Robotics
KinoGen: Customizable Humanoid Loco-Manipulation References via Kinematically Grounded Video Generation
Qi Wu, Lulu Zhao, Sanghyun Hahn et al. (+6 more)
We present KinoGen, a framework that synthesizes customizable humanoid loco-manipulation references through kinematically grounded
video generation. KinoGen conditions a pretrained
video generator on calibrated renders of the target
robot and object in a simulated task instance and recovers the reference by fitting the same models to the generated two-view
video, so that the reference lies in the
robot configuration space by construction without a human body proxy or retargeting. Body, hand, and object priors initialize the reconstruction, and direct optimization against cross-view evidence refines it. Since an edited keyframe is simply another rendered state, a user can prescribe a goal object pose, regenerate the
video around the edited keyframe, and obtain a reference that satisfies the pose exactly. Across eight loco-manipulation tasks, KinoGen yields lower penetration and foot skating than
video-based baselines and preserves over 90% of annotated contacts, and its customized references satisfy every prescribed target with substantially less penetration than direct interpolation. Tracking policies trained on the synthesized references are deployed on a Unitree G1 humanoid with dexterous hands. Project webpage: https://kinogen-humanoid.github.io/.
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All authors Qi Wu, Lulu Zhao, Sanghyun Hahn, Rishi Yennu, Emmy Yang, Dingqi Zhang, Tao Pang, Jiuguang Wang, Kuan Fang
Multimedia
Frame Rate, Not the Grid: A Confound in Single-Clip Optical ENF Video Forensics
Ryan Wyton and Bacha Rehman
The optical electric network frequency, the faint mains-driven flicker imprinted on rolling-shutter
video, is increasingly proposed for verifying where footage was captured and for flagging manipulated or generated
video. This letter shows that reading the mains frequency from a single short clip is confounded by the camera frame rate: at the rates in common use, a harmonic of that rate lands in the same narrow band as the true flicker, so the recovered grid is set by the frame rate, not the supply, and the
estimator returns a confident value instead of abstaining. The underlying aliasing is known; its forensic consequence is not, and is the contribution here. On the 804 clips with a measurable in-band line the flicker feature does not separate genuine from generated
video (area under the curve 0.509), while the recovered grid tracks the frame rate exactly for the generators, and a controlled synthesis confirms the mechanism against ground truth for genuine grids. Single-clip grid, and hence region, identification is therefore unreliable at the standard rates, and a comb-aware
estimator that models the frame-rate harmonics restores the distinction between a genuine grid and a frame-rate comb under controlled conditions.
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Computation and Language
PerSo: Investigating Facets of LLMs' Social Intelligence in Real-World Persuasion Scenarios
Ali Zamani, Soroush Esfahanian, Erfan Shahabi et al. (+3 more)
Understanding the social intelligence of large
language models (LLMs) is increasingly important for applications involving human-AI interaction and multi-
agent environments. Social intelligence comprises two aspects: cognitive intelligence, the ability to infer others' mental states, and behavioral intelligence, the ability to select appropriate social actions based on these inferences to achieve social goals. Persuasion provides a natural testbed in which both aspects are simultaneously at play, yet prior
benchmarks typically evaluate them in isolation using synthetic scenarios. We introduce PerSo, a
benchmark that evaluates both aspects on real-world persuasion dialogues from Reddit's ChangeMyView community, using an LLM-in-the-loop pipeline to construct the evaluation tasks, validated through human annotation. Our evaluation of diverse LLMs shows that performance generally scales with model size but varies across dimensions. The strongest LLM slightly surpasses the best observed human performance on the behavioral task, while the best human participant remains ahead on the cognitive task.
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All authors Ali Zamani, Soroush Esfahanian, Erfan Shahabi, Mohammad Taher Pilehvar, Azadeh Shakery, Yadollah Yaghoobzadeh
Machine Learning
Detection of Potato Plant Leaf Diseases using Convolutional Neural Networks
Haimanot Bitew Alemayehu, Simachew Melaku Ayalew, Chilotie Desalew Mengesha et al. (+3 more)
Potato is an important food-security crop in Ethiopia, but its production is affected by foliar diseases, particularly early blight and late blight. This study develops a lightweight convolutional neural network (CNN) to classify potato leaves into three categories: healthy, early blight, and late blight. The proposed model was trained using 2,215 images from the PlantVillage
dataset and evaluated on an independent field
dataset containing 247 images collected from potato farms in Banja, Awi Zone, Ethiopia. On PlantVillage, the model achieved 99.04% training accuracy, 98.21% validation accuracy, and 98.66% test accuracy. On the field
dataset, it achieved 90.28% accuracy. The study also compares the proposed CNN with VGG16 and MobileNetV2 in terms of classification performance and computational efficiency. The proposed model contains 446,787 trainable parameters, occupies approximately 1.70 MB, and requires an average of 5.58 seconds per training epoch in the reported experimental setup, compared with 9.96 seconds for VGG16 and 7.68 seconds for MobileNetV2. The results demonstrate the potential of a compact CNN for potato leaf disease classification and highlight the importance of independent field evaluation, where image conditions are more variable than in controlled
benchmark datasets. The proposed approach may support preliminary disease identification for smallholder farmers in Ethiopia and similar agricultural settings.
