Data Structures and Algorithms
Subquadratic-Query Algorithms for Finding Another Maximum Matroid Intersection
Makoto Watanabe
Let $\mathcal{M}_1,\mathcal{M}_2$ be two matroids on a common ground set $V$, given by independence oracles, and let $S$ be a maximum-cardinality common independent set. We study the problem of deciding whether there exists another maximum common independent set $T\ne S$, and of outputting one when it exists. Writing $n=|V|$ and $r=|S|$, we give a Las Vegas
algorithm using $\tilde O(n\sqrt r)$ expected independence queries and a deterministic
algorithm using $\tilde O(nr^{2/3})$ queries. As an application, all maximum common independent sets can be enumerated with at most two another-solution calls between consecutive outputs and after the last output; if there are $L$ solutions, exactly $2L-1$ such calls are made. Neither
algorithm constructs the exchange graph. Instead, they perform Kahn-style source peeling through two deletion-only data structures. One side uses the heavy/light categorization of Blikstad, van den Brand, Mukhopadhyay, and Nanongkai. For the opposite side, where no transposed oracle is available, we give a collective randomized classifier and a deterministic capacity-saturation certificate.
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Robotics
WholeBodyWAM: Learning Whole-Body World Action Models with Scalable Motion Priors
Bowei Zhang, Qiyao Zhang, Shuanghao Bai et al. (+8 more)
Humanoid whole-body manipulation requires coordinated whole-body dynamics, yet large-scale trajectories from a target
robot are expensive to collect and difficult to scale. In contrast, whole-body motion from human and
humanoid sources is abundantly available, although such data cannot be directly used as embodiment-specific
robot actions. This work asks whether these scalable motion resources can instead provide a transferable predictive prior for
humanoid world-action modeling. We introduce WholeBodyWAM, a
humanoid world-action model that learns whole-body dynamics from large-scale heterogeneous motion before target-
robot training. We curate UniMotion-4K, a motion corpus spanning more than 4K hours from human
videos, native 3D motion
datasets, and heterogeneous
humanoid platforms, and canonicalize these diverse sources into a unified motion space. A
language-conditioned Motion Expert is then pretrained to predict future whole-body motion without target-
robot action supervision. During
robot post-training, the pretrained Motion Expert is integrated with
Video and Action Experts through asymmetric Mixture-of-
Transformers (MoT)
attention, enabling predictive scene dynamics and whole-body motion to jointly inform embodiment-specific action generation. Experiments show that WholeBodyWAM consistently benefits from increased motion-pretraining scale, improves future-motion prediction and downstream task performance, and transfers effectively to real-world
humanoid manipulation. Moreover, the pretrained motion prior substantially improves data efficiency under limited target-
robot demonstrations.
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All authors Bowei Zhang, Qiyao Zhang, Shuanghao Bai, Xinhua Wang, Meng Li, Yilei Wang, Leiwang Zhang, Jian Tang, Lu Zhou, Lei Sun, Zhengping Che
Cryptography and Security
ISIA-AF: Orchestrating Reproducible Attacks and Multi-Source Data Collection for OT Systems
Stefan Manfred Haratzmüller, Thomas Rosenstatter, Olaf Saßnick et al. (+2 more)
Operational Technology (OT) environments require realistic, reproducible security
datasets, yet existing approaches often lack automation, multi-source data capture, and sufficient documentation for reuse. This paper presents ISIA-AF, a modular attack framework for orchestrating reproducible attack execution and automated
dataset generation on industrial systems. The framework coordinates distributed attack clients, records network traffic and operational data, ultimately leading to a multi-source
dataset. We derive functional and non-functional requirements from prior work and stakeholder discussions, and realise the framework following a design science research approach. A case study on the ISIA testbed, comprising a real industrial system and a simulated process, demonstrates how the framework supports centralised control, low communication overhead, and flexible deployment across network segments. The result is a practical basis for generating extensible, multi-source OT security
datasets for intrusion detection research.
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All authors Stefan Manfred Haratzmüller, Thomas Rosenstatter, Olaf Saßnick, Dalibor Sain, Stefan Huber
Computation and Language
T-SANDHI: Tone Sandhi-aware Adaptive Network with Decoupled Hybrid Injection for Low-resource Taiwanese Hokkien Speech Recognition
Hung-Yang Sung, Chien-Chun Wang, Tien-Hong Lo et al. (+3 more)
In Taiwanese Hokkien automatic
speech recognition (ASR), prior studies often treat tone sandhi as a major challenge under the assumption that models fail to process implicit phonological variations. However, our experiments on Taiwanese Hokkien reveal that
speech foundation models actually handle tone sandhi variations effectively, and the real performance bottleneck stems from a localized confusion between these variations and retained citation tones. To address this, we propose T-SANDHI to explicitly decouple surface acoustics from underlying lexical intent on top of a frozen Whisper backbone. Using a lexicon-guided multi-task learning structure driven by text-derived pseudo labels, our lightweight hybrid injection module integrates independent citation and sandhi phonetic streams via dynamic gating. Extensive evaluation on the TAT-MOE corpus and two blind test sets demonstrates that this explicit disentanglement effectively resolves tonal mapping confusion, outperforming baselines with strict parameter efficiency.
