High Energy Physics (Phenomenology)
Multiboson production at multi-TeV lepton colliders as complementary probes to rare Higgs decays
Dario Buttazzo, Keshav Chaudhary, Roberto Franceschini et al. (+1 more)
We study the potential of very high-energy lepton
colliders to probe effective operators affecting the loop-induced
Higgs boson decays $h\to \gamma\gamma$ and $h\to Z\gamma$, providing constraints complementary to
Higgs coupling measurements at the Large
Hadron Collider. In particular, we consider processes with two and three
bosons in the final state and analyze their individual and combined impact on the effective field theory parameter space. We demonstrate that these processes at a multi-TeV lepton
collider can improve the bounds from rare
Higgs decays and electroweak precision observables, surpassing the projections for High-Luminosity LHC and FCC-ee. This is especially true for triboson processes, whose sensitivity is enhanced by the growth with energy of the new physics cross-sections, making them the dominant constraint at very high energies, potentially competitive with FCC-hh.
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All authors Dario Buttazzo, Keshav Chaudhary, Roberto Franceschini, Arman Korajac
Instrumentation and Methods for Astrophysics
Establishing baseline model performances for optical turbulence forecasting
M. De Sepibus, E. Masciadri, C. Weinberger et al. (+4 more)
Accurate forecasting of optical
turbulence (OT) is useful to optimise
telescope scheduling and maximising observing efficiency, as the performance of adaptive optics systems strongly depends on
turbulence conditions. While various numerical weather prediction and machine learning models are developed to predict these complex
turbulence properties, properly evaluating their added value requires well-defined baseline reference models. This study establishes baseline performance thresholds for key astroclimatic parameters using two distinct models: one forecasting the parameter average over a specific timescale, and the other forecasting the temporal
evolution over the same specific timescale. We apply these frameworks to extensive, multi-year
datasets from the sites of the Very Large
Telescope (VLT) and the Large Binocular
Telescope (LBT) and discuss the possibility of extrapolating reference values of forecast performances for other sites based on their optical
turbulence statistical characteristics. Our results show that baseline predictive accuracy varies according to the observing site, the specific parameter, the chosen timescale, and the forecasting approach. Therefore, an OT forecasting model's accuracy cannot be quantified in absolute terms, but relatively to the baseline.
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All authors M. De Sepibus, E. Masciadri, C. Weinberger, A. Otarola, S. Miesske, C. Veillet, S. Ragland
Data Structures and Algorithms
Sublinear-Time and Semi-Streaming Algorithms for Path Cover and TSP
Sharareh Alipour and Ali Nowzari
We give improved sublinear-time and semi-streaming approximation
algorithms for maximum path cover, $(1,2)$-TSP, and Max-TSP. For each fixed $\eps>0$, our distance-query
algorithm estimates the optimum cost of an $n$-vertex $(1,2)$-TSP instance from above within factor $2-\cstar+\eps=1.413184\ldots+\eps$, with high probability, in $n^{2-\Omega_\eps(1)}$ time, where $\cstar=2(\sec1-\tan1)$. This improves the $3/2+\eps$ factor of direct matching estimation in this subquadratic regime~\cite{BhattacharyaKissSaranurak2023} and of the faster near-linear
estimator~\cite{BehnezhadEtAl2024}. A single random vertex order defines an implicit bipartite graph whose matchings project to path covers. An alternating-permutation analysis shows that its expected maximum matching size is at least $\cstar$ times the optimum path-cover size. On long paths, even the best of polynomially many uniform orders retains only a $\cstar+o(1)$ fraction with high probability. In semi-streaming, we construct deterministic $(5/7-\eps)$-approximate unweighted path covers and $(9/7+\eps+5/(7n))$-approximate $(1,2)$-TSP tours, improving the respective $2/3-\eps$ and $4/3+\eps+1/n$ guarantees in~\cite{AlipourFarokhnejadMoemke2025}. For nonnegative weights, we obtain $2/3-\eps$ for path cover and Max-TSP, improving the earlier asymptotic $7/12-\eps$ Max-TSP bound. These
algorithms use $\poly(1/\eps)$ passes independent of $n$ and $O(n\log(n/\eps)\poly(1/\eps))$ words. We compute two approximate matchings separated by edge deletion. A cycle-compensation lemma accounts for the weight removed when breaking their cycles. For unweighted graphs, a third approximate matching on a simple auxiliary graph with at most $2n$ vertices chooses component connections; a Tutte–Berge count proves the stronger bound.
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Optics
Probing topological edge states in multilayer dielectric photonic nanostructures
Eva Otero, Ivan Toftul, Michael Scalora et al. (+3 more)
Topological photonics has recently attracted a significant
attention, leading to many theoretical studies and predictions. However, direct experimental demonstrations of
topology-driven effects, particularly in nanostructures, are rarely proven. In this work, we experimentally investigate multilayer photonic nanostructures composed of alternating subwavelength dielectric layers, arranged according to the topological Su-Schrieffer-Heeger model. By engineering different edge truncations, we probe different supported topological phases. Our theoretical predictions and experimental measurements reveal strong field confinement at the surface exclusively in the configuration supporting topological edge states, providing their localized character and topological origin. First, we demonstrate enhanced absorption in an ultrathin absorbing overlayer deposited on the surface when the topological surface mode is present, highlighting the potential for surface-sensing applications. Second, we observe enhanced second-harmonic generation in reflection from the surface, demonstrating the potential of topological states for surface enhanced nonlinear optics enabled by the strong localization of the electromagnetic field at the interface.
