Computer Vision
OPIS: An Input-Grounded Benchmark for Multi-Object Memory in Video World Models
Hao Wang, Tao Yu, Liuzhou Zhang et al. (+13 more)
Video world models must preserve the visual state of the world over time, but existing evaluation protocols often rely on generated histories,
video reference, or selected revisit viewpoints that can confound the assessment of a model's true
memory capability. To address this, we introduce OPIS, an input-grounded
benchmark that strictly anchors the assessment to a fixed set of object instances from the initial observation for evaluating multi-object
memory in
video world models. The OPIS
dataset comprises 500 cases across real-world,
embodied-robotic, and game-world domains, providing dense object-level annotations for 12,672 rigid, articulated, and deformable instances. Our object-centric evaluator combines association and explicit visibility
reasoning to hierarchically measure Object (O) Presence (P), Identity (I), and Structure (S), utilizing static or dynamic evaluation tracks based on object kinematics. Across eight image-to-
video or camera-conditioned world models, our proposed OPIS scores range from 48.65 to 56.01. As the reference inventory grows from less than 20 to more than 40 objects, the Presence, Identity, and Structure scores show an overall decline, with the average Identity score falling from 40.22 to 23.11. The results demonstrate that preserving the particular object instances in the input is considerably harder than generating plausible visual elements.
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All authors Hao Wang, Tao Yu, Liuzhou Zhang, HeXin Wang, Haopeng Jin, Yuxuan Zhou, Xinming Wang, Hongzhu Yi, Xinye Li, Yuanlei Wang, Ping Nie, Yan Huang, Yuxuan Zhang, Pengfei Zhou, Yanyan Zou, Wei Yang
Information Theory
Large-Scale Autonomous Discovery of Kissing Number Constructions
Shuxing Yang, Rui Zhao, Junyao Wu et al. (+13 more)
The kissing-number problem is a classical problem in discrete geometry whose exact solution is known in only a few dimensions. Recent artificial-intelligence approaches have begun to discover improved configurations through large-scale numerical and combinatorial search, but converting such searches into general mathematical constructions and rigorous proofs remains challenging. Here we use Qiushi Engine, an autonomous multi-
agent research system, to investigate kissing numbers and obtain new lower bounds in nineteen dimensions: $25$, $27$, $32$–$39$, $43$, $45$, and $49$–$55$. The resulting constructions arise from distinct structural mechanisms, including coordinated motions of contact layers, labelled direction reuse, joint support exchanges, signed-code replacements, cross-shell
lattice constructions, low-overlap
lattice isometries, and spherical-design moment certificates. They yield sharp capacities for parameterized signed-code models, deterministic image-union guarantees, and exact section and projection counts controlled by embedded root systems and anchor-graph statistics. These methods yield, among others, $K(25)\ge197580$, $K(27)\ge201567$, $K(38)\ge591900$, $K(43)\ge2553792$, $K(45)\ge7380090$, and $K(55)\ge53301140$. The autonomous system carried out the construction searches, mathematical analysis and computational verification, while each final result was reduced to explicit mathematical arguments and independently checkable finite certificates. Our results illustrate how autonomous research systems can move beyond optimization within fixed formulations to discover new mathematical representations and constructions at scale.
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All authors Shuxing Yang, Rui Zhao, Junyao Wu, Yize Wang, Fujia Chen, Kaihao Zhu, Wenhao Li, Zichen Li, Yaqi Li, Shenzhan Hong, Yuang Pan, Junjie Yang, Taowen Deng, Jincheng Mi, Hongsheng Chen, Yihao Yang
Robotics
EMPIRIC: Experiment-Driven Learning of Residual World Models for Robot Planning
Yichao Liang, Amber Li, Dat Nguyen et al. (+13 more)
A
robot should be able to learn through experiments how unfamiliar objects behave and interact, then plan with that knowledge. It need not start from scratch: physics engines supply knowledge of motion and contact, but can omit entire mechanisms, such as glue curing, water heating, or wind. We present EMPIRIC, an
agent that learns a residual world model: a physics engine extended with code for the missing mechanisms. The learned programs can introduce new forces, constraints, and hidden state, and
Bayesian inference estimates their parameters and states from noisy observations. The resulting model lets the
agent predict the outcomes of actions, choose informative experiments, and revise its hypotheses when predictions fail. Across five simulated domains, EMPIRIC learns interpretable, reusable models, and solves more tasks with fewer environment interactions than all three baselines. On a physical
robot, it learns wind forces and domino masses to solve a manipulation task. Website and code: https://yichao-liang.github.io/empiric
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All authors Yichao Liang, Amber Li, Dat Nguyen, Emily Bunnapradist, Michelangelo Naim, Sreela Kodali, Matteo Merler, Bowen Li, Kiran Gopinathan, Yiyun Liu, Nikhil Pimpalkhare, Joshua B. Tenenbaum, Adrian Weller, Zenna Tavares, Tom Silver, Kevin Ellis
Computer Vision
LEGAU: Learning Semantic Gaussian Priors for Scalable Category-level Pose Estimation
Hongli Xu, Zhaowei Lu, Junwen Huang et al. (+5 more)
Category-level 6D pose estimation from a single RGB-D observation is inherently under-constrained, since partial visible geometry must be interpreted together with a canonical object structure before a stable pose can be determined. We present LEGAU, a unified framework that jointly predicts NOCS correspondence, object pose and size, and a canonical Semantic Gaussian Field. Rather than treating reconstruction as a detached auxiliary task, LEGAU uses the Gaussian field as a category-conditioned structural prior that participates in
multimodal feature fusion and provides global guidance for local pose
reasoning. Conditioned on a categorical text
embedding, LEGAU processes RGB-D observations through a
transformer-based fusion module that integrates visual,
geometric, and category-level cues, decoding the NOCS map, pose and size information and the Gaussian-based object representation. Extensive experiments on synthetic and real-world
benchmarks show that this coupled pose-shape formulation achieves strong performance in a single-model multi-category setting, with up to 22% on SOPE and competitive transfer to real-world data. These results highlight the benefit of jointly learning canonical correspondence, object shape, and pose
alignment within a unified representation.
