Computers and Society
Argus: Academic Integrity in the Era of Generative AI
David Racovan, Ajay Rawat, Christopher K. May et al. (+1 more)
The rapid proliferation of large
language models (LLMs) in the context of education has introduced significant challenges in enforcement of academic integrity, especially in programming courses. We present Argus, an automated detection system for LLM-assisted student work in undergraduate C programming assignments. Argus integrates behavioral and stylistic indicators to create a holistic picture of the student's progress through an assignment and surfaces anomalies that point to potential misuse of LLM assistance. We quantify and analyze data over six years of Spring semester offerings in a large-enrollment CS2 course at Purdue University using Argus, finding that 45% of enrolled students exhibited patterns consistent with LLM-assisted code development in Spring 2026. To contextualize these results, we analyze the relationship between flagged LLM use and student performance on written, in-person proctored examinations, and find a significant negative correlation. We also explore the problem of mitigating false positives, recognizing that erroneous accusations of academic integrity carry significant consequences for students and instructors alike, particularly in the context of large enrollment courses. We argue that any automated detection system must be accompanied by a structured process for human review. We discuss the consequences for future course design, changing academic policy as these tools become more ubiquitous, and the pedagogical implications of LLM-based tools in computer science education.
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All authors David Racovan, Ajay Rawat, Christopher K. May, Jeffrey A. Turkstra
Computational Complexity
Spectral Methods for the Complexity of Planar Graph Homomorphisms
Ashwin Maran, Jin-Yi Cai and Zhuxiao Tang
We explore the frontier beyond the recently discovered barrier represented by the
quantum automorphism group $qut(M)$ in the classification theory of planar graph homomorphisms $PlGH(M)$. We show that analyzing the spectral relations of $M$ can prove \#P-hardness when traditional vertex separation and domain-reduction methods with planar edge gadgets provably fail due to the $\qut(M)$ barrier. We prove two criteria of \#P-hardness for $PlGH(M)$: a spectral criterion and a determinant criterion. It is known that the core problem for the classification of $PlGH(M)$ for nonnegative
matrices $M$ is for positive definite entry-wise positive
matrices. We use the spectral criterion to show that $PlGH(M)$ is \#P-hard for all circulant
matrices of
prime order $q \ge 3$, while for $q=2$ it is precisely the matchgate case and is P-time computable by the FKT
algorithm (for planar perfect matching). We also prove a complexity dichotomy for $\PlGH$ problems defined by tensor products of 2 by 2
matrices. This gives a complete complexity classification for this class of
matrices, and the FKT
algorithm together with a
holographic transformation is universal—every $PlGH(M)$ is either (1) P-time computable over all graphs, or (2) \#P-hard in general but P-time computable over planar graphs, or (3) \#P-hard over planar graphs; furthermore, $PlGH(M)$ in (2) consists of precisely those computable by FKT with a
holographic transformation.
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Artificial Intelligence
LongCat-DeepResearch Technical Report
Meituan LongCat Team: He Zhu, Yue Xu, Wanli Wu et al. (+27 more)
We present LongCat-DeepResearch, a deep research system that combines an enhanced LongCat model with a multi-
agent workflow for producing comprehensive, evidence-grounded reports. The workflow separates global planning from detailed investigation and coordinates revision at the section level. Multiple planning
agents first explore external sources and refine an actionable research plan, termed ResearchSpec. Research
agents then investigate and draft their assigned sections in parallel, gathering additional evidence in separate contexts as their analyses develop. Once the sections are assembled, global review guides targeted local revisions, reducing reliance on repeated full-report rewriting. This workflow also supports the construction of research tasks and trajectories for the mid-training and post-training of LongCat's general-purpose models. LongCat-DeepResearch achieves 55.25 on DeepResearchBench, 51.35 on DeepResearchBench II, and 79.83 on ResearchRubrics. On an in-house
benchmark, it scores 76.04, ranking second among four compared systems. Development-set analyses show benefits from combining planning perspectives, while further planning refinement has mixed effects. Additional editing improves average automatic readability preference across two
benchmarks, with different trends on each.
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All authors Meituan LongCat Team: He Zhu, Yue Xu, Wanli Wu, Haolin Ren, Yuxin Bian, Jiarui Zhao, Rongzhi Zhang, Quanchi Weng, Jinghao Cui, Yu Fan, Yuhan Liu, Yunhu Ye, Jiyuan Ren, Fengcheng Yuan, Zhao Yang, Jiacheng Zhang, Yuchuan Dai, Ruixuan Xiao, Haozhe Sun, Xiangyuan Liu, Cheng Sun, Yao Du, Yiming Hao, Hongbo Guo, Shuo He, Lei Wang, Xunliang Cai, Yan Chen, Fan Yang, Lingchuan Liu
Distributed Computing
Mixture-of-Kittens: MoE Megakernel for NVL72s
Stuart H. Sul, Nash Brown, Henry Wildermuth et al. (+3 more)
AI accelerator systems are rapidly consolidating into scale-up architectures, where tens to thousands of GPUs communicate over high-bandwidth, single-hop fabrics. We find that existing Mixture-of-Experts (MoE) training systems, optimized for conventional scale-out networks, transfer poorly to this setting, often running slower than a naive baseline built with PyTorch and NCCL. With industry roadmaps pointing toward even larger scale-up domains, understanding the performance tradeoffs of this hardware regime is increasingly important. We present Mixture-of-Kittens (MoK), an MoE training system designed for Nvidia NVL72. MoK builds on three insights that unlock performance on scale-up domains: (1) choosing push- or pull-based communication per operator, (2) restructuring the computation-communication overlap, and (3) fully eliminating CPU-GPU synchronization. MoK distills these insights into a single deterministic training megakernel that fuses
token dispatch, shared and routed expert FFNs, and
token combine. Across MoE layer shapes from four widely used open-weight models, MoK delivers up to $2.37\times$ the throughput of the strongest publicly available baseline. In a production run on 512 GPUs spanning multiple GB300 NVL72 racks, MoK improves end-to-end training throughput by $1.41\times$.
