Data Structures and Algorithms
Solving Vertex Integrity Faster than $2^n$
Sandip Das, Sweta Das, Sk Samim Islam et al. (+3 more)
Vertex Integrity is a classical graph parameter measuring the vulnerability of a network to vertex failures. We settle two fundamental questions concerning its exact exponential complexity. First, we prove that, unless the Exponential Time Hypothesis fails, Vertex Integrity has no $2^{o(n)}n^{O(1)}$-time
algorithm. Secondly, we give a deterministic exact
algorithm running in $O(1.9602^n)$ time and space, improving the straightforward $O(2^n)$ bound.
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All authors Sandip Das, Sweta Das, Sk Samim Islam, Ritam Manna Mitra, Aashirwad Mohapatra, Arkaprava Paul
Artificial Intelligence
Evidence-Inference Reconstruction: When The Evidence Is Recalled But The Reasoning Goes Wrong
Megan Diehl and Ser-Nam Lim
Modern multi-hop LLM
agents are equipped with built-in mechanisms to detect errors in intermediate
reasoning steps. Such errors trigger corrective actions from these
agents, which mostly follow the paradigm of retrying the steps or the
reasoning trajectories. Not only are these retries expensive, we present in this paper that they are also potentially unnecessary. To this end, we introduce Evidence-Inference Reconstruction (EIR), which uses structured state to guide one
retrieval trajectory, accumulating source evidence in the process. We show that as long as the relevant evidence has been collected, EIR is capable of generating the correct answer in a single final model call even if erroneous evidence has been mixed in due to incorrect intermediate
reasoning steps. In one evaluation, using Haiku 4.5 and GPT-4.1 Mini, we evaluate EIR on matched 1,000-question subsets of HotpotQA, 2WikiMultiHopQA, and MuSiQue, showing that EIR improves Answer F1, the overlap between the model's and the correct answer, over the baseline by 8.3–32.8 points, Agentic SSR by 10.6–29.1 points, and Reflexion by 1.1–15.9 points. Additionally, we show that EIR averages 4.85 total model calls per question, compared with 35.29 for Agentic SSR and 12.41 for Reflexion. Together, these results corroborate EIR's central premise: separating evidence
retrieval from the final answer model call can improve answer accuracy while utilizing substantially less computation.
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Sound
Tokens Change, Structure Endures: Spectral Watermarking for Generated Speech
Kanghwi Lee, Kyeongseok Jeong and Jeongmin Liu
Watermarking is a promising tool for establishing the provenance of AI-generated
speech. While many neural audio watermarking methods rely on a separately trained watermark generator,
token-level watermarking is a training-free alternative that operates directly during generation. Its main weakness is retokenization: decoding generated
speech to a waveform and encoding it again can change
token identities and erode the watermark. To make the watermark robust to these changes, we propose Redwing, REtokenization-Durable Watermarking IN Generation. It builds a graph from the
token substitutions observed under retokenization, whose
Laplacian yields a basis that assigns similar values to
tokens likely to substitute for one another. Over this basis,
embedding and detection functions are jointly optimized to preserve watermark signal through retokenization while limiting
embedding distortion and detector variability on unwatermarked
speech. On the Moshi full-duplex system, after eight consecutive passes of Mimi resynthesis, Redwing achieves 80.7% TPR at a calibrated 1% FPR, compared with 8.3% for KGW and at most 7.3% for WMAR. It also has the highest TPR after eight passes through three other neural codecs (77.5-93.0%), and the gains generalize to TTS models at a
speech-quality cost close to that of KGW. These results show that retokenization is not merely a source of noise: its transition structure can be exploited as a design principle for robust
token-level watermarking.
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Artificial Intelligence
Learning Strategies to Break Judges
Guruprerana Shabadi, Aaditya Naik, Rajeev Alur et al. (+1 more)
As AI
agents surpass human performance, it becomes exceedingly hard for system designers to evaluate them directly and understand their failure modes. Consequently,
agents themselves are being deployed extensively to evaluate, judge, and provide feedback on model traces. But this raises an important question: how can we trust the judge? In this work, we propose an
agent-guided method to find weaknesses of agentic judges that expose interpretable failure mechanisms. Our method focuses on mathematical
reasoning and proceeds in two stages: first, we deploy
adversarial agents to mutate a set of sound proofs by introducing errors, attempting to misguide judges—in other words, injecting errors that judges are unable to catch. Then, we distill these attempts into a small set of mutation strategies which allow us to analyze the failure modes of the judges. To ensure that these strategies are not overfit to the initial set of proofs, we evaluate them by applying the mutation strategies to a held-out set of proofs and querying the same judge. We deploy our method on GPT-5.6-sol and Claude Opus 5, paired with their
agent orchestrators, Codex and Claude Code, respectively. These are used both as mutators to introduce errors and as judges to evaluate correctness of mathematical
reasoning. We find that across all the agentic judges, we are able to distill mutation strategies that consistently bypass their evaluations, thereby enabling us to ascertain actionable failure modes. Our analysis also reveals that judge reliability degrades at the frontier: errors in Olympiad-level proofs or graduate-level mathematical texts are detected more consistently, whereas flaws in research-level manuscripts are more likely to escape detection.