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All authors Hongli Xu, Zhaowei Lu, Junwen Huang, Jiaqi Hu, Peter KT Yu, Benjamin Busam, Federico Tombari, Slobodan ilic
Machine Learning
BA-DPO: Bias-Adjusted Direct Preference Optimization for Language Model Alignment
Antonio Ferrara, Alberto Rumi and Francesco Bonchi
Preference-based
alignment methods such as Direct Preference Optimization (DPO) use pairwise preferences labeled by human annotators to fine-tune
language models. However, annotators carry systematic biases toward some attributes: a name that signals a gender or an ethnicity, a persona, a
language variety, a formatting convention, or length. If not properly addressed, these systematic biases can be absorbed and amplified during
alignment. Existing methods address length bias or annotator disagreement, but fail to eliminate biases toward arbitrary attributes. To address this limitation, we propose Bias-Adjusted DPO (BA-DPO), a generalization of DPO that adds one bias parameter per annotator toward responses carrying a declared attribute. We prove that the objective is convex in the bias parameters and that the votes identify each annotator's bias up to a shared constant. The remaining constant is what fixes the aligned model's attribute rate: by default the rate of the reference model, or a target rate, which we use to bring a biased policy to statistical parity. On a corpus with planted biases, DPO drives the attribute from a balanced start to probability 0.96 and BA-DPO removes 81 to 95% of that shift; on MultiPref with real annotators it removes about half of DPO's lengthening. Both hold at 0.5B with full fine-tuning and at 8B with LoRA, at no higher KL than DPO and no loss in judged quality.
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Computer Vision
Mixed-Prior Decision Risk for Open-Set Recognition
L. A. Erlygin, P. D. Proskura and A. A. Zaytsev
In open-set recognition (OSR), a probe must either be identified as one of the known gallery classes or rejected as unknown, so three error types coexist: false acceptance, false rejection, and misidentification. An uncertainty score for selective recognition should rank probes by the risk of the decision the system has made.
Bayesian gallery-aware models such as Holistic Uncertainty Estimation (HolUE) summarize the posterior over known and unknown classes by Kullback–Leibler (KL) divergence components and map them to an uncertainty score with a supervised nonlinear calibrator. We show that the KL summary is not generally monotone in decision risk: linear fusion of the KL components tuned on validation data yields negative filtering quality on several
benchmarks. We propose MPRisk, a mixed-prior posterior decision-risk score that keeps the same
Bayesian posterior but directly scores the error events associated with the selected decision: false-acceptance, misidentification, and false-rejection risks, plus a non-specificity penalty for rejections, enabled by modeling unknown identities as a continuous component. Four nonnegative weights tuned on a validation set suffice for ranking; no nonlinear supervised model is required. Across nine image, audio, and text
benchmarks, MPRisk achieves the best or tied-best Prediction Rejection Ratio at every operating point on the image and audio
benchmarks and on most text operating points, with bootstrap-confirmed gains over HolUE on five
benchmarks (up to $+0.19$ PRR) at comparable or lower runtime.
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Software Engineering
Graph-Based Learning for Multi-Horizon Martian Atmospheric Forecasting
Gary Myler, James Holmes, Manish Patel et al. (+1 more)
Martian weather
forecasting is important for future exploration, but atmospheric behaviour on Mars combines spatial, temporal, vertical, and
dust-driven processes in ways that challenge current modelling and
forecasting approaches. This paper introduces MaGMA (Martian Graph-based Multi-horizon Atmospheric
Forecasting), a graph-based data engineering framework that transforms OpenMARS reanalysis fields into structured learning objects for Martian atmospheric
forecasting. Local atmospheric patches are represented as graph nodes and linked through spatial neighbourhoods, temporal continuity, longer temporal dependencies, and dynamically similar atmospheric states. The model integrates recent atmospheric history, engineered physical descriptors, and vertical atmospheric information to support
forecasting across multiple horizons. We evaluate MaGMA across five unseen Martian years, including regular years and a global
dust storm year. In regular years, the model achieves overall R^2 values of approximately 0.73-0.85. For
dust-column
forecasting, it outperforms classical and deep temporal baselines in most year-horizon comparisons. During the global
dust storm year,
dust-column prediction remains strong at shorter horizons, with R^2 above 0.8 for the first two horizons, while broader multivariate performance declines. The results show that graph-based data engineering can create reusable and diagnostically useful representations for planetary atmospheric
forecasting, while highlighting the need for better learning under rare extreme regimes and improved use of vertical atmospheric structure.
