Computer Vision
Hyungjin Chung, Byeongjun Park, Joonseok Lee et al. (+3 more)
Multi-view
video understanding requires integrating spatial and temporal evidence across multiple, often non-overlapping camera streams: tracking entities as they transition between viewpoints, aligning events across time, and
reasoning about latent 4D continuity rather than any single visible frame. We introduce MVVBench, a
benchmark for multi-view
video reasoning built from real world multi camera
datasets. Questions are curated to be
monocular-ambiguous along both the view and the temporal axis: each question is unanswerable from any single view in the designated input set, and the majority are further unanswerable from any single moment. Each question becomes uniquely solvable only by jointly
reasoning across views and across time. MVVBench spans diverse dynamic scenes and probes six capabilities: implicit/explicit attribute identification, implicit/explicit relative distance, relative camera pose, and compositional counting, with human-authored QA and rigorous verification. Beyond benchmarking, we provide an extensive analysis of when and why current
vision language models succeed or fail, characterizing errors due to temporal mis-localization, cross-view identity breaks, and brittle multi-hop
reasoning. We then study inference-time elicitation strategies that unlock latent multi-view competence—task-specific chain-of-thought scaffolds and structured cross-view evidence aggregation—yielding substantial gains without retraining. Finally, we present preliminary evidence that reinforcement learning with verifiable rewards can elicit some latent multi-view competence in the base model, pointing to training-time approaches as a promising direction for future work. Together, MVVBench offers a rigorous evaluation of 4D multi-view
reasoning and a foundation for future progress toward reliable
embodied perception.
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All authors Hyungjin Chung, Byeongjun Park, Joonseok Lee, Hojun Kim, Jaeho Choi, Byung-Hoon Kim
Robotics
Bundled Contact Gradients: Stabilizing Differentiable Simulation for Deployable Dynamic Tasks
Dyuman Aditya, Jin Cheng, Clemens Schwarke et al. (+4 more)
Differentiable simulation provides analytic gradients of
robot dynamics, enabling fast and sample-efficient first-order policy optimization. However, obtaining smooth and informative gradients through rigid-body contact typically requires softened contact models, often at the expense of physical fidelity and thereby limiting learned policies largely to simulation. This trade-off becomes particularly consequential for dynamic
humanoid motions, where accurate contact dynamics are critical for transferring policies to the real world. Increasing contact stiffness in rigid-body simulation improves the fidelity of interactions, but also makes the dynamics increasingly sensitive to small state perturbations, producing high-variance gradients that can destabilize first-order policy learning. To address this, we propose Bundled Contact Gradients (BCG), a contact-local randomized smoothing framework for differentiable policy learning. When stiff contact is detected, our method evaluates a local bundle of randomized perturbation rollouts around the stiff contact configuration and aggregates their gradient signal thereby reducing gradient variance. We demonstrate the effectiveness of our method by successfully training and transferring dynamic motions zero-shot onto a real-world Unitree G1
humanoid platform.
Videos and supplementary information can be found at https://bundledcontactgradients.github.io/
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All authors Dyuman Aditya, Jin Cheng, Clemens Schwarke, Quan Nguyen, Gaurav Sukhatme, Stelian Coros, Gabriele Fadini
Machine Learning
Low-Bit Recurrent States in Hybrid Language Models
Hongren Chen and Jiayang He
Hybrid
language models maintain fixed-size recurrent states, but existing quantizers typically use eight bits or more. Quantization errors persist according to channel decay rates. We derive distortion weights from the observability Gramian and combine them with normalized state ranges for mixed-precision bit allocation, without calibration data, rotation, or training. We also quantize decay rates logarithmically. With per-
token state quantization, a four-bit mean payload reduces excess negative log-likelihood by factors of 3.3–27.9 relative to the best of seven baselines across three hybrid models; metadata costs vary. At six bits, negative log-likelihood differs from the FP32-state baseline by less than 0.005 nats. Ablations separate gains from variable bit widths, decay weighting, and range normalization. With less frequent write-backs, gains diminish and depend on the model and budget.
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Machine Learning
PORL: Pretrained Offline Reinforcement Learning for the Job Shop Scheduling Problem
Mateo Toro Diz, Jonathan Hoss and Noah Klarmann
The Job Shop Scheduling Problem (JSSP) is a fundamental combinatorial optimization problem in industrial optimization. This work introduces Pretrained Offline Reinforcement Learning (PORL), a hybrid approach that combines simulation-based online pretraining with offline fine-tuning on production-specific data. Reinforcement learning through online interaction enables exploration of general scheduling strategies, but typically relies on simulation environments and may suffer from a simulation-to-reality gap. In contrast, offline RL avoids direct interaction with the environment by learning from historical data, but its performance is strongly influenced by
dataset quality and coverage. PORL combines the strengths of both paradigms by first learning a general scheduling policy through online interaction and subsequently adapting it offline to a target distribution. A KL-divergence-based policy constraint is introduced to limit deviations from the pretrained policy during fine-tuning. The approach is evaluated on JSSP instances with distribution shift and
datasets generated from heuristic, noisy-expert, and random behavioral policies. The results show that PORL consistently achieves lower optimality gaps than standalone offline RL and the considered general scheduling baselines. Furthermore, its advantage over standalone offline RL increases as
dataset quality decreases, indicating reduced sensitivity to the quality and coverage of the available offline data. The results suggest that offline adaptation of pretrained policies is a promising approach for industrial scheduling environments where direct online exploration is impractical.
