorithm) is a simple combinatorial
algorithm that was originally designed as a heuristic for the graph isomorphism problem. However, it has also numerous connections to other areas such as algebraic graph theory, logics, proof complexity, combinatorial optimization and machine learning. We prove that the classical Weisfeiler-Leman
algorithm terminates after $5(n-1)$ iterations. This improves over the previous best upper bound of $O(n \log n)$ by Lichter, Ponomarenko and Schweitzer [LICS 2019], and asymptotically matches the known lower bound of $\Omega(n)$ by Fürer [ICALP 2001]. Additionally, building on our results for the $2$-dimensional case, we obtain an improved upper bound of $O(n^{k-1}/(k-2)! + n^{k-2})$ on the number of iterations performed by the $k$-dimensional Weisfeiler-Leman
algorithm, for every $k \geq 3$. Our arguments actually hold for a larger class of sequences of colorings of $k$-tuples; in this larger class our upper bounds are essentially tight for all $k \geq 3$.
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Computation and Language
Same Words, Different Actions: Paired Turn-Taking Evaluation under Rewritten Dialogue Contexts
Shuofeng Zhao, Hongwei Cai, Wenke Fan et al. (+9 more)
Real-time spoken dialogue systems must distinguish interruptions that require yielding the floor from backchannels that permit continued speaking. Existing
benchmarks typically score events independently and may therefore assign high scores to systems with fixed action preferences rather than context-sensitive decision policies. We introduce ECHO, a paired diagnostic
benchmark for Chinese turn-taking evaluation. ECHO pairs examples with the same overlap transcript but contrasting preceding multi-turn dialogue contexts, with one requiring Yield and the other Keep. It additionally includes off-talk examples for diagnosing unnecessary yielding. We introduce pair accuracy, which requires correct decisions on both members of a pair and assigns no credit to constant-action policies. Experiments on four
speech systems show that three exhibit a severe over-yielding bias: they correctly keep the floor on fewer than 13% of backchannels, resulting in near-zero pairwise success rates equal or less than 4%. While the remaining system remains comparatively balanced across contexts, these findings broadly demonstrate that interruption-only evaluation can severely overestimate practical turn-taking reliability.
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All authors Shuofeng Zhao, Hongwei Cai, Wenke Fan, Qingxiang Guo, Zhou Wang, Dawei Yang, Zhiyang Zhou, Yingxin Shang, Weixu Wang, Lin Yang, Shuran Zhou, Yang Song
Machine Learning
Hybrid Variational Quantum Circuits for Multivariate Regression and High-Dimensional Data Reconstruction
Koffi Ognandon Ayena, Frédéric Holweck, Serge Iovleff et al. (+1 more)
Variational
quantum circuits (VQCs) are parameterized
quantum circuits optimized classically. We propose a hybrid variational
quantum circuit (HVQC) extending VQCs with a classical affine post-measurement layer, enabling vector-valued regression without the linear overhead of independent scalar
circuits. Theoretically, we show that elementary one-and two-
qubit circuits can approximate quadratic functions and products via data re-uploading and
entanglement, providing the foundations of the full architecture. Experimentally, on two synthetic image reconstruction
datasets and the Friedman1
benchmark (40,568 test samples), our HVQC matches Gaussian Process Regression and outperforms XGBoost and Random Forest. An ablation study confirms that both
quantum and classical components are essential, and results highlight the central role of the feature map in hybrid
quantum-classical models.
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All authors Koffi Ognandon Ayena, Frédéric Holweck, Serge Iovleff, Amah S d'Almeida
Neural and Evolutionary Computing
A Spatiotemporal Extension of the Neuromorphic DBSCAN Implementation
Charles P. Rizzo and James S. Plank
DBSCAN is an
algorithm that denoises and clusters data. In prior work, we implemented the DBSCAN
algorithm neuromorphically, introducing two constructions termed “flat” and “systolic”. The “flat” construction prioritizes throughput, while the “systolic” construction trades time for space resulting in a smaller, more hardware-friendly architecture at the cost of throughput. In this work, we offer spatiotemporal extensions of these two constructions to better leverage the spatiotemporal nature of event sensor data. Moreover, as in our prior work, we discuss partial or segmented implementations that further leverage time for space when hardware resources are constrained. All network constructions are provided as open-source implementations.
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Computers and Society
Evaluating Ambient Clinical Scribes in India: The Need for Multilingual Real-World Clinical Conversation Data
Siddharth D Jaiswal, Krithi S, Ashish Makani et al. (+3 more)
Ambient clinical scribes (ACS) are being rapidly deployed at scale across Global South healthcare settings, aiming to reduce clinician documentation time, especially in overburdened environments like India. These ACS are primarily developed or distilled from models built and validated on Global North
speech,
languages and consultation styles. Indian clinical encounters are brief, triadic,
multilingual, code-mixed with low-resource
languages, and conducted in highly resource-constrained, noisy settings – increasing the likelihood of ASR and note-generation errors manyfold. We posit an urgent need to develop a standardized evaluation infrastructure to assess whether these systems are safe, reliable, and well-suited to the Indian healthcare setting. We substantiate our claims through a mixed-methods study – a systematic survey of publicly available patient-clinician conversational
datasets, a quantitative comparison of these
datasets against conversational and cultural markers drawn from the Indian clinical-communication literature, and semi-structured interviews with five organizations building and deploying ACS in India and Africa. Our survey shows that there are no publicly available, large-scale, real-world
benchmarks for ACS in India, with existing
datasets being overwhelmingly synthetic. We note that the available Global North
datasets diverge significantly from the expected conversational and cultural structures of Indian encounters. Finally, our interviews reveal that deploying organizations have each built proprietary, incomparable evaluation pipelines, creating a fragmented ecosystem with no independent and reliable basis for procurement. We call for the development of a publicly shared, real-world,
multilingual benchmark for ACS evaluation and outline the properties and policies such a
benchmark would require.
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All authors Siddharth D Jaiswal, Krithi S, Ashish Makani, Suvrankar Datta, Sunayana Sitaram, Mohit Jain