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All authors Gary Myler, James Holmes, Manish Patel, Amel Bennaceur
Information Retrieval
Mitigating Popularity Bias in Recommendation with Global Listwise Learning and Progressive Bi-Weighting
Tianyu Zhu, Jiandong Ding, Yansong Shi et al. (+2 more)
In recommender systems, user feedback typically follows a long-tail distribution, which leads many recommendation
algorithms to exacerbate popularity bias by disproportionately favoring popular items. To mitigate this issue, recent studies have employed Inverse Propensity Scoring (IPS) to rebalance training data via reweighting user-item interactions. However, the effectiveness of IPS-based approaches is often constrained by locally unbiased objectives and inaccurate propensity estimation. In this paper, we propose Multinomial Likelihood with Bi-Weighting (Mult-BiW) to address these limitations. First, we introduce a debiasing framework, termed Mult-IPS, which integrates multinomial likelihood with IPS to capture global and unbiased user preferences over the entire item set. Second, we develop a Bi-Weighting (BiW) strategy that jointly leverages propensity scores and a collection model, incorporating a smoothing mechanism to enhance the robustness of propensity estimation. We further provide theoretical analyses that establish an upper bound on the empirical bias and characterize the optimal form of the collection model. Third, to mitigate the adverse effects of aggressive reweighting on representation learning, we design a Progressive Bi-Weighting strategy that gradually transitions from discriminative representation learning to popularity debiasing. Extensive experiments on real-world
datasets show that Mult-BiW consistently outperforms state-of-the-art baselines.
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All authors Tianyu Zhu, Jiandong Ding, Yansong Shi, Guoqing Chen, Jian-Yun Nie
Cryptography and Security
GAZEleak: Passcode Inference Against Eye-tracking XR Devices Through External Observation
Hwanjo Heo, Junhee Lee and Jinwoo Kim
Mixed-reality headsets such as Apple
Vision Pro replace the touch screen with gaze-as-pointer interaction: the wearer looks at a target and confirms with an air pinch. Because the display is inside the headset and the eye tracker is walled off from third-party software, such input is widely assumed to be unobservable to bystanders—a built-in defense against the shoulder-surfing that plagues phones and laptops. We present GAZEleak, a side-channel attack that recovers gaze-driven input from external
video of head motion alone, under a strictly local, physical-observer threat model: the adversary only films the wearer from across the room and installs no software on the device. The attack exploits the centrally coupled eye-head motor program: gaze shifts recruit small, target-dependent head reorientations that project into sub-degree pose changes recoverable from commodity
video. GAZEleak implements a measurement-based, sparse-optical-flow inference pipeline for users' 6-digit device passcodes. On a preliminary front-view
dataset from three author-subjects who were aware of the attack hypothesis, GAZEleak places the true code within the top ten guesses for 56% (10 of 18) of test codes under a cross-person protocol with no labeled victim data, and for every code of the most exposed subject. The performance is subject-dependent: no passcode from the least exposed subject reaches the top ten, although its median guessed passcode rank is 12,786 rather than 500,000 expected from an uninformative ordering. These results provide preliminary evidence that gaze-coupled head motion can expose passcode information under controlled conditions, while motivating broader evaluation across users, behaviors, and capture settings.
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Robotics
Do Not Cut When Uncertain: Rejectable and Calibrated Decision Heads for VLA Policies in Robotic Harvesting
Heng Zhang
Vision-
Language-Action (VLA) policies trained with behavior cloning or flow matching are optimized to output an action trajectory, but they cannot express "I don't know" or "I should not act." In robotic harvesting, occlusion makes single-frame decisions fundamentally ambiguous: identical pixels can correspond either to a cuttable stem or to no stem at all. Existing VLAs are forced to commit, leading to high-confidence errors with irreversible consequences. We argue that the failure mode of a VLA is determined not by backbone scale but by its output interface. We propose Rejectable and Calibrated Decision Heads (RCDH), a typed, rejectable, and calibrated output interface that can be attached to a frozen VLA backbone without retraining or new features. RCDH introduces (i) a decision schema with explicit rejection and ordered, conditional decomposition, and (ii) a calibration procedure for risk-aware abstention. We evaluate RCDH on a robotic harvesting platform with controllable leaf occlusion, comparing generative, enumerated, calibrated, and rejectable interfaces. We show that replacing only the output head restores out-of-distribution usability under occlusion while preserving in-distribution performance. We further test whether the ordering of the rejection space is critical. Our results suggest that the right to refuse, rather than a larger model, is the missing interface for reliable manipulation under uncertainty.
