Cryptography and Security
CWE-bench: Refocusing Agentic Security from Exploitation to Defense
Gonzalo Gonzalez-Pumariega, Ajay Jagannath, Adit Jain et al. (+3 more)
Cybersecurity evaluations of AI
agents have focused largely on offense, yet a working exploit does not show that an
agent can make software safe. An attacker needs to exploit only one vulnerability; a defender must find and fix all of them. We introduce CWE-bench, a private
benchmark of 120 held-out repository audit-and-repair tasks spanning 73 CWE types, eight
languages, and all OWASP Top 10 2025 categories.
Agents are given a repository to audit and a description of its threat model, but are not told the weakness, where it occurs, or how many vulnerable sites there are. Each patch must block the exploit while preserving original functionality, as checked by a programmatic verifier. We validated the verifier against a panel of agentic judges, manually reviewing disagreements during development and refining the checks. The best
agents reach 68% pass@1 and 81% pass@4. We further conduct analyses showing that (i)
agents with the same pass@1 differ widely in retry gains and cost, and (ii) no single
agent is best across
languages or vulnerability categories, and difficulty varies far more by vulnerability category than by
language. As the offensive capabilities of
agents improve, CWE-bench provides a broad and rigorous measure of whether their defensive capabilities keep pace.
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All authors Gonzalo Gonzalez-Pumariega, Ajay Jagannath, Adit Jain, Guram Gogia, Soumyadeep Bakshi, Nazneen Rajani
Artificial Intelligence
Understanding Personalization Gains in Whisper
Sergei Katkov and Antonio Liotta
Whisper-style ASR models provide strong zero-shot transcription, but their residual errors are not evenly distributed across
datasets, recording conditions, and metadata-based groups. We study when lightweight low-rank adaptation (LoRA) improves Whisper and whether observed gains require group-specific adapters. Across Common Voice, LibriSpeech, and VoxPopuli, we compare zero-shot Whisper, pooled LoRA, and group-specific LoRA under matched within-group splits. We find that adaptation is not uniformly beneficial: gains are concentrated in difficult Common Voice groups, are consistent on VoxPopuli, and largely disappear on low-error LibriSpeech. Crucially, pooled LoRA often matches, and sometimes clearly outperforms, group-specific LoRA. Gains over zero-shot should therefore be tested against matched pooled adaptation before being interpreted as evidence of personalization.
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Machine Learning
LLM Evaluation is a Forecasting Problem
Rahul Thomas, Adam Block and Micah Goldblum
Standard procedure in machine learning is to randomly split train and test data, emulating IID draws from one underlying data distribution. In practice, models are trained on past data and deployed in the future, which may come from distinct distributions. In this paper, we find that web text is indeed highly non-stationary, as text distributions shift over time. We further observe that model performance changes continuously over time across diverse
benchmarks. Consequently, future model performance is better predicted by measurements made on recent evaluation data, and is best predicted by time series forecasters applied to historic performance rather than by historic performance itself. Choosing which model to deploy based on forecasted accuracy enables more reliable selection of performant models, thus improving downstream accuracy in the regime practitioners care about: deployment. Taken together, our experiments suggest that time should be treated as a fundamental variable in LLM evaluation. We release our code and data.
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Machine Learning
ProbabilityBench: Model Evaluation and Calibration with Exact Ground-Truth Uncertainty
Yuyang Dai, Yuting Tang, Futoshi Futami et al. (+1 more)
Evaluating model uncertainty on real-world data is challenging because each instance provides only a single observed label, leaving true conditional class probabilities unknown. To address this, we introduce ProbabilityBench, a
benchmark that provides exact, instance-level class probabilities across three simulated text-based domains: peer review, covert information transfer, and credit risk. By explicitly defining the full data-generating process for each environment, we analytically compute both exact class probabilities and Bayes error. Furthermore, we provide a theoretical guarantee that rendering these cases into natural-
language memos strictly preserves their underlying generative probabilities. This exactness enables direct measurement of reported probability quality via KL divergence. Across 16 evaluated models, most models on the observed cost-quality Pareto frontiers are decision models, including Jev and Perplexity Decider. Finally, ProbabilityBench serves as a meta-evaluation framework, leveraging known ground-truth values to
benchmark the accuracy of standard calibration metrics and Bayes-error
estimators themselves.
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All authors Yuyang Dai, Yuting Tang, Futoshi Futami, Takashi Ishida
Computer Vision
DIME: Scaling Facial Representation Learning via Differential Masked Autoencoding
Hao Yu, Haoyu Chen, Hui Wei et al. (+3 more)
Scaling up data and model capacity has driven substantial progress in visual representation learning. However, conventional pretext tasks exhibit diminishing returns on fine-grained facial analysis. We attribute this to a scaling bottleneck: these tasks model each image's appearance and are dominated by identity, pose, and illumination, so subtle within-identity facial differences contribute little to the training signal. We introduce DIME, a differential masked autoencoder for large-scale facial representation learning. DIME builds on a simple observation: two images of the same person share identity-specific appearance, so their difference exposes facial changes. DIME reconstructs both masked images in an identity-matched pair and, through an Explicit Differential Direction Supervision (EDDS) objective, aligns the direction of their reconstructed difference with the true difference, while source isolation makes each reconstruction depend only on its own image. Shared appearance cancels in this difference, and because EDDS ignores magnitude, subtle facial changes count as much as large pose or illumination changes. DIME uses identity labels only to form pretraining pairs, requires no dense annotations, and learns dense facial representations with emergent part-level semantics. We pretrain DIME on 20 million high-resolution facial images consolidated from existing face
datasets. DIME's AU detection performance continues to improve as pretraining data scales from 6M to 20M images, with its BP4D F1 increasing by 5.9 points, while MAE gains only 0.5 points on the same subsets. DIME also outperforms specialized facial methods on AU detection, AU intensity estimation, cross-domain anti-spoofing, landmark localization, head pose estimation, face parsing, and face generation.
