Data Structures and Algorithms
PHastR: Faster Minimal Perfect Hashing with Rotated Layouts
Sebastiano Vigna
We present PHastR, a minimal perfect hash function of the PHast family. Like PHast+, the fast-to-build variant of PHast, PHastR finds feasible seeds with bit-parallel operations: its seeds move keys along rings of slots, and to evaluate the seeds of a bucket it cyclically shifts, for each key, a word recording which slots of its ring are used. Unlike PHast+, however, it is almost immune to self-collisions, which make two keys of a bucket collide for (almost) all its seeds: more than a third of the buckets bumped by PHast+ with wrapping and multiplier 3 contain one. With 8-bit seeds, PHastR uses $1.94$ bits per key, essentially the same space as the best variant of PHast+ with wrapping, but it is built in $37\%$ of the time, and its queries are $28\%$ faster on $10^9$ keys, as it bumps $0.5\%$ of the keys instead of $3.9\%$; they are faster also than those of PHast, which takes twelve times as long to build, and about as fast as those of PtrHash, which uses more than one bit per key more. With a larger expected bucket size PHastR comes within $0.02$ bits per key of the space of PHast, using $1.90$ bits per key, and still has faster queries. Its construction also scales better with the number of threads: with all the threads of a 48-core machine it is almost three times faster than that of PHast+. Finally, we argue that when each key can be placed only within a range of $W$ slots, every placement of $n$ random keys on $n$ slots, however found, must bump on large key sets a constant fraction of the keys: we derive a lower bound of about $0.1/W$ on this fraction, and we estimate that the best placement bumps about $1/2W$ of the keys, which is about one tenth of the keys bumped by PHastR.
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Machine Learning
Agentic-MatriBO: Capability-Compiled Agentic Bayesian Optimization for Materials and Chemical Design
Yu Zeng, Yuchuan Chen, Ziyue Zhang et al. (+5 more)
Materials and chemical optimization must choose the next synthesis, characterization, or high-fidelity simulation from sequential observations under a limited budget. Large
language models can use task knowledge to propose adjustments, but composing surrogates, acquisition functions, and search strategies is constrained by compatibility, parameter dependencies, and evidence requirements. We present Agentic-MatriBO, a capability-compiled agentic
Bayesian optimization (BO) framework that represents BO components and selection conditions as composable capabilities. A
language policy proposes BO recipes and candidate plans; a compiler checks proposals against the registered capability catalog; and a deterministic execution layer validates and commits candidates. Oracle observations are stored in research
memory and inform later rounds. We compare Agentic-MatriBO with eight baselines on four offline finite candidate pools—COF, PCE10, Buchwald–Hartwig (BH), and Suzuki–Miyaura—using five seeds per condition; results are reported descriptively. Across the four tasks, Agentic-MatriBO has the best mean endpoint quality after orienting each objective so that higher is better. On COF, it reaches the tabulated optimum in all five seeds both in the end-to-end comparison and after shared initialization, where it also retains an advantage in cumulative quality. Its normalized area under the best-so-far quality curve during the BO stage (nAUC-BO) is substantially higher on COF, near parity with the best baseline on PCE10 and BH, and lower on Suzuki. COF ablations show that removing the capability compiler or evidence gate coincides with lower cumulative quality and fewer exact-optimum hits. The framework provides an inspectable interface for evidence-conditioned BO adaptation.
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All authors Yu Zeng, Yuchuan Chen, Ziyue Zhang, Zijian Wei, Yanyi Su, Guojiang Zhao, Hongshuai Wang, Zhifeng Gao
Software Engineering
Diff Diffusion: Predicting Edit Paths with Diffusion Models for Efficient Code Editing
Xiaolong Xu, Hao Jiang, Bo Jiang et al. (+5 more)
Diffusion large
language models (dLLMs) are appealing for code generation because they can update many
tokens in parallel, yet many are optimized for sequence synthesis rather than edit prediction. This setting is poorly matched to modern AI-assisted IDEs, where edit prediction must infer the next code change from recent edits, the current file, and cursor-local context. Such changes are usually sparse and preserve most source
tokens, so regenerating a full code segment wastes computation. We propose Diff
Diffusion, a
diffusion-inspired LLM framework for code editing that predicts edit paths from an original snippet to its revised form. Diff
Diffusion represents denoising as structured edit operations, and combines this formulation with multi-stage training and efficient parallel decoding. By focusing computation on changed regions, Diff
Diffusion supports accurate low-latency edits and multi-position parallel modification. Across public edit-completion
benchmarks, edit-prediction evaluations, and online validation, Diff
Diffusion matches or leads autoregressive baselines while delivering 2.78 times faster inference on public edit-completion
benchmarks and a 39.0% P50 model-latency reduction in production. This work provides a new modeling perspective for AI applications that require high accuracy and low latency.