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Machine Learning · Cross-listed from stat.ML
GUIDE-FBO: Guidance via Uncertainty Intervention and Distributional Exchange for Federated Bayesian Optimization
Jintao Wei, Chenxi Li and Songhao Wang
Federated Bayesian Optimization (FBO) enables distributed
agents to collaboratively optimize expensive black-box objectives without sharing raw local observations. However, effective knowledge transfer remains challenging under communication constraints and task heterogeneity. We propose GUIDE-FBO, in which
agents exchange compact distributions over the locations of their respective optima inferred from local Gaussian process (GP) posteriors, rather than raw observations, query points, or surrogate parameters. The server merges and reweights these distributional components before returning a subset to each
agent. Each
agent then constructs a
Federated Interventional GP (FI-GP), which preserves the local posterior mean and spatially rescales its covariance for local decision making. For the upper confidence bound (UCB) instantiation, GUIDE-UCB, we prove that any bounded FI-GP uncertainty intervention preserves the leading-order cumulative regret rate of standard GP-UCB. When the transferred distributions place greater support near an optimum than in a suboptimal region, selecting the latter requires greater local posterior uncertainty. Experiments on 12 synthetic
benchmarks and three real-world optimization tasks show that GUIDE-FBO remains effective across settings ranging from homogeneous to severely heterogeneous. Ablation results highlight the importance of spatially localized uncertainty intervention, while the communication analysis shows that GUIDE-FBO exchanges only compact distributional messages.
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Artificial Intelligence
Persona Following Is Not Selective Control: The Neutrality Gap in LLM User Simulation
Jiashen Ren, Wenlin Zhang, Bohan Zhang et al. (+5 more)
Persona prompting is widely used to construct user simulations with large
language models (LLMs), yet it relies on a largely untested assumption: specifying one user attribute should change that attribute alone. We test this assumption and identify a systematic failure of selective control: across all eight black-box LLMs we audit, changing a target attribute also shifts responses on unspecified, non-target attributes. For example, describing a user as more risk-seeking shifts color choices, even though the
prompt never mentions color; we term this cross-attribute influence. Semantic, contextual, and internal analyses collectively suggest that models treat a persona
prompt as evidence about the user and extend the inferred profile to unspecified preferences, a process we call trait-conditioned completion. We next ask whether explicitly specifying non-target attributes restores selective control. When a non-target attribute is assigned a clear direction, models generally follow the declaration and suppress the target attribute's influence. However, when the same attribute is declared neutral, the target continues to affect choices across all five open-weight checkpoints, even when the model correctly reports the declared state. This disparity, the neutrality gap, demonstrates that successful persona following does not imply selective persona control, which additionally requires keeping non-target attributes stable. We operationalize this distinction with a three-state diagnostic that leaves the non-target attribute unspecified or declares it directional or neutral; because directional tests can be passed by simply following the stated persona, the neutral state reveals failures they miss. In a post hoc analysis of independent items, neutral declarations leave 51-81% of items target-sensitive, against at most 1 of 320 item-pole comparisons under directional ones.
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All authors Jiashen Ren, Wenlin Zhang, Bohan Zhang, Xiaopeng Li, Zichuan Fu, Wanyu Wang, Junyi Li, Xiangyu Zhao
Machine Learning
Giuseppe Chiari, Michele Piccoli, Federico Viola et al. (+1 more)
The integration of artificial intelligence into computer-aided design frameworks has sparked a shift in the design of analog integrated
circuits (ICs), transitioning the field from using manual and algorithmic-based solutions to adopting automated and intelligent paradigms. In this scenario, the GDSII file represents the industry-standard database containing the ultimate and most accurate source of information of the analog
circuit, encapsulating the complex physical geometries and parasitic realities that define tape out performance. This paper proposes THEIA, a novel
dataset containing thousands of layout images paired with question-answer conversations, along with a
benchmark that employs a fine-tuned
vision-
language model (VLM) to analyze GDSII files of analog
circuits, enabling designers to interact with and query physical layouts as intuitive, meaningful entities. Experimental results using thousands of analog designs across five realistic tasks demonstrate that the proposed fine-tuned VLM outperforms state-of-the-art general-purpose VLMs by a significant margin (up to 73%), highlighting a fundamental gap between general-purpose
multimodal reasoning and domain-specific layout understanding.
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All authors Giuseppe Chiari, Michele Piccoli, Federico Viola, Davide Zoni
Information Retrieval
Recommendation Ranking Off-Policy Evaluation under Ranking-Dependent Examination via Examination-Relevance Decomposition
Riki Okamura and Toshiharu Sugawara
Off-policy evaluation, which estimates evaluation policy performance from logged data, is key for recommender ranking policies. However, logged clicks cannot distinguish unexamined items from examined non-clicks, causing bias in existing
estimators when the assumed examination structures fail. We propose two
estimators based on the decomposition of clicks into examination and relevance. First, the latent-examination independent inverse propensity score (LE-IIPS)
estimator corrects the IIPS bias using policy examination probability ratios. Second, the examination-decomposed doubly robust (ED-DR)
estimator extends LE-IIPS to a doubly robust framework. ED-DR is unbiased if the examination probabilities are correct regardless of relevance accuracy, or under ranking-independent examination, even if both model estimates are inaccurate. Experiments show that ED-DR achieves a lower MSE than existing methods with large sample sizes, especially when the examination depends on ranking. We also highlight its limitations under small samples or cascade user behavior conditions.