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All authors Haimanot Bitew Alemayehu, Simachew Melaku Ayalew, Chilotie Desalew Mengesha, Ayisheshim Almaw Bogale, Anteneh Birhan Yenew, Abrham Tegegne Bogale
Computer Vision
Reliability-Aware Multimodal Sentiment Prediction Under Local Modality Missingness
Minghao Kong, Deng Yao, Xi Zhang et al. (+4 more)
Multimodal sentiment models are usually evaluated on complete utterances, yet streams often lose a contiguous span of text, audio, or
video. We study local missingness on aligned CMU-MOSEI features. Our reliability-aware network (RM-Net) keeps the original temporal positions, represents unobserved positions with a learned
token, outputs bounded per-modality intensity contributions gated by observation quality, and is trained with dual-view masking of contiguous segments. We fuse five RM-Net seeds with a LoRA-adapted BERT text expert, using a fusion weight that depends only on the visible fraction of content
tokens. Architecture, checkpoint, and fusion choices were made on the validation split. On the held-out split, the fused model reaches a macro-F1 of 0.655 on clean inputs and 0.634 under 40% mixed missingness; its two-view average exceeds the auxiliary-head baseline by 0.024 (95%
video-cluster bootstrap interval [0.003, 0.045]). Because this split was inspected during development, these numbers are not a blind test. For explanations, exact Shapley attributions over the eight modality subsets and position-level occlusion show that top-ranked positions reduce the predicted-class probability faster than random positions (AUC gap 0.113 on validation). We also describe an auditable word-level
alignment pipeline for raw clips; its outputs do not feed the predictive models.
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All authors Minghao Kong, Deng Yao, Xi Zhang, Ying Gao, Jiurun Chen, Zirong Liu, Rongjie Wang
Computation and Language
PrismaCoder: Pushing the Boundaries of Open Multimodal Code Intelligence through Decomposed Rubric Synthesis
Qiushi Sun, Jingyang Gong, Yang Liu et al. (+14 more)
Multimodal code intelligence connects the symbolic world of programs with the visual world their execution creates. Interest in this capability is burgeoning, as models are increasingly asked to produce charts, webpages, animations, and semi-structured documents. Recently, proprietary models have pushed far ahead, producing interactive artifacts even from a text description or a screenshot alone. Yet their data and training remain closed, and open progress is held back by the quantity and quality of available data and by the pipelines and methods for producing it. Quality is the harder of the two, since a sample must be correct as a program, well built, and above all visually right once rendered. No existing signal covers all three. Execution often returns just a pass or fail, code similarity does not map to the visual output, and task-specific verifiers do not generalize. To push open progress further, we start from the data side with PrismaForge, a hierarchical data pipeline centered on a synthesized rubric that decomposes these factors into checkable criteria. The same rubric serves as both filter and reward: it curates the supervised corpus, then drives reinforcement learning on visual-coding tasks. Leveraging PrismaForge for data synthesis and filtering, we build PrismaCoder-2M, the most diverse open
dataset for
multimodal code intelligence and the largest to date. On the model side, we train PrismaCoder-9B and PrismaCoder-35B on this data and release both. They lead the open state of the art by a wide margin and rival leading proprietary systems. Our analyses further show that the proposed rubric-based pipeline improves
multimodal coding performance across model backbones. We release our code and data at https://qiushisun.github.io/PrismaCoder-Home/.
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All authors Qiushi Sun, Jingyang Gong, Yang Liu, Qiaosheng Chen, Zhangyue Yin, Zixian Huang, Hanxu Hu, Jing Xiong, Fangzhi Xu, Haochen Tan, Haoli Bai, Lifeng Shang, Xiaoguang Li, Ben Kao, Qipeng Guo, Lingpeng Kong, Jiahui Gao
Cryptography and Security · Cross-listed from quant-ph
Quantum-Aware Key Rank for Side-Channel Attacks evaluation
Yevgen Kotukh
Side-channel attacks on block ciphers rarely recover the key outright: they leave a residual key space ordered by subkey posteriors, and evaluation laboratories report the key rank of the true key against thresholds near 2^48 and 2^60. No evaluation framework models an adversary who collects traces classically and finishes the search on a
quantum computer, although
quantum search with advice provides exactly that. We give such an adversary a cost model and a metric. For a device whose key has rank r, any
quantum algorithm with classical access to the posteriors needs Omega(sqrt(r)) cipher evaluations, and block search achieves O(sqrt(r)); the per-instance advantage is exactly quadratic, and the super-quadratic exponents reported for search with advice are averages over the prior that apply to no single device. Under the NIST MAXDEPTH budget D and a Grover iteration of g gates and depth d, the quadratic advantage holds only for ranks up to (4D/(pi d))^2 and collapses to a constant factor beyond; with the best published AES-128 oracle and D = 2^40 the window closes near rank 2^58.6. We define the
quantum-aware key rank, the classical rank of equal cost, computable from the rank
estimators laboratories already run, and derive the classical rank a device must retain to keep a given threshold against each depth budget. Block search and amplitude amplification from the product-form posterior state are within a factor of two to four of each other on Hamming-weight posteriors, so the cost of coherent indexing decides between them. Experiments on simulated leakage and a toy cipher confirm the predicted window: at SNR 0.125 a depth-limited
quantum adversary is worth about 22 traces to a classical one, however large its gate budget.
