General Relativity and Quantum Cosmology
Projective Symmetry and Its Breaking in Quadratic Metric-Affine Gravity
Carmen Ferrara, María José Guzmán and Laur Järv
We investigate generalized projective
symmetry and its breaking in four-dimensional parity-even metric-affine
gravity, considering an action linear in
curvature and at most quadratic in torsion and nonmetricity. We derive the action of the generalized projective transformation on the irreducible components of the affine geometry and determine the coupling relations defining the axial, metric-trace, and fully projectively invariant theories. Complete invariance reduces the $12$ gravitational coefficients to a five-parameter family and generates four vectorial Noether identities for the connection equations. Projective invariance makes the connection operator singular, thus we develop a
symmetry-adapted method for solving its
vacuum field equations. We then analyze couplings to Dirac, electromagnetic, and complex Klein-Gordon fields. Locally exact axial-projective transformations correspond to
chiral rotations for massless
fermions at the classical level, while a combination of covectors acts as an Abelian connection linking a locally exact vector-projective representative to scalar $U(1)$
symmetry. Standard Maxwell theory is independent of the affine connection, whereas a torsion-dependent Maxwell-like extension can preserve both electromagnetic and projective invariance through compensating Stückelberg fields. Exact gravitational projective invariance makes the corresponding connection directions nondynamical and forces matter currents sourcing them to vanish. Controlled explicit breaking lifts these zero modes and converts them into auxiliary fields. Thus, projective
symmetry provides a unified principle for identifying affine gauge modes, constraining matter couplings, and relating
symmetry breaking in the gravitational sector to effective matter interactions.
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General Relativity and Quantum Cosmology
On the topology of the space of vacuum initial data sets
Romain Gicquaud and Jonathan Glöckle
We show that the space of
vacuum initial data sets on a closed
manifold often has many non-trivial homotopy groups. The starting point is a result of the second named author, which constructs non-trivial elements in the homotopy groups of the space of initial data sets satisfying the strict dominant energy condition, together with a later result in joint work with Bernd Ammann showing that these elements often persist when the strictness assumption is dropped. In this work, we use a parametrized version of the conformal method to show that these elements may also be represented by maps into the space of
vacuum initial data sets. This requires two results that may be of independent interest: metrics admitting conformal Killing vectors can be removed from the space of metrics without changing its weak homotopy type, and over the remaining metrics the York decomposition can be carried out in families, the TT-tensors forming a trivial
Hilbert bundle. To our knowledge, this is the first result on the global
topology of this space.
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Mesoscale and Nanoscale Physics
Atomic-Scale Imaging of Lattice Relaxation and Topological Flat Bands in Helical Trilayer Graphene
Shudan Jiang, Zonglin Li, Yu Gu et al. (+15 more)
Helical trilayer
graphene (HTG) has emerged as a highly tunable moire
quantum material that hosts strong electronic correlations and nontrivial band
topology. However, the atomic-scale
lattice structure and local electronic properties have remained largely unexplored. Here we present a comprehensive real-space study of HTG using a combination of scanning near-field optical microscopy and low-temperature scanning tunneling microscopy. We directly image supermoire
lattice relaxation, revealing large triangular domains separated by sharp domain walls, as well as stripe domains connected by smoothly varying boundaries. Atomic-scale spectroscopy uncovers flat bands with a honeycomb electronic texture and one-dimensional boundary states confined to domain walls. By systematically varying the twist angle, we identify a magic angle of approximately 1.9{\deg}, substantially larger than the 1.6 degree predicted by theory. Our results establish a direct microscopic link between
lattice relaxation and flat bands in HTG.
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All authors Shudan Jiang, Zonglin Li, Yu Gu, Liang Liu, Dandan Guan, Yaoyi Li, Hao Zheng, Canhua Liu, Kenji Watanabe, Takashi Taniguchi, Shengwei Jiang, Xiaoxue Liu, Zhiwen Shi, Guorui Chen, Jinfeng Jia, Tingxin Li, Can Li, Shiyong Wang
Nuclear Theory
N = 32 Shell Evolution in Calcium Isotopes: Evidence from Macroscopic to Microscopic Models
A. Anupam, C. Dash, Yashraj et al. (+2 more)
The well-established N=32 subshell closure in calcium serves as an important
benchmark for
nuclear structure
evolution toward
neutron-rich
nuclei. Recent charge-radius measurements of potassium isotopes [Nat. Phys. 17, 439-443 (2021)] reveal no corresponding signature of enhanced stability at the same
neutron number. Motivated by this discrepancy, we investigate the structural
evolution of even-even Ca isotopes from N=22 to 36 using macroscopic, relativistic mean-field (RMF), and large-scale shell-model approaches, with the KB3G, GXPF1A, GXPF1B, and FPD6 interactions in the KSHELL framework. The macroscopic analysis, evaluated against seven theoretical frameworks and NNDC data, reveals a coherent change in
neutron structure at N=32, most clearly in the shell-gap energy $\Delta E$ and the three-point binding-energy filter, corroborated by the charge-radius filter. The RMF spectrum with NL3* shows a clear $\nu2p_{3/2}$-$\nu2p_{1/2}$ separation consistent with this signature. KB3G and FPD6 reproduce the N=32 enhancement of $E(2_1^+)$ and reduction of $R_{4/2}$, whereas GXPF1A and GXPF1B give a weaker N=32 signature than N=34 signature, the reverse of the experimental pattern, showing that the two subshells depend differently on the monopole interaction. The deformation parameter and B(E2) values have been obtained with above four interactions and are in good agreement with the experimental results.