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All authors Guruprerana Shabadi, Aaditya Naik, Rajeev Alur, Mayur Naik
Artificial Intelligence
Skill2Env: Capability-Oriented Environment Synthesis from Skills for General Agents
Weiyi Xu, Xiaowen Yang, Wen Da et al. (+7 more)
Executable environments are critical for post-training
agents on tasks that require tool use and multi-step interaction, but constructing executable tasks together with their environments remains difficult to scale. Skills provide reusable domain knowledge, operational procedures, and tool-use instructions, but a substantial gap remains between the information contained in a skill and a concrete, challenging task with a complete executable environment. To address this gap, we introduce Skill2Env, a capability-oriented framework that starts from a skill and uses
agent capability demands to guide task and environment synthesis. Skill2Env represents these demands through reusable difficulty patterns and instantiates them into task blueprints that specify objectives, challenges, environment facts, information boundaries, and acceptance criteria. These blueprints guide the joint construction of task instructions, execution substrates, workspaces, and rubric-based evaluators around source skills. We further propose Iterative Task Hardening, which uses solver execution evidence to identify insufficiently challenging task designs, strengthen or extend their difficulty-pattern instantiations, and revise the corresponding blueprints and environments. Using 1.5K high-scoring trajectories generated from Skill2Env environments for supervised fine-tuning, we observe consistent improvements across a broad range of
agent benchmarks, demonstrating the effectiveness of capability-oriented environment synthesis for
agent post-training.
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All authors Weiyi Xu, Xiaowen Yang, Wen Da, Hang Xu, Canwei Li, Hongjie You, Pusen Dong, Yucheng Zeng, Zhaokai Luo, Mu Chuan
Computer Vision
M3-Score: Fidelity, Memorization and Coverage as Separate Axes for Evaluating Generative Radiology Image Models
Sathiyamohan Nishankar, Pubudu Sanjeewani and Asanka Perera
Quantitative evaluation of generative models for radiology remains challenging. Clinically relevant structures are often small and infrequent, feature spaces learned from natural images may represent them poorly, and a single summary score cannot distinguish limited fidelity from limited diversity. This study proposes the Medical Multi-axis Maximum Mean Discrepancy score (M3-Score), an evaluation framework based on RadioDINO-s16, a frozen
vision transformer pretrained on radiology images. M3-Score reports three complementary axes computed at pre-specified encoder depths: fidelity, measured by an unbiased multi-bandwidth radial basis function (RBF) MMD$^2$ at the final block; memorization, measured by nearest-neighbor distances at 75% depth; and coverage, defined as the fraction of real images with a generated neighbor within their $k$-nearest-neighbor radius at 33% depth. Reference sets are sampled across subjects to limit the influence of correlated slices. On BraTS
brain MRI, the fidelity axis ordered five comparison sets of increasing severity (Spearman $\rho = 1.00$), and a subject-disjoint real set yielded $\mathrm{MMD}^2 = 0$ (permutation $p = 1$). An unconditional denoising
diffusion probabilistic model achieved $\mathrm{MMD}^2 = 0.073$ (95% confidence interval $[0.071, 0.080]$) but covered only 38% of the real distribution. Under progressive mode dropping, $k$-NN
manifold recall increased at all twelve encoder blocks, whereas the proposed coverage
estimator decreased monotonically ($\rho = -1.00$). RadioDINO-s16 features separated real
brain MRI from generated samples with a ROC-AUC of 0.819, compared with 0.555 for InceptionV3 and 0.582 for CLIP. Across a twentyfold range of sample sizes, the mean M3 value varied by a factor of 1.05, compared with 2.52 for the Fréchet Inception Distance.
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Machine Learning
DEALS: Decentralized Expertise-Aware Load Serving for Multi-Agent LLM Systems
Jingjuan Huang, Wenbin Wang, Yanchuan Yin et al. (+2 more)
Multi-
agent systems (MAS) have recently emerged as an effective approach for coordinating large
language model (LLM)-based
agents to solve complex tasks through structured interactions. In practice, MASs often handle a stream of heterogeneous and complex tasks, requiring
agents to decompose each task and then self-organize and self-evolve to adapt to incoming tasks while sharing execution resources. However, most early approaches to MASs rely on centralized controllers or fixed coordination patterns, which can limit scalability or adaptability. In contrast, existing decentralized and dynamic MASs often require training dedicated routers or invoking LLMs for
agent selection, resulting in substantial computational costs and coordination overhead. To address these challenges and enable efficient task-level self-organization and self-
evolution for task- and workload-level collaboration, we propose Decentralized Expertise-Aware Load Serving (DEALS), a decentralized and low-complexity framework that enables
agents to self-organize and dynamically route concurrent tasks for processing. Specifically, each
agent maintains local queues of incoming tasks, and its router decides whether to process a task locally or forward it to a neighbor based on differences in backlog and success rate. Meanwhile, executors process independent tasks concurrently within and across
agents, and partially solved tasks can be resumed by other
agents. Experiments show that DEALS not only improves performance along multiple dimensions (e.g., answer accuracy and task throughput) in both homogeneous and heterogeneous
agent pools, but also balances
agent expertise and workload in a self-organized manner, enabling effective decentralized coordination.
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All authors Jingjuan Huang, Wenbin Wang, Yanchuan Yin, Alvaro Velasquez, Jia Liu
Machine Learning · Cross-listed from q-fin.PM
Taming the Greeks: Option Portfolios with Inductive Biases
Wee Ling Tan, Stephen Roberts and Stefan Zohren
We present an end-to-end deep learning framework for systematic options trading that directly embeds hedging behavior through explicit control of portfolio-level risk exposures. While neural networks trained to optimize risk-adjusted performance have been shown to outperform traditional rules-based strategies, such approaches remain agnostic to the sensitivities of the resulting portfolios with respect to specific underlying risk factors. We propose a general training objective that combines a performance-driven loss with a differentiable risk-sensitivity penalty, enforcing neutrality to selected risk dimensions. Unlike reinforcement learning methods that approximate optimal hedging policies via simulated
market dynamics, our framework operates entirely on historical data and jointly optimizes risk-adjusted returns and targeted risk constraints in a single learning problem. We instantiate the framework on static delta-neutral straddle portfolios with the penalty directed at first-order directional exposure, and evaluate two penalty variants – an exposure-normalized penalty and a Greek-ratio drift penalty. Empirical results on Nasdaq 100 equity options demonstrate that appropriately calibrated regularization simultaneously improves out-of-sample risk-adjusted performance relative to an unregularized baseline while reducing realized directional exposure.
