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01.
arXiv (quant-ph) 2026-06-16

Initiation of Superradiance from Different Collective Spin States

Authors:
Adnan Alabbar↗Zhenghao Zhang↗Girish S. Agarwal↗

arXiv:2606.14949v1 Announce Type: new Abstract: Superradiance is an extensive cooperative spontaneous emission phenomenon. Some atomic collective spin states exhibit it. However, distinct initial states differ in their decay dynamics. Dicke states with different numbers of excitations have their peak emission intensity shifted in time depending on the number of excitations. Emission intensity in atomic coherent states depends on their polarization. Some specific states undergo a squeezing controlled crossover, making the emission character dependent on the amount of squeezing in the state. We present detailed results on the superradiant dynamics of a representative selection of Dicke states. For large N, we are able to predict fairly accurately the pulse profile in each case using the mean field approximation, an approach based on the Fokker Planck Equation. We also present results on the intensity correlation function of the emission.

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02.
arXiv (math.PR) 2026-06-12

Symmetric Cooperative Motion in Higher Dimensions

Authors:
Louigi Addario-Berry↗Gavin Barill↗Hannah Cairns↗Jessica Lin↗

arXiv:2606.13459v1 Announce Type: new Abstract: We prove a distributional convergence result for a multidimensional version of symmetric cooperative motion which was introduced and studied in one dimension in [HRW, SCM1]. Our approach relies on framing the associated recursive distributional equation as a discretization of the porous medium equation. A major challenge is to analyze the behaviour of finite difference schemes which approximate weak solutions of the porous medium equation with unbounded initial data. In overcoming this difficulty, we perform a detailed analysis of the probability mass function of symmetric cooperative motion, in which we introduce several new comparison arguments for the discrete process. Consequently, along the way, we establish a novel multidimensional convergence result for a finite difference scheme approximating the ZKB/Barenblatt solution of the porous medium equation, which is of independent interest.

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03.
arXiv (CS.CV) 2026-06-24

ActiveScope: Actively Seeking and Correcting Perception for MLLMs

Authors:
Yajing Wang↗Chao Bi↗Junshu Sun↗Shufan Shen↗Zhaobo Qi↗Shuhui Wang↗Qingming Huang↗

Multimodal Large Language Models (MLLMs) have demonstrated impressive vision-language understanding, yet still struggle with fine-grained perception in high-resolution images. While existing training-free methods typically rely on attention-based localization or coarse-to-fine search, they are often misled by distractors and fail to locate multiple targets. Our investigation attributes these failures to Contextual Dominance, where salient distractors overwhelm target attention and cause inaccurate localization, and Semantic Bias, where global semantics cause the model to fixate on the most salient concept, resulting in incomplete localization in multi-object scenarios. Built on these insights, we propose ActiveScope, a training-free framework that enhances MLLMs by actively seeking and correcting perception. ActiveScope features two modules. The Semantic Anchor Localization (SAL) utilizes fine-grained semantic anchors to independently localize key targets, thereby mitigating semantic bias. The Interference-Suppressed Refinement (ISR) refines localization by suppressing attention on salient distractions to overcome contextual dominance. Extensive experiments on high-resolution image understanding benchmarks demonstrate that ActiveScope outperforms existing training-free methods (e.g., 96.34 percent accuracy on $V^{*}$ Bench), validating the superiority of the active search and self-correction paradigm. Our code is available at https://github.com/jasmine-ww/ActiveScope.