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Artificial Intelligence
Zhixi Cai, Fucai Ke, Sukai Huang et al. (+4 more)
In complex
embodied visual
reasoning scenarios, an
agent often has only a limited field of view, and the evidence needed to answer a question may be distributed across time, viewpoint, and interacting objects. A model may therefore give a plausible answer without ever observing the relevant object, time, or view that supports it. Current visual
reasoning benchmarks largely evaluate passive observations and final answers, overlooking settings that require active
reasoning and evidence acquisition. We introduce JRDB-AVR, a
benchmark derived from existing real-world JRDB robotics data through a structured question-generation engine that turns this gap into an explicit evaluation: an
embodied agentic system receives a visual
reasoning question, requests bounded observations by timestamp and viewing angle, and is evaluated on both the final answer and the grounded visual evidence supporting it. The
benchmark contains diverse questions over multiple real-world environments involving temporal search, viewpoint selection, and human-oriented compositional
reasoning. We also introduce JRDB-AVR-
Agent, a reference active
reasoning agentic method that maintains an explicit observation-grounded graph-based world model and answers through solving. Experiments reveal a substantial gap between answer accuracy and evidence accuracy in current baselines, showing that current VLMs can produce unsupported correct answers and that active evidence-aware evaluation is necessary for
embodied visual
reasoning. Code and
benchmark are available at https://github.com/ControlNet/JRDB-AVR.
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All authors Zhixi Cai, Fucai Ke, Sukai Huang, Maria Garcia de la Banda, Peter J. Stuckey, Gholamreza Haffari, Hamid Rezatofighi
Information Retrieval
PEAR: Progressive Evidence-Based AutoResearch for Industrial Search Systems
Yifan Wang, Shipeng Zhu, Fei Xiong et al. (+6 more)
AutoResearch improves systems through iterative experimentation:
agents propose candidate modifications, evaluate them, and use the results to guide subsequent exploration. Applying this paradigm to industrial search presents two challenges. (1) Common AutoResearch approaches follow a keep-if-better rule, retaining the highest-scoring candidate for subsequent experiments. Under non-stationary traffic, transient gains may be mistaken for persistent improvements, impairing reliable accumulation of search knowledge. (2) Candidate modifications can be evaluated at multiple fidelity levels, from low-cost proxies to online validation, differing in cost, objective
alignment, and statistical reliability. Existing methods rely on individual signals or task-specific procedures, lacking a unified basis for using evidence across levels to guide search. We introduce Progressive Evidence-Based AutoResearch (PEAR) with two complementary components. Evidence-driven AutoResearch maintains an independent, hypothesis-guided research state for each strategy task within a predefined objective and intervention scope. Each state evolves through a Plan-Execute-Evaluate-Update transition that links experimentation to context-aware evidence interpretation and hypothesis revision. Confidence-Gated Verifier Ladder organizes evaluation into four levels of increasing fidelity: Offline Replay, Shadow-Traffic Evaluation, Rapid Online Evaluation, and Decision-Grade Online Evaluation. A unified confidence-based gate promotes candidates only when evidence supports a statistically significant positive effect, enabling broad low-cost exploration while reserving costly online experiments for promoted candidates. In a real-world industrial search system, strategies optimized with PEAR significantly increased Main Order/DAU by 2.7336% and 3.2957% relative to their respective baselines in two A/B experiments.
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All authors Yifan Wang, Shipeng Zhu, Fei Xiong, Yuqin Yang, Yonghui Huang, Kunyao Wu, Yue Wang, Weichao Meng, Yu Gong
Machine Learning
Fast Learning Rate Transfer in Shallow Linear Networks at Growing Training Horizons
Mana Sakai and Masaaki Imaizumi
Hyperparameter transfer across model width can substantially reduce the cost of tuning large neural networks, but its behavior when the training horizon grows with width is not fully understood. Building on the framework of fast hyperparameter transfer (Ghosh et al., 2026), which formalizes when transfer is effective, we investigate conditions that ensure fast transfer in the growing-horizon regime. Specifically, we study learning-rate transfer in a shallow linear network with a single trainable hidden
matrix, trained by full-batch gradient descent. Under additional spectral assumptions, our main results are threefold. (i) We prove fast learning-rate transfer as $n,T\to\infty$ whenever $T=o(\sqrt{n})$. (ii) We characterize the transfer rates through the finite-width perturbation scale, the first-order sensitivities of the loss and its learning-rate derivative to finite-width perturbations, and the local loss
curvature. (iii) We derive limiting distributions for the optimal learning rate and optimized loss, governed by fluctuations associated with the extreme
eigenvalues of the data Gram
matrix. These results clarify how spectral structure and local loss sensitivities govern learning-rate transfer at growing horizons.
