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02.
arXiv (CS.LG) 2026-06-18

TIGER: Inverting Transformer Gradients via Embedding-Subspace Distance Optimization

Authors:
William Kalikman↗Ivo Petrov↗Dimitar I. Dimitrov↗Martin Vechev↗

arXiv:2606.18312v1 Announce Type: cross Abstract: Federated learning allows multiple clients to jointly train a shared model by sending gradient updates to a central server while keeping raw inputs local. However, prior gradient inversion attacks show that these updates can reveal enough information to reconstruct client inputs. Existing attacks on transformers either optimize dummy inputs to match the true client updates, which is costly and unstable for modern models, or exploit the low rank of attention gradients to identify a subspace containing the true layer embeddings, followed by a discrete membership test for candidate tokens. However, this token test is brittle under numerical noise, i.e., from quantization or Differential Privacy (DP), and scales poorly for encoder models with non-causal attention. We introduce TIGER, a continuous gradient inversion attack that turns this subspace signal into a differentiable objective. Instead of searching over tokens or matching full gradients, TIGER directly optimizes token embeddings to minimize their distance to the subspace. Our experiments demonstrate that on encoder-only models, TIGER substantially improves both reconstruction quality and runtime over existing attacks, while on decoder models, TIGER is more robust than prior subspace-based attacks, enabling the first successful reconstructions in DP-defended federated learning settings.

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03.
arXiv (CS.LG) 2026-06-15

Direct Fisher Score Estimation for Likelihood Maximization

Authors:
Sherman Khoo↗Yakun Wang↗Song Liu↗Mark Beaumont↗

arXiv:2506.06542v2 Announce Type: replace-cross Abstract: We study the problem of likelihood maximization when the likelihood function is intractable but model simulations are readily available. We propose a sequential, gradient-based optimization method that directly models the Fisher score based on a local score matching technique which uses simulations from a localized region around each parameter iterate. By employing a linear parameterization to the surrogate score model, our technique admits a closed-form, least-squares solution. This approach yields a fast, flexible, and efficient approximation to the Fisher score, effectively smoothing the likelihood objective and mitigating the challenges posed by complex likelihood landscapes. We provide theoretical guarantees for our score estimator, including bounds on the bias introduced by the smoothing. Empirical results on a range of synthetic and real-world problems demonstrate the superior performance of our method compared to existing benchmarks.

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04.
arXiv (CS.CL) 2026-06-12

Trait, Not State: The Durability of Reading Identity in Social Highlighting

Authors:
Kazuki Nakayashiki↗Keisuke Watanabe↗

Prior work on a social web highlighter located individuality in selection – which documents a person chooses to highlight – but measured it cross-sectionally. We ask the temporal question: is a reader's selection signature a trait or a state? We freeze each reader's first six months of highlighting as a profile and track its own-vs-other advantage on their later selections at growing gaps (to 24+ months), with negatives drawn from the same calendar era – so supply drift cannot masquerade as personal drift – at a coarse global level and at a fine level whose negatives and controls come from the reader's own interest neighborhood; the anchor cell reproduces the prior cross-sectional level (+0.188 vs +0.169), validating the harness. Four results. Within the same users, the fine-layer advantage shows no statistically detectable paired decline at any horizon (6-12 month retention R = 1.00 [0.85, 1.18], n = 212; the farthest bin is compatible with a modest decline; the only contrast whose interval excludes zero is the coarse layer at 12-24 months, about 13%). The signal is not reducible to repeated domains (~90% survives excluding all profile sources). Within-person drift is slow (a recent-half profile beats the old half by +0.042). Prospectively, personal profiles – even one built from a reader's earliest documents, median 20 months before evaluation – rank their next reads at roughly 3x the AP of every simple non-personal prior tested. We use "trait" operationally (a stable signature under continued engagement); the scope is heavy, long-tenured readers of one platform, and exposure is not separable from choice.

