Computation and Language
Representational Control over Self-Report & Behavior Coherence in LLM Risk-Taking
Rafal Kocielnik, Peiyang Song, Pengrui Han et al. (+4 more)
Self-report is an appealing low-cost probe of an LLM's dispositions, but recent work finds only selective agreement between what models report and how they behave. Prior accounts establish these patterns by prompting black-box LLMs, leaving open whether the gap is a prompting artefact or a fact about how the underlying constructs are represented internally. We investigate risk-taking, a consequential dimension of agentic decision-making, using activation steering to measure self-report and behavior under the same internal intervention. We survey nine steering-vector extraction methods spanning task-specific directives, the model's own task behavior, and dispositional descriptions at two granularities, evaluated on two behavioral tasks and two psychometric instruments across four open-weight LLMs. We find that (1) a shared internal intervention does not ensure shared responsiveness: directions built from trait descriptions move self-report but leave behavior at chance, directions built from the model's own task choices do the reverse, and only task-specific directives reach both, weakly. (2) Diagnosis dissolves that exception: removing surface confounders leaves the directives only 32% of their behavioral effect. The two channels are otherwise steered by near-orthogonal directions, each channel reachable by several independent constructions. (3) An intervention composing one behavior-moving and one self-report-moving direction moves both together; flipping one sign sets them in opposition, with reported and enacted risk pointing in opposite directions on 69-89% of flipped compositions in all models. These findings move the self-report-behavior relationship from a black-box observation to a representational one that can be inspected and controlled, motivating representational checks alongside behavioral evaluation.
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All authors Rafal Kocielnik, Peiyang Song, Pengrui Han, Myrl G. Marmarelis, Ramit Debnath, Dean Mobbs, R. Michael Alvarez
Combinatorics
An upper bound on the proper hat guessing number of graphs
Ioannis Kakatelis
We study the proper hat guessing game on graphs, introduced by Adriaensen et al. in Hat guessing with proper colorings. In this game, the players are seated on the vertices of a graph $G$ and assigned hats from a set of $k$ colors such that the resulting assignment forms a proper coloring. The visibility of each vertex is limited to the hat colors of their neighborhood. Then they must simultaneously output a guess about the color of their own hat. The players win if at least one guess is correct. A parameter related to this problem is the proper hat guessing number $\operatorname{HG}_{P}(G)$ that is the maximum number of colors $m$ such that the players can guarantee a winning strategy. Motivated by the work of Shurman et al. \cite{shurman2026upper}, we establish the first upper bound that depends both on the number of vertices $n$ and the maximum degree $\Delta$ in the case where $\Delta \geq \frac{n}{e+1}$. This result leads us to show that the proper hat guessing number of the binomial random graph $G_{n,1/2}$ is bounded above by $cn$, where $c \approx 1.366$. Finally, we prove that graphs of maximum degree $(1-\gamma)n$ for some fixed $\gamma \in (0,1]$ cannot have $\operatorname{HG}_{P}(G) = (2-o(1))n$.
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Computers and Society
Agent Reliability Profiles in Financial Services
Mike Hsu, Medha Bankhwal, Béatrice Moissinac et al. (+3 more)
AI
agents can take actions. At times, those actions can go beyond what is intended.
Agent reliability can be defined as assurance that an
agent will stay within intended bounds and operate within limits. Today, there is no shared framework or
language for describing, validating, and benchmarking the reliability of agentic deployments in financial services. This makes it difficult for financial institutions, vendors, and regulators to assess and trust
agents at scale, thus limiting the pace of development and adoption. A standardized, shared representation of
agent reliability would fill the gap. This paper introduces the
Agent Reliability Profile, a per-
agent unit of assurance evidence for
agent deployments in financial services. Each Profile records a bounded, falsifiable claim, this agentic system reliably functions within its operating boundary. We define "operating boundary" as an
agent having; (1) a defined autonomy tier, (2) a defined operational design domain, (3) defined classes of action, and (4) a defined control envelope. Production assurance progresses through three levels while the Profile schema remains constant: a Profile Builder compiles a Level 1 Asserted Profile from institutional evidence, a Profile Validator tests the deployment in its own environment to produce a Level 2 Validated Profile, and operation of the same tests by a qualified independent assessor produces a Level 3 Verified Profile. Separately a Benchmarked Profile reports results comparable across institutions under reference conditions. We describe the architecture, the artifact, the assurance ladder, the comparability flag, associated tools, an evaluation methodology, applications for financial institutions and supervisors, limitations, and a staged implementation program.
