Computer Vision
Look Inside Each Video: Rethinking How Video Anomaly Detection Is Evaluated
Inpyo Song and Jangwon Lee
Video anomaly detection (VAD) aims to localize anomalous events by identifying when they occur. A common evaluation pools frame-level anomaly scores across all test
videos and measures whether anomalous frames receive higher scores than normal frames (Micro-AUROC). This pooling creates comparisons both within the same
video and across different
videos. However, the pooled evaluation protocol commonly used in current VAD
benchmarks makes Micro-AUROC heavily dependent on cross-
video comparisons, which do not directly test whether anomalous frames rank above normal frames within the same
video. To quantify this imbalance, we decompose Micro-AUROC into within-
video and cross-
video comparisons. Across four widely used weakly supervised VAD
benchmarks, within-
video pairs account for only 0.071-0.388% of all anomalous-normal frame pairs. This imbalance can make Micro-AUROC a poor indicator of within-
video ranking.
Video-level classifiers that assign a single score to each
video achieve 81.40-97.18% Micro-AUROC while obtaining 50% Within-AUROC. For existing weakly supervised VAD detectors, replacing all frame scores in each
video with their mean sets Within-AUROC to 50%. Across 120 runs, this replacement retains a median 98.8% of the original Micro-AUROC margin above chance. Because the decomposition depends on the evaluation protocol rather than the training supervision, the same distinction applies whenever VAD frame scores are pooled across
videos. High Micro-AUROC alone therefore does not establish temporal localization. Our analysis reveals a limitation of pooled VAD evaluation and motivates reporting within-
video ranking alongside Micro-AUROC.
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Computation and Language
PUMA: A Polish Benchmark for Culturally Grounded Multimodal Understanding
Sławomir Dadas, Michał Perełkiewicz, Rafał Poświata et al. (+3 more)
Large
language models are increasingly moving beyond text processing, adding support for other modalities such as images and audio. While text understanding and generation have been extensively studied,
multimodal data processing capabilities, particularly in the context of cultures and
languages other than English, have not yet been evaluated comprehensively. In this paper, we propose PUMA (Polish Unified
Multimodal Assessment), a novel
benchmark of 900 hand-crafted tasks designed to probe the limits of
multimodal models in the Polish cultural and linguistic context. The
dataset evaluates both cultural understanding and practical skill in processing text, images, audio, and visually rich documents. Our extensive evaluation of frontier commercial models, open-weights models, and specialized smaller systems highlights a significant performance gap. While top commercial models achieve high scores in visual question answering, most models struggle with complex audio or document understanding. We open-source our evaluation framework to advance localized
multimodal AI research.
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All authors Sławomir Dadas, Michał Perełkiewicz, Rafał Poświata, Małgorzata Grębowiec, Bartłomiej Jaworski, Izabela Woźniakowska
Information Theory
Quantum Locally Repairable Codes from Negacyclic and Repeated-Root Cyclic Codes over Small Fields
Ruipan Yang, Qiang Fu and Liangdong Lu
Quantum locally recoverable codes (qLRCs), introduced recently by Golowich and Guruswami, allow any single-qudit erasure to be recovered from a small set of other qudits. Most known constructions require a large alphabet. We systematically investigate qLRCs obtained, via the CSS construction, from classical constacyclic codes over small fields $\Ff_q$ with $q\in\{2,3,4,5,7\}$. First, we prove that a nonzero dual-containing $\lambda$-constacyclic code exists only when $\lambda^2=1$, so that negacyclic and (repeated-root) cyclic codes exhaust the constacyclic route to qLRCs. Second, we show that the locality of a constacyclic code equals the minimum distance of its dual minus one, and we give a simple purity criterion for the resulting
quantum codes. Third, we show that odd-like duadic codes whose splitting is given by $\mu_{-1}$ yield pure qLRCs; specializing to $q$-ary quadratic residue codes of
prime length $p\equiv 3 \pmod 4$ gives an infinite family of pure qLRCs with unbounded minimum distance and certified locality. Finally, by means of concrete computations, we obtain a classification of qLRCs from cyclic, negacyclic, and repeated-root cyclic codes of moderate lengths, which contains the first binary qLRCs from repeated-root cyclic codes and many parameter sets that cyclic codes cannot attain.