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05.
arXiv (CS.LG) 2026-06-16

Next-Latent Prediction Transformers Learn Compact World Models

Authors:
Jayden Teoh↗Manan Tomar↗Kwangjun Ahn↗Edward S. Hu↗Tim Pearce↗Pratyusha Sharma↗Akshay Krishnamurthy↗Riashat Islam↗Alex Lamb↗John Langford↗

arXiv:2511.05963v4 Announce Type: replace Abstract: Transformers replace recurrence with a memory that grows with sequence length and self-attention that enables ad-hoc lookups over past tokens. Consequently, they lack an inherent incentive to compress history into compact latent states with consistent transition rules. This often leads to learning solutions that generalize poorly. We introduce Next-Latent Prediction (NextLat), which extends standard next-token training with self-supervised predictions in the latent space. Specifically, NextLat trains a transformer to learn latent representations that are predictive of its next latent state given the next token. Theoretically, we show that these latents provably converge towards belief states, compressed information about the history necessary to predict the future. This simple auxiliary objective injects a recurrent inductive bias into transformers while leaving their architecture, parallel training efficiency, and inference unchanged. NextLat effectively encourages transformers to form compact internal world models with coherent belief states and transition dynamics – crucial properties not guaranteed by standard next-token prediction alone. Empirically, across benchmarks in world modeling, reasoning, planning, and language modeling, NextLat demonstrates significant gains over standard next-token prediction and other baselines in downstream accuracy, representation compression, and lookahead planning. Furthermore, NextLat enables variable-length self-speculative decoding, accelerating inference by up to 3.3x in language modeling. NextLat offers a simple yet effective paradigm for learning compact, predictive representations in transformers that generalize better. Our code is available at https://github.com/JaydenTeoh/NextLat.

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06.
arXiv (CS.LG) 2026-06-15

Zero-shot generalization of transformer neural operators to larger domains

Authors:
Armand de Villeroch\'e↗Sibo Cheng↗Vincent Le Guen↗Marc Bocquet↗Rem-Sophia Mouradi↗Patrick Armand↗Alban Farchi↗Patrick Massin↗

arXiv:2606.14597v1 Announce Type: new Abstract: Transformer-based neural operators have shown remarkable performance for approximating solution operators of partial differential equations on complex geometries. However, existing approaches implicitly assume a fixed domain size, which limits their ability to generalize at inference. In this work, we investigate domain extension, namely zero-shot inference on spatial domains that are significantly larger than those encountered during training. We argue that this setting fundamentally requires spatial locality and translation equivariance. We propose to implement this locality via a decomposable bias in the attention logits computation, enabling finely controllable locality while remaining fully decomposable into query-key inner products and directly compatible with optimized attention kernels. Combined with rotary positional embeddings, it enables expressive embeddings with controllable spatial support without altering the transformer architecture. We empirically show that our approach substantially improves zero-shot generalization to larger domains across two PDE benchmarks and a 3D industrial atmospheric flow application. Our code and datasets are available at https://github.com/cerea-daml/domain-extension.

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07.
bioRxiv (Bioinfo) 2026-06-11

A systematic imputation framework for sparse, multimodal space biology datasets: application to retinal imaging and omics from the RR9 mission

Authors:
Nagesh↗Sanders↗Costes↗S. V↗Avci↗Sigit↗Agarwal↗Haghighi↗Batool↗Karouia↗Chander↗A. M↗…

Space biology experiments are expensive, logistically complex, and inherently limited in sample size, resulting in datasets that are frequently incomplete and highly heterogeneous (2). Missing data is a fundamental barrier to building reliable computational models of how the human body responds to spaceflight. This work introduces a systematic framework for addressing missing data through imputation. We developed a validated four-stage framework for imputation specifically designed to preserve biological signal needed for digital twin development, while quantifying trade-offs in downstream analyses. Using retinal imaging and omics data from the NASA RR9 mission as a case study (9), we demonstrate how to diagnose why data is missing(10), select and optimize appropriate imputation strategies (5,10), and rigorously evaluate whether imputed data remains biologically meaningful. A key finding of this work is that while imputation substantially improves the performance of predictive models, it can simultaneously obscure subtle biological patterns; a critical trade-off that researchers must understand before applying these methods (11). This framework provides practical, actionable guidance for space biologists and data scientists working with sparse, multimodal datasets in space biology, and represents a foundational step toward more complete and reliable data-driven models of human physiology in extreme environments.