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All authors A. Anupam, C. Dash, Yashraj, R. P. Singh, B. B. Sahu
Materials Science
Cluster-based Structural Similarity for Dataset Visualization and Data Selection for Machine Learning Interatomic Potentials
Yuto Iwasaki and Miguel A. Caro
Machine learning interatomic potentials (MLIPs) are essential components for accelerating simulation-driven materials design. Data-efficient MLIP training relies on data-selection strategies that maximize structural diversity while limiting computationally expensive first-principles calculations. A key challenge in such strategies is evaluating structural similarity, which involves a trade-off between retaining information on individual atomic environments and reducing computational cost. Here, we propose similarity evaluation methods that achieve both representational fidelity and computational efficiency. Our method represents each structure using a small set of characteristic atomic environments identified by k-medoids clustering and computes pairwise similarity through optimal matching between these representatives or their distributions. Molecular
benchmarks demonstrate that our method is approximately 50 times faster than the baseline method while also more clearly distinguishing structures with different chemical compositions. Similarity-based data-selection
benchmarks demonstrate that our methods improve the data efficiency and stability of force prediction in MLIPs.
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Statistical Methodology
Proximal Empirical Bayes for Sparse Regression with Posterior Decision Support
Dimitrios Roxanas
In this work, we revisit estimation and variable selection in sparse Gaussian regression, possibly under affine constraints. We adopt a middle-ground approach between optimisation and fully hierarchical
Bayesian methods, preserving much of the computational efficiency of the former while adding posterior uncertainty quantification. In particular, we develop an empirical Bayes framework that separates shrinkage calibration from posterior-informed selection while using the same log-concave model throughout. The resulting workflow is modular: different methods can be used for noise-variance estimation, shrinkage calibration and posterior sampling. At the same time, these tasks are linked, and numerical choices made at one stage can propagate through the workflow and ultimately affect the decision about which variables to retain. We investigate in detail one instance of the framework based on a Laplace prior, with the amount of shrinkage determined by
stochastic approximation proximal gradient. The proximal machinery used for this calibration also supports posterior simulation. We further show how the empirical Bayes calibration should be modified when homogeneous constraints reduce the number of free parameters. Nonhomogeneous affine information is instead incorporated softly; when this information is strong, posterior sampling can become poorly conditioned, and we develop a preconditioning strategy to improve mixing and posterior exploration. Synthetic experiments examine how scale calibration, posterior run length and sampler conditioning affect estimation, uncertainty quantification and the final sparse decision. On a diabetes
dataset, posterior uncertainty agrees closely with established
Bayesian analyses, while the final decision provides a sparser practical summary.
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Computation and Language
Mid-Harness: Scaling Actions Between Model and Harness for Terminal Agents
Minki Kang, Ryo Hachiuma, Shaokun Zhang et al. (+8 more)
Terminal
agents act through
stochastic model generations, yet the ability to generate a useful action does not ensure its reliable execution. A poor command (e.g., wrong package install) can change the environment in ways that hinder subsequent progress, even when the model could generate a better alternative. We investigate whether allocating test-time compute at the model-harness boundary can improve action reliability and trajectory success, and what makes this allocation effective. To study these questions, we introduce Mid-Harness, which samples and verifies candidate actions before forwarding one for execution, while keeping the generator and harness unchanged. With a TMAX-9B generator, more action sampling yields little benefit under weak verification, whereas a capable verifier can exploit useful alternatives from the same generator. On TerminalBench-Lite, a GPT-5.6 Sol verifier raises Pass@1 from 50.00% for the base
agent to 68.03% with 8 sampled actions. When the same TMAX-9B model serves as the verifier, pairwise verification performs best among the evaluated verification mechanisms. Distilling responses from the stronger verifier into TMAX-9B further improves Pass@1, while leaving the action generator unchanged. With TMAX-9B on TerminalBench-Lite, combining action and trajectory scaling reaches higher success at lower estimated
token cost than generating more trajectories alone. Mid-Harness also improves performance across additional models,
benchmarks, and harnesses. These findings identify action scaling as a promising target for test-time compute scaling in terminal
agents.
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All authors Minki Kang, Ryo Hachiuma, Shaokun Zhang, Subhashree Radhakrishnan, Yonggan Fu, Jindong Jiang, Mingjie Liu, Ehsan Hosseini-Asl, Yi Dong, Yu-Chiang Frank Wang, Byung-Kwan Lee
Optimization and Control
ExactVariance of Random Return in Distributional LQR and Its Application to Mean-Variance Optimal Control
Ruyi Teng, Zifan Wang and Yulong Gao
The classical linear quadratic regulator (LQR) minimizes the expected cumulative return but fails to account for performance variability, rendering it inadequate for risk-aware applications. To address this, we introduce the variance of the cumulative return as a risk measure in LQR. We derive the first exact closed-form expression for the variance of the discounted in?finite horizon return within the discrete-time Distributional LQR framework, for i.i.d. disturbances with symmetric probability densities. For Gaussian disturbances, this expression elegantly simplifies to a form dependent only on the disturbance covariance. Leveraging these theoretical foundations, we formulate a mean-variance optimal control problem that explicitly manages the trade-off between expected return and performance variability. To address the resulting non-convex optimization problem, we propose a novel adjoint gradient descent
algorithm for a penalized formulation of the original problem, and establish that all iterates remain stabilizing and converge to a stationary point of the penalized objective. The effectiveness of this framework and the inherent risk-performance trade-off? are demonstrated through numerical experiments.