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Computer Vision
DAPEVO: Deep Adaptive Patch Frame-Event Visual Odometry
Luca Gandolfi, Simone Nascivera, Roberto Pellerito et al. (+3 more)
Visual odometry is essential for autonomous
navigation in GPS-denied environments, yet RGB-based methods remain vulnerable to motion blur, challenging illumination, and dropped frames. Event cameras complement conventional cameras with high temporal resolution and dynamic range, but their asynchronous measurements complicate reliable correspondence estimation. We present DAPEVO, a learned visual odometry system that estimates image and event correspondences independently at shared patch locations and fuses their correlation evidence before motion refinement. Each tracked patch maintains image and event descriptors, and a learned scalar gate combines modality-specific correlation
embeddings for each patch–frame edge before a shared recurrent refinement and bundle-adjustment update. DAPEVO also supports event-only observations, enabling continued tracking when RGB frames are sparse or unavailable, while modality-aware keyframe culling preserves scarce frame constraints. On UZH-FPV, when retaining only one in six RGB frames, DAPEVO's mean absolute trajectory error (ATE) increases by only 36%, from 1.00 to 1.36m, whereas the ATE of DPVO and RAMP-VO rises by factors of $3.7\times$ and $3.1\times$, respectively. On TartanEvent, DAPEVO similarly remains below 1m ATE at 3Hz RGB input, while DPVO and RAMP-VO exceed 9m. Under degraded RGB input on TartanEvent, DAPEVO achieves an ATE of 0.60m, compared with more than 4m for both DPVO and RAMP-VO, while also outperforming event-only DEVO at 0.87m.
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All authors Luca Gandolfi, Simone Nascivera, Roberto Pellerito, Rong Zou, Chiara Plizzari, Davide Scaramuzza
Computer Vision
OneWorld: Learning Consistent Physics Across Actions in World Models
Ke He, Yichen Ding and Bin Yang
Action-conditioned
video world models aim to predict scene
evolution under different actions, a capability that is essential for reliable planning, decision-making, and interaction in dynamic environments. However, futures generated independently from the same initial scene may each appear plausible while implying incompatible physical properties, such as friction or mass. This inconsistency can lead to contradictory predictions across interventions, making it difficult for the model to maintain a coherent understanding of the underlying world and limiting its reliability for planning and decision-making. To address these issues, we propose OneWorld, a shared-mechanism counterfactual generation framework that jointly models multiple action-conditioned futures under a common latent physical mechanism. A physical mechanism interpreter first infers a distribution over latent mechanisms from each action-outcome branch. These distributions are then aggregated into shared-world evidence, which captures whether the branches admit a common physical explanation while accounting for uncertainty in less informative branches. This evidence constrains flow training and guides sampling, encouraging consistency in the underlying physical mechanism while preserving the distinct outcomes induced by different actions. We further introduce a multi-intervention evaluation protocol in controlled environments, following the interaction settings of ACWM-Phys, to assess whether generated futures can be jointly explained by the same physical parameters, alongside standard measures of single-rollout prediction quality. Experiments in these environments show that OneWorld improves cross-intervention physical consistency while maintaining competitive single-rollout prediction quality.
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Artificial Intelligence
LogicTree-RAG: Logic Tree-guided Retrieval-Augmented Generation for Long-form Patent Drafting
Jiaqi Zhu, Naili Xing, Hexiang Pan et al. (+4 more)
Long-form technical text generation underpins knowledge-intensive workflows, yet remains challenging for large
language models (LLMs) due to the need for globally consistent logical structuring and faithful technical
reasoning beyond local coherence. Patent drafting is a canonical instance of this challenge, demanding holistic generation of a legally compliant and technically exhaustive document through sustained multi-expert collaboration. Existing approaches often focus on partial section generation or rely on manually crafted outlines, limiting scalable automation in realistic settings. In this work, we propose LogicTree-RAG, a logic tree-guided
retrieval-augmented generation framework that induces a hierarchical logic tree as a global organizational backbone to organize and ground technical disclosures, without relying on expert-defined drafting priors. Each node in the logic tree represents a technical element and is constructed through evidence-guided recursive generation. A hybrid traversal mechanism then maps the logic tree into patent sections, enabling controllable and section-balanced generation. Extensive experiments show that LogicTree-RAG consistently improves content quality and
language conformity over strong LLM-based baselines and achieves longer structured generation with high
token efficiency, demonstrating the effectiveness of logic-centric generation for complex technical document drafting.
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All authors Jiaqi Zhu, Naili Xing, Hexiang Pan, Haotian Gao, Jianwei Yin, Xiaokui Xiao, Beng Chin Ooi
Computer Vision
Spackle: Completing Large View Single Image NVS with Adaptive Gaussians
Xuanzhi Liu, Yuhe Zhou, Xinyi Wu et al. (+4 more)
Single-image novel view synthesis (NVS) enables photorealistic rendering of un- observed viewpoints from a single input. Practical NVS systems require two key capabilities: robust reconstruction of occluded regions and high inference effi- ciency. While hybrid decoupled frameworks combining feedforward 3D Gaussian Splatting (3DGS) and
diffusion models show promise for large-view-deviation NVS, they suffer from capacity competition: a fixed number of Gaussians forces resource shifts from visible to newly disoccluded areas, degrading original scene fidelity when the target view deviates significantly from the input. To address this, we propose Spackle, a lightweight residual learning framework that mit- igates capacity competition without sacrificing efficiency. Spackle operates in three stages: predicting base 3DGS attributes from given views, automatically identifying poorly reconstructed regions, and learning a residual 3DGS optimized exclusively for these areas. At inference, we combine the baseline and aug- mented Gaussians for NVS. We conduct comprehensive experiments and show that Spackle achieves state-of-the-art performance on large-view-deviation cases.