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All authors Mike Hsu, Medha Bankhwal, Béatrice Moissinac, Kevin Werbach, Lukasz Szpruch, Bennett Hillenbrand
Portfolio Management
Cities of Signals: Compression, Separation, and the Geometry of Novelty
Marc da Costa Nunes
A cross-sectional signal is a forecast vector over $d$ assets at each date; demeaned and normalized, it is a point on a sphere. Its city is the direction of its time-averaged vector in a common target-aligned frame, a compressed summary. When is the angle between two cities a conservative estimate of the angle between the histories? For independent uniform histories, the probability that the history angle is at least the city angle tends to one when the city angle is at most 90 degrees, with an $O(T^{-1/2})$ error bound uniform in dimension. Selecting for positive average information coefficient (IC) changes the unconditional limit to $\gamma_q>1/2$, about 56% for 20 assets. The bound extends to independent, rotationally symmetric histories with temporal dependence; summable products of lag correlations preserve the conditional limit. A stationary ergodic
theorem allows shared signal movement and identifies the population margin determining the limiting ordering. Among 266,815 positive-IC pairs in AlphaNova's Competition May 2026, the history angle is at least the city angle for 58.9% overall, 74.1% at city angles up to 90 degrees, and 1.5% beyond. These frequencies describe the observed library and do not validate the uniform null: residual agreement is mostly positive, and an empirical second-moment diagnostic cannot be matched within that null by adjusting temporal dependence alone. On the full 776-signal sample, a city angle of at least 60 degrees selects pairs with a 94.3% success rate at the same history-angle threshold, against an 85.0% base rate, and retains 72.5% of pairs. Explicit constructions show the limits of direction-only summaries, retained lengths give sharp bounds, and shared time blocks recover missing information, here only at fine resolution. Persistent positive IC confines cities and bounds their motion; convergence also requires a stable mean direction.
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Solar and Stellar Astrophysics
A forest of emission lines observed by Sunrise III/SUSI above the solar limb
J. S. Castellanos Durán, F. A. Iglesias, S. K. Solanki et al. (+31 more)
We present off-limb spectroscopy using low-straylight data from the Sunrise Spectropolarimeter and Imager (SUSI) onboard the Sunrise III stratospheric
solar observatory. A spatial scan with SUSI's slit almost parallel to the local limb reached from within the
solar disc to 6.5 Mm off-limb. The wavelength range spanned from 407.6 to 410.25 nm. We detected a total of 174 spectral lines in emission off-limb, a 2.5-fold increase over previous reports. Surprisingly, 122 of these lines begin to appear in emission already on the disc, although close to the limb. In addition, one-fifth of relatively prominent emission lines are due to rare-earth and heavy elements and molecules, where the absorption lines of these transitions are typically weak on-disc.
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All authors J. S. Castellanos Durán, F. A. Iglesias, S. K. Solanki, A. Lagg, I. Milic, H. N. Smitha, D. Vukadinović, T. L. Riethmüller, A. Gandorfer, A. Feller, J. Blanco Rodríguez, D. Orozco Suárez, Y. Katsukawa, J. Hölken, J. C. del Toro Iniesta, S. Rao, M. Sánchez Toledo, K. Singh, P. Bernasconi, T. Berkefeld, A. Alvarez-Herrero, M. Kubo, V. Martínez Pillet, B. Grauf, M. Carpenter, A. Bell, H. Strecker, A. L. Siu-Tapia, P. Santamarina Guerrero, Y. Kawabata, T. Matsumoto, R. T. Ishikawa, E. Harnes, T. Oba
High Energy Physics (Phenomenology)
Energy shift and decay width of kaonium from coupled-channel one-loop chiral amplitudes
Alireza Beygi
Using two fitted coupled-channel one-loop
chiral amplitudes, we investigate the strong-interaction energy shift and decay width of the hypothetical Coulomb-bound $K^+ K^-$ hadronic atom, kaonium. We obtain ground-state widths of 1.67 and 0.92 keV, which are both larger than previous kaonium predictions, including the 0.30 keV result obtained from a non-perturbative treatment based on leading-order
chiral perturbation theory. Although the $K^+ K^-$ scattering lengths obtained from the two one-loop amplitudes have nearly equal imaginary parts, their real parts have opposite signs; thus, the leading absorptive contributions to the widths are similar, while the Coulomb corrections act in opposite directions. The kaonium width therefore depends on both the real and imaginary parts of the near-threshold strong amplitude. Despite the increased ground-state width, the first three $s$-wave levels remain separated. In
photon fusion, at fixed pole area and resonance position, the increase in the intrinsic width has a negligible effect on the line shape after including Belle's experimental resolution.
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Computation and Language
Copying Before Suppression: What Drives a Below-Chance Dip During Language Model Training?
Tejas Dahiya and Cole Blondin
Mechanistic interpretability usually studies fully trained models, yet the computations that drive a behaviour can change while the model is still learning the task. On the Indirect Object Identification task, a model should continue with the name mentioned once rather than the name mentioned twice. Pythia models pass through an early training window in which they prefer the repeated name, so accuracy in a choice between the two names falls below one half while
language-model loss on a fixed text sample keeps decreasing across the same window. The window reflects a temporary imbalance between two computations. We identify one cause of the wrong preference by selecting a set of
attention heads that write the repeated name, on
prompts separate from those used for causal evaluation, keeping that selection fixed, and then replacing each head's final-
token output with its average output on a separate set of non-repeated-name
prompts. This improves the correct-minus-repeated logit difference in a separately trained 160M model and in the official 160M, 410M, and 1B models. At 160M, the head that lowers the repeated name in the mature model shows little of its mature behaviour at this point. It directs less than one percent of its
attention to the repeated mention, and its output makes almost no direct contribution to lowering that name's logit. Both properties grow over the interval in which behaviour recovers. Across the 160M, 410M, and 1B models, transplanting the corresponding head's mature parameters into the early checkpoint recovers 35 to 68 percent of the total improvement in the correct-minus-repeated logit difference seen by the end of training. Related early-to-late reversals appear at further Pythia scales, in two independently trained GPT-2 models, and in OLMo. A mature
circuit can therefore conceal a transient causal configuration that shaped behaviour earlier in training.