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Machine Learning
Revisiting Simple Baselines for Relational Deep Learning
Viet The Nguyen, Vijay Prakash Dwivedi and Gunther Gust
Relational deep learning has emerged as a powerful paradigm for learning end-to-end predictive models over relational databases by representing them as temporal heterogeneous graphs. Recent advances based on graph neural networks and
Transformers have achieved strong performance across diverse domains. However, the effectiveness of simpler relational baselines remains underexplored, making it difficult to assess how much architectural complexity is needed to achieve strong predictive performance. Motivated by the observation that predictive signals arise from target attributes, relational attributes, and historical labels, we revisit how effectively these signals can be exploited using simple propagation methods. We adapt fixed low-pass feature propagation to relational entity graphs and train an MLP for prediction. The resulting predictions are further refined using Correct-and-Smooth, a graph-based label propagation
algorithm that incorporates historical labels over task-specific meta-graphs connecting target entities. Across classification and regression tasks on RelBench, an MLP trained on neighborhood-filtered features outperforms one using only target-entity features on most tasks and recovers a substantial portion of the performance of heterogeneous GNNs. Correct-and-Smooth provides further gains from historical labels, although applying it requires a separately constructed meta-graph because connections among target entities are not directly specified by the relational entity graph. Together, our studies revisit the effectiveness of established propagation methods as simple RDL baselines while identifying canonical meta-graph construction as a key challenge for effective label propagation.
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Machine Learning
Generative Representation Learning for Scalable Latent Tokenization
Alan Baade, Kyle Sargent, Vincent Sitzmann et al. (+4 more)
We introduce an approach for self-supervised representation learning designed to be used as a tokenizer for generative modeling. Our objective supervises a representation loss by factorizing the pointwise mutual information of the data, which we estimate by taking information differences between conditional predictions from a learned generative model. We justify our
algorithm theoretically and empirically, demonstrating that our generative approach to representation learning avoids dimensional collapse and feature suppression in cases where commonly used methods like DINO and LeJEPA fail. Moreover, we show our representation learning technique is scalable; expending more compute leads to predictably better latent tokenization. For the first time, we perform a careful analysis of the combined flop efficiency of tokenizer and generative model and demonstrate that our approach is a compute-optimal improvement over direct
diffusion for pixel-space generation, obtaining an FID-50K of 2.18 with JiT-L. Applied to ImageNet and the text-to-image
dataset GPIC, we show tokenizer encoder scaling laws for diffusability, where extrapolating our approach achieves representations in 7x fewer FLOPs than DINOv2 on ImageNet and 8x fewer FLOPs than WebSSL on GPIC.
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All authors Alan Baade, Kyle Sargent, Vincent Sitzmann, Eric Ryan Chan, Justin Johnson, Li Fei-Fei, Gordon Wetzstein
Artificial Intelligence
ActiveEvidence: Decision-Focused Evidence Graphs with Verifiable Acquisition Gaps
Miaobo Hu, Shuhao Hu, Xiaobo Guo et al. (+5 more)
A
multimodal agent must decide whether visual evidence is worth acquiring, which candidate or resolution to query, and when further interaction can no longer change its decision. We study this problem under a strict free-metadata/paid-observation boundary,
stochastic vector costs, and a frozen execution trace, and treat it as decision-certifiable active evidence acquisition. A Counterfactual Evidence Interaction Graph (CEIG) represents unary evidence value, signed second-order interaction, and directed conditional harm while maintaining explicit score enclosures for unresolved interactions; a decision-weighted enclosure objective tightens the bounds that can change action identity. Gap-directed refinement evaluates only interactions that can displace the incumbent acquisition and stops once an $\epsilon$-decision certificate shows that unresolved terms cannot change the selected action beyond a declared tolerance. The certificate is model-relative: it verifies policy-consistent optimality under the pinned learned score, and semantic quality is measured separately through post-freeze labels. Paired continuation rollouts learn the downstream value of paid previews, an online operational cost filter is separated from independent finite-policy-family trajectory certification, and an acquisition monitor with an independently implemented verifier binds every certificate to the frozen trace. On 640 cases split into 384/128/128 training, validation, and test cases, the controller reaches task score 0.842, grounding F1 0.816, evidence recall 0.873, 1.74 accepted calls, 286.5K visual
tokens, and 742ms p95 latency. Dense replay of issued certificates shows 0.992 action agreement and a 0.988 certificate rate while evaluating 19.6 instead of 191.4 expensive interaction terms per decision.
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All authors Miaobo Hu, Shuhao Hu, Xiaobo Guo, Xin Wang, Bokun Wang, Peng Zhang, Daren Zha, Jun Xiao
Robotics
EarlyCommit: Calibrated Word-by-Word Commitment for Robots Acting Before You Finish Speaking
Muhammad Haris Khan
Human listeners can often identify the intended object several words before an instruction ends.
Robots, however, are typically designed to wait until the speaker has finished before deciding how to act. We present EarlyCommit, an open-source tool that lets a
robot commit to the intended object while the instruction is being spoken. After every recognized word, a fine-tuned text encoder scores each object in the scene. The
robot acts when the calibrated probability of the best object reaches a threshold tau set by the user, so 1 - tau is the accepted risk of choosing the wrong object. Otherwise, it waits or asks. The tool includes a streaming
speech front end, a scene and instruction generator with exact word-by-word ground truth, and scripts to train, calibrate, and evaluate. On instructions containing words never seen in training, EarlyCommit commits correctly 1.6 to 2 times as often as keyword matching, is right at least 92 percent of the time when it commits at tau = 0.9, and needs 31 to 35 ms per word on consumer GPUs.
