Optics
Near-field Ultrafast Nondiffractive Laser Nanoscale Cutting of Glass with Quasi-null Interface Roughness
Rajeev Dwivedi, Anton Rudenko, Guodong Zhang et al. (+5 more)
Generally, ultrafast
laser glass dicing is governed by explosive material removal, which restricts the achievable surface roughness. Here we report single-step dicing of aluminosilicate glass with residual interface roughness below 5 nm, producing optical-quality surfaces without post-processing, using near-field-enhanced 0th-order Bessel-Gauss beams. This exceptional interface quality cannot be explained by a conventional
laser ablation mechanism, such as phase explosion, melt expulsion, or fracture-dominated material removal. Instead, quantitative analysis of local thermal dynamics indicates that material removal is consistent with a Hertz-Knudsen-type evaporation process, enabling layer-by-layer removal with precision approaching a few molecular layers. These results suggest that nanoscale optical confinement fundamentally alters the response of transparent materials to ultrafast
laser irradiation and establish a route toward deterministic
laser processing with near-atomic precision.
Read the full abstract ▾
All authors Rajeev Dwivedi, Anton Rudenko, Guodong Zhang, Xxx Sedao, Yaya Lefkir, Ciro D'Amico, Guanghua Cheng, Razvan Stoian
Computation and Language
AdaGEPA: Adaptive Feedback Allocation for Reflective Prompt Optimization
Junyang Chen, Zecheng Wang and Jingbang Chen
Prompt optimization improves the performance of
language-model systems on downstream tasks by refining their
prompts. Classical methods evaluate
prompts on task examples and use the resulting feedback to guide
prompt revisions through reflection. However, when feedback selection does not account for the
prompt's weaknesses, these revisions may improve performance on selected examples without yielding broader task improvements. To address this issue, we propose AdaGEPA, an adaptive feedback-allocation method that uses the
prompt's performance and task structure to select examples for the next
prompt revision. Our method replaces at most one example in each feedback minibatch to target an identified weakness while preserving the remaining feedback context. Across our main experiments on six downstream
benchmarks, AdaGEPA achieves higher mean validation scores than non-adaptive feedback selection under matched rollout budgets. AdaGEPA also finds high-performing
prompts earlier across several tasks. In the initial Schema-Guided Dialogue (SGD) study, its half-budget
prompts outperform the non-adaptive baseline's full-budget
prompts in joint goal accuracy on new dialogues from services seen and unseen during search. Overall, our findings highlight the potential of adaptive feedback allocation to improve both the effectiveness and rollout-budget efficiency of reflective
prompt optimization.
Read the full abstract ▾
Computer Vision
Super-Resolving Unseen Hyperspectral Sensors at Any Scale via Spatial Operators
Ji-Xuan He, Guohang Zhuang, Bo Junge et al. (+6 more)
Achieving cross-sensor generalization and arbitrary-scale reconstruction with a single model remains challenging in hyperspectral super-resolution (HSR). Although recent methods support arbitrary-scale reconstruction, applying them to new sensors or scales beyond the training range often requires additional data and computation to maintain reconstruction quality. To address these challenges, we propose OmniHSR, which predicts band-shared spatial operators rather than spectral values. Cross-Spectral Mapping (CSM) resamples inputs with any number of bands to fixed reference positions and predicts local operators with Gaussian supports. Continuous Operator-Field Reconstruction (COFR) composes these operators into a continuous field and applies them to all original bands for arbitrary-scale reconstruction. Experiments demonstrate that operator prediction outperforms direct spectral-value prediction on all seven
datasets. Trained solely on ARAD with only 0.538M parameters, OmniHSR outperforms all directly transferred baselines on six unseen
datasets without target-domain training data or adaptation. Across twelve upsampling factors from $\times2$ to $\times48$, it improves average PSNR on Pavia U and Chikusei by 0.55 dB over the strongest baseline. It also surpasses baselines trained from scratch or adapted on the target sensor and achieves up to $36\times$ faster inference. Our code will be publicly released soon.
Read the full abstract ▾
All authors Ji-Xuan He, Guohang Zhuang, Bo Junge, Tingyi Li, Lingchen, Miaomiao Cai, Yanan Qiao, Xiujin Liu, Junfeng Fang
Astrophysics of Galaxies
High-Priority Compact-Object Candidates in Young Clusters from the Gaia DR3 Non-Single-Star Catalog
Dejian Liu, Kangteng Chang, Yao Huang et al. (+7 more)
The search for massive compact objects such as black holes (BHs) or
neutron stars (NSs) in
star clusters is longstanding challenge in astrophysics, yet no definitive identification of such compact objects in young clusters has been reported. In this Letter, we construct a sample of high-priority candidates for follow-up investigation. By cross-matching the cluster catalog with the
Gaia Non-Single-
Star database and estimating companion masses, we identify 13 systems with companion masses exceeding 1.4~$M_{\odot}$, three of which have masses above 2.1~$M_{\odot}$. Applying our newly developed Astrometric Mass-Ratio Function (AMRF) classification based on cluster-specific isochrones, we identify three systems whose companions cannot readily be explained by a single main-sequence
star or an unresolved close binary. These systems have inferred companion masses exceeding 2.1~$M_{\odot}$, suggesting that they may be high-mass compact-object candidates. Joint
Gaia and radial velocity orbit fitting for a comparison subset yields companion masses broadly consistent with the astrometric estimates, supporting the use of our framework for target prioritization. These unconfirmed systems are valuable targets for multi-epoch spectroscopy, high-resolution imaging, and future
Gaia epoch astrometry.