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All authors Hung-Yang Sung, Chien-Chun Wang, Tien-Hong Lo, Yu-Sheng Tsao, Yung-Chang Hsu, Berlin Chen
Robotics
WAVE-Go: World-Model Navigation with Adaptive Execution for Wheel-Legged Robots
Mingyi Li, Ji Li, Zhihao Ouyang et al. (+2 more)
World models can anticipate the consequences of
navigation actions, but predicted action sequences may become invalid during execution, especially when wheel-legged
robots encounter dynamic obstacles or change locomotion modes. We propose WAVE-Go, an image-goal
navigation framework that separates world-action prediction from interruptible command execution. Its executor adaptively selects an action prefix and cancels pending commands when updated observations invalidate execution. A conditional-risk formulation specifies prefix selection under an estimated cumulative failure budget, while posture and locomotion-mode transitions require clearance, stability, and task-evidence checks. In the reported
navigation evaluation, WAVE-Go achieves 74.1% in-distribution success and 63.3% dynamic out-of-distribution success, exceeding the strongest baseline by 4.7 and 7.7 percentage points, respectively, while reducing collisions from 4.4 to 2.9 per 100 m. Compared with interruptible fixed four-command execution, WAVE-Go raises success by 4.0 percentage points while reducing replanning frequency by 51.2% and collision rate by 6.5%. Execution ablations also show that runtime interruption improves success, collision rate, and reaction latency at the cost of additional replanning. These results support adaptive, interruptible execution as a means of balancing
navigation performance and planning overhead. Code is available at https://github.com/vigorlee/wave-go.
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All authors Mingyi Li, Ji Li, Zhihao Ouyang, Yage He, Börje F. Karlsson
Robotics
OmniRisk: Omnidirectional Trajectory-Risk Learning for Agile Quadrotor Dynamic Avoidance
Yifan He, Yang Liu, Wenhao Zhao et al. (+8 more)
Agile quadrotor avoidance of fast-moving obstacles requires anticipating collisions and selecting feasible maneuvers within short reaction windows. Reliable predictive avoidance remains challenging because sparse range observations do not directly reveal obstacle motion, while online trajectory optimizers either scale poorly with obstacle count or remain efficient at the expense of reliability in dense, high-speed encounters. We present OmniRisk, an omnidirectional planning framework that learns trajectory-level risk offline for efficient onboard evasion. A fixed-dimensional tensor combines
LiDAR range panoramas, dynamic masks, and Cartesian surface velocities to represent geometry and motion jointly. We formulate an asymmetric risk field aligned with obstacle velocity that emphasizes approaching interactions and attenuates receding ones. Accumulating this risk along predicted relative trajectories provides dense supervision and discourages unnecessary hesitation after obstacles pass. A dual-branch circular convolutional network predicts terminal boundary states and dynamic risks for candidate primitives over an omnidirectional anchor
lattice in a single forward pass, followed by selection and closed-form reconstruction of the selected candidate primitive. This formulation removes online risk accumulation along trajectories and makes risk-inference cost independent of obstacle count. OmniRisk enables efficient onboard avoidance, with real-world flights demonstrating consecutive evasive maneuvers at relative encounter speeds up to 15 m/s without fine-tuning. Code is available at https://github.com/VANdexj/OmniRisk.
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All authors Yifan He, Yang Liu, Wenhao Zhao, Hai Lin, Deping Zhang, Mingze Ma, Xin Zhou, Fei Gao, Huan Yu, Zipeng Dai, Ziming Ding
Machine Learning
Transformation Laws in Neural Representations: Structure, Realisability, and Construction
Yuan Sun
How neural representations preserve the structure of input changes connects representation analysis with internal intervention. We study operable representational content through compatible actions of reference transformations on neural features. We characterise when a transformation descends through an encoder, and give a linear setting in which the defect is governed by the transformation's demand for discarded information, measured in the metric the representation induces. On a rectifier the failure to realise a transformation has two distinguishable sources — what the source region has already made unrecoverable, and what it costs to satisfy every region the transformation visits with one operator — and for a measured harmonic carrier the same question has a closed answer: a linear realisation exists exactly when the retained harmonic blocks are invariant under the action. Using colour as the in-depth instance, we find that hue orbits in frozen visual features concentrate 84–88% of their energy in the first two harmonics with rotation planes shared across shapes, that this organisation is substantially inherited from input and architecture and is reshaped by training and depth, and that the measured structure supports prediction, transport from new starting states, and composition — with global and local realisations differing sharply in which they achieve. Guided by the measurements, we construct a compact interface whose rotation action is fixed by the structure and never fitted: it reads hue zero-shot at 3.4$^\circ$ median error on unseen shapes. Theory, structural measurement, and construction together establish transformation laws as a concrete object connecting the understanding of neural representations to their design.
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Information Retrieval
Single-Token Expected-Value Scoring for Cold-Start Candidate Ranking
Qihang Wang, Jinwei Tan, Mengyuan Shi et al. (+8 more)
AI-assisted sourcing streamlines candidate review, reducing the administrative burden of manual screening for recruiters. However, deploying
language models as production rankers remains challenging. Zero-shot Large
Language Models (LLMs) may produce unstable, non-deterministic scores and rank less accurately, while conventional deep neural rankers require millions of logged interactions that a low-traffic, niche sourcing platform does not produce. What is available instead is a few hundred thousand ordinal relevance labels – small by ranker-training standards, but sufficient when a pretrained
language model already encodes the general world knowledge the task depends on. We present single-
token expected-value scoring, a ranking primitive that casts candidate-job relevance as an ordinal classification over the grade
tokens {1, ..., 5} and reads the relevance score as the expectation of the first-
token probability distribution. Because the score comes from a single decoding step rather than open-ended generation, it is a deterministic function of the model's logits, requires no output parsing, and serves at low latency. To learn the non-linear interdependencies of heterogeneous hiring criteria from this supervision alone, we fine-tune a Small
Language Model (SLM) with a hybrid ordinal regression loss combining a Mean Squared Error term, which preserves ordinal distance, with a categorical Cross-
Entropy term, which sharpens class boundaries. We evaluate along two dimensions – Jobseeker Relevance and Employer Relevance – using NDCG@10 and low relevance rate. Offline, our fine-tuned model outperforms a heuristic baseline and zero-shot LLMs. An end-to-end simulation shows the same direction at larger magnitude (+54.2% Jobseeker NDCG@10, -46.7% low relevance rate), and a live online experiment reduces employer low-relevance by 27.3% and raises employer keep rate by 7.07%.