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All authors Eva Otero, Ivan Toftul, Michael Scalora, Jose Trull, Crina Cojocaru, Yuri Kivshar
Robotics
RoboFQE: Rollout-Free Evaluation of Visual Robot Policies from Offline Trajectories
Yunfu Deng, Sangwoo Shin, Brahma S. Pavse et al. (+1 more)
Evaluating
robot policies before deployment typically requires producing new trajectories through real-world execution, simulators, or learned world models. These rollout-based approaches impose infrastructure requirements; simulator and world-model evaluators additionally face prediction errors that can compound over the evaluation horizon. In this paper, we study off-policy evaluation as an alternative to rollout-based
robot-policy evaluation methods. Specifically, we introduce RoboFQE, an evaluator that estimates the discounted expected return of a target policy from offline
robot trajectories without estimating a behavior-policy density, combining fitted Q-evaluation with a pretrained
vision foundation model and learned terminal success annotation to bridge the gap between standard OPE
benchmarks and continuous-control visual manipulation. Controlled experiments across simulated and real-world manipulation tasks show that RoboFQE produces accurate discounted-return estimates and preserves policy rankings even on long-horizon manipulation tasks where
video world-model evaluators are degraded by autoregressive prediction errors. Ablation experiments show that pretrained
vision encoders substantially outperform from-scratch alternatives, particularly on long-horizon tasks, and that learned terminal success annotation enables evaluation without ground-truth reward labels with only slight reduction of accuracy.
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All authors Yunfu Deng, Sangwoo Shin, Brahma S. Pavse, Josiah P. Hanna
Probability
A Randomized Approximation Algorithm for Doubly Free-Boundary Macdonald Processes
Zhongyang Li
We construct an approximate sampler for finite doubly free-boundary Macdonald rail-yard processes with Macdonald, boundary, and edge parameters in $(0,1)$. The construction allows arbitrary finite sequences of additions and removals of horizontal or vertical strips and all four boundary types. It reverses a finite commutation–reflection history from a partition sequence whose entries are equal, with the common partition weighted by both boundaries. We derive local reconstruction kernels from the skew Cauchy, Littlewood, and branching identities. Bulk and boundary updates combine integer increments with conditional shape sampling at a prescribed size; same-sign swaps use finite conditional resampling. For $K$ reflection periods and an initial partition-size cutoff $M$, the
algorithm terminates almost surely and samples the resulting approximating law exactly. We prove total variation convergence to the target process as $K,M\to\infty$ and give explicit bounds for both truncation errors, decaying geometrically in the cutoffs for fixed input parameters. Under fixed parameter bounds, we also bound the expected operation count of direct enumeration in a unit-cost model. We further construct an exact rejection sampler for the initial partition law without a size cutoff or evaluation of its normalizing constant. Using this sampler removes the size-truncation error, leaving only the error from the finite reflection history.
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Robotics
UniW(A)M: A Unified Mixture-of-Experts Framework for World and World-Action Models
Jingbo Xu, Jianjie Fang, Zhuohang Li et al. (+9 more)
World models (WMs) and world-action models (WAMs) focus on future world prediction and executable action generation, respectively. Although these models learn shared physical dynamics and interaction semantics from embodied trajectories, they are typically trained in isolation, which restricts knowledge reuse between prediction and control. The central challenge is therefore to develop a unified learning framework that shares these dynamics while preserving the distinct modeling capabilities required by each paradigm. We propose UniW(A)M, a mixture-of-experts world-(action) model built upon
diffusion Transformers that unifies action-conditioned world modeling and diverse WAM variants. Shared experts capture common dynamics, while atomic capability experts are reused across interfaces so that each interface keeps its own conditional pathway. At deployment, a plug-in adaptive interface router (PAIR) learns from a small subset of target-task chunk scenarios to select a suitable WAM interface for each execution chunk, balancing task success and execution efficiency while keeping the backbone frozen. We evaluate UniW(A)M on LIBERO, RoboTwin 2.0, and three real-world manipulation tasks, where it achieves 88.3% average real-world success versus 76.7% for the strongest baseline. On WorldArena, it leads action-conditioned world modeling on all three tasks. AC-WM co-training contributes to action generation on every action interface and PAIR raises OOD success from 63.3% to 76.7%.