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Robotics
Principal Steering Subspaces for Online Adaptation of Frozen Generative Robot Policies
Jialeng Ni, Nathan Zhao and Kunpeng Song
Generative
robot policies provide expressive behavior priors, but updating a large
diffusion or flow-matching model through online interaction is costly. Latent-space reinforcement learning avoids updating the pretrained generator by controlling its initial sampling noise, yet high-dimensional noise can have strongly anisotropic effects on decoded actions. We introduce Principal Steering Subspaces (PSS), a forward-query interface that constructs a fixed low-dimensional control basis from finite-difference decoder responses. Soft Actor-Critic controls the leading response directions, while the orthogonal complement is independently resampled from the Gaussian prior at each query. On three RoboMimic tasks with
diffusion and flow-matching policies, response spectra reveal substantial concentration. Across five matched task-generator pairs, the training curves indicate that PSS generally converges faster and exhibits more stable late-training behavior than full-latent control, while achieving stronger final performance overall. Controlled
Diffusion-Square ablations further show that leading-response directions outperform random and least-responsive subspaces of equal dimension. We further integrate PSS with a frozen, closed-source 3B-parameter
vision-
language-action (VLA) policy in a
humanoid learning system with synchronous transition collection, reset-time optimization, and latency-aware asynchronous deployment. In an exploratory screwdriver-placement evaluation, success is observed in 2/10 trials for the frozen VLA policy and 6/10 after SAC+PSS adaptation. These results support decoder-response geometry as a practical basis for online adaptation of frozen generative
robot policies.
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Machine Learning
Beyond Fixed Features: Architecture-Dependent Sensitivity to Node Representations under Heterophily
Priyanath Maji, Sidharth Gaur and Rajavinoth Paul Durai
Graph Neural Networks (GNNs) perform well on homophilic graphs but struggle in heterophilic settings, where connected nodes often carry dissimilar labels. Existing evaluations typically compare architectures under a fixed node-feature representation, leaving unclear whether conclusions about heterophily robustness remain stable as the input representation changes. We address this question by constructing parallel feature variants of two large-scale heterophilic
benchmarks, Roman-Empire and Amazon-Ratings, pairing each graph with representations ranging from static fastText vectors to contextual
Transformer embeddings and evaluating seven GNN architectures across these representations. We find that the effect of representation varies across architectures: on Roman-Empire, the contextual gain ranges from 2.38 percentage points for GCN-sep to 13.67 points for GAT, with H2GCN gaining 8.77 points. On Amazon-Ratings, where node text is limited to short product titles, GAT improves by 6.78 points from fastText to MPNet, while GCN-sep changes by only 0.20 points. These results show that architectural performance is conditional on node representation: the same representation change can produce different magnitudes of performance gain across architectures, so architecture and representation cannot be treated as independent evaluation factors. A rank-correlation analysis on these two
benchmarks further shows that the relative ordering of architectures remains highly stable across representations, isolating differential sensitivity, rather than ranking instability, as the primary effect.
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Cryptography and Security
SecProbe: Adaptive Evaluation of Coding Agents on Cybersecurity Vulnerabilities
Xiaonan Luo, Yue Huang, Kehan Guo et al. (+9 more)
Assessing cybersecurity vulnerability awareness in coding
agents requires evaluations that reveal capability gaps and remain informative as models evolve. Static
benchmarks offer fixed coverage and difficulty, while scarce vulnerable repositories and costly expert authoring limit their renewal at scale. We introduce SecProbe, a framework for adaptive evaluation that combines Item Response Theory (IRT) with on-demand synthesis of repository-scale vulnerability-repair tasks. From observed performance, SecProbe estimates
agent ability and identifies where additional evidence is most informative, selecting existing tasks or synthesizing new ones accordingly. As one use case, we construct 353 tasks spanning six programming
languages and 151 CWE types and evaluate nine frontier models with two
agent harnesses. Success rates peak at 28.33%, highlighting substantial gaps in vulnerability recognition and repair. Compared with random and one-shot baselines, SecProbe achieves comparable
agent ability estimates while requiring
agents to solve up to 29.5% fewer tasks. These results support adaptive evaluation as an efficient and discriminative approach to assessing cybersecurity vulnerability awareness.
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All authors Xiaonan Luo, Yue Huang, Kehan Guo, Ping He, Chuan Zou, Chujie Gao, Lichi Li, Yuchen Ma, Zhangchen Xu, Zichen Chen, Yufei Han, Xiangliang Zhang
Distributed Computing
EfficientAgent: What Makes KV Cache Offloading Work for Concurrent Agents?