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All authors Xiaolong Xu, Hao Jiang, Bo Jiang, Zhiwen Deng, Ningyuan Sun, Bingzhou Chen, Jin Chen, Chenxing Wei
Statistical Methodology
Spatial Kernel Restricted Canonical Correlation Analysis: Population Targets, Identifiability, and Stability
Zhen Miao, Yilong Qu and Nancy R. Zhang
Relating observations of different categories across space requires methods that accommodate distinct sampling locations and spatial dependence. Spatial kernel restricted canonical correlation analysis (skrCCA) uses spatial weights to identify associated feature combinations across data categories. Here, we develop a statistical theory of its covariance form after feature reduction. Conditional on the observed locations and under a mean-zero model, the pairwise association
matrices target kernel-weighted averages of cross-covariances. For prespecified directions, we derive the exact null variance under independence between blocks. We establish recovery and stability bounds in two- and multiblock settings under explicit population models, and show that exact cyclic updates have convergent objective values. The variance characterization shows how spatial weighting and within-block dependence jointly determine the effective information available for estimating association. The recovery bounds separate statistical and optimization error, relating stability to population separation and the strength of connections between blocks. These results clarify what fitted spatial association axes estimate and provide conditions under which they can be recovered reliably.
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Computer Vision
QK-Edit: Revisiting Attention-based Injection in MM-DiT for Image and Video Editing
Tiancheng Shen, Zilong Huang, Xiangtai Li et al. (+6 more)
Multimodal Diffusion Transformers (MM-DiTs) have recently emerged as a powerful framework for unified text-
vision synthesis, surpassing traditional U-Net architectures in generative tasks. One key innovation lies in its
Multimodal Self-
Attention (MM-SA) interaction, where image and text
tokens are concatenated and processed via self-
attention. However, this mechanism poses significant challenges for editing, rendering conventional U-Net-based
attention manipulation methods ineffective. To address this limitation, we propose QK-Edit, a training-free framework that exploits the unique
attention dynamics of MM-DiTs for precise text-guided image and
video editing. By introducing a novel query-key manipulation strategy, our method isolates and adjusts critical
attention components to achieve an optimal balance between
prompt fidelity and structural consistency. This enables seamless edits across various tasks, including object addition, object removal, object replacement, changing background, changing material, changing color, and style transformation. Notably, it can be easily implemented with feature replacement in inference. QK-Edit demonstrates superior editing performance on state-of-the-art models, such as FLUX and HunyuanVideo, effectively bridging the gap between generative power and editable flexibility in MM-DiTs, and paving the way for scalable
multimodal content creation.
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All authors Tiancheng Shen, Zilong Huang, Xiangtai Li, Zhijie Lin, Jiyang Liu, Yitong Wang, Jiashi Feng, Ming-Hsuan Yang, Jun Hao Liew
Signal Processing
Shuf-Scatter: Permutation-Based DLI Suppression for OFDM Ambient Backscatter
Salim Chabani, Riadh Ouzane, Salah Eddine Zegrar et al. (+1 more)
Direct Link Interference (DLI) remains a major bottleneck in Orthogonal Frequency Division Multiplexing (OFDM)-based Ambient Backscatter Communication (AmBC) systems. This letter proposes Shuf-Scatter, a scheme that applies a deterministic permutation to the reflected OFDM waveform to create a distinct correlation-domain signature. By exploiting the quasi-orthogonality between the reshuffled backscatter signal and the direct-link component, the proposed receiver effectively mitigates DLI and enables reliable detection without requiring dedicated spectral resources, or instantaneous Backscatter-Link (BL) Channel State Information (CSI). Analytical and simulation results demonstrate that Shuf-Scatter achieves superior Bit Error Rate (BER) performance under strong DLI while preserving the integrity of the legacy OFDM transmission.
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All authors Salim Chabani, Riadh Ouzane, Salah Eddine Zegrar, Huseyin Arslan
Artificial Intelligence
Benchmarking and Training Reward Models for Cross-Source Evidence Grounding in Search Agents
Haochen Zhao, Minsheng Hao, Zheng Li et al. (+8 more)
Existing approaches to training and evaluating search
agents focus heavily on final-answer quality, largely neglecting how generated answers map back to retrieved evidence. Recent methods track citations to enforce grounding, but citation support alone does not establish source independence or whether an answer appropriately handles conflicting evidence. We argue that credibility in open-web
retrieval depends on principled cross-source evidence grounding that accounts for source independence and systematically attributes and adjudicates conflicting evidence, while hierarchical judgments of these grounding decisions provide a unified signal for training and evaluation. To this end, we propose an evaluation framework, Cross-Source Evidence Grounding (CSEG), that employs a fine-grained, six-layer decision tree over the complete search trajectory. We introduce CSEG-Bench and trainable reward models to provide targeted supervision following this framework. Our approach helps eliminating the severe data skew of natural trajectories by providing targeted coverage over complex, deep-layer decision paths, such as cross-source conflict resolution and nuanced attribution, that standard rollouts fail to capture. Specifically, (1) we define the hierarchy at label, node, and path levels; (2) we construct 1,160 verified trajectories over nine canonical paths by rewriting answers on real retrieved document pools, with independent frontier judges reproducing the entire gold path and human experts deciding inclusion; (3) we train CSEG-RM, which reaches 83.9% exact accuracy on our
benchmark while lying on the performance-
token efficiency Pareto frontier. Experiments that use this reward alone to supervise a frozen-
retrieval answer policy raise the independently judged mean label from 2.83 to 4.19, and show that learning the hierarchy generalizes across layers at a fraction of frontier-judge cost.
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All authors Haochen Zhao, Minsheng Hao, Zheng Li, Yifan Li, Zikang Liu, Bowen Qin, Yuhao Cheng, Chuang Fan, Haosheng Zou, Jeff Chen, Sujian Li
Mathematical Physics
How many switches does two-Hamiltonian universality require?