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All authors Stuart H. Sul, Nash Brown, Henry Wildermuth, William Lin, Federico Cassano, Christopher Ré
Software Engineering
The Uneven Decline of Collective Knowledge Production: Evidence from Stack Overflow After Generative AI
Myokyung Han, Taegyoon Kim, Jinhyuk Yun et al. (+1 more)
Generative AI (Gen AI) is reshaping how individuals learn and work, but its consequences for collective knowledge, the shared body of knowledge that online communities produce together, remain poorly understood. Prior work has documented an aggregate decline in participation on knowledge-sharing platforms, but it remains unclear which specific kinds of knowledge are being lost first. We study this question using Stack Overflow, one of the largest online communities for software engineering, treating the release of ChatGPT-3.5 as a natural shock. Analyzing over two million questions posted between 2020 and 2025, we track how two dimensions of collective knowledge, difficulty and data availability, change following Gen AI's release. Using diverse methods and robust checks, we find consistent patterns. Easy questions decline sharply while difficult questions become more common, a pattern corroborated by rising code complexity. Data-rich topics and tags lose share of questions, while data-scarce ones gain ground. The two dimensions also interact: the decline in easy questions is concentrated specifically within data-rich domains, while difficult questions increase regardless of data availability. This pattern extends beyond Python across programming
languages, with more prevalent
languages showing sharper shifts. Together, our findings reveal that Gen AI's impact on collective knowledge is uneven, eroding easy, accessible knowledge first while more complex, less common knowledge persists.
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All authors Myokyung Han, Taegyoon Kim, Jinhyuk Yun, Lanu Kim
Number Theory
Strongly Pseudoperfect Numbers
Audrey Wang and Joshua Zelinsky
A positive integer $n$ is said to be strongly pseudoperfect if there is a subset $S$ of the positive divisors of $n$ such that $d \in S$ if and only if $n/d \in S$, and $\sum_{d \in S} d = 2n$. We construct new infinite families of strongly pseudoperfect numbers, and also prove new restrictions on when a positive integer $n$ can be strongly pseudoperfect.
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Information Theory
A Hyperbolic Bound for File Retrieval in DNA-Based Data Storage
Constantinos Vasilios Argyris Vlachos and Daniella Bar-Lev
In DNA-based storage systems, data are retrieved by sequencing molecules sampled from a DNA pool. We study how the way coding redundancy is shared between files affects their expected
retrieval times. Our focus lies on the case of two files that are encoded by a systematic linear code over an arbitrary finite field. We consider the
conjecture that the sum obtained by dividing each file dimension by its expected
retrieval time is at most one whenever the dimension of at least one file is more than one. For this, we develop a
geometric view of the
retrieval process. As molecules are sampled, we follow the growing span of the corresponding columns of the generator
matrix and track how much of this span comes from each file. Among the samples that enlarge the overall span, this lets us compare those that make progress toward recovering both files with those that make progress toward neither. We call a column mixed if the corresponding encoded symbol combines information from both files. In this work, we sharpen a projection bound and use it to control the effect of mixed columns. We prove the
conjecture whenever the total information dimension is at least twice the number of mixed columns plus two. For equal-sized files, this allows up to one fewer mixed column than the dimension of either file and extends the previous result for codes with no mixed columns.
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Computer Vision
AerialDojo-200K: A Large-Scale Benchmark Suite for Open-World Aerial Object-Goal Search
Tongtong Feng, Xin Wang, Haoran Hou et al. (+9 more)
Open-world aerial object-goal search is a foundational yet challenging task, requiring aerial
agents to autonomously explore large-scale, unstructured three-dimensional environments and reach target objects specified by semantic descriptions or reference images, rather than following route-specific instructions. However, research in this task remains at a nascent stage and relies on small, environment-specific
benchmarks with heterogeneous action spaces and data formats. These limitations hinder large-scale training and cross-
benchmark evaluation, constraining the scalability and generalizability of aerial
agents. To address this problem, we propose AerialDojo-200K, a large-scale
benchmark suite for open-world aerial object-goal search, with 3 times as many scenes and 18.7 times as many task instances as the largest existing
benchmark for this task. Specifically, we construct 42 simulation scenes spanning four scene families and 21 scene types, including 18 urban, 12 natural, six infrastructure, and six disaster scenes. To ensure data quality, 12 annotators spent two months manually annotating 109 landmarks, 2099 target objects, and 2099 object anchors across these scenes. We further construct 205,732 task instances, comprising over 100K semantic-goal and over 100K image-goal instances across Base, Standard, and Long-Horizon settings. Each task instance includes a collision-free reference trajectory and corresponding multi-view
video recordings. We also develop a unified evaluation framework with a scene partition comprising 21 in-distribution scenes and 21 out-of-distribution scenes. Finally, our evaluation of five open-source and four closed-source
multimodal large
language models reveals that there is still a long way to go toward achieving general-purpose aerial
agents. All can be found at https://fengtt42.github.io/AerialDojo/.
