Astrophysics of Galaxies
Large dust grains escaping from a supernova remnant
Evgenii O. Vasiliev
Dust grains produced in the
supernova (SN) ejecta at early ages are partially destroyed in hot gas of the SN remnant. Large grains owing to their inertia can overtake the forward shock front of the remnant and move through the ambient interstellar medium. Using one-dimensional gas dynamics including the dust destruction we study the transport of dust grains inside the SN remnant and the ambient medium. Here we estimate the surviving and escaping mass fractions depending on the cut-off (maximum) size in a power-law distribution of grain sizes. We found that these fractions follow closely each other at late age of the SN remnant, i.e. almost all grains, which have survived in the remnant, leave it for the ambient interstellar medium. At the age of 30 kyr – the onset of the radiative phase with density $n\sim 1$ cm$^{-3}$ with solar
metallicity – the escaping/surviving mass fractions for the cut-off size of several microns reach about $0.7-0.8$. The major part of these grains have size larger than $1~\mu$m and initially located at distances $\gtrsim 0.2$ of the ejecta core radius. Later dust grains at velocity of several hundreds km/s escaping the remnant continue to be destroyed due to kinetic sputtering in the interstellar medium. Grains with sizes of several microns are destroyed marginally and therefore will survive in the interstellar gas. After decelerating of grains in the medium the surviving mass fraction of grains produced in the SN remnant, which evolves in an ambient gas with $n$ from 1 to 100 cm$^{-3}$, is from $\sim 0.5$ to $\sim 0.3$, respectively, for the cut-off grain size $a_{cut} = 10~\mu$m. A decrease of the cut-off size in the distribution results in lower surviving mass fraction.
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Strongly Correlated Electrons
Beyond Kitaev exchange: $\Gamma'$-driven magnetism in Ag$_3$LiRh$_2$O$_6$
Efrain Insuasti Pazmino, Shi Feng, Ramesh Dhakal et al. (+4 more)
In Kitaev magnets, frustration arises from bond-dependent
spin anisotropic interactions, which can potentially realize sought-after
quantum spin liquid phases. However, material realizations with dominating Kitaev exchange are scarce and the broad parameter space of extended Kitaev models remains largely unexplored experimentally. Here, we use density-functional theory, cluster many-body,
spin-wave, and density-
matrix renormalization calculations to show that the recently discovered honeycomb rhodate Ag$_3$LiRh$_2$O$_6$ hosts an off-diagonal exchange $\Gamma_1'$ that is second in magnitude only to the ferromagnetic Kitaev exchange $K_1$. This combination of $K_1$ and $\Gamma_1'$ explains key experimental observations, an unusually high
magnetic ordering temperature of 94 K at ambient pressure and its rapid decrease under hydrostatic pressure as Ag$_3$LiRh$_2$O$_6$ evolves from ferromagnetic toward zigzag order within the honeycomb planes. Our
spin-wave calculations further predict an unusual
topology of the
magnon bands arising from the $\Gamma_1'$ term in the
spin Hamiltonian of the honeycomb layer. We show that the large trigonal splitting of $t_{2g}$ states enhances $\Gamma_1'$, the hitherto underappreciated term in Kitaev magnets, and opens the way to new interesting regimes of the extended Kitaev model.
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All authors Efrain Insuasti Pazmino, Shi Feng, Ramesh Dhakal, Ioannis Rousochatzakis, Stephen M. Winter, Johannes Knolle, Alexander A. Tsirlin
Software Engineering
When Does the Dependency Graph Help Code-to-Architecture Mapping?
Rakhshanda Jabeen, Morgan Ericsson, Jonas Nordqvist et al. (+1 more)
Architectural technical debt arises when a system's implementation drifts from its inten ded modular decomposition, while the mapping between source files and architectural modu les is missing, incomplete, or costly to maintain manually. We formulate code-to-archite cture mapping as semi-supervised node classification over a directed multi-relational de pendency graph, with files as nodes, typed dependencies as edges, and lexical features d erived from code identifiers and directory layout. Starting from a small labeled sample, an iterative self-training procedure expands the mapping to the remaining files. We com pare a graph convolutional network (GCN), a graph
attention network (GAT), and a graph-f ree multi-layer perceptron (MLP), using identical features and training procedures for t he learned models, against a Naive Bayes reference. Across 100 runs on ten open-source J ava and C/C++ systems, all three neural models outperform the reference on both F1 metri cs. GAT achieves the highest averages of system-level median macro and micro F1, at 83.5 and 92.2, respectively, whereas MLP remains competitive or superior on several systems. On Chrome, the largest system with 18,343 files, GAT and GCN exceed MLP by 22 and 19 ma cro-F1 points, respectively, and have substantially fewer persistently misclassified fil es. Connectivity and homophily are associated with where neural message passing helps, w hile multi-relational aggregation outperforms a single merged edge type on every system for both GCN and GAT. A follow-up study finds that several model rankings depend on the training seed, qualifying smaller advantages without removing the large graph benefit ob served on Chrome.
