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01.
arXiv (CS.CL) 2026-06-19

Learning to Prompt: Improving Student Engagement with Adaptive LLM-based High-School Tutoring

Authors:
Po-Chin Chang↗Nicholas Hogan↗Aske Plaat↗Michiel T. van der Meer↗

LLMs can personalize education, although current static-prompt tutoring systems struggle to adapt to diverse academic disciplines. We develop and test a system with subject-aware prompting, based on 14 pedagogical features (e.g., tutor scaffolding, student understanding) extracted from raw transcripts. We first train a prompt routing model in a simulation environment, and then deploy it for online adaptation with actual high-school students. The simulation benchmark shows the router outperforming two static baselines ($0.694$ vs. $0.647$ and $0.64$, $p

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02.
arXiv (CS.AI) 2026-06-11

What Limits Does Quantization Place on Dense Top-$k$ Retrieval? A Theoretical Study

Authors:
Koki Okajima↗Tsukasa Yoshida↗

arXiv:2606.11780v1 Announce Type: cross Abstract: We establish conditions for embedding a corpus of $N$ documents as $d$-dimensional vectors such that every $k$-subset $S \subseteq [N]$ is realizable as a result of top-$k$ retrieval by some query vector. Recent work shows that $d = O(k)$ suffices for such embeddings to exist in $\mathbb{R}^d$, independently of $N$. We theoretically prove that this corpus-independent bound is specific to infinite precision. With $B$ bits per coordinate, perfect top-$k$ retrieval requires $Bd = \Omega(k \ln N)$; thus, at any fixed precision, the dimension must grow at least logarithmically with $N$. Specializing to a $\ell_2$-normalized $B$-bit uniform scalar quantization model, we also identify a threshold on the precision $B^{*} = O(\ln \ln N)$ below which no dimension suffices, together with two further regimes that bound the feasible $(B, d)$ pairs. Our result implies that in practical vector databases and dense retrieval systems where quantization is standard, the embedding dimension and possibly the precision must grow with the corpus size.

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03.
arXiv (CS.AI) 2026-06-16

Direction-Conditioned Policies via Compositional Subgoal Scoring for Online Goal-Conditioned Reinforcement Learning

Authors:
Swaminathan S K↗Damiya Gondha↗Theyanesh Eswaramoorthy Rajahkrishnan↗Aritra Hazra↗

arXiv:2606.16515v1 Announce Type: cross Abstract: Hamilton-Jacobi-Bellman theory implies that the optimal goal-conditioned action depends on the goal only through the gradient of the goal-reaching distance at the current state, yet standard online GCRL still conditions the actor on the raw goal – a signal that is geometrically uninformative when the goal is far from the data distribution. We propose Direction-Conditioned Policies (DCP), a fully online method that decomposes goal-reaching into two components sharing one InfoNCE representation $\psi$: a subgoal-scoring step that selects a visited state $z_t$ aligned with the final goal $g$ in $\psi_g$, and a direction-conditioned actor that consumes the unit direction $d_t$ and magnitude $r_t$ from $\psi(s_t)$ to $\psi(z_t)$. The two components train jointly, factor cleanly at deployment (subgoal scoring is removed, while direction conditioning remains with $g$ in place of $z_t$), and admit independent modification at the same $(d_t,r_t)$ interface. We prove three results. First, direction sufficiency under HJB: the optimal action under control-affine dynamics depends on the goal only through the value gradient. Second, a quantitative bound showing that, under mild conditions on the learned representation and assuming the scoring rule returns an on-path $z_t$, the actor's conditioning input at training and at deployment coincide up to representation error and geodesic slack. Third, a controllable-subspace characterization of when directional conditioning fails. Across nine environments, DCP improves over Contrastive RL on most final metrics, with the largest gains on manipulation and obstacle-interaction tasks; a qualitative analysis of the learned $\psi$-distance landscape shows the contrastive representation behaves as an online quasimetric encoding environment topology, and the single failure case (AntSoccer) localizes to a learned-gradient pathology that the theory anticipates.