Read the full abstract ▾
All authors Dejian Liu, Kangteng Chang, Yao Huang, Yingjie Li, Xianjin Shen, Zehao Lin, Jingjing Li, Zhenyu Wu, Jianrong Shi, Ye Xu
Computer Vision
CoVisco: Codec-Native Vision Encoder with Native Token Compression for Unified Image-Video Understanding
Yulong Liu, Xiaotian Han, Junyuan Shang et al. (+6 more)
Vision-
language models face a fundamental scaling bottleneck: the number of visual
tokens grows with both temporal duration and spatial resolution, making long-
video understanding expensive for the
vision encoder and the
language model. Existing methods often compress visual
tokens after dense encoding, creating a mismatch between the representation used during training and the compact interface required at deployment. We present CoVisco, a codec-native
vision encoder with native
token compression for unified image-
video understanding. By combining codec-native input support with segmented
attention, CoVisco can encode long visual inputs in a single forward pass without forming dense patch-to-patch interactions across all frames. Each temporal segment is equipped with learnable abstract
tokens that learn a compact segment-level representation, while fine-grained patch
tokens remain available throughout the encoder. Alternating intra-segment and abstract-communication layers preserve
video-level context through the abstract-
token channel. A lightweight selector further exposes either abstract
tokens alone or abstract
tokens augmented with a runtime-selected subset of patch
tokens, yielding a compact visual interface that reduces the visual context and prefill burden of downstream MLLMs while retaining fine-grained evidence when needed. Pretrained with contrastive objectives on 565M image–text pairs and 6.4M
videos, CoVisco shows competitive performance on
video-oriented
embedding and
multimodal understanding
benchmarks. In the evaluated four-segment, 64-frame setting, abstract-only inference uses only 400 visual
tokens while achieving
video-understanding performance close to, and on some
benchmarks exceeding, OneVision-Encoder. Selected patch
tokens further improve fine-grained
video reasoning. Project URL: https://github.com/ernie-research/CoVisco.git
Read the full abstract ▾
All authors Yulong Liu, Xiaotian Han, Junyuan Shang, Yuchen Ding, Zhenyu Zhang, Shuohuan Wang, Guibo Zhu, Sirui Han, Dianhai Yu
Cosmology and Nongalactic Astrophysics
Raising the Optical Depth to Reionization with Dark Matter Decay
Yu-Ning Wang, Paulo Montero-Camacho, Ziwei Wang et al. (+2 more)
DESI DR2
baryon acoustic oscillation (BAO) measurements have sharpened a discrepancy with
cosmic microwave background (CMB) inferences that can be recast as a preference for $\tau_{\rm reio}\simeq0.09$, above the $\simeq0.06$ inferred from large-scale CMB polarization. We test whether electromagnetic energy injection from dark matter (DM) decaying through $\chi\to e^+e^-$ can supply this additional optical depth. Adopting an asymmetric, simulation-calibrated Gompertzian model for astrophysical reionization, we derive $\tau_{\rm reio}$ self-consistently from the free-electron history including DM decay. We jointly constrain cosmological and reionization parameters and the decay rate using ACT+Planck CMB temperature and polarization anisotropies, CMB
lensing, BAO, and
quasar damping-wing observations. For $m_\chi=1$ GeV, DM decay raises the marginalized mean optical depth from 0.064 to 0.072 under a flat decay-rate prior, without significantly improving the fit. Reweighting the chains with log-uniform priors lowers the mean optical depth, indicating prior dependence. The additional optical depth arises primarily from a broad ionization tail through the
cosmic dark ages, rather than an earlier onset of astrophysical reionization. For DM masses from 2 MeV to 7 GeV, the 68% credible intervals remain below $\tau_{\rm reio}\simeq0.08$, a
benchmark previously found sufficient to bring both the
neutrino-mass tension and the exclusion of $\Lambda$CDM below $2\sigma$. The DM lifetime constraints complement
photon,
cosmic-ray, and gas-heating bounds and are competitive with previous CMB limits. DM decay therefore shifts the inferred optical depth upward, but the posterior-favored increase remains insufficient to substantially alleviate the CMB–BAO discrepancy.
Read the full abstract ▾
All authors Yu-Ning Wang, Paulo Montero-Camacho, Ziwei Wang, Yin Li, Yue-Lin Sming Tsai
Quantum Physics
Fault-tolerant cost of shallow QAOA on near-symmetric optimization problems
Jernej Rudi Finžgar, Martin Leib and Elisabeth Wybo
Montanaro and Zhou proved that depth-one
Quantum Approximate Optimization
Algorithm (QAOA) finds the planted solution of certain near-symmetric constraint satisfaction problems with probability $\Omega(1)$, and observed exponential runtimes for general-purpose classical solvers on explicit realizations, providing strong evidence of an empirical exponential speedup of low-depth QAOA. We ask what such a
circuit costs fault-tolerantly. Although the cost
Hamiltonian carries $\Theta(n^\ell)$ clauses, the rotation angle at which QAOA succeeds shrinks as $n^{1-\ell}$, and we show that small-angle Clifford$+T$ rotation synthesis reduces the non-Clifford cost per
circuit to $\widetilde O(n^2)$ for every clause locality $\ell$. Compiling the
circuit, however, requires the explicit clause list. We find that the mechanism underlying constant QAOA success fixes the degree-one
Fourier coefficients of the cost, whose signs reveal the planted solution. However, this leakage need not reveal the solution: we design families in which the quadratic non-Clifford scaling and hard exact optimization coexist.