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All authors Rakhshanda Jabeen, Morgan Ericsson, Jonas Nordqvist, Anna Wingkvist
Machine Learning
Learning to Compute Is Not Learning to Retain or Reuse in Looped Transformers
Peilong Zhou, Hongtao Zhang, Yulong Huang et al. (+3 more)
Looped
transformers compute by applying one block repeatedly, so they can run for more loops than they were trained with. Recurrent reasoners are known to overthink: iterating past the trained depth can turn a correct answer into a wrong one. Such a late error can reflect failed computation, loss of retention, or a state that later computation cannot reuse. We separate these cases in in-context Mealy-machine execution, where a simulator supplies intermediate targets and exact counterfactuals for hidden-state interventions. Models trained to answer at loop 32 are fully correct there but keep only 22% of these answers at loop 64. Retention depends on what training asks for: supervising one randomly chosen position per example, instead of always the last, keeps 99.8%, and randomizing the number of training loops also helps; truncating gradients, as in progressive training against overthinking, does not. The computation itself is exact: position $k$ is computed at loop $k$, and swapping its loop-$k$ hidden vector for the one from another input redirects every later position to that input's exact continuation (237/237 pairs in each of ten models). Retention, however, is not reuse. Even in models that keep nearly all answers, holding each position $k$ fixed from loop $k$ drops accuracy to 4–6%, whereas holding it from loop $k+4$ is harmless. A released recurrent maze network trained to avoid overthinking behaves the same way. Learning to compute is thus not learning to retain, and avoiding overthinking does not make a state reusable when its answer first appears.
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All authors Peilong Zhou, Hongtao Zhang, Yulong Huang, Cangyuan Li, Mingyu Chen, Ying Wang
Computation and Language
Likelihood Is Not Authorship: Can We Reliably Measure Human Contribution in AI-Assisted Writing?
Hasan Amin, Zhuoran Lu and Ming Yin
AI-text detection generally asks whether a machine wrote a text. As AI becomes an increasingly integral and accepted part of writing, the more relevant question is no longer whether AI was used, but how much of the text reflects human contribution. A prominent line of work estimates this by measuring how predictive a person's
prompt is of the final text. We show that this measure can be easily manipulated. We introduce SPECIFICITY PADDING that simply asks the model to copy a few random IDs from the
prompt, and that alone makes a title-only request look more human than one that supplies the article's key ideas. A context-swap audit then holds each document fixed, and only shows or hides the IDs to the evaluator. The score of the unchanged text swings by a third to half of its scale, and it rises for every document we test, on news and scientific abstracts, across varied LLMs from 3.8B to 753B parameters. The same articles can average 85% human or 15% human depending only on which
prompt is on record, and even with no
prompt at all, the output-only variant rewards the random strings themselves. An exact identity, the credit ledger, explains why. The score is a surprisal-weighted average over the text, so rare strings that the context makes copyable collect the credit, whatever they mean. A two-point bound marks a deeper limit. If the
prompt could have been machine-written, every transcript-only method must, in the worst case, miss by half the contribution at stake. Likelihood-based scores alone cannot reliably establish human contribution without evidence of what the person added and how the text was produced.
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Cryptography and Security
Analyzing and Mitigating Poisoning Attacks on Flow-Based Intrusion Detection
Florian Spelter, Johannes F. Loevenich, Tobias Hürten et al. (+4 more)
Although Artificial Intelligence (AI)-based Intrusion Detection Systems (IDSs) are increasingly deployed in operational networks, their dependence on large-scale training data introduces a critical vulnerability to stealthy poisoning attacks. We demonstrate that latent backdoors can be successfully implanted in flow-based IDS models by poisoning as little as 0.001% of the training data, maintaining near-nominal performance on clean inputs while ensuring high attack success rates. To address this threat, we propose a diagnostic-first methodology that localizes trigger-relevant features through constrained perturbation analysis and detects poisoning-induced behavioral drift by comparison with a distilled baseline model. The proposed methodology further enables targeted mitigation via automated data sanitization or
neuron pruning. Our findings expose the operational risks of near-undetectable backdoors and provide a scalable approach to analyzing, quantifying, and mitigating backdoor exposure in AI-based IDS.
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All authors Florian Spelter, Johannes F. Loevenich, Tobias Hürten, Erik Adler, Maxime Schwarzer, Laurin Holz, Roberto Rigolin F. Lopes
Robotics
Refine Actions If Needed: Adaptive Action Chunk Refinement for Robot Manipulation
Hanlin Liu, Zichen Zhou, Jiacheng Zhang et al. (+6 more)
Computationally lightweight manipulation policies predict executable actions with low inference latency, but prediction errors can limit task success. Iterative generative policies can achieve higher task success rates, but repeated network evaluations increase inference latency. Refining action proposals from computationally lightweight policies can combine these strengths, but the amount of refinement needed varies across states. We introduce RAIN (Refine Actions If Needed), a framework that predicts action errors at different refinement levels to decide whether and how much to refine each executable proposal, before performing any refinement. We train an ensemble to predict these errors by comparing action proposals and their refined versions with expert actions from offline demonstrations, without requiring explicit labels of refinement needs. Calibration balances uncertainty, computation, and temporal consistency. We evaluate RAIN on eight ManiSkill tasks and four real-
robot tasks. In simulation, our policy achieves 66.6% success, approaching the best evaluated
diffusion-policy success rate with 5.71 times lower policy latency. On the real
robot, success increases from 55.0% with Action Chunking with
Transformers (ACT) to 72.5%, with comparable average successful completion time. Matched controls show that dynamic routing maintains the strongest observed fixed-refinement success level with 26.6% fewer refiner evaluations. Project page: https://rain-policy.github.io/.