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All authors Hao Yu, Haoyu Chen, Hui Wei, Yan Jiang, Nicu Sebe, Guoying Zhao
Machine Learning
MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation
Protyay Dey, Ayush Roy, Hyuna Cho et al. (+2 more)
Functional
brain connectome represents neural connectivity as a
matrix of pairwise interactions between
brain regions. Generation of functional connectomes is not only a question of validity; rather, having satisfied the constraints on the correlation
matrix, the next step is to recover the class-conditional geometry buried under coarse labels. We propose MAGNET, a
Manifold-Aware Graph
Diffusion Network which uses a normalized-Cholesky representation of the
manifold of correlation
matrices that guarantees validity. MAGNET lifts noisy latent states into ROI-level region
tokens and performs denoising with a relational inductive bias over
brain atlas regions. To deal with structural problems induced by coarse labels, MAGNET employs class-anchored conditioning, amortized structural bridge, and relevance-preserving corruption. Across ABIDE, ADNI, and OASIS-3, MAGNET consistently achieves favorable results compared to previous
manifold-aware approaches, demonstrating improvements of 7-21% in class-conditional fidelity ($\alpha,\beta$-F1) across three cohorts and better sampling efficiency. Moreover, while training only with strict binary labels, MAGNET is capable of maintaining clinical heterogeneity through fine substructure of the connectomes in a zero-shot setting, improving subclass covariance
alignment ($\lambda$-MSE) by over 30%. These results suggest that
geometric validity is a necessary but insufficient condition for clinical utility in connectome synthesis. Moreover, efforts in making the
diffusion denoising class-conditional
manifold aware finds utility beyond the highly curved
brain connectome generation as this is a critical problem in various general settings. The code repository can be found at https://github.com/protyayofficial/MAGNET.
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All authors Protyay Dey, Ayush Roy, Hyuna Cho, Won Hwa Kim, Vishnu Suresh Lokhande
Systems and Control
Wing Rock Modeling and Control for Flight-Critical Systems
Eugene Lavretsky and Miroslav Krstic
Aircraft equipped with delta wings are designed to be aerodynamically efficient in high-speed flight. However, at low-speed flight conditions with sufficiently high angle of attack, these vehicles often have lightly damped, close to unstable lateral-directional dynamics, such as the dutch roll mode. As a result, a delta wing aircraft often exhibits the so-called wing rock (WR) phenomenon, with a back and forth rocking motion, while the aircraft sideslip angle, the roll and the yaw angular rates remain small. In this paper, the WR dynamics are defined as a lightly damped dutch roll mode excited by airmass vortical external input, acting at or near the dutch roll natural frequency. The vortical signal comes from
Fourier-Galerkin approximation of the Navier-Stokes equation, with no-penetration boundary conditions and the resulting airmass velocities are calculated by the Lorenz dynamical system of three coupled bilinear ordinary differential equations. In that context, the Lorenz bilinear dynamics become the disturbance-generator exosystem, acting on the lightly damped lateral-directional aircraft mode at or near its natural frequency, and thus exciting it to produce near oscillatory wing rocking motion. Using the proposed model, a flight control design trade study is conducted to mitigate and suppress the WR phenomenon.
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Computer Vision
MaskSense: Confronting the Visual Exploration Trap in Masked Image Generation
Yawen Shao, Jie Xiao, Kai Zhu et al. (+6 more)
Reinforcement learning (RL) has shown strong potential for aligning generative models with human intent, but adapting RL to masked generative models (MGMs) remains largely underexplored. Existing approaches that directly adapt Group Relative Policy Optimization (GRPO) to MGMs leave a foundational conflict unaddressed: MGMs' exploitative decoding nature is inherently at odds with the exploratory diversity that GRPO presupposes. In this work, we reveal a structural decoding bias termed the visual exploration trap: confidence-based sampling trades the exploration of diverse subject realizations for the greedy resolution of low-uncertainty background regions, causing premature collapse of the generative exploration space and starving GRPO of the rollout diversity it depends on. To this end, we propose MaskSense, a novel RL framework for MGMs that confronts the visual exploration trap at both the sampling and optimization stages. Specifically, we introduce a semantic anchored routing sampling mechanism that leverages semantic priors to preserve high-
entropy subject
token exploration while amplifying intra-group reward variance through exploratory-exploitative routing to yield more discriminative advantage estimates. Furthermore, we design a global
entropy transition anchoring strategy to identify the most consequential decoding steps and concentrate gradient updates on them. Extensive experiments on multiple text-to-image
benchmarks demonstrate that MaskSense substantially improves upon the base model, with GenEval accuracy improving from 54% to 86% and HPS from 28.89 to 36.39, achieving state-of-the-art performance.