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Computation and Language
LongSocialBench: Do Long-Context LLMs Understand Online Discussion Threads?
Xinyi Liu, Rinat Khaziev, Dilek Hakkani-Tür et al. (+1 more)
Long-context LLMs can now ingest entire online discussion threads, but understanding their social discourse requires more than reading a long document: models must track parent-reply relations, turning points, scoped subtrees, cross-branch contrasts, and participant trajectories. To test this structure-aware social
reasoning, we introduce LongSocialBench, a
benchmark of 1,462 verified human-authored multiple-choice items drawn from 94 complete Hacker News, Stack Exchange, and Reddit r/ChangeMyView episodes, with a median length of approximately 73K
tokens. Each item pairs a complete serialized discussion and reply structure with a four-option question, requiring models to recover structured social evidence. Released items are verified for answerability, option uniqueness, and evidence grounding. Across 18 models and 29 evaluation settings, current long-context workflows remain far below human performance. The best individual result comes from Claude-Opus-4.7, which reaches 63.0% when prompted to eliminate incorrect options before answering, compared with 72.4% for independent human readers. Averaged across all 18 models, the full-context Baseline scores 43.9%. Supplying the gold evidence scope raises this to 55.0%, showing that substantial errors remain even after the relevant thread region is identified. LongSocialBench shows that the missing capability is not context access or prompting alone, but social understanding over structured reply trees.
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All authors Xinyi Liu, Rinat Khaziev, Dilek Hakkani-Tür, Tarek F. Abdelzaher
Machine Learning
Sharp Convergence and Sample Complexity of Policy Mirror Descent for Average-Reward MDPs
Enes Arda and Atilla Eryilmaz
Policy mirror descent (PMD) has a mature finite-time theory in discounted Markov decision processes (MDPs), but less is known in the average-reward setting, a more natural objective for many control applications. We give a finite-time, finite-sample analysis of PMD in ergodic average-reward MDPs built around a single master recursion that governs convergence for any critic, without external regularization. Its specializations yield linear rates for exact, inexact-tabular, and linear function approximation (LFA) updates. For exact PMD, the rate is tight in the worst case, and convergence can become superlinear. We complement these convergence results with critics whose end-to-end sample complexities improve the prior best policy-gradient dependence from $|S||A|\,t_{\mathrm{mix}}^3$ to $|S|\,t_{\mathrm{mix}}^3+|S||A|\,t_{\mathrm{mix}}^2$ in the tabular setting and from $|S||A|\,t_{\mathrm{mix}}^5$ to $d\,t_{\mathrm{mix}}^3$ under LFA. Matching information-theoretic lower bounds establish that both critics are optimal up to logarithmic factors.
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Artificial Intelligence
CUAWright: A Minimal Unified Interface for Digital Agents
Yadong Lu, Theodore Lee, Yifei Li et al. (+5 more)
The prevailing approach to computer-use
agents couples a model with a domain-specific harness: a browser or desktop environment equipped with human engineered tools that are fixed before task execution. As models' coding capabilities improve, the GUI native and static harness prevents them from direct programmatic operation on system state, as well as flexible construction of tools. To this end, we introduce CUAWright, a minimal terminal harness of roughly 3K lines of code that uses bash commands as its sole action interface, and a file system as its evolvable space for dynamically creating tools and managing the context. We conduct comprehensive experiments across a wide range of digital tasks, and demonstrate that by giving the
agent a minimal, programmable interface, it achieves substantially stronger results compared to their GUI or hybrid CLI interface across a wide range of tasks. On OSWorld 2.0, CUAWright delivers a 33.2% relative improvement in partial reward while reducing estimated cost by 37.5% compared with the published GPT-5.5 baseline. On Online-Mind2Web and the long horizon Odysseys
benchmark, CUAWright substantially outperforms GUI native harness by 4.7% and 44.0% in success rate, respectively. Furthermore, we found the gains extend to CAD applications that require accurate visual understanding and CLI interaction: on CADGenBench and BenchCAD, our unified harness yields 8.1%-41.6% relative improvements over other CLI based harnesses with GPT-5.5. Together, these results suggest digital environments are far more programmable than their GUI interfaces imply, and a minimal terminal-focused harness is the key for better performance and efficiency.
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All authors Yadong Lu, Theodore Lee, Yifei Li, Lawrence Keunho Jang, Tianci Xue, Yu Su, Huan Sun, Ahmed Hassan Awadallah
Materials Science
Distilling universal machine-learning potentials for moiré lattices across one million atoms
Thomas Huang, Yueyao Fan, Kaichen Xie et al. (+7 more)
Atomic reconstruction reshapes moiré materials across multiple scales, from local structure and polarization textures to global electronic
topology, yet direct ab initio modeling becomes prohibitive for large superstructures such as marginal-twist-angle moirés and moiré-of-moirés. We develop MoiréMLIP by fine-tuning a universal atomistic model on the density functional theory labeled Moiré Kaleidoscope
dataset, which spans transition metal dichalcogenide compositions,
symmetries, stackings, and twist angles. MoiréMLIP reproduces ab initio reconstruction with force errors of 6–8\,meV/Å and transfers to smaller twist angles and unseen structures. Knowledge
distillation yields MoiréMLIP-mini, which retains this accuracy while extending single-GPU inference to one million atoms. Applied to an alternate-twist MoTe$_2$ trilayer, it reveals a hierarchical polarization network spanning tens of nanometers arising from large moiré-
cell distortions sharply localized along the moiré-of-moiré domain walls. These results overcome key accuracy, transferability, and scaling bottlenecks of existing atomistic models and enable predictive simulations across emergent moiré length scales.
