Science & Mathematics

Given an incentive-compatible mechanism for a single agent on a restricted domain of valuations, we consider its incentive-compatible extensions to the entire valuation space. We identify two extreme extensions: the "keep-the-menu" extension, which minimizes buyer payoffs and maximizes canonical prices pointwise, and the "keep-the-pricing" extension, which maximizes buyer payoffs and minimizes canonical prices pointwise.

Recent agents have demonstrated strong capabilities in generating images from textual prompts, motivating their utilization for scientific diagram generation. Scientific diagrams naturally span diverse types, each characterized by distinct visual structures and content organization. Consequently, during peer review and other scientific evaluation, human experts naturally consider type-specific criteria when assessing their quality. However, existing VLM-based evaluation benchmarks fail to reflect the above human evaluation practices for two main reasons: 1) benchmark datasets typically lack a systematic categorization of different diagram types and thus adopt the same generic criteria for all diagrams, and 2) their evaluation criteria are often designed heuristically, without explicit alignment to human evaluation preferences. Motivated by these limitations, we introduce SciDiagBench, which consists of a benchmark dataset that systematically categorizes scientific diagrams into 11 types and a human preference-guided evaluation method. Specifically, SciDiagBench contains 165 scientific diagrams across 10 broad domains, with types including graphical abstracts, technical roadmaps, and illustrative cases. Based on this dataset, SciDiagBench develops an evaluation method that leverages a preference-guided calibration process to incorporate human preferences into the generation of type-specific criteria. Ultimately, SciDiagBench enables more reliable automated evaluation that better aligns with human evaluation across diverse scientific diagrams.

Scaling the training of Large Language Model (LLM) agents typically requires a large collection of executable environments, whose construction, maintenance, and operation constrain the expansion of training experience. Existing offline trajectories offer an opportunity to reuse interaction experience, yet effectively refining the current policy without revisiting these environments remains challenging: supervised fine-tuning (SFT) can lead to catastrophic forgetting, while online reinforcement learning requires real-time interaction with the environment. To address this challenge, we propose Step-Advantage Policy Distillation (SAPD), a method for learning from offline trajectories using relative step advantages. SAPD decomposes multi-turn trajectories into individual decision contexts for on-policy distillation, allowing the student to generate responses conditioned on recorded histories, and introduces relative step advantage signals to stabilize training. The entire process requires neither new environment observations nor additional multi-turn interactions. Experiments on ALFWorld, WebShop, and Search-based QA show that SAPD improves performance across different teacher configurations, mitigates catastrophic forgetting compared with SFT, and runs at least $4.7\times$ faster per training step than online OPD. We will release our code soon.

This paper presents UvA-MT's submission to the WMT 2026 General Machine Translation shared task, competing in the unconstrained track across all 23 translation directions. This year, we fully leverage the test-time methods of Large Language Models (LLMs) for machine translation. Specifically, (1) we use sequential sampling with a refinement prompt to generate a pool of translation candidates for each source segment, a strategy shown to outperform traditional parallel sampling; and (2) we employ LLMs as pairwise judges to select the best candidate via a round-robin voting mechanism, with offline evaluation on the WMT25 benchmark showing a very clear improvement over point-wise best-of-N selection. Combined, these two strategies form our WMT26 submission.

Atmospheric turbulence and channel loss represent the primary limiting factors in ground-to-satellite quantum communication systems, yet their joint impact remains insufficiently characterized across diverse operational conditions. In this work, we develop a comprehensive and open atmospheric channel model for quantum optical links along ground-to-satellite paths, integrating altitude-dependent molecular and aerosol absorptions. The model is validated with available data from missions such as Micius and Hispasat, including a secondary viability assessment for a hypothetical Starlink-based quantum communication network, where we evaluate the predicted channel behavior in terms of single-photon transmittance and power balance, alongside the channel capacity of the link for uplink and downlink exclusively. Our comparative analysis demonstrates the ability of our channel model to predict real data under simple atmospheric behavior assumptions, which offer practical guidelines for the design and optimization of future quantum satellite missions.