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04.
arXiv (CS.CL) 2026-06-15

Token-Level LLM Collaboration via FusionRoute

Authors:
Nuoya Xiong↗Yuhang Zhou↗Hanqing Zeng↗Zhaorun Chen↗Furong Huang↗Shuchao Bi↗Lizhu Zhang↗Zhuokai Zhao↗

Large language models (LLMs) exhibit strengths across diverse domains. However, achieving strong performance across these domains with a single general-purpose model typically requires scaling to sizes that are prohibitively expensive to train and deploy. On the other hand, while smaller domain-specialized models are much more efficient, they struggle to generalize beyond their training distributions. To address this dilemma, we propose FusionRoute, a robust and effective token-level multi-LLM collaboration framework in which a lightweight router simultaneously (i) selects the most suitable expert at each decoding step and (ii) contributes a complementary logit that refines or corrects the selected expert's next-token distribution via logit addition. Unlike existing token-level collaboration methods that rely solely on fixed expert outputs, we provide a theoretical analysis showing that pure expert-only routing is fundamentally limited: unless strong global coverage assumptions hold, it cannot in general realize the optimal decoding policy. By augmenting expert selection with a trainable complementary generator, FusionRoute expands the effective policy class and enables recovery of optimal value functions under mild conditions. Empirically, across both Llama-3 and Gemma-2 families and diverse benchmarks spanning mathematical reasoning, code generation, and instruction following, FusionRoute outperforms both sequence- and token-level collaboration, model merging, and direct fine-tuning, while remaining competitive with domain experts on their respective tasks.

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

M-CTX: Exact and Scalable Spatial Context Retrieval for Trajectory Analytics

Authors:
Kun Ma↗Qilong Han↗Chengjing Song↗Jingzheng Yao↗Xiao Han↗Yuee Zhou↗Changmao Wu↗

arXiv:2606.15244v1 Announce Type: new Abstract: Modern trajectory predictors increasingly condition on external spatial context, such as map geometry, signed distance fields (SDFs), and nearby moving agents. While this context improves prediction quality, constructing it for every training anchor has become a hidden systems bottleneck. In a representative maritime AIS pipeline, spatial context construction requires roughly 17 CPU-days for a 5.48M-anchor corpus, dominating the cost of the downstream predictor. We present M-CTX, an exact and scalable spatial context-retrieval framework for trajectory analytics. M-CTX recasts context construction as an ingest-once, query-many spatial database workload and replaces three brute-force stages – OSM range retrieval, SDF computation, and moving-vessel neighbour lookup – with composable, index-backed operators. Its learned range-index backend, BR-LZ, provides recall-complete MBR-overlap range retrieval and reduces candidate amplification by 1.1x–2.7x relative to global-expansion one-curve baselines. Across four maritime regions, eight baseline systems, synthetic workloads with up to 40M spatial features, and 10^7-record AIS streams, M-CTX reproduces the reference context exactly. On the 5.48M-anchor corpus, it reduces context construction from about 17 CPU-days to 1.8 hours, a measured 226x end-to-end speed-up. An optional storage mode further compresses SDF context by 64x with only a 0.04 m ADE change. These results establish exact spatial context retrieval as a first-class database problem in modern trajectory analytics. Code and datasets are publicly available at https://github.com/mark000071/M-CTX-Traj.

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

ScholarSum: Student-Teacher Abstractive Summarization via Knowledge Graph Reasoning and Reflective Refinement

Authors:
Bohou Zhang↗Xiaoyu Tao↗Mingyue Cheng↗Huijie Liu↗Qi Liu↗

Abstractive summarization plays a crucial role in enabling efficient understanding of scientific literature, yet it inherently demands both linguistic fluency and factual faithfulness. Existing approaches often fail to reconcile these two requirements. Extractive methods rely on rigid sentence splicing that disrupts macro-level logical coherence, while large language model (LLM)-based generative approaches, despite mastering linguistic fluency, exhibit limited factual consistency. In this work, we propose ScholarSum, a hierarchical reflective graph-based framework that emulates a student-teacher writing process for fluent and faithful scientific summarization. ScholarSum first organizes the document into a hierarchical knowledge graph by segmenting it into semantically coherent units, whose multi-layered community structure captures global logic and macro-level themes. Guided by this global structure, the student generates an initial draft, which is subsequently refined through fine-grained evidence retrieval. To ensure factual consistency, a teacher-like reviewer then iteratively examines the draft, identifies unsupported content, and prompts targeted re-retrieval and rewriting until the summary meets rigorous quality standards. Extensive experiments demonstrate that ScholarSum significantly outperforms previous baselines in terms of both completeness and faithfulness. Our code is available at https://github.com/Xiaoyu-Tao/ScholarSum.