Matas Garbaravičius and Michael M. Wolf
Any pair of sufficiently generic
Hamiltonians on $\mathbb{C}^d$ generates the full
unitary group, i.e., any $W\in U(d)$ can be synthesized to arbitrary precision by alternating between the two evolutions for suitably chosen durations. For any given pair of
Hamiltonians, the minimal number of switches required to cover the entire group is at least $d$ and unbounded in general. Despite this, we show that it scales linearly in $d$ in expectation. Assuming that the two spectra are sufficiently generic, we derive constructive upper bounds on the minimal switching number as a function of the change-of-basis
matrix between the two eigenbases. In particular, if this
matrix has a column whose entries all have equal moduli, as for a complex Hadamard
matrix, then $9d$ switches suffice to reach every
unitary. To obtain these results, we construct a short exact representation of any
unitary as a product of
unitaries that are alternately diagonal in two given bases. All bounds are extended to non-generic spectra and bounded
evolution times.
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Optimization and Control
Minimal norm control problems for linear mean-field control systems
Manfen An, Rui Gao and Yanqing Wang
This paper studies controllability and minimal norm control problems (NP problems) for mean-field
stochastic differential equation (MF-SDE) systems. We first establish an equivalent rank criterion for terminal controllability, namely, that the
diffusion matrices $D$ and $D+\bar D$ are both of full row rank. We then derive an explicit Riccati-based representation of the minimal norm control by applying a parameterization technique. A key step is to identify the value $P(0)$ of an associated Riccati solution with the controllability Gramian of a controllable auxiliary system, which yields the parameter required in the construction. The resulting representation provides a basis for the future development and analysis of numerical methods for NP problems.
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Machine Learning
Checking the Checkers: A Synthesis of Verifier Noise and Robustness in RLVR
Savyasachi Gupta and Aniruddha Srinivas Joshi
Reinforcement learning with verifiable rewards (RLVR) has become a principal recipe for improving the
reasoning of large
language models (LLMs). It rests on the assumption that the checker scoring each response is itself correct. In practice, rule-based checkers reject correct answers in unexpected formats, unit tests accept incorrect programs, and LLM judges can be led to approve empty or
adversarial responses. This work surveys 58 studies that measure, analyze, or mitigate such verifier errors. We ask what errors verifiers make, how they affect training, which mitigations work against which errors, and how the findings extend to self-generated, rubric- and judge-based, and verifier-free rewards. The evidence suggests that the structure of an error matters more than its rate. Random errors mostly slow learning, though under a fixed budget or KL regularization they also lower final accuracy. False positives that the policy can learn to trigger can cause plateaus or collapse, sometimes even with a low initial error rate. Whether a verifier wrongly rejects or accepts depends on what it checks and who built it, not on its family. Its errors shift as the policy improves, and most mitigations have been tested against only one kind of error. We compile the findings into comparison tables, reconcile apparent conflicts in the literature, and propose a reporting protocol to make future results comparable.
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Information Retrieval
Mitigating Popularity Bias in Sequential Recommendations with Sparse Prior-Regularized Objective
Anton Pembek, Anton Klenitskiy, Alexander Savchenko et al. (+1 more)
Popularity bias is a well-known challenge in recommender systems, often leading to the over-exposure of popular items and reduced catalog coverage. In this work, we propose PR-entmax, a sparse prior-regularized objective that makes popularity correction context-dependent. We derive our method from a generalized variational principle in which an item popularity prior is incorporated directly into a sparse probability mapping, yielding a popularity-aware active support: the set of items receiving non-zero probabilities and hence non-zero gradients is determined jointly by contextual relevance and item popularity. As a result, the prior affects not only item probabilities, but also which items participate in gradient updates during training. The method requires no architectural changes and can be integrated into existing training pipelines by modifying only the loss function. At inference time, the prior term is removed, and items are ranked by the learned contextual relevance alone. Experiments across multiple
benchmark datasets and sequential recommendation architectures show that PR-entmax consistently reduces popularity bias while preserving or improving recommendation relevance, advancing the relevance-debiasing trade-off.
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All authors Anton Pembek, Anton Klenitskiy, Alexander Savchenko, Alexey Vasilev
Optics
Practical Mid-infrared Dual-comb Spectroscopy with Absolute Frequency Referencing Based on a Single-cavity Dual-comb Fiber Laser
Akifumi Asahara and Kaoru Minoshima
We demonstrate a practical mid-infrared (MIR) dual-comb spectroscopy (DCS) method based on a bidirectional dual-comb fiber
laser. By exploiting the inherently high relative stability and mutual coherence of the dual-comb
laser, we stabilize the MIR dual-comb modes with sufficient precision using only piezoelectric control of the repetition frequencies. This simple configuration enables correction of residual timing jitter and phase noise in interferograms while preserving the absolute optical frequency scale defined by the experimentally obtained comb parameters, without requiring an external continuous-wave
laser reference or tight phase-locking. Importantly, the proposed method does not merely produce apparently clean interferograms through signal processing, but enables coherent averaging while retaining both the physically meaningful interferometric waveforms and well-defined absolute frequency information. The proposed system is experimentally validated through MIR spectroscopy of N$_2$O around 3.9 $\mu$m, achieving a signal-to-noise ratio of approximately 690 through coherent averaging of 10,000 interferograms over 65 s, along with the acquisition of an absorption spectrum consistent with the reference data. These results demonstrate a simple and practical approach to absolute-frequency-referenced MIR-DCS.