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Computers and Society
Consistently Good vs. Occasionally Great: A Rubric for Open-Ended Feedback Quality from Humans and Machines
Binglin Chen, Rajarshi Haldar, Max Fowler et al. (+2 more)
Providing high-quality feedback on student work is essential for learning, yet delivering such feedback at scale remains challenging. In this paper, we focus on feedback for open-ended short answer questions in introductory programming, with the goal of nudging students toward success on reattempts without revealing the correct answer. We develop a five-criteria rubric grounded in educational literature for evaluating feedback quality: (1) acknowledging correct portions of the student answer, (2) identifying at least one flaw (if present), (3) providing actionable guidance for improvement, (4) maintaining appropriate concealment of the answer, and (5) using an appropriate conversational tone. Using this rubric, we compare feedback generated by a frontier LLM (OpenAI o1) to feedback from nine teaching assistants across 90 student responses, with three researchers and an LLM independently scoring all feedback. Our results show that while one TA often produced the best feedback, the LLM demonstrated consistently higher average performance than TAs, as evaluated by humans. However, we also uncover significant self-preference bias when using LLMs to evaluate feedback quality: the LLM systematically rated its own outputs higher than human experts did. This bias, which research suggests persists even in cross-model evaluation, raises important methodological concerns for researchers employing LLM-based evaluation. We provide detailed characterization of both TA and LLM performance, analyze sources of variance in TA feedback quality, and discuss implications for deploying LLM-generated feedback in educational settings.
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All authors Binglin Chen, Rajarshi Haldar, Max Fowler, Matthew West, Craig Zilles
Computer Vision
GaussVid: Sparse-View Gaussian Splatting with 3D-Aware Video Diffusion Priors
Xinhui Liu, Can Wang, Wei Jiang et al. (+2 more)
3D Gaussian Splatting (3DGS) has achieved remarkable success in novel view synthesis; however, reconstructions under sparse views often exhibit noticeable artifacts. While recent
video diffusion models provide strong spatio-temporal priors for 3DGS restoration, directly fine-tuning them for restoration is suboptimal, as they lack awareness of the underlying multi-camera geometry, resulting in multi-view inconsistencies. In this work, we propose a novel 3D-aware
video restoration framework designed to enhance the quality of sparse 3DGS reconstruction. Specifically, we construct a large-scale 3DGS
video dataset to enable specialized fine-tuning. To bridge the gap between 2D
video generation and 3D multi-view constraints, we introduce a camera-conditioned
geometric prior. By using the first and last frames as boundary anchors and encoding the corresponding camera relationships, we explicitly inject spatial structure into the
video generation pipeline. This boundary-anchored, camera-aware prior guides the network toward geometrically grounded restoration that remains coherent across viewpoints. Extensive experiments show that, among
video-prior restoration methods, our approach attains the best pixel- and structure-level fidelity (PSNR/SSIM) and improves multi-view consistency, while remaining competitive in perceptual quality (LPIPS).