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All authors Jingbo Xu, Jianjie Fang, Zhuohang Li, Zihao Song, Zhaolu Wang, Xvyuan Liu, Xin Zhang, Haisheng Su, Yinzhou Tang, Chen Gao, Wei Wu, Yong Li
Computer Vision
CryoTracer: A Synthetic-to-Real System for Filament Instance Tracing in Cryo-EM
Radosław Kuczbański, Wojciech Kozłowski, Mateusz Grzesiuk et al. (+4 more)
Particle picking in cryo-electron microscopy is commonly formulated as an object-detection problem in which individual particles are represented by bounding boxes, a natural representation for globular particles but a poor match for filaments that can extend across an entire micrograph. Existing methods reconstruct filament trajectories by linking local predictions in a separate post-processing stage, decoupling detection from instance formation and creating potential ambiguities at gaps and crossings. Directly preserving filament identity is particularly useful for polymorphic specimens, where segments originating from the same filament should remain associated. However, learning such instance-level representations is challenging because expert centerline annotations are scarce and costly to obtain. We introduce CryoTracer, a synthetic-to-real framework that addresses this limitation through procedural micrograph generation,
transformer-based polyline prediction, and specimen-specific fine-tuning. Procedural pretraining exposes the model to diverse filament geometries, intersections, and imaging conditions before adaptation using a limited number of fully annotated experimental micrographs. CryoTracer represents filaments as ordered centerline polylines and optimizes their geometry using an orientation-invariant discrete Fréchet objective, enabling consistent learning regardless of tracing direction. Evaluation on established and newly expert-annotated
datasets shows that CryoTracer improves instance-level filament recovery over existing particle-detection approaches. These results establish end-to-end polyline prediction as a practical alternative to detection-and-linking pipelines for recovering complete filament instances with limited micrograph annotation budgets. Code and data are available at https://github.com/cryotracer/cryotracer
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All authors Radosław Kuczbański, Wojciech Kozłowski, Mateusz Grzesiuk, Konrad Karanowski, Lukas Frey, Andrzej Rzepiela, Maciej Zięba
Machine Learning
A Refresh Interval Does Not Fully Specify a Refresh Schedule: Learning-Rate-Dependent Placement in Kronecker-Preconditioned Language Model Training
Xinyu Wang, Zhiwei Zhai, Yu Gu et al. (+6 more)
Kronecker-preconditioned optimizers recompute a cached
curvature eigendecomposition only periodically, and implementations and adaptive methods control this through the refresh frequency. We show that frequency leaves part of a schedule free: at a fixed number of refreshes, when they occur changes held-out loss at the same compute. Training GPT-style models with the Mousse optimizer across model sizes, we move the middle refreshes of a four-refresh schedule while fixing the first and last. On FineWeb the two placements exchanged rank as the learning rate rose (later placement was better at low rates, earlier placement at high ones), so a single-learning-rate comparison can report either ranking. The reversal held under controls including the published optimizer internals, and placement still mattered at the published refresh density. The learning rate at which the preference flips fell with model size, consistent with a held-out smaller model, and shifted with training length. A spectral-width staleness score predicted the same ranking at every learning rate and missed the reversal. Refresh schedules should be reported in full and compared across the learning rates at which they will be used.
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All authors Xinyu Wang, Zhiwei Zhai, Yu Gu, Zijun Yu, Sicheng Lyu, Jingrui Tian, Lei Ding, Ziyang Song, Xiao-Wen Chang
Computer Vision
GeoReader: Learning Latent Geometry for Spatial Reasoning in Vision-Language Models
Rong Li, Shijie Li, Jiangyong Huang et al. (+7 more)
Vision-
language models (VLMs) struggle with metric and multi-view relationships that remain implicit in RGB observations. Geometry foundation models recover this structure from RGB, but existing methods either fuse the estimated features with visual
tokens and learn to use them from question answering alone, or align VLM latents with geometry features. Capturing geometry, however, does not ensure its use: zeroing the aligned latents of a released feature-
alignment model leaves its accuracy unchanged. We introduce GeoReader, a framework that learns
geometric representations for the VLM that reads them. First, we train lightweight
Geometric Readers to produce continuous input
tokens, supervising them with geometry descriptions decoded by a frozen VLM, so that the Readers express geometry in a form the target model can already read. We then freeze the Readers to preserve this interface while fine-tuning the VLM on spatial QA, with geometry injection conditioned on the question. Geometry descriptions serve only as training targets: at inference, the VLM answers directly from the continuous
geometric tokens without generating a geometry chain of thought. With geometry predicted from RGB images, our approach reaches 94.3 on MindCube-Tiny with Qwen3-VL-4B and, with a multi-source QA mixture, an average of 72.5 across six additional spatial
benchmarks with Qwen3.5-9B. These results suggest that a useful
geometric representation is one the receiving model can use, not merely one from which geometry can be reconstructed.
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All authors Rong Li, Shijie Li, Jiangyong Huang, Lingdong Kong, Jiaming Zhou, Dongyue Lu, Zeying Gong, Baoxiong Jia, Junwei Liang, Siyuan Huang
Information Theory
On Han- and Shearer-Type Information Inequalities: Equality Conditions and Combinatorial Applications
Gunank Jakhar, Gowtham R. Kurri, Suryajith Chillara et al. (+1 more)
Madiman and Tetali established strong and weak fractional inequalities that subsume classical
entropy subset inequalities such as Han's inequality and Shearer's lemma. In this work, we derive a decomposition of the gap in the strong fractional inequality and use it to establish the necessary and sufficient conditions for equality in the strong form of Han's inequality. As a consequence, we characterize the equality conditions for a strong Loomis-Whitnet-type projection inequality of Balister and Bollobas (2012). Finally, we apply the weak fractional inequality to an extremal graph theory problem, recovering and extending results of Sason (2022) and Boucheron et al. (2013), whose approaches rely on Han's inequality.