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Sound
FLOMSS: Generative Music Source Separation by Flow Matching
Shuyang Cui, Zhi Zhong, Keisuke Toyama et al. (+10 more)
Music source separation (MSS) has been conventionally addressed by discriminative methods. Recently, flow matching has emerged as a strong generative paradigm for modeling complex data distributions, with successful applications across diverse domains. These advances motivate us to systematically investigate flow matching for MSS in a four-source (Vocals, Bass, Drums, Other) setting in full-band stereo. Specifically, we investigate prediction targets, flow paths, single-source versus multi-source modeling, and mixture consistency constraints. Beyond the signal-level metrics commonly used for MSS, such as signal-to-distortion ratio, we further evaluate MSS methods using
embedding-based metrics, as well as a listening test, providing a comprehensive
benchmark. On MUSDB18-HQ, our proposed model exhibits competitive performance in both objective and subjective tests, highlighting the potential of flow matching for generative MSS. Demo: https://ik4sumii.github.io/FLOMSS/
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All authors Shuyang Cui, Zhi Zhong, Keisuke Toyama, Koichi Saito, Qiyu Wu, Yukara Ikemiya, Junghyun Koo, Kin Wai Cheuk, Woosung Choi, Giorgio Fabbro, Wei-Hsiang Liao, Shusuke Takahashi, Yuki Mitsufuji
Cryptography and Security
Same Attack, Different Judge: A First Controlled Adversarial Comparison of Jev, LLM Judges, and Purpose-Built Safety Classifiers
Xiang Li, Aichun Huang, Mingming Zhang et al. (+1 more)
TypeSafe AI's Jev is a new kind of safety judge. It generates no text and no label: it answers questions the developer defines in advance and returns a probability for each answer. The vendor admits that
adversarial input can change these answers but gives no numbers. In this paper, we present the first controlled
adversarial evaluation of Jev. We build a test harness that sends every system the same payload under the same policy, and run it on four public-data tasks, from rule-based ones (input moderation, injection detection, routing) to a judgment-based one (phishing detection), with fifty attack variants from thirteen families. The targets are Jev, five GPT and GLM judges, and two local classifiers (Llama Guard 3,
Prompt Guard 2). We find that Jev is the most robust system on input moderation: eight forgeries of its structured input bypass none of its decisions, and a sentence asking only why someone might make a harmful request bypasses 9% of its blocks, against 56% for GPT-6 Astra. On phishing detection. However, the same forgeries bypass 19-29% of the emails it flags, and even without attack Jev detects only 62% of phishing emails, against 77-81% for the GLM judges. Jev is also the fastest judge, but it flags the most benign requests (27%). The local classifiers fail differently: a training-example statement makes Llama Guard 3 miss 62% of the phishing it had flagged. These results suggest that Jev resists attacks on tasks with a clear rule to check but is more exposed on tasks that call for an overall judgment.
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All authors Xiang Li, Aichun Huang, Mingming Zhang, Zheli Liu
General Relativity and Quantum Cosmology
Classical and quantum scattering of massive Klein–Gordon fields in asymptotically flat spacetimes
Mikhail Molodyk
We use microlocal tools to define distinguished incoming and outgoing
vacuum states of the
quantum theory of massive Klein–Gordon fields in non-trapping asymptotically flat spacetimes, including radiative perturbations of Minkowski space. The asymptotic
vacuum states satisfy the Hadamard condition, and the incoming and outgoing particle representations are unitarily equivalent, or in other words the
quantum S-
matrix is well-defined. These results are based on a description of scattering of classical fields which keeps track of the asymptotic distinction between positive- and negative-frequency solutions of the Klein–Gordon equation. Our emphasis is on developing the theory without recourse to explicit asymptotics for classical waves.
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Machine Learning
Follow the Perturbed Leader for Linear Bandits: $\sqrt{T}$ Regret over General Action Sets
Chen-Yu Wei
We give the first
adversarial linear bandit
algorithm based on Follow-the-Perturbed-Leader (FTPL) that achieves $\sqrt{T}$ regret over general action sets. Our
algorithm attains $\widetilde{\mathcal{O}}(d^{5/4}\sqrt{T})$ regret using only linear optimization oracles, and the near-optimal $\widetilde{\mathcal{O}}(d\sqrt{T})$ regret when an optimal-design exploration distribution is further available. This improves on the $T^{2/3}$ regret of the FTPL
algorithm of Dani and Hayes (2006) for general action sets, and generalizes the $\sqrt{T}$ regret of Lévy et al. (2026) over two special action sets (hypercube and Euclidean ball). Compared to previous oracle-efficient linear bandit
algorithms based on continuous exponential weights, the FTPL approach generally has better computational efficiency, and is much simpler to implement. The key algorithmic element is a perturbation distribution that adapts to the past reward
estimators. Its simplest form, an adaptive Gaussian perturbation, is straightforward to implement and already attains the regret bound above. We further extend it to two general constructions, random-weighting and elliptical perturbations, each covering distributions from light tails (Gaussian) to heavy tails (Student-$t_2$). On the technical side, we develop a systematic way to analyze the stability term in the FTPL regret through the Fisher information of the perturbation distribution. This builds a general framework for FTPL beyond existing ones such as the hazard rate and the FTRL-FTPL duality.