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All authors Xinhui Liu, Can Wang, Wei Jiang, Wei Wang, Dong Xu
Computer Vision
BC-IHV: Conditioning the Color Space for Stable Rectified-Flow Low-Light Enhancement
Yi Ai, Zheng Chen, Yuanhao Cai et al. (+2 more)
Low-light image enhancement (LLIE) must correct ambiguous exposure without overwriting structure already supported by the input. Generative transport can model exposure ambiguity; however, its flexibility may also alter observable geometry and chromatic content. Moreover, fixed invertible color coordinates are usually treated only as representations, although their inverse mappings reshape the RGB-domain gradients received by the enhancement network. To address these issues, we propose Structure-Anchored Rectified Flow (SA-RF), which maintains correspondence through separate chromaticity/intensity stems, a scale-matched condition pyramid, and HybridAda. HybridAda assigns location-specific
retrieval to spatial cross-
attention and global exposure modulation to pooled AdaLN. We further introduce BC-IHV, a learnable Box–Cox polar color space whose analytically invertible intensity mapping controls the inverse-gradient dynamic range through a single exponent. This allows the representation to balance dark-range expansion and gradient conditioning instead of adopting a fixed linear or logarithmic law. Experiments on three LOL
benchmarks, blind image-quality evaluation, and cross-
dataset tests demonstrate consistent reconstruction and perceptual advantages over the sota. Controlled studies further support the effectiveness of both the proposed framework and color representation.
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All authors Yi Ai, Zheng Chen, Yuanhao Cai, Yulun Zhang, Xiaokang Yang
Artificial Intelligence
AI Watchdog: Agent Interfaces for Detecting and Defending Against Manipulative Dark Patterns in AI Conversations
Rachel Poonsiriwong, Chayapatr Archiwaranguprok, Constanze Albrecht et al. (+3 more)
Conversational AI increasingly shapes consequential decisions, yet users have limited support for recognizing and resisting manipulation. We present AI Watchdog, a browser-based
agent interface that monitors live conversations, detects five dark-pattern categories, including sycophancy, brand bias, anthropomorphization, sneaking, and harmful generation, and alerts users when they occur. Its open-weight turn-level classifier supports independent deployment and a path toward local inference, preserving user
privacy while remaining separate from the conversational AI. We evaluated AI Watchdog in a preregistered, five-condition between-subjects experiment (N = 150) comparing a no-intervention control with four configurations varying nudge timing (prebunking vs. just-in-time) and engagement mode (without vs. with cognitive forcing). Results show that participants rarely flagged manipulative turns across all conditions, and post-task awareness did not differ significantly across groups. However, just-in-time warnings without cognitive forcing were the only intervention to significantly reduce compliance with AI-steered recommendations containing dark patterns, lowering compliance from 71.7% to 53.7%, an 18 percentage-point reduction. Exploratory analyses further showed that lower misinformation susceptibility was associated with greater flagging but not lower compliance, while higher AI trust was associated with greater compliance and lower reported awareness. Together, these findings suggest that explicit recognition of conversational dark patterns and behavioral resistance to AI steering may be distinct outcomes, motivating further investigation of timely, low-friction defensive interfaces.
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All authors Rachel Poonsiriwong, Chayapatr Archiwaranguprok, Constanze Albrecht, Monchai Lertsutthiwong, Pattie Maes, Pat Pataranutaporn
Machine Learning
More Experts, Worse Dynamics: Inverse Scaling and Spectral Bias in Mixture-of-Experts State-Space Models
Chandresh Pandey
Mixture-of-Experts (MoE) architectures are commonly motivated as a way to increase expressivity by decomposing complex systems into simpler local dynamics. This intuition has recently been extended to spectral state-space models, where mixing stable operators is assumed to enable adaptation to heterogeneous or regime-switching time series. We critically evaluate this assumption in a controlled synthetic setting designed to isolate dynamical rather than representational challenges. We study a next-step prediction task on sequences composed of three regimes: chaotic dynamics generated by the Mackey-Glass system, a stable oscillatory regime, and a noise-dominated autoregressive regime. Across extensive ablations including capacity scaling, oracle routing, frozen-expert variants, and comparisons to output-level MoE baselines, operator-level mixture models consistently fail to outperform a single-expert baseline. Increasing the number of experts leads to inverse scaling, routing collapses or fails to induce meaningful specialization, and even perfect regime supervision does not prevent degradation in global performance. Furthermore, we show that apparent improvements in mean squared error on chaotic trajectories can be misleading. Phase-space analysis reveals that lower error often arises from temporal smoothing that destroys the geometry of the underlying attractor rather than from faithful modeling of the dynamics. These results identify a likely limitation of operator interpolation under the studied parameterization and training protocol, and underscore the need for geometry-aware evaluation when assessing regime-switching dynamical systems.