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All authors Hanlin Liu, Zichen Zhou, Jiacheng Zhang, Liuyuan Na, Jin Jin, Guangyan Chen, Yufeng Yue, Gen Li, Fangqiang Ding
Programming Languages
Verifying deployed bytecode against executable EVM semantics, with exact gas and no added axioms
Seulkee Baek
Much of Ethereum's most consequential code is immutable bytecode: old output of unverified compilers, and protocol logic run as contract code. We show that such bytecode can be verified as it is, without trusting what produced it, without simplifying the machine it runs on, and with no axioms beyond Lean's own. Blanc, a Lean 4 verification stack, reads fixed runtime bytes via kernel-checked certificates and states every
theorem against Jaune, an executable semantics of the EVM and block processing that passes all 34,205 blockchain-test cases of Ethereum's fixture release v20.0.2 that involve only forks that have governed mainnet since May 2025. Eight artifacts, deployed from 2017 to 2026, come from seven compiler versions and an assembler. For the EIP-7002 contract, we prove that the end-of-block call to it, whose failure would invalidate the block, succeeds whatever its storage holds, and that each block's withdrawal requests are exactly the oldest pending submissions; mainnet met these
theorems' starting premises when Prague began. For a Uniswap V2 pair, we prove that after any valid history whose hashed slots stay apart, from any checkpoint representing its Solidity model's state, the storage represents that model replayed over exactly the committed calls, a balanced liquidity ledger stays balanced, and, under a premise on
token answers,
geometric-mean reserves per share fall only through the protocol fee. Exact gas makes liveness provable: a tracked WETH9 holder without code can withdraw by a transaction whose gas has a closed form, matching all 3,748 comparable mainnet receipts of its shape in 30,000 blocks. On Curve pool bytes compiled with the lock bug exploited in 2023, Jaune executes such a reentrancy from deployment; for bytes from a fixed compiler, we prove the lock excludes it. AI
agents wrote the proofs in fifteen days under one author's direction, atop an existing stack.
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Computer Vision
Physics-Shaped, Not Physical: Auditing Physics-Guided Underwater Image Restoration
Mochamad Rizal Fauzan and Irgi Surya
Physics-guided networks for underwater image restoration predict transmission and backscatter and invert the image-formation model, yet rarely test whether this physics does any work. We audit one representative design, PhysGuide-Net, with four controlled experiments: five-seed ablations on UIEB with zero-shot evaluation on the complete LSUI (4279 pairs) and AUIED3K (3000 pairs) sets; a data-efficiency study with a capacity-matched control that removes the formation model; and a validation of the predicted maps against measured range on Sea-thru. With all 800 training images the physics module gives no zero-shot gain (mean PSNR differences of at most 0.16 dB; all 95% intervals include zero) at 3.4x the FLOPs. With 80 images it helps more (+0.65 dB on UIEB, +1.48 dB on AUIED3K; seed-to-seed s.d. on AUIED3K from 1.3 to 0.5 dB), although not individually significant after correction, and the control does not reproduce this (0.8-1.0 dB lower). Yet the predicted transmission does not behave like a physical quantity: its correlation with range is no stronger than for the range map of a different image (pooled difference +0.02, versus -0.06 for the dark-channel prior), and a Beer-Lambert fit explains 0.4% of its variance. The effect of the module also changes sign with degradation severity between
datasets. The benefit thus comes from a physics-shaped inductive bias, not from physics. We further show that single-run ablations can reverse sign and document implementation defects that invalidate loss ablations.
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Computation and Language
PRISMATIC : Prescription Risk Inspection System for Multi-Agent Treatment Interaction in Clinical Decision-Making
Ziyang Ling, Shuyu Lu and Ziyi Chen
Medical prescribing errors for multimorbid patients, who require complex drug regimens, are a major patient safety concern. These errors often stem from fragmented decision-making across medical specialties, a critical coordination failure that leads to adverse drug events and rehospitalizations. To address this coordination challenge, we introduce PRISMATIC, a multi-
agent architecture where specialized
agents, each representing a workflow role, collaboratively generate and refine treatment plans. Operating on a blackboard architecture, the system first structures patient information from clinical notes, then iteratively develops safe, rational, and interpretable drug regimens. The
agents' collaboration is governed by two novel adaptive coordination mechanisms: (1) Dynamic Self-Updating Weight Adjustment (DSWA), a long-term mechanism that adjusts the influence weights of
agents based on historical case outcomes, enabling the system to adapt and trust more reliable specialists over time. (2) Discrepancy Feedback Precise Calibration (DFPC), a short-term mechanism where
agents provide iterative feedback to resolve disagreements and eliminate prescription conflicts for the current patient. Experiments on patient records from the MIMIC-IV database show that PRISMATIC significantly reduces drug conflicts, improves prescription coverage, and enhances treatment consistency compared to strong LLM baselines. Our work demonstrates that structuring clinical co-treatment as a multi-
agent coordination problem offers a promising path toward safer and more coherent multidisease management.