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07.
arXiv (CS.CL) 2026-06-19

ReNikud: Audio-Supervised Hebrew Grapheme-to-Phoneme Conversion

Authors:
Maxim Melichov↗Yakov Kolani↗Morris Alper↗

Grapheme-to-phoneme (G2P) conversion for Modern Hebrew is needed for applications like text-to-speech (TTS), but is challenging due to the language's abjad writing system, which leaves vowels largely unwritten, creating substantial ambiguity. Standard approaches first predict vowel diacritics (nikud) to produce International Phonetic Alphabet (IPA) transcriptions, but this is limited: vocalization data is scarce and laborious to produce, it does not specify features such as lexical stress, and it reflects formal grammatical rules rather than everyday spoken pronunciation. Direct sequence-to-sequence IPA prediction, meanwhile, struggles on limited data and fails to exploit the character-level alignment characteristic of abjads. Our method, ReNikud, overcomes these limitations with two key insights: (1) Weak audio supervision via a phoneme-based automatic speech recognition (ASR) pseudo-labeling pipeline on thousands of hours of unlabeled Hebrew audio, yielding phonemic transcriptions that reflect natural spoken norms without manual annotation. (2) A pseudo-vocalization architecture that predicts IPA phonemes at each character position, enforcing character-level alignment as an inductive bias. Results on existing Hebrew G2P benchmarks and the new targeted MILIM benchmark for spoken Hebrew show that ReNikud surpasses previous state-of-the-art methods. We will release our code and trained models to support further work on Hebrew TTS and speech technologies.

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08.
arXiv (math.PR) 2026-06-17

On Injectivity of Phase Retrieval

Authors:
Zhangsong Li↗

arXiv:2606.17922v1 Announce Type: cross Abstract: In this short note, we prove that if $A \in \mathbb C^{N \times M}$ with $N=4M-5$ has i.i.d.\ standard complex Gaussian entries, then the probability that the phase retrieval map generated by $A$ is not injective is positive. This proves Part (1) of a conjecture of Cynthia Vinzant, which was later restated by Afonso S. Bandeira in [BDL+26]. The main result of this paper was obtained using generative AI, in particular the Rethlas system.

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09.
arXiv (CS.CL) 2026-06-17

LVLMs and Humans Ground Differently in Referential Communication

Authors:
Peter Zeng↗Weiling Li↗Amie Paige↗Zhengxiang Wang↗Panagiotis Kaliosis↗Dimitris Samaras↗Gregory Zelinsky↗Susan Brennan↗Owen Rambow↗

For generative AI agents to partner effectively with human users, the ability to accurately predict human intent is critical. But this ability to collaborate remains limited by a critical deficit: an inability to model common ground. We present a referential communication experiment with a factorial design involving director-matcher pairs (human-human, human-AI, AI-human, and AI-AI) that interact with multiple turns in repeated rounds to match pictures of objects not associated with any obvious lexicalized labels. We show that LVLMs cannot interactively generate and resolve referring expressions in a way that enables smooth communication, a crucial skill that underlies human language use. We release our corpus of 356 dialogues (89 pairs over 4 rounds each) along with the online pipeline for data collection and the tools for analyzing accuracy, efficiency, and lexical overlap.

