Peer Revenue Fully funded

Papers

Fresh from arXiv, typeset properly. Highlighted words trade on the market.

All of arXiv

Tuesday 13 October

Statistical Computation

How to Train Your Intercept: A Comparison of Intercept Update Strategies in Coordinate Descent

Johan Larsson and Frederik Fabricius-Bjerre

Coordinate descent solvers for regularized generalized linear models (GLMs) differ in how they update the intercept. This seemingly routine choice can substantially affect convergence when the response is imbalanced. For direct coordinate descent on the original GLM loss, we compare a conservative step based on worst-case curvature, a safeguarded Newton step based on current curvature, and repeated Newton steps that solve the intercept subproblem. The conservative update can leave much of the interaction between the intercept and the coefficients unresolved, producing a slowdown when that interaction controls convergence. A single Newton step avoids this attenuation and, in our experiments, follows nearly the same outer trajectory as exact minimization while requiring fewer inner steps. Simulated and real-data experiments, including comparisons across six production solvers and a controlled intervention within skglm, support this explanation. We also show how the same mechanism appears in quadratic-approximation methods and extends to multinomial models with rare classes. We recommend a safeguarded Newton update for direct coordinate descent and full intercept optimization within frozen local quadratic surrogates.
Read the full abstract

Analysis of PDEs

Orthonormal Strichartz estimates over fractal time

Guoxia Feng, Jinbong Lee and Sanghyuk Lee

We study Strichartz estimates for orthonormal families of initial data when time is integrated against an $\alpha$-dimensional Frostman measure $\mu$, $0<\alpha\le1$, with coefficients measured in $\ell^\beta$. For the fractional Schrödinger, wave and Klein–Gordon equations, we obtain orthogonality gains in the admissible region and necessary conditions on $\beta$. For the fractional Schrödinger equation the exponents are sharp up to an endpoint; the optimality of the threshold $\beta=q/2$ requires $\mu$ to be Ahlfors regular. For the Schrödinger equation in dimensions $d\ge2$, we prove a Lorentz endpoint estimate and, if $d>2\alpha$, the critical exponent $\beta=q/2$ at every pair $2\le q,r<\infty$ with \[\frac d2(\frac12-\frac1r)\ge\frac\alpha q, \quad r>\frac{dq}{d-\alpha};\] on $r=\infty$ we obtain a restricted weak-type estimate. Since the constants depend on $\mu$ only through its Frostman constant, Lebesgue measure yields global-in-time critical estimates in this range when $d\ge3$. The proofs combine Frostman fractional integration, a spectral near–far argument at the endpoint, and asymmetric Hilbert–Schmidt bounds with orthogonal frequency-block summation. We also obtain further estimates in the low-dimensional cases $d=1$ (fractional Schrödinger) and $d=2$ (wave and Klein–Gordon) with $\alpha>1/2$, and formulate the consequences for densities of freely evolving states.
Read the full abstract

Functional Analysis

Completely symmetric subspaces of noncommutative $L_p$-spaces

Jinghao Huang

Let $2<p<\infty$. We prove that every infinite-dimensional closed subspace $F$ of a noncommutative $L_p$-space contains a subspace completely isomorphic to $\ell_p$, $C_p$, $R_p$, or $C_p\cap R_p$ and completely complemented in $F$. This answers the question of Junge and Xu on the existence of completely symmetric basic sequences.
Read the full abstract

Robotics

Contractor Filters

Simon Rohou

We introduce Contractor Filters (CFs), a set-membership architecture for nonlinear continuous-time state estimation with bounded uncertainties and sporadic outliers. CFs represent feasible states symbolically by compositions of contractors rather than rematerializing a geometric enclosure after each update. This provides an abstraction layer between the set-theoretic filtering equations and their numerical realization, so prediction, correction, ambiguity, and outlier handling can be expressed within one algebra without prescribing a state geometry. We derive an exact nominal recursion and a guaranteed robust enclosure, with validated flow inclusions preserving soundness. Optional sub-paving compression then provides finite outer representations. CFs are evaluated against a robust particle filter on range-only and terrain-based localization under outliers, probabilistic model mismatch, association ambiguity, and multimodality. CFs retain the simulated truth in all tested configurations, while particle-filter reliability degrades under persistent bias and spatial aliasing. Contractor strength and compression budget control the trade-off between paving tightness and computational cost, while preserving the enclosure guarantee.
Read the full abstract

Computer Vision

Scaling Vision Transformers Without Training: Prune-and-Merge Token Compression

Vatsal, Soumil Malhotra, Priyanshu Chahal et al. (+1 more)

Vision Transformers achieve strong performance but incur high computational costs from quadratic token interactions. Training-free token reduction methods improve efficiency, but their robustness remains underexplored. We propose Prune-and Merge ViT, a training-free framework for off-the-shelf models. It combines class attention-based pruning with similarity-based merging to reduce redundancy while preserving semantic information. To evaluate robustness we introduce two metrics. Relative Accuracy Drop (RAD) measures relative accuracy degradation under transformations, and Prediction Flip Rate (PFR) quantifies prediction consistency. On pretrained ViT and DeiT, Prune-and-Merge reduces computation competitively while maintaining robustness and better accuracy than existing token reduction methods. Our analysis suggests that hybrid token reduction does not introduce hallucination-like behavior and preserves prediction stability for common image transformations.
Read the full abstract

All authors Vatsal, Soumil Malhotra, Priyanshu Chahal, Sanjeev Kumar

Robotics

Data-Free Continual Learning for World Action Model with Rectified Generative Replay