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Quantum Physics
Logical information localisation in stabiliser codes via single-qubit measurements
Jelena Mackeprang, Hemant Sharma and Jonas Helsen
Stabiliser path finding (SPF) has previously been introduced as a method to localise logical information in a stabiliser code undergoing loss onto a single pre-specified target
qubit, using only one round of single-
qubit measurements. When working with limited resources and flying
qubits, this fast read-out of logical information is a helpful tool for fault-tolerant communication. In this work, we provide a broad analytical and computational study of localisation via SPF. We introduce $g$-SPF, where the task is to localise the logical information onto a set of at most $g$ unspecified target
qubits. Through analytical arguments based on percolation theory and the disjointness of stabiliser codes, we prove that for i.i.d.
qubit loss with probability $p<1/2$ and sufficiently large planar surface codes, localisation via $g$-SPF for constant $g$ succeeds with a probability converging to one, which establishes a localisation threshold. Furthermore, we propose and implement two
algorithms to solve SPF. The first is exact and formulates SPF as an integer linear program, whereas the second is heuristic and formulates SPF as a decoding problem. We validate both
algorithms by numerically reproducing the localisation threshold for the surface code and demonstrate that the heuristic
algorithm is considerably faster. Our work significantly reduces the time required to solve SPF compared to current state-of-the-art
algorithms, allowing us to study localisation in substantially larger stabiliser codes than previously considered in the literature. Together, these theoretical and computational results open the door to various applications, such as fault-tolerant teleportation and efficient logical fusion.
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Rings and Algebras
A Koteljanskii inequality for permanents
Suvrit Sra
We prove a permanental analogue of Koteljanskii's inequality. If $A$ is an inverse $M$-
matrix that becomes symmetric after a positive diagonal similarity, then $\mathrm{per}(A_{S\cup T})\,\mathrm{per}(A_{S\cap T})\ge\mathrm{per}(A_S)\,\mathrm{per}(A_T)$ for all $S,T\subseteq[n]$, where $A_S$ is the principal submatrix indexed by $S$. The proof expresses permanents as moments of a complex Gaussian vector and uses Ginibre's correlation inequality. Finally, an explicit counterexample shows that
symmetry cannot be dropped.
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Distributed Computing
LatencyLab: A DPDK-Based P4 Pipeline Latency Measurement Framework for FPGA SmartNICs
Pavani Kuppili, Zhaoyang Han, Yicheng Qian et al. (+4 more)
P4-programmable FPGA SmartNICs place packet processing directly on the wire, but open FPGA P4 toolflows do not expose timestamping at the pipeline boundary, so the latency a P4 program adds on the target FPGA is rarely measured. This paper presents LatencyLab, a DPDK-based measurement framework for FPGA P4 pipeline latency that needs neither PHC/PTP support on the datapath nor clock synchronization. The FPGA's two ports share a network segment, so the switch multicasts a copy of each probe packet to both: one copy passes through the VitisNetP4 pipeline, the other through a matched bypass path. A kernel-bypass DPDK receiver busy-polls both ports and timestamps every packet with the CPU timestamp counter (TSC) as it is retrieved from the NIC's receive circular buffer. The arrival-time difference of the two copies isolates the pipeline latency after calibration against a null bitstream carrying the same traffic; transmit time cancel in the subtraction. We evaluate four VitisNetP4 programs on an AMD Alveo U280, probing each with a 20,000-packet trace measured ten times per session over five independent sessions, all TSC-timestamped and reflected for hardware timestamping. The measured latency distributions are tight and reproducible: 99% of packets fall within 20 ns of the median, session medians repeating within 1 to 2 ns (FiveTuple 107/137 ns, Forward 149 ns, RemoveHeader 177 ns, Checksum 364 ns at 250 MHz). Two independent checks agree with the framework: a kernel-free reflector returns every probe pair to a ConnectX-5 NIC whose adapter clock reproduces the measured distributions within a few nanoseconds,
quantile by
quantile, and every measured packet falls 18 to 22 clock cycles below the vendor's worst-case latency bound.
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All authors Pavani Kuppili, Zhaoyang Han, Yicheng Qian, Suranga Handagala, Michael Zink, Miriam Leeser, Robert Ricci
Astrophysics of Galaxies
A New Sample of Closely Separated Dual and Binary AGN Candidates Revealed with the Radio Fundamental Catalog
Emma Schwartzman, Ryan Pfeifle, Tracy Clarke et al. (+3 more)
The Big Multi-AGN Catalog (BigMAC) provides the first literature-complete compilation of confirmed and candidate dual, binary, and recoiling active
galactic nuclei (AGN). The BigMAC provides a basis for the first population-wide
radio study of multiple AGN (multi-AGN). The
Radio Fundamental Catalog (RFC) is composed of four decades of Very Long Baseline Interferometry (VLBI) observations capable of resolving parsec-scale structure. All BigMAC candidate and confirmed multi-AGN were cross-matched to the RFC, highlighting a total of 241 BigMAC sources with available RFC imaging. We present the first detailed analysis of the ten systems exhibiting multiple compact components, most likely to be
radio multi-AGN. Using multi-frequency
radio and supporting multiwavelength information, we assess whether each system is more consistent with dual/binary AGN or intrinsic jet structure. The dual/binary nature of six systems is further supported by our
radio analysis, while the four remaining systems are more plausibly explained by compact jet activity. Several candidate multi-AGN systems exhibit projected separations of only a few to tens of parsecs. In many cases, the parsec-scale
radio separations are orders of magnitude smaller than the separations inferred from the observations that originally motivated their selection, emphasizing the importance of VLBI follow-up. Because all ten sources are members of the International Celestial Reference Frame (ICRF) and display significant intrinsic
radio structure, they also represent potential sources of astrometric error. This work establishes the RFC as an archival resource for characterizing the closest SMBH pairs while improving future ICRF realizations.