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All authors Thomas Huang, Yueyao Fan, Kaichen Xie, Bolun Li, Kaijie Yang, Jenna A. Bilbrey, Eric Bylaska, Peter V. Sushko, Di Xiao, Ting Cao
Earth and Planetary Astrophysics
Closing the Gap: A Test of the Streaming Instability Using Simulations and Observations of the Cold-Classical Kuiper Belt Size Distribution
Weston Hall, Jacob B. Simon, Jeonghoon Lim et al. (+2 more)
How planetesimals form is a major open question in studies of
planet formation, as the growth of smaller solids must overcome barriers such as radial drift, fragmentation, and bouncing. The leading model is that planetesimals form by the gravitational collapse of dense particle clumps that have been aerodynamically collected via a process known as the streaming instability (SI). While the SI has passed several observational tests, a critical test remains: a successful comparison with the size distribution of
Solar System planetesimals. In this work, we conducted a series of SI simulations for a range of input parameters and combine a more careful accounting of initial planetesimal mass with more advanced statistical techniques compared to previous studies in order to compare the simulation outcomes against observations of the most
primordial population in the
solar system: the Cold Classical Kuiper Belt (CCKB). Using both cumulative distribution function (CDF) model parameters and distribution comparison methods like the Kolomogorov-Smirnov test, we show that, when planetesimal masses are measured at formation and analyzed with modern statistical methods, the SI-predicted size distributions can quantitatively reproduce the high-mass end of the observed CCKB size distribution, though the extent of the agreement depends on the physical parameters chosen, providing further support for the SI theory of planetesimal formation.
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All authors Weston Hall, Jacob B. Simon, Jeonghoon Lim, Daniel Carrera, Matthew Small
Neurons and Cognition
Multimodal Physiological Decoding Reveals Individualized Arousal Dynamics in Closed-Loop Neurofeedback
Anirudh Natarajan and Paul Sajda
Most arousal-based
brain-computer interfaces (BCIs) use
EEG to decode cognitive state. But arousal is an autonomic process. We examined if peripheral physiological signals give a better decode of task-related arousal. We used a public
dataset from a difficult boundary-avoidance flight task. In this task, participants received
EEG-based BCI neurofeedback, sham feedback, or no feedback. We trained a
multimodal deep learning decoder on heart rate, heart rate variability (HRV), respiration, electrodermal activity, and pupil diameter. We compared it with an
EEG-only decoder and with the original filter-bank common spatial pattern (FBCSP) decoder. All analyses were offline. The peripheral decoder had a within-subject AUC of 93.0%. The
EEG-only decoder had 85.2% and the FBCSP decoder had 79.8%. Only the peripheral decoder showed the Yerkes-Dodson inverted-U relation between decoded arousal and performance. The feedback conditions did not change the arousal trajectories. Thus, the BCI benefit possibly comes from regulation at critical moments in the task, not from a continuous shift. We calculated a time-varying optimal arousal trajectory from control trials. Per-trial deviations from this trajectory predicted performance (r = -0.28 to -0.24, p < 0.01). Baseline HRV and gamma power changed the arousal-performance curve of each subject. We used these values to divide subjects into arousal-sensitive and arousal-tolerant groups. Control bands specific to each group increased trial separation (Cohen's d from 1.21 to 1.32 and from 0.85 to 1.10). These results show that peripheral signals are better than
EEG for continuous arousal decoding. They also give a specification for individualized closed-loop arousal regulation. A prospective study must test this specification, for example with personalized transcutaneous vagus nerve stimulation.
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Artificial Intelligence
The Independence Prior of SAEs Fragments Visual Concepts
Tommaso Mencattini, Giorgos Nikolaou, Donato Crisostomi et al. (+4 more)
Sparse Autoencoders (SAEs) decompose model activations into sparse combinations of interpretable dictionary atoms. Although SAEs are grounded in the Linear Representation Hypothesis (LRH), their objective smuggles in an additional prior: concepts across patches are treated as independent, an assumption clearly violated by natural images and by the activations they induce. We therefore specialize LRH to
vision through the Markov-Field Linear Representation Hypothesis (MFLRH), which adds the missing spatial dependencies to the LRH assumptions. We thus propose Spatial-SAE as an amortized MAP
estimator under the MFLRH. Spatial-SAE consistently outperforms standard SAEs in concept recovery and interpretability. Across four variants, it achieves a 96% average win rate on synthetic concept recovery and improves interpretability on DINOv2 activations, at a reconstruction cost concentrated in high spatial frequencies.