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Machine Learning
VEX-Bench: Benchmarking Verification Complexity of LLM-Generated Misinformation
Hanxun Huang, Yutao Wu, Qizhou Wang et al. (+9 more)
Large
language models (LLMs) have made misinformation inexpensive to produce but not to verify, creating a growing asymmetry in the information ecosystem. Under tight time, labor, and budget constraints, media organizations, platforms, and fact-checkers rely on screening to prioritize which content to verify. We introduce VEX-Bench, a unified
benchmark for evaluating the verification complexity of LLM-generated misinformation, as perceived during screening, across models and generation methods. Verification complexity is assessed along multiple dimensions derived from journalistic and fact-checking practices, capturing checkability, harm potential, source credibility signals, imposter legitimacy, and expected verification effort. We define the VEX score as an integrated measure combining elicitation yield and verification complexity to quantify how generated content consumes limited verification capacity. We construct a
benchmark spanning two misinformation categories, 6 high-stakes domains, and 60 real-world topics, and evaluate 7 frontier LLMs and 7 generation methods, yielding 5{,}880 articles. We employ an LLM-as-judge for scalable evaluation and validate it using content-analysis methodology, including ordinal Krippendorff $\alpha$ for inter-annotator reliability, complemented by fact-checking
agents for verification. Our findings show that no single method dominates all dimensions, underscoring the need for multi-dimensional evaluation. LLMs can generate high-VEX misinformation at 3$\times$ to 169$\times$ lower cost than
agent-based verification. Such content is often prioritized during screening, consuming scarce verification resources and introducing a systematic risk of misallocation in resource-constrained verification systems. The code is publicly available in our \href{https://github.com/HanxunH/VEX-Bench}{GitHub repository}.
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All authors Hanxun Huang, Yutao Wu, Qizhou Wang, Silvia Montaña-Niño, Yige Li, Xiang Zheng, Elif Buse Doyuran, Phoebe Matich, Xiao Liu, Xingjun Ma, Sarah Erfani, Christopher Leckie
Computational Complexity · Cross-listed from math.MG
The complexity of computing the covering radius of a Euclidean lattice
Frank Vallentin
In this note, we prove that the covering radius problem for Euclidean
lattices is complete for the second level of the
polynomial hierarchy. The note also documents the author's first experiment with generative AI as a tool for mathematical research.
Artificial Intelligence
WebPageBench: Event-Level Verification and Controlled UI-Variant Generation for Web Agents
Anton Emelyanov, Maria Tikhonova, Zaven Martirosian et al. (+2 more)
We present WebPageBench, an open framework for evaluating web
agents in which every task is verified from the interface's own event log. Six instrumented mock sites with brand identifiers removed (a marketplace, a bookstore, a grocery service, rail ticketing, hotel search and a document cabinet) emit typed events with parameters as a user or an
agent acts. A task declares the events it requires, and success is decided by matching them, with no judge model and no scraping of rendered pages. The same instrumentation supports controlled UI variation: one configuration switch re-renders a task through a different implementation of a single control while the
prompt and the success conditions stay completely identical, so sensitivity to interface form can be measured under a fixed task specification. The WebPageBench release consists of three components: 152 tasks, divided into 65 canonical scenarios and 87 control variants across light/dark UI-modes; a common runner evaluated with six browser/DOM harness configurations and five screenshot-only GUI-
agent families; and a public leaderboard of 24 model-harness pairs. On the public 152-task leaderboard the gap between what
agents declare finished and what the log confirms reaches 41 points (one configuration declares every task finished and satisfies the conditions on 59%).
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All authors Anton Emelyanov, Maria Tikhonova, Zaven Martirosian, Sergei Averkiev, Alena Fenogenova
Artificial Intelligence
AutoDataBench: Can Agents Write the Data That Feeds the Self-Improvement Loop?
Haotian Luo, Haoyu Wang, Zeyu Qin et al. (+5 more)
Recent gains in
language model capability have come more from data than from architecture. Frontier labs and data companies produce verifiable agentic tasks, which supervised finetuning and reinforcement learning then turn into capability.This production line still rests on human labour and on human-in-the-loop collaboration. Automating task creation would let data production scale with compute rather than with expert headcount, would extend to more domains, and would enable a key step in recursive self-improvement (RSI). Current evaluations of an
agent's ability to write such tasks measure how a model performs after training on what the
agent produced. That does not match common practice in the data industry, where data is delivered sample by sample and each sample is accepted against a set of criteria rather than put straight into training. No existing evaluation asks whether an individual task meets the acceptance criteria of a data pipeline. We therefore introduce AutoDataBench. Given an original
benchmark task and a record of the target model attempting it, an
agent must write a new task for the same suite that meets practical acceptance standards on validity, novelty, difficulty and behavioural coverage. Across three
benchmarks of executable
agent tasks, no
agent we evaluate scores above 20 out of 100 at the default time budget of 45 minutes. Giving the strongest
agent four times as long improves its score substantially, while the cost of one usable task stays almost unchanged. Current
agents can write training tasks of the required quality, but not efficiently. AutoDataBench provides a direct measure of an
agent's capacity for autonomous data synthesis: one artifact at a time, judged against the criteria a production pipeline would apply, and without a training run. Code and data are available at https://github.com/StarDewXXX/AutoDataBench.
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All authors Haotian Luo, Haoyu Wang, Zeyu Qin, Huanjin Yao, Yibo Wang, Zhuotao Tian, Shuai Wang, Jiaya Jia
Computer Vision
Proxy2World: Learning to Generate Worlds From Lightweight Proxies without Seeing Them
Hongli Xu, Weilong Yan, Anbang Wang et al. (+3 more)
Lightweight scene proxies let creators control scene layout and motion while leaving room for imagination in appearance, lighting, and visual effects. However, a suitable proxy is not uniquely defined, making paired proxy-
video data difficult to construct automatically at scale. We present Proxy2World, a controllable world model that learns these complementary capabilities from ordinary posed RGBD
videos, without training on authored proxy-
video pairs. The model jointly learns depth-conditioned RGB generation and joint RGBD generation through cross-modal flow matching. Learning both tasks enables proxy-camera hybrid denoising at inference to follow the proxy structure while producing natural, detailed visuals. We further introduce ProxyBench to evaluate this capability across a diverse set of scenes, camera trajectories, and subject motions. Experiments on ProxyBench show that Proxy2World achieves a better balance between structural adherence and visual quality than camera-controlled and geometry-conditioned methods, supported by quantitative metrics, VLM assessments, human evaluations and diverse qualitative results.