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04.
arXiv (quant-ph) 2026-06-12

Efficient certification of intractable quantum states with few Pauli measurements

Authors:
Sami Abdul Sater↗Maxime Garnier↗Thierry Martinez↗Harold Ollivier↗Ulysse Chabaud↗

arXiv:2511.07300v2 Announce Type: replace Abstract: Efficient verification of quantum computational resources is crucial as experiments advance toward fault-tolerance. Universal quantum computation can be achieved by consuming resource states through simple Pauli measurements, yet a significant gap remains between states that are easy to certify and those required for universality. We focus on Clifford-enhanced Product States, a class of resource states obtained by applying Clifford circuits to a product of single-qubit, potentially magic, states. While essential for universal computation, the certification of such states has previously relied on query oracles that are \#P-hard to implement, leaving their efficient, oracle-free verification an open challenge. In this work, we demonstrate that such classically intractable resource states can be efficiently verified using only Pauli measurements. Our protocol achieves sample- and time-efficiency in both i.i.d.\ and adversarial settings. This work fills a gap in Pauli-based certification, providing a new practical pathway to verify resource states that drive universal Pauli-based quantum computation.

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07.
arXiv (CS.CV) 2026-06-11

Semantically-Aware Diver Activity Recognition Framework for Effective Underwater Multi-Human-Robot Collaboration

Authors:
Sadman Sakib Enan↗Junaed Sattar↗

Effective multi-human-robot collaboration is essential for expanding human-led operations in the challenging and high-risk underwater environment. For autonomous underwater vehicles (AUVs) to become true teammates, they must be able to comprehend their surroundings and recognize a diver's activities to offer assistance and ensure safety. Towards this goal, we introduce DAR-Net, a novel transformer-based framework that analyzes complex underwater scenes to classify diver activities. Our contribution lies in a semantically guided learning formulation that couples transformer-based temporal reasoning with pixel-level scene supervision. This multi-loss training strategy explicitly aligns global activity recognition with local human-robot interaction semantics, which is particularly critical in low-visibility underwater conditions. To address the significant challenge of data scarcity in this domain, we present the first-ever Underwater Diver Activity (UDA) dataset, a foundational resource containing over 2,600 annotated images with pixel-level masks. Through rigorous experimental evaluations in a controlled environment, we demonstrate that DAR-Net achieves promising accuracy in recognizing six distinct diver activities, outperforming state-of-the-art models. While this dataset provides a crucial baseline, our work serves as a pioneering step, laying the groundwork for future research and facilitating the development of more intelligent, collaborative underwater robotic systems.

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08.
arXiv (CS.AI) 2026-06-18

A Link between Shock-wave Theory and Symmetry-reduced Stochastic Gradient Descent for Artificial Neural Networks

Authors:
Taiki Miyagawa↗

arXiv:2606.18303v1 Announce Type: cross Abstract: We develop a mathematically explicit link between shock-wave theory and the symmetry-quotiented learning dynamics of stochastic gradient descent, drawing on differential geometry, Lie group theory, and fluid mechanics. Specifically, after quotienting parameter symmetries and applying local-entropy coarse-graining, the effective dynamics satisfy a viscous Hamilton–Jacobi equation on the quotient manifold. Moreover, under the assumption that the raw parameter dynamics can be summarized by a gradient field on the quotiented space, the gradient of the coarse-grained loss function obeys a Burgers-type equation, and shock formation can be established rigorously. We apply our theory to multilayer perceptrons, convolutional neural networks, Transformers, and mean-field networks, and show that they obey the Hamilton–Jacobi or Burgers-type equations. We conjecture that this framework also yields practical diagnostics for deep learning. In architectures such as Transformers, raw parameter norms are often distorted by symmetry redundancy and may therefore be misleading, whereas symmetry-corrected quotient observables provide a principled basis for monitoring, forecasting, and controlling training-phase transitions.