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All authors Xuanzhi Liu, Yuhe Zhou, Xinyi Wu, Zhenyao Wu, Jinghao Chen, Ruize Han, Song Wang
Artificial Intelligence
Financial Fragility in Societies of LLM Agents: Coordination Failures and Stabilizing Mechanisms
Zhenhao Fu, Ruipeng Xu and Qibing Ren
Individually protective decisions can produce avoidable collective failures. As large
language model (LLM)
agents take on greater roles in financial decision-making, financial AI safety must therefore be considered not only at the level of individual
agents, but also at the level of the systems they jointly create. We study this problem with FRAIL, a controlled experimental framework that places LLM
agents in three dynamic financial environments—bank runs, debt rollover, and reward crowdfunding—where
agents' decisions reshape the financial conditions faced by others. Across seven leading LLMs, we find widespread collective fragility even when no
agent is instructed to destabilize the system: 77% of baseline bank-run episodes and 83% of debt-rollover episodes end in failure. We then compare three interaction mechanisms based on compensated commitments, centralized commitment agreements, and participant-led coalitions. All three improve aggregate outcomes, but no single mechanism performs best across all financial structures. Across mechanisms, successful stabilization shares a common temporal pattern: broad commitment forms early, before defensive behavior becomes self-reinforcing. Our findings show that individually capable
agents do not automatically form safe financial systems, highlighting system-level evaluation and interaction design as central problems for financial AI safety. Code is available at https://anonymous.4open.science/r/FinFrail-CF26.
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Artificial Intelligence
MACBT: A Multi-Agent Cognitive Behavioral Therapy Decision Support System with Longitudinal Memory
De Jiang, Shuo Zhang, Weiwei Liao et al. (+4 more)
Cognitive behavioral therapy (CBT) is an evidence-based first-line treatment for depression, yet its scale is constrained by the time clinicians spend on pre-session preparation, post-session documentation, and longitudinal cognitive-pathology tracking. We present a clinician-facing AI decision-support system that combines a multi-
agent CBT framework (MACBT) with a CBT-specific longitudinal
memory module (CD
Memory). MACBT encodes the five-stage CBT workflow (assessment, Socratic questioning, cognitive restructuring, behavioral experiments, and treatment monitoring) into five collaborative
agents. CD
Memory tracks cognitive-distortion type, frequency, severity, and restructuring efficacy across sessions to generate pre-session pathology reports and intervention-priority recommendations. We construct a Chinese CBT dialogue corpus via dual-role large
language model simulation and train a Qwen3-14B backbone with supervised fine-tuning and direct preference optimization. Evaluation with GPT-4 judges shows MACBT outperforms MeChat, SoulChat, PsyChat, and CPsyCounX in professionalism (2.62) and clinical authenticity (2.25). The full
memory-augmented system further improves session quality by 12.6% and achieves a longitudinal mean of 2.29 on cross-session continuity, intervention progression, and personalization.
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All authors De Jiang, Shuo Zhang, Weiwei Liao, Jianying Zhang, Chuanhui Yu, Hongen Liao, Kehong Yuan
Neural and Evolutionary Computing
Landscape Limits of Quantum-Inspired Evolutionary Optimization across 256 continuous functions
Rishi Govind, Ferdin Sagai Don Bosco, Kasturi Venkata Srikanth et al. (+4 more)
Quantum-inspired evolutionary optimization (QIEO) represents design variables as a set of
qubits and searches a continuous, multi-dimensional landscape through rotation of the
qubit's amplitude pair. Every generation rotates those amplitudes toward a single elite, which corresponds to that generation's best. The update is cheap, almost parameter-free, and well-suited for massive parallel implementation, which has encouraged its adoption in engineering, design, and planning applications. However, there are critical issues with this formulation, principally, the treatment of design variables as independent probability components which make it incapable of exploiting local
curvature, anisotropy, or variable coupling. Despite this, QIEO is believed to hold promise, and has been used extensively to solve real-world problems, with significant qualitative and computational advantage over its classical counterpart, Genetic
Algorithm (GA). A collection of 256 (actually 508; 256 unshifted + 252 shifted, 4 could not be shifted) continuous function are selected from the prior works, in such a way that they represent eleven landscape characteristics, namely continuity, differentiability, separability, scalability, modality, convexity, conditioning,
symmetry, maximum dimensionality, dimension dependency, and the coupling pattern of the design variables. These functions are then solved by three QIEO variants, two GA encodings and Hansen's Covariance
Matrix Adaptation
Evolution Strategy (CMA-ES). The results are evaluated in terms of computational cost, solution precision, and specialization across landscape characteristics. They identify the conditions under which QIEO provides competitive performance, clarify where its independent-variable representation becomes limiting, and establish whether particular QIEO variants offer advantages for specific landscape characteristics.