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10.
arXiv (CS.AI) 2026-06-16

Is Code Better Than Language for Algorithmic Reasoning

Authors:
Terry Tong↗Yu Feng↗Surbhi Goel↗Dan Roth↗

arXiv:2606.15589v1 Announce Type: cross Abstract: For tool-augmented language models, comparing natural-language reasoning with code-execution pipelines is difficult because the comparison changes both the intermediate representation and the execution mechanism. We separate these factors with an intermediate intervention: the model expresses its reasoning as executable code, and the language model simulates that code in context to produce an answer. On a 40-task verifiable algorithmic benchmark, deterministic code execution outperforms natural-language reasoning by +31.6pp. We observe that the intermediate intervention is not meaningfully different from natural-language reasoning (+0.15pp). These results suggest that, in our evaluated setting, changing the intermediate representation alone does not explain the tool-use advantage, providing evidence for the performance gains requiring reliable external execution. We formalize this intuition with a simple statistical decision-theoretic model that characterizes when execution dominates end-to-end risk in our disentangled trace-generation/execution regime. We validate our theory using a reconstruction intervention that leverages a proxy language model to infer natural-language reasoning traces from code representations, recovering performance comparable to the original natural-language reasoning pipeline. All experiments are at https://github.com/TerryTong-Git/ToolProj.

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11.
arXiv (CS.LG) 2026-06-11

PCA-Enhanced Adaptive NVAR Framework for High-Resolution Sea Surface Temperature Forecasting in the East Sea

Authors:
Sherkhon Azimov↗Susana L\'opez-Moreno↗Eric Dolores-Cuenca↗JinYong Choi↗Sangil Kim↗

arXiv:2606.12141v1 Announce Type: new Abstract: Accurate forecasting of sea surface temperature (SST) in regional seas such as the East Sea is crucial for monitoring marine ecosystems, assessing climate risks, managing fisheries, and conducting naval operations. Traditional numerical ocean models provide reliable predictions but are computationally expensive and often unsuitable for real-time forecasting. Many deep learning methods also struggle with high-dimensional spatiotemporal ocean data and experience error accumulation over longer forecasting periods. This study builds on our previously proposed Adaptive Next-Generation Reservoir Computing (Adaptive NVAR) framework, initially introduced and tested on synthetic dynamical systems, and extends it to ocean forecasting. We present a reduced-order forecasting framework that combines Singular Value Decomposition (SVD) with Adaptive NVAR to predict SST dynamics in the East Sea. SST fields are compressed into a low-dimensional representation using SVD, which extracts dominant modes of ocean variability. Adaptive NVAR models the temporal evolution of these latent states, and the predicted states are reconstructed into SST forecasts. We evaluate the framework using regional ocean datasets and compare it with the standard NG-RC/NVAR. Results show that Adaptive NVAR consistently achieves lower forecasting errors across multiple prediction horizons. In addition, SVD reduces computational complexity, resulting in a fast and scalable framework suitable for real-time ocean forecasting.

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

TruncProof: A Guardrail for LLM-based JSON Generation under Token-Length Constraints

Authors:
Yoshio Kato↗Shuhei Tarashima↗

The LLM-based generation of machine-readable outputs such as JSON has attracted significant attention for integration with external systems. However, existing approaches cannot strictly enforce the maximum number of tokens to be generated, leading to infinite generation or truncated outputs that cause a system malfunction. To address this limitation, we propose TruncProof, a novel grammar-constrained generation method that enables LLMs to produce grammatically valid JSONs while adhering to a predefined token limit. By leveraging the properties of LL(1) parsers, TruncProof efficiently approximates the minimum number of tokens required to complete a grammatically valid output at each decoding step. Experiments on the Text-to-JSON instruction tasks demonstrate that TruncProof successfully generates syntactically correct outputs even under strict token constraints. Furthermore, we show that TruncProof can be effectively combined with advanced decoding strategies, resulting in outputs that are not only grammatically valid but also semantically accurate.