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Computer Vision
FIRM-Video: Check Before You Score for Reliable Text-to-Video Reward Modeling
Peiyuan Zhang, Xiangyu Zhao, Hongbo Liu et al. (+8 more)
Reliable reward models are essential for text-to-
video evaluation and
alignment. However, the trade-off between evaluation accuracy and inference efficiency places high demands on the quality of training supervision. Existing approaches often rely on holistic judges with fixed rubrics or open-ended
reasoning, leading to incomplete inspection, unfaithful justification, and entangled attribution. We introduce FIRM-
Video, a unified checklist-driven data construction framework based on a check-before-score principle: construct dimension-specific checklists, verify each criterion against temporal visual evidence, and aggregate only verified decisions. For Instruction Following, FIRM-
Video decomposes
prompts into weighted atomic requirements; for World Coherence, it constructs
prompt-calibrated, target-specific checks grounded in visible entities and actions; and for Perceptual Quality, it applies a generic taxonomy of visual defects. The verified criteria and scores are further transformed into natural-
language analyses for end-to-end reward modeling. Subsequently, we construct FIRM-
Video-90K with 88,044 dimension-specific instances from 29,348
videos, and introduce FIRM-
Video-Bench with 750 point-wise human annotations across 250
videos. The Qwen3-VL-based FIRM-
Video-8B achieves the best overall MAE on FIRM-
Video-Bench while consistently delivering the highest VBench Total, Quality, and Semantic Scores in Best-of-8 sampling across three
video generators.
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All authors Peiyuan Zhang, Xiangyu Zhao, Hongbo Liu, Xiaoxing Hu, Mingxin Liu, Shuran Ma, Yunhang Shen, Jian Hu, Haihan Gao, Haoyu Cao, Xue Yang
Computer Vision
Towards Bitstream-corrupted Harsh Visual Understanding: Through Bitstream Language Modeling as Robust Semantic Priors
Chaoran Huang, Fangcheng Li, Tianyi Liu et al. (+2 more)
Bitstream-corrupted Harsh Visual Understanding (BcHVU) aims to understand harshly degraded
videos originally decoded from a severely corrupted bitstream in real-world multimedia communication. The ill-posed nature of BcHVU poses a major challenge for existing
vision models, as even subtle bitstream corruption can lead to irreversible pixel distortion and significant semantic loss. To address these challenges in BcHVU, we propose Bitstream
Language Modeling as Robust Semantic Priors (BLMSP), a framework for learning and injecting bitstream-native semantic cues. Our proposed BLMSP framework learns to extract bitstream-native semantic cues by bitstream
language modeling, and leverages them as priors by injecting into off-the-shelf
vision models of BcHVU tasks. Specifically, we present a
Video Bitstream Byte Model (VBBM) that integrates byte-level modeling and cross-codec semantic
distillation, enabling it to interpret robust semantics from byte sequences in multiple corrupted bitstream formats. The learned bitstream semantics are leveraged as robust priors and fused into BcHVU model backbones for improving the quality of
video restoration, captioning, and human pose estimation. To train BLMSP, we construct a large-scale multi-source Corrupted-bitstream Harsh-
video Paired (CHP)
dataset containing 607k corrupted bitstream segments and 287k paired harsh
video clips. Extensive experimental results show that the learned bitstream priors improve
video restoration, captioning, and human pose estimation by 2.51 dB in PSNR, 0.20 in CIDEr, and 0.18 in PCK@0.2 on average, respectively. These results demonstrate that corrupted bitstream can serve as robust semantic priors in solving pixel distortion and semantic loss in BcHVU.