Kaipeng Fang, Hangxu Liu, Chaojian Shi et al. (+5 more)

World action models (WAMs) built on pretrained video generation models adapt quickly to new robotic tasks, but they forget previously learned tasks under sequential fine-tuning. Experience replay (ER) mitigates such forgetting by rehearsing data of previous tasks, yet this data is often unavailable in robotics, where a policy is deployed as weights only and its training data is not shared along with it. In this work, we ask whether a WAM can achieve data-free continual learning by regenerating its own past. Our key observation is that a WAM can indeed autoregressively regenerate complete rollouts of previously learned tasks and replay them to mitigate forgetting. However, replaying such raw rollouts still lags far behind replaying real data, which we attribute to two issues. First, the WAM is unable to recognize task completion and keeps generating purposeless motion after the task is done. Second, under open-loop generation, the generated actions gradually drift away from the generated observations. To address both issues, we present rectified generative replay (R$^2$-WAM), which truncates generated rollouts at task completion with an agentic evaluator and filters out rollouts whose actions are physically inconsistent with the observations. On the four LIBERO suites and five real-world tasks, R$^2$-WAM reduces forgetting by over 90% relative to sequential fine-tuning and nearly matches ER without accessing any data from previous tasks. Our analysis further reveals that forgetting in WAMs concentrates in inverse dynamics modeling: a forgotten WAM still produces visually plausible observations of previous tasks while its generated actions fail. Project page: https://kaipengfang.github.io/r2-wam/
Read the full abstract

All authors Kaipeng Fang, Hangxu Liu, Chaojian Shi, Yunsong Zhou, Hongzi Zhu, Minyi Guo, Jingkuan Song, Lianli Gao

Machine Learning (Statistics)

Weighting-Based Strategies for Classification: Analysis and Local Peak Dynamics with CART Trees

Abdoulaye Sakho, Emmanuel Malherbe and Erwan Scornet

Tabular learning tasks often suffer from class imbalance, which is often addressed via weighting-based strategies. In this work, we compare the naive approach (unweighted) with the weighting-based rebalancing strategies. Somewhat surprisingly, we demonstrate that, asymptotically, as the predictor complexity increases with sample size, weighting strategies yield predictive performance identical to unweighted approaches across all evaluation metrics, up to a shift in the final threshold. To go beyond, we theoretically study the estimation of a linear regression function through weighted and unweighted CART trees with limited depths. In this setting, we prove that the points in the PR space for both strategies are distributed along the same curve, thus illustrating the similar nonasymptotic performance of the two strategies. Then, we numerically identify more complex synthetic settings where performance gaps, evaluated via fixed classifier complexity, favor weighting strategies. Thus, to guide practitioners, we introduce an indicator that aims at signaling whether a data set may benefit from weighting.
Read the full abstract

Cosmology and Nongalactic Astrophysics

Validation of harmonic-space likelihood methods for LiteBIRD B-mode polarisation analysis

G. Galloni, P. Campeti, C. Gimeno-Amo et al. (+112 more)

The LiteBIRD satellite mission aims to measure the tensor-to-scalar ratio $r$ with unprecedented sensitivity through observations of the cosmic microwave background (CMB) $B$-mode polarisation. Robust constraints on $r$ require accurate likelihood modelling across a wide range of angular scales. In this work, we validate harmonic-space likelihood methods against pixel-based benchmarks using 20\,000 end-to-end simulations that include CMB, instrumental noise, and Galactic foreground residuals processed through a realistic component-separation pipeline. We compare a debiased pixel-based likelihood, which serves as our benchmark on large scales ($\ell\leq32$), with an offset Hamimeche & Lewis (oHL) likelihood applicable across the full multipole range. We systematically explore the space of analysis settings, identifying the number of simulations used for covariance estimation and the choice of beam window function as the most critical factors. Over the full multipole range considered ($\ell\leq128$), where the benchmark combines the pixel-based likelihood at $\ell\leq32$ with a harmonic one at smaller scales, the 68% upper bound on $r$ from the oHL likelihood is $19$–$29\%$ larger than the benchmark one, depending on the beam window function. This demonstrates its viability as a computationally efficient baseline for LiteBIRD data analysis and cosmological inference from the polarisation $B$-mode field.
Read the full abstract

All authors G. Galloni, P. Campeti, C. Gimeno-Amo, A. Carones, A. Rizzieri, Y. Chinone, N. Krachmalnicoff, E. Martínez-González, L. Pagano, D. Adak, E. Allys, A. Anand, K. Aryan, J. Aumont, C. Baccigalupi, A. J. Banday, R. B. Barreiro, N. Bartolo, A. Basyrov, M. Bersanelli, A. Besnard, F. Bouchet, N. Brancadori, T. Brinckmann, F. J. Casas, L. Castelló Gomar, L. Colombo, F. Columbro, A. Coppolecchia, M. Cruz, F. Cuttaia, P. de Bernardis, E. de la Hoz, M. De Lucia, S. Della Torre, C. Demay, P. Diego-Palazuelos, J. J. Díaz García, F. Finelli, C. Franceschet, U. Fuskeland, M. Galloway, K. Ganga, M. Gerbino, T. Ghigna, S. Giardiello, E. Gjerløw, M. Gomes, A. Gruppuso, S. E. Harper, M. Hazumi, S. Henrot-Versillé, L. T. Hergt, E. Hivon, K. Ikuma, H. Ishino, K. Kohri, L. Lamagna, M. Lattanzi, C. Leloup, M. Lembo, F. Levrier, A. I. Lonappan, M. López-Caniego, D. Maino, V. Maranchery, S. Masi, S. Matarrese, S. Micheli, M. Migliaccio, M. Monelli, L. Montier, G. Morgante, M. Morshed, L. Mousset, M. Najafi, T. Namikawa, P. Natoli, I. Obata, A. Occhiuzzi, A. Paiella, D. Paoletti, S. Paradiso, G. Pascual-Cisneros, F. Piacentini, G. Piccirilli, M. Pinchera, G. Polenta, L. Porcelli, M. Reinecke, M. Remazeilles, A. Ritacco, J. A. Rubiño-Martín, M. Ruiz-Granda, Y. Sakurai, L. Salvati, J. Sanghavi, D. Scott, D. Shenoy, M. Shiraishi, G. Signorelli, S. Stellati, R. M. Sullivan, K. Tassis, L. Terenzi, M. Tomasi, H. Tran, L. Vacher, B. van Tent, P. Vielva, S. Vinzl, D. J. Watts, I. K. Wehus, G. Weymann-Despres, for the LiteBIRD Collaboration