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13.
arXiv (CS.AI) 2026-06-24

A global log for medical AI

Authors:
Ayush Noori↗Aaron E. Boussina↗Hai Ho Bich↗James Anibal↗Julia Maslinski↗Manuel Burger↗Martin Faltys↗Adam Rodman↗Alan Karthikesalingam↗Alessandro Blasimme↗Annelia Itwaru↗Ben Kaplan↗…

arXiv:2510.04033v2 Announce Type: replace Abstract: Modern computer systems rely on syslog, a universal protocol that records critical events across heterogeneous infrastructure. Medicine's rapidly growing AI stack has no equivalent. As medicine deploys AI tools at scale, there is no standard way to record how, when, by whom, and for whom these models are used. Without such records, it is difficult to measure real-world performance and outcomes, detect adverse events, or identify bias and dataset drift. Here we introduce MedLog, a protocol for event-level logging of medical AI. Each time an AI model interacts with a human, another algorithm, or an automated workflow, MedLog creates a record. Each record contains nine core fields: header, model, user, target, inputs, artifacts, outputs, outcomes, and feedback. We apply MedLog across four deployments in the US, Switzerland, and Vietnam: ICU deterioration prediction, tetanus progression monitoring from wearable signals, automated sepsis quality reporting, and patient attendance prediction. MedLog records capture model behavior, workflow interactions, and downstream outcomes, including AI performance degradation during severe weather events in patient attendance prediction and increased laboratory testing after ICU deterioration alerts. MedLog limits the data footprint through risk-based sampling, lifecycle-aware retention policies, and write-behind caching, enabling deployment in low-resource settings. It also supports detailed traces for complex, agentic, or multi-stage workflows, creating a foundation for continuous monitoring, auditing, and improvement of medical AI.

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14.
medRxiv (Medicine) 2026-06-11

Advancing Clinical Implementation of Cardiovascular Polygenic Risk Scores Through Patient-Level Robustness Assessment

Authors:
de La Harpe↗Vaucher↗Kutalik↗Fellay↗Thorball↗C. W↗

Background and Aims: Polygenic risk scores (PRSs) for atherosclerotic cardiovascular disease (ASCVD) can perform equivalently at the population level yet disagree for individual patients. We examined whether such intra-individual variability reflects genuinely complementary risk information or mainly statistical and methodological uncertainty, and whether it affects clinical classification once PRSs are integrated into SCORE2-OP. Methods: In 4,137 ASCVD-free participants of the CoLaus|PsyCoLaus cohort (478 incident events over a median 14.4 years), we identified 16 ASCVD-PRSs with practically equivalent population-level performance using Bayesian equivalence testing. We quantified intra-individual variability (standard deviation, coefficient of variation, intraclass correlation, Cohen's kappa, extreme discordance), tested whether discordance exceeded chance, decomposed scores into shared and unique genetic components, and assessed variability after integration into SCORE2-OP, benchmarked against perturbation of systolic blood pressure. Results: For a typical individual, risk estimates varied by 18 percentile points across PRSs. Discordance matched chance expectations under a shared-signal model, with no distinct phenotypic profile among discordant individuals, and predictive power resided overwhelmingly in the shared genetic component. Variability tracked PRS size and weighting rather than distinct variants. After integration into SCORE2-OP, 75.6% of participants were placed in different categories by at least one model and 54.6% as both low and high risk; instability was concentrated near guideline thresholds and far exceeded that from blood-pressure measurement error. Conclusions: Equivalent population-level performance is not sufficient to treat PRSs as interchangeable at the individual level, and methodological standardisation and pragmatic clinical trials remain necessary to determine whether PRS integration improves long-term cardiovascular outcomes.

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15.
arXiv (CS.CL) 2026-06-19

ShoppingBench: A Real-World Intent-Grounded Shopping Benchmark for LLM-based Agents

Authors:
Jiangyuan Wang↗Kejun Xiao↗Qi Sun↗Huaipeng Zhao↗Tao Luo↗Jian Dong Zhang↗Xiaoyi Zeng↗