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All authors Tommaso Mencattini, Giorgos Nikolaou, Donato Crisostomi, Thomas Fel, Francesco Montagna, Emanuele Rodolà, Francesco Locatello
Mesoscale and Nanoscale Physics
Exchange-controlled quantum beats and entanglement in an exciton–bimodal-cavity system
Cesar E. P. Villegas, Victor P. Brasil, Pedro A. S. Contri et al. (+2 more)
We theoretically demonstrate tunable population transfer and
entanglement generation in a two-
exciton system coupled to a bimodal optical cavity, including an
exciton-
exciton exchange interaction. By using dressed-state transformations and Löwdin partitioning, we derive effective
Hamiltonians that identify three dynamical time scales: local polaritonic oscillations, long-period dressed-state beats, and direct excitonic exchange. We apply the framework to
graphene nanoribbon
excitons, using excitonic energies, transition dipoles, and lifetimes obtained from first-principles calculations to parameterize the model. In the small- and moderate-exchange regimes, the dynamics become multiscale, and the beat channel sustains sizable
quantum entanglement over long dissipative time windows. In the large-exchange regime, the excitonic
Hamiltonian dominates and drives nearly complete population transfer between excitonic states over many coherent cycles. The resulting analytical expressions provide direct design rules connecting the beat period, transfer amplitude, and coherent-cycle number to the exchange interaction, cavity detuning, and light–matter coupling. Our results establish a controllable route for engineering long-lived
entanglement and coherent excitonic operations in
exciton–bimodal-cavity systems.
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All authors Cesar E. P. Villegas, Victor P. Brasil, Pedro A. S. Contri, Kevin Lizárraga, Jonas Maziero
Combinatorics
Restrictions of the dot action representation
Hsin-Chieh Liao, Martha Precup and John Shareshian
Regular semisimple Hessenberg varieties are subvarieties of flag varieties defined for any reductive group. Their
cohomology carries a representation of the associated Weyl group, known as the dot action representation. In type $A$, these representations can be computed using the chromatic quasisymmetric functions of naturally labeled unit interval graphs. We prove that restricting the dot action representation of any Weyl group to a type $A$
parabolic subgroup yields a representation isomorphic to a direct sum of type $A$ dot action representations. This work is motivated by the
conjecture that an analogous property holds for all
parabolic subgroups. In particular, for classical Weyl groups of types $B/C$ and $D$, we use chromatic symmetric functions to provide a streamlined, combinatorial formula for the restriction to a specific maximal type $A$
parabolic subgroup. As an application, we prove a
conjecture of Lesnevich regarding the representations associated with ideals of types $B$ and $C$ identified under the standard poset isomorphism. Specifically, we show that the corresponding dot action representations are isomorphic if and only if the ideals determine the same reflection sets in their respective root systems. Furthermore, we show that a Catalan number counts the total number of such ideals.
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Machine Learning
Mingtian Tan, Palash Goyal, Mihir Parmar et al. (+6 more)
Real-world time series are frequently driven by exogenous events and structural shifts, rendering conventional
forecasting based solely on historical numerical observations insufficient. While
language models can retrieve external news, standard
retrieval-augmented approaches struggle with high noise, missing signals, and an inability to reason causally about event impacts. We propose SEER (Self-Evolving Event
Reasoning and
Retrieval), a closed-loop framework that dynamically optimizes event conditioning for time series
forecasting. SEER translates prediction errors into two decoupled feedback mechanisms: (i) a reflective
retrieval memory that refines subsequent search queries and filters spurious noise, and (ii) a persistent causal knowledge base that distills transferable domain dynamics. SEER enforces strict chronological boundaries across both event
retrieval and reflection, preventing look-ahead bias and data leakage. Across six volatile time-series
benchmarks, SEER consistently outperforms state-of-the-art time series foundation models and
language model baselines.
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All authors Mingtian Tan, Palash Goyal, Mihir Parmar, Sarkar Snigdha Sarathi Das, Chun-Liang Li, Nanyun Peng, Thomas Hartvigsen, Jinsung Yoon, Tomas Pfister
Machine Learning
Physics is the Best Teacher: Consistency Learning for Time-Invariant Operators of Chaotic Dynamics
Lufang Chiang, Jiachen Yao, Thomas Y.L. Lin et al. (+1 more)
Accelerating the prediction of long-term behavior in chaotic systems is crucial in scientific computing. However, existing methods rely on numerical solvers or autoregressive models that advance one small step at a time, which makes long horizons expensive. We instead view this problem as learning the system's time-invariant
evolution operator, which jumps the state across a large time span in a single evaluation. To this end, we derive the consistency equations a time-invariant operator must satisfy, with differential and compositional objectives in physical time. These equations also connect the learned operator to the physics-prescribed instant dynamics, enabling physics
embedding in consistency learning. Across five chaotic systems, we find that physics-distilled consistency makes both short-term trajectories and long-term statistics more accurate. The learned operator survives temporal extrapolation and requires one-tenth as many evaluations as autoregressive rollout, offering an efficient route to long-term simulation of chaotic dynamics.
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All authors Lufang Chiang, Jiachen Yao, Thomas Y.L. Lin, Anima Anandkumar
Chemical Physics
PyCC: A Reference Quantum Chemistry Python Package for Education, Validation, and Rapid Prototyping
T. Daniel Crawford, Benjamin G. Peyton, Zhe Wang et al. (+6 more)
PyCC is a Python-based
quantum chemistry package that emphasizes coupled cluster and related methods. Developed over the last five years, PyCC's purpose is to provide a clear reference implementation that will serve as a validation suite for production code, a platform for rapid prototyping and testing of new methods, and an educational resource for newcomers to the field. The package covers both closed- and open-shell single-determinant references in spatial- and
spin-orbital formulations for the coupled cluster hierarchy through triples-including methods such as CC3, as well as for MP2 and CI methods. PyCC provides a broad range of molecular properties such as analytic first and second energy derivatives including gradients, Hessians, polarizabilities and atomic polar tensors, as well as non-energy-derivative properties, such as velocity-gauge atomic polar tensors and diagonal Born-Oppenheimer corrections, plus dynamic linear response functions, and excitation energies. The package includes a number of unique capabilities, such as real-time CC propagation (up through CC3), atomic axial tensors for simulating vibrational circular dichroism spectra at the MP2 and CI levels, and local correlation methods including perturbation-aware correlation domains. The code is designed around a shared Wavefunction base and a method-agnostic derivative layer that enables a new method to inherit the full property set once it supplies its reduced densities and their first-order responses. PyCC is fully open source and available on GitHub under the BSD 3-clause license.