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09.
arXiv (CS.LG) 2026-06-16

Learning the Geometry of Data: A Mathematical Review of Shape Space Analysis

Authors:
Gary P. T. Choi↗Khanh Dao Duc↗Shira Faigenbaum-Golovin↗Karen Habermann↗Emmanuel Hartman↗Christoph von Tycowicz↗Chi Zhang↗Wenjun Zhao↗Felix Zhou↗

arXiv:2606.17022v1 Announce Type: cross Abstract: A central objective of machine learning is to identify structure and patterns in data. Advances in data acquisition have increasingly produced datasets whose observations possess rich geometric form, giving rise to shape spaces that encode variability in object geometry. Such datasets arise across a wide range of disciplines, including biology, medicine, anthropology, and computer vision, where subtle geometric differences often carry important scientific information. Traditional machine learning methods, however, are frequently ill-equipped to account for the nonlinear geometric structure underlying these data. This survey synthesizes a rapidly growing body of work on shape space analysis, which provides a mathematical and computational framework for the study of geometric data. Drawing on ideas from differential geometry, statistics, and machine learning, we organize the literature around a common analytical pipeline: shape representation and parameterization, the rigorous construction of robust geodesic metrics, statistical analysis on shape spaces, and geometry-aware learning methods. We discuss how these tools enable the characterization of shape variability, the comparison of geometric objects, and the analysis of structural trajectories across populations and time. To illustrate the breadth of the field, we highlight applications spanning multiple scales of biological organization, including studies of subcellular morphology and primate tooth evolution. Across these and many other domains, researchers face common challenges arising from complex, nonlinear, and often unaligned geometric variation. The review concludes by identifying key theoretical and computational challenges, as well as emerging opportunities driven by increasingly large and diverse geometric datasets.

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10.
arXiv (CS.AI) 2026-06-17

SketchXplain: Intuitive Visual Explanations of Image Classifiers with Sketches

Authors:
Wencan Zhang↗Mario Michelessa↗Xuejun Zhao↗Brian Y. Lim↗

arXiv:2606.17646v1 Announce Type: cross Abstract: Saliency map visualizations explain image-based AI predictions by pointing to regions, but these are often unintuitive and semantically unclear, leaving an interpretability gap. We argue that AI explanations should be intuitive – coherent to user knowledge, yet simple and selective to accelerate interpretation. Inspired by artistic drawings, we propose SketchXplain to generate sketch-based visual explanations for intuitive image-based explainable AI (XAI). Combining techniques in saliency maps, concept-bottleneck models, and sketch optimization, SketchXplain integrates saliency to select coherent observation artifacts, concepts for knowledge coherence, cues to represent them, and abstraction for simplicity. Evaluating on face expression recognition, modeling and user studies showed that SketchXplain supported quicker interpretation with more aligned visualizations than saliency maps or simple drawings. Further evaluation on skin lesion diagnosis found that SketchXplain more coherently visualized disease symptoms, better supporting lay diagnosis. Thus, this work illustrates the value of sketches for intuitive, simple, coherent, and quick image-based XAI visualizations.

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11.
arXiv (CS.CV) 2026-06-19

SA-VIS: Sparse frame Annotations for training Video Instance Segmentation

Authors:
Edoardo Mello Rella↗Ajad Chhatkuli↗Shipra Jain↗Ender Konukoglu↗Luc Van Gool↗

Recent online video instance segmentation (VIS) methods have achieved impressive results, thus becoming the preferred approach to segment instances in videos. Despite the resurgence of impressive single image models, the online (or semi-online) VIS approaches outperform single-image models (e.g., based on SAM) by using long sequences of densely annotated frames during training. However,such a training setup of VIS is expensive in the sense of compute as well as dense annotations required. In order to solve these major flaws, we argue that the effective modeling of the instances and their evolution in videos do not require densely annotated frames. To that end, we propose a simple and effective module, called Past-frames Feature Propagation (PFP) which aggregates low-dimensional features from the image encoder of multiple frames. This simple low-compute module provides tremendous learning capability in using sparse video frame labels for end-to-end training. Combined with a light-weight frame-specific Instance Queries, our Sparse frame Annotation VIS (SA-VIS) significantly improves performance over its baseline. Most interestingly, our simple design that avoids complexities effectively bridges the gap in accuracy between training on sparsely and densely annotated video sequences. This translates to a mere 0.4% drop in performance of SA-VIS when using annotations for only 1/5 of the images in the dataset. Empirically, SA-VIS shows strong improvements over the baseline on YouTube-VIS 2019/2021/2022 and Occluded VIS (OVIS) and an over 1% improvement in AP on the state-of-the-art in a limited annotations scenario.