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All authors Gunank Jakhar, Gowtham R. Kurri, Suryajith Chillara, Vinod M. Prabhakaran
General Topology
Chain Continuity and Scott-Closed Interpolation
Yuxu Chen
We prove that every chain-continuous dcpo is continuous and that its way-below relation is the relational square of its chain-below relation, answering a question of Erné. A subalgebra
lattice obtained from a locally finite Jónsson
algebra shows that the chain-below relation itself need not interpolate. For approximation by Scott-closed sets, we give a finite-upper-bound characterization on lower-set
lattices and characterize interpolation by an extension property for directed lower sets. This yields a countably based, completely distributive algebraic
lattice on which chain and directed approximation coincide, whereas Scott-closed approximation fails to interpolate, answering questions of Erné and of Ho and Zhao.
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Computer Vision
Orientation Estimation of Cryo-EM Images for Molecules with Arbitrary Symmetry by Semidefinite Programming
Yunpeng Shi, Amit Singer, Jinxin Wang et al. (+1 more)
This paper extends the non-unique games framework introduced in Bandeira et al. (2020) to address symmetric structures. Precisely, we consider the problem \[ \begin{aligned} \underset{g_1,\ldots,g_n\in \mathcal{G}}{\operatorname{min}}\,\, \frac{1}{|S|}\sum_{i,j=1}^n \sum_{s\in S} f_{ij}(g_i s g_j^{-1}), \end{aligned} \] where $\mathcal{G}$ is a compact group, $S\subset \mathcal{G}$ is a finite subgroup of
symmetry elements, and $f_{ij}:\mathcal{G}\rightarrow \mathbb{R}$ is a loss function. A notable example is common lines based orientation estimation of cryo-EM images for molecules with point group
symmetry $S$, where $\mathcal{G}$ is the rotation group $\operatorname{SO}(3)$. With a semidefinite programming relaxation, we turn the problem into a convex program that is solved with the alternating direction method of multipliers. Unlike existing approaches which are tailored to specific
symmetry groups, ours provides a unified computational framework for addressing all point group
symmetries. We demonstrate the applicability of the proposed
algorithm on the cryo-EM orientation estimation problem on both simulated and experimental
datasets. In particular, our
algorithm is effective in generating high-quality ab initio models from only a few class averages computed from experimental images.
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All authors Yunpeng Shi, Amit Singer, Jinxin Wang, Ruiyi Yang
Computer Vision
Just Respect the Semantics: Image-Token Compression via Pre-LLM Semantic Variable-Resolution Tokenization and In-LLM Relevance Pruning
Tianhao Fu, Austin Wang, Charles Chen et al. (+5 more)
Image-
token compression must reduce computation without hurting the semantics. We introduce Varest, guided by semantic fungibility:
tokens within a coherent semantic region can be summarized coarsely, while object boundaries and literal text require finer distinctions. Object and text localizers guide variable-resolution merging, preserving spatial coverage and finer detail at boundaries and text. Learned [REL] registers then prune visual
tokens after early decoder processing. Training across budgets enables one checkpoint per backbone. Across three backbones and 13
benchmarks, Varest outperforms 15 baselines in mean performance retention at every evaluated budget, retaining 86.10-92.33% of full-
token performance with only 5.56% of the original layer-averaged image-
token count.
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All authors Tianhao Fu, Austin Wang, Charles Chen, Roby Aldave-Garza, Victor Xie, Xiyue Jiao, Yucheng Chen, Martin Tin
Rings and Algebras
Classification of $3$-dimensional unital commutative algebras
Maqpal Eraliyeva and Ivan Kaygorodov
We present the complete classification of non-isomorphic complex $3$-dimensional unital commutative
algebras. Every such
algebra decomposes canonically as $\mathbb Ce_1\oplus V$, where $e_1$ is the unit and $V$ is the kernel of the trace form; the induced traceless $2$-dimensional commutative
algebra on $V$ is one of four
algebras, and the classification reduces to the classification of symmetric bilinear forms on $V$ up to the automorphisms of this kernel. The answer consists of six
algebras, three one-parameter families, one two-parameter family and one three-parameter family.