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All authors Tongtong Feng, Xin Wang, Haoran Hou, Ren Wang, Weiran Wang, Shaokai Zhu, Ziqi Jia, Hao Wang, Yu-Wei Zhan, Zongyuan Wu, Jinghao Cui, Wenwu Zhu
Programming Languages
Irene: Equivalence Checking of Hybrid Quantum Programs via Structure-Preserving Symbolic Reduction
Jingyu Ke, Jingyang Li and Guoqiang Li
Equivalence checking is essential for validating compiler transformations of hybrid
quantum programs, which combine
quantum operations, measurements, and classical control. Measurement-dependent control limits
unitary reasoning, while dependencies between classical outcomes and
quantum operations can enlarge intermediate symbolic states. We present Irene, an equivalence-checking framework for bounded hybrid
quantum programs based on structure-preserving symbolic reduction. The framework progressively simplifies equivalence obligations through three levels of
reasoning. At the gate level, algebraic identities simplify
unitary regions. At the hybrid path-sum (HPS) level, reduced symbolic execution states are represented as typed graphs, whose isomorphism certifies equivalence. Remaining obligations are handled by density kernels that characterize transformations of input density operators into observable outputs, allowing comparison even when internal measurement histories differ. Residual coefficient differences are encoded as SMT queries. A common set of symbolic reductions supports HPS and density-kernel
reasoning by preserving factored Boolean and arithmetic expressions, eliminating reducible dependencies before expanding residual sums. We evaluate Irene against five equivalence checkers on 1,982 program pairs from seven
benchmark suites. Irene solves 1,584 pairs (79.92%), compared with 57.52% for MQT QCEC, the baseline with the highest aggregate coverage, with a mean end-to-end time of 3.93 seconds per solved pair. Applied as an equivalence-checking oracle, Irene also identifies 15 previously unknown bugs in
quantum compilers, including Qiskit, Cirq, and PennyLane.
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Machine Learning
FLOORA: A Human-Aligned Domain-Specific Language Model for Architectural Design
Sahand Rezaei-Shoshtari, Patryk Wozniczka, Shu Ishida et al. (+14 more)
Foundation models are powerful generators, but many engineering domains require structured representations that general-purpose systems handle poorly. We introduce FLOORA (Floor Layout Optimization with RL
Alignment), a family of small domain-specific
language (DSL) models for architectural layout generation. With specialized data and
alignment, our 0.6B model outperforms much larger frontier models, achieving VLM judge win rates up to 92.0% on out-of-distribution real-world buildings and 96.0% on synthetic buildings. Human evaluations further corroborate these results, with FLOORA selected as the best model in 89.3% of evaluations. FLOORA combines a
token-efficient DSL, custom tokenization, domain-specific pretraining, supervised fine-tuning (SFT), and reinforcement learning (RL) with learned human-preference and verifiable rewards. This pipeline improves architectural and
geometric validity, supported by extensive empirical evaluation and ablation studies. Although focused on architecture, our results suggest that similar domain-specific recipes may be useful in other engineering domains with structured, verifiable outputs.
Datasets, models, and inference code are available at https://github.com/AutodeskAILab/floora.
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All authors Sahand Rezaei-Shoshtari, Patryk Wozniczka, Shu Ishida, Gregg Streuber, Farnoosh Javadi, Jeffrey Landes, Angela Ju, Muhammad Azam, Bryan Lim, Johan Luttun, Indrajeet Haldar, Jonathan Shaw, Beatriz Guerra, Ivan Sosnovik, James Stoddart, Robert Giaquinto, Adam Gaier
Machine Learning
Understanding Decision-Making Mechanisms in Neural Routing Solvers
Fatemeh Askari, Mazdak Teymourian, Mohammad Izadi et al. (+1 more)
Neural Combinatorial Optimization (NCO) has achieved strong empirical success, yet the internal mechanisms driving model decisions remain largely unexplored. In this paper, we investigate three representative autoregressive NCO models spanning two encoder-decoder configurations: AM and POMO (heavy-encoder, light-decoder), and LEHD (light-encoder, heavy-decoder). Through behavioral analyses, representation probing, and causal interventions, we examine how these models construct solutions and use internal representations during decoding. Our results suggest that AM and POMO predominantly follow a persistent
geometric pattern throughout solution construction, whereas LEHD contains linearly accessible information about multiple future actions. Causal experiments further provide evidence for the role of future-node representations in LEHD's decision-making. We also observe that LEHD relies strongly on the current-node representation for immediate local decisions, while the start-node representation plays a broader navigational role over the subsequent route. Cross-instance
alignment analyses additionally indicate that LEHD maps current-node representations into a relatively shared latent region, which may provide a stable reference for evaluating subsequent decisions. Across the Traveling Salesman Problem and the Capacitated Vehicle Routing Problem, these results reveal distinct decision-making patterns across these architecturally distinct solvers and provide a foundation for more interpretable analyses of NCO solvers. Code and additional visualizations are provided in the https://github.com/NCO-Interpretability/NCO-Interpretability.