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12.
arXiv (quant-ph) 2026-06-12

Invariant Measures and Weak-Magic-Injection Asymptotics in Random Monitored Quantum Circuits

Authors:
Guocheng Zhen↗Xuanrong Yang↗Chengkai Zhu↗Ranyiliu Chen↗Xin Wang↗

arXiv:2606.13470v1 Announce Type: new Abstract: Monitored quantum circuits provide a natural setting in which scrambling, measurements, and measurement-conditioned updates compete within a stochastic many-body dynamics. From the viewpoint of nonstabilizer resource theory, this competition is especially relevant because Clifford-compatible operations preserve the stabilizer structure, while weak non-Clifford perturbations inject magic resource. Most of the existing understanding of monitored quantum circuits has been shaped by numerical simulations and phenomenological descriptions, while a rigorous dynamics theory remains less developed. In this paper, we address this gap by developing an analytical framework which lays a rigorous mathematical foundation for the study of random monitored quantum dynamics. Specifically, we study a class of monitored quantum circuits driven by random Clifford. We prove the existence and uniqueness of the stationary law, which gives an ergodic description of the long-time dynamics. We then resolve the leading asymptotics of steady magic in the weak-magic-injection limit. This tangent description makes the contrast between resource measures transparent: in odd-prime local dimension, the steady Gross–Wigner mana has a linear leading asymptotic, whereas in qubit systems the steady 2-stabilizer Rényi entropy has a quadratic leading asymptotic. These different powers reflect the distinct local geometries of the two resource measures near the stabilizer layer. In this way, this work develops an analytical framework that first establishes the stationary ergodic dynamics of random monitored quantum circuits.

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13.
arXiv (CS.CL) 2026-06-18

Lost in a Single Vector: Improving Long-Document Retrieval with Chunk Evidence Aggregation

Authors:
Shanshan Lyu↗Yiwei Wang↗Yujun Cai↗Jiafeng Guo↗Shenghua Liu↗

Dense retrieval ranks one query vector against one document vector. On long documents, this interface can fail when a short but decisive span is weakened during document encoding before ranking. We study this failure mode as document-side early compression and introduce the Evidence Dilution Index (EDI) to measure how far a document-level representation falls below the strongest chunk-level evidence within the same gold document. Guided by this view, we propose DICE (Document Inference via Chunk Evidence), a training-free document-side strategy that splits documents into chunks, encodes them independently with a frozen model, and aggregates them back into a single vector while preserving the standard one-query-one-document interface. On LongEmbed, DICE improves retrieval across four backbones, with the largest gains on slices beyond 4k tokens: for Dream, Passkey >4k rises from 30.0 to 90.0 and Needle >4k from 23.3 to 74.0. Across 12,779 filtered samples, DICE yields lower EDI than the single-vector baseline in 92.8% of cases. These results establish document-level encoding as a practical and underexplored lever for long-document retrieval.

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14.
arXiv (CS.LG) 2026-06-12

Viral Proteins Reveal Geometry of Protein Language Models

Authors:
Arthur Bigot↗Harmon Bhasin↗Core Francisco Park↗Eugene Shakhnovich↗Dianzhuo Wang↗

arXiv:2606.12609v1 Announce Type: new Abstract: Protein language models are trained on highly imbalanced datasets, raising the question of how they represent underrepresented biological sequences. Using viral proteins as a case study across ESM model families, we identify a dominant nativeness axis in embedding space, aligned with masked reconstruction perplexity, that orders sequences from well-modeled cellular proteins through viral proteins to shuffled and random sequences. Scaling contracts this axis unevenly across viral families. Despite this, protein language model embeddings retain viral-specific signal: viral proteins remain linearly separable beyond zero-shot perplexity and shallow sequence features. Together, these results suggest that pLM representations are structured by a general notion of nativeness while preserving information specific to distinct biological groups.