Read the full abstract ▾
Optimization and Control
Sharp Quadratic Majorants of Weighted Power Means of Quadratic Forms
Alexey Peregudin
We study when concave weighted power means of positive semidefinite quadratic forms admit sharp quadratic upper bounds. The starting point is the scalar fact that each such mean is the lower envelope of its supporting affine majorants. After the scalar variables are replaced by quadratic forms, the resulting bounds may cease to be extremal among quadratic majorants. We prove that sharpness is preserved under a lower-envelope convexity condition on the joint numerical range. In that case, a nonlinear inequality involving power means of quadratic forms is certified, with no gap, by a single linear
matrix inequality. The condition is weaker than convexity of the full augmented range and holds automatically for two quadratic forms. This yields a sharp Peter-Paul-type majorant for the whole concave power-mean scale, including Yuan's lemma and the weighted
geometric mean as special cases. We also derive variational formulas for the extrema of sums of power means of quadratic forms on the unit sphere; under the same lower-envelope condition, the maximization becomes an exact convex
eigenvalue minimization. A finite-horizon control application illustrates how the certificate can be used constructively.
Read the full abstract ▾
Computer Vision
Yanshu Li, Jiaqian Li, Canran Xiao et al. (+3 more)
Large
vision-
language models (LVLMs) exhibit strong
multimodal in-context learning (ICL) capabilities, yet this ability degrades substantially as model size decreases. Knowledge
distillation offers a natural way to bridge this gap, but existing methods primarily align output distributions or hidden representations directly. Such
alignment teaches the student what the teacher predicts without revealing which evidence in the complex context causally supports that prediction. Consequently, a student can imitate the teacher's answer while continuing to rely on
language priors,
prompt structure, or other spurious cues. To address this limitation, we introduce
Multimodal Causal
Distillation (MCD), a
distillation framework that transfers how a strong teacher uses
multimodal evidence during ICL. MCD uses structure-preserving
token interventions to identify and verify causal evidence, then transfers how the teacher responds when that evidence is retained or removed. This design connects
distillation to the causal patterns by which the model uses contextual evidence during
multimodal ICL. Experiments across three LVLM families and seven
benchmarks show that MCD improves student performance by 7.23 points on average and outperforms vanilla
distillation by 4.68 points, while further analyses confirm the generalizability of these gains.
Read the full abstract ▾
All authors Yanshu Li, Jiaqian Li, Canran Xiao, Xi Xiao, Tianyang Wang, Yongtai Liu
Probability
A stochastic Carleson embedding theorem with constant $e$ and the vector of Riesz transforms
Komla Domelevo, Johanna Fladung, Spyridon Kakaroumpas et al. (+2 more)
We prove a continuous-time Carleson
embedding theorem with constant $e$ for a system of square-integrable continuous martingales whose quadratic covariations mimic the generalised Cauchy–Riemann relations. The argument is based on a Bellman function and a multidimensional Itô formula. As an application we transfer the estimate to the upper half-space via the Gundy–Varopoulos representation and obtain a Carleson
embedding, still with constant $e$, for the vector consisting of a function and its Riesz transforms in arbitrary dimension.
Read the full abstract ▾
All authors Komla Domelevo, Johanna Fladung, Spyridon Kakaroumpas, Paul Montobbio, Stefanie Petermichl
Quantum Physics
Verifiable quantum advantage based on polynomials with planted structures
Markus Bläser, Michael Gullans, Dominik Hangleiter et al. (+2 more)
A central question in the theory of
quantum advantage is whether there are
quantum advantage protocols with similar resource requirements as random
circuit sampling that are also verifiable just from the classical outputs of the
quantum computation. Here, we develop the idea of simulation secrets for verifiable advantage. A verifier can use a simulation secret to evaluate a cross-
entropy test faster than it would take a classical adversary to pass the test. We instantiate this idea using IQP
circuits described by cubic
polynomials with planted independent spaces. These correspond to the largest independent set in the orbit of a
polynomial under the general linear group and yield a low-rank stabilizer decomposition of the corresponding state. We
conjecture that large independent spaces are invisible to a computationally bounded adversary, and therefore they cannot exploit them to pass the protocol. A second
conjecture regards the fine-grained complexity of producing samples that pass the cross-
entropy test for uniformly random
polynomials. Under these
conjectures, our scheme results in a
polynomial gap between the verification time and the time a classical adversary would need to pass the protocol—both are exponential. It has a potential application to generating classically certifiable randomness, since the output distributions have high min-
entropy. We estimate that the planted
polynomial scheme is implementable using 100 logical
qubits at logical error rates around $10^{-6}$.