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Machine Learning
CAST: History-Adaptive Spectral Clipping for LLM Pretraining
Jiacheng Li, Jianchao Tan, Kefeng Zhang et al. (+2 more)
Soft spectral clipping (SSC) reduces gradient singular values, but its effect depends on the gradient scale and clipping threshold. We introduce CAST (Clipping with Adaptive Scales and Thresholds), a history-adaptive extension of SSC. AdamW's previous second moment sets row and column scales. An exponential moving average of the clipped output's estimated spectral norm sets each
matrix's threshold. CAST restores the original coordinates before the AdamW update. We train a mixture-of-experts (MoE) model with 19.08B total parameters for 83.89B
tokens. CAST achieves lower training and mean validation losses than nine gradient-processing baselines, with the fewest relative gradient spikes. It reaches the best baseline's final smoothed training loss in 7.2% fewer steps.
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All authors Jiacheng Li, Jianchao Tan, Kefeng Zhang, Yuchen Xie, Xunliang Cai
Instrumentation and Methods for Astrophysics
Identifying Planetary Nebulae Candidates from IPHAS and VPHAS+ Surveys via YOLO Deep Learning Models
Titouan Sérandour, Anne-Laure Méalier, Bruno Colicchio et al. (+5 more)
We present a deep learning detection method capable of effectively identifying planetary nebulae (PNe) on IPHAS and VPHAS+ surveys. Our detection pipeline is based on the YOLO architecture and is adaptable to other
datasets. Using the HiPS system, we preprocess and augment the $r$, $i$, and $H{\alpha}$ bands to produce two red, green, and blue (RGB) image
datasets: (1) $640 \times 640$ px images at full resolution, and (2) $1 280 \times 1 280$ px images designed to capture extended objects up to $0.2$ deg. Images of $\sim 2600$ PNe are labelled using the HASH database. The loss function and evaluation metrics are adapted to the diffuse and low-contrast nature of PNe; a very low intersection over union (IoU) threshold ($1\%$) is used. A $k$-fold cross validation shows that the YOLOv12m model achieves an average precision of $0.979$ on the first
dataset. At a confidence threshold of $0.15$, it achieves a precision rate of $98.7\%$ and a recall rate of $97.4\%$. We deployed our system (on $1280 \times 1280$ images) on the full surveys' coverage ($\sim 3755$ deg$^2$), and present a preliminary analysis of the detection results. The system is robust against artifacts such as CCD corner defects and HiPS tile joints. Our convolutional-neural-network-based model achieved excellent results on the test set, comparable to-or exceeding-that of a recent Swin
Transformer approach. However, a challenge persists when applying deep learning models to extensive and densely populated Galactic fields: the development of an automated system that can effectively discriminate PNe from mimics that human experts are able to distinguish (e.g., H II regions). We outline possible improvements for future work.
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All authors Titouan Sérandour, Anne-Laure Méalier, Bruno Colicchio, Dominique Daniel, Philippe Gilberton, Philippe Oros, Thomas Boch, Djaffar Ould Abdeslam
Robotics
Mixed-Reality Feedback Control: Towards Blending of Control and Digital Twinning
Melisa Soydan and Mario Gleirscher
Technologies, such as co-simulation, in particular, digital twins (DTs) are used for comprehensive and reliable prototyping, verification and validation (V&V) of control systems. There, the combination of feedback loops across multiple realities (e.g., simulations, physical setups) enables various forms of experimentation with
robots and autonomous systems. However, the handling of multiple realities requires a more specific support by the modelling techniques used in control engineering. To streamline the latter in the system life cycle, we propose a framework for mixing (i.e., fusing, defusing) control input and feedback signals (i.e., stimuli, responses) in simultaneously operated plants for multiple realities.
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Computation and Language
Qwen-MT-Lumen at WMT2026: Instruction-Specialized Translation via Quality-Aware Training and On-Policy Distillation
Hao Wang, Yangyang Liu, Xiaohu Zhao et al. (+11 more)
We present Lumen, The open-source model is available at, our submission to the WMT 2026 General Machine Translation Shared Task. We focus on instruction-specialized translation, where systems must produce accurate translations while satisfying fine-grained requirements on style, terminology, and structured output. Lumen follows a four-stage pipeline. First, continued pre-training and supervised fine-tuning jointly strengthen multilingual translation competence and the ability to follow translation-specific instructions, such as terminology, style, and format constraints. Second, we extend the originally offline M^{2}PO preference-optimization framework into an online preference-optimization stage: the current policy samples candidate translations, an LLM judge assigns continuous direct-assessment scores, and the resulting multi-pair preferences are used to update the model. Third, on-policy
distillation transfers stronger translation-instruction-following behavior from a larger teacher model to the Qwen3-14B student, improving translation-instruction adherence while retaining a 14B primary translation backbone. Finally, structure-aware inference and postprocessing preserve required formats, while selective repair addresses clear structural or decoding failures before submission. We participate in 23 translation directions. Under an absolute 1–5 LLM-judge rubric that evaluates translation-instruction following and translation quality separately, Lumen reaches an overall mean score of 4.60. Despite using a 14B translation backbone, it achieves performance comparable to leading proprietary systems in both translation quality and translation-instruction following.