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Algebraic Geometry
Aspects of deformation invariance of plurigenera of Kähler varieties
Jiawei Feng, Yi Li and Sheng Rao
We study several aspects of deformation invariance of plurigenera in Kähler families over a smooth curve. First, for a relatively flat coherent sheaf, we characterize local constancy of fiberwise cohomology dimension by equivalent conditions in terms of extension, base change, torsion freeness of higher direct images, and the vanishing of jumping classes. Then, combining this criterion with the Kähler minimal model program, we prove invariance of plurigenera for Kähler families of threefolds whose central fiber has canonical singularities, and for smooth Kähler families of maximal Albanese dimension. We also establish deformation invariance of log plurigenera when the central fiber is projective, and is of log general type. We prove the invariance of higher cohomological plurigenera assuming that the canonical bundle of the total space is semipositive. Finally, we extend Z.~Li–Z.~Wang's deformation invariance result for generalized polarized pairs with abundant nef parts from projective families to Moishezon families.
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Artificial Intelligence
Empowering Edge Agents: A Systematic Analysis of Edge-Cloud Collaboration in LLM Agent Execution
Pengcheng Huang, Zhenghao Liu, Shixin Ren et al. (+5 more)
Edge-cloud collaboration combines the lower inference overhead and greater data locality of edge-side execution with the stronger capabilities of cloud-hosted models. However, as LLM
agents increasingly operate in long-horizon scenarios, cloud participation becomes interleaved throughout execution rather than a one-shot decision for each query. Such interleaved participation introduces trajectory-level effects that remain insufficiently understood. To address this gap, we introduce EC2-Bench, a
benchmark for evaluating edge-cloud collaboration in long-horizon
agent execution. It provides executable digital-workspace
agent tasks and a unified evaluation framework covering task performance, resource usage, and
privacy exposure. Using this
benchmark, we empirically study diverse forms of edge-cloud collaboration. Our study reveals three findings. First, collaboration achieves a favorable balance among task performance, resource usage, and
privacy exposure compared with edge-only and cloud-only execution. Second, as edge models become stronger, collaborative execution approaches cloud-only performance, and performance differences across strategies shrink, while
privacy exposure remains strongly strategy-dependent. Third, stronger edge capability shifts preferred collaboration from externally controlled cloud participation and direct cloud execution toward more edge-controlled collaboration. Further analyses identify cloud-invocation decision-making and instruction following as important edge-side abilities for effective collaboration. These findings provide a systematic understanding of edge-cloud collaboration in long-horizon
agent execution and insights for designing future edge
agents that effectively collaborate with cloud models. Code and resources are available at https://github.com/OpenBMB/EC2-Bench.
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All authors Pengcheng Huang, Zhenghao Liu, Shixin Ren, Shi Yu, Sijia Yao, Yukun Yan, Chaojun Xiao, Maosong Sun
Mathematical Physics
$L^2$ convergence rates for singular stochastic wave dynamics
Nikolay Barashkov, Giacomo Di Gesù and Petri Laarne
We establish quantitative hypocoercivity for singular
stochastic damped wave equations arising in
stochastic quantization. Building on the finite-dimensional argument of Cao, Lu, and Wang [Arch. Rat. Mech. Anal. 2023], we derive a dimension-free criterion that replaces uniform semiconvexity by a log-Sobolev inequality together with exponential lower-tail bounds for the bottom of the Hessian spectrum. We verify this criterion for the two-dimensional $\Phi^4_2$ and sine-Gordon models and for the three-dimensional $\Phi^4_3$ model, through estimates on the lowest
eigenvalue of associated singular random Schrödinger operators. Combined with the continuum limit established in a companion paper, this yields exponential $L^2$ convergence to
equilibrium for the $\Phi^4_2$ dynamics and for sine-Gordon with $\beta^2<2\pi$. For $\Phi^4_2$, we verify that in a weak-coupling, small-mass regime, optimizing the damping yields a kinetic relaxation rate of at least order $m$. This yields a genuine acceleration over the order-$m^2$ relaxation rate of the overdamped dynamics. The
lattice estimates are uniform in the discretization for sine-Gordon throughout $\beta^2<4\pi$, and for $\Phi^4_3$ at fixed volume, including regimes beyond those for which the continuum dynamics is vpresently constructed.
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Information Retrieval
When Inputs Receive No Score: Evaluating Open-Web Event Admission on the Complete Stream
Shengqi Zhou, Zhigang Liu, Wenxiang Zhu et al. (+4 more)
A Web item can link to the correct event without contributing information worth organizing. Evaluating item-level admission therefore begins with complete inputs, including those a processing pipeline fails to score. We study how this upstream selection changes ranking comparisons and constrains operational decisions. Across six instruction-model backbones, three strategies, and 602 human-annotated evaluation items, systems score different populations with different class compositions. Comparing rankings on shared inputs separates these population differences from discrimination. Score availability also limits attainable retention: four configurations score only ten of twelve positive development items, making a 90% full-stream target unreachable by threshold adjustment. Paired precision experiments show that this constraint depends on execution configuration as well as model choice. Qwen scores the same number of evaluation items under INT4 and INT8, yet exchanges four scored items for four others. Extraction-recovery diagnostics further show that obtaining more scores can increase false acceptance under an unchanged threshold. We connect these observations through a complete-stream evaluation protocol that checks calibration feasibility and separates upstream score loss from decision loss. The resulting account explains which inputs a system can score, what its operating point can achieve, and what it actually admits.