Existing benchmarks in e-commerce primarily focus on basic user intents, such as finding or purchasing products. However, real-world users often pursue more complex goals, such as applying vouchers, managing budgets, and finding multi-products seller. To bridge this gap, we propose ShoppingBench, a novel end-to-end shopping benchmark designed to encompass increasingly challenging levels of grounded intent. Specifically, we propose a scalable framework to simulate user instructions based on various intents derived from sampled real-world products. To facilitate consistent and reliable evaluations, we provide a large-scale shopping sandbox that serves as an interactive simulated environment, incorporating over 2.5 million real-world products. Experimental results demonstrate that even state-of-the-art language agents (such as GPT-4.1) achieve absolute success rates under 50% on our benchmark tasks, highlighting the significant challenges posed by our ShoppingBench. In addition, we propose a trajectory distillation strategy and leverage supervised fine-tuning, along with reinforcement learning on synthetic trajectories, to distill the capabilities of a large language agent into a smaller one. As a result, our trained agent achieves competitive performance compared to GPT-4.1.

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16.
medRxiv (Medicine) 2026-06-11

A continental-scale scenario modelling framework for evaluating infant RSV immunisation strategies across Europe

Authors:
Viola↗Mazzoli↗Paolotti↗Rizzo↗Zino↗Gozzi↗

Background. The recent approval of long-acting monoclonal antibodies (la-mAbs) and a maternal vaccine (MV) in the EU enables universal RSV prevention in infants. Modelling studies are widely used to quantify the population-level impact of alternative immunisation strategies. However, existing assessments of new RSV immunisation products focus on national or sub-national settings. Methods. We developed an age-stratified, stochastic compartmental model of RSV transmission for 28 EU/EEA countries. It combines literature-based parameters on RSV natural history and product efficacy with country-specific demographic and contact patterns. After model calibration against age- and country-specific RSV hospitalisation rates, we designed scenarios for both la-mAbs and MV at four coverage levels, with and without catch-up immunisation for infants under six months at season onset. We then evaluated each scenario against a no-immunisation baseline. Results. At 95% coverage, the cross-country median reduction in RSV hospitalisations over one season in infants under 12 months is 29.9% for la-mAbs (country median range: 27.7-33.9%) and 22.4% for MV (20.0-25.6%), scaling linearly with coverage. Out of all averted hospitalisations, 78.3% (90% CI: [67.3, 92.7]%) are concentrated in infants aged 0-2 months for la-mAbs and 72.7% (90% CI: [61.4, 88.6]%) for MV. A catch-up campaign nearly doubles the overall reduction in RSV hospitalisations. Conclusions. Despite country-specific heterogeneities, impact of la-mAbs and MV is comparable across settings and herd-immunity effects are largely negligible. This supports harmonised European guidelines on coverage targets. Seasonal catch-up campaigns emerge as an effective lever to maximise the impact of immunisation programmes.

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17.
Nature (Science) 2026-06-22

Will AI spark a scientific renaissance — or a diffuse monoculture?

Authors:
Xizhe Zhang↗

Artificial intelligence’s ability to enrich science will depend not only on model capability, but also on whether researchers, reviewers and funders reward originality over speed. Artificial intelligence’s ability to enrich science will depend not only on model capability, but also on whether researchers, reviewers and funders reward originality over speed.

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

Power Battery Detection

Authors:
Xiaoqi Zhao↗Peiqian Cao↗Chenyang Yu↗Zonglei Feng↗Lihe Zhang↗Hanqi Liu↗Jiaming Zuo↗Youwei Pang↗Jinsong Ouyang↗Weisi Lin↗Georges El Fakhri↗Huchuan Lu↗…

Power batteries are essential components in electric vehicles, where internal structural defects can pose serious safety risks. We conduct a comprehensive study on a new task, power battery detection (PBD), which aims to localize the dense endpoints of cathode and anode plates from industrial X-ray images for quality inspection. Manual inspection is inefficient and error-prone, while traditional vision algorithms struggle with densely packed plates, low contrast, scale variation, and imaging artifacts. To address this issue and drive more attention into this meaningful task, we present PBD5K, the first large-scale benchmark for this task, consisting of 5,000 X-ray images from nine battery types with fine-grained annotations and eight types of real-world visual interference. To support scalable and consistent labeling, we develop an intelligent annotation pipeline that combines image filtering, model-assisted pre-labeling, cross-verification, and layered quality evaluation. We formulate PBD as a point-level segmentation problem and propose MDCNeXt, a model designed to extract and integrate multi-dimensional structure clues including point, line, and count information from the plate itself. To improve discrimination between plates and suppress visual interference, MDCNeXt incorporates two state space modules. The first is a prompt-filtered module that learns contrastive relationships guided by task-specific prompts. The second is a density-aware reordering module that refines segmentation in regions with high plate density. In addition, we propose a distance-adaptive mask generation strategy to provide robust supervision under varying spatial distributions of anode and cathode positions. The source code and datasets will be publicly available at \href{https://github.com/Xiaoqi-Zhao-DLUT/X-ray-PBD}{PBD5K}.