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08.
arXiv (CS.AI) 2026-06-11

Diffusing to Coordinate: Efficient Online Multi-Agent Diffusion Policies

Authors:
Zhuoran Li↗Hai Zhong↗Xun Wang↗Qingxin Xia↗Lihua Zhang↗Longbo Huang↗

arXiv:2602.18291v2 Announce Type: replace Abstract: Online Multi-Agent Reinforcement Learning (MARL) is a prominent framework for efficient agent coordination. Crucially, enhancing policy expressiveness is pivotal for achieving superior performance. Diffusion-based generative models are well-positioned to meet this demand, having demonstrated remarkable expressiveness and multimodal representation in image generation and offline settings. Yet, their potential in online MARL remains largely under-explored. A major obstacle is that the intractable likelihoods of diffusion models impede entropy-based exploration and coordination. To tackle this challenge, we propose among the first \underline{O}nline off-policy \underline{MA}RL framework using \underline{D}iffusion policies (OMAD) to orchestrate coordination. Our key innovation is a relaxed policy objective that maximizes scaled joint entropy, facilitating effective exploration without relying on tractable likelihood. Complementing this, within the centralized training with decentralized execution (CTDE) paradigm, we employ a joint distributional value function to optimize decentralized diffusion policies. It leverages tractable entropy-augmented targets to guide the simultaneous updates of diffusion policies, thereby ensuring stable coordination. Extensive evaluations on MPE and MAMuJoCo establish our method as the new state-of-the-art across $10$ diverse tasks, demonstrating a remarkable $2.5\times$ to $5\times$ improvement in sample efficiency.

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09.
arXiv (CS.AI) 2026-06-18

LLM-Evolved Domain-Independent Heuristics for Symbolic AI Planning

Authors:
Elliot Gestrin↗Jendrik Seipp↗

arXiv:2605.29649v2 Announce Type: replace Abstract: Heuristic search is the dominant paradigm in symbolic AI planning, and the strongest heuristics are the result of decades of work by planning researchers. Recent work has shown that large language models (LLMs) can design heuristics for individual planning domains, but no LLM-generated heuristic has so far worked on arbitrary planning tasks. In this paper, we use evolutionary search to produce the first LLM-generated domain-independent heuristics that exceed the hand-engineered state of the art. We let an LLM mutate parent heuristics written in C++, store candidates in a MAP-Elites archive keyed on informedness and speed and calculate fitness scores by blending coverage with solving time. To place the evolved programs in context, we additionally benchmark a broad set of hand-engineered heuristics on their informedness-speed tradeoff, which to our knowledge has not been done before. On unseen testing domains, our best evolved heuristic solves more tasks than even the strongest baseline, with our full heuristic suite spanning the Pareto frontier of said tradeoff. We also find that seeding evolution from the trivial blind heuristic outperforms seeding from the strong FF heuristic, even when the resulting program is itself an FF variant, and that LLM reasoning effort affects how often candidates compile much more than the quality of those that do. Because the evolved programs are plain C++, they slot into existing planners as drop-in replacements and inherit the soundness and completeness guarantees of the underlying search.

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10.
arXiv (quant-ph) 2026-06-24

Logical qubits with erasure conversion using metastable neutral atoms

Authors:
Bichen Zhang↗Genyue Liu↗Guillaume Bornet↗Sebastian P. Horvath↗Pai Peng↗Shuo Ma↗Shilin Huang↗Shruti Puri↗Jeff D. Thompson↗

arXiv:2506.13724v2 Announce Type: replace Abstract: Implementing large-scale quantum algorithms with practical advantage will require fault-tolerance achieved through quantum error correction, but the associated overhead is prohibitive. This overhead can be reduced by engineering physical qubits with fewer errors, and by shaping the residual errors to be more easily correctable. In this work, we demonstrate quantum error correcting codes and logical qubit circuits in a metastable ytterbium-171 nuclear spin qubit with a noise bias towards erasure errors. These errors can be located separately from any syndrome information diagnosing the error, and we demonstrate adaptive circuit execution based on erasure information. We show that dephasing errors on the qubit during coherent transport can be strongly suppressed, and implement entangling gates that maintain a high fidelity in the presence of gate beam inhomogeneity or pointing errors. Furthermore, we demonstrate logical qubit encoding in the [[4, 2, 2]] code, with error correction during decoding based on mid-circuit erasure measurements despite the fact that the code is too small to correct any Pauli errors. Finally, we demonstrate logical qubit teleportation between multiple code blocks with conditionally selected ancillas based on mid-circuit erasure checks, a key part of leakage-robust error correction schemes using neutral atoms.