Read the full abstract ▾
All authors Markus Bläser, Michael Gullans, Dominik Hangleiter, Yuxuan Liu, Youming Qiao
Combinatorics
Log-concavity and Approximate Counting for Totally Unimodular Polytopes
Jonathan Leake and Maryam Mohammadi Yekta
We present a new lower bound on the number of
lattice points of all totally unimodular polytopes, generalizing previous lower bounds on contingency tables, integer flows, and beyond. Our bound is based on the Gurvits capacity convex optimization problem, and thus our result implies an efficient deterministic
algorithm for approximate counting of the
lattice points up to an explicit exponential factor. We achieve our bounds by showing that the associated generating
polynomials fit into a new general class of log-concave
polynomials called VLC ("variable-wise log-concavity”). This also implies a
conjecture of Ferroni and Higashitani on the evaluations of the Ehrhart
polynomials of unimodular polytopes. The essential ingredient for these results is the resolution of Barvinok's log-concavity
conjecture for contingency tables on lines, which was proven using ChatGPT 6 Astra. We
conjecture a generalization of Barvinok's
conjecture, which we believe will lead to stronger and more general bounds.
Read the full abstract ▾
Quantum Physics
Theodore M. Mahaffey, Chaohan Cui, Saikat Guha et al. (+1 more)
We introduce an
entanglement distillation protocol that utilizes an arbitrary $[[n,k,d]]$ stabilizer code to convert $n$ raw copies of an $N$-
qubit Greenberger-Horne-Zeilinger (GHZ) state into $k$ logical copies in the presence of Pauli noise. Our explicit formulation of the scheme on a
star network
topology is efficiently scalable to arbitrary $N$, requires only local operations, and minimizes the impact of errors on idle physical
qubits, enabling the distribution of high-fidelity entangled logical states between any number of
quantum processors. We report the results of numerical experiments using the 5-
qubit code, toric code, and $[[144,12,12]]$ bivariate bicyle code to protect against independent single-
qubit depolarizing noise on all
qubits. Our encoding scheme improves the final GHZ state fidelity relative to a single bare GHZ state sent over the same noisy channel for depolarizing rates up to or above $6 \%$ for all codes tested. Additionally, we use the stabilizer formalism to prove that our protocol can be applied to any $N$-
qubit Calderbank-Shor-Steane (CSS) stabilizer state, from which the GHZ state emerges as a special case. This result explicitly establishes the working principle of previous stabilizer-based Bell pair and GHZ state
distillation schemes, and generalizes such protocols beyond the traditionally considered states. We also show that some stabilizer codes can distill any stabilizer state with our protocol.
Read the full abstract ▾
All authors Theodore M. Mahaffey, Chaohan Cui, Saikat Guha, Murphy Yuezhen Niu
Computer Vision
Haoxiang Shi, Zaijing Li, Muhe Ding et al. (+3 more)
Vision-
Language Navigation (VLN) requires
embodied agents to generate actions based on instructions and observations. General-purpose
multimodal agents offer a promising basis for this task, but selecting plausible local actions does not ensure that execution remains consistent with the intended route, particularly in long-horizon tasks. Moreover, the accumulated interaction history increases the input required for subsequent decisions, resulting in a significant inference overhead. To this end, we introduce \method, an Agentic VLN framework that includes a Goal
Agent that sets adaptive goals for local actions, a Verify
Agent that dynamically verifies whether a goal has been completed, a
Memory Agent for
multimodal context compression, and a Visuomotor
Agent to execute adaptive goals. Specifically, the Goal
Agent formulates adaptive goals based on the instruction, current observation, and execution history. Then the Visuomotor
Agent executes
navigation actions to achieve each goal, while the Verify
Agent uses a goal-specific verification question to dynamically assess whether the observed outcomes satisfy the intended completion condition. Verified goal completion then marks a boundary for the
Memory Agent to compress the corresponding
multimodal interaction history while preserving information needed for subsequent
navigation. We evaluate
navigation on R2R-CE and RxR-CE, examine framework variants across three model backbones, and study context
evolution during execution. For Real-World evaluation, \method achieves 83.3% success and 1.51\,m
navigation error across eight challenging routes evaluated three times each.
Read the full abstract ▾
All authors Haoxiang Shi, Zaijing Li, Muhe Ding, Xiang Deng, Yaowei Wang, Liqiang Nie
Mathematical Physics
Cluster Attention Neural Operators for Solving Parametric Partial Differential Equations
Ming Zhong, Antonio Colanera, Gianluigi Rozza et al. (+1 more)
Traditional simulations of parametric partial differential equations (PDEs) rely on repetitive computations for each parameter, which makes high-fidelity design impractical. Neural operators address this issue by learning solution operators, accelerating parameter-space mapping by orders of magnitude. Recent
Transformer-based neural operators attempt to capture global dependencies, but often at the cost of quadratic
attention complexity. Transolver resolves this problem by projecting physical states into a reduced slice space for
attention computation. Although fast, this projection sacrifices fine spatial information. Moreover, by operating in this reduced space with shared weights across
attention heads, it may constrain the model's flexibility, thereby limiting its capacity to capture complex phenomena. To address these issues, we propose the Cluster
Attention Neural Operator (CANO), which reformulates
attention via a novel cross-
attention mechanism that dynamically clusters queries while preserving full-resolution keys and values. This avoids slice compression loss and removes weight-sharing limits. At the same time, the model remains fast without losing global interactions. Empirically, CANO achieves state-of-the-art performance across canonical PDE
benchmarks, covering fluid and solid dynamics (e.g., Navier-Stokes, Airfoil, Plasticity), irregular unstructured geometries (e.g., Pipe
Turbulence, Composites), and long-term temporal rollouts. Across solid deformation and turbulent flow
benchmarks, CANO achieves lower errors than baselines and exhibits strong
geometric adaptability and temporal consistency.