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All authors Chaoran Huang, Fangcheng Li, Tianyi Liu, Wenyang Liu, Kejun Wu
Artificial Intelligence
LLM4LLM: Bridging Kernel Benchmarks and Real Deployment via Closed-Loop Agentic Optimization
Hui Zeng, Pengfei Yang, Yanxin Chen et al. (+2 more)
Large
language models have become increasingly capable
agents for low-level code and kernel optimization, but isolated kernel
benchmarks provide only a proxy for the deployment behavior that matters in
language-model inference. We identify a
benchmark-to-deployment gap: candidate kernels that appear correct and fast in standalone harnesses can exhibit different performance, safety, or phase behavior after integration into a real inference workload. We introduce LLM4LLM, a deployment-aware closed-loop optimization framework that starts from a target inference script, extracts phase-aware optimization tasks, searches with an experience-guided episodic
agent, and accepts patches through in-model validation. Across ten
language-model inference workloads on A100 and H100 GPUs, LLM4LLM improves end-to-end latency for every evaluated model, achieving 3.91$\times$/6.98$\times$
geometric-mean speedups on A100/H100; as supporting kernel-level evidence, it also attains up to 2.745$\times$ GeoMean speedup on KernelBench Level 2.
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All authors Hui Zeng, Pengfei Yang, Yanxin Chen, Fusong Ju, Xinran Wei
Artificial Intelligence
GameXpert-Bench: How Far Are Coding Agents from Expert Game Development?
Kun Chen, Haorong Hong, Peizhong Gao et al. (+7 more)
Recent large
language models (LLMs) can operate as coding
agents that build complete games from natural
language requests. Game development is especially demanding because program logic, visual and audio content, interfaces, interaction and playability must function together in one executable artifact. Measuring this capability therefore requires evaluation of both game product and the development process. Existing
benchmarks often assess the game development capabilities of LLMs by evaluating the final artifact or an isolated development stage. Our analysis of complete human-
agent development trajectories identifies three stages that together span the lifecycle of game development with a coding
agent: initial game generation, bug diagnosis and repair, and optimization over multiple turns. Therefore, we introduce GameXpert-Bench, which operationalizes the three lifecycle stages as three complementary
benchmark tracks. GameGen evaluates complete game creation from a single request in an empty workspace. GameFix evaluates diagnosis and repair when defects are reported or left for the
agent to discover. GameOpt evaluates cumulative optimization through request chains seeded by real development trajectories between users and
agents. We evaluate each track using live game interaction, deterministic behavioral tests, or final product criteria with regression checks. The suite contains 97 generation tasks across 11 genres; 100 repair tasks from 50 game levels verified by humans, each with 19-27 injected bugs; and 17 optimization chains with six turns and 102 requests. Across the three tracks, current
agents are more reliable at producing playable foundations and implementing explicit requirements than at discovering defects, verifying runtime behavior, and preserving functionality across changes.
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All authors Kun Chen, Haorong Hong, Peizhong Gao, Jianfeng Lin, Tongxu Luo, Yuxuan Xie, Chenxu Liu, Jieling He, Zhongyuan Liu, Zeno Zeng
Computation and Language
GUI-Primitives: Diagnosing Spatial Reasoning Failures in Vision-Language GUI Grounding
Md Abrar Jahin and Md Rizwan Parvez
Computer-use
agents ground natural-
language instructions in screenshots to locate interface elements, yet existing
benchmarks do not isolate whether models bind relational
language to the correct element. We introduce GUI-Primitives, a 994-item
benchmark of contrastive instruction pairs over seven spatial relations in graphical user interfaces (left/right, above/below, containment,
alignment, proximity, list ordinal, occlusion). Each pair holds the screenshot and anchor fixed while changing the relation expression, so the correct target moves between two designated candidates. Five annotators validate a 196-item subset ($\kappa = 0.94$ well-formedness; $\kappa = 0.79$ target selection). Nineteen
vision-
language models reach at most $32\%$ strict point-in-box accuracy. Because models emit unconstrained coordinates, we classify each prediction by the candidate region it falls within. Predictions fall outside both candidates on $60-92\%$ of items. Conditional on falling within a candidate region, target selection reaches 0.82-0.90 for horizontal position, vertical position, proximity, and list ordinal, but does not differ significantly from 0.50 for containment and occlusion: most failures reflect candidate localization rather than relation understanding. Across ten models,
benchmark accuracy correlates with ScreenSpot-Pro accuracy (Spearman $\rho = +0.74$), an exploratory association at this sample size. Marking the two designated candidates raises selection accuracy by 35–57 percentage points, an oracle diagnostic that supplies the candidate set rather than a deployable method. We release the
benchmark, predictions, and code.