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All authors Yawen Shao, Jie Xiao, Kai Zhu, Yu Liu, Hongchen Luo, Xueyang Fu, Yang Cao, Wei Zhai, Zheng-Jun Zha
Machine Learning
Mixture of GFlowNets
Omar G. Younis, Joseph D. Viviano, Chirayu Anant Haryan et al. (+3 more)
Generative Flow Networks (GFlowNets) learn a policy that samples compositional objects in proportion to an unnormalized reward $R(x)$. This makes them useful wherever many diverse, high-reward candidates are needed and oracle queries are expensive. In practice, a single GFlowNet often fails to cover all modes of a complex distribution, and the obvious ways to scale compute saturate: larger batches and data-parallel gradient averaging draw every trajectory from one, possibly biased, policy, so extra rollouts rediscover known modes instead of finding new ones. We explore whether this mode-bias problem (mode collapse in the extreme) can be addressed with a population of asynchronously trained policies which divide the state space between them. Mixture of GFlowNets (MoG) replaces the single forward policy with a community of $K$ independent GFlowNets coupled through one jointly trained soft classifier, which assigns each member its own region of the state space through reward shaping. Mixing the members under their learned partition functions provably recovers the target $p(x)\propto R(x)$. MoG exchange (MoGX) also lets the classifier route sampled trajectories: a trajectory the classifier assigns to another member is sent to that member and trained on there, with no importance sampling. We evaluate both methods by keeping the environment-interaction budget fixed while scaling the community size, turning parallel compute into mode coverage without extra oracle calls. On HyperGrid, across six reward landscapes and communities of up to $K{=}64$, MoG and MoGX keep finding new modes as $K$ grows, while data-parallel training saturates. On a genomic ideotype-design task, MoGX covers $39%$ of the $1024$ optima at $K{=}64$, against $31\%$ for independent
agents and $20\%$ for data-parallel training. MoG also has a lower per-iteration synchronization cost than data-parallel training.
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All authors Omar G. Younis, Joseph D. Viviano, Chirayu Anant Haryan, MD Vasimuddin, Sanchit Misra, Yoshua Bengio
Computational Complexity · Cross-listed from math.CO
Sensitivity of Rotation Symmetric Boolean Functions via Binary Necklaces
Yuan Li and Jing Zhang
Rotation symmetric Boolean functions are Boolean functions invariant under cyclic permutation of their variables and can be represented by binary necklaces. We express local sensitivity as a weighted bichromatic degree in the necklace quotient and derive an exact formula for the multiplicities between neighboring necklaces. For an odd
prime number of variables, we obtain explicit sensitivity formulas and show that multiplicity corrections occur only on an asymptotically negligible family of necklaces. For monomial rotation symmetric Boolean functions, we prove that every odd-degree function has maximum sensitivity, determine the sensitivity of every quadratic function, and determine the sensitivity of the consecutive quartic family. We also determine exactly the sensitivity of cyclic run-threshold functions for arbitrary length and threshold.
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Systems and Control
Multi-Agent Deferred-Decision Trajectory Optimization with Anytime-Valid Target Retention for Moving Candidates
Farbod Siahkali, Mengxue Hou and Vijay Gupta
We study multi-
agent terminal interception when one of several moving candidates is the unknown target and both target identity and candidate terminal locations are uncertain. We develop a receding-horizon deferred-decision trajectory optimization (DDTO) controller that combines conformal terminal-location prediction, sequential candidate rejection, capacity-constrained assignment, and candidate-contingent planning. Under exchangeability conditions for terminal and identity residuals, with predictable querying, we establish fixed-time terminal-location coverage and anytime-valid target retention. At the final replan, when one control step remains, we bound the joint probability that a tightened-set DDTO plan passes nonlinear rollout verification but capture fails. The closed-loop controller applies fallback guidance to any
agent without a verified DDTO plan and, across 100 paired scenarios, captures 79% of targets under the strict control-effort budget and 85% when that budget is relaxed.
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Computers and Society
Governing Copyright Responsibly in the Era of Generative AI
D. Kochetkov
Large
language models are trained on collections that may include scholarly works, while authors generally lack work-level information about whether and how their works were used. This paper asks how copyright governance frames and operationalises the protection of such authors, and what a responsible alternative would require. Forty legislative, judicial and soft-law instruments issued between 2019 and 2026 were coded deductively against a framework derived from Responsible Research and Innovation theory, yielding 1,337 category assignments across 835 segments. The European Union and the United States are the primary cases; the United Kingdom, China and international bodies serve as comparators. Normative orientation and the distribution of responsibility dominate the corpus, while reflexivity is the least represented category and reaches its minimum in the US sub-corpus. Both primary jurisdictions frame protection procedurally rather than as an entitlement, and no instrument in the corpus creates a general author-facing attribution duty for the use of works in AI training. From the coded gaps, the paper derives a six-instrument governance design and a capacity-based allocation of responsibility among developers, publishers, collecting societies, funders, institutions, regulators, and individual authors.