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All authors Fatemeh Askari, Mazdak Teymourian, Mohammad Izadi, Mahdieh Soleymani Baghshah
Artificial Intelligence
SMat-Attention: Structured Long-Context Sequence Modeling
Emile Anand, Abdullah Ateyeh, Archer Wang et al. (+1 more)
Long-context sequence models face a fundamental tradeoff: softmax
attention uses flexible
token-level interactions at quadratic cost, whereas linear
attention obtains linear-time training and constant-time decoding by compressing history into a fixed-size state. In this work, we ask whether we can connect these regimes through a tunable notion of structure. To this end, we introduce Structured
Matrix Attention (SMat-
Attention) via a family of causal masks with structured long-range routing whose row supports have VC-dimension $d$. In our construction, $d=1$ recovers the standard causal mask, and increasing $d$ permits richer subset-routing patterns. We give chunkwise forward and backward
algorithms to enable hardware-efficiency. For sequences of length $T$, the hard-routing construction takes $O(T^{2-3/d}+T)$ work, despite the mask being dense, for our prescribed family. In fixed-horizon streaming, decoding after the distant prefix takes constant time per
token using $O(T^{1-1/d})$ cached states. SMat-
Attention therefore makes VC-dimension an explicit knob governing access-pattern complexity, prefill cost, and decoding
memory. Empirically, subset-routing and rule-assisted multi-key
retrieval experiments illustrate the masks' routing expressiveness. Extensions to Mamba-2 and Gated DeltaNet using learned routing with top-$k$ query reads retain subquadratic prefill, improve recall accuracy over the backbones in several settings, and achieve comparable small-scale
language-modeling performance.
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All authors Emile Anand, Abdullah Ateyeh, Archer Wang, Marin Soljačić
Machine Learning
Introducing the CZAR Loss: A Tailored Objective Function for Financial Log-Return Predictions
Joel Pfeffer, J. M. Diederik Kruijssen, Florian Stecker et al. (+1 more)
In quantitative finance, standard regression losses are misaligned with the economics of return prediction. As the conditional mean of financial log-returns is close to zero, symmetric losses such as the mean squared and mean absolute errors make the constant zero forecast a near-optimal solution, penalizing models with genuine but noisy directional skill. This applies both during training, where predictions shrink toward zero, and during evaluation, where trivial forecasters can lead loss-based rankings. Under a Gaussian linear prediction model, we show that all symmetric monotonic losses share a universal breakeven directional accuracy against the zero predictor, which rises sharply and becomes unobtainable as the prediction noise approaches the standard deviation of the returns. We introduce the CZAR (Composite Zero-Agnostic Return) loss function, a piecewise quadratic loss built around five key requirements: convexity in the prediction, asymmetry with true return direction that vanishes at zero, near-linear penalization of undershoots and wrong-direction predictions, divergence for large errors, and an adaptive loss floor for evaluation. CZAR is provably convex in the prediction at fixed true value, has closed-form gradient and Hessian suitable for custom objectives in gradient-boosted libraries, and its four hyperparameters reduce to a single choice through correlated defaults. In idealized tests, the minimum directional accuracy required for a CZAR-evaluated forecaster to outperform the zero predictor under mean log loss remains near the 50% chance level, whereas the corresponding threshold for symmetric losses rises sharply with prediction noise. This advantage persists under heavy-tailed return distributions. In a LightGBM experiment on intraday BTC log-returns, CZAR-trained models reduce the `zero-returns bias' and improve directional accuracy on large-magnitude returns.
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All authors Joel Pfeffer, J. M. Diederik Kruijssen, Florian Stecker, Steven N. Longmore
Quantum Physics
From Projected Subspaces to Full-Space Implementations: Representation-Equivalence Audits for Open-Shell ADAPT-VQE
Lucia Malíčková and Petr Klenovský
A variational state optimized in a
symmetry-projected space need not be the state prepared by exponentiating the corresponding unprojected parent generators. For an orthogonal target-space projector $P$, $Q=I-P$, and an anti-Hermitian generator $A$, exact equivalence for all amplitudes requires target-space invariance, $QAP=0$. We formulate this condition as a representation-equivalence audit for open-shell ADAPT-VQE. For a canonical CAS(15e,9o) type-1 Cu(II)
Hamiltonian, 274 of 323 bare Hartree–Fock-origin single, double, and triple excitations violate this condition. Projected-doublet ADAPT reaches 1.5719 m$E_h$ with 121 operators, whereas the same amplitudes in the corresponding full-space sequence give 464.730 m$E_h$ error, 0.07343 fidelity with the projected state, and 54.45% quartet weight. Reoptimizing the same 121 parent generators in the same order under the hard constraint $p_D\ge0.999$ reduces the best converged error to 8.482 m$E_h$, showing that the catastrophic same-angle failure is primarily a representation/parameter-transfer failure but is not completely repaired by full-space reoptimization of the fixed sequence. An independent 12-
qubit NO
benchmark exhibits the same mechanism more mildly. A
spin-preserving generalized full-space construction removes the representation ambiguity and reaches 1.493 and 1.181 m$E_h$ at 18 and 24
qubits. For the 18-
qubit positive control, selectively refining only four Suzuki-2 factors and re-transpiling with a fixed compiler seed passes the predeclared
circuit-equivalence gate at 19,660 controlled-X (CX) gates and depth 23,016.
Circuit synthesis and final-energy measurement therefore constitute separate validation layers. The contribution is a validation framework, not a new
spin-adapted ADAPT
algorithm.