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All authors Rishi Govind, Ferdin Sagai Don Bosco, Kasturi Venkata Srikanth, Aman Mittal, Abhishek Singh, Aditya Singh, Abhishek Chopra
Artificial Intelligence
Lorenzo Molfetta, Wai-Chung Kwan, Giacomo Frisoni et al. (+5 more)
Improving
reasoning abilities in Large
Language Models (LLMs) requires high-quality data that exposes difficult decisions, competing alternatives, and their consequences. Data scarcity is driven by the low quality of synthetic data and the cost of human labeling. We introduce Self-Play Search
Distillation (SPSD), a framework for generating superhuman synthetic data via self-play of MuZero-like networks trained on board games. SPSD uses executable environments to turn search into structured
reasoning problems. At each state, the expert identifies a preferred decision, plausible alternatives, plausible opponent replies, and value estimates. By converting the self-play search records into superhuman chains-of-thought, we train LLMs with environment-grounded supervision. Although trained only on self-play search records, SPSD transfers to unseen mathematics. On Qwen3-4B-Base, it raises the mean over six mathematics
benchmarks from 24.1 to 36.6 while increasing the held-out-game win rate from 15% to 45%. SPSD offers an annotation-efficient way to create high-quality synthetic data for improving LLM performance in
reasoning tasks.
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All authors Lorenzo Molfetta, Wai-Chung Kwan, Giacomo Frisoni, Luca Ragazzi, Gianluca Moro, Pavlos Vougiouklis, Jeff Z. Pan, Pasquale Minervini
Computation and Language
Estimating and Orthogonalizing Unknown Pre-training Gradients for Continual Fine-tuning of Large Language Models
Bing Wang, Changchun Li, Xin-Qiang Cai et al. (+4 more)
Continual fine-tuning is essential for large
language models (LLMs) to dynamically adapt to real-world environments, yet it inevitably suffers from catastrophic forgetting, particularly the performance degradation of previous tasks and LLMs' general-purpose knowledge. Although existing methods, such as orthogonal gradient projection, mitigate the forgetting across various fine-tuning tasks, they fundamentally fail to preserve pre-training LLMs' inherent general-purpose knowledge because the original data and gradients of off-the-shelf pre-training LLMs required by these methods are strictly unknown and highly diverse. To bridge this critical gap, we propose EoupCT, a novel framework designed to Estimate and Orthogonalize Unknown Pre-training gradients for Continual LLM fine-Tuning. Specifically, EoupCT estimates pre-training gradients by dynamically generating pseudo data that is most susceptible to forgetting for new tasks through a learnable soft
prompt equipped with Gumbel-Softmax relaxation. Furthermore, we formulate a multi-objective optimization problem and introduce a first-order efficient Pareto optimizer that jointly optimizes LLM parameters and the soft
prompt, rigorously enforcing orthogonality between new task updates and the estimated pre-training gradients. Extensive experiments across multiple LLMs demonstrate that EoupCT effectively preserves both task-specific proficiency and inherent general-purpose knowledge, successfully mitigating the catastrophic forgetting.
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All authors Bing Wang, Changchun Li, Xin-Qiang Cai, Lin Yuanbo Wu, Ximing Li, Gang Niu, Masashi Sugiyama
Computer Vision
ManiVid: Unified and Explainable Forensic Analysis of Manipulated Videos
Hengrui Kang, Zhonghao Yan, Yuxuan Yang et al. (+7 more)
Rapid advances in AI-generated
video (AIGV) have increased the risks posed by deceptive
video manipulation. Unlike fully synthetic
videos, manipulated
videos retain most source content and alter only localized regions, making forensic analysis particularly challenging. Existing
video forgery research faces two limitations in both data and methodology: (1) High-quality
datasets and
benchmarks tailored for manipulated
videos remain scarce. (2)
Multimodal large
language models (MLLMs) extend forgery analysis beyond binary classification but struggle to use low-level forensic cues and provide precise pixel-level grounding. Specifically, we introduce ManiVid, a unified forensic analysis task covering forgery detection, artifact grounding, and anomaly explanation for manipulated
videos. We construct ManiVid-38K, the first
dataset to combine paired, open-vocabulary localized manipulations of general
videos with authenticity labels, forgery masks, and anomaly explanations. It comprises about 19K manually verified real-fake
video pairs, mostly at 1080P resolution, generated under 2 paradigms with 15 powerful generation models. We sample 1K pairs for ManiVidBench, balanced across six manipulation types and generation models for fair evaluation. We further propose ManiVidLens, a unified framework for explainable
video forgery analysis. Its Forensic Evidence Router supplies shared low-level forensic evidence for
multimodal reasoning and
video segmentation. Its
Prompt Distill Module converts grounding states into semantic and
geometric prompts and distills spatial priors for mask decoding and full-
video propagation. ManiVidLens achieves relative gains over the strongest comparison methods in artifact grounding (+21.1% mIoU; +21.3% J&F) and anomaly explanation (+131.3% ROUGE-L; +9.9% CSS). Its forgery detection remains comparable to dedicated classifiers (0.914 Acc; 0.913 F1).