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Systems and Control
FedRIC: Heterogeneity-Aware Federated Learning with Hard Deadline Constraints in Open RAN
Adhwaa Al-Chaab, Ayman Younis and Dario Pompili
Open
Radio Access Networks (O-RANs) bring intelligence to disaggregated RANs through virtualized platforms and open interfaces, but the stringent latency budgets of the RAN Intelligent Controller (RIC) and heterogeneous traffic conditions make real-time deployment costly. Centralized learning is ill-suited to the RIC due to its computational burden, timing constraints, and communication overhead, motivating
Federated Learning (FL) as a distributed alternative. We formulate FL in O-RAN as a Mixed-Integer Nonlinear Program (MINLP) that minimizes communication cost and learning time under heterogeneity in User Equipment (UE) compute, traffic, and channel conditions, and decompose it into a UE Assignment (UA) subproblem and a Computation and Bandwidth Allocation (CBA) subproblem. We then propose FedRIC, a heterogeneity-aware FL framework combining compressed gradient descent, slice-aware resource allocation, and multilevel hierarchical aggregation. Experiments show that FedRIC outperforms FedCOM, FedAvg, and QFedAvg in convergence rate, communication cost, and latency.
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Databases
JetStream: Generating Verified Query Accelerators for Existing Database Systems
Akhilesh Balasingam, Amadou Ngom, Geoffrey X. Yu et al. (+1 more)
Recent work has shown that LLMs can synthesize highly specialized database systems for fixed workloads, but these systems replace the DBMS's native storage and assume static data: users must migrate their data, often give up ad-hoc queries, and lose acceleration once the data changes. We present JetStream, which instead generates query-specific accelerators that extend an existing DBMS. Given a set of parametrized query templates, a staged agentic workflow synthesizes accelerators that run directly over the DBMS's native tables and may maintain custom auxiliary state, while the DBMS remains responsible for storage, transactions, recovery, and all other queries. A fixed, engine-neutral substrate handles engine-specific hooks and keeps auxiliary state transactionally consistent under writes. JetStream generates two kinds of accelerators: tested accelerators, which may use any optimization and are validated by differential testing, and verified accelerators which trade some performance for a machine-checked proof that every answer satisfies a trusted specification derived from the SQL. On TPC-H at SF=20, JetStream achieves a $315 \times$ geomean read speedup over DuckDB ($93 \times$ verified) and retains $120 \times$ under a continuous update stream of 10,000 modified rows per second.
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All authors Akhilesh Balasingam, Amadou Ngom, Geoffrey X. Yu, Tim Kraska
Multimedia
Lluna: Collaborative Unimodal Models for Missing-Modality Inference
Haoyue Bai, Yinyi Luo, Heqing Huang et al. (+3 more)
Pretrained unimodal models offer a flexible route to
multimodal prediction through collaboration, but this collaboration can become less accurate than a surviving unimodal model when modalities are missing. We study this setting using fixed surviving-model anchors and find that collaborative gains can weaken or reverse as inputs disappear, even when the anchor's own input is unchanged. Our diagnostics further reveal reduced recovery of modality-specific information after aggregation and state-dependent collaborative representations. We propose Lluna, which combines a direct private prediction path from adapted unimodal features with a common graph-based collaboration branch that provides a residual correction. Progressive Collaboration Consistency organizes richer-to-poorer guidance along single-modality removals, aligning normalized common representations while task supervision adapts predictions to the available evidence. Across eight
datasets spanning affective computing, scene understanding, and medical prediction, Lluna achieves the best evaluated average performance over non-full input states, improving Mean-Missing by 4.26% on average relative to the strongest evaluated baseline. The fixed-anchor diagnostic also shows positive gains in mean collaboration under minimal inputs on all eight tasks. Code is available at https://github.com/AIFrontierLab/Lluna.
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All authors Haoyue Bai, Yinyi Luo, Heqing Huang, Xiaoyuan Yi, James Evans, Jindong Wang
Machine Learning
The Physics Is the Network: Fast, Training-Free Neural Emulation
Luka Todorovic, Kaushal Gianchandani and Sai Ravela
Neural emulation need not require learning a substitute for known physics. We investigate PolyNets that couple a continuous-time neural representation of
polynomial dynamics with a simple recurrent network for discrete-time integration. When the governing coefficients are known, the resulting tensor graph requires no training and preserves the prescribed numerical scheme while enabling efficient hardware execution and parallel simulation. Using the one-dimensional Korteweg-de Vries equation, we compare PolyNet with CPU and GPU finite-difference Runge-Kutta solvers and three trained neural models: a state-map emulator, a physics-informed predictor, and a learned-tendency integrator. Analytical
soliton solutions provide a reference for amplitude and phase errors across grid resolutions, 10^6 dispersively scaled time steps, ensembles of up to 10^4 trajectories, and varying initial amplitudes. PolyNet achieves accuracy comparable to the numerical solvers, including at amplitudes outside the learned models' training conditions. It combines this fidelity with high GPU throughput, approximately constant over the tested resolution range, and favorable ensemble scaling.