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All authors T. Daniel Crawford, Benjamin G. Peyton, Zhe Wang, Aparna Krishnan, Brendan M. Shumberger, Jose Madriaga, Joshua Atta-Kumi, Aaron De Clercq, Ritvik Prabhu
Dynamical Systems
Pointwise Convergence of Random Ergodic Averages at the \(L^1\) Endpoint
Will Burstein, Lorenzo Catani and Ben Krause
In his highly influential paper, On the maximal ergodic
theorem for certain subsets of the integers, Bourgain introduced the study of pointwise convergence of ergodic averages along randomly generated subsets of the integers, developing a robust \(L^p\)-theory for \(p>1\). More recently, this theory was partially complemented at the \(L^1\) endpoint by work of LaVictoire, who treated averages along random sequences that are “slightly denser" than the set of squares, for which universal \(L^1\) pointwise convergence is known to fail. In this work, we prove that, almost surely, for every measure-preserving system and every integrable function, the ergodic averages formed by sampling along random sequences with quadratic growth converge almost everywhere. Our proof combines harmonic-analytic methods developed by M.~Christ with a “random sampling" of quadratic forms, thereby overcoming the main uniformity obstruction.
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Fluid Dynamics
Transparent 3D-printed flow cells for porous media studies with proof-of-concept on two-phase flow-reversal asymmetry
Yann Dumay, J. Kevin Pierce, Julie Delhaie et al. (+1 more)
Porous media experiments commonly rely on geometry-controlled and optically transparent experimental flow
cells. Examples include studies of flow structure, solute mixing, multiphase flow, bacterial dynamics, and reactive transport. Here, we provide a reproducible and open-source protocol to fabricate transparent monolithic porous flow
cells by stereolithography 3D printing. The protocol covers digital design, resin printing, internal flushing, and resin coating. The flushing and coating steps are central to the workflow, enabling enclosed single-material
cells to be cleared of uncured resin and made optically transparent for direct imaging. The protocol produces optically transparent, millimetre-scale, and quasi-two-dimensional flow
cells suitable for simultaneous pressure and image-based measurements. As a proof of concept, we study flow-reversal asymmetry in steady oil-air two-phase flow in quasi-two-dimensional Hele-Shaw
cells containing either triangular or cylindrical obstacles. Directional differences are quantified from pressure measurements and from image-based active-width measurements. The triangular geometry produces a clear directional response, whereas the cylindrical geometry remains symmetric within experimental uncertainty. These results demonstrate that our fabrication protocol can link digitally designed pore geometries to macroscopic pressure response and steady-state two-phase flow organisation. By making the design files, scripts, and manufacturing workflow openly available, we aim to make 3D-printed flow
cells readily adaptable to other experimental systems and research fields.
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All authors Yann Dumay, J. Kevin Pierce, Julie Delhaie, Erika Eiser
Computer Vision
Where Does the Semantic Gain Come From? A Reproduction and Extension of Semantic Knowledge-driven Contrastive Learning for Long-Tailed Recognition
Sushrut Ghimire
Semantic Knowledge-driven Contrastive Learning (SKCL) uses a
language model to decide which classes are related, and pulls each image towards the prototypes of its semantic neighbours. On CIFAR-100-LT (beta = 100) it reports 54.02% top-1 accuracy, 2.01 points above Balanced Contrastive Learning (BCL), the method it builds on. The code and the class descriptions are not public. I reimplement SKCL, BCL and ConCutMix in one framework, check it against the public baseline code, and run every configuration with three seeds. The two baselines reproduce within 1.5 points, but SKCL built on BCL, as the paper describes it, ends up 0.56 points below BCL. To find out why, I add SKCL to the authors' own ConCutMix code. Trained for the paper's 300 epochs, it reaches 53.69, only 0.33 below the published number. At the same budget, however, the semantic graph adds just 0.23 points over ConCutMix, while training ConCutMix for 100 more epochs adds 1.04. Together with ConCutMix's published lead over BCL (1.15), this explains the claimed gain. A BCL model that never sees the graph already shares 41.2% of the graph's top-2 neighbours with its own most-confused classes (2.0% by chance), which shows why the graph adds so little on these
benchmarks. I also test several changes to SKCL. Combining it with the CutMix branch improves it by 1.06 points, and an adaptive version of the graph improves it slightly (+0.34 and +0.28 in two codebases), although these gains are within seed noise.