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All authors Hengrui Kang, Zhonghao Yan, Yuxuan Yang, Ruoyan Jing, Yuncheng Guo, Hao Chen, Kongming Liang, Zhanyu Ma, Conghui He, Weijia Li
Machine Learning
EPOC: Endpoint-Preserving Online Correction With Compressed Residual State for Multi-Horizon Time Series Forecasting
Takumi Fujimoto and Hiroaki Nishi
Completed forecasts provide residual feedback, but retaining full residual blocks increases auxiliary state. We propose Endpoint-Preserving Online Correction (EPOC) with a compressed residual state. It stores low-order discrete cosine transform (DCT) coefficients and the final value of the preceding residual block. Each channel shares the endpoint across component-wise online ridge regressions using current-forecast coefficients. We evaluate eight multivariate series, DLinear and PatchTST, three seeds, and two training variants: 96 matched fixed-base conditions at a 24-step horizon. EPOC achieves mean condition-wise reductions in mean squared error (MSE) and mean absolute error (MAE) of 15.40% and 9.35% from the uncorrected base, respectively, with a median of 6,352 B in retained auxiliary arrays. EPOC also outperforms the $\delta$-Adapter, COSA, FAC, and OMPB in paired MSE on most conditions while retaining less state. Full ELF achieves the largest mean MSE reduction, 19.29%, but its median retained state is 474,048 B ($\times$75 relative to EPOC). Equal-size summary controls favor the endpoint by 1.65–2.20% in paired MSE; a coefficient-reconstructed endpoint yields similar accuracy to the observed endpoint, highlighting its role as a shared input. Increasing the retained DCT component count from 4 to 8 adds 1.00 percentage point of MSE reduction for 5,728 B. On jointly trained bases, EPOC lowers MSE by 16.69–20.15% relative to globally blended TEFL-style adapters applied to the same base. Code and numerical records are available at [https://github.com/keiotakmin/endpoint-preserving-residual-correction](https://github.com/keiotakmin/endpoint-preserving-residual-correction).
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Computer Vision
UltraG-Bench: A Multi-task Benchmark for assessing Large Vision-Language Models on Pixel-level Evidence Grounding in Ultrasound
Quanhao Zhu, Bo Xu, Rui Lin et al. (+7 more)
Ultrasound is one of the most widely used medical imaging modalities, and recent large
vision-
language models(VLMs) have shown increasing capabilities in ultrasound image understanding. However, these models fail to provide pixel-level visual evidence aligned with their semantic predictions, and their fine-grained grounding capability in ultrasound remains largely unclear. We introduce UltraG-Bench, a large-scale multi-task
benchmark for evaluating pixel-level evidence grounding in ultrasound. UltraG-Bench is built by annotating 40 public ultrasound
segmentation datasets spanning 13 anatomical categories, and comprises three progressive tasks: instruction-guided
segmentation, evidence-grounded VQA, and evidence-grounded report generation, with 331125, 666779, and 138832 annotations, respectively. Comprehensive evaluation of 14 state-of-the-art models reveals a substantial gap between semantic understanding and fine-grained pixel-level localization. We further propose UltraG-
Agent, which combines the semantic
reasoning capabilities of a VLM with the ultrasound-specific
segmentation capability of UltraSAM3. Experiments show that UltraG-
Agent substantially improves both semantic prediction and pixel-level visual grounding. Our
dataset and code are available at https://github.com/zhuqh19/UltraG-Bench.
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All authors Quanhao Zhu, Bo Xu, Rui Lin, Chenyuan Wang, Yu Shao, Boling Zhu, Jiuyan Sun, Liang Zhao, Hongfei Lin, Feng Xia
Cryptography and Security
How to break the Miranda signature scheme over matrix Gabidulin codes
Adrien Vinçotte
The Miranda signature scheme relies on masking a
matrix code which disposes of a masked underlying structure, and the knowledge of which allows for efficient error decoding. We consider a Gabidulin code which is expanded into a
matrix code, that is only \Fq-linear. An additional masking is then applied to it. The attack proposed here shares similarities with that of [Le26, https://arxiv.org/abs/2608.03328] on the EGMC encryption scheme, which also follows the paradigm described above. It consists in recovering the Fqm-linear structure of a masked
matrix Gabidulin code by reducing to a MinRank instance to be solved over the extension field Fqm, but where the
matrices have coefficients in Fq. Such an instance can be efficiently solved. However, unlike the previous attack, it is possible to reduce in
polynomial time to such a MinRank instance in the case of the Miranda signature scheme. This results in a particularly efficient key recovery attack against the parameters proposed for Miranda. For example, for the proposed parameter set with m=79, the complexity drops from 146 bits to 46 bits in this attack.
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Computation and Language
Training-Free Pronunciation Transcription via Text-Constrained Acoustic Rescoring
Hikaru Asano, Yotaro Kubo and So Kuroki
Accurate and efficient pronunciation transcription is essential for preparing text-to-
speech training data at scale. Existing approaches have different limitations: grapheme-to-pronunciation (G2P) and
speech-to-pronunciation (S2P) methods each capture only partial information, using only text or only
speech, while
speech-and-text-to-pronunciation (ST2P) methods use both but require costly pronunciation-annotated data. To address this problem, we propose a training-free ST2P pipeline that integrates both lexical and acoustic information at inference time. Lexical resources and G2P tools generate text-constrained candidates, and a left-to-right greedy search selects the best one using whole-sequence negative log-likelihoods from frozen pretrained S2P models. On three Japanese corpora, our method reduces Character Error Rate (CER) from 0.60–1.40% (text-only baseline) to 0.04–0.17% with reference transcripts, and 0.64–1.58% with ASR transcripts. It outperforms all baselines, including a trained ST2P model and commercial
multimodal LLMs. Our greedy search method is 3–3.5$\times$ faster than beam search at similar CER, and the cascade is 2$\times$ faster than direct decoding ensuring the efficiency and accuracy. In Spanish, French, and preliminary English, it also surpasses four open
multimodal LLMs and the best traditional methods.