Kunming Shao, Jierun Chen, Jiangnan Yu et al. (+7 more)
LLM
agents resend their whole conversation on every turn, and most of it was already processed on the previous turn. Serving systems avoid recomputing it by caching its key-value (KV) state and, when GPU
memory runs out, by offloading that state to host
memory. For
agents, offloading gives inconsistent results: on the same coding-
agent workload it speeds up one deployment, slows down another, and changes nothing on a third, even where loading a
token back is several times cheaper than recomputing it. The reason is that cached state must survive until it is used again. While one
agent waits for its tool, the server processes the contexts of all other
agents, so an
agent's prefix is reused only if the host tier holds the reusable context of the whole
agent pool, which we call the reuse working set. A smaller tier keeps writing state that is evicted before anyone reads it. We present EfficientAgent, which sizes and manages the host tier by this working set. A stack-distance model estimates the working set from
agent histories to size the host tier; its predictions, made before the experiments, located the capacity at which offloading starts to pay. When the tier is too small, a runtime policy stops writing large refills of evicted context and keeps extending prefixes that are still cached; when the tier is large enough, it writes everything. On SWE-bench Verified coding
agents, a host tier sized to the estimated working set cuts recomputed
prompt tokens by 93% and end-to-end time by 39%. With a small fixed tier, the policy cuts recomputation by 35%; with a large tier, it avoids the 4.3-fold increase caused by always filtering writes. Across three GPU types and two models, offloading pays off when the GPU has little compute per byte of host bandwidth and the host tier holds the working set. Code is available at https://github.com/KunmingSHAO/efficientagent_release.
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All authors Kunming Shao, Jierun Chen, Jiangnan Yu, Xiao-Hui Li, Chaofan Tao, Yanli Wang, Huanxin Lin, Kwang-Ting Cheng, Chi Ying Tsui, Haoli Bai
Machine Learning
Marginal Calibration Masks Generator Differences in Held-Out Evaluation of Multivariate Time Series, with an Application to ERA5 Weather Sequences
Mustafa Özaytaç and Özge Karadağ Ataş
Generative models for multivariate time series are routinely evaluated with pooled distributional metrics computed after marginal calibration. We show that, under a held-out protocol, the calibrated Kolmogorov-Smirnov (KS) distance differs from a real-versus-real floor by at most the distance of the calibrated sample to the training block, so that it carries little information about the generator. We audit a three-discriminator GAN with evolutionary weight adaptation (StatD2GAN) against re-implemented RCGAN and TimeGAN baselines on ERA5 weather sequences under a pre-specified protocol: the final two calendar years are held out behind a 168 hour embargo, calibration is fitted on the training block only, and the decision rules were frozen in advance. Evidence comes from 25 matched (location, seed) pairs across five locations, tested with Holm-corrected Wilcoxon signed-rank tests. First, isotonic calibration drives KS to a median of 1.019 times a per-location noise-and-shift floor, and neither baseline is distinguishable from StatD2GAN on it. Second, the sorted-representation discriminator is the only component whose removal significantly degrades cross-variable dependence (Kendall tau MAE +0.116, Holm p = 0.002); a rank-transformed variant also degrades it, consistent with
quantile supervision rather than copula matching. Third, calibration raises physical constraint violations; projection removes them, but the largest marginal change (0.0110, Lhasa) exceeds the pre-specified tolerance in three locations. Fourth, the re-implemented TimeGAN is significantly better than StatD2GAN on Kendall tau and ACF MAE, RCGAN is worse on Kendall tau MAE and constraint violations, and the pre-specified collapse criterion was not met. We recommend floor-referenced marginal evaluation, matched-pair testing, and dependence and sequence-level metrics.
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Machine Learning
Oracle-Efficient Online Classification with Stochastic Inputs and Adversarial Outputs
Gon Buzaglo and Elad Hazan
We consider binary prediction with i.i.d. contexts from an unknown distribution and adaptively chosen losses. We show that a simple Follow-the-Perturbed-Leader
algorithm using a Gaussian perturbation for each observed context achieves $\widetilde O(\sqrt{T\log N})$ regret for a class of $N$ experts, while requiring one optimization-oracle call per round and no explicit enumeration of the class. For an infinite hypothesis class $\mathcal H$, the same
algorithm achieves $\widetilde O(\sqrt{T\operatorname{VC}(\mathcal H)})$ regret. This resolves an open problem posed by Lazaric and Munos (2012), showing that hybrid classification is computationally as easy as statistical learning. As an application, we reduce the problem of contextual bandits with $K$ actions to classification through uniform exploration, achieving $\widetilde O(K^{2/3}T^{2/3}(\log N)^{1/3})$ regret. This matches the best known dependence on the horizon while removing the context-distribution access required by prior oracle-efficient methods.
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Computation and Language
Positions Are Not Facts: The Mismatch Between KV Caches and Memory
Changhai Zhou, Yuhua Zhou, Shiyang Zhang et al. (+5 more)
When a fact changes, how should a
language model update the history stored in its key-value (KV) cache? Hiding the old record is cheap, but it may still contain needed details or answer questions about the past. We compare hiding whole records, hiding only replaced values, and deleting old text and recomputing the cache. In a controlled quantity task, masking makes all eight models prefer the new value more strongly, yet six lose complete answers through unit errors or failure to stop; keeping the unit preserves all current answers. Later states also retain useful information from earlier records: on multi-hop updates, rebuilding these states at unchanged positions lowers historical accuracy by 20-41 percentage points, whereas moving the existing states has little effect. Keeping object dependencies and unchanged revision passages prevents many losses. Recognition is a separate challenge. Learned readouts recover distinctions missed by fixed cache similarities on synthetic record pairs. On natural text, text-detector-selected masks show no clear advantage over random masks at the same rate in 14 same-model detector-generator comparisons. Query-dependent access can avoid some losses, with additional storage or access costs. These findings identify what must be preserved beyond the replaced value when using a KV cache as updatable
memory.