Optimization and Control

Non-Potential Congestion Games with a Continuum of Players

Alix Dupont, Quentin Petit and Adrien Séguret

We study a class of congestion games with a continuum of heterogeneous players and a finite set of resources, without assuming the existence of a potential function. Players differ in their individual characteristics and may contribute differently to congestion, which is represented through general aggregate features of the population choosing each resource. We characterize the conditions under which such a game admits a potential and show that this structure generally fails when players have heterogeneous impacts on congestion. To analyze the non-potential case, we introduce the concept of Stochastic Variational Nash Equilibrium, in which players observe idiosyncratic cost shocks before selecting a resource. We show that equilibrium choices are pure conditional on players' characteristics and shock realizations, whereas the induced aggregate allocation is probabilistic. Under Gumbel-distributed shocks, the equilibrium is equivalent to a continuum-player logit Quantal Response Equilibrium. Using a fixed-point formulation and the Schauder–Tychonoff theorem, we establish existence for every positive noise level and recover a pure Nash equilibrium in the zero-noise limit. Under a coordinate-wise monotonicity condition on aggregate interaction costs, we further prove uniqueness and convergence of the regularized allocations to the pure equilibrium. An application to electric vehicle charging illustrates how heterogeneous energy demands jointly affect station choice, congestion, and market shares.
Read the full abstract

Computer Vision

COMPASS: Contextual Moment Prior Alignment for Generalizable AIGI Detection

Zhan Yang, Tiancheng Shen, Weijie Lyu et al. (+4 more)

Generalizable AI-generated image (AIGI) and deepfake detection remain challenging due to the asymmetry phenomenon, where adaptation to limited training distributions can degrade the transferable representations of Vision Foundation Models (VFMs). Recent subspace adaptation methods mitigate this issue by freezing high-magnitude principal components while adapting the residual subspace. However, such magnitude-based decomposition is data-agnostic and does not explicitly account for target-relevant semantics, potentially leading to semantic misalignment. We propose COMPASS (COntextual Moment Prior Alignment for Subspace Separation), a data-aware subspace adaptation framework that uses a small unlabeled Semantic Proxy Set to estimate target activation statistics. COMPASS incorporates activation covariance into the decomposition of pre-trained weights, preserving target-relevant representations as a stable semantic reference while the residual adapter learns forgery-related variations. The proxy calibration requires no gradient-based optimization. Extensive experiments demonstrate strong generalization across cross-dataset, cross-method, joint cross-dataset-method, and open-world cross-generator evaluations, achieving state-of-the-art performance on both Deepfake and AIGI detection benchmarks.
Read the full abstract

All authors Zhan Yang, Tiancheng Shen, Weijie Lyu, Shuo Chen, Bernard Ghanem, Yibo Yang, Ming-Hsuan Yang

Probability

Distribution tail of the distance to infinity in supercritical bond percolation

Julien Verges

We give an estimate for the distribution tail of the passage time between $0$ and $\infty$ in first-passage percolation on $\mathbb Z^d$, when the weight distribution is bounded and has a supercritical atom at $0$. A special case involves the number of disjoint cutsets between $0$ and $\infty$ in supercritical bond percolation. Besides, we show that the rate function for the upper-tail deviations of the point-to-point passage time is asymptotically polynomial near $0$.
Read the full abstract

Optimization and Control

Lifted Restricted Isometry Property and Fast Algorithm for Spectral Compressed Sensing With Near-Optimal Sample Complexity

Xunmeng Wu, Jian-Feng Cai, Zongben Xu et al. (+1 more)

This paper studies the spectral compressed sensing problem, which aims to recover an $r$-spectrally-sparse signal of length $N$ from linear compressive measurements. We reformulate the problem as the recovery of a low-rank Hankel matrix. Motivated by this formulation, we propose a Lifted Restricted Isometry Property (LRIP) for the measurement matrix and show that Gaussian measurement matrices satisfy LRIP under certain conditions. Unlike classical RIP, LRIP imposes a rank constraint in the lifted matrix domain while taking measurements in the signal domain through the adjoint Hankel operator. To solve the resulting nonconvex problem efficiently, we introduce a Fast Iterative Hard Thresholding (FIHT) algorithm. Based on LRIP, we show that FIHT converges linearly to the true signal with high probability using $\mathcal{O}(r \ln r \ln^2 N)$ scaled Gaussian measurements, which is near-optimal up to log factors and improves the state-of-the-art order of $r^2$ for nonconvex Hankel methods. Extensive numerical results demonstrate the superiority of FIHT as compared to state-of-the-art convex methods in terms of phase transitions, computational time, and robustness to noise.
Read the full abstract