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Artificial Intelligence
JevSoup: System-One Routing for Training-Free LoRA Composition
Xiuying Wang, Jiahua Cheng, Shuotian Li et al. (+4 more)
Building adaptable AI systems requires effective coordination of specialized capabilities across diverse tasks. Low-rank adaptation (LoRA) enables modular expertise, but existing routing approaches may require auxiliary data, additional training, or autoregressive decoding. We propose JevSoup, a training-free framework separating System One expert routing from System Two execution. Using only the input and expert descriptions, Jev selects two experts through structured probabilities. JevSoup retains the leading expert's update, projects the second onto the orthogonal complement of the first update's row space, and combines them with equal weights. Across 14 PorTAL tasks and three Qwen3 scales, JepSoup achieves absolute gains of up to 1.19% in task-macro and 1.21% in sample-micro accuracy over the strongest evaluated external baselines. Our code is available at https://github.com/Leowang980/JevSoup.
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All authors Xiuying Wang, Jiahua Cheng, Shuotian Li, Yufan Cheng, Bowen Deng, Zhexuan Bai, Yichen Li
Machine Learning
Robust to Which Model Change? A Unified Evaluation of Robust Counterfactual Explanations
Marcin Kostrzewa and Maciej Zięba
Robust counterfactual explanations promise recourse that still works after the model behind it changes. Whether they keep that promise depends on what the change is. A small perturbation of the parameters, retraining on new data, and a new architecture are different events, and each existing method is evaluated against the one it was built for. Reported robustness scores, therefore, answer different questions and cannot be compared. We propose a unified cross-family evaluation protocol that holds factual instances and generated counterfactuals fixed while testing every method against the same eight types of model change. The
benchmark compares six robust methods and two standard baselines on four tabular
datasets. It characterizes every changed classifier through its outputs and reports empirical robustness together with coverage, base validity, and proximity. We find that relative performance and failure modes vary across change families. Bounded parameter perturbations change 0.95% of test predictions on average, compared with 4.9% for bootstrap retraining. Methods with guarantees for these perturbations do not necessarily transfer to other changes. RobX transfers most consistently in our experiments, although greater stability can require larger interventions. We argue that robust CFE methods should be evaluated through a common protocol that specifies the model changes, measures their realized behavioral magnitude, and keeps generation performance separate from robustness.
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Distributed Computing
Cross-Backend QIEO: Universal Runtime Portability across OpenMP5, CUDA, HIP, and Multi-Language Interfaces
Aman Mittal, Ferdin Sagai Don Bosco, Kasturi Venkata Srikanth et al. (+3 more)
Quantum-inspired
algorithms emulate
quantum mechanical principles, such as, superposition, interference, and probabilistic amplitude
evolution, on classical hardware by representing candidate solutions as
qubit vectors and evolving them through rotation-gate operators. This approach offers higher optimization performance without physical
qubits, and has been shown to achieve order-of-magnitude speedups (10–80$\times$) over traditional solvers on combinatorial, high-dimensional NP-hard problems. A critical barrier to adoption, however, is the lack of a unified execution framework that delivers both algorithmic performance and hardware portability. We present Cross-Backend
Quantum Inspired Evolutionary Optimizer (QIEO), the runtime core of BQP's BQPhy solver, which addresses this gap through a single-source-of-truth architecture. One C++ implementation of the QIEO
algorithm is compiled once per hardware target and exposed to multiple high-level
languages via thin binding layers. The framework dispatches to CPU (sequential), OpenMP~5 (multi-core), CUDA (NVIDIA), and HIP (AMD) backends at runtime, adapting kernels to each device's
memory hierarchy and warp/wavefront execution model. The framework's real-world utility is validated through binding demonstrations that share the identical C++ runtime. BQPhy's Python library is demonstrated on a neural network hyperparameter optimisation achieving 88.60% test accuracy on MNIST. BQPhy's MATLAB's Toolkit is tested on wind farm layout optimisation attaining $365\,399 \pm 4\,552$~MWh/yr, which is statistically indistinguishable from particle swarm optimisation and $+7.6\%$ above genetic
algorithms on a 32-variable constrained engineering problem. The Julia package tackles the Lotka–Volterra parameter estimation where BQPhy replaces native Julia solvers on the same residual, cutting mean SSE by $2.1\times$.
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All authors Aman Mittal, Ferdin Sagai Don Bosco, Kasturi Venkata Srikanth, Abhishek Singh, Aditya Singh, Abhishek Chopra
Robotics
Causeway: Restoring Task Accessibility for Instruction Switching in VLA Policies
Qingzi Wang, Kaixi Feng, Guangyao Shi et al. (+3 more)
Vision-
language-action (VLA) policies can execute many tasks from standard initial states, yet a new instruction may fail after another task has altered the
robot's physical state. We study instruction switching, where a new task is issued during or after the execution of a different one. We observe that a target task that is reliably completed from its standard initial states can become inaccessible from states produced by a preceding task. We call such states task islands. We propose Causeway, a training-free inference-time intervention. Given the current state and a re-entry pose for the target task, Causeway back-propagates through the frozen decoding computation and applies a state-directed write within the action-stream representation. The VLA decodes the return motion itself, without parameter updates, a new action head, or external action generation. Across 71 cross-object pairs, three switch timings, and three VLA architectures on LIBERO-Goal, Causeway raises bare-switch success from 3-26% to 47-65% and increases the rate of reaching the handoff neighborhood by 42-72 percentage points across models. Additional experiments on LIBERO-Object and a real xArm platform show that the recovery extends beyond the main LIBERO-Goal setting, both in simulation and on a
robot.