A published laboratory number can be verified only when its raw file and extraction method are known. This paper presents the value-level provenance architecture of APS ELN, an electronic laboratory notebook used by both researchers and language-model agents. The architecture was motivated by a production audit in a materials laboratory covering projects performed over almost 5 years. Tracing eight baseline studies from sample to stored value showed that the initial ELN structure preserved the link from sample to raw file. Still, the link from raw file to stored value was lost, mainly due to human factors: manual entries carried no file reference or extractor version. To close this gap, the database design and workflow were reworked so that every stored value records its source file, the data file reader/viewer (DFR) plugin and version, any interactive analysis selection, or the server-side formula for derived quantities. An automated drift check re-runs each DFR on the stored files. In two end-to-end validation studies recorded by agents in X-ray reflectivity and tribology, all stored values retained verifiable provenance with zero drift. A comparison with five other platforms shows that none binds individual stored numbers to both their source files and extractor versions.

Cavitation diagnosis in industrial pipeline systems is challenging because acoustic responses vary substantially with operating conditions, while similar time-frequency patterns may correspond to different cavitation states. To address this issue, we propose a physical operating-condition-guided vision-language matching (POC-VLM) for cavitation diagnosis. POC-VLM first constructs physical operating-condition-aware semantic descriptions by explicitly incorporating sample-specific operating variables into candidate cavitation texts. A time-frequency-conditioned text adaptation module then dynamically aligns these physical semantics with the observed acoustic response, followed by global-local vision-language matching to capture both holistic and localized discriminative patterns. In parallel, a K-nearest neighbours-based non-parametric physical operating-condition prior estimates the physical plausibility of each candidate state and is fused with the vision-language semantic evidence at the decision level. Experiments on three real-world cavitation datasets show that POC-VLM consistently outperforms representative data-driven, multimodal and domain-specific diagnostic methods. Further analyses verify the complementary roles of vision-language semantic matching and physical operating-condition priors, while demonstrating robust performance under varying sampling rates, noise levels and observation durations.

Hamilton-Jacobi (HJ) reachability analysis offers formal safety guarantees for control systems, but training accurate neural certificates remains difficult, particularly when unsafe regions are hard to characterize. We propose a verification-guided training framework that addresses this by using probabilistic enumeration of safety constraints to detect forward-invariance violations, i.e., states where the learned certificate incorrectly classifies unsafe behavior. This produces a compact, targeted set of counterexamples that more precisely captures the failure boundary than inputs generated by standard adversarial attack methods. Crucially, using this representation to fine-tune a pretrained HJ value function yields larger certified regions and reduces certification time across all tested benchmarks. We further introduce a shielding mechanism based on the refined certificates to enforce safety constraints at deployment time. We evaluate the proposed safety filter in a realistic, physics-based Isaac Sim environment and through real-world autonomous racing experiments, achieving zero safety constraint violations compared to a nominal controller and a backup policy based on Linear Quadratic Regulation (LQR). These results demonstrate the effectiveness of the proposed shielding mechanism in ensuring safe operation under high-speed, safety-critical conditions.

Code reuse in modern software ecosystems facilitates the spread of 1-day vulnerabilities, posing significant security risks. Determining if patches are present is crucial, but traditional methods suffer from limited usability and accuracy, often depending on a fragile compilation process. In our prior work, we introduced Lares, the first compilation-free framework for patch presence testing that combines large language models (LLMs) with SMT solvers. However, Lares treats the LLM and the SMT solver as two separate, parallel decision channels: the symbolic channel (Z3) succeeded in only 20% of code cases, and the remaining cases fell back to the neural channel (the LLM) for the final decision. Letting the LLM act as the final arbiter turns verification into an informal, non-deterministic process that is prone to hallucination errors, while the two channels never reinforce each other. To overcome these limitations, we propose SyLares, which reformulates compilation-free patch presence testing as neuro-symbolic reasoning. Instead of letting the LLM emit a final verdict, SyLares uses the LLM as a neural feature extractor that perceives heterogeneous source code and decompiled pseudocode and produces specification-compliant SMT-LIB inputs, and delegates the final, deterministic judgment to the symbolic Z3 solver. This neural-perceives, symbolic-decides coupling is realized by a Code2SMT reasoning engine that combines deterministic syntactic parsing with LLM-driven code normalization and symbol alignment, so that every case is reduced to a formal equivalence query rather than an opaque LLM guess.