All authors Xunmeng Wu, Jian-Feng Cai, Zongben Xu, Zai Yang

Quantum Physics

Optimal Lower Bound on the Sample Complexity of Coherent Classical Shadows for Mixed States

Pavel Svetlichnyy

We study coherent classical shadows protocols for an unknown $d$-dimensional quantum state of rank at most $r$, allowing an arbitrary joint measurement on all available copies. For observables with operator norm at most one and centered squared Frobenius norm at most $F$, we study the minumum number of copies $T$ needed for the protocol to successed with propbability $1 - \delta$. We prove a lower bound on $T$, that depends both on $\delta$ and the number $M$ of post-measurement observables. Denoting $\ell=\max\{\log(2M),\log(1/\delta)\}$, we show that $T=\Omega\left(\min\left\{\frac{\sqrt{rF\ell}}{\varepsilon},\frac{(F\ell)^{2/3}}{\varepsilon^{4/3}}\right\}+\frac{\ell}{\varepsilon^2}\right)$ under the mild assumption $\log(2M)\lesssim d\min\{r,F\}$. The lower bound nearly matches the known upper bound and matches the conjectural strengthening of the known upper bound. The proof is inspired by the framework of the proof of the lower bound for Boolean Hidden Matching. We develop a Gaussian-lift noise kernel that transfers forward and reverse Gaussian hypercontractivity to the Grassmannian, allowing us to bound the distinguishing advantage in an auxiliary game with a revealed query observable. We use Schur–Weyl structure and posterior moment bounds to control arbitrary coherent measurements on the source side.
Read the full abstract

Computer Vision

FlowSim: Rendering-Free Closed-Loop Simulation for Evaluating End-to-End Driving Systems

Hanfeng Wu, Alvaro Marcos-Ramiro, Michael Schmidt et al. (+1 more)

Closed-loop evaluation of end-to-end (E2E) driving systems typically relies on generating sensor observations and re-processing them through the perception encoder, introducing substantial computational cost and a sim2real gap between simulated and real sensor observations. In this paper, we ask a more fundamental question: is image-space simulation necessary for closed-loop evaluation of E2E driving systems? We propose FlowSim, a rendering-free simulation paradigm that predicts the internal latent representation directly from a previous latent and target-frame symbolic scene conditions. The predicted latent is then processed by the original frozen downstream modules, eliminating image rendering and perception re-encoding. Explicit cross-frame agent association improves latent transition prediction and rollout stability, while variable-horizon simulation enables long-horizon prediction from a common real anchor without accumulating intermediate rollout errors. Experiments on nuScenes and NavSim demonstrate that FlowSim preserves downstream behavior competitively with, and often exceeds image-space simulation in source-to-reference agreement across variable simulation horizons and both dense and sparse latent representations. By predicting the latent directly, both instantiations of FlowSim simulate each step in 0.04–0.06s, substantially faster than the image-space simulation methods they are compared against. These results establish FlowSim as an efficient and practical alternative to conventional image-space simulation methods.
Read the full abstract

All authors Hanfeng Wu, Alvaro Marcos-Ramiro, Michael Schmidt, Christoph Stiller

Quantum Physics

Lossless Reductions and Hybrid Sampling for Phylogeny Using the Maximum Compatibility Criterion

Orson Ye, James Bonfield, Joshua Cudby et al. (+2 more)

Maximum compatibility provides a criterion for phylogenetic reconstruction in which the objective is to retain a maximum set of mutually compatible characters. For binary characters, pairwise compatibility can be represented by a weighted graph whose vertices correspond to characters and whose edges connect compatible pairs. A leading algorithm by Cherry [BMC Bioinformatics, 18(1):127, 2017] uses this representation to identify maximum compatible character sets as maximum weight cliques. We formulate the problem as maximum weight independent set enumeration on the complement graph and use deterministic postprocessing to map sampled bit strings to maximal feasible solutions. To explore the prospects of quantum computational advantage, we further construct bespoke correction-in-the-loop Quantum Approximate Optimisation Algorithm instances and find that traditional Lagrange multiplier choices may not be optimal for the enumeration problem on noisy devices. We also introduce lossless graph reductions with reconstruction rules that preserve the complete set of optimal solutions. Our results indicate that graph reduction and sampling can be effectively combined to address the enumeration bottleneck in maximum compatibility phylogeny, while retaining exact reconstruction when the residual optimum set is completely resolved. We enhance Cherry's approach to maximum compatibility by introducing graph reduction techniques and quantum sampling. Our classical benchmarks show a clear difference between finding one optimum and recovering the optimum set in practice. We demonstrate the utility of our methods on graphs constructed from SARS-CoV-2 sequences.
Read the full abstract

All authors Orson Ye, James Bonfield, Joshua Cudby, Richard Durbin, Sergii Strelchuk