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All authors Qingzi Wang, Kaixi Feng, Guangyao Shi, Xiyang Wu, Ang Li, Dinesh Manocha
Sound · Cross-listed from eess.AS
Music Source Separation via Stem Discovery
V. Valtteri Kallinen, Eloi Moliner, Lauri Juvela et al. (+1 more)
Music source separation (MSS) methods aim to extract stems from music mixtures, which is important, for example, in karaoke, music remixing, and pedagogical applications. While earlier research on MSS systems has been dominated by models targeting narrow sets of general stems, there have recently been attempts to support broader source definitions. One of these methods uses audio queries to provide direct and descriptive control over the desired separation targets based on the sound itself. However, query-based separation remains cumbersome due to the need to provide audio examples with features matching the sources contained within mixtures. This paper proposes Music Source Separation via Stem Discovery (MuS3D), a query-based source separation framework that iteratively discovers active sources from the mixture. On correctly detected sources, our model matches manually queried baselines and surpasses state-of-the-art text-based models. Subjective evaluation indicates encoding artifacts as the limiting factor in current generative separation. The findings suggest that audio-based query representations offer an effective and automatable interface for source separation.
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All authors V. Valtteri Kallinen, Eloi Moliner, Lauri Juvela, Vesa Välimäki
Cryptography and Security
Machine Unlearning for Large Language Models: Foundations, Advances, and Agentic Extensions
Xiaoyu Xu, Minxin Du, Li Bai et al. (+8 more)
Machine unlearning aims to remove target influence while preserving other capabilities. This survey compares methods,
benchmarks, and evidence across large
language models and systems using
retrieval,
memory, tools, and interacting
agents. A five-layer framework connects removal requests, system boundaries, target locations, interventions, and supported claims. A seven-stage lifecycle and six evidence dimensions guide comparison. The review shows that target construction, retained data, and recovery tests affect reported outcomes. Evidence from model evaluations remains insufficient to establish removal across external state and subsequent updates, motivating evaluation that tracks dependencies and tests whether target influence returns.
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All authors Xiaoyu Xu, Minxin Du, Li Bai, Junxu Liu, Yaxin Xiao, Kun Fang, Liu Yang, Huadi Zheng, Peizhao Hu, Qingqing Ye, Haibo Hu
Computation and Language
ToolSearcher: Optimizing Tool Selection at Scale via Reinforcement Learning
Zhenlong Dai, Xujie Song, Zitong Wang et al. (+7 more)
Large
language models (LLMs) excel at natural
language processing but struggle to interact with external environments. Tool learning provides a promising way to extend LLMs into actionable
agents, where tool selection is a critical prerequisite for successful tool use. Existing work often assumes a small or predefined set of tools, leaving large-scale tool selection underexplored. Real-world repositories contain a vast and diverse array of tools, making it difficult for LLMs to effectively search, distinguish, and compose tools under context-length constraints. We identify large-scale tool selection as a new challenge for agentic reinforcement learning, highlighting that existing RL methods for knowledge-based question answering are inadequate for selecting tools while considering compatibility. To address this challenge, we propose ToolSearcher, a novel RL framework for effective multi-turn search and fine-grained optimization in large-scale tool selection. Specifically, we introduce category-constrained tool discrimination to improve the model's ability to distinguish functionally similar tools, event-level search modeling to explicitly optimize the discovery of target tools during multi-turn search, and trajectory-aligned credit allocation to provide fine-grained reward signals for different stages of the search-selection process. Extensive experiments on large-scale tool selection
benchmarks demonstrate that ToolSearcher consistently outperforms a set of strong baselines in challenging settings involving iterative search and complex tool composition.
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All authors Zhenlong Dai, Xujie Song, Zitong Wang, Tong Niu, Jian liu, Weiqiang Wang, Xiu Tang, Sai Wu, Chang Yao, Jingyuan Chen
Social and Information Networks
Network Analysis in Communication Research: A Review of Methods, Theories, and Research Practices
Pengjia Cui
Network analysis has become a standard way of representing consultation, sourcing, media use,
attention, and influence in communication research. This review examines 31 applications across communication subfields and asks of each what communication problem it addresses, which explanatory resource it invokes, and what its research practice makes observable. The three dimensions form a comparison framework and yield three propositions with stated scope and refutation conditions: resource relevance is task- and criterion-dependent; commonality at one level of aggregation does not imply commonality at another; and influence estimates are conditional on relationship type, group stage, and time. A short bibliometric section describes how the literature's cited references are organised: method references are paired with one another more often than year-specific margins predict (ratio 1.36) and with theoretical references less often (0.82), a pattern consistent with a methodological toolkit that circulates apart from the commitments that license its use. The review closes with designs that would test each proposition.