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All authors Emma Schwartzman, Ryan Pfeifle, Tracy Clarke, Nathan Secrest, Henrique Schmitt, Barry Rothberg
Human-Computer Interaction
Richard: Voice-First Mobile Interaction for Persistent Tasks
Xinyang Chen
Mobile terminals need to provide application and network services while supporting users' control over their
attention. We explore voice-first interaction organized around requests and delegated tasks, allowing users to leave a conversation and later inspect, revise, and retrieve the work. We present Richard, a system prototype that manages voice sessions, task execution, and result delivery separately, linking them through persistent request records. Conversation and task views provide visual feedback, while the backend coordinates immediate responses, dedicated service operations, and
agent tasks. Request revisions, execution states, and notifications remain associated with the relevant task. We examine this design through Android functional records, controlled lifecycle verification, and execution records of a real programming request. Controlled verification reproduces revision, execution after confirmation, and result retention; deployed-service records show backend progress and failure feedback after client disconnection. These observations inform the design of task continuity, user control, and service integration in mobile voice interaction, providing an implementation basis for personal computing devices that accommodate intermittent user participation.
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Computation and Language
Overview of BioASQ 2026: The fourteenth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering
Anastasios Nentidis, Georgios Katsimpras, Anastasia Krithara et al. (+18 more)
This paper presents an overview of the fourteenth edition of the BioASQ challenge, organized in the context of the Conference and Labs of the Evaluation Forum (CLEF) 2026. BioASQ is an international challenge series that supports progress in biomedical
language processing tasks ranging from semantic indexing and information extraction to question answering and summarization. In 2026, BioASQ included six shared tasks: a) Task 14b on biomedical semantic question answering. b) Task Synergy14 on question answering for developing biomedical top- ics. c) Task MultiClinSum-2 on
multilingual clinical summarization. d) Task BioNNE-R on extracting relations between nested named entities in Russian and English. e) Task ELCardioCC on clinical coding in cardiology. f) Task GutBrainIE on gut-
brain interplay information extrac- tion. Across these six tasks, 87 distinct teams participated, submitting more than 1000 runs overall. As in previous editions, several submissions reached competitive performance, reflecting the continued progress of state-of-the-art methods across biomedical
language processing tasks.
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All authors Anastasios Nentidis, Georgios Katsimpras, Anastasia Krithara, Martin Krallinger, Miguel Rodríguez-Ortega, Eduard Rodriguez-López, Natalia Loukachevitch, Igor Rozhkov, Elena Tutubalina, Dimitris Dimitriadis, Vasiliki Patsiou, Grigorios Tsoumakas, George Giannakoulas, Alexandra Bekiaridou, Athanasios Samaras, Giorgio Maria Di Nunzio, Nicola Ferro, Stefano Marchesin, Marco Martinelli, Gianmaria Silvello, Georgios Paliouras
Quantum Physics
The quantum query complexity of the semigroup product problem
Troy Lee and Miklos Santha
We study the
quantum query complexity of computing a
semigroup product $x_1\cdots x_n$, when one query reveals one input element and the multiplication table is given. For a finite aperiodic
semigroup of size $N-1$, the argument of Aaronson, Grier, and Schaeffer gives an upper bound of $\sqrt n\,(N\log(nN+2))^{O(N)}$ queries. To obtain query bounds that reflect algebraic structure, we study the product breadth $\beta$: the smallest bound such that every input word has a subsequence of at most $\beta$ letters with the same product. For nontrivial finite commutative aperiodic monoids, the bounded-error
quantum query complexity is $\Theta(\min\{n,\sqrt{n\beta}\})$, and is thus characterized by product breadth. We further show that if such a monoid $M$ has aperiodicity index $k$ (the least positive integer satisfying $x^k=x^{k+1}$ for every $x\in M$), then $\beta=O(k\log(|M|+1)\log\log(|M|+2))$. For monoids with a stable partial order in which the identity is the minimum element, we prove that the bounded-error
quantum query complexity is at most $\sqrt{n+1}((\beta+2)\log(n+2))^{O(\log(\beta+2))}$. For arbitrary finite aperiodic
semigroups of order $N-1$, we improve the bound of Aaronson, Grier, and Schaeffer, obtaining a bounded-error
quantum query complexity of at most \[ \min\left\{n,\sqrt n\, \log^{O((N\log(N+2))^{1/3})}(n+2)\right\}. \] The dependence on
semigroup size is nearly tight: the bounded-depth Dyck lower bound of Ambainis et al. yields aperiodic monoids requiring $\sqrt n\,2^{\Omega(N^{1/3})}$
quantum queries in the relevant parameter range.
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Robotics
EWAM: Emergent Depth-Wise Specialization in a Unified Embodied Model – From Semantic Understanding through Visual Foresight to Action
Hao Wang, Jiajun Wen, Jingzhi Liu et al. (+21 more)
Vision-
language-action (VLA) policies emphasize semantic understanding, whereas world-action models (WAMs) learn predictive representations of environment dynamics. Systems that expose a policy to both sources often still concentrate action computation on a single expert. We present EWAM, an action-centric unified
embodied model whose asymmetric joint
attention lets action
tokens read semantic, current-visual, predicted-future, and action information at every layer while the perceptual experts retain their distinct roles. Without layer-wise supervision, EWAM develops an emergent depth-wise specialization: action queries attend mainly to
vision-
language features in shallow layers, to predicted future frames in intermediate layers, and to action
tokens themselves in deep layers. This handoff replicates across tasks and is stable across denoising steps. Checkpoint tracking and causal interventions show that it is learned and that action generation depends on it. EWAM is pretrained in two separate regimes, one on cross-embodiment
robot trajectories and one on human
egocentric video. In simulation and real-
robot experiments, it surpasses existing VLA, WAM, and hybrid baselines. Human
egocentric data improve both cross-embodiment transfer and real-
robot robustness, and subtask-phase supervision improves long-horizon completion. Together, these results suggest that unified
embodied learning can induce an ordered internal progression from semantic understanding, through visual foresight, to action formation.