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Robotics
MOSAIC: Mode- and Support-Aware Inference-Time Control of Robot Diffusion Models
Sabrina Bodmer, Rahel Rickenbach, René Zurbrügg et al. (+2 more)
Generative policies such as
diffusion models provide expressive data-driven priors for
robot behavior, yet the situations a
robot faces at execution time rarely match its training data and impose constraints and objectives the policy never learned. Existing inference-time guidance methods either rely on gradients that can push samples away from the learned prior, or on sampling procedures that can be inefficient, collapse modes, or average them into out-of-distribution predictions. We introduce MOSAIC, a mode- and support-aware framework for gradient-free inference-time control of robotic
diffusion models. MOSAIC treats the initial noise and all
stochastic reverse-step perturbations as a joint input to the pretrained denoising process and adapts a distribution over this input using importance weights computed from fully denoised outputs. Physical feasibility and consistency with the learned support are balanced using adaptive penalties, while mode-wise importance-weighted updates preserve modes and avoid destructive averaging. The same formulation can jointly steer multiple independently trained
diffusion models through shared coupling costs, enabling coordinated behavior without requiring a joint model. We evaluate MOSAIC on navigation, dexterous grasping, motion planning with unseen geometry, and collaborative box manipulation with independently trained
robot priors. Across these settings, MOSAIC adapts and composes frozen
diffusion priors under new deployment constraints without retraining.
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All authors Sabrina Bodmer, Rahel Rickenbach, René Zurbrügg, Melanie N. Zeilinger, Colin Jones
Artificial Intelligence
Glasseek: A Transparent Recipe for Training and Trustworthy Evaluation of DeepSearch Agents
Cong Pang, Jiaqi Su, Chengrui Wu et al. (+14 more)
Deep search enables
language agents to solve complex information-seeking tasks through iterative web exploration. However, developing robust open-source
agents remains challenging for three reasons. First, opaque trajectory-construction pipelines make training data difficult to audit and reproduce. Second, context compression can create a substantial mismatch between training and inference. Third,
agents may retrieve
benchmark content from the open web during evaluation, contaminating the results and overstating their performance. To overcome these bottlenecks, we introduce Glasseek, a fully transparent deep search framework spanning the complete development lifecycle, releasing all data, model weights, training recipes, and code. Specifically, for data construction, we establish a verifiable trajectory curation pipeline that systematically generates high-quality exploration paths. For training, we propose Compaction-Managed GSPO (CM-GSPO), which uses the same policy for search and summarization while optimizing only execution sub-trajectories, enabling end-to-end search-policy training across context-compaction boundaries. For evaluation, we conduct the first systematic audit of test-set leakage in open-web
agents and release a modular anti-leakage plugin that seamlessly integrates into existing evaluation harnesses. Under this rigorous evaluation protocol, our 9B
agent outperforms frontier proprietary
agents across almost all deep research
benchmarks spanning diverse task types, and achieves the best overall performance among recent open-weight
agents, setting a new state of the art at the 9B scale.
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All authors Cong Pang, Jiaqi Su, Chengrui Wu, Kuihao Yan, Tianrui Zhang, Zixuan Qiu, Chao Lin, Zixiu Lin, Zixuan Chen, Zelin Xiao, Tiankuo Yao, Jiawei Hong, Zixuan Chen, Dan Xu, Lewei Lu, Wenjie Wang, Xin Lou
Machine Learning
A physics-coupled graph neural operator for mid-plane six-component stress tensor field prediction in concrete penetration
Wenpu Du, Peng Zhou, Yunlong Xia et al. (+5 more)
The penetration-resistant design of concrete structures requires the spatiotemporal
evolution of the stress field within the target, whereas in design only the strength grade, the geometry and the projectile parameters are known, and the aggregate arrangement is not. Empirical formulae and scalar-output surrogates serve design screening, and mesoscale finite element models resolve the aggregate-scale response, but one solution cannot cover random aggregate samples at scale, and no framework links mesoscale heterogeneity to field-level prediction. A physics-coupled graph neural operator (PC-GNO) is proposed, which learns in function space the
evolution of the mid-plane six-component stress tensor field. The training data are generated by full-scale, aggregate-resolved finite element models and comprise 359 cases at the impact velocities of 100, 135 and 165 m/s, corresponding to compressive strengths of 14, 23 and 48 MPa; the data source is verified by laboratory measurements. The coupling strength of message passing is constructed from the impedance contrast across the material interface, and the update is constrained by spectral normalization. Under the single-step primary setting, the mean accuracy of the three levels is 80.16% (91.45%, 77.80% and 71.22%), i.e. 27.81, 42.46 and 34.16 percentage points above the preceding-frame field of the same window; with 20 664 parameters it exceeds the 87.9% of the best
Fourier neural operator configuration (about 812 times as many parameters) and the 71.0% of the closed-form linear regression. The output is the complete six-component tensor field rather than a fixed engineering quantity, so that strength criteria and energy indicators follow from one set of weights. For field-level response prediction in concrete penetration, this result provides a data-driven route, with conditions of applicability defined by the training and the evaluation.