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Computer Vision
RSIgen: Reciprocal Self-Improvement for Agentic Visual Generation
Yidi Liu, Haozhe Wang, Jie Xiao et al. (+3 more)
Agentic visual generation produces experience that can improve the native understanding (
agent) and generation roles of a unified
multimodal backbone, but task success alone does not determine which role should learn. Generator updates can also make the
agent's action supervision stale. We introduce RSIgen, a task-centric reciprocal self-improvement framework linking learning allocation, supervision refresh, and recursive curriculum construction. Dynamic Responsibility Routing (DRR) uses matched interventions on evidence, inputs, and supported actions to select learning recipients and construct compatible training targets. After generator learning, RSIgen re-evaluates the relative utility of tested actions with branch inputs held fixed to refresh
agent supervision; the retained roles then construct the next curriculum. Across BAGEL, Qwen-Image, and Wan-Image-7B, RSIgen improves AgentGen-Bench Full Overall9 by 3.2–3.3 points over a three-cycle SearchGen adaptation with action costs. Wan controls support allocation gains at matched role-level training exposure, refresh gains at a matched adaptation budget, and benefits from updated curriculum sources while recipients continue learning. In blind evaluation on 200 real-user requests, pooled tie-adjusted preference scores against the same baseline reach 70.0% for task completion and 64.9% for visual quality.
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All authors Yidi Liu, Haozhe Wang, Jie Xiao, Kai Zhu, Xueyang Fu, Zheng-Jun Zha
High Energy Astrophysical Phenomena
The compact jets of the black hole X-ray binary Swift J1753.5-0127 during its 2023-2024 outburst
Noa Grollimund, Stéphane Corbel, Lauren Rhodes et al. (+14 more)
X-ray binaries are excellent laboratories to study the coupling between
accretion and ejection phenomena around compact objects through multiwavelength observations. Swift J1753.5-0127 is a black hole low-mass X-ray binary discovered in outburst in 2005 that remained active for 11 years, followed by two mini-outbursts. We investigate the behavior of the compact jets of Swift J1753.5-0127 during its 2023-2024 outburst. We report a dense multiwavelength monitoring in the
radio, NIR/optical/UV and X-ray bands. Using new and archival luminosity measurements, we construct a refined, smoother
radio/X-ray plane that covers the steep/outlier and shallow/transition branches of the
radio/X-ray correlation. We also present spectral energy distributions (SEDs) constructed using quasi-simultaneous observations from four epochs. Swift J1753.5-0127 underwent a faint, hard-only, double-peaked outburst, followed by a re-brightening in the optical. The updated
radio/X-ray plane offers a clear view on the region occupied by this source, similar to MAXI J1348-630, but at lower
radio luminosities than H 1743-322, and we confirm its hybrid behavior. The broad-band SED fitting reveals that a cool, truncated
accretion disk dominates the NIR/optical/UV band. We find that the compact jets have a variable but weak contribution in the NIR, with a break frequency $\nu_\mathrm{break} \lesssim 5 \times 10^{12}~\mathrm{Hz}$, providing additional evidence that
radio-quiet sources are also infrared-quiet. We also find evidence that the
radio spectral index evolves with X-ray luminosity, transitioning from inverted values near quiescence to flatter or negative values at higher luminosities. In the context of adiabatic jet models, this trend may indicate that the jet geometry becomes increasingly confined as the
accretion rate rises.
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All authors Noa Grollimund, Stéphane Corbel, Lauren Rhodes, David M. Russell, M. Cristina Baglio, Francesco Carotenuto, Tristan Bouchet, Payaswini Saikia, Rob P. Fender, Sara E. Motta, Piergiorgio Casella, Kevin Alabarta, Fraser Lewis, Andrew K. Hughes, Fraser J. Cowie, Justine Crook-Mansour, Xian Zhang
Computer Vision
RadarGSOcc: Doppler-Aware 3D Semantic Occupancy Prediction Using 4D Radar Tensor with Geometric-Semantic Feature Extraction
Christofel Rio Goenawan, Dong-Hee Paek and Seung-Hyun Kong
3D semantic occupancy prediction is crucial for autonomous driving, but camera-based methods degrade in low-light conditions and
LiDAR-based methods are sensitive to adverse weather. 4D radar is more robust, yet its sparsity and the high cost of radar tensor processing limit its use. We propose Radar
Geometric-Semantic Occupancy Prediction (RadarGSOcc), an efficient framework that directly processes 4D radar tensors by separately extracting
geometric and semantic features, including Doppler information, with a deformable
attention-based
geometric refinement module as its key component. We also introduce the K-Radar 3DSemOcc
benchmark, the first large-scale
benchmark for 3D semantic occupancy prediction using 4D radar tensors, built on the K-Radar
dataset with refined
LiDAR-based annotations. RadarGSOcc predicts 15 classes within a 51.2m range and achieves 38.2% IoU, 7.6% mIoU, and 25.5% weighted mIoU, outperforming RadarOcc by 17% relative (25.5 vs. 21.9) in weighted mIoU while using fewer parameters and lower computation. It substantially improves truck prediction while trading off car accuracy, outperforms camera-based approaches, and narrows the gap with
LiDAR-based methods. To assess robustness without per-frame ground truth in adverse weather, we further propose a novel proxy metric, Environment-Based KL Divergence (EBKL), under which 4D radar proves a reliable and efficient modality for 3D semantic occupancy prediction. The
dataset and code will be publicly available.