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All authors Hao Wang, Jiajun Wen, Jingzhi Liu, Shuoshuo Xue, Zhiliang Chen, Min Lin, Yicheng Chang, Xiaoyu Guo, Yukang Zhuo, Zheng Chong, Yunshuang Nie, Jian Zhang, Weijia Liufu, Qingman Wu, Heming Xu, Bingchang Song, Dantong Wu, Zhiyuan Wang, Hang Xu, Jianhua Han, Bokui Chen, Shen Zhao, Rui Li, Xiaodan Liang
Computation and Language
UBTree: Parallel Tree Drafting via Unigram and Bigram Models for Speculative Decoding
Chumeng Liang, Linxuan Wang, Xinyu Peng et al. (+7 more)
Speculative decoding accelerates
language model inference by verifying multiple draft
tokens in a single target-model pass. Recent parallel drafters have achieved breakthrough performance in frontier production models, but their effectiveness deteriorates as the
entropy of target distributions increases due to insufficient draft diversity. To overcome this bottleneck without sacrificing parallelism, we introduce UBTree, a parallel drafter that couples a Unigram proposer with a Bigram selector to construct drafting Trees. The unigram proposer is trained with the standard cross-
entropy objective to generate candidate
tokens independently for each position, while a lightweight bigram selector predicts transition scores between adjacent candidate pairs. Unlike the proposer, the selector is trained with a renormalized KL objective on high-temperature data. This tree-native training broadens the supervision beyond the greedy path, encouraging plausible alternative branches that improve the chance of accepting additional
tokens during tree verification. Across seven standardized
benchmarks with Qwen3-4B and Qwen3-8B, UBTree achieves an average speedup of $5.84$–$6.94\times$ over autoregressive decoding and outperforms DARTree in all 28 comparisons. Production-scale evaluation further demonstrates UBTree's advantage over frontier baselines such as DSpark.
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All authors Chumeng Liang, Linxuan Wang, Xinyu Peng, Huabin Liu, XiaoXu Guo, Yuxin Chen, Ge Liu, Guang Lin, Qifan Song, Jianguo Li
Robotics
When Instructions Retrieve Trajectories: Diagnosing and Mitigating Generalization Failures in VLA Models
Hung-Jen Chen, Yu-Hsun Hou, Yan-Hong Chen et al. (+4 more)
Vision-
language-action (VLA) models can exceed 90% success on in-distribution tasks and withstand nuisance changes that preserve the required action, yet fail under counterfactual changes that demand a different action. Aggregate robustness scores can therefore conceal a more specific failure, in which a policy responds to both
language and
vision yet does not combine them to select the action the task requires. We call this failure instruction-action binding. Instructions cue familiar trajectory families, and visual feedback adjusts their execution. Behavioral analyses of fine-tuned $\pi_{0.5}$ and GR00T-N1.7 policies reveal that failed rollouts often retain the source behavior or switch to another demonstrated task. These switches show that
language is not simply ignored. Readouts and interventions connect these choices to task-conditioned internal states. Our analysis of the imitation objective shows how narrow conditional action support can leave grounded and instruction-keyed solutions indistinguishable on the demonstrations. This motivates Equivariant Counterfactual Training (ECT), which acts at two levels. ECT data supply valid demonstrations in which the same instruction requires different actions in distinguishable scenes, while the ECT loss trains each demonstration with its counterpart in the same update. In a controlled LIBERO-PRO comparison, full ECT raises $\pi_{0.5}$'s mean position-swap success from 36% to 59%. On CALVIN, where counterparts already occur in the original data, the ECT loss improves five-task completion without new demonstrations. On a real UR5e under a fixed demonstration budget, full ECT raises unseen-position success from 8% to 88%.
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All authors Hung-Jen Chen, Yu-Hsun Hou, Yan-Hong Chen, Yan-Fu Chen, Binghua Cai, Min Sun, Chun-Yi Lee
Robotics
PhasePlan: Ordered Future-Phase Planning for Robot Brain Models
Xiaoyu Yang, Yafei Zhang, Wensheng Li et al. (+2 more)
Robot brain models integrate
vision,
language, and
robot state to generate actions for complex manipulation tasks. Most predict fixed-length action chunks that may span multiple task phases. This can obscure phase transitions and favor frequent action patterns, compromising action timing in dynamic environments. We propose \method, an ordered future-phase planning method for
robot brain models. From current
multimodal observations, it predicts the task phase at each future action position. The resulting planning representations condition the corresponding actions, preserving temporal
alignment between task progress and action generation. Training first learns the planner, then freezes it during action-model adaptation to maintain stable phase representations. We instantiate \method on pretrained $\pi_{0.5}$ and AcrossWAM1.0
robot brain models. Detailed quantitative evaluation uses the $\pi_{0.5}$ implementation. On conveyor-belt manipulation, \method reduces offline joint-action error by approximately 22.5% relative to the original $\pi_{0.5}$ model. It also improves phase-transition modeling and cross-phase action prediction. These results demonstrate the value of ordered future-phase planning for continuous action generation.