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15.
arXiv (CS.CL) 2026-06-12

Identifiability Without Gaussianity: Symbolic World Models and Near-Infinite Temporal Consistency

Authors:
Seth Dobrin↗{\L}ukasz Chmiel↗

Klindt, LeCun, and Balestriero (arXiv:2605.26379) proved that Joint-Embedding Predictive Architectures (JEPAs) achieve linear identifiability, the linear recovery of the world's true latent variables, if and only if the world's latent dynamics follow a Gaussian, stationary process. This Gaussian boundary implies a fundamental limit on temporal consistency: for any non-Gaussian physical system, the representation error of a statistical World Model grows monotonically with time. We prove that this limit is an artifact of the statistical alignment mechanism, not a property of World Models in general. We introduce the Physics-Grounded Symbolic Architecture (PGSA) and prove three results: (1) a PGSA achieves exact linear identifiability for all physical regimes, regardless of the latent distribution; (2) the per-step error of a PGSA is bounded by numerical precision alone; and (3) as a direct consequence, a PGSA maintains temporal consistency for an unbounded number of transitions, a property we term near-infinite temporal consistency. We further prove that statistical World Models cannot achieve this property for any non-Gaussian system, regardless of model capacity or the volume of training data. The algebraic cores of four of the theorems are formalized in Lean 4 with Mathlib4 v4.31.0 (zero sorry placeholders); the Klindt et al. converse is taken as an external premise. The contrast establishes that symbolic grounding in the causal generator of the world's dynamics is the sufficient condition and, in non-Gaussian regimes, the only condition for near-infinite temporal consistency.

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16.
medRxiv (Medicine) 2026-06-11

A global cross-sectional survey of health professionals' interest-confidence gaps in value-based health care implementation: a learning needs assessment

Authors:
Lewis↗Andrews↗Laing↗

Abstract Objectives Value-Based Health Care (VBHC) increasingly guides health system redesign internationally. Despite the increasing availability of VBHC education, gaps remain between health professionals' conceptual understanding of VBHC and their confidence to implement it in practice. This study assessed perceived learning needs and preferences of healthcare professionals across foundational topics essential to VBHC implementation. Design Cross-sectional online survey study Setting and participants The survey was distributed to the global VBHC community and yielded 518 responses. Most respondents were based in the UK and Ireland (51%) and 65% had more than 10 years of experience in the health sector. Participants represented a variety of professional backgrounds, including clinicians (34%), operational or executive managers and leaders (22%), and life sciences or procurement professionals (13%). Primary and secondary outcome measures Primary outcome measures included self-reported interest and confidence across 15 VBHC domains and the magnitude of the gap between them. Secondary outcomes included perceived implementation challenges and preferred VBHC learning approaches, including prior engagement with VBHC-related learning. Results Respondents identified substantial VBHC implementation challenges, including implementing outcome measurement (62.4%), conflicting priorities (57.7%), and resistance to change (56.8%). Interest in all VBHC domains was high (median >= 80/10), while confidence to implement remained substantially lower across most domains (median

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17.
arXiv (CS.CV) 2026-06-16