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Robotics
Physical Time as a Missing Dimension of Robot Policy Memory Dynamics
Yushen Liang, Yue Peng, Baosheng Jin et al. (+6 more)
Existing
robot policy
memories typically evolve with observation sequences, implicitly treating each update as the same amount of physical time. Yet identical observation histories can span different durations and require different actions, exposing physical time as a missing dimension of policy
memory. We introduce Temporal Flow Policy (TFP), which learns
memory dynamics to retain past events and evolve the
memory state over measured time intervals. Observations and prior
memory determine the content and time scales of each transition; elapsed time determines how far the state advances. TFP conditions
diffusion and flow-matching policies through this
memory state and is trained with their native imitation objectives. We evaluate TFP with
Diffusion Policy and $\pi_{0.5}$ through controlled comparisons of
memory designs. These experiments show that successful timing depends on both measured intervals and retained history. TFP achieves higher average success than the recurrent and state-space
memories we equip with the same intervals and than a variant that feeds the interval to a learned gate.
Memory readouts recover the time since an event and motion information after occlusion. Changing the supplied intervals systematically delays or advances actions, linking time inputs to behavior. Real-
robot tasks requiring timing or
memory, together with LIBERO evaluations, demonstrate benefits in physical and general manipulation. These results suggest viewing
robot memory not as stored observation history, but as a state that retains past events and evolves with physical time.
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All authors Yushen Liang, Yue Peng, Baosheng Jin, Tianluo Zhang, Xinyu Zhang, Shuyi Zhou, Zhuoran Chen, Xinqi Liu, Shenji Wan
Cryptography and Security
Tulva: Practical Asynchronous Threshold FHE
Valerio Cini, Antonio Guimarães, Russell W. F. Lai et al. (+2 more)
We present Tulva, a practical threshold FHE scheme which equips a base FHE scheme with an asynchronous threshold decryption mechanism in a modular way. Our scheme simultaneously (1) is compatible with virtually any base FHE scheme and leaves its ciphertexts unchanged apart from an appended random tag, (2) supports a wide range of threshold profiles $(t,K)$ and ring degrees $\geq 256$ with 6-25 bits partial decryption per plaintext bit, and (3) achieves security in the presence of partial decryption queries on challenge ciphertexts based on the standard LWE assumption. We provide an open-source implementation,
benchmark its threshold decryption performance and evaluate the end-to-end costs for typical
privacy-preserving Ethereum Virtual Machine (EVM) use cases. For 16-out-of-512 threshold and a publicly preprocessed ciphertext, computing secret-dependent partial decryption and public share combination requires only 3.7 ms and 0.5 ms, respectively, on commodity hardware. Compared with the state-of-the-art synchronous scheme [Zyskind et al., CCS'25], we replace their ciphertext-independent yet interactive preparation of secret-correlated randomness with a public, ciphertext-dependent 0.5 s preprocessing step, eliminate interaction in the online phase while maintaining similar decryption time on the same machine, and support 32$\times$ more total users.
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All authors Valerio Cini, Antonio Guimarães, Russell W. F. Lai, Michał Osadnik, Ivy K. Y. Woo
Artificial Intelligence
The Wealth of Agents: Scaling Agentic AI Across Developers
Austin W. Hanjie, Carlos E. Jimenez, Zirui Wang et al. (+1 more)
Today's agentic systems are typically designed, built, and deployed by single developers with full control over all participating
agents. While this simplifies development, it limits the potential scale of agentic systems. We study multi-developer agentic systems (MDAs) which enable developers to collaboratively build agentic systems to push frontier capability beyond what any individual developer could achieve alone. We construct empirical and theoretical models of developers to compare MDAs with single-developer agentic systems (SDAs). On GAIA, a general assistant
benchmark, a population of specialized developers whose individual
agents do not exceed 35% accuracy reaches 70% when their
agents are combined in an MDA. This MDA performs 12 percentage points above the median general-purpose SDA built with several times each specialist's development budget. Our theoretical models show how MDAs can incentivize specialization and encourage more efficient use of aggregated developer resources. Put together, our results make a preliminary case for MDAs as a new scaling axis by enabling developers with diverse capabilities and resources to advance agentic AI collaboratively.
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All authors Austin W. Hanjie, Carlos E. Jimenez, Zirui Wang, Karthik Narasimhan
Computation and Language
VASPO: Self-Distillation with Privileged Value Estimation
Fei Wu, Zhenrong Zhang, Jianshu Zhang et al. (+1 more)
On-policy self-
distillation provides dense supervision for
reasoning models via a privileged self-teacher. However, this supervision reflects local
token preferences rather than the expected return of an unguided student continuation. Recent methods combine this signal with GRPO. They use GRPO for update directions and the teacher for signal scaling. However, we demonstrate a more effective integration within PPO. Specifically, we identify complementary positional dynamics: privileged information enhances a critic's value-prediction accuracy toward the response's end, whereas self-teacher guidance is strong initially, drops mid-generation, and rebounds finally. These insights motivate Value-Augmented Self-
Distillation Policy Optimization (VASPO), wherein a self-teacher and critic evaluate identical privileged information at student-generated prefixes. The critic computes
token-level PPO advantages from predicted continuation returns, while the self-teacher modulates selected positive advantages via bounded probability ratios. Across six mathematical
reasoning benchmarks, VASPO improves mean accuracy over PPO by 2.59 and 1.81 percentage points on Qwen3-4B and OLMo3-7B variants, respectively, and achieves the highest mean accuracy among evaluated methods on Knights and Knaves. Our code is available at https://github.com/cii030/VASPO.