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11.
medRxiv (Medicine) 2026-06-10

"We don't complain; it's just part of being a woman": frequency, knowledge, and sociocultural beliefs about dysmenorrhoea in a South African university cohort

Authors:
Bedwell↗G. J↗Madden↗V. J↗Isaacs↗Khorommbi↗Moloi↗Papaioannou↗Solomons↗Sudan↗Parker↗

Introduction Dysmenorrhoea is highly prevalent globally and interferes with engagement in education, work, social participation, and quality of life. Although evidence suggests that sociocultural beliefs influence how menstrual pain is understood and managed, relatively little research has explored dysmenorrhoea-related knowledge and beliefs within South Africa. This study aimed to (1) determine the frequency of dysmenorrhoea, (2) assess dysmenorrhoea-related knowledge and compare knowledge between menstruating and non-menstruating individuals, and (3) explore commonly held generational, cultural, and religious beliefs related to dysmenorrhoea in a South African university cohort. Methods We analysed data collected as part of a cross-sectional survey conducted among staff and students at a South African university. Participants completed demographic questions, items assessing dysmenorrhoea-related knowledge, and an adapted Working Ability, Location, Intensity, Days of Pain, Dysmenorrhoea (WaLIDD) questionnaire. Participants were also invited to provide free-text responses describing generational, cultural, and religious beliefs about dysmenorrhoea. Quantitative data were analysed descriptively and compared between menstruating and non-menstruating participants. Free-text responses were analysed using reflexive thematic analysis. Results A total of 863 participants completed the survey, including 578 current or past menstruators. The frequency (95%CI) of dysmenorrhoea was 75.4% (71.7-78.9). Most participants were classified as having moderate (53%) or severe (31%) dysmenorrhoea on the WaLIDD scale. Awareness of dysmenorrhoea was higher among participants who had menstruated than among those who had never menstruated (80.4% vs 55.3%, p

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12.
arXiv (CS.AI) 2026-06-24

DynamicPO: Dynamic Preference Optimization for Recommendation

Authors:
Xingyu Hu↗Kai Zhang↗Jiancan Wu↗Shuli Wang↗Chi Wang↗Wenshuai Chen↗Yinhua Zhu↗Haitao Wang↗Xingxing Wang↗Xiang Wang↗

arXiv:2605.00327v3 Announce Type: replace-cross Abstract: In large language model (LLM)-based recommendation systems, direct preference optimization (DPO) effectively aligns recommendations with user preferences, requiring multi-negative objective functions to leverage abundant implicit-feedback negatives and sharpen preference boundaries. However, our empirical analyses reveal a counterintuitive phenomenon, preference optimization collapse, where increasing the number of negative samples can lead to performance degradation despite a continuously decreasing training loss. We further theoretically demonstrate that this collapse arises from gradient suppression, caused by the dominance of easily discriminable negatives over boundary-critical negatives that truly define user preference boundaries. As a result, boundary-relevant signals are under-optimized, weakening the model's decision boundary. Motivated by these observations, we propose DynamicPO (Dynamic Preference Optimization), a lightweight and plug-and-play framework comprising two adaptive mechanisms: Dynamic Boundary Negative Selection, which identifies and prioritizes informative negatives near the model's decision boundary, and Dual-Margin Dynamic beta Adjustment, which calibrates optimization strength per sample according to boundary ambiguity. Extensive experiments on three public datasets show that DynamicPO effectively prevents optimization collapse and improves recommendation accuracy on multi-negative preference optimization methods, with negligible computational overhead. Our code and datasets are available at https://github.com/xingyuHuxingyu/DynamicPO.