Reasoning in Computer Vision: Taxonomy, Models, Tasks, and Methodologies

Authors:
Ayushman Sarkar↗Zhenyu Yu↗Mohd Yamani Idna Idris↗

Visual reasoning matters for many computer vision tasks that go beyond surface-level object detection and classification. Despite progress in relational, symbolic, temporal, causal, and commonsense reasoning, existing surveys typically cover only one part of the problem, such as visual question answering, scene-graph generation, neuro-symbolic AI, or multimodal chain-of-thought, and rarely analyze reasoning types, methodologies, and evaluation protocols together. This survey addresses that gap. Following a structured literature review, we group visual reasoning into five major types (relational, symbolic, temporal, causal, and commonsense) and examine how each is implemented across methods that range from graph-based models, memory networks, attention mechanisms, and neuro-symbolic systems to reasoning with vision-language models (VLMs) and multimodal large language models (MLLMs), including visual chain-of-thought, visual programming, and tool-augmented and test-time reasoning. We then review evaluation protocols for functional correctness, structural consistency, and causal validity, and we analyze their limits in generalizability, reproducibility, faithfulness, and explanatory power. We also identify open challenges: scaling to complex scenes, integrating symbolic and neural paradigms more deeply, the shortage of comprehensive benchmarks, language-prior shortcuts and hallucination in foundation models, and reasoning under weak supervision. Finally, we set out a research agenda for vision systems and argue that connecting perception and reasoning is necessary for transparent, trustworthy, and cross-domain models, especially in high-stakes settings such as autonomous driving and medical diagnostics.

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18.
Nature (Science) 2026-06-17

Revealing competitive interfacial reactions in high-energy Li–S batteries

Authors:
Shiyuan Zhou↗

Charge transfer at solid–liquid interfaces plays a critical role in various energy-storage systems1, particularly under dynamically varying reactant concentrations. Deciphering these intricate reaction pathways remains a substantial challenge, notably in lithium–sulfur (Li–S) batteries, in which achieving high energy density requires efficient conversion of highly concentrated lithium polysulfides (LiPSs)2,3. However, the mechanisms governing lithium sulfide (Li2S) deposition and dissolution under lean electrolyte conditions remain poorly understood. Here, using in situ liquid-cell electron microscopy, we directly visualize concentration-driven phase segregation at the electrode–electrolyte interface. Within these high-concentration interfacial layers (HCILs), competitive surface and solution dictate the charge-transfer dynamics and ultimately govern Li2S deposition at different phase boundaries. Density functional theory (DFT) calculations reveal that the aggregation of LiPSs alters molecular geometry, electronic properties and orbital hybridization, collectively facilitating charge transfer through highly concentrated LiPSs clusters. Guided by these insights, we design optimized electrodes that balance interfacial reaction pathways, enabling fast charging (4 C, 26.8 mA cm−2) and achieving high energy densities exceeding 400 Wh kg−1. These findings provide mechanistic understanding of interfacial reactions under practical working conditions and offer a design strategy to advance Li–S batteries. Visualization of concentration-driven phase segregation within high-concentration interfacial layers in the context of high-energy lithium–sulfur batteries using liquid-cell electrochemical transmission electron microscopy reveals competitive interfacial reactions under lean electrolyte conditions at different phase boundaries.

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19.
arXiv (CS.LG) 2026-06-12

Bulk-Calibrated Credal Ambiguity Sets: Fast, Tractable Decision Making under Out-of-Sample Contamination

Authors:
Mengqi Chen↗Thomas B. Berrett↗Theodoros Damoulas↗Michele Caprio↗

arXiv:2601.21324v2 Announce Type: replace-cross Abstract: Distributionally robust optimisation (DRO) minimises the worst-case expected loss over an ambiguity set that can capture distributional shifts in out-of-sample environments. While Huber (linear-vacuous) contamination is a classical minimal-assumption model for an $\varepsilon$-fraction of arbitrary perturbations, including it in an ambiguity set can make the worst-case risk infinite and the DRO objective vacuous unless one imposes strong boundedness or support assumptions. We address these challenges by introducing bulk-calibrated credal ambiguity sets: we learn a high-mass bulk set from data while considering contamination inside the bulk and bounding the remaining tail contribution separately. This leads to a closed-form, finite $\mathrm{mean}+\sup$ robust objective and tractable linear or second-order cone programs for common losses and bulk geometries. Through this framework, we highlight and exploit the equivalence between the imprecise probability (IP) notion of upper expectation and the worst-case risk, demonstrating how IP credal sets translate into DRO objectives with interpretable tolerance levels. Experiments on heavy-tailed inventory control, geographically shifted house-price regression, and demographically shifted text classification show competitive robustness-accuracy trade-offs and efficient optimisation times, using Bayesian, frequentist, or empirical reference distributions.