Quantum Physics

Ergotropy of multipartite entangled states in the quantum atmosphere

Ruopu Sun and Xiaofen Huang

We investigate the ergotropy of tripartite Greenberger-Horne-Zeilinger (GHZ) and W states in the quantum atmosphere of a Schwarzschild black hole, focusing on how the extractable work is redistributed under Hawking-induced thermalization. We find that the two classes of multipartite entanglement exhibit qualitatively different ergotropy structures. For GHZ states, the ergotropies associated with different accessible subsystems organize into two degenerate families, whereas for W states they obey a linear averaging relation with no counterpart in the GHZ case. Under symmetric gravitational thermal corrections, the two GHZ families remain separated by a constant energy gap that is independent of the radial position. We further show that the Hartle-Hawking constant $D_{HH}$ and the normalized radial distance $r/r_h$ play competing roles in the evolution of extractable work: increasing $D_{HH}$ suppresses its recovery toward the asymptotic Hawking limit, while increasing $r/r_h$ enhances the ergotropy accessible to the observers. These results show that the redistribution of extractable work in a black-hole environment is governed not only by gravitationally induced thermalization but also by the structure of the underlying multipartite entanglement, providing a thermodynamic signature that distinguishes GHZ- and W-type quantum correlations in curved spacetime.
Read the full abstract

Chemical Physics

Can Machine Learning Potentials Be More Accurate Than the Methods They Are Trained On?

Pavlo O. Dral, Yi-Fan Hou, Yuxinxin Chen et al. (+4 more)

Although machine learning potentials (MLPs) generalize across configurational and chemical compound spaces, their accuracy is commonly believed to be limited by their training methods, with improvements beyond this limit achieved so far only by refitting to experimental observables. In this Perspective, we argue that MLPs can also be made more accurate than their training methods non-empirically by generalizing across a third space, that of quantum-chemical methods. We propose routes to method-transferable MLPs that predict at unseen methods, with working implementations in both density-functional and wavefunction-based theories showing that they can be more accurate than any of their training methods. Aiming beyond the accuracy of the training methods could also make universal MLPs more accurate than single-method models, even when the method-transferable MLPs remain less accurate than those QM methods. Method-transferable MLPs will shape the construction of training datasets and the evaluation of models, pushing the accuracy limits of chemical simulations.
Read the full abstract

All authors Pavlo O. Dral, Yi-Fan Hou, Yuxinxin Chen, Hassan Nawaz, Mateusz Witkowski, Szymon Śmiga, Ireneusz Grabowski

Machine Learning

ChampionKV: Head-Wise Adaptive Budget Allocation for KV Cache Compression

Yuechuan Chen, Yanda Guo, Xiaoxi Liu et al. (+4 more)

Some observation-window-based KV cache compressors use prompt attention to identify important tokens but allocate uniform or predefined budgets across heads, overlooking input-dependent differences in budget demand. We propose ChampionKV, an input-adaptive head-wise budget allocator for long-context KV cache compression. It uses the maximum aggregated observation-window attention score as a lightweight empirical proxy for attention concentration, assigning larger budgets to heads with dispersed attention and smaller budgets to concentrated heads. An abnormal-head mask and a floor-budget mechanism improve robustness under weak attention responses. ChampionKV plugs into compressors such as SnapKV and PyramidKV without changing their token-selection procedures. On LongBench and RULER, both Champion variants consistently improve over their SnapKV and PyramidKV base compressors; the improvements are largest and statistically significant on Llama3.1-70B-Instruct, especially under tight budgets, whereas results on the tested 7B/8B models remain competitive but mixed. ChampionKV does not uniformly outperform Ada-KV, whose global cross-head Top-K selection retains an advantage in several settings. Controlled analysis further shows improved retained attention mass over the corresponding base compressors at negligible overhead.
Read the full abstract

All authors Yuechuan Chen, Yanda Guo, Xiaoxi Liu, Weilin Ruan, Huanbo Xu, Ruyi Wang, Wei Zhu

Artificial Intelligence

SHELDON: Reasoning through Dual Search over Hypothesis and Observation Spaces

Harshit Joshi, Priyank Shethia and Monica S. Lam

Test-time scaling has enabled LLMs to solve increasingly complex reasoning, discovery, and optimization tasks. However, existing approaches often gather observations while seeking evidence for current hypotheses, then generate new hypotheses conditioned on those hypotheses and their associated evidence. This couples observation discovery to existing hypotheses, favoring local refinements over exploration of unknown hypotheses regions. Inspired by the hypothetico-deductive method (Hempel, 1945) and dual-space models of scientific discovery (Klahr & Dunbar, 1988), we introduce SHELDON, a scientific reasoning harness that separately represents and jointly explores hypothesis and observation spaces. By actively seeking new observations along with new hypotheses, SHELDON can discover observations that its current hypotheses do not explain. These unexplained observations expose gaps in its understanding, motivate new investigations, and guide the generation of hypotheses beyond refinements of existing ones. Across diagnosis, abstract reasoning, and optimization benchmarks, SHELDON outperforms evaluated baselines by up to 26.7 points. Additional analysis shows that SHELDON generates up to 4.5x more novel solutions.
Read the full abstract

Neurons and Cognition

Rewiring of adult neural networks - learning from structural plasticity across multiple scales

Michael Fauth, Han Lu, Jannik Luboeinski et al. (+36 more)

Synaptic connections are continuously formed and eliminated even in adult neuronal networks. This process - called structural plasticity - allows networks to change their topology to learn and adapt to their environment or compensate for lesions. Yet, in contrast to synaptic plasticity, structural plasticity is less well studied and understood. We here present the current state of knowledge as well as open problems in the field along three guiding questions: (1) What regulates structural plasticity in biological neural networks? (2) What are the functional consequences and advantages of structural plasticity in adult neural networks? (3) How can our understanding of structural plasticity be translated into clinical and technological applications? We argue that understanding structural plasticity is a problem involving multiple temporal and spatial scales. Solving this problem thus requires collaborative efforts of experimentalists, theoreticians as well as computational modelers, and has the potential for comprehensive neuroscientific insights that can guide the development of medical applications, artificial neural networks as well as neuromorphic computing.
Read the full abstract