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Geometric Topology
Integral face-edge duality for knot triangulations
Sai Kambampati
For an ordered ideal triangulation of a
knot exterior in $S^3$, we give a cochain construction relating its face
matrix $C$ to its preferred-longitude gluing
matrices $A,B$. The obstruction to uniqueness is the first
cohomology of the ideal compactification. Its vanishing yields nullity doubling and the FAMED
matrix identity, including a constrained formulation for singular face
matrices. A separate rank argument under a strict angle structure supplies a proposed proof of Wong's generalized FAMED
conjecture. Over the integers, the construction gives a direct-sum decomposition of $\ker_{\mathbb{Z}}B$ and an exact sequence from the two face cokernels to the gluing cokernel, with quotient $\mathbb{Z}/2\mathbb{Z}$. In the unhalved longitude convention this implies $|\det B|=2|\det C|^2$. We also obtain cyclic face torsion, order-reversal invariance of Smith normal form, and affine face coordinates for fixed lifted gluing systems. The determinant-one assertion and the full Andersen–Kashaev volume
conjecture remain open here.
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Statistical Methodology
Network-Constrained Matching and Randomization-Based Inference for Causal Effects
Zhejia Dong and Youjin Lee
Matching is widely used to mimic randomized experiments by forming matched sets in which treated and control units differ only randomly with respect to observed
covariates. However, when the study population consists of interconnected units from a single network or a small number of networks, matching solely on observed
covariates may produce matched units that are more closely connected in the network than would occur by chance. This increased network proximity within matched sets can induce spurious associations between treatment and outcome when both variables exhibit similar autocorrelation patterns on the network. To reduce spurious associations while preserving the validity of causal comparisons, we propose a new matching method that matches units with similar
covariates subject to additional network-proximity constraints. For post-matching inference, we propose a randomization-based procedure for testing the sharp null hypothesis of no causal effect. The inference uses the asymptotic normal approximation and accommodates statistical dependence among test statistics obtained from each matched set without requiring explicit specifications of their correlation structures. We demonstrate the validity and utility of the proposed methods through simulation studies and apply them to the Framingham Heart Study. The matching method and subsequent inference procedure are implemented in the R package netmatchRI.
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Geophysics
Eigenwaves: Finite-frequency Eigenrays in Elastic Anisotropic Media
Zvi Koren
The Eigenray method solves two-point boundary-value ray-tracing problems in heterogeneous anisotropic elastic media using a variational formulation based on the eikonal equation. Here, we extend the method to finite frequencies using the trajectory-mechanics formulation in which finite-frequency effects are represented by a frequency-dependent potential that couples phase and amplitude. We show that finite-frequency propagation can be described within the same variational framework through an equivalent effective medium, preserving the structure of the original method while introducing corrections to trajectories, phase, amplitudes,
geometric spreading, wavefront
curvature, and caustic behavior. The resulting Eigenwave formulation requires only local modifications of the ray velocity and its derivatives and reduces to classical ray theory in the high-frequency limit. Numerical examples involving multipathing and cusp caustics demonstrate accurate modeling of finite-frequency propagation effects. While ray-based amplitudes become unreliable near caustics, a phase-space (Maslov) treatment successfully reproduces finite-difference wavefield solutions. These results establish Eigenwave theory as a unified framework connecting ray theory and finite-frequency wave propagation in heterogeneous anisotropic media.
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Machine Learning
Progressive Multi-Ancestor Bit-Depth Distillation
Adil Mubashir Chaudhry, Osama Ahmad, Zubair Khalid et al. (+1 more)
Model compression strategies are widely employed to reduce
memory footprint and network complexity, particularly for devices with constrained computational,
memory, and energy resources. Prior works that rely on simultaneous conversion from floating-point high-precision (FP32) to integer low-precision (INT4) representations and
distillation into smaller models suffer from unstable training and drastic degradation of prediction performance. To address these limitations, we propose a unified framework, known as Progressive Multi-Ancestor Bit-depth
Distillation (PMABD), that progressively compresses the network while transferring knowledge through a growing pool of higher-precision ancestor teachers. PMABD generates a sequence of intermediate teachers that each learn from all higher-precision ancestors and jointly supervise the final target student. This multi-ancestor, multi-stage design stabilizes ultra-low-bit quantization by lowering quantization noise profiles across training and ensuring stable quantization. Experiments on CIFAR-10/100 with ResNet-20/32/18, and Tiny-ImageNet with MobileNetV2 show that PMABD outperforms state-of-the-art compression frameworks, results in 1.06$\%$ increase in performance of W2A2 (ResNet-18/CIFAR-100) student model. We show that a saturation-based stopping criterion contributes to improve the performance of our final student.