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All authors Qihang Wang, Jinwei Tan, Mengyuan Shi, Mayank Sharma, Shuai Zhao, Fuxian Li, Ryan Yan, Alexander P. Kreuzer, Mohit Jain, Dheeraj Toshniwal, Manoj Seethamsetty
Human-Computer Interaction
Misgendering as Breakdown in Human-Machine Communication: How AI Companion Chatbot Users Experience and Repair Misgendering
Julia Liu, Qing Xiao, Leona Yinglang Pang et al. (+3 more)
In recent years, large
language model-based AI companion and role play chatbots have grown increasingly popular. People turn to these chatbots for emotional support and to engage in romantic and erotic role play. Although prior research suggests that digital role play can help people explore their gender and sexuality, LLM based technologies are also replete with gender and sexuality biases. In this study, we examine one way that AI chatbots can harm users: misgendering. In order to study chatbot misgendering we qualitatively analyzed 326 posts mentioning misgendering that were shared in AI companion or role play subreddits. We document how chatbot misgendering takes place and how, in response, users engage in ongoing work to curate their gender presentation to prevent and repair misgendering. We discuss how researchers and designers can mitigate chatbot misgendering and consider the implications of using AI chatbots for identity exploration.
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All authors Julia Liu, Qing Xiao, Leona Yinglang Pang, Haiyi Zhu, Hong Shen, Jordan Taylor
Artificial Intelligence
WFM: Wiki Foundation Model for Complex Agentic Reasoning
Junnan Dong, Linhao Luo, Senlei Zhang et al. (+9 more)
Real-world
agents fundamentally require persistent non-parametric knowledge for dynamic
reasoning, i.e., long-term
memory and
retrieval-augmented generation. While graphs have shown reliable advantages in providing structured evidence, the sparse graph representations naturally restrict machine readability and semantic density required for complex agentic workflows. Driven by this limitation, the entire industry is witnessing a paradigm shift from traditional sparse graphs to LLM Wiki, an
agent-native knowledge representation that couples dense document contexts with markdown files containing multi-layered topological linkages. However, parameterizing such rich semantics is challenging to encode dense textual contexts using traditional sparse graph
embeddings. Moreover, learning LLM Wiki with existing graph encoders could overwhelm distributed system overheads that hinder deployment in large-scale commercial scenarios. To this end, we propose a novel paradigm Wiki Foundation Model, i.e., WFM, tailored for scalable,
agent-native representation and
retrieval. Specifically, (i) we formalize a Wiki Graph schema that seamlessly bridges fine-grained structures with dense contexts, maintaining explicit
topologies alongside continuous semantics; (ii) A query-conditioned attentive aggregation is tailored for rich wiki message passing and explicit
attention variance regularization; (iii) We engineer an infrastructural NCCL boundary exchange protocol that hoists static partition indices and leverages fixed-shape GPU-to-GPU collectives, bypassing CPU serialization and
memory copy overheads. Extensive evaluations across five long-term
agent memory and multi-hop
reasoning benchmarks demonstrate the remarkable performance of WFM, while achieving a 10.5 times training acceleration on distributed clusters.
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All authors Junnan Dong, Linhao Luo, Senlei Zhang, Gong Chen, Taian Guo, Yifei Yu, Rong Tao, Tao Guo, Qian-Wen Zhang, Siyu An, Ruizhi Qiao, Xing Sun
Human-Computer Interaction
Replication Studies: Not Just a Copy
Yiheng Liang, Kim Marriott and Helen C. Purchase
Replication studies revisit previous experimental findings for multiple purposes, including assessing reliability, exploring generalisability, and evaluating research methods. Such studies entail design decisions that a single replication label cannot fully capture, making their designs difficult to describe and compare. We present REPVIS2, a validated design space that describes how a replication study relates to a reference study across eight practical dimensions, each coded as identical, similar, or different. We refined our initial design space, REPVIS1, by applying it to a corpus of replication studies and validated REPVIS2 against a separate corpus. We then characterised 86 replication studies from 51 papers in visualisation through paired reading of the replication and reference reports. Studies often retained the task while changing the procedure, interface, environment, participant population, evidence, or analysis. Additions beyond the replication core were also common. REPVIS2 makes replication design explicit for characterisation, reporting, and planning. An interactive visualisation and supplementary materials are available at https://replication-study.github.io/.