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Numerical Analysis
Weighted Laplacian Flow: A Deterministic Particle Flow with Exponential Convergence
Weiye Gan, Tangjun Wang and Zuoqiang Shi
We introduce weighted
Laplacian flow (WLF), a deterministic particle-flow framework for sampling from a target distribution known only up to normalization. The proposed method evolves the logarithmic density ratio through a transport equation and determines the particle velocity from a target-weighted Poisson problem, resulting in a nonlocal and kernel-free mechanism for redistributing mass. For bounded smooth domains, we establish global well-posedness of the flow for Lipschitz initial data and prove that the flow contracts the discrepancy between the evolving and target densities at an exact exponential rate in terms of the oscillation of the log-density ratio, as well as in the associated projective metric. The relative
entropy also satisfies a precise dissipation relation involving the two directional Kullback–Leibler divergences. In addition, we identify a variational interpretation of WLF. The method can be viewed as a gradient flow of the reverse Kullback–Leibler divergence under a target-anchored metric. Consequently, the method achieves an explicit relaxation scale without requiring log-concavity or spectral-gap assumptions on the target distribution. Numerical experiments on
multimodal, heavy-tailed, and high-dimensional targets demonstrate the effectiveness of the proposed approach in capturing long-range mass transport.
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Artificial Intelligence
Beyond Success and Failure: Length-Aware Contrastive Learning for GUI Agents
Chengyang Gu, Le Zhang, Jingbo Zhou et al. (+6 more)
Graphical User Interface (GUI)
agents powered by
Multimodal Large
Language Models (MLLMs) have shown strong potential for automating tasks across diverse digital environments, where reinforcement learning (RL) has become a dominant training paradigm. However, widely used methods such as Group Relative Policy Optimization (GRPO) suffer from reward-gradient misalignment, leading to inefficient and unstable optimization. Recent work addresses this issue by reformulating RL with verifiable rewards (RLVR) as contrastive or classification-based objectives, which improve stability by eliminating problematic gradient behaviors. Despite this progress, existing contrastive RLVR methods rely primarily on outcome-level supervision and fail to capture fine-grained differences in trajectory quality within the same outcome category. In this paper, we propose Length-Aware Contrastive Learning for GUI
Agents (LACL-GUI), a contrastive RLVR framework that incorporates trajectory-level quality signals into policy optimization. LACL-GUI introduces structured preferences within both successful and failed trajectories, encouraging concise successful executions and differentiating failure quality based on divergence from successful trajectories, while preserving optimization stability. Experiments on GUI
agent benchmarks show that LACL-GUI provides more effective learning signals and consistently improves
agent performance over prior methods, highlighting the value of trajectory-level supervision in contrastive RLVR.