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

Interaction-enabled topological pumping of Rydberg electrons

Authors:
Chenxi Huang↗Tao Chen↗Kaden R. A. Hazzard↗Jacob P. Covey↗Bryce Gadway↗

arXiv:2606.15126v1 Announce Type: cross Abstract: Topological pumping is a paradigmatic realization of quantized transport in band systems, yet its fate in strongly correlated regimes, especially with long-range interactions, remains largely unexplored. Here we report the experimental observation of interaction-enabled topological pumping of correlated Rydberg electrons in a synthetic lattice. We show that dipolar exchange interactions induce a controllable shift of the underlying topological singularity in parameter space, such that a fixed pumping trajectory can be driven through successive topological transitions by tuning the interaction strength alone. This leads to the emergence and breakdown of quantized transport. The observations are consistent with an effective Rice-Mele description with interaction-renormalized onsite potentials and are supported by characterizing the adiabaticity and robustness to control trajectory imperfections. Our results establish a platform for exploring interaction-controlled topological transport beyond perturbative regimes and open a route toward engineering correlated topological matter in synthetic quantum systems.

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

DOG-DPO:Dynamic Optimization in Geometry for Safety Alignment

Authors:
Yi Nian↗Tiankai Yang↗Yudi Zhang↗Qi Pan↗Zelong Xu↗Shenzhe Zhu↗Qingqing Luan↗Yue Huang↗Xiangliang Zhang↗Yue Zhao↗

arXiv:2606.07678v2 Announce Type: replace-cross Abstract: Safety alignment for large language models relies on preference data, but current pipelines often train on large, redundant datasets. Existing data selection methods typically score each preference pair independently, collapsing directional preference information into scalar quality or diversity scores. This sample-centric view is especially limiting in multi-dataset settings, where shared safety directions coexist with dataset-specific residual risks. We propose DOG-DPO, a training-free data selection framework that treats preference pairs as structured geometric signals. DOG-DPO first represents each preference pair as a direction in model representation space. It then decomposes multi-dataset preference geometry into a global anchor subspace and dataset-specific residual subspaces. Finally, it selects subsets by maximizing diversity-based coverage, encouraging broad, non-redundant coverage of alignment directions before DPO training. Across six safety benchmarks and two model backbones, DOG-DPO achieves a strong utility-robustness trade-off using only 11% of the preference pairs. It recovers most of the safety gains of full-data training while remaining entirely teacher-free, training-free, and substantially faster than representative selection baselines.

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22.
bioRxiv (Bioinfo) 2026-06-10

Pseudoperplexity Probes Memorization in Protein Language Models

Authors:
Plaikner↗Ploner↗Sewald↗Senoner↗Franz↗Brenner↗Heinzinger↗Rost↗

Protein Language Models (pLMs) have significantly advanced computational biology. Yet their scale and reliance on redundant training data raise a fundamental question: do pLMs generalize the statistical grammar of proteins, or do they simply memorize their training data? To investigate this, we used pseudoperplexity as a probe for sequence-level memorization, comparing ProtT5's pseudoperplexity on a pre-training proxy dataset against a post-training holdout of genuinely novel sequences. To ensure a valid comparison, we matched the datasets by sequence length, cluster size, and taxonomic family. As a statistical baseline, we trained n-gram language models; analysis of higher-order n-gram composition and a statistically significant divergence in perplexity confirmed that the post-training sequences were genuinely novel at the local sequence level. ProtT5 showed a statistically significant difference in pseudoperplexity between seen and unseen sequences, though further analysis revealed this memorization signal to be modest. These findings suggest that ProtT5 exhibits detectable but limited memorization of its training data as measured by a pseudoperplexity-based probe.