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All authors Changhai Zhou, Yuhua Zhou, Shiyang Zhang, Jun Gao, Zhen Li, Hua Wu, Hanchao Yu, Haifeng Wang
Computer Vision
ENet-GP: Unified Document Image Restoration
Sujal Burad, Aakanksha, A. N. Rajagopalan et al. (+1 more)
Reliable document digitization in uncontrolled capture settings is challenging because real images exhibit multiple interacting degradations rather than a single isolated distortion. Documents thus captured are affected simultaneously by
geometric distortions, like page warping, as well as photometric degradations such as non-uniform illumination, and blurring. However, most existing approaches address these factors independently and are evaluated on
benchmarks containing only one distortion type, limiting their real-world applicability. We introduce GutenDoc, a large-scale
dataset of high-resolution dense-text documents with physically grounded compound degradations. Using physics-based rendering, our
dataset jointly models
geometric warping and diverse photometric effects, enabling systematic evaluation under realistic capture conditions. We further propose a unified restoration framework that jointly corrects
geometric and photometric distortions within a single-network and single-training setup, without the need for degradation-specific retraining or sequential inference passes. Extensive experiments show that our method remains competitive on established single-distortion
benchmarks while substantially improving robustness under compound degradations, providing a practical solution for real-world document digitization.
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All authors Sujal Burad, Aakanksha, A. N. Rajagopalan, Sumit Shekar
Machine Learning · Cross-listed from eess.AS
YuE2: Unifying Symbolic and Audio Music Generation at Frontier Quality
Ruibin Yuan, Jiahao Pan, Junyan Jiang et al. (+32 more)
Symbolic models make melody, harmony, rhythm, and form explicit but typically stop before a finished recording; audio models produce complete songs while leaving composition implicit. We introduce YuE2, which unifies symbolic and audio music generation at frontier quality through symbolic planning. A single AR-NAR Mixture-of-
Transformers (MoT) first writes a readable score specifying melody and harmony, expands it into semantic music
tokens, and realizes it as full-song audio. In comparisons using the same checkpoint, experts prefer symbolic planning for overall quality and musicality, with 49.3% of overall preferences versus 34.6% without planning. Experts also favor the unified model over a separate
language model and
diffusion Transformer. On WildSongBench, YuE2 scores 6.73 on SongBench Global Avg, exceeding all evaluated public baselines. Selecting from eight candidates (best-of-8), YuE2 reaches 6.96, the highest observed mean among all evaluated systems. Expert listening further establishes its competitiveness with proprietary song generators, favoring best-of-8 over Suno v4.5 and yielding nearly balanced preferences against Suno v5. To learn this generation process from recordings without aligned scores, we introduce MERT2 and SheetSage2 to supply semantic and symbolic supervision. MERT2 sets a new state of the art in music representation learning, surpassing previous best results on 14 of 15 MARBLE metrics; SheetSage2 leads 12 of 15
benchmark-metric pairs in our lead-sheet transcription comparison. The same checkpoint follows score edits while largely preserving unedited musical content and generates zero-shot covers without cover-specific training. Its readable score also enables agentic music editing, with external
language models translating user feedback into revisions of the composition.
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All authors Ruibin Yuan, Jiahao Pan, Junyan Jiang, Zhiyue Wu, Ziya Zhou, Jiankai Sun, Yizhi Li, Ge Zhang, Yicheng Gu, Zeyue Tian, Junyu Dai, Hanfeng Lin, Kai Li, Shangda Wu, Xuanjie Liu, Jiaming Wang, Zihan Liu, Yue Wang, Yinghao Ma, Hanzhi Yin, Kangrui Chen, Xinyue Zhang, Ziyang Ma, Mengqi Liao, Hejia Zhao, Guowei Huang, Chao Yan, Lei Ke, Jianwei Yu, Bei Liu, Joe Guo, Liumeng Xue, Gus Xia, Wei Xue, Yike Guo
Sound
Transformer-based Neural Beamforming for Real-Time Speech Enhancement on Smart Low-Power Hearable Devices
Luca Bompani, Marco Fariselli, Giovanni Oltrecolli et al. (+1 more)
Accurate, efficient, and low-latency spatial beamforming is a key component in emerging smart hearable devices, enhancing
speech while suppressing noise and interference. However, handling multiple input sources under strict real-time constraints poses significant challenges for the low-power, resource-constrained microcontroller units (MCUs) used in hearables. We present an optimized methodology for the real-time execution of a neural-network-based minimum variance distortionless response (MVDR) beamformer on MCUs. Using six microphones and a three-stage mixed-precision scheme (float32 MVDR, int8 CNN, float16
Transformer), the pipeline pairs a CNN that estimates the MVDR weights with a lightweight
Transformer that applies a per-frame correction. By time-slicing weight estimation with beamforming, it achieves a 15~ms per-frame latency while refreshing a complete set of CNN-derived weights every 564~ms. The deployed mixed-precision pipeline attains a short-time objective intelligibility (STOI) of 97.65%, a scale-invariant signal-to-noise ratio (SI-SNR) of 20.26~dB, and a wideband PESQ of 3.676 at an average power of 45.9~mW. A
speech activity detection (SAD) module (98.5% accuracy, 0.62~mJ per inference) bypasses the pipeline during silence; under realistic deployment conditions, the system exceeds the 16~h all-day target on a 100~mAh battery, with an estimated lifetime of up to $\sim$20~h. To our knowledge, this is the first real-time multi-channel
Transformer-based neural beamforming pipeline deployed on an MCU-class device.