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All authors Wenpu Du, Peng Zhou, Yunlong Xia, Sinuo Xin, Congcong Zhang, Boyang Zhang, Yi Zhang, Wenzheng Xu
Machine Learning
Intrinsic Geometry Matching: learning observation-equivariant process geometry
Manuel Lecha, Michael Bronstein and Iolo Jones
Real data are often arbitrary, indirect observations of an unknown underlying process, and those observations can distort its geometry. Existing
geometric methods equip data with the induced geometry of the ambient space, but this entangles the true geometry of the process with the distortion of the observation. To address this, we introduce Intrinsic Geometry Matching (IGM): a scalable approach to learn the intrinsic geometry of the underlying process. Specifically, we learn the carré du champ operator of the process, allowing us to recover its
diffusion geometry. Our method uses the fact that data often come with natural perturbations, like steps in a time series, to recover the intrinsic carré du champ while transforming naturally with changes in observation: it is observation-equivariant. Furthermore, since this equivariance relates the geometry of different spaces, we can train a model using additional sources of data, even when that data lies in another space. We evaluate IGM on molecular dynamics, image, and
video data and find better performance and scaling than existing neural and graph-based
geometric tools.
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Machine Learning
Constant-Depth Rational-Weight GNNs and their Connection to Schanuel's Conjecture
Daumantas Kojelis and Carsten Lutz
A landmark result states that the expressive power of graph neural networks (GNNs) is bounded from above by the 1-dimensional Weisfeiler-Leman (1-WL) test. We ask whether a constant-depth GNN can match the expressive power of 1-WL while using only rational weights. We prove that this is the case when Schanuel's
conjecture, a famous open problem from transcendental number theory, is taken to be true. Our GNN construction works even with integer weights, and more generally with a broad range of rational weights. As the activation function, it may use logistic sigmoid,
hyperbolic tangent, or related functions. It is uniform across all graph sizes. We also prove a partial converse: if a GNN with exponentiation,
hyperbolic sine or
hyperbolic cosine as activation matches the expressive power of the 1-WL test in a slightly more expressive setting where graphs may contain certain auxiliary vertices, then this implies Schanuel's
conjecture restricted to the numbers that arise in computations of the GNN. We also conduct experiments showing that the constructed GNNs indeed match the 1-WL test.
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Machine Learning
Generative Models as Adaptive Trust Regions for Discrete Bayesian Optimisation
Michael Dodds, Erik Bodin, Sebastian W. Ober et al. (+3 more)
Bayesian optimisation (BO) is a powerful framework for sample-efficient optimisation of expensive black-box functions, but its application to high-dimensional discrete scientific design remains challenging. Local trust-region BO methods such as TuRBO address high dimensionality by restricting search to adaptively sized neighbourhoods. However, a direct discrete analogue based on edit distance can be poorly suited to structured discrete objects: neighbourhoods grow combinatorially, and metric proximity does not capture the dependencies that determine whether a design is plausible or useful. Motivated by the growing availability of pretrained generative models, we introduce GenTuRBO, which replaces the
geometric trust region with an adaptively scaled proposal distribution defined by a pretrained generator. The generator provides a learned prior over discrete transformations, encoding coordinated changes without explicitly enumerating edit-distance neighbourhoods. GenTuRBO performs strongly across all three domains and achieves the highest mean final scores on Ehrlich sequence optimisation and Sokoban coordination.
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All authors Michael Dodds, Erik Bodin, Sebastian W. Ober, Tom Diethe, Carl Henrik Ek, Henry Moss
Machine Learning
TabGift: A Context-Efficient Foundation Model for Tabular Data Generation
Yu-Sheng Li, Jun-Peng Jiang and Han-Jia Ye
Tabular data generation aims to synthesize realistic samples from limited observations while faithfully capturing the underlying data distribution. Tabular foundation models (TFMs), pretrained on diverse tabular tasks and adapted through in-context learning, have demonstrated strong predictive capabilities, suggesting the potential of extending this paradigm to tabular data generation. However, the strong predictive performance of TFMs often relies on sufficiently large context sets, whereas tabular generation is typically needed precisely when target data are scarce. Extending the foundation-model paradigm to generation therefore requires reliable distribution inference from only a limited context. To this end, we propose TabGift, a context-efficient tabular foundation model enabling both efficient distribution modeling and effective generation from only a few context samples. TabGift maps heterogeneous tables into a shared latent space with a pretrained tabular encoder, where an in-context
diffusion model learns to denoise latent samples conditioned on a small set of clean examples. This design transfers generative priors across
datasets and enables distribution inference from limited context, while a lightweight
dataset-specific MLP decoder maps generated representations back to the original data space. Experiments on 7 classification and 11 regression
datasets show that TabGift consistently outperforms representative baselines in synthetic data quality, with particularly strong gains under limited-context settings, while achieving substantially faster adaptation and generation.
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Computer Vision
Self-Supervised Learning of Semantically Consistent Visual Features via a Simple 3D Prior
Sebastian Day and Oisin Mac Aodha
Visual features extracted from
vision models like DINOv2 or Stable
Diffusion enable high quality semantic correspondence between images. However, such features exhibit documented limitations that hinder accurate correspondence estimation such as the inability to disambiguate the left from right for categories exhibiting
symmetries or repeated parts. Previous attempts to improve these features rely on using supervised data such as annotated keypoint pairs or camera pose information, or make restrictive topological assumptions, such as modelling the object of interest as a 3D sphere. We introduce a new self-supervised approach that allows us to learn features for topologically complex object categories using a simple prior. Instead of relying on an expensive instance-level analysis by synthesis pipeline, our approach makes use of contrastive learning and distribution matching at the global
dataset-level to learn the coarse shape and appearance of a category. Combined with additional
geometric losses, this enables us to replicate the global and local statistics of visual features which match those that are extracted from real images. Quantitative results are presented across several challenging object categories, demonstrating the effectiveness of our approach.