Read the full abstract ▾
All authors Ming Zhong, Antonio Colanera, Gianluigi Rozza, Zhenya Yan
Computation and Language
The Concrete-Arbitrary Gap: Kinship Reasoning in LLMs Is Not Indifferent to Presentation
Thomas Pashby
We test whether large
language models solve formally matched kinship problems equally well when relations are expressed in familiar vocabulary or by explicitly defined nonce predicates. Across 500 paired graphs, concrete accuracy exceeds arbitrary accuracy by 35.6 percentage points in local Qwen3.8-27B, 26.6 in Gemma 4 26B-A4B, 12.0 in Gemma 4 31B, and 5.4 in Qwen3.8-Max. All four paired gaps are statistically resolved.
Reasoning budgets and
prompt-
language interventions can substantially reduce the difference, showing that it is modifiable rather than a fixed incapacity. The minimal conclusion is behavioral: on these tasks, the models' manifested relational competence is not indifferent to presentation. Explicit definitions provide the formal relations but do not make nonce predicates as usable as familiar vocabulary embedded in learned linguistic associations.
Read the full abstract ▾
Machine Learning
TRACE: Trajectory Selection for Parallel Scaling of Search Agents
Qisheng Zhou, Zhen Xiong and Qiaoyu Tan
Parallel search may generate a correct answer that final-answer voting fails to select. We formulate this consolidation stage as trajectory selection and introduce TRACE (Trajectory Ranking with Aggregated Cross-Rollout Evidence), a lightweight learned selector that ranks completed trajectories using the search evidence behind their answers. TRACE preserves individual query and evidence occurrences, connects rollouts through shared content or document identity, and propagates information across these relations. Each candidate answer then reads the updated states of its own trajectory, preserving
retrieval provenance while incorporating evidence from related rollouts. Trained with answer-level supervision over frozen text
embeddings, TRACE returns an existing answer without additional search or autoregressive aggregation. One selector per search setting transfers across rollout policies and
agent backbones without
agent-specific fine-tuning, improving over voting across six WebQA policies and six long-horizon
dataset-backbone combinations at $K=16$. On Qwen2.5-14B Base/SFT WebQA pools, TRACE achieves 45.2/49.2% EM, compared with 43.9/48.0% for the strongest Qwen3-32B generative aggregators. On long-horizon FRAMES,
GAIA, and BrowseComp, it reaches 78.6% average accuracy, exceeding majority voting by 3.1 percentage points. On Base WebQA pools, TRACE with only 8 rollouts comes within 0.4 points of majority voting over 64. TRACE also achieves at least $10\times$ higher processing throughput than SolAgg, SummAgg, and AggAgent across all seven WebQA
benchmarks. These results show that reusing cross-rollout search evidence provides an effective and efficient alternative to heavyweight generative aggregation for parallel search. Code is available at https://github.com/Jaasssoooonnnnn/TRACE.
Read the full abstract ▾
Machine Learning
Patient-Centered Treatment Planning for Chronic Multimorbidity: A Hierarchical Reinforcement Learning Framework for Preference Modeling
Nafiseh Payani, Soham Das, G. Anthony Wilson et al. (+1 more)
Patient preference, defined as a patient's demonstrated willingness and capacity to adhere to clinical recommendations, is a primary determinant of therapeutic effect yet remains structurally absent from existing computational treatment planning models. We address this gap by presenting patient-centered factored-action hierarchical option-critic (FAHOC), a hierarchical reinforcement learning (HRL) framework that jointly learns high-level options corresponding to therapeutic strategies and factored intra-option policies that decompose the joint action space into disease- and intervention-specific subcomponents, while imposing a cooperation-aware action masking mechanism. This enables structured exploration, improved credit assignment across hierarchy levels, and more interpretable decision pathways, while enforcing patients' preferences. Formal guarantees establish that cooperative patients achieve higher optimal expected health outcomes than non-cooperative patients, and that the factored Q-function approximation error is provably bounded. The framework is evaluated using longitudinal data collected from approximately 50,000 comorbid hypertension and type 2 diabetes mellitus patients from five hospitals in the Southeast U.S. FAHOC achieves a quality-adjusted life year expectancy equivalent improvement of 0.669 (vs -0.133 observed clinician practice), correctly identifies cooperative patients in 95.9% of cases and never violates a patient's preference in held-out test, demonstrating that HRL with explicit preference constraints can support preference-consistent, clinically safe decision-making in multimorbidity management.
Read the full abstract ▾
All authors Nafiseh Payani, Soham Das, G. Anthony Wilson, Anahita Khojandi
High Energy Physics (Phenomenology)
Schur elimination of brane-localised Higgs mass spectra, and the criteria for the $\epsilon\to0$/$N\to\infty$ non-commutativity
Roberto Barceló and Subhadip Mitra
In many 5D extensions of the Standard Model, the
Higgs boson is localised to a single point along the extra dimension, where it couples to bulk
fermions. Computing the
fermion mass spectrum then requires a boundary regulator, and removing it before or after summing the Kaluza-Klein tower can give different answers, as shown in Ref.~\cite{Barcelo:2014kha}. Because the coupling reaches any KK mode through only two overlap numbers, the whole tower can be eliminated exactly by a Schur complement; the disagreement between the two orders of limit then reduces to a single sum, whose discontinuity we prove directly. This discontinuity needs two conditions to appear: a coupling sharp enough to leave the sum conditionally convergent, and a second channel with complementary behaviour at the point of contact. Neither condition depends on having an extra dimension, or even a discrete tower to sum. The same test applies to contact interactions in ordinary scattering, the imaginary part behind the optical
theorem, and seesaw, portal, and clockwork constructions. Whether the two conditions are met determines whether a similar ambiguity plays any role.