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All authors Hongli Xu, Weilong Yan, Anbang Wang, Chunyu Zou, Siyu Hong, Jingwei Huang
Computer Vision
Detection of Adversarial Attacks on Super-Resolvers Using Spectral Features
Emma J. Reid, Haley Duba-Sullivan and Tony G. Allen
The integration of deep learning models into image preprocessing pipelines such as super-resolution introduces a largely unexplored attack vector for adversaries targeting downstream tasks. To ensure trustworthiness of critical imaging pipelines, we must be able to detect
adversarial behavior within preprocessing models. In this paper, we propose a spectral-based detection method for identifying
adversarial attacks embedded in super-resolution model weights. More specifically, we use the radially-averaged power spectral density as a discriminative feature to train an extreme gradient boosting (XGBoost) detector, demonstrating detectability of model-level threats in super-resolution networks. We further
benchmark our detector against magnitude- and phase-based
Fourier spectrum detectors, evaluating each method across a range of training and cross-architecture scenarios. Our proposed detector out-performs the comparison detectors in most of these scenarios and indicates that high-frequency features are most informative for detecting AdvSR attacks across SR architectures.
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Machine Learning
Addressing Spatial Indistinguishability in Spatiotemporal Prediction via Optimal Transport-Guided Masking
Guangyu Wang and Jiawei Tong
Spatiotemporal prediction aims to learn discriminative representations from correlated temporal signals over spatial structures for accurate future inference. A central challenge is spatial indistinguishability: different nodes may share similar historical patterns yet evolve toward divergent futures, severely degrading
forecasting performance in real-world sensor networks. Existing
embedding-based and graph neural network (GNN)-based approaches can partially detect such ambiguous nodes but rely on historical similarity, struggling to capture future behavioral divergence. We propose STOT (SpatioTemporal Optimal Transport), a self-supervised framework that resolves spatiotemporal ambiguity via structured masking guided by optimal transport. Our key idea treats indistinguishability as a disambiguation problem: future states are inferred by exploiting concurrent spatial correlations and their time-varying similarity. We design a similarity-aware metric for dynamic inter-node relationships and an optimal transport-based masking strategy to emphasize ambiguous positions during pre-training. A batch consistency constraint preserves semantic coherence, while a random-walk masking mechanism promotes structured context exploration. Experiments on six real-world
datasets show that STOT performs competitively with state-of-the-art baselines on the evaluated
benchmarks and improved interpretability through transport-plan visualizations.
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Computer Vision
Verifying the Linear Representation Hypothesis: How Interpretable Are Vision SAEs?
Teodor Chiaburu, Franz Motzkus, Frank Haußer et al. (+1 more)
Vision Sparse Autoencoders (SAEs) have become a popular tool in Mechanistic Interpretability due to their presumed ability to disentangle complex features learned by a model into monosemantic concepts. Despite their growing popularity, evaluating their interpretability remains an active topic of research. The bedrock motivating the adoption of SAEs is the Linear Representation Hypothesis (LRH), which claims that polysemantic features can be projected onto a (near) orthogonal basis of sparse, human-understandable representations. Yet, most current frameworks evaluate proxies such as the sparsity of SAE features or the coherence of the inferred dictionary, implicitly assuming that these reflect
alignment with human perception. In this paper, we provide empirical evidence that measuring the interpretability of SAE concepts is more difficult than these proxies suggest. To this end, we adapt the Autointerpretability Score (AIS) - previously shown to align with human judgments in Natural
Language Processing - to
vision tasks and validate our approach in a dedicated user study. We evaluate SAE concept quality using both standard metrics and our adapted AIS. We find that established interpretability metrics for SAEs correlate neither with one another nor with AIS, indicating that no single reference-free metric, whether grounded in the LRH or not, is sufficient for verifying the interpretability of
vision SAEs. We argue these findings support recent calls for more verifiable, ground-truth-anchored design and evaluation of explanation methods.
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All authors Teodor Chiaburu, Franz Motzkus, Frank Haußer, Felix Bießmann
Computational Complexity · Cross-listed from math.PR
Anticoncentration of Complex Gaussian Hafnians
Priyanshu Pant
Let $G_{2n}$ be a complex symmetric random
matrix whose entries above the diagonal are independent standard circular complex Gaussians, and let $H_n=\operatorname{haf}(G_{2n})$. We prove the uniform shifted anticoncentration bound $$ \Pr\!\left( \left| \frac{H_n}{\sqrt{(2n-1)!!}}-z \right| \le \varepsilon \right) \le 2\sqrt{\frac n\pi}\,\varepsilon^2 $$ for every $z\in\mathbb C$ and $\varepsilon>0$. This establishes a local anticoncentration property that supports hardness arguments for
quantum advantage in Gaussian
boson sampling.