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All authors Xiaoyu Yang, Yafei Zhang, Wensheng Li, Qing Zhan, Nan Wu
Robotics
TACTIC: Temporal and Context-Aware LLM Tactical Planning for Roadside LiDAR Attacks
Yiming Gao and Shaocheng Luo
Physical
LiDAR attacks are often evaluated using fixed primitives and manually selected parameters, despite their strong dependence on surrounding traffic. We present TACTIC, a scene-aware framework that uses a
multimodal large
language model (MLLM) to coordinate state-adaptive roadside
LiDAR attacks. Under a gray-box threat model, TACTIC relies only on an attacker-operated roadside perception stack, without accessing the victim
LiDAR's native point clouds or internal processing. Local perception provides metric vehicle states, while the MLLM combines these measurements with roadside imagery to infer relational traffic context and construct a semantic scene graph. Based on this representation, TACTIC selects and configures two complementary primitives: push-away, which shifts the perceived range of a lead vehicle, and phantom-obstacle braking, which triggers emergency braking through obstacle injection. Measured traffic states and empirically calibrated constraints ground the generated tactics in physically feasible operating regions. To accommodate MLLM latency, TACTIC overlaps
reasoning and execution asynchronously while high-rate local perception detects scene changes and triggers replanning. Across 280 randomized CARLA trials, the full policy achieves a 100% collision rate, compared with 35% for a fixed rule, 60% for random selection, and 75% for a restricted LLM using mode selection with default parameters. Joint physical-and-image input achieves 100% success, versus 65% with physical measurements alone and 75% with imagery alone, while asynchronous $\Delta$ refresh reduces scene-mutation response from 7.4 s to 2.0 s. These results show that scene-dependent tactical planning can expose context-sensitive
LiDAR failure modes that fixed attack policies may miss.
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Systems and Control
On the Input Sensitivity of METANET Models and the Robustness of Dynamic Calibration
Cameron Hickert, Shreyaa Raghavan and Cathy Wu
While traffic modeling and control rely upon effective calibration of macroscopic traffic simulation models like METANET, recent empirical work shows these models can exhibit severe sensitivity to input noise. This paper explains this phenomenon through a string-stability analysis, demonstrating that a calibrated METANET model can amplify small additive perturbations to boundary conditions along the corridor, causing the simulated state to diverge from the nominal baseline. The input sensitivity in off-nominal cases may compromise the model's capabilities for counterfactual analysis, which is required for use cases of interest like the design of large-scale variable speed limits. To address these issues, this work demonstrates how a dynamic, time-varying approach to calibrate model parameters can achieve robustness and improved accuracy of the resulting macrosimulation. We show analytically that under stated regularity and perturbation assumptions, dynamic calibration achieves a tighter cost deviation bound than static calibration, and we validate this behavior in highway environments with synthetic and real-world data. Ultimately, this can enable more trustworthy traffic simulation and more effective evaluation of control strategies.
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Machine Learning
What Limits Recursive Reasoning Models: Optimization, Architecture and Test-Time Scaling
Yuliana Shakhvalieva, Dmitrii Kharchev, Viacheslav Bezrukov et al. (+4 more)
Recursive
reasoning models apply a small shared
Transformer block many times to refine a latent state. This gives them large effective depth with few parameters and makes them strong on algorithmic tasks. Such compact solvers are natural candidates for tools that an LLM can call on narrow algorithmic subproblems. However, existing models such as HRM, TRM and URM differ in architecture, gradient propagation and training procedure simultaneously. This makes it hard to tell what drives their performance, and their optimization is still poorly understood and often unstable. In this work we address both of these gaps. First, we study these questions under a unified experimental pipeline spanning six algorithmic domains. Individual controlled ablations are performed on representative domains, while the resulting recipe is evaluated across the full suite. The study reveals a surprisingly simple recipe for stable and generalizable recursive
reasoning: an intermediate gradient horizon, large physical batches and controlled updates of the recurrent state. An explicit hierarchical architecture is not needed. Second, we combine these findings into a stable 13.6M-parameter model that achieves the strongest overall performance among the evaluated recursive baselines, with particularly large gains on out-of-distribution generalization. It raises Arithmetic OOD accuracy to 71.2%, from 36.2% for the strongest baseline, while reaching 98.41% on Sudoku and 59.5% pass@2 on ARC-AGI-1. Our results show that, within the recursive architectures studied here, performance depends strongly on how recurrence is optimized and stabilized. More broadly, it shows how AI systems can be improved by optimizing their components one at a time.