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All authors Shengqi Zhou, Zhigang Liu, Wenxiang Zhu, Boyong Du, Kairui Yang, Haicheng Huang, Weijian Bu
Sound
SSEmb: Training a Generalizable Speech Retrieval Model Using Only Automatic Speech Recognition Data
Sangkwon Park, Dongwook Lee, Youngho Cho et al. (+3 more)
End-to-end
speech-to-text
retrieval is becoming an important capability for voice
agents, allowing spoken queries to retrieve relevant documents without intermediate transcription. Training these retrievers typically depends on large collections of spoken query-document pairs that are costly to acquire and difficult to extend across domains and
languages. We introduce \ours, which replaces such task-specific supervision with
speech-transcript pairs from standard ASR corpora. A lightweight adapter projects the output of a frozen
speech encoder into the
retrieval space of a frozen pretrained text
embedding model. \ours is competitive with cascaded ASR-then-retrieve systems while reducing inference latency, and outperforms existing publicly available
speech embedding models. It also transfers to text-to-
speech and
speech-to-
speech retrieval without additional training. Controlled analyses validate the
speech-transcript
alignment paradigm, demonstrating robustness to reduced training data and consistent gains from larger corpora. Further ablations of loss functions and trainable modules establish practical guidelines for the framework. Model weights and codes are to be released.
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All authors Sangkwon Park, Dongwook Lee, Youngho Cho, Donghun Kang, Heeseung Kim, Sungroh Yoon
Materials Science
Orthoexciton-to-paraexciton conversion rate in Cu$_2$O at sub-Kelvin temperatures
Yusuke Morita, Yukihiro Akiyama, Yuki Haita et al. (+2 more)
We measured the conversion rate from 1s orthoexcitons to 1s paraexcitons in Cu$_2$O below 1 K using a dilution refrigerator. To determine the conversion rate, we performed time-resolved luminescence spectroscopy of 1s orthoexcitons and 1s paraexcitons confined in a strain-induced trap potential. From the time
evolution of the ${\Gamma}$$_3$$^{-}$-LO-
phonon-assisted luminescence of 1s orthoexcitons, we obtained a conversion rate of $5.9 \times 10^7$ s${^{-1}}$ at a mixing-chamber temperature of 64 mK under an applied stress of 2.2 kbar. Between 64 and 800 mK, the conversion rate depends only weakly on temperature, and it is less than half of the value of $1.6 \times 10^8$ s${^{-1}}$ reported at 2 K under the same applied stress. The conversion rate therefore continues to decrease below 2 K, contrary to the previous expectation that it becomes nearly constant below about 1.7 K. We discuss these results in terms of the momentum distribution of orthoexcitons, which does not reach
equilibrium with the
lattice during the conversion at these low temperatures. These findings call for a theory that accounts for both the small magnitude of the conversion rate and its weak temperature dependence below 1 K.
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All authors Yusuke Morita, Yukihiro Akiyama, Yuki Haita, Yuga Hirota, Kosuke Yoshioka
Earth and Planetary Astrophysics
Tracing the Influence of Distant companions on Exoplanetary Systems (TIDES) I. Highly Eccentric and Misaligned Binary Companions to Hot Jupiters
Marvin Morgan, Jerry W. Xuan, Brendan P. Bowler et al. (+9 more)
The origin of hot Jupiters remains an open question. A significant fraction reside in binary or multiple
star systems, suggesting that stellar companions may play an important role in shaping their present-day architectures. However, the orbits of the vast majority of these stellar companions remain poorly constrained, leaving their role in hot Jupiter
evolution an open question. We present the Tracing the Influence of Distant companions on Exoplanetary Systems (TIDES) survey, a campaign to map the orbits of stellar companions in systems hosting hot Jupiters to assess the extent to which binaries play a role in hot Jupiter formation and migration. Here, we introduce our survey design and new orbital fits for the first six systems in our sample—$\tau$ Boo, WASP-2, WASP-11, WASP-76, HAT-P-14, and HAT-P-16—by combining previous observations of the stellar companions together with new Keck/NIRC2 imaging and Keck/KPIC relative radial velocities. Among these six systems, the four with the most precisely constrained orbits exhibit high eccentricities ($e \geq 0.8$), and three of these systems also display significant
planet–binary misalignments ($\Delta i \geq 20^\circ$). This suggests the orbital properties of stellar companions to hot Jupiter host
stars are qualitatively different than field binaries and binaries hosting inner transiting small
planets. These orbital architectures are consistent with stellar companions shaping the
evolution of hot Jupiters, either through high-eccentricity migration or by participating in an earlier episode of dynamical
evolution that ultimately led to hot Jupiter formation. Continued orbital monitoring of the full sample will reveal whether outer stellar companions play a causal role in the
evolution of hot Jupiters.