All authors Michael Fauth, Han Lu, Jannik Luboeinski, Johanna Senk, Simone Albani, Valentina Armuzza, Paolo Carloni, Richmond L. Crisostomo, Hermann Cuntz, Ulrich Egert, Eugenio F. Fornasiero, Tobias Gemmeke, Claus C. Hilgetag, Sabine M. Hölter, Patrick Inoue, Björn Kampa, James C. Knight, Susanne Kunkel, Thomas Nowotny, Samora Okujeni, Nicola Palomero-Gallagher, Mariia Popova, Petra Ritter, Giulia Rossetti, Michael Schirner, Marc Spehr, Leon Stefanovski, Gaia Tavosanis, Agata Witkowska, Felix Wolf, Willem Wybo, Sandra Diaz-Pier, Maximilian Lenz, Abigail Morrison, Florian Röhrbein, Stefan Rotter, Johannes Schemmel, Christian Tetzlaff, Andreas Vlachos

Machine Learning

FRACTION: Fraud Explanation Generation via Agentic Graph Context Navigation

Cecelia Soh, Jun Hu, Yuan Li et al. (+6 more)

Graph fraud explanation generation emerges as an important task, which requires jointly leveraging graph structure and content information to produce human-readable explanations for a given risk label. An intuitive approach is to adapt existing graph-enhanced large language models (LLMs), which inject graph structure into LLMs via graph-conditioned instruction tuning or structure-aware embeddings. However, existing methods face two key challenges in graph fraud explanation generation: a lack of evaluation protocols tailored to explanation quality, and a mismatch between the granularity of graph information they provide and what explanation generation requires. In this paper, we propose FRaud Explanation Generation via Agentic Graph Context NavigaTION (FRACTION), a framework for graph fraud explanation generation. To evaluate explanation quality, we introduce a task-specific rubric that assesses faithfulness, graph grounding, helpfulness, consistency, and conciseness, and validate it against human judgments. To focus on fine-grained details of relevant neighbors without reading the entire neighborhood, we equip a language model with graph tools that progressively disclose neighbor records. To train our graph agent, we propose a rubric-guided trajectory distillation strategy through which the agent learns both tool-use behavior and structured explanation generation. Experimental results on three fraud datasets demonstrate consistent improvements over state-of-the-art baselines under a human-aligned rubric.
Read the full abstract

All authors Cecelia Soh, Jun Hu, Yuan Li, Cheng Chen, Hui Liu, Yanjia Zhu, Hao He, Bryan Hooi, Bingsheng He

Applied Physics

Device Model Linking Singlet and Triplet Spin-State Properties to Organic Photovoltaic Device Characteristics

Jun Du, Liming Liu and Jun Yan

This work proposes an exciton-kinetics coupled drift-diffusion (EKDD) model that bridges device-level charge transport with singlet and triplet excited-state kinetics. State populations and reversible photophysical transitions are solved self-consistently, enabling transient and steady-state device response calculations. We investigate how LE-CT, CT-CS, and LE-T energy offsets, together with triplet-transfer kinetics, govern charge generation, recombination, voltage loss, and extraction in organic photovoltaics. We identify CT-CS offset as a primary, largely overlooked factor controlling charge generation and voltage loss. A positive offset makes CT3-CS separation exothermic and improves generation, but lowers CS energy and increases voltage loss; a negative offset makes separation endothermic and highly temperature-sensitive. The model reproduces triplet-mediated recombination in PM6:Y6 and material-dependent triplet recycling in D18:NFA blends. We propose design rules for balanced generation and voltage loss via two charge-transfer channels: the LE-CT-CS pathway favors a moderate LE-CT offset of about 0.15-0.20 eV and a weakly exothermic CT-CS offset of about 0.05-0.075 eV; the T-CT3-CS pathway requires the same CT-CS alignment plus a small CT-T offset, no more than about 0.1 eV, corresponding to a recycling factor K greater than about 10^-2, to maintain fast T-to-CT3 recycling. CT-CS alignment is the main determinant of the charge-generation-voltage-loss trade-off, while triplet-recycling kinetics determines whether this balance survives at the device level. This work provides a unified theoretical description from exciton generation to free charge collection, offering a convenient tool for transient and steady-state analysis in organic photovoltaics.
Read the full abstract

Information Theory

The Gauss–Newton Method for Phase Retrieval from Coded Diffraction Patterns

Meng Huang and Tianjiao Wang

We study the minimum-norm Gauss–Newton method for phase retrieval from random coded diffraction patterns with additive intensity noise. For bounded symmetric masks satisfying the standard complex moment conditions, we establish convergence guarantees with quadratic error recursions in both the resampled and fixed-data settings. In the resampled setting, $O(\log^2 n)$ masks per independent block suffice to obtain a spectral initializer within a constant-relative-radius neighborhood and to establish a local quadratic error recursion with an additive noise term. Consequently, in the noiseless case, the method achieves relative accuracy $\epsilon$ using $O(\log\log(1/\epsilon)\log^2 n)$ masks. In the fixed-data setting, $O(\log^6(en))$ masks suffice for spectral initialization within a neighborhood whose relative radius is proportional to $\log^{-5/2}(nL)$. Within this neighborhood, the method satisfies a linear contraction bound and eventually a quadratic error recursion. In particular, in the noiseless case, the method initially converges linearly and enters a quadratic convergence phase after $O(\log n)$ iterations, without acquiring additional masks. Numerical experiments on synthetic signals and a natural image demonstrate the method's efficiency relative to state-of-the-art algorithms.
Read the full abstract