Hyperspectral Image (HSI) reconstruction has made substantial progress with the deep unfolding framework by decomposing the problem into a projection module and a denoiser module. Nevertheless, existing methods still exhibit limitations of insufficient matching with HSI data. The issues lie in two aspects: 1) the data-consistency projection module applies a fixed gradient descent step while ignoring the spectral content adaptivity of HSI; 2) the denoiser module either suffers from high computational complexity or is limited to the local receptive field, failing to efficiently capture the global spatial-spectral dependencies. In this paper, we propose a deep unfolding framework centered on a two-stage manifold learning strategy to obtain a degradation-free structural prior, which is then strategically injected into both the projection and denoiser modules. In the data-consistency projection, a prior-guided spectral weight mechanism is introduced to adaptively modulate the weight of spectral band. In the denoiser module, a manifold-guided Mamba is proposed to achieve the structure-aware extraction of spatial-spectral features through prior modulation. Quantitative and visual comparisons on synthetic and real-world datasets show that the proposed method significantly outperforms existing state-of-the-art approaches, while maintaining the structural interpretability of the unfolding framework. The code is available at https://github.com/SqLiu67/MPG-DU.

Anticipating how users will interact with their surroundings is essential for proactive systems that provide timely assistance in AR, VR, and embodied applications. Prior work typically forecasts hand-object interactions directly from historical observations, leaving intention and contact geometry implicit. However, similar observation histories can precede semantically or geometrically distinct futures. Inspired by behavioral evidence that gaze can provide early cues for underlying intention, we propose Gaze2HOI, a gaze-aware framework that formulates hand-object interaction forecasting as a hierarchical problem comprising intention inference, contact grounding, and coupled hand-object motion generation. Specifically, Gaze2HOI first weights gaze cues by fixation strength and recency, and uses them to modulate the visual representations of a vision-language model (VLM) through Temporal Gaze Saliency Modulation (TGSM), thereby reducing semantic ambiguity in intention inference. Subsequently, an Anticipatory Contact Grounding (ACG) module maps these structured intentions into explicit contact, support, and placement fields, effectively mitigating geometric ambiguity. Driven by these explicit conditions, a diffusion-based model jointly generates the 6-DoF object trajectories and future articulated motion of both hands. To evaluate Gaze2HOI, we further introduce EgoAIM, a phase-aware benchmark that organizes egocentric hand-object interactions into four phases defined by evolving hand-object relations and provides fine-grained intention annotations. Extensive experiments demonstrate that Gaze2HOI achieves state-of-the-art performance in both intention inference and motion forecasting, even approaching oracle-level end-to-end performance in specific interaction phases. Project page: https://gaze2hoi.github.io/.

One of the central questions in space complexity is whether nondeterministic logspace computations ($\textsf{NL}$) can be simulated in unambiguous logspace ($\textsf{UL}$). A stronger notion, \emph{reach unambiguity} ($\textsf{RUL} \subseteq \textsf{UL} \cap \textsf{co}\textsf{UL}$), requires every configuration reachable from the start to have exactly one computation path (Buntrock et.al. 91). While directed graph reachability is $\textsf{NL}$-complete, planar graph reachability is in $\textsf{UL}$ and undirected graph reachability is in deterministic logspace ($\textsf{L}$). In this work, we study the space complexity of reachability problem for restricted directed graph families and show the following. - For reach unambiguous graphs (graphs with at most one path from start vertex to every vertex), Lange (Lange 97) showed that reachability is in $\textsf{RUL}$. As our main result, we show that for simple path graphs (introduced in Kannan et.al. 08), which contains reach unambiguous graphs), where each vertex has at most one simple path from the start, we show that the reachability problem lies in $\textsf{UL} \cap \textsf{co}\textsf{UL}$. - Our first result can also be equivalently stated as: the recognition problem for simple path is in $\textsf{UL} \cap \textsf{co}\textsf{UL}$ if and only if the reachability problem restricted to simple path graphs is also in $\textsf{UL} \cap \textsf{co}\textsf{UL}$. Inspired by this, we investigate the complexity of graph recognition versus graph reachability for graph classes. Observe that for any graph class, solving reachability (for the class) also solves the recognition problem for that class. We show that for reach unambiguous graphs, solving recognition is as hard as solving reachability (making both of them $\textsf{RUL}$-complete). (Abstract shortened to meet arXiv limits)