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Distributed Computing
MODAIC: Coordinated Asynchrony for Decentralized Multimodal Learning
Yanhang Shi, Xiaoyu Wang, Houwei Cao et al. (+2 more)
Multimodal decentralized learning enables collaboration across heterogeneous edge devices without sharing raw data, but differing modality sets create coupled computation and communication bottlenecks.
Multimodal agents support cross-modal
alignment while training more model components and sharing bandwidth across multiple modality overlays. Under bandwidth limits, these
agents can become hot spots: transfers miss aggregation windows, and their updates become stale or underrepresented, degrading worst-
agent accuracy. Asynchrony removes global blocking but does not by itself coordinate local cross-modal interaction with timely peer-update exchange. We present MODAIC (Modality-Decoupled Asynchrony with Interleaved Coordination), a wait-free system that coordinates local modality progress, peer-to-peer update delivery, and aggregation under heterogeneous compute and bandwidth. Hierarchical asynchrony lets modalities progress independently while enforcing reciprocal feature exchange; load-aware update fetching overlaps training with communication, limits concurrent serving load, and reuses late arrivals; and consensus-aware peer selection uses lightweight metadata to account for update availability, peer participation, and loss similarity without active probing. We implement MODAIC as a containerized multi-
agent framework and evaluate it on local and cross-region cloud testbeds. Across AVE and KU-HAR, MODAIC improves average accuracy and
multimodal worst-
agent performance. On AVE at 200 Mbps per
agent, it improves
multimodal worst-
agent accuracy by 11.0 percentage points at 1000 seconds over the strongest baseline. On the cloud testbed, it reduces time-to-accuracy by up to 30%. Ablations show that simpler load-balancing and delayed-aggregation variants leave a worst-
agent accuracy gap, and stress tests show tolerance to WAN variability and partial tracker unavailability.
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All authors Yanhang Shi, Xiaoyu Wang, Houwei Cao, Jian Li, Yong Liu
Computer Vision
AUGUstus: Autonomous Driving VLA Model with Unified Generation and Understanding
Yuqi Ye, Caigao Jiang, Zhuonan Wang et al. (+12 more)
Vision-
language-action (VLA) models show strong potential for end-to-end autonomous driving by leveraging world knowledge and understanding to excel in long-tail scenarios. However, current VLAs have two limitations: 1) Degeneration into
vision-action (VA) models. For low latency, regression/flow-based methods directly generate actions but underuse VLM understanding, causing unsafe, unexplainable actions. 2) Autoregressive high latency. Autoregressive methods sequentially generate chain-of-thought (CoT) and action
tokens, fully leveraging VLM understanding, but high latency hinders deployment. To enable low-latency deployment while using CoT to assist action generation, we propose AUGUstus, an Autonomous-driving VLA model with Unified Generation and Understanding. We unify
diffusion-based action generation and autoregressive CoT understanding in one model, where understanding only serves generation. Joint CoT supervision preserves VLM spatial understanding in shared representation for safer, more accurate trajectories. AUGUstus-mode orders action generation before CoT, so CoT supervises reliable actions during training while inference needs only action output, enabling low latency. AUGUstus-GRPO, a unified GRPO
algorithm, jointly optimizes action and CoT, improving action performance. Experiments show AUGUstus achieves 94.6 PDMS on NAVSIMv1 and 90.7 EPDMS on NAVSIMv2, SOTA planning without scorer module. On nuScenes, it achieves average L2 error 0.27 m and average collision rate 0.05%, best among compared methods. To validate AUGUstus practicality, we train on in-house data with 512 NVIDIA H800 GPUs for ~2 days, evaluate on an internal 1,000 km
dataset, and preliminarily deploy on a real vehicle, achieving 12 Hz operation. Project page: https://augustus-ad.github.io
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All authors Yuqi Ye, Caigao Jiang, Zhuonan Wang, Congyuan Pei, Chao Qu, Changhao Peng, Junhong Lin, Qize Yu, Zetian Song, Guangzhao Fu, Chongruo Wu, Shiyu Huang, Shuchang Zhou, Honggou Yang, Wei Gao
Machine Learning
From Suboptimal to Optimal: One-Step Latent Flow Policies for Offline Reinforcement Learning
Shenghua Lu, Zixuan Yang, Rongxu Zhao et al. (+3 more)
Offline reinforcement learning aims to learn effective policies from fixed
datasets without environment interaction. Although generative models such as
diffusion and flow matching enhance policy expressiveness, they typically start from uninformative Gaussian noise and generate actions through multi-step iteration, leading to high computational costs and failing to effectively leverage suboptimal trajectories in
datasets. We propose MeanFlow2Better (MF2B), which reformulates offline RL as generative decision chunk optimization from current performance status to better performance status in a high-level latent decision space. MF2B maps trajectory chunks to high-level latent decision representations through an encoder, placing generation starting points within a structured decision space sculpted by behavioral data. The model learns a shared conditional mean velocity field in this space, constructing two flow mappings: from Gaussian-Induced Decision Sources toward positive behavioral decision regions, and from suboptimal decisions toward high-value decision regions. Both flow mappings complete decision refinement through one-step ODE integration over the unit time interval $\Delta t=1$, requiring neither multi-step iterative sampling nor backpropagation through time. Furthermore, we propose hierarchical chunk-level advantage to guide the flow direction toward high-return regions, where the high-level decisions cover multiple low-level actions, leading to sequence length compression from $T$ to $K \approx T/H$ and significantly lower computational overhead. On the D4RL Gym-MuJoCo
benchmark, MF2B achieves an average score of 90.91, matching or surpassing strong baselines.