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All authors Yuliana Shakhvalieva, Dmitrii Kharchev, Viacheslav Bezrukov, Inessa Fedorova, Dmitry Bocharov, Ivan Oseledets, Valerii Ternovskii
Strongly Correlated Electrons
Strong Dimerization and Field-Induced Reconstruction of the Low-Energy Spectrum in $\mathrm{Cu}_3(\mathrm{OH})_4(\mathrm{HCO}_2)_2$
Sk Saniur Rahaman and S. R. Hassan
We investigate the field-dependent low-energy thermodynamics of the distorted triangular
quantum antiferromagnet $\mathrm{Cu}_3(\mathrm{OH})_4(\mathrm{HCO}_2)_2$ using a sector-resolved Superblock Diagonalization Method (SBDM) supplemented by a transfer-
matrix treatment of weakly coupled layers. The strong exchange hierarchy, dominated by the Cu1–Cu1 intradimer coupling $J_2=150\,\mathrm{K}$, separates a high-energy dimer sector from a much softer
magnetic manifold formed predominantly by the Cu2 moments. In the 24-site cluster, sixteen Cu1
spins form the strongly bound sector while eight Cu2
spins remain magnetically active; polarization of these eight
spins gives $S^z=4$ compared with $S^z_{\rm sat}=12$, providing a direct microscopic origin for the one-third magnetization scale. The finite-temperature thermodynamics reveals a non-monotonic field
evolution of the low-energy scale: the dominant $C/T$ feature softens with increasing field, reaches a minimum near the field region around $2\,\mathrm{T}$, and subsequently hardens as the low-temperature magnetization approaches $M_{\rm sat}/3$. Temperature and field sweeps thus expose a common field-induced spectral reconstruction, while the strongly reduced
entropy reflects the restricted number of thermally active degrees of freedom below the dimer excitation scale. Our results identify strong-dimer-induced reduction of the active
magnetic Hilbert space, followed by field-driven reorganization of the residual
spin sector, as the common microscopic origin of the one-third
magnetic response and the non-monotonic low-temperature thermodynamics.
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Quantum Physics
Single-shot state preparation thresholds in quantum LDPC codes: soundness, confinement, and statistical mechanics
Yuanchen Zhao, Yi Yuan, Zhengyi Han et al. (+1 more)
Single-shot error correction does not by itself establish equally direct and robust logical-state initialization. We study preparation of CSS
quantum low-density parity-check code states using one noisy round of the code's checks. We prove that linear confinement over a weight range growing at least logarithmically, together with at least logarithmic distance, suffices for a nonzero threshold under independent local-
stochastic readout and data noise in the relevant CSS sector. The threshold is certified by ideal recovery of a decoder-defined logical channel. The key is a correction rule that preserves the logical class under local syndrome changes, allowing the proof to proceed without a local-
stochastic bound on the preparation residual. This gives a single-block guarantee for
quantum expander codes without dimension jumps or batching. Under the stronger linear-soundness hypothesis, we establish local-
stochastic residual noise, enabling composition with compatible fault-tolerant gadgets. For a retained-record formulation, we also derive an exact statistical-mechanical representation of optimal final recovery at fixed preparation decoder. A decoder-dependent logical-sector order parameter characterizes asymptotically successful block decoding. Monte Carlo results for the three-dimensional toric code provide finite-size evidence for a threshold beyond both linear sufficient conditions. These results distinguish logical robustness from local-
stochastic residual-noise control and identify decoder consistency as a route to single-shot initialization.
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All authors Yuanchen Zhao, Yi Yuan, Zhengyi Han, Dong E. Liu
Artificial Intelligence
AIMS: An Agentic AI Framework for Sim-to-Real Multi-Modal ISAC
Yijie Bian, Kai Zhang, Wei Guo et al. (+4 more)
Multi-modal integrated sensing and communication (
ISAC) enables environmental perception and reliable connectivity for intelligent wireless networks. Data-driven multi-modal
ISAC models depend heavily on annotated real-world data to learn relationships across sensing and wireless observations, thereby constraining scalable deployment. Although synthetic data generation reduces the burden, adapting existing simulation pipelines to a target deployment requires consistent scene, sensing, wireless, and learning configurations, while mismatches among these coupled components impair sim-to-real transferability. To address the challenge, we propose an agentic artificial intelligence (AI) framework for sim-to-real multi-modal
ISAC, named AIMS. Given a natural-
language deployment request specifying the target task, deployment conditions, and real-data budget, AIMS derives a deployment-specific sim-to-real configuration and coordinates its execution to produce a deployment-specific task model. A two-
agent architecture coordinates scene construction with task learning. A scene construction
agent generates geographically grounded, synchronized sensing and wireless records from shared physical states, while a scene understanding
agent configures task-relevant modalities and mixture-of-experts (MoE) learning for zero-shot inference or few-shot adaptation. Structured domain knowledge guides dependency-aware planning, while validation evidence supports feedback-driven revision of affected decisions. Experiments on the real-world DeepSense 6G
dataset demonstrate improved vehicle detection and beam prediction over the considered simulation and fusion baselines. A separate orchestration
benchmark evaluates task interpretation, dependency
reasoning, and feedback-driven replanning across diverse deployment requests, showing improved plan correctness with structured domain knowledge and validation feedback.
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All authors Yijie Bian, Kai Zhang, Wei Guo, Zixin Wang, Shenghui Song, Jun Zhang, Khaled B. Letaief
Human-Computer Interaction
Diptych: Scoped, AI-Interpreted Comparison for Reference Listening in Music Production
Chongjun Zhong, Abhinaba Roy, Archishman Ghosh et al. (+2 more)
Reference listening is a common strategy in music production, but current comparison tools often obscure a key human judgment: deciding what should be compared. We present Diptych, an AI-assisted system that lets users define comparison scope across whole tracks or independently selected segments, while inspecting structured audio features and scope-specific AI interpretations. We evaluated Diptych in a within-participants study with 12 musicians, complemented by source-blinded ratings from four expert listeners. Participants used the system to surface additional differences, nine of ten of which received at least partial expert support, and reported good usability and greater clarity about possible next steps. These findings suggest that AI support for creative comparison should prioritize user-defined scope, inspectable evidence, and actionable guidance, while avoiding authoritative judgments that exceed what the evidence can support.