Read the full abstract ▾
Software Engineering
DoGBench: Can Agents Meet Expert Standards for User-Facing Documentation?
Frances Liu, Manny Silva, Paige Calvert et al. (+2 more)
We introduce DoGBENCH (Documentation Generation
Benchmark), to our knowledge, the first
benchmark for generating and maintaining real user-facing software documentation. It asks whether an
agent can produce documentation that experienced technical writers would accept in review. The
benchmark contains 292 items from open source projects, including Helm, PostHog, and Mautic. Each item gives the
agent a pre-change repository and a trigger, such as a code pull request or a reported documentation gap. The
agent must first decide whether the documentation needs an update. For items that need one, the
agent must produce an acceptable patch in one attempt. For items that do not need updates, the
agent must abstain. Task-specific rubrics, validated with project maintainers, score each patch on accuracy, completeness, reader guidance, placement, and repository conventions. The composite score combines patch quality with correct abstention, and a score of 100 means an
agent meets every requirement for the task. Scores should not be interpreted as a percentage of an expert's capability. We evaluated seven
agents. The highest-scoring
agent reached 47.3 out of 100 on the 117-item held-out split. In a separate audit of 1,267 patches, the most common failure modes were task-completion gaps (45.5%), technical inaccuracies (36.6%), and incomplete conceptual or reference coverage (32.5%). Analysis of the corresponding trajectories identified three key patterns associated with these failures: (1) describing interfaces without examining how readers use them (36.0%), (2) missing decisive evidence and filling the gaps with plausible assumptions (33.1%), and (3) stopping after finding the first plausible documentation surface and leaving other affected pages stale (30.1%).
Read the full abstract ▾
All authors Frances Liu, Manny Silva, Paige Calvert, Ayu Adiati, Sarah Sanders
Quantum Physics
Kelvin Koor and Patrick Rebentrost
We introduce the notion of stochastically evolving
Hamiltonians (SEHs), which are local
Hamiltonians whose coefficients are governed by
stochastic differential equations (SDEs). After developing some theoretical aspects of these objects, we consider the task of
Hamiltonian learning for SEHs. We reanalyse and make necessary extensions to the Huang-Tong-Fang-Su protocol, which first achieved the Heisenberg limit for
Hamiltonian coefficient learning, under these circumstances. We delineate theoretically the regimes where the Heisenberg limit can and cannot be retained. Along the way we develop a few tools in
quantum sensing and
Hamiltonian simulation that may be of independent interest under similar settings in
quantum information processing. The broad aim is to encourage the exploration of various settings in
quantum information processing in which the control parameters are governed by SDEs.
Read the full abstract ▾
Solar and Stellar Astrophysics
Towards breaking the degeneracy between stellar ages and unresolved multiplicity
Friedrich Anders, Anna B. A. Queiroz, Sagar Malhotra et al. (+2 more)
Stellar age is the central desirable parameter we need to disentangle different
Galactic formation scenarios. Nevertheless, no
stellar age dating method is completely model-independent. Even in the best-case scenarios, present-day
stellar age estimates are only precise to $\sim7\%$ and accurate to $\sim10-15\%$. A main drawback of state-of-the-art
stellar parameter inference codes like StarHorse is that they do not take into account unresolved
stellar multiplicity. Especially in the case of nearly equal-mass binaries or higher-order systems on the main sequence, properly taking into account unresolved multiplicity is imperative for the next generation of
stellar surveys. Simulation-based inference (SBI) is a robust and flexible approach for dealing with complex models where traditional likelihood-based methods become unfeasible. Here we sketch how SBI can be used to obtain precise
stellar ages, masses, binary mass ratios, and other
stellar parameters from spectroscopic, photometric, and astrometric measurements.
Read the full abstract ▾
All authors Friedrich Anders, Anna B. A. Queiroz, Sagar Malhotra, Cristina Chiappini, Arman Khalatyan
Human-Computer Interaction
Understanding Parents' Complex Views of AI for Children's Pretend Play
Sungho Oh, Mohammad Namvarpour, Maxi Heitmayer et al. (+2 more)
AI could support children's pretend play, but it could also direct the play on behalf of children. Whether AI should have roles in children's lives is controversial because its influence on children remains uncertain. We conducted semi-structured interviews with 10 U.S. parents, each with at least one child aged 4-15. During the interview, we described the concept of AI-supported pretend play and provided participants with two boundary-case storyboards. We analyzed the interview data through codebook thematic analysis, using inductive coding and affinity diagramming organized around the research questions, and then used qualitative systems mapping to examine relationships within and across themes. We found that the same characteristics of AI, e.g., ability to assume characters, responsiveness, and adaptability, were seen by parents as potentially useful but also concerning. Parents imagined that AI could make role-based play accessible to all children or help parents participate in family play. However, they opposed the idea of AI for children's play without a clear understanding of how it works and its long-term influence on their children. Parents worried about children's loss of imagination and creativity, emotional attachment to AI, reduced human interaction, inappropriate behavior by AI and/or children, and their inability to manage children's AI use. Parents viewed AI not only as a play tool but also as a social actor and a possible perturbation in the existing family dynamics. The appropriateness of AI and child–AI interactions therefore emerged as a requirement for AI in children's pretend play, in addition to technical safeguards and parental control. We contribute an integrated account of parents' interdependent judgments and emphasize the need for longitudinal research with children and their diverse families.