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All authors Luca Bompani, Marco Fariselli, Giovanni Oltrecolli, Francesco Conti
Machine Learning
Collaborative Synthetic Data for Privacy-Preserving Financial Fraud Detection Across Organizational Silos
Simeon Allmendinger, Domenique Zipperling, Burhanettin Bahadir Kibar et al. (+1 more)
Organizations seek analytical value from AI, yet relevant data are often fragmented across organizations and constrained by
privacy. This is acute in financial fraud detection, where rare fraud cases and imbalanced local
datasets limit decision-relevant analytics.
Federated learning enables collaboration without direct data sharing but does not resolve minority-class scarcity. Synthetic data generation can help, yet lightweight methods are interpolation-bound, while generative models require substantial data and computation. Existing collaborative generative approaches often rely on
federated learning, imposing considerable organization-side training burdens. In this paper, we examine CollaFuse as a collaborative
diffusion-based alternative for fraud detection and evaluate it across five fraud
datasets. Compared with classical oversampling, local generative baselines, and centralized
diffusion benchmarks, CollaFuse does not achieve the highest local fidelity but improves downstream fraud detection more consistently across most
datasets. These findings suggest that synthetic data create analytical value less through local realism than through transferable cross-organizational structure.
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All authors Simeon Allmendinger, Domenique Zipperling, Burhanettin Bahadir Kibar, Niklas Kühl
Information Theory
Concurrent Coded Signal-Multiplexing Ranging for Half-Duplex Asynchronous Networks
Zijian Zhang and Yuan Shen
Signal-multiplexing network ranging (SM-NR) shares broadcasts across node pairs, but its sequential operation leads to a ranging cycle that grows linearly with network size. This paper proposes a concurrent coded SM-NR (CC-SM-NR) framework for asynchronous half-duplex networks. Firstly, the CC-SM-NR protocol coordinates concurrent transmissions through binary transmit-listen codewords. The transmit-listen schedule defined by these codewords ensures reciprocal observations subject to a finite concurrency limit. Then, we derive the exact minimum number of transmit-listen rounds without a concurrency limit, which reveals that the minimum grows logarithmically with network size. To account for practical scenarios, we establish the necessary and sufficient conditions for the constant-weight feasibility of codewords under a finite concurrency limit. Subsequently, we propose a low-complexity scheduling
algorithm that achieves the minimum round count within the constant-weight codeword class. To support higher observation redundancy, this scheduling design is extended through a greedy construction. Finally, simulation results demonstrate the effectiveness of the proposed schemes for network ranging.
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Computers and Society
An Empirical Study on how Computing Students Interact with Large Language Models for Learning Purposes
Opetunde Ibitoye and Saheed Popoola
Large
Language Models (LLM) have become popular tools in education. Therefore, understanding how students interact with LLMs for learning purposes is an important aspect of the responsible design and development of LLM based learning tools. However, existing research has focused on evaluating model performance, specific use cases, and students' attitudes toward LLM in education. There has been limited empirical work exploring students' interaction strategies with LLMs in computing education. This study draws on Self Regulated Learning (SRL) theory and conducts a survey of 225 undergraduate and graduate computing students to investigate how students use LLM tools during learning, the perceived learning impact, and the strategies they rely upon when using these tools. Students used LLMs across nine functional categories, most often to explain concepts, learn new material, find information, debug, and generate ideas. They also adopted some strategies such as supplying detailed context and iterative prompting. The most common difficulties encountered with LLM tools were incorrect information, generic responses, and difficulty trusting the model's
reasoning. We synthesized these patterns through an SRL lens and offer a Probe Monitor Shape heuristic to map students' LLM interaction strategies onto the phases of self-regulated learning.
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Machine Learning
ResDiffFRG: Residual Diffusion for Multiple Appropriate Facial Reaction Generation
Shizhe Liu, Jiayan Gu, Xiangyu Kong et al. (+1 more)
In dyadic human speaker-listener conversations, the listener's facial reactions allows the speaker to accurately perceive the listener's emotional states. Since human facial reactions are non-deterministic, the ability to generate multiple appropriate human-like facial reactions is crucial for realistic human-
agent interactions. Although
diffusion models are naturally suited to such one-to-many generation, existing
diffusion-based Multiple Appropriate Facial Reaction Generation (MAFRG) methods attempt to denoise random Gaussian initialisations directly into multiple appropriate facial reactions (AFRs). These random initialisations are usually not well-aligned with the target listener facial reaction, which requires complex denoising trajectories from these initialisations, and subsequently creates substantial opportunities for deviations away from the range of trajectories leading to appropriate AFRs. Given the inherent mimicry between the human listener's and speaker's facial behaviours, we address the above denoising trajectory issue by leveraging this strong prior. Specifically, we propose ResDiffFRG, a novel
diffusion-based MAFRG framework that explicitly anchors the
diffusion process to the speaker behaviour by defining its
diffusion target as the residual between the speaker anchor and an AFR. The denoiser only needs to model the comparatively small, reaction-specific residual needed to transform this anchor into an AFR, rather than reconstructing the complete reaction from an unstructured state. Extensive experiments show that ResDiffFRG achieves large improvements in correlation-based appropriateness over existing methods. Our denoising trajectory analysis showed that even at the start of the denoising trajectory, ResDiffFRG already achieves a higher facial-reaction correlation score than the Gaussian
Diffusion baseline does after completing 60% of its denoising trajectory.