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All authors Marvin Morgan, Jerry W. Xuan, Brendan P. Bowler, Kyle Franson, Michael C. Liu, Konstantin Batygin, Heather A. Knutson, Björn Benneke, Julianne Cronin, Luke Finnerty, Chih-Chun Hsu, Aniket Sanghi
Computation and Language
Perturbation, Not Provenance: Why Activation Steering Does Not Make a Robust Text Watermark
Sergey Pletenev
Linear directions in the activation space of large
language models causally and universally influence model behavior. This suggests that they might serve as a basis for a robust text watermark. The idea is to steer the generation along a specific semantic direction and then recover the mark by probing the activations of the re-encoded text. We test this hypothesis by conducting a series of experiments. We vary the strength of the steering and use four different directions (formality, sentiment, complexity, and a random control) across two different model families. We also test two paraphrasing attacks: a sentence-level one and a stronger paragraph-level one. The results show that activation-steering watermarks are outperformed by a standard logit-based watermark (KGW) in terms of detection accuracy. At a matched level of fluency (within 1.15 times the original text's perplexity), KGW achieved an AUC of 0.81 against a sentence-level paraphraser, while the best steering direction only reached 0.65. A stronger paragraph-level paraphraser lowered both (0.65 for KGW and 0.59 for steering), narrowing the gap but not reversing it. On Qwen, it left the two indistinguishable but at near-chance levels for both. We further showed that the detectability provided by steering was not semantic: a random direction was just as detectable as a meaningful one. The signal was independent of which layer was probed and whether a surface feature control was used. We conclude that, in this case, semantic activation does not encode a localized, recoverable semantic mark, but rather a global, content-independent shift that can only be strongly detected when the text is degraded.
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Materials Science
Polarons in Lead-Halide Perovskites: What the Structural Evidence Does and Does Not Establish
Inhee Maeng, Young Mi Lee, Jinwoo Park et al. (+2 more)
Photogenerated carriers in lead-halide perovskites are widely believed to move as large polarons, whose accompanying
lattice distortion is thought to screen the carrier from charged defects. Three recent findings call this picture into question. First, time-resolved structural probes now measure the photoinduced
lattice distortion directly; yet calculations show that thermal motion of the
lattice alone can raise the carrier effective mass-an increase previously taken as evidence for polarons-so that it remains unclear whether the
lattice distortion around a carrier is really a polaron. Second, the carrier-
lattice coupling strengthens as the material is thinned from 3D to 2D. In the two main families of 2D perovskites, Ruddlesden-Popper and Dion-Jacobson, the carrier couples to the
lattice through different channels, and their contrasting behavior is set by which coupling channel dominates rather than by
lattice stiffness alone. Third, the first direct test of defect tolerance does not point to polaron screening; its authors attribute defect tolerance to the soft, disordered
lattice. New transport data show that polaron screening reduces scattering between carriers, but not that it protects carriers from defects. Polaron formation, even if established, therefore does not establish polarons as the cause of defect tolerance. Separating the two requires measuring the
lattice distortion as a function of excitation density, over a range spanning the low densities of a solar
cell under sunlight and the much higher densities used in most ultrafast experiments. On this basis, we propose experiments that can clarify how photogenerated carriers behave in lead-halide perovskites.
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All authors Inhee Maeng, Young Mi Lee, Jinwoo Park, Seung Jae Oh, Min-Cherl Jung
Computer Vision
SPARCL: Spatially Adaptive Regional Coverage for Active Learning in Chest X-ray Analysis
Juho Jung, Seongjae Kang, Mijoo Kim et al. (+4 more)
Pixel-level annotation for chest X-ray (CXR) lesion segmentation is expensive, making active learning (AL) essential. However, existing AL strategies fail to account for the spatial distribution of selected samples, resulting in inconsistent performance across focal (e.g., aortic calcification) and diffuse (e.g., nodules) pathologies. In this paper, we propose SPARCL (Spatially Adaptive Regional Coverage for Active Learning) to dynamically balance predictive uncertainty with cumulative spatial coverage. By employing a concave coverage function, SPARCL imposes diminishing returns on previously annotated regions, naturally directing the annotation budget toward under-labeled areas. Furthermore, an
entropy gate reverts the
algorithm to standard uncertainty sampling when spatial variation is absent. This coverage objective is monotone submodular, guaranteeing near-optimal greedy batch selection. To address how aggregate metrics like Dice obscure regional failures, we further introduce two metrics—Spatial Inconsistency (SI) and Performance Coverage (PC)—to quantify anatomical
fairness across AL rounds. Evaluated on an in-house calcification cohort and public CXR
benchmarks, SPARCL reaches 95% of fully supervised performance using only 24% of the data, compared to 29% for the best baseline. Ultimately, SPARCL ensures robust spatial reliability, surfacing regional gains that aggregate metrics miss.