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All authors Fei Wu, Zhenrong Zhang, Jianshu Zhang, Jun Du
Machine Learning
Bayesian Learning to Select Fair Classifiers
Saatvik Kher, Hyungrok Do, Judy Zhong et al. (+1 more)
Evaluations of classifiers (AI or human or
agents) often assume a batch setting in which every
agent produces labels on every example in a test set, which can be expensive or impractical in many contexts. This motivates online policies that adaptively select
agents to query as examples arrive and update policies as outcomes are observed. We study the problem of selecting, in an online stream, the
agent that best balances (i) predictive performance and (ii) subgroup
fairness of a sensitive attribute, and that allows for delayed observations of true class labels. We formulate this as a
Bayesian multi-armed bandit problem using Thompson Sampling, explicitly balancing accuracy and
fairness. We conduct empirical evaluations on real-world
datasets spanning problems involving tabular classifiers, large
language models, and human labelers, demonstrating substantial reductions in regret relative to baselines. Our theoretical contributions include guarantees on the performance of the selected
agent that incorporate both the accuracy and
fairness aspects of the problem.
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All authors Saatvik Kher, Hyungrok Do, Judy Zhong, Padhraic Smyth
Machine Learning
A Two-Level Clinical Novelty Prioritization Framework for Disease Trajectories Discovered from Longitudinal Electronic Health Records
Jin-Jia Zheng and Vincent S. Tseng
Graph-based analysis of electronic health records (EHRs) yields disease-trajectory catalogs, but structural support does not establish semantic novelty. We present a two-level framework to prioritize 99,527 branching motifs previously discovered by an upstream patient-disjoint, train-only MIMIC-IV v3.1 pipeline and frozen after validation-based catalog selection. The framework separates path-level and node-to-endpoint semantic sparsity from reliability-adjusted clinical priority. Three deterministic textualizations per motif or node-target pair were embedded with BAAI/bge-small-en-v1.5 and calibrated against 166,972 pre-2023 PubMed references using mean top-20 cosine distance. Positive recombination occurred in 8.63% of motifs and had negligible effect on global ranking, whereas specificity and support/GNN reliability terms changed clinical-priority ordering. Robustness analyses across neighbor counts, templates, encoders, generic-label taxonomies, score settings, PrimeKG, and corrected density-based RND showed that broad ordering was more stable than hard top-k membership. A semantic-dose analysis did not support a monotonic relation between score and forecasting benefit after endpoint and
token-count adjustment (1.96 days per SD; 95% CI, -0.75 to 4.68). In the frozen catalog-q90 high-semantic subgroup, branch-enhanced forecasting reduced conditional event-time MAE by 20.1 days (95% CI, 13.3-27.0) and sMAPE by 2.37 percentage points (95% CI, 1.81-2.93), while discrimination and endpoint-ranking differences were inconclusive. Corrected RND showed low tail agreement with the primary score (Spearman rho = 0.430; q90 Jaccard = 0.125) despite a similar aggregate timing association. These findings support representation-aware, uncertainty-aware literature-relative prioritization of discovered trajectories rather than causal discovery or clinician-adjudicated novelty.
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Data Structures and Algorithms
Claw-Free and Line Graph Edge Deletion on Block Graphs
Rajat Adak
Edge deletion problems ask for the fewest edges whose deletion gives a graph with a specified property. We study two such problems on block graphs. First making the graph claw-free, and second making it a line graph. Since every line graph is claw-free, the second problem is at least as difficult as the first. We show that on block graphs the two optimum values are always equal, and both are attained by the line graph of a forest. The main step is a completion argument, in which starting from any claw-free solution, we put back edges to get some nice structure inside each block still maintaining the claw-free property. This reduces both edge-deletion problems to the same problem of selecting vertices within the blocks of the input graph. We solve it by dynamic programming in $O(n+m)$ time, which also produces an optimal deletion set and a forest whose line graph is what remains after the completion argument. We also extend the method to $K_{1,r+1}$-free edge deletion for every fixed $r$.
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Computer Vision
VascuSR: Iterative Anatomy-Aware Diffusion for CT Super-Resolution
Hairong Ni, Jiahe Huang, Minghao Guo et al. (+2 more)
Generative super-resolution (SR) can recover sharp detail in computed
tomography (CT) images from severely downsampled observations, but the missing high-frequency content is not uniquely determined by the low-resolution input. Consequently, visually plausible reconstructions may contain anatomically unsupported vascular detail, such as missing, spurious, or incorrectly connected vessels. To overcome this challenge, we introduce VascuSR, an iterative anatomy-aware
diffusion framework that combines image reconstruction with explicit 3D vascular guidance. First, to provide richer anatomical guidance, we use an explicit 3D vascular representation that captures structural information beyond individual slices. This representation also simplifies vascular refinement by allowing a lightweight 3D generative refiner to focus on vascular structure rather than complex CT appearance. Second, to guide slice-wise SR, a lightweight anatomy controller injects the refined structure into the
diffusion decoder, while iterative feedback updates this guidance from each new SR volume. On re-annotated MSD Task08 and HVM
datasets, VascuSR outperforms interpolation and learning-based SR baselines in segmentation accuracy (Dice and clDice) and 3D vascular structure (connectivity and mean centerline distance), while maintaining image quality comparable to its SR backbone. Compared with learning-based baselines, VascuSR reduces class-averaged connected-component counts by over 20% on both
datasets and mean centerline distance by over 10% on MSD.