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High Energy Physics (Experiment)
Probing the proton structure with minimum bias and low-pt dimuon events at hadron colliders
Kyle Cormier
This paper presents two methods for probing the
proton structure and the number of partonic interactions in
proton-
proton collisions using dimuon production and minimum bias events. At the LHC these targeted measurements can provide information on the average number of partonic scatters in different event classes, their dependence on the partonic momentum fraction, x, and potentially partonic flavour. Within a simple mostly
geometric model of multiparton interactions as quasi-independent scatters, the measurements can be used to make inference about the
proton-
proton overlap dependence on impact parameter and its partonic-x dependence.
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Artificial Intelligence
Knowing the User Is Not Knowing What We Established: Grounding Awareness in Personal Agents
Jiajun Chen, Xinyue Chen, Yicheng Jiang et al. (+1 more)
Personal-
agent memory has largely focused on learning what users know, prefer, and want. However, knowing the user does not necessarily mean knowing what the user and the
agent have established together. An assistant's proposal should not become an authorized instruction merely because it is remembered. We introduce Grounding Awareness, the ability to distinguish accepted, unacknowledged, revised, and scoped arrangements when selecting actions. We develop a controlled
benchmark to test whether
agents can tell the difference between proposals users have accepted and those they have not, and whether they apply accepted arrangements only where appropriate. Across multiple
agents and
memory systems, we find that strong
memory performance does not ensure Grounding Awareness. More surprisingly, providing more complete user information can increase execution of unacknowledged proposals, while persistent
memory can cause
agents to treat these proposals as if they were user instructions. We trace these failures through
memory writing,
retrieval, and action selection. Finally, we introduce Grounding Layer, a lightweight, training-free extension that explicitly tracks grounding status and checks consequential actions, substantially improving
agent behavior without modifying models or
memory systems.
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All authors Jiajun Chen, Xinyue Chen, Yicheng Jiang, Hua Shen
Machine Learning
ProAS: A Progress-Aware Advantage Shaping Framework for Multi-Turn Agentic Reinforcement Learning
Bowen Ding, Mingda Wan, Yuhan Chen et al. (+7 more)
Reinforcement learning (RL) has emerged as the primary paradigm for optimizing Large
Language Model (LLM)
agents, yet efficient critic-free
algorithms (e.g., GRPO) suffer from a fundamental uniform credit assignment dilemma that broadcasts trajectory-level advantages equally to all actions. Our analysis reveals that this uniformity inherently reinforces structurally invalid actions in preferred trajectories and ignores the downstream cascading influence of early pivotal deviations in unpreferred ones. To resolve this without introducing critic overhead, we propose Progress-Aware Advantage Shaping (ProAS), a plug-and-play framework that leverages a dynamically evolving Agentic Reference Graph (ARG) to achieve fine-grained credit assignment via Masking Unreasonable Incentives (MUI) and Asymmetric Penalty Redistribution (APR). Extensive experiments across Qwen2.5-1.5B-Instruct and Qwen2.5-7B-Instruct demonstrate that ProAS universally elevates mainstream group-based baselines to new state-of-the-art performance, achieving peak success rates of 91.9% and 94.3% on ALFWorld, alongside 66.7% and 70.8% on WebShop, respectively. Furthermore, it accelerates training convergence by up to $2.44\times$ while maintaining strong robustness against imperfect or distilled ARGs. Codes: https://github.com/LINs-lab/ProAS.
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All authors Bowen Ding, Mingda Wan, Yuhan Chen, Jiyao Yuan, Qi Zhu, Futing Wang, Heyuan Deng, Fei Mi, Lifeng Shang, Tao Lin
Computational Engineering
Physics-Informed Deep Learning in Oncology
Scindhiya Laxmi, Vivek S. Yadav and Ameya D. Jagtap
Deep learning has enabled substantial advances in oncology, including cancer detection and diagnosis, medical image analysis, tumor characterization, treatment planning, response assessment, and outcome prediction. However, conventional data-driven models often require large annotated
datasets, exhibit limited generalization across heterogeneous clinical settings, and lack explicit mechanisms for incorporating the physical, physiological, and biological principles underlying cancer development and treatment. Physics-informed deep learning (PIDL) integrates governing physical laws, mechanistic models, physiological constraints, and domain knowledge into deep learning frameworks, providing a principled approach for learning from sparse, noisy, and heterogeneous oncological data. This review presents a task-oriented synthesis of PIDL methods across oncology, encompassing neural networks, Kolmogorov-Arnold networks, convolutional and
vision architectures, neural operators, generative models, and hybrid physics-data-driven frameworks. Their applications are examined across cancer detection and diagnosis, image reconstruction and analysis, tumor characterization, quantitative imaging, tumor progression, treatment response, radiation therapy, treatment planning, and
multimodal clinical modeling. Based on the synthesized methodological and application-level evidence, we develop task-optimized PIDL workflows that align the clinical task with appropriate data representations, physical constraints, model architectures, and validation strategies. We further analyze key challenges, including physical-model fidelity, computational scalability, uncertainty quantification, domain generalization, standardized benchmarking, and clinical validation.