Chemical Physics

Development of a Minimal Auxiliary Basis for the Particle–Particle Random Phase Approximation (ppRPA): Accelerating Excited-State Calculations in Diradical Nanoparticles and Periodic Systems

Yuchen Wang and Zhenyu Li

Singlet–triplet gaps set the energy scale of the two-spin manifold in molecular quantum information materials, and such systems frequently carry diradical character: metal nanoclusters, metal–organic frameworks with radical linkers, and the nitrogen–vacancy center in diamond. Time-dependent density functional theory is the standard tool at this size but describes multireference systems poorly. A closed-shell reference is inadequate for a multiconfigurational ground state, and the adiabatic kernel excludes doubly excited configurations. The particle–particle random phase approximation (\pprpa{}) changes the particle number rather than the spin. Singlet and triplet states descend from one closed-shell reference, and double excitations enter on the same footing as single ones. Computational cost has confined \pprpa{} to small systems, and the two-electron integrals that drive it are factorized through an auxiliary basis. We develop \pprpa{}–as, which reduces that basis to a minimal set of atom-centered Gaussian shells whose exponents follow from free-atom radii and one global scaling factor. The approximation enters only the excited-state step, so both calculations proceed from the same self-consistent reference. We test molecules and clusters of up to 286 atoms, including heavy elements, and seven point defects in four periodic hosts, obtaining both multiplicities and their splitting without symmetry breaking or spin projection. We optimize the scaling factor and give guidance on choosing it. An order-of-magnitude smaller auxiliary basis delivers a speed-up of up to eightfold while staying within $0.03$~eV of conventional \pprpa{}, bringing diradical nanoclusters and periodic systems within reach.
Read the full abstract

Computation and Language

Machine–human agreement in marking undergraduate assignments is specific to model, prompt and institution

Giulio Corsi, Maryam Abo-Tabik, Yael Benn et al. (+4 more)

Large language models (LLMs) promise to automate one of higher education's most resource-intensive activities: assessing student writing. Existing evaluations are typically limited to a single model, a single prompt and a single institution. Using 761 undergraduate psychology assignments from three UK universities, we evaluated three frontier LLMs, individually and in ensemble, on accuracy, prompt sensitivity, error independence, generalisability and evidential basis. Agreement between human and machine marks varied widely, with no model or prompt optimal across institutions. All models compressed marks towards a common central tendency, overvaluing the weakest assignments and undervaluing the strongest. Because this error was shared, ensembling failed to remove it. Machine marks also rewarded linguistic features more strongly than human marks. Since human-machine agreement was a joint product of model, prompt and institution, we conclude that current LLMs lack evaluative capacity transferable across contexts, and that single-context validation overstates readiness for deployment.
Read the full abstract

All authors Giulio Corsi, Maryam Abo-Tabik, Yael Benn, Georgiana Thorpe Apreutesei, Roni Tibon, Alexandru Marcoci, Deborah Talmi

Computer Vision

Dynamic Interpolation for Training-Time Prediction Reshaping in Low-Light Image Enhancement

Peirui Zhu, Shijie Hao, Jingxi Liao et al. (+2 more)

Low-light image enhancement (LLIE) is an important image/video processing problem for reliable visual systems under poor illumination, yet it remains challenging due to the strong coupling among insufficient illumination, noise amplification, and structural degradation. Existing methods typically rely on sophisticated network architectures or engineered objective functions. While effective, the former increases model complexity and deployment burden, whereas the latter relies on empirically tuned loss weights. We revisit supervised LLIE from a training-dynamics perspective and observe that brightness-related errors tend to converge early, while residual noise and structural artifacts often remain insufficiently optimized in later stages. To address this issue, we propose a Dynamic Interpolation Training-only Module (DITM), which introduces no additional learnable parameters and adaptively reshapes the model prediction for supervised feed-forward LLIE training. Specifically, DITM employs brightness-aware interpolation, in which the model prediction is combined with a mean brightness-aligned image constructed by rescaling the model prediction according to the mean brightness of the ground-truth image. By reducing the relative dominance of global brightness discrepancy, DITM makes residual local errors more influential during backpropagation after global luminance has largely stabilized, thereby facilitating artifact suppression and fine-detail restoration in later training stages. DITM is used only during training and is fully removed at inference, introducing no additional inference cost. Extensive experiments on multiple benchmark datasets and CNN-, Transformer-, and Mamba-based LLIE backbones show that DITM consistently improves fidelity and perceptual quality while preserving the inference-time architecture and introducing no additional inference-time computation.
Read the full abstract

All authors Peirui Zhu, Shijie Hao, Jingxi Liao, Richang Hong, Meng Wang

Data Structures and Algorithms

Inclusion-wise extremal knapsack problems are (mostly) hard to approximate

Fabio Ciccarelli, Fabio Furini, Ulrich Pferschy et al. (+1 more)