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Computation and Language
Mnemon: Raw Records, Fast Judgments, Slow Thoughts
Guangren Wang
Long-term
memory lets an LLM assistant use a history it can no longer reread, and most
memory systems build it by rewriting conversations into facts, graphs or typed
memories at write time. We argue that the work of
memory divides, as thinking does, into two systems. Most of it is fast System 1 work: many small, independent yes/no judgments about records, such as whether a record is needed or no longer current, which a decision model makes by the dozen in a third of a second. Only a little is slow System 2 work: writing a few search queries, naming what the reply needs and composing the answer, which an LLM does well but slowly. We present Mnemon, a
memory agent built on this division. It keeps conversations as raw, dated records; an LLM (System 2) plans searches over them, a decision model, Jev (System 1), judges what the searches return, and rules with explicit budgets turn the judgments into a small View for an unchanged answering model. A background pass consolidates each record once into topic timelines, value histories and standing instructions linked to the records, so that questions about a whole conversation reach evidence their own searches miss. Because nothing is decided about a record when it is written, the same
agent can read any store that returns dated records. With gpt-4.1-mini answering, as in a public re-evaluation of 14 systems, Mnemon scores 91.7% on LoCoMo, the highest among them, and 83.8% on LongMemEval-S, from under 4k
tokens of context per question, with the lowest effective cost index on LoCoMo. With a
reasoning model answering, it reaches 92.2% on LoCoMo and 94.4% on LongMemEval-S, the latter on par with the best published results. From 100K to 10M
tokens of history on BEAM, its cost per question grows by a factor of 1.11. On the same records, Jev separates gold evidence better than two LLMs and is 3-11 times faster.
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Machine Learning
ABC: Advantage-Based Control Variates for Reinforcement Learning with Verifiable Rewards
Hsiao-Ru Pan, Florent Draye and Bernhard Schölkopf
Recent progress in reinforcement learning with verifiable rewards (RLVR) has highlighted the effectiveness of simple critic-free policy-gradient methods such as Group Relative Policy Optimization (GRPO). In contrast, actor-critic methods rely on learned value functions whose approximation error can introduce bias through commonly used advantage
estimators such as temporal-difference error. Motivated by this observation, we revisit trajectory-level control variates through an advantage-value formulation, which we call Advantage-Based Control Variates (ABC). This formulation reveals that the covariance structure is closely related to the return decomposition used in Direct Advantage Estimation (DAE). Finally, we combine ABC with DAE into a single actor-critic
algorithm and evaluate it in an offline-to-online RLVR setting, where the critic is first trained on previously collected trajectories and adapted during online learning. On mathematical
reasoning tasks, ABC achieves performance competitive with GRPO using substantially fewer online optimization steps.
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Artificial Intelligence
Mirror-Score: Calibrated, Inference-only Scoring Exposes the Limits of Sequence-compatibility Ranking in D-peptide Design
Jiada Li
D-peptides combine protease resistance with high target specificity, but computational design of D-peptide binders remains immature. Mirror-Peptidizer introduced an in silico mirror-image screening pipeline using target reflection, backbone generation, and ProteinMPNN sequence design, but its raw ProteinMPNN negative log-likelihood (NLL) ranking was not validated against measured affinities, and only 4 of 9 tested MDM2 designs bound detectably. We introduce Mirror-Score, a calibrated, inference-only scoring framework for heterochiral D-peptide/L-
protein complexes, and a public
benchmark of 31
crystal complexes across four target families, including 18 with literature-verified affinities. Raw ProteinMPNN NLL is not a valid affinity ranker: its pooled Spearman correlation with affinity is 0.19, and correlations reverse between MDM2/CHIP (+0.62) and gp41 (-0.70). We therefore evaluate Boltz-2 mirror-space cofolding confidence. For the complete viral-entry family (7 structures representing 3 peptides), interface predicted local distance difference test (pLDDT) achieves structure-level leave-one-out Spearman rho = 0.90 (p = 0.006) and correctly orders all three peptides by affinity, whereas NLL fails (structure-level rho = 0.18). Because only three independent chemotypes are represented, this result indicates directional consistency rather than a statistically validated predictor. Cross-family calibration does not transfer at current sample sizes, supporting family-matched calibration as the practical deployment mode. We also specify a prospective design protocol for the antimicrobial-resistance targets LasR and LecB from Pseudomonas aeruginosa, including mirrored structures, ligand-derived hotspot maps,
diffusion-model-ready inputs, and Mirror-Score ranking. Code,
benchmark data, structures, and analysis scripts are openly available at https://github.com/Jiadalee/Mirror-Score.
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Artificial Intelligence
GeoWind2Plan: Mission-Time 3D Urban Wind Prediction for Energy-Efficient UAV Planning
Shaoxiang Qin, Yucheng Zhao, Fuyuan Lyu et al. (+6 more)
In urban low-altitude flight, buildings reshape ambient wind into spatially varying 3D flow, making unmanned aerial vehicle (
UAV) energy depend on local wind exposure as well as path length. However, building-resolved wind is rarely available when a mission must be planned. Computational fluid dynamics (CFD) produces high-fidelity urban flow fields, but each simulation is tied to a fixed inflow boundary condition and can take hours to days, which is incompatible with urban
UAV missions that typically last minutes to tens of minutes. We present GeoWind2Plan, a geometry-to-wind-to-planning framework for mission-time 3D urban wind prediction and energy-efficient
UAV planning. Given only a background wind vector, 3D building geometry, and a start-goal pair, GeoWind2Plan transforms the building geometry into a reference-wind frame, predicts mission-relevant 3D wind patches with a localized geometry-conditioned neural operator, stitches them into a local wind field, and optimizes a feasible 3D path and speed profile using a physically grounded
UAV energy model. Rather than pursuing CFD-perfect reconstruction, GeoWind2Plan targets decision-useful wind prediction: trajectories are planned with predicted wind and evaluated under high-fidelity CFD wind. Across held-out urban domains, wind speeds, and mission wind-angle regimes, GeoWind2Plan performs corridor-localized wind inference in about 3 s, compared with roughly 8 h for CFD. Under CFD evaluation, trajectories planned with GeoWind2Plan reduce energy by 6.9%, 12.7%, and 4.5% in tailwind, headwind, and crosswind missions relative to wind-agnostic planning, recovering 87.9%, 85.7%, and 75.0% of CFD-reference savings. These results show that fast, corridor-localized 3D urban wind prediction can make wind-aware
UAV energy planning practical at mission time. Code/
dataset: https://github.com/DUAL-Xiao/GeoWind2Plan/ . Demo: https://geowind2plan.com .