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Distributed Computing
Zero-I/O Fault Recovery for Sharded Deep Learning via Dynamic Framework Dependency Rebinding
Genlang Chen and Junyi Zhu
Distributed model training at scale is frequently interrupted by transient network failures, conventionally forcing cluster managers to abort all processes and roll back to the latest checkpoint. While periodic checkpointing provides durability, frequent snapshotting introduces severe storage backpressure: our measurements on a 1.216B-parameter decoder reveal that per-update asynchronous checkpointing incurs up to a +656.7% latency overhead, consumes 32.5 GiB of host
memory, and generates 3.39 TB/hour of storage traffic. To eliminate this overhead, we present AccelPact, a parallel runtime enabling zero-I/O in-
memory fault recovery for sharded distributed training. When communication fails at a committed optimizer step, device
memory remains quiescent and uncorrupted, yet continuation fails because frameworks like PyTorch FSDP cache internal communication handles across module wrappers and parameter hierarchies. AccelPact resolves this dependency invalidation via a non-invasive reference-rebinding mechanism coordinated by an out-of-band Gloo consensus protocol. On 16 NVIDIA RTX 5880 GPUs training full-parameter Mistral-7B, AccelPact eliminates checkpoint replay, yielding a 1.197x whole-run goodput improvement over cold restart and 1.194x over NVRx checkpoint restoration at checkpoint age 5, rising to 1.698x at age 18. Across ten successive fault injections, all 16 ranks maintain bit-identical parameter state with zero numerical drift. Across 4-to-16 GPU cluster
topologies, reference rebinding executes in constant time (0.493-0.518 ms). Operating directly on native communicator instances, AccelPact requires zero application-code modifications and avoids compiler graph breaks under torch.compile, providing an efficient foundation for resilient deep learning.
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Numerical Analysis
A Lattice Boltzmann Method with Adaptive Relaxation Parameter and Lax–Friedrichs-Type Equilibrium for Hyperbolic Systems
Yifan Zhang, Ping Lin, Jin Zhao et al. (+1 more)
In this paper, a
lattice Boltzmann method with an adaptive relaxation parameter and a Lax–Friedrichs-type
equilibrium is proposed for
hyperbolic systems with source terms. The
equilibrium distribution recovers the conservative variables and physical fluxes while incorporating dissipation determined by characteristic-speed bounds. To balance accuracy and robustness, the relaxation parameter is selected from a local smoothness indicator based on characteristic projections. In smooth regions, the parameter approaches the low-dissipation limit, retaining second-order accuracy; near discontinuities, it is automatically reduced to introduce localized dissipation and suppress nonphysical oscillations. The stabilization acts directly through the local collision step and preserves the standard collide-and-stream structure, without a posteriori recomputation or interface-based limiting. Maxwell iteration establishes second-order consistency in smooth regions, and a weighted $L^2$-stability estimate is proved for linear
hyperbolic systems with periodic boundary conditions under a standard CFL condition. Numerical experiments for scalar advection, Euler, shallow-water, and reactive Euler equations demonstrate the expected accuracy for smooth solutions and robust resolution of challenging one- and two-dimensional discontinuous problems, including wet–dry fronts and reactive discontinuities. A large-scale simulation of a 2D cellular detonation further demonstrates the capability of the method to resolve long-time multidimensional shock–reaction interactions.
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All authors Yifan Zhang, Ping Lin, Jin Zhao, Weifeng Zhao
Machine Learning
MoRE: Mixture of Reused Experts
Eric S. Qiu, Utku Umur Acikalin, Justin Lovelace et al. (+4 more)
Mixture-of-Experts (MoE) architectures decouple model capacity from computational cost, yet incur high
memory footprints as parameters grow linearly with the number of experts. Recurrent
Transformers achieve parameter efficiency by reusing layer weights, but typically lack the capacity for competitive
language modeling. We propose Mixture of Reused Experts (MoRE), a hybrid that shares expert pools across groups of adjacent layers. Each layer retains its own router but selects from a larger shared pool, expanding the diversity of routing combinations without additional parameters. To enable shared experts to distinguish between layers, we introduce lightweight learnable depth
embeddings that condition each layer's input before routing. Experiments across three model scales (114M-1.15B parameters) show that MoRE consistently achieves lower perplexity and stronger downstream performance than standard MoEs and state-of-the-art weight-sharing architectures at matched compute and parameter budgets, with only minimal modifications to existing MoE implementations.
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All authors Eric S. Qiu, Utku Umur Acikalin, Justin Lovelace, Christian Belardi, Arjun B. Mulchandani, Carla P. Gomes, Kilian Q. Weinberger
Robotics
TacBPM: A Tactile-conditioned Behavior Prior Model for Dexterous Reorientation
Jie Yin, Wanli Xing, Zeyuan Zhao et al. (+3 more)
Dexterous in-hand manipulation requires policies that coordinate high-DoF hand joints through intermittent, contact-rich interaction. Beyond target-orientation tracking, such policies must discover finger gaits that preserve object stability while adapting to geometry, anisotropy, pose, contact, and sensing changes. We propose \method, a tactile-conditioned behavior prior model for dexterous reorientation. \method distills multi-scale sphere specialists into a latent controller and lets downstream policies reuse the fixed tactile prior through residual latent actions, reducing renewed exploration from raw joint commands. The prior conditions on tactile-proprioceptive history so latent behavior reflects the current hand-object interaction. We evaluate arbitrary-pose transfer across anisotropic objects, commanded-axis rotation, and an arm-hand Grasp-to-AnyPose task in which the
robot must grasp, lift, transport, and reach goal poses for novel tool geometries and generalized placements. Extensive experiments demonstrate that the proposed method accelerates training and enables stable policies where matched raw-action PPO remains near failure, with successful sim-to-real transfer in in-hand and arm-hand tasks.