We study two inclusion-wise extremal variants of the 0-1 Knapsack Problem (KP). Given $n$ items with integer weights and nonnegative costs or profits, the Minimum-Cost Maximal Knapsack Packing Problem seeks a minimum-cost packing for capacity $c$ to which no item can be added, while the Maximum-Profit Minimal Knapsack Cover Problem seeks a maximum-profit cover for threshold $d$ from which no item can be removed. Both problems are weakly $\mathrm{NP}$-hard and pseudo-polynomially solvable, as is the classical KP, but their approximability is fundamentally different: unless $\mathrm{P}=\mathrm{NP}$, with positive values neither problem admits a polynomial-time $2^{p(n)}$-approximation for any polynomial $p$; allowing zero values rules out every multiplicative factor. Even with unit costs for packing and unit profits for covering, neither admits an $n^{1-\varepsilon}$-approximation for any fixed $\varepsilon>0$, whereas a trivial $n$-approximation exists. We identify restrictions that restore approximability. If every item weighs at least a fixed fraction of the capacity, respectively of the threshold, both problems can be solved exactly in polynomial time. For the cover problem, if each weight $w_i$ satisfies $w_i-1\ge\delta(d-1)$, we give an approximation scheme for arbitrary profits whose running time is polynomial in $n$, $1/\varepsilon$, and $1/\delta$. If values equal weights, a fully polynomial-time approximation scheme (FPTAS) was already known for the packing problem; we give an FPTAS for the cover problem, improving on the previously known factor $2$. If value-to-weight ratios differ by at most a factor $\kappa$, we obtain a $(1+\varepsilon)\kappa$-approximation for both problems.
Read the full abstract

All authors Fabio Ciccarelli, Fabio Furini, Ulrich Pferschy, Elena Rener

General Topology

Generalized Topological Structures Induced by $H^{*}$-Open Sets and Their Separation Properties

Neeraj Kumar Tomar, Komal Priya and Amit Ujlayan

In this paper, we investigate the family of $H^{*}$-open sets from the perspective of generalized topology and examine the structural and separation properties arising from it. For a topological space $(\mathbb{X},\tau)$, the family of all $H^{*}$-open subsets of $\mathbb{X}$ is considered as a natural candidate for an $H^{*}$-generated generalized topology. We establish conditions under which this family forms a strong generalized topology and identify additional assumptions that allow the resulting structure to be treated as an ordinary topology. This distinction provides a systematic framework for studying $H^{*}$-open sets without imposing finite-intersection properties in advance. Within this framework, we investigate the corresponding $H^{*}$-interior and $H^{*}$-closure operators and analyze separation properties determined by the $H^{*}$-open structure. When the $H^{*}$-generated family forms a topology, the resulting associated space is used to relate generalized $H^{*}$-separation properties to their classical topological counterparts. We further examine compactness-type properties, subspace behaviour, and preservation under suitable mappings. Examples are included to illustrate how the structure generated by $H^{*}$-open sets may exhibit separation behaviour different from that of the original topology. The results provide a unified setting for connecting $H^{*}$-open-set theory with generalized topological structures and offer a basis for further investigation of separation and covering properties in this framework.
Read the full abstract

Optimization and Control

Optimal Control of Mean-field FBSDEs and Classical Solutions of the Related Generalized HJB Equations

Ziyu Huang

In this article, we study optimal control problems driven by mean-field forward-backward stochastic differential equations (FBSDEs). The aim is two-fold: (i) to provide the unique optimal control; (ii) to establish classical solutions of the associated generalized Hamilton-Jacobi-Bellman (HJB) equations on the space of probability measures. For (i), we study the coupled system arising from the maximum principle, which involves both the state FBSDEs and the adjoint FBSDEs. This system is non-standard and more complicated than that in standard mean-field type control (MFTC) problems. By extending the method of continuation in the coefficients to our framework, we establish the well-posedness result, and subsequently obtain the differentiability of the value functional with respect to the distribution argument — thereby extending existing results for MFTC problems. For (ii), since the flow property does not generally hold for control problems of FBSDEs, standard dynamic-programming arguments cannot be applied directly. We therefore develop probabilistic approaches to the classical solvability of generalized HJB equations under three distinct sets of conditions. The auxiliary processes arising in these arguments differ significantly from those in standard MFTC problems. We establish new connections between the value function and the adjoint processes, derive equations for the relevant decoupling field, and obtain first- and second-order generalized HJB equations, including a coupled system on the space of probability measures. The standard HJB equations for MFTC problems are recovered as special cases. Our approach yields classical solutions without requiring non-degeneracy of the diffusion.
Read the full abstract

Computation and Language

LatentRemedy: Efficient Remedy For Non-Prefix KV Cache Reuse in Multi-Agent Systems

Zhiwei Chen, Zhongtao Rao, Waikit Xiu et al. (+4 more)

Non-prefix KV cache reuse is a promising approach for improving inference efficiency in multi-agent systems as agents share the same context blocks. However, when agents have conflicting tasks, directly reusing non-prefix KV caches across agents can severely degrade model performance, causing up to a 90% drop. Existing remedies, including selective recomputation and offset-based calibration, recover a limited fraction of this loss. We argue that the problem lies not only in contaminated KV representations but also in the agent's difficulty interpreting reused context under its own instructions. To this end, we introduce LatentRemedy, which uses a few task-conditioned latent tokens to help the target agent leverage reused context without recomputing the shared block. Specifically, we use an MLP to map the last hidden state of the target prefix into a small number of latent tokens, which are placed after the reused KV block to interact with the shared context. To guide subsequent predictions toward the target task, we train the MLP through full-prefill distillation. Experiments show that LatentRemedy consistently outperforms existing reuse baselines on conflicting tasks while maintaining TTFT close to direct reuse as cache size grows. Code and data are available at https://github.com/chenzhwsysu57/kvreuse.
Read the full abstract

All authors Zhiwei Chen, Zhongtao Rao, Waikit Xiu, Yuxiang Zhang, Shuting Cui, Jian Chen, Tianxiang Zhao