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All authors Shaoxiang Qin, Yucheng Zhao, Fuyuan Lyu, Di Zhou, Jiachen Yao, Xue Liu, Anima Anandkumar, Liangzhu Leon Wang, Xiongye Xiao
Probability
Scale operators for Cramér–Lundberg type Markov additive processes
Kei Noba
In this paper, we consider Markov additive processes (MAPs) whose additive components are $\bR$-valued and have no positive jumps. Specifically, we assume that the modulators are Feller processes with only finitely many jumps on each bounded time interval and that the additive components also have only finitely many jumps on each bounded time interval. For such MAPs, we characterize the associated scale operators and use them to solve the two-sided exit problem, which concerns the first entrance time into one half-line on the event that it is reached before the other, and to characterize the potential measures of the processes killed when their additive components exit an interval. Our proofs rely on properties of analogues of local times, properties of the exit systems obtained by pairing them with suitable kernels, and results on the extension of $C_0$-
semigroups to $C_0$-groups.
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Computers and Society
What if automating AI R&D triggers an intelligence explosion?
Alan Chan, Christoph Winter, Andrew Barto et al. (+19 more)
In contrast to even a year ago, AI systems now write most of the code inside the companies that build them. As more of the AI research and development (R&D) pipeline is automated, could AI progress radically accelerate in an "intelligence explosion," where years of advances are compressed into months or less? Preliminary evidence suggests that it could. In this work, we assess this evidence, analyze an intelligence explosion's potential impacts, and propose policy responses. AI systems are on track to automate most AI R&D work within a few years, and possibly all of it. If this triggers an intelligence explosion, it could dramatically bring forward AI's benefits, but also pose extreme risks: capabilities growth could accelerate far beyond what society can keep up with, humanity could lose control over superhuman AI systems, and checks on power within and between states, companies, and branches of government could be severely eroded. Although there remains much uncertainty about these possibilities, the high stakes warrant serious further
attention. Policymakers should urgently obtain more visibility into the automation of AI R&D, develop ways to steer and constrain an intelligence explosion, and prepare society to adapt to an intelligence explosion's impacts.
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All authors Alan Chan, Christoph Winter, Andrew Barto, Jakub Pachocki, Geoffrey Hinton, Eric Horvitz, Yoshua Bengio, Dawn Song, Jack Clark, Hilary Greaves, Anton Korinek, Samuel Hammond, Thore Graepel, Ben Bariach, Philip H. S. Torr, Sheila A. McIlraith, Jeff Clune, Sam Manning, Girish Sastry, Tom Davidson, Daniel Eth, Sören Mindermann
Numerical Analysis
Slip Wall Boundary Conditions in Curved Domains for Finite Element ALE Hydrodynamics
Nabil Atallah, Kevin Sweet, Paul Moujaes et al. (+3 more)
We present a high-order finite element arbitrary Lagrangian-Eulerian (ALE) method for hydrodynamics with slip-wall boundary conditions on analytically curved domains. The method combines distinct Lagrangian, remesh, and remap phases while preserving the
geometric features of curved boundaries and allowing tangential motion of boundary nodes. In the Lagrangian phase, we use weak boundary condition enforcement to impose the slip-wall condition without freezing tangential motion. In the mesh optimization phase, we adapt TMOP (target-
matrix optimization paradigm) to analytic walls by replacing selected boundary coordinate degrees of freedom with curve or surface parameters. The Newton solve then moves boundary nodes tangentially with respect to the prescribed geometry while carrying the drift introduced by the Lagrangian phase. In the remap phase, we transfer the solution from the old mesh to the optimized mesh by a conservative advection-based remap over pseudo-time. We present numerical results in two and three dimensions demonstrating the method on high-order curved meshes and showing that it preserves boundary fidelity while producing the expected hydrodynamic solution features.
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All authors Nabil Atallah, Kevin Sweet, Paul Moujaes, Jan Nikl, Ketan Mittal, Vladimir Z. Tomov
Artificial Intelligence
PowerZooJax: A JAX-based Power System Benchmark for Reinforcement Learning
Zhanhua Pan, Xiao Liu, Zhilong Cao et al. (+2 more)
Power system operation is a safety-critical sequential decision-making problem, making it a natural testbed for reinforcement learning (RL). However, existing RL environments for power systems are often narrow in scope and computationally limited by CPU-based simulation workflows, making large-scale evaluation difficult. We introduce PowerZooJax, a JAX-based
benchmark suite for RL in power system operation. It provides five constrained Markov decision process tasks spanning generation, transmission, distribution, distributed energy resources, and data center microgrid. By rewriting power flow, economic dispatch,
market clearing, and device dynamics as JAX computation graphs, PowerZooJax keeps the entire training and evaluation loop on the GPU. Experiments show substantial speedups over CPU-based simulations and demonstrate standardized evaluation of policy returns, safety violations, and out-of-distribution stress conditions. Our open-source
benchmark is available at: https://github.com/powerzoojax/PowerZooJax.