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All authors Chongjun Zhong, Abhinaba Roy, Archishman Ghosh, Kejun Zhang, Dorien Herremans
Quantum Physics
Mario Guillaume Cecile, Nis-Luca van Hülst, Tomohiro Hashizume et al. (+19 more)
We present a comprehensive review of
quantum approaches for solving partial differential equations (PDEs) arising in computational fluid dynamics (CFD). We examine fully
quantum approaches, including
quantum linear system
algorithms (QLSAs), ranging from the Harrow–Hassidim–Lloyd (HHL)
algorithm to
quantum singular value transformation (QSVT),
Hamiltonian simulation, and
quantum lattice Boltzmann methods (QLBMs), while emphasizing hybrid
quantum–classical approaches, including
quantum physics-informed neural networks (QPINNs) and amplitude-encoded variational PDE solvers. We focus on hardware-agnostic
algorithms compatible with present noisy processors and emerging fault-tolerant architectures. For each framework, we analyze the mathematical formulation, algorithmic structure, and principal limitations. We also examine tensor-network (TN) representations, since CFD fields, differential operators, and geometrical information can often be encoded efficiently in low-rank form. The TN formalism bridges CFD discretizations and
quantum states, operators, and
circuits, enabling compact representations to be translated into tensor-programmable variational
quantum algorithms (TP-VQAs). We further review
benchmark problems, including Poisson, reaction,
diffusion, and nonlinear model equations, and assess how well
quantum algorithms capture key features of fluid dynamics. Our analysis highlights that potential
quantum advantage is highly problem dependent and governed by condition number, representational complexity, state preparation, and measurement constraints. We outline capabilities, limitations, and challenges toward scalable
quantum algorithms for CFD.
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All authors Mario Guillaume Cecile, Nis-Luca van Hülst, Tomohiro Hashizume, Pia Siegl, Abhishek Setty, José Diogo da Costa Jesus, Paul Over, Sergio Bengoechea, Muhammad Umer, Spyros Tserkis, Eleftherios Mastorakis, Tristan Kraft, Francisco Cárdenas-López, Leonardo Scandurra, Thomas Rung, Felix Motzoi, Belda Yesil, Barbara Kraus, Martin Kiffner, Dimitris G. Angelakis, Eugene de Villiers, Dieter Jaksch
Differential Geometry
Non-Hermitian Yang–Mills Connections with Vanishing Chern Classes
Guangzhen Ren
We construct non-flat non-Hermitian Yang–Mills connections with zero Einstein constant on smoothly trivial rank-two bundles over compact Kähler surfaces, with positive harmonic metrics and stable induced and adjoint holomorphic bundles. Thus vanishing Chern classes do not force flatness even under stability on both sides. The local model comes from a known complex anti-self-dual ansatz; the stable descents yield explicit global
moduli phenomena. For a fixed stable bundle, the Kaledin–Verbitsky map contracts a complex line containing flat and non-flat points in the connected component of the Hermitian–Einstein connection. Contraction also occurs in the two-sided stable locus. The pair of holomorphic projections has a fibre containing both flat and non-flat points. An energy identity and a spectral-gap estimate quantify local flatness with the induced holomorphic structure fixed.
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Computer Vision
Reconstructing the Dynamic World: A Representation-Centric View of 4D Scene Reconstruction
Ziren Gong, Guo Chen, Yongjia Li et al. (+13 more)
4D scene reconstruction aims to recover the evolving geometry, appearance, and motion of dynamic environments from visual observations. Despite substantial progress in neural scene representations, reconstructing dynamic scenes remains challenging due to non-rigid motion, occlusions, temporal inconsistencies, and the trade-offs between reconstruction fidelity and computational efficiency. Recent advances in Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have introduced diverse approaches to representing and reconstructing dynamic scenes, yet their relationships, underlying design choices, and evaluation protocols remain fragmented. In this paper, we present a unified perspective on 4D scene reconstruction, organizing existing methods around their scene representations, temporal modeling strategies, reconstruction pipelines, and optimization objectives. Through this framework, we examine how different design choices affect
geometric fidelity, appearance consistency, motion representation, and computational efficiency. We further consolidate commonly used
datasets and evaluation metrics, identify limitations in current experimental practices, and discuss open challenges in reconstructing complex, dynamic real-world environments. By connecting methodological developments with their underlying assumptions and evaluation evidence, this work provides a structured foundation for understanding existing approaches and identifying future research directions. An evolving collection of relevant papers and resources is available at https://github.com/ZiyangYan/Awesome-4D-Scene-Reconstruction.
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All authors Ziren Gong, Guo Chen, Yongjia Li, Yihua Shao, Fabio Tosi, Stefano Mattoccia, Matteo Poggi, Hao Tang, Fei Ma, Shuyan Li, Ziyang Yan, Nicu Sebe, Ling Shao, Jianfei Cai, Qi Tian, Ming-Hsuan Yang
Quantum Physics
Haah's 3D cubic code thermalizes rapidly
Sebastian Stengele, Libor Caha, Ángela Capel et al. (+1 more)
Haah's 3D cubic code was introduced as a candidate for a self-correcting
quantum memory in three dimensions, using the absence of string logical operators to obstruct thermal errors. It has been a long-standing open question whether it is a self-correcting
quantum memory. Even though the
memory time was explored numerically, no analytical upper bound was known. Here, we settle this question rigorously and show that the cubic code thermalizes rapidly at every positive temperature. Our proof relies on the framework of [Commun. Math. Phys. 407, 195 (2026)] to turn a decay-of-correlations condition into a modified logarithmic Sobolev inequality for the Davies generator. To the best of our knowledge, this is the first analytic proof of rapid mixing for all positive temperatures for any fracton code.
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All authors Sebastian Stengele, Libor Caha, Ángela Capel, Simone Warzel