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All authors Hao Wang, Yangyang Liu, Xiaohu Zhao, Heng Liu, Zifu Shang, Tianhao Li, Ruize Gao, Jialong Tang, Liangying Shao, Haoran Wei, Baosong Yang, Linlong Xu, Longyue Wang, Weihua Luo
Analysis of PDEs
On blow-up for the supercritical defocusing nonlinear scalar wave equation
Feng Shao, Dongyi Wei and Zhifei Zhang
We study finite-time singularity formation for the scalar defocusing nonlinear wave equation $-\partial_t^2u+\Delta u=u^p$. First, for fixed $d\geq4$ and odd $p\geq3$, we establish a conditional blow-up result under the assumption that a smooth self-similar imploding profile exists for the leading-order relativistic Euler system, with associated normalized coefficients satisfying quantitative analytic radius conditions. Second, we verify both the profile and the analytic radius conditions in dimensions four and five for all sufficiently large odd $p$. Using finite speed of propagation, we obtain a dimension-independent threshold $p_0$ such that, for every $d\geq4$ and every odd $p\geq p_0$, finite-time blow-up occurs for some smooth, compactly supported, real-valued initial data. The proof combines a normalized formulation that separates the leading periodic oscillation from the unknown modulation, growth of the spatial analytic radius induced by an outgoing property, and frequency-dependent
matrix multipliers that control the cumulative growth caused by the periodic linearized potential.
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Systems and Control
Iteratively Convex Frequency Data-Driven Synthesis of Structured MIMO Controllers
Luuk Marceau Spin, Thom Samuels, Dragan Kostic et al. (+3 more)
Frequency-response-function-based controller design enables synthesis directly from measured data, avoiding the need for accurate parametric plant models. This paper presents a generalized IQC-based approach for convex synthesis of structured
MIMO feedback and feedforward controllers from frequency response function data. The framework supports flexible controller parametrizations, multiple performance objectives, and both singular-value and element-wise performance shaping of
MIMO controllers within a unified frequency-domain formulation. To facilitate practical deployment, the synthesis approach is embedded within a bootstrapped controller design methodology that progressively increases controller flexibility while refining the performance specifications. The resulting approach is validated experimentally on the motion stage of an industrial wirebonder, where clear performance improvement is demonstrated through
MIMO $\mathcal{H}_\infty$ synthesis, element-wise feedback shaping, and dynamic feedforward design. The results demonstrate that high-performance multivariable controllers can be synthesized directly from measured FRF data in a practical and systematic manner.
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All authors Luuk Marceau Spin, Thom Samuels, Dragan Kostic, Maurice W. P. M. H. Heemels, Nathan van de Wouw, Roland Toth
Group Theory
Prefixes of Coxeter elements are Coxeter elements of the reflection subgroups they generate
Patrick Wegener
Let $(W,S)$ be a Coxeter system of finite rank, let $T$ be its set of reflections and let $c \in W$ be a Coxeter element. For $x$ in the interval $[1,c]_T$ of the absolute order, let $P(x)$ be the subgroup generated by the reflections $t\le_T x$. We prove that $x$ is a Coxeter element of $P(x)$ with respect to the canonical Coxeter generators of $P(x)$, that $P(x)$ is generated by the factors of any reduced reflection factorization of $x$, and that the Hurwitz action on the reduced reflection factorizations of $x$ is transitive. The proof reduces the general case to the case of a Coxeter system whose Coxeter
matrix has entries in $\{1,2,3,\infty\}$, which was treated by Hubery and Krause.
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Computation and Language
Beyond Reachability: A Relation-Semantic Reasoning Framework for Biomedical Multi-hop QA
Ziyang Ling, Ziyi Chen, Ronald X.Xu et al. (+1 more)
Biomedical multi-hop
reasoning over knowledge graphs requires identifying a semantically valid relation sequence that supports the answer. Existing methods can retrieve correct answers, yet often rank them poorly within a large pool of seemingly relevant candidates. Even when a correct answer is retrieved, its associated evidence and
reasoning path may fail to support the relation composition required by the question. We term this disconnect between answer reachability and evidential validity the reachable-but-invalid problem. We present ReSemReason, which shares a question-induced distribution over typed, directed relation schemas between candidate discovery and evidence selection. Schema compatibility guides graph search; ReSemAttn then uses relation-conditioned key/value projections and an uncertainty-weighted path prior to attend to candidate-specific evidence. A separate absolute-support score complements normalized
attention when all retrieved paths are weak. The reported experiments on BioHopR, PrimeKGQA, and MedReason show improvements in answer ranking and answer-set quality. Path-discrimination analyses examine the distinction between endpoint correctness and evidential support. We also specify matched controls for search,
attention, and schema uncertainty.
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All authors Ziyang Ling, Ziyi Chen, Ronald X.Xu, Mingzhai Sun
Robotics
Coordi-pi: Coordination-Aware Dual-Hand Action Representation for Bimanual Dexterous Manipulation
Yiming Meng, Yanming Shao, Weijie Shen et al. (+4 more)
Vision-
language-action (VLA) models have shown strong potential for general-purpose robotic manipulation, but extending them to bimanual dexterous manipulation introduces a high-dimensional and structured action space. Existing bimanual VLA policies commonly concatenate right- and left-side arm–hand actions at each control timestep, leaving cross-hand interactions implicit within a single action vector. We introduce Coordi-$\pi$, a structured action representation for bimanual dexterous manipulation. Coordi-$\pi$ represents the right- and left-side arm–hand actions as individual action
tokens and organizes them into an interleaved sequence, explicitly exposing the two sides within the action sequence. To explicitly encode hand identity and temporal synchronization in this interleaved representation, a learnable hand-identity
embedding distinguishes the two sides, while sequence–control-timestep factorized rotary positional encoding (RoPE) separates sequence position from control timestep, allowing actions at the same control timestep to share a temporal coordinate while retaining distinct sequence positions. Across all five bimanual dexterous manipulation tasks in DexJoCo, Coordi-$\pi$ achieves state-of-the-art performance under both rand-obj and rand-full, improving the average success rate over $\pi_{0.5}$ from 31.9% to 51.3% and from 18.3% to 32.3%, respectively. Coordi-$\pi$ also consistently outperforms $\pi_{0.5}$ across four real-world bimanual dexterous manipulation tasks.