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All authors Jie Yin, Wanli Xing, Zeyuan Zhao, Xuezhou Zhu, Zhijie Deng, Kaifeng Zhang
Machine Learning · Cross-listed from eess.SP
Beyond Direct Sensing: Harnessing Indirect Observations from Third-Party Sensors in Vehicle Tracking
Gaofeng Dong, Vamsi Eyunni, Pragya Sharma et al. (+2 more)
Vehicle tracking is fundamental to applications ranging from urban mobility and public safety to security and defense. Conventional tracking relies on direct access to sensors that provide strong observations such as vehicle identity and location. In practice, however, factors such as ownership,
privacy, cost, and operational constraints may limit directly accessible sensors, leaving sparse observations and long tracking gaps. Meanwhile, many additional third-party sensing assets may be present across the environment but remain inaccessible at the raw-data level, preventing their direct integration into the tracking system. In this work, we investigate whether weak, indirect observations with uncertain spatial and temporal cues can complement sparse direct sensing for vehicle tracking. Specifically, we propose GrayTrack, which fuses weak anonymous events with sparse direct observations using a road-constrained particle filter. We build a CARLA-Mininet-WiFi pipeline to evaluate the system under controlled conditions, generating direct observations from accessible cameras and indirect observations from third-party cameras. Our learning-based detector achieves an F1 score of 0.989 for anonymous vehicle passages. Further, incorporating indirect third-party observations reduces trajectory RMSE by 60.1% and catastrophic track loss from 35.8% to 0.3%. These results demonstrate that GrayTrack can effectively exploit weak indirect observations to extend tracking capabilities.
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All authors Gaofeng Dong, Vamsi Eyunni, Pragya Sharma, Kang Yang, Mani Srivastava
Robotics
Approximating High Dimensional Self-Motion Manifolds via Deep Generative Models
Haitao Gao, Yang Song and Liao Wu
Self-motion
manifold (SMM) characterizes the
geometric structure of the infinite inverse kinematic solutions set of a redundant manipulator at a fixed end-effector pose, and its efficient recovery underpins feasible and global optimal motion planning. Existing methods such as null-space continuation and learning-based methods are formulated around the assumption that an SMM is a curve, and do not extend to higher redundancy orders. We instead adopt a probabilistic view: SMMs are the support of the conditional posterior over configurations given a target pose, so that recovering it reduces to sampling from a learned distribution and separating its disjoint components by clustering. The formulation is independent of the
manifold dimension and requires no architectural change as the redundancy order grows. In this work, we demonstrate that our method can approximate 1-D SMMs with performance comparable to the latest null-space continuation and learning-based approach, and that it is the first method capable of approximating highly redundant 4-D SMMs in a 7R manipulator for position tasks. Project website: \href{https://github.com/accuracy-maker/high-dimenstional-self-motion-
manifold-approximation}{https://github.com/accuracy-maker/high-dimenstional-self-motion-
manifold-approximation}
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Robotics
Characterizing Replay Retention Under Dynamics Shift in Model-Based Reinforcement Learning
Everest Yang, Skye Thompson and George D. Konidaris
Adapting to changes in
robot dynamics requires learning from new data without discarding experience that may still be useful. In continual model-based reinforcement learning (RL), replay collected before a dynamics change can slow adaptation, while removing it unnecessarily reduces available training data and can be especially costly if earlier dynamics return. We study when recent transitions are preferable to the full replay history. Two quantities characterize this trade-off: change magnitude and age-staleness area under the curve (AUC), measuring how well transition age separates stale from fresh data. Forgetting stale data helps after large permanent shifts but hurts when dynamics recur and older data becomes useful again. Choosing a replay strategy therefore depends on predicting when older data will help or hurt. We test these effects across two locomotion morphologies, two model-based RL
algorithms, and Real-World RL
benchmark perturbations. Because ground-truth staleness labels are unavailable on deployed
robots, we evaluate whether an
estimator built from interaction data can still provide the quantities needed to choose a replay strategy after permanent changes. Our results show that replay retention depends on change magnitude and on how the dynamics evolve.
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Data Structures and Algorithms
Xi Chen, Animesh Fatehpuria, Shyamal Patel et al. (+1 more)
We study the challenging problem of learning depth-three
circuits in the mistake-bound model of (realizable) online learning, which is a more difficult model than distribution-free PAC learning. Prior
algorithms for this problem, due to Servedio and Tan [ST17], could only learn
polynomial-size depth-three
circuits of poly$(n)$ size over $\{0,1\}^n$ with a running time of $2^{n - \Omega(n/\log n)}$, and hence they ran in time $N^{1-o(1)}$ where $N=2^n$ is the running time of a naive memorization-based approach. In this work we substantially improve on the [ST17] result: for any constant $\gamma\geq1$, we give an
algorithm that learns depth-three
circuits of size $n^\gamma$ with running time \[ 2^{n-c_\gamma n}, \] where $c_\gamma>0$ depends only on $\gamma$ and not on $n$. Hence we achieve a
polynomial savings over the naive approach for learning any
polynomial-size depth-three
circuit. The main driving force behind our improvement is an improved bound on the approximate degree of width-$k$ CNFs. Inspired by Szegedy [Sze04] and Magniez et al. [MNRS11], the rough idea of our construction is to use a Chebyshev
polynomial to efficiently amplify the spectral gap of a carefully designed random walk. This is combined with a random-restriction-like approach to separately learn different subfunctions corresponding to different assignments to a randomly chosen set of variables, using the Perceptron
algorithm over a specially designed feature space. A simplified warmup instantiation of our approach achieves $c_\gamma = \exp(-O(\gamma))$; by augmenting this warmup with further ingredients we obtain the sharp form of our result, which achieves $c_\gamma=\Omega(1)/\gamma$.