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All authors Shizhe Liu, Jiayan Gu, Xiangyu Kong, Siyang Song
Robotics
AnyStep-WAM: Budget-Aligned Distillation and Adaptive Inference for World Action Models
Rui Wang, Xiangyu Wang, Donglin Yang et al. (+4 more)
World-action models (WAMs) couple predictive visual modeling with action generation, typically relying on iterative denoising with a fixed denoising steps. However, manipulation tasks contain actions chunks with varying sensitivity to generation errors: critical actions require precision, while less sensitive actions allow faster generation with fewer denoising steps. Here we introduce AnyStep World Action Model, a general framework for tunable-budget prediction and scene-dependent computation allocation. Our budget-aligned teacher-trajectory
distillation trains interval-conditioned flow maps using explicit frozen-teacher transitions and shared low-rank adapters, supporting action generation from one-step prediction to multi-step refinement. Building on this capability, a lightweight risk-benefit scheduler predicts teacher-
curvature-based difficulty and budget-specific student-teacher fidelity from a single one-step preview, selecting the smallest budget predicted to satisfy risk-adaptive fidelity requirements. We evaluate our framework on three widely used WAMs Motus, FastWAM, and LingBotVA using RoboTwin 2.0. Our method reduces average denoising steps by 60.2%, 49.8%, and 85.28%, respectively, while maintaining baseline task success rates. In particular, our AnyStep training substantially improves model performance under a one-step denoising budget, increasing task success rates by 7.07%, 12.08%, and 8.94% on Motus, FastWAM, and LingBotVA, respectively. Experiments on six real-world manipulation tasks further validate its effectiveness.
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All authors Rui Wang, Xiangyu Wang, Donglin Yang, Yibo Li, Canyang Chen, Zhongrui Wang, Xiaojuan Qi
Machine Learning
Task-Aware Discretization of Differentiable Logic Gate Networks
Thore Gerlach
Differentiable logic gate networks (DLGNs) enable gradient-based training of highly efficient Boolean networks by relaxing discrete logic gates during training and discretizing them for inference. Standard approaches make this discretization decision locally, typically through argmax selection and confidence- or
entropy-based convergence criteria. We show that local discretization can be task-suboptimal even for globally optimal relaxed solutions, with high gate confidence providing no general guarantee, and derive bounds relating task-aware gate selection to tractable interventions in the relaxed network. Motivated by these results, we study first-order downstream task information for progressive discretization and characterize when this local approximation is reliable. Experiments on convolutional DLGNs reveal a strong locality dependence: first-order scores become unreliable when directly optimized over nonlocal interventions, but accurately assess local argmax decisions for progressive freezing.
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Machine Learning
PQ-HSA: Reusing Product-Quantized Scores for Hybrid Sparse-Approximate Attention
Kunming Shao, Jierun Chen, Yanli Wang et al. (+4 more)
At each decoding step a
language model attends over the key-value (KV) cache of every earlier
token, so at long context the
attention call is bounded by
memory bandwidth. Sparse
attention reads only a subset of keys chosen by a cheap score estimate, and most methods give the unread
tokens zero weight. The output then draws on only a small fraction of the KV cache, and accuracy drops at small budgets, most on tasks that aggregate information across the context. An inverted-file product-quantization (IVF-PQ) index over the cached keys computes an approximate score for every indexed
token in order to rank them; after ranking, those scores approximate the
attention logits of the
tokens left out. PQ-HSA (hybrid sparse-approximate
attention) attends the selected
tokens with their original keys and values, and the unselected
tokens, the background, enter the same softmax through those scores, summed per inverted list and multiplied by the list's mean value. At 128K and a 1-2%
retrieval budget, PQ-HSA is more accurate than Quest and SnapKV on Llama-3.1-8B and Qwen3-30B-A3B and stays close to full
attention in macro accuracy; with the same selector, the background term raises macro accuracy on the 8B model from 0.71 to 0.83. In the same 128K setting, inside vLLM on one NVIDIA H20, the decode
attention call runs 1.6x faster than the FlashAttention-3 kernel; the speedup grows with context length, and a cost model fitted on 8B to 30B models gives the context length at which it begins. A vLLM plugin runs PQ-HSA on two engine versions without changes to the engine source; code is available at https://github.com/KunmingSHAO/pqhsa_release.
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All authors Kunming Shao, Jierun Chen, Yanli Wang, Ruoyu Wang, Haoli Bai, Kwang-Ting Cheng, Chi Ying Tsui
Information Retrieval
Beyond the Beam: Constructive Repair and Candidate Completion for Generative Recommendation
Zijun Zhao, Peng Zhang, Gang Zhang et al. (+3 more)
Generative recommenders retrieve items by generating identifiers, but a valid identifier can remain outside the beam after catalog expansion. This raises two connected questions: which failures can identifier assignment repair, and how should
retrieval proceed beyond the initial beam? We characterize assignment repair with a fixed generator and retained old identifiers. Output-invariance certificates identify failures shared by all admissible assignments. Under a common effective prefix, coupled support and ranking constraints give the exact feasible interval of new-item counts for target recovery. Building on this characterization, Beyond the Beam (BB) obtains minimum-replacement repairs through an integral flow formulation, selects a shared map and adapts the generator. At inference, generative likelihood and collaborative evidence define one score for ranking, candidate priority and stopping. Retained prefix bounds guide candidate completion and certify its global Top-$K$ when the stopping condition is met. Exhaustive finite-catalog evaluation confirms construction in every feasible case. Across three Amazon Reviews categories and three random seeds, the full T5 procedure improves mean Recall@10 by 15.5–46.3% and NDCG@10 by 15.2–44.4% over the best-performing evaluated generative baseline for each
dataset and metric. Matched controls show that shared construction and adaptation improve new-target ranking and certification efficiency on Beauty and Toys. Combined scoring and candidate completion improve NDCG@10 across all three
datasets with both T5 and decoder-only LC-Rec.