We evaluate the quality of feedback and practice exercises generated by GPT-5.1 from handwritten university mathematics answers and their grades, and examine whether receiving this feedback improves subsequent exam performance. We use six weekly bonus tests in an English (n=343) and a Dutch (n=236) cohort. Tests 1 and 3 used a crossover feedback design; otherwise, all attendees received feedback. To evaluate the feedback, six reviewers assessed 157 messages from three English-cohort tasks using six criteria scored from 0 to 3. The practice exercises received the highest proportion of strong ratings (95.5%), while diagnostic specificity (41.1%) and clarity and tone (33.4%) received fewer. Feedback for the lowest-scoring answers was rated as less actionable, suggesting that these students may need more targeted guidance from instructors. Comparisons of related exam questions showed mixed results, with no statistically significant differences after adjustment across four comparisons. Greater attendance and feedback exposure were associated with higher exam scores, but the contribution of feedback could not be separated from attendance and practice. Our findings show the potential of an integrated framework merging instructor guidance with automated feedback generation to provide personalised learning support at scale.

Multispectral object detection requires detectors to exploit material-dependent spectral responses while maintaining consistent localization under geometric variations. Existing MOD detectors learn discriminative spectral representations, yet lack a unified mechanism that preserves spectral order, adapts spectral-context fusion across pyramid levels, and regulates geometric responses under rotation. To address these limitations, we propose S$^3$E-Det, a strict spatial-spectral equivariant detector that couples ordered spectral modeling with transformation-consistent prediction. Spectral Order-aware Sampling Calibration (SOSC) imposes local order constraints on input-derived spectral features through first-order variation, while Common-Differential Equivariant Fusion (CDEF) adapts spectral-context aggregation across pyramid levels. A compact Geometric Alignment Head (GAH) reconciles invariant and equivariant output properties within a unified prediction stage. Across MODA, HOD3K, and DroneVehicle, S$^3$E-Det combines high detection accuracy with a compact parameter footprint and reduced rotation-induced variation. Code is available at https://github.com/Riiluo/S3E-Det.

Multispectral aerial object detection aims to exploit spectral-spatial cues to address object detection challenges in complex aerial scenes. Existing methods mainly focus on spectral-spatial decoupling and object-aware spectral-spatial fusion to improve spectral utilization. However, two critical issues remain underexplored: 1) severe foreground-background imbalance caused by large scene coverage hinders reliable object recognition; 2) aerial objects exhibit substantial local variations in scale, orientation, and structure, which hinder accurate localization. To address these issues, we propose SGLDet, a spectral-aware global-local dynamic modeling framework for multispectral aerial object detection, which is motivated by the observation that object semantics are jointly determined by global spectral consistency and scene context, while precise localization requires dynamic feature modulation conditioned on the input spectral and spatial properties. To realize these properties, SGLDet consists of three coordinated components: a Spectral-driven Global Dynamic Modeling module that aggregates spectrally related features across the scene via similarity-guided sparse matching; a Multi-scale Global Context Integration module that constructs a compact scene memory for scene-guided contextual reasoning; and a Spectral-aware Input-dependent Calibration Aggregation module that performs expert-kernel aggregation with scene-guided spectral-spatial calibration to enable adaptive local feature modulation. Extensive experiments on MODA, HOD3K, and DroneVehicle demonstrate that SGLDet achieves superior performance with competitive computational cost. Code is available at https://github.com/Riiluo/SGLDet.