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All authors Xi Chen, Animesh Fatehpuria, Shyamal Patel, Rocco Servedio
Robotics
LUMO: Designing Luminous Contact Morphology for Repeatable Whole-Finger Contact Observation
Dong Ho Kang, Youngsu Ko and Luis Sentis
A low-impedance
robot finger reports through joint torque how strongly it is loaded, but the same torque can arise from a small force near the fingertip or a large force near the joint. Resolving the force therefore requires knowing where along the finger contact occurred. LUMO makes that location externally observable. Embedded LEDs illuminate a compliant silicone pad, and contact deforms the pad so that light emerging from the finger's side changes in a pattern set by where the load acts. Because the same structure also carries the contact load, we optimize its cross-section, including the pad profile, rigid carrier, and lateral void, for two behaviors at once. Mechanically, the pad conforms under low preload while the carrier increasingly restricts further deformation as load rises. Optically, different contact locations produce separated responses on the finger's side. The search uses rigid–soft contact simulation, ray tracing, and multi-objective
Bayesian optimization. Across two silicones, six contact locations, and 10- and 30-mm spherical indenters, the optimized morphologies improve neighboring-location separation relative to variation from re-establishing contact by \(15\)–\(59\%\). Estimating contact location from the optical response using the known LED spacing and combining it with joint torque gives \(1.44~\mathrm{N}\) normal-force MAE over 931 samples. In a two-finger hand, localized side responses appear on several links simultaneously during grasps.
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Robotics
Energy-Regularized Imitation Learning for Force- and Work-Aware Robotic Manipulation
Toshiki Otani, Hiromu Taketsugu and Norimichi Ukita
This paper studies energy-aware manipulation as a physically grounded learning problem. We define a joint-space mechanical-work proxy from joint torque and angular displacement, and train a differentiable energy predictor that estimates this work from
robot states and actions. The predictor converts a non-differentiable simulator-side physical quantity into a differentiable regularizer for fine-tuning a pretrained manipulation policy. We instantiate the framework with RVT-2 on RLBench and evaluate 12 manipulation tasks involving object contact, articulated motion, placement, pushing, and sweeping. The proposed fine-tuning reduces the average mechanical work from 208.8J to 204.4J (i.e., 2.1% reduction), while the mean task success rate also increases slightly from 86.2% to 86.9%. These results show that work-aware policy optimization can suppress physically inefficient motion without requiring an explicit differentiable dynamics model.
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Artificial Intelligence
Time-Aligned Evolving Concept Graphs for Scientific Relation Forecasting
Fred Sun, Jingze Wang, Minkun Xu et al. (+1 more)
Forecasting scientific relations can guide discovery by identifying promising connections before they emerge. Existing approaches often model concept semantics and graph structure separately or summarize semantics over coarse historical snapshots, leaving semantic representations potentially misaligned with rapidly evolving graph evidence. We propose a time-aligned evolving concept graph framework that jointly models semantic and structural
evolution. Its core idea is to treat dated papers as shared update events, reconstructing semantic and structural states from the same publication history through each prediction time. Pair-level fusion combines these states to forecast first co-occurrence, relation formation, and conditional relation type. Holding architecture and training fixed, refreshing context alongside graph updates improves mean relation AUPRC by 16.6% over frozen context. On a graph built from 187,848 papers with 270,687 concepts and 7.45 million co-occurrence links, the complete framework improves mean relation AUROC from 0.9290 for the strongest evaluated baseline to 0.9722, with mean population-weighted AUPRC 0.005778.
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All authors Fred Sun, Jingze Wang, Minkun Xu, Shangqi Guo
Cryptography and Security
Bridging the Opacity: Evidence-Backed Cross-Chain Transaction Correspondence Reconstruction Across Heterogeneous Blockchains
Dan Lin, Huan Xiao, Ziwei Li et al. (+4 more)
Cross-chain bridges enable interoperability, but they also break the transaction trails needed to trace illicit funds. Third-party investigators typically cannot access the source-to-destination mappings maintained by bridge backends, and our survey of 131 bridges finds that only 16.79% provide complete public tracking. Existing approaches depend on official APIs, EVM-specific assumptions, or fragile temporal heuristics, limiting their ability to trace transfers across heterogeneous ledgers. We present XSplicer, an evidence-driven system for reconstructing cross-chain transaction correspondence (xTCR) without privileged access to bridge backends. XSplicer derives unified semantic specifications from public protocol documentation and transaction examples, translates them into lightweight parsers and verifiers, and links source and destination transactions by prioritizing hard evidence and using soft clues only when necessary. We evaluate XSplicer on seven bridge protocols spanning EVM, Bitcoin, and Solana. XSplicer achieves 92.5% global recovery rate and up to 98.61% on individual protocols. Under
adversarial noise, its hard-evidence verifier retains the correct match in 100% of tested cases, while soft-clue matching degrades as ambiguity increases. In two real-world case studies, XSplicer recovers more than 1,900 historical transaction pairs after Multichain ceased operations and identifies 754 illicit cross-chain transfers worth 105.6 million USD in the Bybit laundering incident. These results show that public protocol invariants can support practical cross-chain forensics without privileged bridge mappings.