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23.
arXiv (CS.AI) 2026-06-16

AlignCoder: Aligning Retrieval with Target Intent for Repository-Level Code Completion

Authors:
Tianyue Jiang↗Yanli Wang↗Yanlin Wang↗Daya Guo↗Ensheng Shi↗Yuchi Ma↗Jiachi Chen↗Zibin Zheng↗

arXiv:2601.19697v2 Announce Type: replace-cross Abstract: Repository-level code completion remains a challenging task for existing code large language models (code LLMs) due to their limited understanding of repository-specific context and domain knowledge. While retrieval-augmented generation (RAG) approaches have shown promise by retrieving relevant code snippets as cross-file context, they suffer from two fundamental problems: misalignment between the query and the target code in the retrieval process, and the inability of existing retrieval methods to effectively utilize the inference information. To address these challenges, we propose AlignCoder, a repository-level code completion framework that introduces a query enhancement mechanism and a reinforcement learning based retriever training method. Our approach generates multiple candidate completions to construct an enhanced query that bridges the semantic gap between the initial query and the target code. Additionally, we employ reinforcement learning to train an AlignRetriever that learns to leverage inference information in the enhanced query for more accurate retrieval. We evaluate AlignCoder on two widely-used benchmarks (CrossCodeEval and RepoEval) across five backbone code LLMs, demonstrating an 18.1% improvement in EM score compared to baselines on the CrossCodeEval benchmark. The results show that our framework achieves superior performance and exhibits high generalizability across various code LLMs and programming languages.

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24.
arXiv (CS.CL) 2026-06-17

RooseBERT: A New Deal For Political Language Modelling

Authors:
Deborah Dore↗Elena Cabrio↗Serena Villata↗

The increasing amount of political debates and politics-related discussions calls for the definition of novel computational methods to automatically analyse such content with the final goal of lightening up political deliberation to citizens. However, the specificity of the political language and the argumentative form of these debates (employing hidden communication strategies and leveraging implicit arguments) make this task very challenging, even for current general-purpose pre-trained Language Models (LMs). To address this, we introduce a novel pre-trained LM for political discourse language called RooseBERT. Pre-training a LM on a specialised domain presents different technical and linguistic challenges, requiring extensive computational resources and large-scale data. RooseBERT has been trained on large political debate and speech corpora (11GB) in English. To evaluate its performances, we fine-tuned it on multiple downstream tasks related to political debate analysis, i.e., stance detection, sentiment analysis, argument component detection and classification, argument relation prediction and classification, policy classification, named entity recognition (NER). Our results show improvements over general-purpose LMs on the majority of these tasks, highlighting how domain-specific pre-training enhances performance in political debate analysis. We release RooseBERT for the research community.

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25.
arXiv (CS.CL) 2026-06-11

Massive Open-Vocabulary Keyword Spotting

Authors:
Leonor Barreiros↗Raul Monteiro↗Afonso Mendes↗Gon\c{c}alo M. Correia↗

Automatic speech recognition systems have been shown to under-perform when it comes to transcribing words rarely seen in the training data, namely specialized terminology. Open-vocabulary keyword spotting, combined with contextual biasing, has been shown to mitigate this issue. However, existing systems can only handle glossaries of a few hundred terms without becoming an infeasible bottleneck. We propose a system that stores features with a memory footprint up to 128 times smaller than a comparable baseline and allows users to process massive databases while remaining open-vocabulary. Without fine-tuning the speech recognition model, our system achieves a comparable entity recall as uncompressed solutions, even in languages not seen during training.

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