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All authors Yiming Meng, Yanming Shao, Weijie Shen, Kunyang Lin, Jiayu Sun, Ping Luo, Yao Mu
Quantum Physics
Quantum-enhanced phase interrogation of a fiber-optic strain sensor
Romain Dalidet, Marialuisa Capezzuto, Hugo Degert et al. (+7 more)
Fiber-optic sensors enable high-sensitivity measurements of various physical parameters. Here, we demonstrate a
quantum-enhanced interrogation of an all-fiber-optic Fabry-Perot resonator, employing entangled two-
photon interference to probe directly strain-induced optical phase shifts. The resonator is interrogated by a narrow-linewidth
laser that is frequency-locked to its resonance mode, thereby exploiting the cavity's inherent sensitivity to variations in the fiber length. To access the phase fluctuations transferred to the interrogation field due to external perturbations on the sensor, we implement an in-fiber Mach-Zehnder interferometer readout. This configuration allows the sensor to operate both with classical coherent light and with entangled
photon pairs generated via spontaneous parametric down-conversion. Harnessing two-
photon entangled states corresponding to an N = 2 NOON state, we experimentally demonstrate a reduction of the measurement noise below the standard
quantum limit. These results provide clear evidence of the
quantum advantage in enhancing fiber-optic sensors under practical constraints on optical power.
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All authors Romain Dalidet, Marialuisa Capezzuto, Hugo Degert, Davide D'Ambrosio, Saverio Avino, Anthony Martin, Sebastien Tanzilli, Laurent Labonte, Paolo De Natale, Gianluca Gagliardi
High Energy Physics (Phenomenology)
Quantum-enhanced Search for Galactic Dark Photons Using Cat-State Cavities
Aoying Wang, Yanjie Zeng, Yanyan Cai et al. (+4 more)
We present a
quantum-enhanced search for the Galactic dark-
photon background (GDPB), a relativistic and anisotropic vector field that can be produced by dark-matter decay or annihilation in the Milky Way
halo. We reinterpret an existing
quantum-enhanced dark-
photon dataset obtained with a superconducting
circuit QED platform originally developed for wave-like dark-
photon dark-matter searches, consisting of a high-$Q$ niobium cavity in the $\mathrm{TM}_{010}$ mode dispersively coupled to a transmon
qubit. By preparing four-component cat states and performing repeated
quantum non-demolition parity measurements, analyzed with a hidden Markov model, we extract
photon-transition probabilities for multiple cat amplitudes and integration times. The coupling to the GDPB is governed by a polarization- and angle-dependent cavity overlap function, which predicts a distinctive diurnal modulation of the excitation rate for both longitudinal and transverse polarizations. From the absence of such modulation, we set 90% confidence-level bounds on the kinetic-mixing parameter $\epsilon$ for two
benchmark production mechanisms (parametric and cascade decay) over the relativistic mass range $m_X \ll \omega_c$.
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All authors Aoying Wang, Yanjie Zeng, Yanyan Cai, Jing Shu, Bin Xu, Yuan Xu, Pan Zheng
Systems and Control
Severity-Aware Minimum Security Sets for Accelerated Preventive SCOPF Using Machine Learning
Mohammad Iman Alizadeh, Saeed Zeynali, Salah Ghamizi et al. (+1 more)
This paper proposes a severity-aware machine learning framework for reducing the computational burden of preventive AC security-constrained optimal power flow (PSCOPF). The central idea is to identify a compact minimum security set (MSS) containing only the non-dominated contingencies and associated post-contingency constraints that materially influence the preventive operating point while satisfying a prescribed severity tolerance. An offline procedure combines AC security assessment, severity-based ranking, non-dominated screening, and iterative reduced PSCOPF. After each reduced PSCOPF solution, exhaustive security assessment over all credible contingencies identifies additional contingency–constraint pairs whose residual severity exceeds the specified threshold. Severity is characterized using the normalized number, accumulated magnitude, maximum magnitude, and average magnitude of post-contingency violations. The resulting operating-point-dependent MSSs are used as labels for a supervised multi-label learning model that predicts the security elements required to construct the reduced PSCOPF directly from the pre-contingency state. The proposed framework therefore focuses the optimization on the contingencies and constraints that affect preventive control decisions, reducing problem size and computation time while explicitly controlling residual post-contingency severity. Results on the IEEE 60-bus and 118-bus systems, the latter having an approximately seven-times-larger PSCOPF formulation, demonstrate substantial computational reductions with limited impact on the preventive operating solution.