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All authors Dan Lin, Huan Xiao, Ziwei Li, Xiapu Luo, Jiachi Chen, Jiajing Wu, Zibin Zheng
Computation and Language
TeochewBench: A Human-Reviewed Benchmark for Teochew Hanzi Translation
Jianan Wu
Teochew has a substantial speaker community and exhibits distinctive lexical, syntactic, and pragmatic features, yet textual resources for evaluating large
language models remain limited. We present TeochewBench, a human-reviewed
benchmark comprising 300 Teochew Hanzi expressions for evaluating translation from Teochew Hanzi into Mandarin Chinese and English. The
dataset covers five categories: basic vocabulary; everyday sentences; Teochew-specific expressions; tone, politeness, and context; and idiomatic, ambiguous, and culturally specific expressions. A primary Teochew-speaking reviewer examined all entries individually and revised them as needed, while two additional Teochew speakers verified selected items. Our main evaluation covers 11 official general-purpose post-trained models on the reviewed
dataset in both translation directions, yielding 6,600 predictions. Two official base checkpoints provide 1,200 predictions for supplementary diagnostics, bringing the total to 13 models and 7,800 predictions. We additionally include a Hanzi-copy control, which returns the source input unchanged, to assess how shared Hanzi affect automatic scores for translation into Mandarin Chinese. Qwen3.5-27B achieved the highest overall chrF-style score among the evaluated checkpoints, at 60.63, followed by Qwen2.5-72B-Instruct at 56.61, Gemma-3-27B-IT at 56.36, and GLM-4-32B-0414 at 55.82. Across the 11 main-evaluation models, the mean chrF-style score decreased from 69.25 for low-specificity items to 27.52 for high-specificity items. High-specificity expressions received lower scores and exhibited smaller cross-model differences, suggesting that they constitute a shared low-scoring region across the model families evaluated here. The Hanzi-copy control further indicates that surface overlap in low-specificity items can substantially affect automatic scores for translation into Mandarin Chinese.
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Information Retrieval
PageRecall: Measuring Page Selection in Literature-Grounded Question Answering
Aaditya Chauhan
We describe our system for LitTraceQA (GroundLM @ EMNLP 2026): given a research question, retrieve the relevant papers from a pool of 27,487, cite the page and the table or figure where the answer lives, and answer in a requested format. Our main finding is that evidence grounding is limited by
retrieval, not by reading. The page selector put the annotator's page, which we call the gold page, in front of the model that locates evidence only about half the time (52.6% gold-page recall), while that model, given the page, cited the right one in 45 of the 48 locators it emitted (94%). When the page was missing it rarely said so: of 45 such cases it returned nothing 14 times, a wrong page 24 times, and a correct page 7 times, so the pipeline failed quietly almost twice as often as it failed visibly. Since the failure was that the right page was never shown, the fix is to stop choosing: each retrieved paper fits in the model's context, so we show it whole. Page ranking survives only as a fallback inside papers too long to fit, which no test-split paper was, and gold-page recall reaches 100% on the papers we can parse. Separately, questions that identify their target by position rather than content, such as "the first author of the 24th reference", are served by parsing rather than
retrieval: we resolve the bibliography into an addressable list, which also supplies identifiers the evidence metric scores. The final system scores 0.762 paper $F_1$, 0.441 evidence $F_1$ and 0.920 multiple-choice accuracy on the held-out test split. Because the pipeline depends on a closed model without seed control, we release a harness that verifies the paper's central claims against committed artifacts.
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Robotics
Weiliang Deng, Zhengyang Dang, Yao Mu et al. (+1 more)
Aerial grasping is a remarkable capability exhibited by predatory birds, allowing them to capture prey through highly coordinated maneuvers in flight. Inspired by this capability, researchers have developed various formulations to reproduce such maneuvers through trajectory optimization. However, two limitations remain in practice. First, the resulting optimization problem is highly nonconvex and sensitive to initialization, making high-quality solutions difficult to obtain under a limited computational budget. Second, prescribed numerical objectives are human-designed abstractions that describe successful grasping through a limited set of mathematically tractable quantities and may not fully capture what determines task success. We investigate how learning can address these limitations within an analytical planner. Accordingly, a trajectory prior is first learned from optimized motions and then evolved through a CEM-based process that evaluates sampled initializations with the deployed optimizer and retains favorable ones as new supervision. An Execution-Aware Critic learns from contact, lift, and completion outcomes to assess whether the optimized trajectories are likely to succeed in physical execution. Its frozen energy can further serve as a differentiable grasping cost, allowing execution data to directly shape trajectory generation. Simulation and real-world experiments demonstrate improved optimization reliability, trajectory consistency, and grasping performance.
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All authors Weiliang Deng, Zhengyang Dang, Yao Mu, Ximin Lyu
Computational Engineering
The Exact Approximation Ratio of Uniformly Rotated Coordinate-wise Median in the Euclidean Plane
Song Zichen
Uniformly rotated coordinate-wise median chooses a random orthonormal coordinate system, takes a median in each coordinate, and maps the resulting point back to the Euclidean plane. We determine its exact worst-case expected approximation ratio when social cost is the $L_p$ norm of the
agents' Euclidean distances and $1<p<2$. The ratio is \(\frac{2^{2-1/p}}{\pi}\int_0^{\pi/2}(\cos^p\theta+\sin^p\theta)^{1/p}\dd\theta\). This expression was previously established as a lower bound by Chan, Lin, and Wang; our contribution is the matching upper bound. The proof establishes a strengthened coordinate-wise median inequality relative to an arbitrary reference point. Its right-hand side is linear in a sum of direction-dependent norms, which permits direct averaging over rotations without the loss incurred by passing through a $p$th-moment bound. We give all auxiliary inequalities and a self-contained proof of tightness using the established two-cluster-and-outlier construction. The upper bound holds for every finite profile and every measurable choice within the coordinate median intervals, while odd-size profiles suffice for the matching lower bound. The result characterizes this fixed mechanism, rather than the optimal approximation ratio among all randomized strategyproof mechanisms.