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All authors Shenghua Lu, Zixuan Yang, Rongxu Zhao, Bohao Qu, Jifeng Hu, Hechang Chen
Computation and Language
Predicting Mental Health Help-Seeking Behaviour on Reddit: A Survival Analysis of Linguistic and Behavioural Signals
Nisar Ahmed, Ammara Kazmi, Irtaza Bashir Raja et al. (+1 more)
Most prior work on predicting mental health help-seeking treats it as post-hoc classification of users already active in clinical communities, which cannot tell whether someone approaching a transition can be identified in advance. We instead frame help-seeking as a time-to-event problem and ask which Reddit users will eventually seek mental health support, and how imminent that transition is. We build a two-arm cohort: a control frame of 6,337 ordinary users with genuine right-censoring (12.1% transition rate) to answer who will transition, and a clinical frame of 5,023 eventual transitioners to answer how soon. Features come only from posts written before any clinical activity, and user-grouped five-fold cross-validation plus a four-year temporal holdout guard against leakage. We compare TF-IDF, MentalBERT
embeddings and behavioural activity features using Cox proportional hazards models, Random Survival Forests and an LSTM sequential model. Behavioural features alone outperform text for predicting who will seek help (C-index 0.797 vs. 0.763), and combining both is best (0.823 under cross-validation, 0.856 on the holdout). The pattern reverses for predicting how soon a known transition will happen: text remains a stable predictor (C-index 0.698) while behavioural features become unstable. Structural engagement patterns therefore carry information about future help-seeking beyond linguistic content, and separating who from when yields a cleaner analysis.
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All authors Nisar Ahmed, Ammara Kazmi, Irtaza Bashir Raja, Didier Devaurs
Social and Information Networks
Tracing Money Flows in the Multi-Token Cryptocurrency Network
Natkamon Tovanich and Célestin Coquidé
Tracing money flows in cryptocurrency transaction networks is challenging because funds can move across accounts, transactions, and
token layers. We propose a transaction-aware graph in which nodes represent account–asset states within transactions, while edges encode observed transfers, inferred asset transformations, and temporal inter-transaction sequences. Applied to Aave borrow–repay flows, we compare graph construction and edge-
pruning strategies and extract shortest paths using a heuristic weighting. Our results reveal a trade-off between reachability and interpretability and uncover frequent
token sequences underlying how borrowed assets are transformed before repayment. This work lays a foundation for general, interpretable multi-
token flow tracing.
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Artificial Intelligence
PRAISE: A Reproducible Workflow for Non-Programmer Scientists to Implement Published AI Models
Max Achterweust, Stefan Groot Nibbelink, Vincent J. de Beer et al. (+7 more)
Artificial Intelligence (AI) models are increasingly developed, published and used across scientific fields. Researchers, students and industry professionals who want to apply these models to their own research questions often lack the programming and data science expertise to do so. Existing guidelines cover single steps, but not the route from a published model to working predictions on one's own data. Here we present the hands-on workflow PRAISE, a Practical, Reproducible AI-model Implementation for Science and Education, that helps non-programmers implement published AI models step by step. Because implementations rarely work the first time, PRAISE anticipates common failures and provides a fallback route for each. As a proof of principle, three BSc chemistry students and their tutor applied PRAISE without prior programming or data science knowledge. They reproduced, validated and extended a state-of-the-art model that predicts enzyme-substrate interactions. This shows that with PRAISE, implementing an AI model from the literature becomes a learnable skill, one that turns non-programmer scientists from users into evaluators and improvers of AI models.
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All authors Max Achterweust, Stefan Groot Nibbelink, Vincent J. de Beer, Stefan Schipper, Zeynep Yüksektepe, Pedro Andrade da Silva, Aurelia Peppelaar, Martijn J. van der Willigen, Christophe B. Minkenberg, Robert F. van der Willigen
Information Retrieval
WeGR: Unified Generative Retrieval for Industrial Search Systems in WeChat
Jiajun Han, Guoxin Ma, Felix et al. (+9 more)
Generative
retrieval (GR) offers a promising paradigm for industrial search by unifying candidate generation within an autoregressive framework. However, its effectiveness is limited by two key challenges. First, existing Semantic ID (SID) tokenizers struggle to incorporate collaborative signals while maintaining effective codebook utilization, limiting their ability to distinguish items and achieve high-quality
retrieval. Second, existing reinforcement learning (RL) methods suffer from severe reward sparsity under exact SID matching, leaving many sampled trajectories without effective learning signals and hindering preference optimization. To address these challenges, we propose WeGR, a unified generative
retrieval framework consisting of two complementary components. Specifically, the Collaborative and Collapse-free Tokenizer (CoCo-VAE) incorporates query-to-item and item-to-item collaborative signals while mitigating codebook collapse through codebook refresh and complementary regularization, enabling more informative and balanced SID representations. Building upon these SIDs, Dynamic Horizon Policy Optimization (DHPO) progressively expands both reward evaluation and policy optimization horizons, reducing learning difficulty while leveraging behavioral reward signals at different levels of the SID hierarchy, without relying on auxiliary reward models. Extensive offline experiments demonstrate the effectiveness of WeGR in improving
retrieval and ranking performance. Moreover, a two-month online A/B test in WeChat E-commerce Search demonstrates relative improvements of 2.539% in orders, 1.508% in click-through rate, and 1.381% in conversion rate, while reducing online CPU and GPU usage by 25.5% and 26.1%, respectively. The system has been successfully deployed across multiple search scenarios in WeChat, serving billions of users, generating tens of millions of PVs daily.