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All authors Hairong Ni, Jiahe Huang, Minghao Guo, Wojciech Matusik, Rose Yu
Robotics
Graph-Reweighted Behavior Cloning with Human Interventions for Precise Robotic Manipulation
Xiaoman Li, Zhiqiang Yang, Jiayu Hu et al. (+6 more)
Long-horizon precise manipulation produces mixed-quality data for post-training
vision-
language-action (VLA) models: even successful demonstrations and intervention-assisted rollouts may contain detours, hesitations, failed attempts, and imperfect recoveries. The key challenge is to identify which action chunks actually advance the task, rather than uniformly cloning or discarding imperfect trajectories. We propose Graph-Reweighted Behavior Cloning (GR-BC), an offline iterative post-training framework that builds a task-conditioned goal-reachability graph over demonstrations and iteratively collected rollouts, including both successful and failed episodes with human interventions. Reverse graph search computes minimal remaining steps to task success within the
dataset support, and each action chunk is weighted by how much its execution reduces this potential. This consequence-based weighting reuses recoverable segments from failed trajectories, leverages interventions as recovery paths, and down-weights locally regressive chunks even inside successful trajectories. New rollouts are incorporated by appending them to the graph and rerunning search, without training a reward model, action-conditioned critic, or progress predictor. Across four precise manipulation tasks and three VLA backbones, GR-BC improves single-round supervised fine-tuning and reaches 91% success after a few offline iterations, without trajectory comparisons or progress labels. More details and
videos are available at: https://gr-bc.github.io
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All authors Xiaoman Li, Zhiqiang Yang, Jiayu Hu, Jipeng Kong, Yang Zhang, Jingwei Zhang, Fangzheng Yan, Chunhua Shen, Chenjia Bai
Computation and Language
ReaDy: Recurrent States as Dynamic Context for Speculative Drafting in Hybrid Linear Attention Large Language Models
Bohan Sun, Zunhai Su, Xialie Zhuang et al. (+3 more)
Speculative decoding accelerates autoregressive generation by drafting multiple
tokens per step, yet feature-based drafters rely on target hidden features whose contextual emphasis may not align with the needs of subsequent draft positions. Hybrid linear
attention (HLA) models maintain compact recurrent states that accumulate historical associations, offering an efficient source of complementary context for speculative drafting. We introduce ReaDy, the first speculative decoding method, to the best of our knowledge, to leverage HLA recurrent states for position-specific context
retrieval during drafting. ReaDy equips each draft position with a learned query to retrieve supplementary context directly from the target's committed recurrent states. A lightweight state reader integrates this context into draft representations through residual updates, while low-rank state reads and fused operators keep additional computation and I/O overhead low. Building on DSpark's parallel backbone and sequential prediction head, ReaDy employs a lightweight two-stage adaptation strategy, with reader capacity, state sources, and residual placement examined through ablation studies. Across seven
benchmarks spanning mathematics, code generation, and dialogue, ReaDy achieves an average $3.16\times$ decoding speedup over autoregressive decoding with Qwen3.8-27B on a single NVIDIA B200 GPU. It outperforms DSpark on every
benchmark, improving throughput by 7.97% and acceptance length by 8.50% on average. Our code is available at https://github.com/StartLuxLabs/ReaDy.
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All authors Bohan Sun, Zunhai Su, Xialie Zhuang, Jianjin Li, Ngai Wong, Chuan-Wei Kuo
Machine Learning
Graph-Enhanced Disease Trajectory Discovery and Temporal Endpoint Forecasting from Longitudinal Electronic Health Records
Jin-Jia Zheng and Vincent S. Tseng
Longitudinal electronic health records (EHRs) can reveal disease-progression structure, while useful forecasting requires leakage-controlled discovery, censor-aware outcomes, and inspectable trajectory evidence. We developed a graph-enhanced framework in MIMIC-IV v3.1 that splits patients before graph construction, learns train-only consecutive-admission transitions, and freezes endpoint-specific branching-motif policies selected on validation data. The final catalog contained 99,527 motifs, and a time-aware
Transformer retrieved at most 24 patient-compatible motifs per endpoint through endpoint-specific cross-
attention. Across five training seeds, micro-AUPRC increased from 0.9330 to 0.9357, while event-time MAE decreased from 676.9 to 657.7 days (reduction 19.2 days; 95% paired patient-cluster bootstrap CI, 13.8-24.8). Timing effects were heterogeneous: MAE worsened for events within 730 days but improved by 58.9 days for events occurring after 730 days. In an exploratory internal temporal split, branch conditioning reduced conditional event-time MAE by 26.9 days (95% CI, 23.0-30.8) without evidence of better micro-AUPRC. Corrected survival evaluation over the supported 0-2,555-day interval did not establish macro C-index or integrated Brier score superiority over a matched history-only discrete-hazard model. A directed, degree-matched GNN repair substantially improved unseen-edge discovery, yet replacing GNN-derived scores within fixed motif identities did not improve downstream forecasting. These results position branching motifs as structured trajectory context whose main value is long-range timing support rather than uniformly stronger endpoint discrimination, survival prediction, or causal explanation.