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All authors Zhanhua Pan, Xiao Liu, Zhilong Cao, Jianhong Wang, Dawei Qiu
Strongly Correlated Electrons
Excitonic Stripe Order in the Two-Orbital Hubbard-Kanamori Model
Rafael D. Soares, Luke Staszewski, Chunhan Feng et al. (+1 more)
Excitonic condensation and stripe formation are two distinct manifestations of electronic correlations. While excitonic order naturally arises in multi-orbital systems, stripe order is a prominent feature of doped correlated-electron models. Here, we investigate an excitonic analogue of stripe order in the two–orbital Hubbard-Kanamori model on the square
lattice, characterized by a spatial modulation of inter-orbital particle-hole coherence and, in the orbital-parity-symmetric limit, spontaneous breaking of a relative orbital $\mathbb{Z}_2$
symmetry. Using unrestricted real-space Hartree-Fock calculations complemented by random-phase-approximation instability analysis, we determine how the Kanamori interactions select different excitonic channels. The sign of Hund's exchange controls the
spin character of the condensate:
ferromagnetic Hund coupling favors triplet excitonic order, whereas
antiferromagnetic Hund coupling stabilizes singlet excitonic order. Upon doping, commensurate excitonic density waves develop incommensurate textures, including excitonic stripes and, in the triplet sector, spiral excitonic density waves. We further show that
crystal-field splitting strongly reorganizes the excitonic instability by modifying inter-orbital nesting and can stabilize triplet excitonic order even in the absence of pair hopping. Our results establish excitonic stripes as a distinct
symmetry-broken state of multi-orbital correlated systems and identify microscopic routes for their stabilization.
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All authors Rafael D. Soares, Luke Staszewski, Chunhan Feng, Alexander Wietek
Statistics Theory
From Weak to Strong Testing in Gaussian Models
Ansh Nagda and Alexander S. Wein
We study the computational complexity of hypothesis testing in the spiked Wigner model, a prototypical model for detecting low-rank structure in a large random
matrix. Below the "BBP"
eigenvalue transition, it is expected that strong detection — with both type I and II errors vanishing — requires exponential time. Assuming this as a
conjecture, we determine the limits of
polynomial-time weak detection, exactly characterizing the possible tradeoffs between type I and II errors. Specifically, the optimal tradeoff is achieved by a particular linear spectral statistic. Thus, the question of weak detection is entirely reduced to that of strong detection. The proof builds on ideas of Nagda-Raghavendra (2025) and Moitra-Wein (2025). The low-degree likelihood ratio (LDLR) plays a key role: any test that slightly beats the LDLR can be boosted to have an even higher success probability. This leads us to establish a computational analogue of the Neyman-Pearson lemma for a subclass of additive Gaussian models: for a given super-
polynomial runtime, the best possible tradeoff between type I and II errors is either the one achieved by thresholding the LDLR, or the trivial tradeoff that results from strong detection.
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Machine Learning
Improving scalable oversight with co-trained monitors
Joseph H. Rudoler, Kevin Tan, Benedict Tessler et al. (+2 more)
Worker-monitor setups are a promising approach to AI oversight, but training workers against fixed monitors can incentivize monitor evasion. We study whether this failure mode can be avoided by co-training the monitor alongside the worker, and explore both supervised and self-supervised approaches. In the supervised setting, we prove a characterization: monitoring is possible with vanishing error and query rates exactly when the class of possible monitor functions has finite Littlestone dimension. This connects worker monitoring with an established literature on
adversarial online learning. For self-supervision, we propose a co-training procedure based on test-time
distillation: the monitor uses additional test-time compute to generate training labels, then trains its standard-compute policy on those labels. For majority-vote labels, we give a finite-sample sharpening guarantee under adaptive worker distributions with action coverage, that shows that the monitor's verdicts converge to its initial modal verdicts. We stress-test the former in code-security settings where the worker is trained adversarially to fool the monitor. Our results suggest that adaptive monitors are better at keeping pace with evolving worker strategies, while fixed monitors are more vulnerable to evasion.
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All authors Joseph H. Rudoler, Kevin Tan, Benedict Tessler, Timothy Kong, Enric Boix Adserà
High Energy Physics (Theory)
On the Derivation of Twisted K-Theory from M-Theory
Nikita Golub, Hisham Sati and Urs Schreiber
In a restricted untwisted sector, Diaconescu, Moore & Witten (2000, 2003) gave a
quantum M-theoretic consistency check, at the level of partition functions, of what one may call "Hypothesis K", namely that
magnetic and electric type IIA brane charges are jointly quantized in K-theory. We show that the more recently discussed "Hypothesis H" — that
magnetic and electric charges in 11D are jointly quantized in coHomotopy — together with a topological smallness condition on the M-theory circle, systematically implies that IIA charges are quantized in a nonabelian deformation of twisted K-theory by the previously neglected non-linear NS1-brane charge. This is essentially a derivation of twisted K-theory from IR-completed 11D SuGra, and conversely a further consistency check on Hypothesis H from Hypothesis K. We motivate and precisely state this result, discuss its structural implications, and outline the elaborate algebro-topological proof.