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13.
arXiv (CS.CV) 2026-06-12

Skill-3D: Evolving Scene-Aware Skills for Agentic 3D Spatial Reasoning

Authors:
Haoyuan Li↗Zhengdong Hu↗Jun Wang↗Hehe Fan↗Yi Yang↗

This paper explores agentic 3D spatial understanding, i.e., MLLM agents performing 3D reasoning through tool use. Existing methods often misuse tools and exhibit biased tool preferences under 3D scenarios, leaving the agentic paradigm with only marginal gains over non-agentic strategies. We reveal that 3D spatial reasoning tasks are heterogeneous across scenes, while these agents apply a uniform tool-use strategy to all scenes rather than selecting tools according to the specific scene and task. To address this, we propose Skill-3D, a framework that learns self-evolving scene-aware skills. Specifically, Skill-3D identifies the task scene and records the agent's tool-use trajectory into a Scene Memory, where successful trajectories from similar scenes are aggregated and distilled into a reusable scene-aware skill, with failed ones attached to the skill as lessons. During training, once a similar scene recurs, the corresponding skill is injected to guide the agent, producing new trajectories whose successes and failures further refine the skill, forming a loop in which the memory and the skill library co-evolve. Experiments show that Skill-3D substantially improves tool utilization in 3D spatial reasoning (from 39% to 78% on VSI-Bench), driving the agent toward correct and sufficient tool use. For instance, it improves Gemini-3-Flash by 67% on MMSI-Bench. Furthermore, we conduct agentic post-training over skill-guided trajectories, which boosts Qwen3-VL-8B by 60% on VSI-Bench.

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14.
medRxiv (Medicine) 2026-06-22

GCH1 p.Ser80Asn Confers Risk for Parkinson's Disease in East Asian Populations

Authors:
Tay↗Y. W↗Lee↗A. L↗Schee↗J. P↗Lin↗C. H↗Tan↗E. K↗Shin↗J. H↗…

Introduction: GCH1 has been implicated in Parkinson's disease (PD), but its risks variants and associations are not well defined. Objectives: To investigate the clinical relevance and PD risk associated with the GCH1 p.Ser80Asn variant. Methods: We first identified a segregating GCH1 p.Ser80Asn variant in a Malaysian Chinese PD family via whole genome sequencing (WGS). We assessed its risk association using multi-ancestry WGS data from the Global Parkinson's Genetics Program (GP2) (n=22,372PD vs n=8,826Controls) and meta-analysis of East Asian (EAS) cohorts (n=4,712PD vs 38,733Controls). Clinico-demographic details of affected variant carriers were collated. Results: The GCH1 p.Ser80Asn variant was enriched in GP2 EAS PD populations (n=9/2,757; 0.33%) but not detected in other ancestries. Meta-analysis revealed increased PD risk in EAS populations (odds ratio:5.1; 95%CI:2.3-10.7; p=2.89x10-5). Affected carriers (mean age at onset:56.3+-12.5 years) had additional occurrence of dystonia, while dementia was rare. Conclusions: The GCH1 p.Ser80Asn variant is a rare, EAS-enriched risk variant for PD.

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15.
Nature (Science) 2026-06-24

Long-sought chemical inhibitors of β-arrestin proteins

Authors: Unknown Author

Proteins called β-arrestins regulate signalling through members of the G protein-coupled receptor (GPCR) superfamily. Small molecules that bind directly to the β-arrestins and inhibit their activities are the first chemical tools to probe their biology, opening avenues for transducer-targeted, pathway-specific GPCR therapeutics. Three small molecules disrupt the engagement of β-arrestins with G-protein-coupled receptor proteins.

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16.
arXiv (CS.LG) 2026-06-18

Ultrafast On-chip Online Learning via Spline Locality in Kolmogorov-Arnold Networks

Authors:
Duc Hoang↗Aarush Gupta↗Philip Harris↗

arXiv:2602.02056v3 Announce Type: replace-cross Abstract: Ultrafast online learning is essential for high-frequency systems, such as controls for quantum computing and nuclear fusion, where adaptation must occur on sub-microsecond timescales. Meeting these requirements demands low-latency, fixed-precision computation under strict memory constraints, a regime in which conventional Multi-Layer Perceptrons (MLPs) are both inefficient and numerically unstable. We identify key properties of Kolmogorov-Arnold Networks (KANs) that align with these constraints. Specifically, we show that: (i) KAN updates exploiting B-spline locality are sparse, enabling superior on-chip resource scaling, and (ii) KANs are inherently robust to fixed-point quantization. By implementing fixed-point online training on Field-Programmable Gate Arrays (FPGAs), a representative platform for on-chip computation, we demonstrate that KAN-based online learners are significantly more efficient and expressive than MLPs across a range of low-latency and resource-constrained tasks. To our knowledge, this work is the first to demonstrate model-free online learning at sub-microsecond latencies.