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All authors Zijun Zhao, Peng Zhang, Gang Zhang, Yuanchi Ma, Hui He, Zhendong Niu
Human-Computer Interaction
Accounting for Stochasticity in Studies of Large Language Model Refusal
Emma Lurie, Stephanie T. Wang, Sorelle A. Friedler et al. (+1 more)
We present preliminary empirical evidence that single-observation queries are insufficient for evaluations of LLM refusal behaviors. Using a longitudinal
auditing system, we issued identical
prompts 100 times each across four dates to GPT-4.1 for two socially salient topics across 20 Wikipedia sources. Refusal outcomes were consistent with a stable Bernoulli process, yet 20% of sources fell within a decision-boundary region where a single query is largely uninformative. Reliable quantification of refusals required between 15 and 25 repeated queries, well above the single-observation standard common in existing evaluations.
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All authors Emma Lurie, Stephanie T. Wang, Sorelle A. Friedler, Danaé Metaxa
Sound
DuraS2ST: Chain-of-Thought and Reinforcement Learning for Duration-Aligned Speech-to-Speech Translation
Yayue Deng, Dingdong Wang, Yuxuan Hu et al. (+7 more)
Speech-to-
speech translation (S2ST) in time-sensitive applications such as
video dubbing requires not only semantic fidelity and speaker preservation, but also strict duration consistency to avoid audio-visual misalignment. However, existing S2ST systems largely generate target
speech without explicit temporal planning, making duration control an unresolved challenge. We introduce DuraS2ST, a duration-aligned
reasoning framework that enables a single
speech language model to first generate an explicit chain-of-thought (CoT) for planning target wording and phonetic length, and then synthesize the corresponding
speech tokens. To support this paradigm, we construct DuraSet-440K, a high-quality duration-aligned CoT corpus for supervised initialization. We further optimize the model with multi-modal multi-dimensional reinforcement learning, using a Duration Margin Reward to balance translation quality and duration consistency, and Modality-Aware Reward Attribution to assign rewards to appropriate
token spans. Experiments on CVSS-T show that DuraS2ST achieves a strong balance between translation quality and duration consistency, outperforming competitive open-source and commercial baselines. Project page: https://github.com/Mia11939/DuraS2ST.
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All authors Yayue Deng, Dingdong Wang, Yuxuan Hu, Jinyu Li, Yanqing Liu, Yuanyuan Wang, Weidong Chen, Helen M. Meng, Shujie Liu, Xixin Wu
Machine Learning · Cross-listed from stat.ML
Multi-Marginal Inverse Optimal Transport for Contrastive Learning Via Explicit Anchor-Positive-Negative Coupling
Ngoc-Hai Nguyen, Thuan Nguyen, Prakash Ishwar et al. (+1 more)
Inverse Optimal Transport (OT) based methods for representation learning learn representations such that the global OT coupling between a pair of data marginals in the representation space, concentrates on the positive pairs. This is in contrast to previous methods that primarily focused on pairwise matching. However, these methods $\textit{DO NOT}$ utilize negative pairs and hence are not truly contrastive in their approach. We show that this leads to issues of dimensional collapse and hence degraded downstream performance. To alleviate this, we develop a novel multi-marginal (MM) inverse OT (IOT) contrastive learning (CL) approach called Neg-MMIOT-CL, which learns representations such that the global multi-marginal OT (MMOT) coupling between a triple of data marginals, with respect to a carefully designed ground-cost between triplets of data points in the representation space, concentrates on the anchor-positive-negative $\textit{triplets}$. For a latent class model, we empirically show that Neg-MMIOT-CL alleviates dimensional collapse. Furthermore, for a specific choice of ground cost for all triplets in representation space, we prove that the optimal representation configuration for Neg-MMIOT-CL exhibits equiangular property for within-class and across-class representations, which translates to Neural-Collapse when the representation dimension is larger than the number of classes minus one – a result that is $\textit{previously established only}$ for pairwise contrastive learning methods. Finally, we propose Neg-IOT-CL-PushPull, that is a computationally efficient alternative to Neg-MMIOT-CL, alleviating the high cost of computing MMOT plans needed during implementation. We apply these methods on both synthetic and real-world
datasets and show significant improvements over existing OT-based contrastive learning methods.
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All authors Ngoc-Hai Nguyen, Thuan Nguyen, Prakash Ishwar, Shuchin Aeron
Machine Learning · Cross-listed from stat.ML
Weighted Spline-Expanded Networks with Distributional Balancing for Continuous Treatment Effects
Shucheng Liu, Chan Park and Guanhua Chen
Estimating causal effects with continuous treatments in observational studies is challenging due to confounding, model misspecification, and high-dimensional
covariates. We propose the Weighted Spline-Expanded Network (WSENet), an end-to-end neural framework that addresses these challenges by combining
covariate balancing, structured treatment
embedding, and bias-corrected outcome estimation. WSENet first applies Distance
Covariate Optimal Weights to induce distributional independence between
covariates and treatment without relying on parametric models. It then learns the conditional outcome via a structured network that fuses outcome-relevant representations of
covariates with a spline-expanded treatment input, enabling smooth and flexible modeling of the dose-response relationship. To mitigate residual bias, we introduce Weighted Targeted Regularization, a correction technique based on efficient influence functions that yields a doubly robust
estimator. Extensive evaluations on semi-synthetic and real-world
datasets, including high-dimensional genomic and environmental health data, demonstrate that WSENet consistently outperforms existing baselines in both accuracy and stability.
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