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Quantum Physics
Explicitly correlated ground states with a feedback-free variational hybrid algorithm
Benjamin Corbett and Akimasa Miyake
Explicitly correlated methods, which represent dominant short-range correlations by a diagonal Jastrow-type correlator, are designed to accurately describe systems of strongly correlated electrons, such as the fully periodic 2D Fermi–Hubbard model. However, as the reference state is compact in momentum space whereas the correlator acts naturally in real space, there is a fundamental mismatch of bases that classical
algorithms resolve only by restricting the reference or the correlator. Here we propose a hybrid
algorithm in which a
quantum processor prepares a low-
entanglement momentum-space
matrix product state (MPS), precomputed classically by the transcorrelated method at the cost of a nonvariational energy, rotates it to real space, and measures in bases fixed by the
Hamiltonian. Because the measurement bases are independent of the correlator, the correlator can be variationally optimized entirely in classical post-processing with no
quantum-classical feedback loop. We demonstrate our
algorithm on 32
qubits of Quantinuum's H2-2 with a bond-dimension-4 MPS for the half-filled $4 \times 4$
lattice and achieve a relative ground-state energy error of $16 \pm 4\%$ after postselection, reduced to $3.5 \pm 0.5\%$ by calibration using low-order moments from the MPS. Compared with other feedback-free hybrid
algorithms, even under generous assumptions, our method requires fewer samples than
quantum-selected configuration interaction and fewer two-
qubit gates than
quantum Krylov methods.
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Algebraic Geometry
Cominuscule Richardson varieties
Anders Skovsted Buch
A Richardson variety in a cominuscule flag variety is defined by a skew shape of boxes. We prove that any cominuscule Richardson variety has a unique decomposition as a product of indecomposable cominuscule Richardson varieties, which are given by the connected components of the skew shape. We also prove that the first Chow group and the Picard group of a cominuscule Richardson variety are dual to each other; both are free abelian groups of rank equal to the number of indecomposable factors. Along the way, we construct an explicit desingularization of any cominuscule Richardson variety of classical Lie type as a tower of (isotropic) Grassmannian bundles. Our results imply that the
Hilbert polynomial of the minimal ample line bundle is a well-defined
geometric invariant of any cominuscule Richardson variety. We give a positive formula for this
Hilbert polynomial.
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Human-Computer Interaction
Communicating Uncertainty in AI-Supported Nurse Scheduling: Expert Co-Design and Evaluation of an Explainable AI Prototype
Alice Rollwagen, Emily Schiller, Anton Hummel et al. (+3 more)
Nurse scheduling is a complex optimization problem in which nurses are assigned to shifts to meet staffing demands while complying with working-time limits, qualification requirements, and other operational and regulatory constraints. Artificial intelligence is increasingly being used to support this process by forecasting future staffing needs and providing advice on how to improve shift scheduling. Effective human-AI collaboration in nurse scheduling requires users to understand, interpret, and appropriately act on AI forecasts and advice. However, it remains insufficiently understood how healthcare professionals use transparency mechanisms like uncertainty communication and AI advice explanations when making staffing decisions. Applying a human-centered design approach, we iteratively developed a prototype decision support system for nurse scheduling and evaluated it through a mixed-methods user study with expert users. Using thematic analysis, questionnaire data, and eye tracking, we examine how healthcare professionals interact with the system and how uncertainty communication and explanations of AI advice influence understanding, decision confidence, trust, and usability. We found that the uncertainty communicated in AI forecasts was frequently misinterpreted and reduced decision confidence. Explanations were generally perceived positively, but did not significantly affect decision confidence or advice acceptance. We conclude with eight design implications derived from our findings, providing insights for the design of more transparent and effective AI-supported decision support tools in healthcare.
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All authors Alice Rollwagen, Emily Schiller, Anton Hummel, Kathrin Ebertsch, Andreas Riener, Jan-Philipp Steghöfer
Game Theory
CLS Hardness of 2D Min-Max Optimization with Product Constraints
Alkis Kalavasis, Panagiotis Patsilinakos, Stratis Skoulakis et al. (+1 more)
We study the complexity of computing approximate first-order Nash
equilibria in min-max optimization problems with one dimensional min and max players and product constraints. For black-box access to the objective, we prove that any
algorithm requires $\Omega(1/\sqrt{\epsilon})$ queries to find an $\epsilon$-first-order Nash point. In the white-box setting, we establish CLS-hardness by reducing from End-of-Potential-Line. These results show that even the simplest min-max setting with one dimensional min and max players is at least as hard as CLS and therefore does not admit
polynomial-time
algorithms unless $\mathsf{CLS}\subseteq \mathsf{P}$.
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All authors Alkis Kalavasis, Panagiotis Patsilinakos, Stratis Skoulakis, Manolis Zampetakis
Machine Learning
Model-Based Online Decision Making via Generative Trajectory Planning
Haldun Balim, Yilun Du and Na Li
Online decision-making faces a tension between efficient inference and long-horizon
reasoning. Value-based methods rely on short-horizon bootstrapping that limits the propagation of long-term information, while trajectory optimization methods plan explicitly but must solve a high-dimensional optimization problem at every decision step. We propose Generative Trajectory Planning (GTP), a model-based framework that addresses this tension by learning a generative model as a proposal distribution over action sequences. Instead of optimizing sequences from scratch, GTP samples candidate sequences from a
diffusion prior and refines them using learned dynamics, reward, and value models, enabling efficient trajectory-level
reasoning. Across continuous control and long-horizon manipulation tasks, GTP matches or exceeds state-of-the-art model-free,
diffusion-policy, and model-based baselines, and remains stable in regimes where strong baselines degrade. Our results suggest that generative models are best understood not as policies, but as structured proposal distributions for planning, offering a new perspective on how to integrate generative modeling with model-based decision-making.