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Artificial Intelligence
Environmental requirements for the use of social information by artificial life agents using evolved plastic artificial neural networks
Hugh Charterton, James M. Borg and Aniko Ekart
Evolved Plastic Artificial Neural Networks (EPANNs) consist of two principal processes, the first,
evolution, and the second, development and in-life learning. In the context of the origins of social = learning, very few studies have been carried out using ALIFE models based on EPANN requirements. Studies in this field have usually involved an imitative teacher/pupil relationship. This, however, ignores the possibility that the observed behaviour is a consequence of social information cues rather than direct imitation or teaching. Starting with the first of the EPANN processes (
evolution), a series of experiments was undertaken using artificial neural network (ANN) based
agents in a variety of foraging environments to examine under what minimal environmental conditions the use of social information might have evolved, as measured by the number of generations taken to meet a specified fitness criterion. NEAT (Neuroevolution of Augmenting
Topologies) was the ANN used as its evolutionary
algorithm would evolve a network's
topology as well its weights. Unintentionally, in the experiment there was a simple network
topology based on the location of the nearest food item which enabled
agents to swiftly meet the fitness criterion. With this
topology, additional information, social or otherwise, was not required and could have proved to be a hindrance. However, this does indicate that for the use of social information to have evolved, it would require a greater degree of complexity in the environment to do so.
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Artificial Intelligence
TermJudge: A Document-Level Metric Judging, Not Counting, Terminology in Machine Translation Evaluation
Nicolas Dahan, Fran{\cc}ois Yvon and Rachel Bawden
Existing automatic metrics for evaluating terminological use in machine translation (MT) penalise any divergence from a fixed reference, conflating translation errors with the valid terminological variation that human translators routinely produce. We introduce TermJudge, a document-level terminology metric that assigns an interpretable verdict to every term occurrence: glossary-conforming occurrences are settled deterministically, while divergences are assessed under a two-step LLM-as-judge procedure using the full document context: the first detects and labels terminology errors; the second sorts valid document-level variations from inconsistencies. Validated against expert error annotations and document-level human MQM scores, TermJudge ranks first in both system- and segment-level meta-evaluation, ahead of glossary-conformity and quality-estimation baselines. When applied to eight systems translating academic documents, under two prompting conditions, we observe that glossary injection improves terminology translation in all paired comparisons, by removing genuine errors rather than valid variation. TermJudge is released as open-source code.
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Mathematical Software
A performance enhancement of the Payne-Hanek range reduction algorithm
Tue Ly
Range reduction plays a crucial role in the accuracy and performance of evaluating trigonometric functions, and is often the primary bottleneck for large floating-point inputs. While fast
algorithms such as Cody–Waite work efficiently over narrow intervals, the Payne–Hanek
algorithm remains the standard technique for accurate reduction across large floating-point inputs. However, many existing implementations of Payne–Hanek suffer from high latency due to heavy branching, conversion overheads, and the use of multi-word integer arithmetic, which hinders SIMD vectorization. In this paper, we analyze and present a branch-free variation of the Payne–Hanek
algorithm using only floating-point arithmetic. Our method operates directly over large double-precision inputs ($|x| \ge 2^{16}$) and is well suited to hardware with FMA instructions. We formulate the precision constraints in terms of a truncation error budget, construct a compact lookup table indexed by the input exponent, and prove that the scaled reduced argument has absolute error below $2^{-110}$ and relative error below $2^{-60}$ for every input, including worst cases. The same routine can serve both as the complete range reduction of a single-stage implementation and as the fast path of a correctly rounded one, achieving higher throughput than existing implementations and lower latency than those returning a double-double reduced argument. The
algorithm is currently implemented in the LLVM libc project.
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Artificial Intelligence
Automated Feature Engineering, AutoML, and Decision-Focused Learning for Improved Energy Consumption Forecasting
Nasser Alkhulaifi
The rising cost and demand for energy, together with environmental sustainability goals, create major challenges for energy management. Energy Consumption
Forecasting (ECF) supports planning by predicting future consumption, but Machine Learning (ML) models for ECF often depend on expert-driven Feature Engineering (FE). This thesis addresses that dependence through three contributions. First, it establishes and evaluates a comprehensive FE pipeline for ECF and investigates domain-specific features. Second, it introduces AutoEnergy, a domain-tailored automated FE
algorithm that generates interpretable features from timestamps and lagged consumption and integrates with AutoML for end-to-end ECF modelling. Across eighteen real-world energy
datasets spanning residential, commercial, industrial, renewable, and grid domains, AutoEnergy reduces
forecasting error by 19.52%-84.72% relative to baseline AutoML and established automated FE methods, while running 1.31-4.41 times faster, with gains varying by
dataset. Third, AutoEnergy is integrated with Decision-Focused Learning (DFL) for a Battery Energy Storage System problem, jointly
forecasting electricity prices and demand while optimising charging and discharging decisions. On a real-world UK property
dataset, this approach reduces operating costs by 22.9%-56.5% compared with the same DFL models without automated FE. Overall, the results show that domain-specific automated FE can reduce reliance on manual feature design, improve
forecasting accuracy, and translate predictive gains into measurable operational benefits in energy management.
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