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All authors Mohammad Iman Alizadeh, Saeed Zeynali, Salah Ghamizi, Sylvain Kubler
Analysis of PDEs
Global well-posedness of the energy-critical nonlinear Schrödinger equation on the three-dimensional torus with additive Lévy noise
Bin Liu and Fan Xu
This paper establishes global well-posedness for the defocusing energy-critical nonlinear Schrödinger equation on the three-dimensional
torus with a class of additive Lévy noises. For arbitrary deterministic initial data in the energy space, we construct probabilistically strong solutions that are pathwise unique in the critical solution class. Continuous dependence on initial data, convergence of finite-activity approximations, and a vanishing-noise limit hold on finite time intervals. When the effective noise is nonzero, quantitative bounds for mass exit times imply almost-sure unboundedness of the mass and exclude free scattering. The expected energy then admits a cubic lower bound in time. The analysis couples pathwise critical perturbation theory with energy estimates adapted to Poisson compensation and stopping-time arguments based on the exact mass balance.
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Numerical Analysis
Finite Volume Methods
Robert Eymard, Thierry Gallouët and Raphaèle Herbin
This chapter focuses on finite volume methods. The finite volume method is a discretization method that is well suited for the numerical simulation of various types (for instance, elliptic, parabolic, or
hyperbolic) of conservation laws; it has been extensively used in several engineering fields, such as fluid mechanics, heat and mass transfer, or petroleum engineering. Some of the important features of the finite volume method are similar to those of the finite element method: it may be used on arbitrary geometries, using structured or unstructured meshes, and it leads to robust schemes. The finite volume method is locally conservative because it is based on a “balance” approach: a local balance is written on each discretization
cell that is often called “control volume;” by the divergence formula, an integral formulation of the fluxes over the boundary of the control volume is then obtained. The fluxes on the boundary are discretized with respect to the discrete unknowns.
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Statistical Methodology
Generalized Odds for Ordinal Outcomes: A Causal Inference Framework Adjusting for Covariates
Runnian Huang, Dandan Shi, Lan Liu et al. (+1 more)
Background: Generalized Odds (Gen Odds) provide summaries for ordinal outcomes without requiring the proportional odds assumption. However, conventional Gen Odds compares independently drawn treatment and control outcomes and targets a population-level contrast rather than an individual-level contrast. We developed an individual-level causal formulation of Gen Odds and examined its identification. Methods: We defined the Causal Gen Odds using the joint distribution of potential outcomes and identified it under conditional independence of the potential outcomes given baseline
covariates. We developed a model-based
estimator using nonproportional cumulative logistic regression and introduced an odds-ratio sensitivity parameter to assess departures from conditional independence. We evaluated the
estimators through simulations and illustrated them using data from the ACCORD Study. Results: In simulations, the conditional-independence
estimator was approximately unbiased with 95.5% confidence interval coverage under conditional independence, but bias and undercoverage increased with residual dependence. When the sensitivity-analysis
estimator incorporated the data-generating dependence parameter, bias remained below 0.01 and coverage ranged from 93.7% to 96.1% across examined settings. In the ACCORD Study, the Causal Gen Odds for right-foot pre-tibial edema was 0.909 (95% CI: 0.880 to 0.940) under conditional independence and remained stable across the examined sensitivity range. Conclusions: The Causal Gen Odds provides an individual-level estimand that complements the conventional population-level Gen Odds. This framework enables estimation of this contrast while explicitly assessing its robustness to assumptions about dependence between potential outcomes.
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All authors Runnian Huang, Dandan Shi, Lan Liu, Yunjian Yin
Robotics
One-shot Human Video-Guided Adaptation for Vision-Language-Action Models
Qi Chen, Yuwei Wu, Quan Chen et al. (+5 more)
Vision-
language-action (VLA) models have recently achieved remarkable progress by leveraging large-scale
robot datasets and foundation models for general-purpose
robot manipulation. However, their ability to rapidly adapt to novel manipulation tasks beyond the training distribution remains limited. Existing approaches often require task-specific
robot demonstrations and additional training, which are labor- and compute-intensive, limiting the scalability of VLA adaptation to open-world scenarios. To reduce this dependence on target-task
robot data, we propose CapPro, a framework that enables a VLA policy to adapt to previously unseen manipulation tasks using only a single human demonstration
video. CapPro consists of two components. First, a phase-aware human
video representation module constructs a compact
video representation through learned visual
token selection and learns task-relevant query representations under phase-caption supervision. Second, a
video-conditioned adaptation module jointly incorporates the compact
video and query-derived representations into the action-conditioning context of the pretrained VLA policy. % This design introduces task-specific guidance while largely preserving the original VLA action-generation backbone. In this way, the human demonstration provides task-specific execution guidance rather than an embodiment-aligned trajectory to imitate, without requiring target-task
robot demonstrations or target-task fine-tuning. Experiments across source and unseen target tasks show that CapPro improves average performance across unseen target groups while maintaining comparable performance on the source tasks. These results demonstrate the effectiveness of human-
video guidance for cross-task VLA adaptation.
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All authors Qi Chen, Yuwei Wu, Quan Chen, Kunpeng Gao, Jin Chen, Che Sun, Zhi Gao, Yunde Jia
Functional Analysis
Continuous and compact embeddings for a class of modular sequence spaces
Pierre-A. Vuillermot
In this article we prove the existence of continuous and compact
embeddings for a class of modular sequence spaces generated by Orlicz functions. The class under consideration contains sequence spaces of Orlicz-Sobolev type, including certain special cases that have played an important role in a number of applications regarding problems arising in non-
equilibrium statistical mechanics, as well as Nakano's class of Lebesgue sequence spaces with variable exponents. In a few exceptional cases we provide necessary and sufficient conditions for such
embeddings to hold, in many others only sufficient conditions and in that situation we clearly indicate why these are not necessary by means of additional criteria and specific examples. Our results are complementary to those already available in the literature. We illustrate them with several concrete examples.