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20.
arXiv (quant-ph) 2026-06-16

Sharp Transitions for Subsystem Complexity

Authors:
Yale Fan↗Nicholas Hunter-Jones↗Andreas Karch↗Shivan Mittal↗

arXiv:2510.18832v2 Announce Type: replace-cross Abstract: The circuit complexity of time-evolved pure quantum states grows linearly in time for an exponentially long time. This behavior has been proven in certain models, is conjectured to hold for generic quantum many-body systems, and is believed to be dual to the long-time growth of black hole interiors in AdS/CFT. Achieving a similar understanding for mixed states remains an important problem. In this work, we study the circuit complexity of time-evolved subsystems of pure quantum states. We find that for greater-than-half subsystem sizes, the complexity grows linearly in time for an exponentially long time, similarly to that of the full state. However, for less-than-half subsystem sizes, the complexity rises and then falls, returning to low complexity as the subsystem equilibrates. Notably, the transition between these two regimes occurs sharply at half system size. We use holographic duality to map out this picture of subsystem complexity dynamics and rigorously prove the existence of the sharp transition in random quantum circuits. Furthermore, we use holography to predict features of complexity growth at finite temperature that lie beyond the reach of techniques based on random quantum circuits. In particular, at finite temperature, we argue for an additional sharp transition at a critical less-than-half subsystem size. Below this critical value, the subsystem complexity saturates nearly instantaneously rather than exhibiting a rise and fall. This novel phenomenon, as well as an analogous transition above half system size, provides a target for future studies based on rigorous methods.

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

SimSiam Naming Game: A Unified Approach for Emergent Communication and Representation Learning

Authors:
Nguyen Le Hoang↗Tadahiro Taniguchi↗Tianwei Fang↗Akira Taniguchi↗Masatoshi Nagano↗

Emergent Communication (EmCom) investigates how agents develop symbolic communication through interaction without predefined language. Recent frameworks, such as the Metropolis–Hastings Naming Game (MHNG), formulate EmCom as the learning of shared external representations negotiated through interaction under joint attention, without explicit success or reward feedback. However, MHNG relies on sampling-based updates that suffer from high rejection rates in high-dimensional perceptual spaces, making the learning process sample-inefficient for complex visual datasets. In this work, we propose the SimSiam Naming Game (SSNG), a feedback-free EmCom framework that replaces sampling-based updates with a symmetric, self-supervised representation alignment objective between autonomous agents. Building on a variational inference–based probabilistic interpretation of self-supervised learning, SSNG formulates symbol emergence as an alignment process between agents' latent representations mediated by message exchange. To enable end-to-end gradient-based optimization, discrete symbolic messages are learned via a Gumbel–Softmax relaxation, preserving the discrete nature of communication while maintaining differentiability. Experiments on CIFAR-10 and ImageNet-100 show that the emergent messages learned by SSNG achieve substantially higher linear-probe classification accuracy than those produced by referential games, reconstruction games, and MHNG. These results indicate that self-supervised representation alignment provides an effective mechanism for feedback-free EmCom in multi-agent systems.

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22.
arXiv (quant-ph) 2026-06-15

Certification of the genuine resolution of photon number resolving detectors

Authors:
Jef Pauwels↗Towsif Taher↗Roope Uola↗Boris Korzh↗Nicolas Brunner↗Pavel Sekatski↗

arXiv:2606.14365v1 Announce Type: new Abstract: Photon-number-resolving (PNR) detectors are essential components of photonic quantum technologies, yet thus far, no practical metric exists to certify how many photons they can genuinely resolve in a single measurement. Here we introduce an operational framework for quantifying the capability of a PNR detector to distinguish between different numbers of photons, i.e. its genuine resolution. In turn, we develop a practical and scalable protocol for certifying the genuine resolution of a detector, which is based on coherent state probes. We apply the method to a 28-pixel photon-number-resolving superconducting nanowire single-photon detector (PNR-SNSPD) and certify genuine four-outcome resolution. Our work highlights the critical requirements in terms of detector efficiency towards achieving high genuine resolution. This approach provides an operational benchmark for PNR detectors and fills a crucial gap in the characterization of photonic quantum devices.