Read the full abstract ▾
All authors Sungho Oh, Mohammad Namvarpour, Maxi Heitmayer, Minahil Khalid, Afsaneh Razi
Statistics Theory
Is the regression $F$-test doubly robust?
Lucy Xia, Oliver Y. Feng, Yang Feng et al. (+2 more)
We study the robustness of the $F$-test in random design linear models, and reach a somewhat nuanced conclusion. On the positive side, one of our main results is that the size of the test is close to its nominal level as soon as either the distribution of the normalised error vector is close to uniform on the unit sphere, or the design
matrix, after applying a suitable column space-preserving orthogonalisation scheme, is close to being uniformly distributed. This provides a sense in which the $F$-test is doubly robust. Our conclusion is reached by establishing a Kolmogorov to Wasserstein distance Hölder continuity property controlling the departure of the $F$-statistic from its notional $F$-distribution under the null. Writing $n$, $p$ and $p_0$ for the sample size and the dimensions of the full and null models respectively, we prove that the Hölder exponent is $1/3$ when $\min(p-p_0,n-p) = 1$ and $1/2$ when $\min(p-p_0,n-p) \geq 2$. On the other hand, these exponents are relatively small and cannot be improved in general, revealing that the size of the test may depart from its nominal level quite quickly as we move away from settings where the test is exact. In some cases, our conclusions may be improved by working with a local Kolmogorov distance that focuses on discrepancies between distribution functions in the right tail.
Read the full abstract ▾
All authors Lucy Xia, Oliver Y. Feng, Yang Feng, Min Xu, Richard J. Samworth
Number Theory
On a question of Jones on tracelike vectors, cusp forms, and von Neumann algebras
Nikolaos Diamantis and Larry Rolen
Using the theory of von Neumann
algebras, Vaughan Jones showed abstractly that there is a bridge between holomorphic cusp forms and random
matrices. This bridge is supplied by a distinguished function he called a tracelike vector. He posed the following problem: construct a tracelike vector to make this bridge explicit. We carry out Jones'
vision for the full modular group. We construct holomorphic tracelike vectors for every finite-covolume Fuchsian group throughout the permissible Bergman range. For the modular group at the critical parameter $s=13$, we use the resulting unit tracelike vector to assign explicit group coefficients to cusp form multiplication operators, and form positive finite random
matrices from a shared Haar-distributed
unitary matrix. Almost surely, their normalised traces recover all joint operator moments, first as the
matrix size tends to infinity, and then as the group-word cutoff tends to infinity. We also identify the limiting mixed second moment with a series of Petersson norms that involve Maass-Shimura operators of cusp forms. The exact nature of our use of AI is detailed in the introduction.
Read the full abstract ▾
Artificial Intelligence
OSWorld-Science: A Benchmark of Computer Use Agents for Learning and Using Scientific Software
Dingyuan Dai, Heli Qi, Lei Liu et al. (+28 more)
Scientific software presents a demanding test for computer-using
agents based on visual
language models (VLMs): completing a research workflow requires interpreting specialized interfaces, manipulating scientific objects, and producing verifiable results. We thus introduce OSWorld-Science, a
benchmark and evaluation environment that combines scientifically meaningful tasks, artifact-based evaluation, and an efficient
agent harness for studying computer use in the scientific domain. The
benchmark contains 12 VLMs and 146 high-quality tasks across several scientific domains and software configurations, covering workflows such as molecular drawing and retrosynthesis, pathology image analysis, statistical computing, and physical simulation. Tasks are developed through expert proposals and iterative human–AI co-design, with selection guided by scientific value and difficulty. Task-specific execution-based evaluators inspect application states and generated artifacts, including molecular structures,
segmentation masks, plots, and numerical results, and award partial credit for incomplete outcomes. Our special harness integrates model adapters, interaction-loop control, and trajectory logging to support comparisons of models and interaction strategies. Our results show that current state-of-the-art VLMs with a strong harness still face challenges in addressing key questions in the scientific domains. We also analyze the benchmarking results across multi-linguistics,
reasoning efforts, context length and other factors and derive several important conclusions and directions to assist future development. Overall, we provide an integrated framework connecting expert-defined scientific goals to verifiable software outcomes, enabling systematic evaluation of both
agent capabilities and harness design in scientific workflows.