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Group Theory
Constructions and automorphisms of quandles
Valeriy G. Bardakov and Birama Sangare
We prove that if M is an abelian group and X = Alex(M, phi) is a Latin Alexander quandle with id - phi in Aut(M), then Aut(X) is isomorphic to M semidirect C_Aut(M)(phi), Inn(X) is isomorphic to M semidirect <phi> and Out(X) is isomorphic to C_Aut(M)(phi)/<phi>. As a consequence, we obtain a description of the automorphism groups of direct products of the cyclic commutative medial quandles C_{2n+1} and the dihedral quandles R_{2n+1}, and we find their orders. Furthermore, we correct a result of Bardakov-Fedoseev [
Algebra and Logic 63 (2024), no. 2, 111-142] on multiplication of quandles and prove that the Borodin-Neshchadim-Simonov and Markhinina-Nasybullov quandles can be constructed from conjugation, core, and generalized Alexander quandles, using the operation of multiplication of quandles. Finally, we consider the quotients of a quandle Q, namely the medialization Q^med, the commutativization Q^com, and the trivialization Q^tr. We prove that the conjugation quandle Conj(G) is medial if and only if gamma_2(G) <= Z(G); we find bounds for |Conj(G)^med| and prove that Core(G)^com is isomorphic to Core(G/<<g^3>>). We describe commutative generalized Alexander quandles and, as a consequence, find the commutativization of the dihedral quandle: R_n^com is isomorphic to R_gcd(n,3).
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Optimization and Control
Optimal Decay Matrix Control Barrier Functions: Soundness and Completeness for Matrix Safety Constraints
Samuel G. Gessow, Albert Wu, Aaron D. Ames et al. (+1 more)
Many safety requirements, such as keeping a manipulator away from kinematic singularities, a network connected, or a system observable, amount to keeping a state-dependent
matrix positive definite.
Matrix control barrier functions (MCBFs) enforce such requirements with a semidefinite program that minimally modifies a nominal control input. The MCBF constraint bounds how fast the safety margin, the smallest
eigenvalue of the
matrix, may decay. Yet a fixed decay rule is conservative, rejecting safe controls that drive the state quickly toward, but never across, the boundary of the safe set. Scalar optimal decay (SOD) addresses this issue by scaling the decay term by a scalar weight that is optimized together with the control. For scalar barrier functions, SOD then rejects a control at the boundary only if the control would decrease the margin. For MCBFs, however, SOD can reject controls that leave the margin unchanged, and near the safety boundary SOD accepts such controls only at a high cost. We introduce
matrix optimal decay (MOD), which replaces the scalar weight with a
matrix multiplier while keeping the semidefinite program convex. MOD is provably sound and complete, certifying at the boundary exactly the controls that do not decrease the margin. If some control can strictly increase the margin at every boundary point, the MOD-MCBF filtered controller is continuous and renders the safe set forward invariant. We also add an optional recovery condition that drives the system toward the safe set after a violation without affecting soundness or completeness. On a planar two-link arm avoiding singularities, MOD-MCBF certifies safe motions that both a fixed decay rule and SOD reject, and tracks an end-effector target more closely than SOD.
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All authors Samuel G. Gessow, Albert Wu, Aaron D. Ames, Brett T. Lopez
Computation and Language
From Relation-Specific Heads to Shared Interventions: Contextual Entrainment in Language Models
Jinghan Zheng and Jiajun Shi
Large
language models rely on
prompt context to perform in-context learning, follow formats, and adapt to local evidence. This ability, however, also raises a robustness concern: models may reuse irrelevant
tokens from earlier context when answering a later query. Prior work identifies this behavior as contextual entrainment and finds
attention heads whose ablation reduces it, arguing that these heads are largely task- or relation-specific. But are the mechanisms behind contextual entrainment truly isolated by relation, or do relation-conditioned searches recover reusable components shared across relations? Using factual-distractor relation-completion
prompts across 19 lexical-semantic subtypes, we identify
attention heads whose ablation reduces entrainment and test how the resulting head sets transfer recovery ability across exact relations, semantic families, and mixed relation distributions. Same-family transfer exceeds different-family transfer, family residuals weaken once globally shared heads are removed, and subtype-specific searches repeatedly rediscover many global heads. Clean-completion and context-use probes further suggest that this shared intervention can reduce harmful copying while preserving ordinary relation completion and simple in-context abilities. Our findings provide a more precise perspective on contextual entrainment and its mitigation: harmful context following is not best addressed by searching for isolated relation-specific
circuits, but by using heterogeneous attack
prompts to reveal reusable intervention targets whose effects are shaped by semantic relation structure.
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