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All authors Juho Jung, Seongjae Kang, Mijoo Kim, Sungduk Cho, Yunkyoung Jun, Sunghoon Joo, Yoona Hwang
High Energy Physics (Theory)
Construction and geometric properties of the fuzzy $CP^3$ onion
Kohei Uno and Tomohiro Inagaki
We consider the
matrix model of a seven-dimensional
quantum space built from concentric fuzzy $CP^3$'s of increasing
matrix size, and show that its continuum limit is the metric cone $C(CP^3)$ over $CP^3$, not flat $R^7$. Every ingredient is fixed before any comparison, so this geometry is a prediction rather than an input. The two geometries agree in the $s$ wave ($p=0$) but separate in the higher angular sectors (harmonics on $CP^3$ of degree $p\geq1$): in each sector tested, the spectrum of the free
Laplacian converges at second order to that of the cone, and at 20480 layers the cone is matched to $1\times10^{-9}$ for $p\leq8$, whereas flat space misses by 8-18%. The free propagator converges to that of the cone at the same order, and heat
diffusion reproduces the $r^6$ measure and the Bessel spectrum of the cone. The maps between neighbouring layers are obtained in closed form from the polar decomposition of the second-quantized Schwinger-
boson lift, using a known operator identity, and coincide with the Kováčik-Tekel coefficient-copying prescription. For $CP^1$ the construction reduces, away from the outer boundary, to the Kováčik-Tekel fuzzy onion. Modes along the twistor fibre are exactly degenerate with the harmonics on $S^4$.
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Robotics
Which Trajectory Is Safer? Combining Rule and Learning for Safety Evaluation
Xuewen Luo, Linlian Jiang, Bo Yu et al. (+3 more)
Collision rate remains a dominant metric for evaluating autonomous driving trajectory safety, but its binary nature obscures substantial safety differences among collision-free trajectories. Existing surrogate-safety metrics provide finer-grained assessments, yet often trade off physical interpretability against adaptivity to interaction context. We introduce FutureRisk, a continuous metric that quantifies the latent future risk of a trajectory by how closely and persistently its realized interactions approach unsafe states. At its core, our Context-Adaptive Risk Model (CARM) combines a context-dependent safety-time distribution with an independently supervised critical-event-time distribution. Its safety criterion is bounded around a closed-form physical prior, while a bounded residual provides additional adaptation without abandoning this physical anchor. Experiments on DeepAccident and Bench2Drive show that FutureRisk achieves stronger agreement with native collision labels than existing surrogate-safety measures, discriminates risk among collision-free trajectories, and transfers zero-shot across
datasets. Comparisons across four planners further show that FutureRisk reveals safety differences hidden by binary collision outcomes. Together, these results establish FutureRisk as a fine-grained, interpretable, and context-adaptive metric for trajectory safety evaluation. Code is available at https://github.com/Xuewen2025/safety-evaluation.
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All authors Xuewen Luo, Linlian Jiang, Bo Yu, Fan Ding, Xinxin Zuo, Yang Wang
Computation and Language
BRIDGE: Selective Boundary Repair with Dependency Graph Evidence in Multi-Agent Systems
Encheng Su, Pengze Li, Ziheng Hua et al. (+12 more)
Multi-
agent collaboration has emerged as a critical paradigm for tackling complex, long-horizon tasks; however, while coordination protocols continue to advance, systemic failures at the inter-
agent collaboration interface remain a pervasive bottleneck. In collaborative workflows, systems frequently suffer from broken handoffs—where critical task context is omitted, misrouted, or ignored despite each
agent emitting locally plausible responses. To address this challenge, we present \method, a verifiable runtime control framework that treats handoff repair as selective intervention over explicit dependency graphs. Central to \method is an integrated fault-attribution and calibration pipeline, \dataset, which decouples execution dependencies and injects counterfactual handoff perturbations to provide empirical supervision for runtime intervention. Leveraging this diagnostic foundation, \method dynamically compiles observable interaction traces into a provenance graph to pinpoint unresolved dependencies, without accessing private
agent states. It then evaluates candidate repairs through an empirical gate that weighs expected gains against disruption risks, and executes repairs only when formal local postconditions hold. Extensive evaluations across demanding
benchmarks demonstrate that \method boosts task success by up to 12.3 absolute percentage points on SILO-BENCH and 11.1 on TeamBench. Across both backbones, \method maintains strict cost-effectiveness, adding as little as 2.0% marginal
token overhead. Furthermore, on 562 handoff-relevant traces from the MAST
benchmark, \method achieves 69.8% F1 in a single pass, establishing a reliable operational boundary for verifiable coordination repair.
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All authors Encheng Su, Pengze Li, Ziheng Hua, Qiucheng Yu, Jianyu Wu, Chen Tang, Yizhou Wang, Lintao Wang, Junjia Xiang, Xinzhu Ma, Xi Chen, Shixiang Tang, Houqiang Li, Xi Chen, Aoran Wang
Materials Science
Learning from fracture: Teaching language models to design through physical experience
Bo Ni
How can
language models learn to optimize physical designs through experience? We introduce FracLearn, an experience-abstraction-generalization framework evaluated through a deliberately unfamiliar, Tetris-inspired fracture game. The model first proposes architectures and examines simulated fracture responses to accumulate experience. Abstraction organizes these observations into conditional principles linking reinforcement placement, crack
evolution and load transfer, with supporting evidence and failure conditions. Generalization challenges the model to combine these principles in new architectures and surpass the earlier design record. Campaigns demonstrate progressive refinement and recovery through hypothesis revision, yielding a best composite score 11.0% above the experience-stage record. Fracture analyses connect improved performance to crack deflection and temporary trapping as toughening mechanisms, alongside greater residual load capacity. Learning occurs through external knowledge and decisions, without model-weight updates. This approach offers a route to scientific
agents that turn computational experiments into reusable physical knowledge for materials discovery and engineering design.
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