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17.
medRxiv (Medicine) 2026-06-24

Beyond Single Biomarkers: A Graph Neural Network Framework for Multivariable Prediction of Clinical Outcomes from Brain Imaging

Authors:
Esmaelpoor↗Kadkhodamohammadi↗Peng↗Jelfs↗Mao↗Ghafouri↗Shader↗

Understanding brain-behavior relationships requires models capturing the distributed, interactive, and multiscale nature of neural systems. Traditional univariate approaches and single-biomarker models are inherently limited in this context, as they fail to represent dependencies across regions and the hierarchical organization of brain networks. In this study, we propose a graph-based multivariable framework for brain imaging analysis that integrates key organizational principles of brain function-including segregation, integration, modularity, and temporal dynamics-within a unified graph neural network architecture. The framework represents brain data as hierarchical graphs, where node features encode regional activation and temporal variability, and graph structure captures interactions within and between functional modules. The proposed approach is evaluated using functional near-infrared spectroscopy (fNIRS) data as a case study, where subject-specific brain graphs are constructed from task-based recordings acquired shortly after cochlear implant activation to predict speech understanding outcomes one year later. Under leave-one-subject-out validation, the model demonstrates strong predictive performance (R = 0.73, p < 0.001), outperforming previously reported single-biomarker approaches. Perturbation-based analyses further show that predictions are driven by distributed patterns of activity and interaction across regions and modalities, rather than isolated features. These results illustrate the capability of the proposed framework to capture complex brain organization and highlight its potential as a generalizable platform for multivariable analysis and prediction in neuroimaging applications beyond the specific clinical use case considered here.

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18.
arXiv (CS.CV) 2026-06-16

Lesion-DDPM: Lesion-Enhanced 3D Diffusion for MS MRI Synthesis

Authors:
Weidong Zhang↗Yongchan Jung↗Shafayat Mowla Anik↗Furen Xiao↗Vasudevan Janarthanan↗Enkhzaya Chuluunbaatar↗Byeong Kil Lee↗Jeeho Ryoo↗

3D FLAIR MRI is widely recommended as one of the standard MRI sequences for brain imaging in multiple sclerosis (MS), but publicly available MS datasets remain relatively small and vary across scanners, acquisition protocols, and lesion patterns. This scarcity and variability hinder the development of robust neuroimaging machine learning models and are particularly challenging for generative models that aim to synthesize images while preserving small, sparse lesions. We propose Lesion-DDPM, a 3D conditional diffusion framework for lesion-aware FLAIR synthesis that incorporates multi-level anatomical mask injection together with a lesion-weighted reconstruction loss to emphasize lesion voxels while maintaining global brain structure. Using a curated subset of the MSLesSeg dataset, we compare Lesion-DDPM with representative state-of-the-art GAN- and diffusion-based models, assessing both image-generation metrics and downstream 3D U-Net segmentation. In our experiments, Lesion-DDPM achieved the lowest lesion-region reconstruction error among all methods. In a downstream 3D U-Net lesion segmentation task, a model trained only on Lesion-DDPM-generated scans and evaluated on real MRIs reached a Dice score of 0.616 compared with 0.569 for the best competing synthetic dataset. When Lesion-DDPM images were added to the real training set, the Dice score further increased to 0.685.