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All authors Jiajun Han, Guoxin Ma, Felix, Liangsheng Xu, Ruiqi Yu, Yingdi Guo, Zongbing Wang, Xinhua Ye, Chenyan Hua, Xin Xie, Pengcheng Yang, Haijun Wu
Robotics
Joint-Feasibility Margin Feedback for Bounded-Input Second-Order Control Barrier Functions
Siyuan Yang and Shaoming He
Multiple second-order control barrier function constraints can exhaust their common control authority under bounded acceleration, even when the current state remains feasible. We define a signed local joint-feasibility margin whose sign exactly identifies whether the current barrier half-spaces intersect the admissible input set. For unit-scaled distance constraints, it has acceleration units and a support-function dual that identifies weighted constraint conflicts. We predict the margin one sample ahead for nine task-directed candidates, selecting either the least-deviating candidate that attains a desired margin or the candidate with the largest predicted margin before solving the same hard HOCBF quadratic program. A controlled one-step case shows that, at the state predicted under the hard-QP command, two constraints remain individually feasible but become jointly infeasible, whereas the margin-feedback controller retains a positive next margin. Separately, across 20 paired initial conditions, the margin-feedback controller retains the hard-QP command in a low-pressure task and acts at positive margin in a critical task before the hard-QP problem becomes infeasible; its trajectories remain feasible at that time and complete the task. Source code is publicly available. https://github.com/xirhxq/cbf/tree/robio2026-preventive-regulation
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Computer Vision
The Value of Being Wrong: Self-Mined Visual In-Context Learning from Errors
Shu Zhao and Tan Yu
Visual in-context learning (ICL) enables
vision-
language models (VLMs) to adapt at test time via demonstration examples, but its effectiveness depends critically on the examples selected. Recent counterfactual selection methods improve ICL by constructing demonstrations that expose how individual attribute changes affect the answer. However, they require multi-stage pipelines involving attribute extraction, caption engineering, and composed image matching. We propose a simpler alternative grounded in a key observation: a model's own prediction errors are natural counterfactual demonstrations that directly encode its failure modes. Our method, SMILE (Self-Mined Visual In-Context Learning from Errors), builds a model-specific pool of hard negatives offline by recording the target VLM's own mistakes. At test time, the prediction serves as a direction signal that surfaces, in the semantic
embedding space, the past mistakes most likely to recur for the current query. This prediction-conditioned identification provides a unified mechanism for both closed-set classification and open-ended visual question answering without task-specific design. Across four
benchmarks and four VLMs, SMILE delivers an average task-score gain of +3.09 points over the SOTA baseline at 5x lower latency. Project page: https://tree-shu-zhao.github.io/SMILE/
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Artificial Intelligence
MADDIE: Multi-Agent Drug Discovery Engine
Ye Liu, Reza Averly, Avery Meyer et al. (+4 more)
Drug discovery remains a resource-intensive and time-consuming process, involving billions of dollars and a decade of development to advance a drug to
market. While many in silico tools have accelerated this process, operating them still demands substantial time, effort, and expertise from medicinal chemists. Autonomous systems built on Large
Language Models (LLMs) have emerged as a promising solution, yet current designs limit their performance. Existing single-
agent systems scale poorly to long-horizon, multi-step, and realistic tasks, whereas multi-
agent systems typically execute predefined workflows that cannot capture the iterative, non-linear nature of a real drug discovery campaign. Here, we present MADDIE, a multi-
agent drug discovery engine. It comprises a central
agent, Orchestrator, and four specialized worker
agents: HitIdentifier, LeadOptimizer, Synthesizer, and Evaluator, each representing a critical step in a drug discovery campaign. Under Orchestrator's orchestration and coordination, these worker
agents are composed into flexible workflows, enabling MADDIE to adapt its strategy and navigate the drug discovery pipeline end-to-end. Across 50
protein targets, MADDIE produces molecules satisfying key property requirements and their synthesis routes, outperforming existing agentic systems, generative models, and general-purpose LLMs by a wide margin. Our analysis reveals how these
agents collaborate and that MADDIE can employ different strategies across targets to achieve the campaign's goal, while still being efficient in time and
tokens. Together, they demonstrate MADDIE as a low-cost, effective, and efficient autonomous engine for accelerating drug discovery. Code and
dataset is available via https://github.com/ninglab/MADDIE.
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All authors Ye Liu, Reza Averly, Avery Meyer, Frazier N. Baker, Ruoxi Gao, Daniel Adu-Ampratwum, Xia Ning