Read the full abstract ▾
All authors Dingyuan Dai, Heli Qi, Lei Liu, Yinxi Li, Baiding Chen, Zijun Dou, Qingcheng Zeng, Qi Kang, Oliver Sun, Eric Wang, Bo Zhou, Haixin Wang, Yufan Du, Shi Bo, Ruihan Lin, Mengqi Yuan, Dunjie Lu, Steven Dillmann, Yiming Shi, Tina Su, Amy Xin, Minghao Liu, Xi Wang, Xu Huang, Ge Zhang, Pengyu Nie, Zhen Yang, Jie Tang, Juanzi Li, Weihao Xuan, Tianyu Liu
Cryptography and Security
CodeMimicry: Exploiting Safety Generalization Lag in Large Language Models via Structured Code Completion
Zhen Liang, Hai Huang and Wentao Chen
Large
language models have achieved remarkable capabilities across diverse domains, yet their safety
alignment remains vulnerable to jailbreak attacks. In this work, we identify a previously underexplored failure mode - safety generalization lag - where
alignment trained predominantly on natural
language fails to transfer to the code domain. We show that this lag induces a code-completion blind spot, allowing malicious intent embedded within syntactically valid code to evade safety mechanisms. To exploit this vulnerability, we propose CodeMimicry, a fully automated black-box jailbreak framework that generates structured, object-oriented code
prompts to induce harmful outputs via code completion. Experiments on 8 state-of-the-art commercial LLMs demonstrate that CodeMimicry achieves a 96.25% attack success rate with 1.51 queries on average, significantly outperforming both template-based and optimization-based baselines. Beyond empirical performance, we provide a mechanistic analysis of code-based jailbreaks through latent space representations, including projection onto refusal-related directions and activation steering. This analysis offers an explanation of how CodeMimicry bypasses safety mechanisms in code-related domains. Our findings reveal a weakness in current safety
alignment and highlight the need for robust
alignments in structured domains such as code.
Read the full abstract ▾
Machine Learning
Do Better Goal Representations Improve Goal-Conditioned Reinforcement Learning?
Syed Nazmus Sakib, Abdul Monaf Chowdhury, Nafiul Haque et al. (+2 more)
Goal-conditioned reinforcement learning (GCRL) relies heavily on how target goals are represented to the policy. While recent methods encode goals via temporal distance, occupancy, or controllability, it remains unclear how much downstream performance actually depends on representation quality. We study this in offline GCRL by constructing an exact temporal-distance goal representation in deterministic mazes. We then systematically corrupt its
geometric quality while keeping the downstream learner fixed. Across OGBench
navigation tasks and two
algorithms, large changes in goal-representation quality produce almost no change in performance. However, applying the same interventions to the
agent's current state more than doubles success, revealing the state pathway as the true bottleneck. Building on this insight, we show that simple random
Fourier positional encodings substantially improve performance on the hardest
navigation tasks without map information or objective modifications. Overall, our findings suggest that in state-based offline
navigation, improving how the
agent's current state is represented matters far more than refining the goal representation. Code will be released soon.
Read the full abstract ▾
All authors Syed Nazmus Sakib, Abdul Monaf Chowdhury, Nafiul Haque, Shifat E Arman, Md Mehedi Hasan
Analysis of PDEs
Low Mach number limit of a two-phase flow model in $\mathbb{R}^3$
Fucai Li, Jinkai Ni, Zhipeng Zhang et al. (+1 more)
We study the simultaneous low Mach number limit of a compressible Navier–Stokes–Euler two-phase system in $\mathbb R^3$, in which the pressure terms in both phases are scaled by $\varepsilon^{-2}$. For sufficiently small $H^3$-perturbations around the constant
equilibrium, we establish the global well-posedness of the scaled compressible system and derive global energy-dissipation estimates that are uniform with respect to the Mach number $\varepsilon$. A key feature of the analysis is the degenerate dissipation structure: viscosity acts only on the Navier–Stokes phase, while the drag coupling transfers dissipation to the inviscid Euler phase through the relaxation mode. We then prove the global well-posedness and large-time decay of the limiting incompressible two-phase system, and show that the relative velocity $u-\omega$ decays faster than the full velocity pair. Finally, for the well-prepared initial data, we introduce pressure-corrected acoustic variables to remove the singular pressure mismatch and establish a global-in-time $H^2$-error. As a result, the scaled compressible solutions converge uniformly in time to the corresponding solution of the limiting incompressible system.
Read the full abstract ▾
All authors Fucai Li, Jinkai Ni, Zhipeng Zhang, Zhu Zhang
Computer Vision
Learning Where to Look: Anatomical Grounding and Guided Attention for Cardiac MRI Vision-Language Models
Bangwei Guo, Xiao Chen, Boris Mailhe et al. (+8 more)
Cardiac
magnetic resonance imaging (CMR) enables assessment of cardiac anatomy, ventricular function, and myocardial tissue characteristics. Clinicians interpret these images by identifying cardiac structures and focusing on the regions relevant to each clinical question, motivating anatomically guided
vision-
language models (VLMs). Yet CMR-specific supervision for anatomical localisation and clinical question answering remains limited. To address this gap, we investigate fine-grained CMR visual question answering through anatomical grounding and guided
attention. We construct 128,915 anatomical-grounding and 42,799 clinical QA pairs across short-axis cine, late gadolinium enhancement, and long-axis cine. These
datasets support anatomical recognition, localisation, and clinical assessment without requiring paired reports for individual training images. To help the model learn where to look, we introduce Cardiac Anatomy-Routed
Attention (CARA), which selects predicted anatomical priors according to the question and guides decoder
attention with learned task-specific strengths. Combining anatomical grounding pretraining with CARA yields our model, CARA-VL. Experiments demonstrate CARA-VL's strengths in clinical assessment and regional localisation across CMR imaging settings, with promising generalization to an external clinical cohort. Together, our data and method provide a practical framework for studying and advancing cardiac visual understanding in VLMs. We will release the QA data derived from public
datasets upon publication.
Read the full abstract ▾
All authors Bangwei Guo, Xiao Chen, Boris Mailhe, Jia Yao, Yiqing Wang, Ankush Mukherjee, Yikang Liu, Zheyuan Zhang, Hang Yu, Terrence Chen, Shanhui Sun