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19.
arXiv (quant-ph) 2026-06-16

Complete Relational Description of Spin in a Quantum Background

Authors:
Hannah Troger↗Ofek Bengyat↗Thomas D. Galley↗Marios Christodoulou↗

arXiv:2606.15873v1 Announce Type: new Abstract: The standard description of the state of a spin in quantum mechanics presupposes externally fixed directions – a classical background. Can a spin be fully described instead in relation to other quantum mechanical systems? Poulin suggested twenty years ago group averaging over rotations the joint state of a fundamental spin and a reference spin with large angular momentum which, however, yields a classical bit in a probabilistic mixture. We revisit this idea and show that when the quantum reference system is augmented to two large spins, the standard quantum mechanical description of a spin is recovered in the limit of large quantum numbers for the reference system.

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20.
arXiv (CS.CL) 2026-06-24

A P\={a}ninian Foundation for Indic Language Processing

Authors:
Ritwik Banerjee↗Lav R. Varshney↗

More than a billion people communicate in Indic languages, yet the natural language processing infrastructure serving them remains fragmented and underdeveloped. The cause is structural: the field organizes its tools and benchmarks around individual languages or small subsets of genealogical language families, building separate analyzers, parsers, and datasets for each language and starting over for the next. This overlooks a deep regularity. Through more than two millennia of convergence around Sanskrit, Indic languages came to share a morphosyntactic architecture formalized in P\={a}nini's grammar, the Ast\={a}dhy\={a}y\={i}. This cuts across genealogical lines, uniting languages through a common framework. We argue that this P\={a}ninian framework supplies a unifying computational architecture the field has lacked, and that benchmarks grounded explicitly in it would make Indic language systems more accurate, more data-efficient, and more transferable, effectively merging many apparently disparate and sparse Indic language resources into a single high-resource metalanguage bedrock. We propose a four-part benchmark suite to render this shared architecture explicit, measurable, and ready to be leveraged for practical applications. Moreover, we underscore the question it raises for interpretability research: whether neural models trained on these languages come to represent P\={a}nini's categories on their own.

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21.
arXiv (CS.LG) 2026-06-16

Dynestyx: A Probabilistic Programming Library for Dynamical Systems

Authors:
Daniel Waxman↗Dmitry Batenkov↗John Feser↗Andy Zane↗Eli Bingham↗Youssef Marzouk↗Matthew E. Levine↗

arXiv:2606.16985v1 Announce Type: cross Abstract: State-space models (SSMs) are the standard formalism for Bayesian treatment of dynamical systems, with natural applications in statistics, signal processing, and machine learning. Despite their importance in both theory and application, dynamical systems have proven difficult to incorporate in modern probabilistic programming languages (PPLs), making state-of-the-art methods less accessible to practitioners and introducing friction in following the "Bayesian workflow." We introduce dynestyx, a probabilistic programming library with first-class support for SSMs, including state-of-the-art methods in the estimation of both states and parameters. Through a single, unified interface, users may specify arbitrary priors for discrete-time or continuous-time dynamical systems, perform inference over mixed-effect data, and make state and parameter estimates with principled uncertainty quantification.

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22.
arXiv (CS.CL) 2026-06-24

Predicting Poets' Origins from Verse: A Computational Analysis of Regional Linguistic Fingerprints in the Complete Tang Poems

Authors:
Chi-Sheng Chen↗Hung-Yun Liu↗

We ask whether the geographic origin of Tang-dynasty poets leaves a detectable linguistic trace in their work. Aggregating every poem attributed to each author in the Complete Tang Poems (Quan Tang Shi) and linking poets to their administrative circuit of origin via the China Biographical Database (CBDB), we build a poet-level corpus of 357 poets across the ten Tang circuits and frame origin prediction as multi-class classification. Using character $n$-gram TF-IDF together with interpretable domain features (imagery, season, and allusion), classical and neural models predict a poet's broad region (South vs.\ North) at $0.69$ accuracy, well above the $0.53$ majority baseline, and finer circuit-level origin above chance. Beyond classification, three findings emerge. (i) Linguistic distance between circuits grows with geographic distance (Mantel $r=0.40$, $p\approx0.09$ over nine circuits), evidence of a distance-decay effect in poetic language. (ii) The signal interacts with time: South/North separability is at chance in the High Tang and strongest in the Late Tang, consistent with court-driven homogenization at the empire's height followed by regional divergence. (iii) The model's confident errors are historically meaningful – in the Early Tang, every misclassification is a southern poet read as northern, reflecting the prestige of the northern court idiom. We further show that, when given the whole corpus through a hierarchical frozen-encoder representation, a classical-Chinese transformer (GuwenBERT) only matches – not beats – simple TF-IDF, and that combining them adds nothing, indicating that character $n$-grams already capture the regional signal. Our results position interpretable machine learning as a hypothesis generator for literary history.