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Information Retrieval
QReason: Query-Focused Decoupled Chain-of-Thought for Efficient Passage Reranking
Yang Zhang, Wenhan Liu, Qiannan Zhu et al. (+2 more)
Passage reranking plays a crucial role in information
retrieval by refining the ordering of candidate passages to better reflect relevance. Existing listwise LLM rerankers with Chain-of-Thought (CoT)
reasoning can handle complex queries effectively, but they suffer from substantial redundancy and high latency due to sliding-window strategies, which repeatedly generate highly similar CoTs. To address this, we propose QReason, a decoupled framework that separates query-focused
reasoning from window-specific passage relevance assessment. Specifically, QReason introduces a dedicated rewriter that generates a ranking-oriented
reasoning query once, capturing the query's core intent while avoiding redundant
reasoning, and then reuses it across all windows with a non-
reasoning reranker. The rewriter is trained via a two-stage process that first uses supervised fine-tuning with relevant-passage guidance through semantic evidence to produce deeply grounded, query-focused CoTs. It then applies reinforcement learning to align CoT generation with both the inference-time setting and the reranking objective, optimizing listwise metrics and passage-level discrimination to produce reusable
reasoning chains for reranking. Experiments on the BRIGHT
benchmark demonstrate that QReason significantly reduces redundant
reasoning, achieves ranking performance comparable to or better than strong
reasoning-based rerankers, and outperforms existing query rewriting models.
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All authors Yang Zhang, Wenhan Liu, Qiannan Zhu, Mingming Li, Yuanfei Huang
Data Structures and Algorithms
Odd Cycle Transversal on $H$-free graphs
Esther Galby, Paloma T. de Lima, Andrea Munaro et al. (+1 more)
Odd Cycle Transversal is a classic $\mathsf{NP}$-hard graph optimization problem asking for a minimum-weight set of vertices whose deletion makes the input graph bipartite, or equivalently, a maximum-weight induced bipartite subgraph. We show that Odd Cycle Transversal is quasi-
polynomial-time solvable on $kP_4$-free graphs, for every fixed $k \in \mathbb{N}$. In fact, we provide an $n^{O_k(\log n)}$-time
algorithm for the more general \textsc{Max-Weight List $2$-Colorable Induced Subgraph}, where the notation $O_{k}(\cdot)$ hides factors depending on $k$. Paired with known results from the literature, this allows us to obtain a complete complexity dichotomy for these two problems on $H$-free graphs into cases solvable in quasi-
polynomial time and cases which are $\mathsf{NP}$-hard, in particular resolving an open problem of Agrawal, Lima, Lokshtanov, Saurabh, and Sharma [SODA 2024]. Our
algorithms are based on a new structural tool that may be of independent interest. We introduce the notion of $H$-amiable family and show that, for every fixed graph $H$ without isolated vertices and every fixed $k\ge2$, every $kH$-free graph admits an $H$-amiable family of quasi-
polynomial size that can be constructed in quasi-
polynomial time. Besides yielding the aforementioned
algorithms, this result gives, for every fixed connected graph $H$ and every fixed $k\ge2$, a reduction from Max-Weight Independent Set on $kH$-free graphs to the same problem on $H$-free graphs with $n^{O_{H,k}(\log n)}$ overhead. In this setting, it improves the $n^{O_{H,k}(\log^3 n)}$ overhead obtained by specializing the general reduction of Gartland and Lokshtanov [FOCS 2020].
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All authors Esther Galby, Paloma T. de Lima, Andrea Munaro, Amir Nikabadi
Computation and Language
From annotation to reasoning: Culture in language models
Daniel Hershcovich, Alexander Conroy and Jens Bjerring-Hansen
How should we evaluate
language models when more than one interpretation can be right? Cultural
benchmarks often test factual knowledge, agreement with survey responses, or recognition of a predefined meaning. These tasks leave open whether a model can explain how a cultural reference works in a particular text, support a reading with evidence, or revise it after criticism. This is a question of interpretive depth, complementary to the breadth of cultural coverage. We argue that literary interpretation offers a useful setting for studying these capabilities. We focus on cultural referencing and reuse: how texts invoke, repeat, and transform earlier expressions across historical and linguistic contexts. Our central claim is that literary scholars can disagree about an interpretation while recognizing the quality of its support. We propose linking evidence-centered
benchmarks, evaluation that preserves scholarly disagreement, and model-development experiments on literary data, contextual resources, and scholarly feedback. Danish literature provides a concrete starting point, with implications for other
languages and domains. The aim is to develop alternative evaluation strategies that go beyond conventional
benchmark metrics and guide model development toward cultural robustness in AI systems.
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Computers and Society
Large language models underestimate and partly misrepresent cultural variation in everyday norms
Kimmo Eriksson, Irina Vartanova and Pontus Strimling
A key aspect of culture is a society's norms about everyday behavior. How accurately do large
language models (LLMs) represent cultural differences in such norms? To answer this question we used the recent Global Study of Everyday Norms (GSEN), which collected ratings of 150 scenarios in 90 societies, as the human
benchmark. We prompted GPT-5 to estimate each society's average rating for every scenario, and later repeated the
benchmark in three other LLMs: GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro. Compared to GSEN estimates, all four LLMs misrepresented cultural variation in two ways. First, they greatly underestimated its magnitude, estimating differences between societies to be, on average, less than half their measured size. Second, for many scenarios the LLMs poorly identified the pattern of variation, that is, which societies judged the behavior less acceptable and which societies judged it more acceptable. The pattern of variation was identified better for scenarios that elicit concerns about vulgarity, especially scenarios involving kissing and flirting. We also found that norms in more developed societies tended to be estimated somewhat more accurately, and that prompting in local survey
languages rather than English produced only a modest improvement in accuracy. Local-
language prompting also reduced, but did not remove, the underestimation of between-society differences. Cultural differences in everyday norms are only weakly and unevenly represented by LLMs.
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