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Disordered Systems and Neural Networks
Resolving Structural Uncertainty in Stochastic Dynamical Systems via Ensemble-Based Bayesian Inference
Francesco Zambelli, Valeria D'Andrea and Manlio De Domenico
Recovering the interaction structure of complex
stochastic dynamical systems from sparse time-series observations is an inherently ill-posed inverse problem. The posterior over network configurations is high-dimensional, highly degenerate, and shaped by the non-
equilibrium nature of the available data. To address this, we propose a fully
Bayesian, ensemble-based inference framework. Our approach grounds a continuous-time
Stochastic Differential Equation (SDE) likelihood formulation—acting mathematically as an Onsager-Machlup quasi-potential for non-
equilibrium paths—within a non-parametric Minimum Description Length (MDL) structural prior. This framework rigorously penalizes model complexity and avoids kinetic overfitting by forcing continuous parameters into a finite vocabulary of discrete categories. The joint posterior is optimized using a block-coordinate scheme combining sub-quadratic Markov Chain Monte Carlo (MCMC) topological sampling with deterministic Nelder-Mead parameter optimization. Using highly non-linear macroscopic population dynamics as a
benchmark, we demonstrate that inference requires non-stationary transient data to resolve causal information. Furthermore, we prove that propagating structural uncertainty via ensemble-based posterior analysis produces significantly more robust and reliable predictions for downstream perturbation outcomes than traditional single-point (MAP) estimates. The framework is highly generalizable, capturing fundamental topological constraints even under severe generative model mismatches, offering a robust tool for uncertainty-aware inference in complex
stochastic systems.
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Computer Vision
Discriminative Score Function: Turning Pretrained Models into Functional Generative Priors
Junhoo Lee, Hyeonjin Kim, Sangbum Han et al. (+1 more)
Discriminative models are trained with task-specific objectives such as classification, detection, or representation learning, yet their practical utility depends on generalizing beyond the training task to unseen samples from the same domain. This suggests that a discriminative model may retain an implicit trace of the data
manifold beyond its task labels. We ask whether this trace can be exposed as a generative signal without task-specific guidance. We introduce the Discriminative Score Function (DSF), which extracts an image-space generative field from the loss-gradient geometry of a pretrained discriminative model. DSF provides a training-free update rule that moves images toward model-consistent visual regions without retraining or task-specific targets. We apply DSF to diverse pretrained discriminative models, including ResNet classifiers, DETR detectors, and DINOv2 ViT encoders. DSF generates unconditional images from noise and supports conditional guidance, editing, inpainting, and explanation through the same field. DSF-generated samples also provide effective zero-shot quantization calibration when real images or labels are unavailable, preserving feature geometry better than data-free calibration baselines. These results show that discriminative training leaves a generative functional signal for synthesis and calibration. Project page: https://junhoo.me/dsf.
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All authors Junhoo Lee, Hyeonjin Kim, Sangbum Han, Nojun Kwak
Computer Vision
HyperLoop: Hyperspectral-Assisted Loop Closure Detection in 3D-SLAM for Planetary Exploration
Annie Bhalla, Dennis Rotondi, Riccardo Giubilato et al. (+2 more)
Autonomous navigation of planetary rovers is challenged by visually repetitive, featureless terrain. SLAM systems based on 3D Gaussian Splatting (3DGS) offer dense mapping but rely on RGB appearance descriptors that degrade under perceptual aliasing - a common failure mode in extraterrestrial environments where physically distinct locations appear visually identical. This paper presents HyperLoop, the first framework integrating hyperspectral imaging (HSI) into the loop closure module of a 3DGS-based SLAM system. Hyperspectral descriptors encode material-level spectral signatures complementing RGB features, providing robustness to illumination changes and visual repetitiveness. Two HSI descriptor families are introduced: Bag-of-Spectral-Words (BoSW), encoding spectral material distributions via TF-IDF weighted histograms, and PCA-K VLAD (PCAK-VLAD), capturing first-order residual statistics of PCA-compressed spectral signatures. Both are evaluated with SIFT-VLAD and NetVLAD RGB descriptors under Strict-AND and Weighted fusion strategies on a custom
dataset from the Moon-Mars Outdoor Test Site (MMOTS) at DLR, comprising synchronized RGB-D and hyperspectral sequences across four planetary analog traverses (65.5 m total). A key finding is that the sensor's Red-NIR range (600-860 nm) provides limited discriminability for rock terrain: the best HSI fusion (SIFT-VLAD + BoSW, Strict-AND) achieves 5.04 cm ATE RMSE, within 1.8% of SIFT-VLAD alone (4.95 cm). The gain over NetVLAD (8.81 cm) is attributable to SIFT-VLAD, not HSI fusion. This motivates future adoption of SWIR sensors capturing diagnostic mineral absorption features absent from the 600-860 nm range for planetary SLAM missions.
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All authors Annie Bhalla, Dennis Rotondi, Riccardo Giubilato, Kai O. Arras, Wolfgang Stuerzl