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Computational Engineering
Research on the Price Prediction Algorithms of Major Cryptocurrencies and a Basic Transaction Framework
Shengjian Chen
Through long-term observation and time series analysis of Bitcoin and Ethereum, we found the similarity in long-term consistent price trends, especially in the weekly K-line. We denoised and smoothed the historical data of their prices, and further derived the periodicity of their price change trends and the timing of buying and selling. We have for the first time proposed that the full application of central bank digital currencies (CBDC) is a key signal for investors to reduce their holdings of cryptocurrencies or even exit the
market. In addition, we proposed momentum opening/closing prices to replace the traditional nominal opening/closing prices to accurately describe the price trends of the 24/7 financial trading
market. We found that the cryptocurrency
market can be regarded as a relatively independent financial
market, thereby designing a safer and more efficient arbitrage strategy.
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Machine Learning
LIGE-GR: A Smooth Leap from Ranking to Generative Recommendation in the LLM Era
Venkat Srinivas, Chenzhang He, Sam Woodmansee et al. (+63 more)
The remarkable success of large
language models (LLMs) has provided important inspiration for the next generation of recommender systems. Structurally, recommendation and
language generation share a similarity: both aim to produce an ordered sequence that optimizes the user's experience. However, how to precisely absorb the essence of the LLM paradigm into mature industrial recommender systems remains an open problem. There are two challenges. First, it is unclear how to incorporate the LLM paradigm – sequence-level generation and optimization – into recommendation. Second, real-world recommender systems are mature systems that have been iteratively customized for years around specific products, business constraints, serving infrastructure, and organizational ownership. Replacing such systems wholesale is often technically risky and organizationally disruptive. In this paper, we propose LIGE-GR, a listwise generation and evaluation recommendation framework that upgrades from a traditional ranking system (itemwise recommendation) toward a generative recommendation paradigm. Instead of rebuilding the entire recommendation stack from scratch, LIGE-GR generalizes the existing pointwise recommendation system into a listwise generation system. This allows mature recommender systems to benefit from listwise optimization while preserving compatibility with existing models, value functions, and serving infrastructure. We validate LIGE-GR in short-
video recommendation on Instagram Reels and Facebook
Video. On these recommendation surfaces, LIGE-GR improves time spent by 1.14 percent on Instagram Reels and 0.72 percent on Facebook
Video, while requiring only modest additional inference resources.
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All authors Venkat Srinivas, Chenzhang He, Sam Woodmansee, Shawn Lian, Wenjie Hu, Renjie Jiang, Ziheng Huang, Xinyuan Zhang, Zhihao Zheng, Zhuoran Yu, Rui Li, Lei Yuan, Ziwei Li, Jimmy Jia, Mert Terzihan, Ekrem Kocaguneli, Yiming Liao, Zhichen Zhao, Yue Yin, Yue Weng, Wanli Ma, Xufeng Cai, Weimiao Wu, Yezhou Huang, Du Zhang, Yukun Ding, Aaron Johnston, Yueming Wang, Zhaojie Gong, Yuting Zhang, Serena Li, Adithya Ganesh, Boying Liu, Haichuan Yang, Xialu Li, Matt Ma, Qunshu Zhang, Andrew Ton, Nathan Berrebbi, Neel Pawar, John Joshua Miller, Jayant Subramanian, Praveen Rathinavelu, Cheng Huang, Aadhar Sachdeva, Josh Karns, Andres Aaron Gutierrez, Neil Agarwal, Gustas Pladis, Vladimir Batygin, Gopal Ray, Aditya Priyadarshi, Shantanu Patil, Zhe Wang, Penny Pan, Yiping Han, Arun Singh, Guangdeng Liao, Bi Xue, Xinyao Hu, Yang Song, Yisong Song, Meihong Wang, Haotian Wu, Deepak Agarwal, Ji Liu
Game Theory · Cross-listed from econ.TH
Dynamic Pooling and Regional Participation in Deceased-Donor Organ Allocation
Genta Okada
Moving from geographically fragmented to pooled waiting lists in deceased-donor organ transplantation can improve efficiency, but it raises concerns about regional
fairness and participation incentives. This paper studies Pareto gains from such transitions in a multi-class queueing model with impatient
agents, perishable items, and a tractable homogeneous compatibility friction. In contrast to standard approaches that emphasize static match quality or overlook regional incentives in dynamic contexts, this analysis identifies dynamic risk pooling as the primary source of Pareto improvements for regions. Pooling reduces organ waste resulting from
stochastic supply-demand fluctuations, temporal mismatches, and random compatibility outcomes. Although this dynamic surplus theoretically enables Pareto improvements, a purely utilitarian policy that treats all patients equally across regions often fails to achieve these improvements when regions are asymmetric. In these situations, utilitarian redistribution removes an excessive number of organs from high-supply regions and violates their participation constraints. To address this conflict, it is demonstrated that the set of achievable utilities can be represented by mixtures of static priority index policies. Building on this structure, the Constrained Pareto Frontier Search (CPFS) is proposed as a
polynomial-time procedure that computes an approximately optimal allocation policy with arbitrarily small participation-constraint violations. The results indicate that, by adjusting priority indices to ensure redistribution remains within the gains from pooled supply, demand, and compatibility risk, policymakers can achieve global efficiency while maintaining local participation incentives.
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