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Machine Learning
Neural networks for spectral optimization
Alexis de Villeroché, Beniamin Bogosel, Stéphane Breuils et al. (+2 more)
Given a functional dependent on the spectrum of a differential operator, we address the problem of finding a domain which optimizes this functional. PDE solvers might be used to tackle this optimization. It is however computationally expensive. We propose two neural network models which learn the spectrum directly from the geometry of the domain and can be used to optimize the domain from one or more
eigenvalues. We investigate two representations. The first encodes the domain through
Fourier coefficients and a light MLP, which is efficient on
star-shaped geometries, achieving a precision of 0.2%. Through a rescaling of the coefficients the designed models satisfy the scaling law of the
eigenvalues. Additionally, averaging the outputs of the trained surrogates over rotations and reflections induces invariance for these transformations. The second is a model that takes the landscape function, the indicator function and the gradient of the landscape function. A Gram-Schmidt process produces orthogonal eigenfunctions as output of the model along with the associated
eigenvalues. The landscape model reaches 1% mean relative error on the first ten
eigenvalues, compared with 4% for an FNO model. Replacing the landscape by an SDF worsened both prediction and optimization errors. The trained model also generalizes from synthetic shapes to domains given as classical image
dataset. The resulting surrogates of both approaches recover classical spectral optima such as the disk for the first
eigenvalue or the conjectured minima of higher
eigenvalues. This confirms that our models produce accurate differentiable estimates of
eigenvalues, which can be used in shape optimization problems involving spectral quantities.
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All authors Alexis de Villeroché, Beniamin Bogosel, Stéphane Breuils, Dorin Bucur, Jacques-Olivier Lachaud
Statistical Methodology
Graph-Split Bayesian Causal Forest for Spatial Heterogeneous Treatment Effect Estimation
Shuren He, Huiyan Sang and Ligang Lu
In spatial observational studies, treatment assignment and outcomes often exhibit spatial dependence patterns, and treatment effects may vary across space and subpopulations due to both measured and unmeasured spatially structured confounders. Accounting for spatial dependence while estimating heterogeneous treatment effects (HTEs) is a central task in spatial causal inference. Causal
Bayesian additive regression tree methods are popular nonparametric methods for modeling and estimating HTEs. Despite their flexibility and uncertainty quantification, the axis-aligned split rules often adopted in these models are not suitable for modeling spatial structures. We propose a spatial structure-aware
Bayesian nonparametric method, called Graph-Split
Bayesian Causal Forest (GSBCF), that integrates graph-split
Bayesian additive regression trees (GS-BART) with the
Bayesian causal forest propensity-score regression framework for spatial heterogeneous causal inference. Spatial confounding is accommodated through graph-guided split rules in modeling decision trees of the prognostic and HTE functions. We develop an efficient informed proposal sampling
algorithm for posterior computation, enabling full
Bayesian inference of the spatial conditional average treatment effect function. Simulations and a real data study demonstrate substantially improved estimation accuracy and uncertainty quantification over existing causal BART methods.
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Number Theory
p-adic Rigidity and Supercongruences at Rank Two Attractors
Yu Fu
We study a $p$-adic
rigidity phenomenon suggested by rank two attractors. Near an ordinary special fiber of a Calabi–Yau family, the excellent Frobenius of Beukers and Vlasenko acts on the parameter disk. Achinger–Zdanowicz construct a canonical $W_2$-lift of the Frobenius twist, while Brantner–Taelman construct a canonical formal lift. We
conjecture that the Dwork and Achinger–Zdanowicz first-order obstruction maps agree and that the fixed points of the excellent Frobenius are precisely the parameters of the Brantner–Taelman canonical formal lifts. Our main
theorem gives a cohomological criterion in arbitrary dimension. Under its hypotheses, a Frobenius-stable rank two factor of the $p$-adic
cohomology containing the holomorphic class forces that class to span a Cartier-stable line and gives supercongruences modulo $p^{2s}$ for every $s\geq1$. Moreover, if the hypotheses of the Beukers–Vlasenko excellent lift
theorem hold, then the excellent Frobenius fixes the corresponding parameter. We apply the criterion to the Hulek–Verrill $A_4$ family at $t_*=-1/7$, a parameter predicted to be a rank two attractor. Assuming Dummigan's
Conjecture~1.1, we prove that the excellent Frobenius fixes $t_*$, the supercongruences hold, and the fiber with parameter $t_*\bmod p^2$ is the Achinger–Zdanowicz canonical lift at every
prime where the required Dwork ordinarity conditions are satisfied. A CM fiber of the mirror quartic K3 pencil gives a second application. We also prove that the $q$-power map on a split formal
torus has the unit section as its unique periodic point in the identity residue disk over every finite extension of $W(\mathbb{F}_q)[1/p]$, where $q$ is the cardinality of the residue field.
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High Energy Physics (Phenomenology)
Hybrid Neural Simulation-Based Inference for Robust Applications and Limited-Budget Scenarios
Sean Benevedes, Mani Dehghan, Aishik Ghosh et al. (+1 more)
We develop two hybrid techniques that approach the performance of neural simulation-based inference (NSBI) analyses while substantially reducing the computational cost of inference and preserving some or all of the reliability guarantees of parametric methods. The first approach is broadly applicable, while the second is tailored to a class of particle physics analyses that admit a semi-parametric NSBI formulation. With only a modest compromise in raw sensitivity, these methods represent an important step toward computationally efficient NSBI in offline analyses and also open the door to the exploration of trigger-level applications in the future. Based on our comparison studies, we recommend the use of our first approach, Latent Categories, for robust and efficient inference.
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All authors Sean Benevedes, Mani Dehghan, Aishik Ghosh, Tae Hyoun Park