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23.
arXiv (CS.LG) 2026-06-17

On Surjectivity of Neural Networks: Can you elicit any behavior from your model?

Authors:
Haozhe Jiang↗Nika Haghtalab↗

arXiv:2508.19445v3 Announce Type: replace Abstract: Given a trained neural network, can any specified output be generated by some input? Equivalently, does the network correspond to a function that is surjective? In generative models, surjectivity implies that any output, including harmful or undesirable content, can in principle be generated by the networks, raising concerns about model safety and jailbreak vulnerabilities. In this paper, we prove that many fundamental building blocks of modern neural architectures, such as networks with pre-layer normalization and linear-attention modules, are almost always surjective. As corollaries, widely used generative frameworks, including GPT-style transformers and diffusion models with deterministic ODE solvers, admit inverse mappings for arbitrary outputs. By studying surjectivity of these modern and commonly used neural architectures, we contribute a formalism that sheds light on their unavoidable vulnerability to a broad class of adversarial attacks.

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24.
arXiv (CS.AI) 2026-06-18

Forecasting what Matters: Decision-Focused RL for Controlled EV Charging with Unknown Departure Times

Authors:
Giuseppe Gabriele↗Fabio Pavirani↗Seyed Soroush Karimi Madahi↗Chris Develder↗

arXiv:2606.19199v1 Announce Type: cross Abstract: The recent growth of EV adoption poses challenges for power systems, including increased peak demand and potential grid instability. Smart control of EV charging – e.g., based on reinforcement learning (RL) – can alleviate these issues by learning temporal and contextual patterns from historical data. Yet, in real-world scenarios, key features, such as departure time, often are unavailable. This, in turn, makes it harder for an RL agent to learn and execute an effective charging policy. To mitigate this uncertainty, a trained forecaster can approximate the unknown features from available data. However, since these forecasting models are typically trained for accuracy (rather than their impact on a downstream agent's decision quality), their errors may propagate and hinder the overall performance of a controller that is using the forecasts. To avoid this, we propose a decision-focused RL (DF-RL) framework in which the forecaster is trained end-to-end, i.e., with feedback from the charging policy actions taken by the RL agent. Such joint training of both the forecaster and controller ultimately results in higher-quality actions: our proposed DF-RL method yields superior charging decisions compared to other baselines, achieving up to a 14% improvement in total reward and a 55% reduction of unsupplied energy (i.e., charging that failed to happen because the EV already left), relative to the RL method without departure time forecasting.

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25.
arXiv (CS.CV) 2026-06-18

Physics-IQ Verified

Authors:
Tim R\"adsch↗Yuki M Asano↗Hilde Kuehne↗Stefan Bauer↗Priyank Jaini↗Robert Geirhos↗Carsten T. L\"uth↗

Video generative models ( VGMs) have become a new frontier that can be used not just for video generation but for a multitude of downstream tasks, including world modeling. To advance these tasks, a good video model must understand the physical reality of the world. Evaluating this understanding is an emerging field and has led to the Physics-IQ benchmark, which quantifies this explicitly by comparing model-generated videos to real-world videos of physical experiments. In this work, we present a systematic audit of the Physics-IQ benchmark, expose shortcomings and propose three solutions that sharpen how we can measure physical understanding of VGMs. Specifically, we improve prompt and ground-truth quality to reduce the influence of confounding factors and further introduce a sample-level scoring system that weights each sample and metric equally. Our resulting benchmark, Physics-IQ Verified, refines 57.6\% of all samples and improves over 34.8\% of prompts. In a comparison study using six image-to-video generative models, we observe moderate but meaningful ranking changes (Kendall's $\tau = 0.46$). We hope Physics-IQ Verified advances the community by providing a more reliable signal toward physically accurate VGMs. The code for the benchmark can be accessed at https://github.com/google-deepmind/physics-iq-benchmark

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