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23.
arXiv (CS.AI) 2026-06-15

I'm Sorry Driver, I'm Afraid I Can't Do That: Appraising the Safety of LLMs within Automotive Contexts

Authors:
Shaun Feakins↗Ibrahim Habli↗Kim Littler↗Robert Palin↗

arXiv:2606.14327v1 Announce Type: cross Abstract: This paper appraises recent frameworks within AI development to integrate LLMs into control tasks in automotive contexts from the perspective of safety assurance. This work has built upon the rapid integration of LLMs across automotive settings. However, we find that at present, these frameworks face significant challenges, limiting their efficacy in real-time safety-critical contexts. Firstly, we consider conceptual challenges, including the fact that deployers are faced with a dual challenge, wherein they must assure a model which has been developed upstream, i.e. as general-purpose tools by the large AI labs, in a downstream context, i.e. into specific vehicle architectures. Secondly, we consider concrete challenges from across existing standards. We show that there are currently both fundamental engineering constraints covered in ISO21448, such as latency, and novel LLM-specific issues, such as alignment-related issues covered in ISO/PAS8800. We ground both examples in a concrete introductory, experimental case study exploring an existing open-source repository, Talk2Drive. We present a safety argument in order to make explicit the limitations of existing solutions. Nonetheless, given that the use of LLMs in automotive contexts is being explored at a technical level and operationalised, we propose potential assurance mechanisms for LLM-related hazardous events going forward.

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24.
arXiv (math.PR) 2026-06-12

Diffusion approximations for interacting stochastic systems with reflection and control

Authors:
Thoa Thieu↗Roderick Melnik↗

arXiv:2601.05895v2 Announce Type: replace Abstract: We study diffusion approximations for a class of interacting stochastic systems with reflection and control. Motivated by interacting stochastic dynamics subject to feedback mechanisms and boundary constraints, we consider diffusion-scaled stochastic processes incorporating stochastic fluctuations, state-dependent interactions, and reflection. Under suitable assumptions, we establish convergence in distribution of the scaled processes to systems of interacting reflected stochastic differential equations of Ornstein-Uhlenbeck type. The limiting dynamics capture key features of constrained multi-agent systems, including mean-reverting behavior, interaction effects, and confinement within bounded domains through Skorokhod reflection. The analysis combines diffusion-scaling arguments, stability estimates, and continuity properties of the Skorokhod map to connect discrete stochastic systems with their reflected diffusion limits. To illustrate the framework, we present numerical examples motivated by crowd dynamics and neural population dynamics. The simulations demonstrate qualitative agreement between the finite stochastic systems and the corresponding reflected diffusion models and illustrate how diffusion approximations can provide tractable descriptions of interacting stochastic systems with constraints.

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25.
arXiv (CS.AI) 2026-06-19

RIVET: Robust Idempotent Voice Attribute Editing

Authors:
Dareen Alharthi↗Bhuvan Koduru↗Rita Singh↗Bhiksha Raj↗

arXiv:2606.19629v1 Announce Type: cross Abstract: Voice attribute editing models modify characteristics such as age and gender while preserving speaker identity. In large-scale speech datasets, however, attribute annotations are often noisy or inconsistent, which can cause conditional generative models to produce unstable edits. In this work, we show that idempotency provides an effective mechanism for improving robustness to noisy labels. An idempotent operator is one for which repeated application does not change the result, i.e., f(f(x)) = f(x). Enforcing this property acts as an implicit regularizer that reduces sensitivity to mislabeled examples. We introduce RIVET, a training framework that incorporates an idempotency objective to improve robustness to label noise. We evaluate RIVET under controlled label noise and on the GLOBE dataset with naturally noisy annotations. RIVET improves editing success and better preserves speaker identity than standard training, showing that idempotency improves robustness in voice editing models.

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