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Computer Vision
OLiVES: An Outdoor Low-Light Video Benchmark for Enhancement and Segmentation
Yini Li, Joanne Lin, David Blackham et al. (+2 more)
Low-light
video processing remains an underexplored problem in computer
vision, primarily due to the scarcity of high-quality ground-truth data. Existing public
datasets exhibit several limitations, including restricted motion diversity, limited scene coverage, weak degradations, reliance on auxiliary modalities such as event data, and misalignment between low-light inputs and ground truth. Furthermore, current
datasets typically address either low-light enhancement or scene understanding, whereas these tasks should be considered jointly. We introduce OLiVES, a
benchmark dataset for outdoor low-light
videos with dynamic scenes, designed to support both low-light
video enhancement (LLVE) and
video object segmentation (VOS). The
dataset addresses key content gaps by incorporating a diverse range of natural textures, animals, occlusions, dynamic activities, and close-up scenes. To the best of our knowledge, OLiVES is the first outdoor low-light
video dataset captured using professional cinema-grade cameras. It provides over 25,000 aligned normal- and low-light frame pairs, along with over 46,000 VOS annotations. We
benchmark state-of-the-art LLVE and VOS methods on OLiVES and show that training on it improves performance on both tasks. The OLiVES project page is available at: https://anonymous-OLiVES.github.io/.
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All authors Yini Li, Joanne Lin, David Blackham, David Bull, Nantheera Anantrasirichai
Computational Geometry · Cross-listed from cond-mat.mtrl-sci
Structural identification in general crystalline systems via Voronoi cell topology
Emanuel A. Lazar and Chris H. Rycroft
Widely used methods for identifying
crystal structure in atomistic simulations are limited in practice to a handful of simple structures, leaving many important crystalline systems without automated classification techniques. Voronoi
topology analysis offers a route to classifying arbitrary crystalline and defect environments, but realizing this potential first requires enumerating the Voronoi
cell topologies that can arise under thermal perturbation of a structure of interest. We present an
algorithm that deterministically enumerates all Voronoi
cell topologies arising from generic infinitesimal perturbations of an arbitrary
crystal structure, producing structural classifiers that extend automated identification to general crystalline systems. We demonstrate the approach on several structures of general interest.
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Machine Learning
Hindsight-Privileged RLVR: Single-Rollout Token Credit Without Group Sampling
Wei Chen, Yubing Wu, Zhou Xu et al. (+9 more)
Reinforcement learning with verifiable rewards (RLVR) trains
language models from checked task outcomes, but assigning a terminal reward to individual
reasoning tokens or actions remains difficult. Group-relative methods such as Group Relative Policy Optimization (GRPO) sample several attempts per
prompt, but lose their reward-based learning signal when all attempts receive the same outcome. We propose Hindsight-Privileged RLVR (HP-RLVR), a single-rollout method that turns a completed trajectory's own future into
token-level update weights. The generating policy snapshot scores each eligible
token under its causal history and under a hindsight context containing the realized future and verifier feedback. A normalized Jensen–Shannon divergence between the two views weights the reward relative to a historical baseline, yielding bounded
token credit for both successful and failed attempts, with no reference solution or learned critic. In paired Qwen3-8B runs over a shared $100$-step schedule, HP-RLVR trains $1.21\times$, $1.46\times$, and $2.30\times$ faster than GRPO on mathematics, code generation, and ALFWorld, using one eighth the rollouts per
prompt. Accuracy is comparable overall, with HP-RLVR ahead on mathematics at all three sizes and GRPO ahead on code and ALFWorld. In ALFWorld, $43.2\%$ (4B) and $49.0\%$ (8B) of GRPO's gradient-norm records are zero, versus none for HP-RLVR.
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All authors Wei Chen, Yubing Wu, Zhou Xu, Haoyue Zhang, Delu Zeng, Jiahao Wang, Junmei Yang, Pan tiewen, Jingfeng Liu, Zhiheng Zhou, Bo Li, Min Chen
Computer Vision
Quantifying privacy risks in foundation model representations of brain MRI
Imen Bajar, Héber Hwang Arcolezi, Antoine Boutet et al. (+2 more)
Pretrained representations are increasingly used to share and reuse information from medical images without exposing the original scans, but it is unclear how much patient-specific information these representations retain. We investigate this question for
brain magnetic resonance imaging (MRI) by evaluating five frozen pretrained encoders spanning different architectures, pretraining objectives, and training domains on two longitudinal T1-weighted
datasets, OASIS-2 and MIRIAD. Under an attacker model with access to released
embeddings, the corresponding public encoder, and auxiliary MRI scans, we evaluate patient linkage and sensitive-attribute inference. Across the evaluated representations, we observe substantial subject-specific information: rank-1 patient linkage reaches up to 99% on MIRIAD and exceeds 90% on OASIS-2 for the highest-risk representation, despite the absence of an identification objective during encoder training. We further show that the released
embeddings retain information about a demographic attribute, with recorded-sex prediction reaching balanced accuracy of 90.9% on OASIS-2 and 95.5% on MIRIAD. Overall, our results demonstrate that replacing raw
brain MRI with learned
embeddings does not, by itself, eliminate patient-linkage or attribute-inference risk, and is therefore insufficient on its own to establish anonymity.
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All authors Imen Bajar, Héber Hwang Arcolezi, Antoine Boutet, Carole Frindel, Mohamed Maouche