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All authors Adil Mubashir Chaudhry, Osama Ahmad, Zubair Khalid, Murtaza Taj
Number Theory
Moments and Large Geometric Monodromy for Linearly Constrained Kloosterman Families
Hamed Ebadi
Let $q=p^a$ with $p$
prime, $\lambda\in\mathbb F_p^\times$, and $\psi_q=\psi\circ\mathrm{Tr}_{\mathbb F_q/\mathbb F_p}$ for a nontrivial additive character $\psi$ of $\mathbb F_p$. For $n\ge2$ we study $\Sigma_n(h)=\sum_{u\in(\mathbb F_q^\times)^n,\sum_i h_i u_i=-\lambda}\psi_q(\sum_i u_i^{-1})$. On $\mathbb G_m^n$ we construct a lisse sheaf $\mathcal W_n$, pure of weight $n+1$ and rank $r(n)=2^n-\binom n{\lfloor n/2\rfloor}$, whose Frobenius traces equal $(-1)^{n-1}q\Sigma_n(h)$ minus an explicit
polynomial in $q$, for $h\in(\mathbb F_q^\times)^n$. For odd $p$, its
geometric monodromy has identity component $\mathrm{SL}_{r(n)}$ and cyclic component group of order divisible by $p$. The full group is $\{g:\det(g)^p=1\}$ for $n=2$, and for $n\ge3$ using boundary unipotence from the companion preprint arXiv:2610.03081. For $p=2$, the sheaf is self-dual and its
geometric monodromy is $\mathrm{SO}_{r(n)}$ for odd $n$ and $\mathrm{Sp}_{r(n)}$ for even $n$. After normalization to weight zero, the arithmetic and
geometric groups coincide, using the same companion input for odd $p$ and $n\ge3$. As $q\to\infty$ with $p$ fixed, we obtain Sato-Tate laws for centered, normalized sums with error $O(q^{-1/2})$ for
polynomial tests and formulas for all power moments. For $p=2$, limiting moments are Gaussian through order $r(n)-1$, with an excess of one above the orthogonal value at order $r(n)$ for odd $n$; if $n+1$ is a power of two, every Frobenius on $\mathcal W_n$ has
eigenvalue $q^{(n+1)/2}$. The proof uses $\sum_h|\Sigma_n(h)|^4=c_pq^{3n-2}+O(q^{3n-2-\delta(n)})$ for $n\ge3$, where $c_p=2$ for odd $p$, $c_2=3$ and $\delta(n)=\min(1,(n-2)/2)$, plane sections of the Cayley cubic, exact Frobenius-Schur identities, Laumon's local
Fourier transforms and Artin-Schreier theory. For $n=2$ we identify twisted classical Kloosterman sums and compute the fourth moment exactly.
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Computation and Language
Representation-Aligned Auxiliary Supervision for Language Model Adaptation
Kyuyoung Kim, Peiyao Sheng, Ashwin Hebbar et al. (+9 more)
Language models exhibit strong
reasoning capabilities, yet adapting them to structured domains remains challenging and can yield inconsistent outcomes. We identify representation compatibility, the extent to which a model effectively processes a representation for a structured task, as a key factor in adaptation. We study this in chess, which provides a controlled testbed with precise semantics, computable optimal actions, and multiple state representations, including a symbolic encoding (FEN) and a spatial format (ASCII). We find that models often process semantically equivalent inputs substantially differently, affecting both learning and generalization. Building on this observation, we propose representation-aligned auxiliary supervision, which uses environment-derived tasks expressed in compatible representations to improve adaptation to structured domains. Across models and representations, auxiliary supervision consistently improves optimal-move prediction relative to target-only training under identical target data. Tasks that expose environment dynamics provide larger and most consistent gains than surface-level or static supervision, while remaining competitive with substantially increasing the amount of target-task data. Moreover, ASCII-trained models transfer more effectively to FEN than FEN-trained models do to ASCII, even surpassing the FEN target-only baseline on FEN evaluation. The gains also extend beyond optimal-move prediction to open-ended, factually grounded commentary generation. Overall, our results show that auxiliary supervision in model-compatible representations can enable effective adaptation in structured domains.
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All authors Kyuyoung Kim, Peiyao Sheng, Ashwin Hebbar, Peiyang Xu, Yunfei Xie, Kevin Wang, Rui Xin, Chen Wei, Zhangyang Wang, Jinwoo Shin, Pramod Viswanath, Sewoong Oh
High Energy Physics (Phenomenology)
Dijet acoplanarity as a function of flattenicity in proton-proton collisions at $\sqrt{s}=13$ TeV
Jesus Eduardo Muñoz Mendez and Antonio Ortiz
One of the most important discoveries at the LHC is the presence of effects associated with
quark-
gluon plasma (QGP) formation in
proton-
proton (pp) collisions. The origin of these observations remains unknown. Since event selection in many studies relies on multiplicity-based
estimators known to introduce selection biases, alternative approaches must be explored to elucidate the origin of these phenomena. This contribution presents a comparison of different event classifiers used in LHC experiments, aiming to assess their mutual compatibility. Results for pp collisions at $\sqrt{s}=13$ TeV simulated with PYTHIA 8 indicate that the different classifiers introduce biases that can obscure any evidence of QGP formation in small systems. However, these biases are significantly reduced or absent in events triggered with flattenicity. This finding motivates the application of flattenicity in dijet acoplanarity studies used to search for jet quenching effects. While multiplicity-based
estimators produce an azimuthal broadening in high-multiplicity compared with minimum-bias pp collisions, even in PYTHIA 8 that does not incorporate jet quenching, events triggered with flattenicity show no such broadening. This suggests that this novel tool could be exploited by LHC experiments to refine jet-quenching searches in small systems.
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Representation Theory
The Benson–Etingof–Ostrik conjecture for finite tensor categories
Kevin Coulembier and Johannes Flake
We prove that every finite symmetric tensor category over an algebraically closed field of positive characteristic admits a symmetric tensor functor to a higher Verlinde category. This was conjectured for arbitrary symmetric tensor categories of moderate growth by Benson, Etingof and Ostrik. The proof uses higher Frobenius functors, introduced by the authors in [CF1]. The main effort is directed towards a proof of a
conjecture from [CF1] about thick tensor ideals in the representation category of elementary abelian $p$-groups, which implies that the higher Frobenius functors are symmetric monoidal. In fact, we prove a refinement of the latter
conjecture that relates tensoring with restricted Steinberg modules to an iterated generalised
cohomology of modules of elementary abelian $p$-groups.
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