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All authors Chengyang Gu, Le Zhang, Jingbo Zhou, Yize Chen, Yu Shi, Siqi Bao, Zheng-Fan Wu, Hua Wu, Hui Xiong
Computation and Language
Training a Knowledge Base: Supervised Structure Learning for Agent-Curated Document Stores
Yu Pan and Hongfeng Yu
Retrieval-augmented generation treats the document store as a frozen input, and the offline pipelines that do build structure over it build it unsupervised – a whole corpus indexed at uniform effort, with no signal about which structure a question will need. We instead treat the knowledge base as a non-parametric model trained on (question, answer) pairs: a curator
agent answers a supervised question against the current store, is shown the gold answer, then edits the store. The store carries forward, and we evaluate the curated store with a test set, on two contamination-free
benchmarks: KBGym, a fictional-universe generator we release, and PhantomWiki. Generalization is probed with four question groups of decreasing overlap with the training set: the trained questions themselves, and unseen questions sharing both of their keys with training, one key, or neither. The curated store's advantage grows with overlap – from parity where no key was shared, through +0.176 F1 where both keys were, to 25% fewer actions at +0.294 F1 on the trained questions, the one
cell significant on both
benchmarks – while matching HippoRAG's gains with 1,913 links against its 196,112: per point of corpus covered, 1.5x the action saving and 2.1x the accuracy gain. Accuracy rises steadily with the share of the corpus the indexes cover, so training on more questions widens coverage, and with it the generalization.
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Computer Vision
Towards Alias-Free 4D Gaussian Representations with Motion-Aware Filtering
Ankit Dhiman, Kunal A Kathare, Pranav Vignesh et al. (+2 more)
Novel-view synthesis of dynamic scenes, crucial for AR/VR applications, remains a challenging problem. Recent methods adapt representations like 3D Gaussian Splatting (3DGS) and Neural Radiance Fields (NeRF) for dynamic scenes by incorporating time as the fourth dimension (4D representations). These 4D representations still suffer from aliasing artifacts, especially when generating novel views from divergent viewpoints (zoom-in/zoom-out operations). While using 3D smoothing filters like those proposed in Mip-Splatting might seem like a possible solution, they fail to account for local motion and also exhibit aliasing. To address this, we propose a motion-aware 3D smoothing filter specifically designed for 4D representations. Our approach adapts the filter strength based on local motion information, effectively mitigating aliasing without compromising rendering quality. This is achieved by estimating the joint density function of time and focal-to-depth ratio using a non-parametric estimation method. During inference, we sample from this joint distribution to determine the appropriate smoothing filter. This flexible strategy can be integrated with various 4D representations. Our evaluations on standard
datasets demonstrate superior performance compared to state-of-the-art methods.
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All authors Ankit Dhiman, Kunal A Kathare, Pranav Vignesh, Lokesh R Boregowda, Venkatesh Babu Radhakrishnan
Computation and Language
Do Large Language Models Perform Well on Comprehending Poetic Logic in Modern Chinese Poetry?
Tian Lan, Shanshan Wang, Zehua Duo et al. (+4 more)
Large
Language Models (LLMs) have achieved significant progress across a wide range of natural
language processing (NLP) tasks, yet their ability to understand literary texts, particularly modern Chinese poetry, remains largely unexplored. The unique literary characteristics of modern Chinese poetry necessitate a distinct form of
reasoning for effective comprehension. Unlike conventional texts that convey clear information, the unique "poetic logic" of modern Chinese poetry requires a holistic
reasoning approach that goes beyond superficial semantic analysis to be understood. However, current evaluation paradigms largely ignore this critical dimension. To address this gap, we propose Peony, the first
benchmark specifically designed for evaluating the poetic logic of modern Chinese poetry. We define poetic logic as four tasks across three levels, namely stanza, line, and imagery, and systematically evaluate and analyze six mainstream LLMs based on Peony. We evaluate these models under both non-thinking and thinking configurations. The experimental results reveal the limitations of current LLMs in understanding the poetic logic of modern Chinese poetry and validate the effectiveness and necessity of Peony. Our data and code will be available.
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All authors Tian Lan, Shanshan Wang, Zehua Duo, Jiang Li, Guanglai Gao, Derek F. Wong, Xiangdong Su
Distributed Computing
A Concurrent Queue System for Multi-GPU Platforms: Application to Bellman-Ford SSSP
Beyza Cavusoglu
This paper presents the design and implementation of a multi-GPU concurrent queue system using NVIDIA's NVSHMEM. The Bellman-Ford
algorithm is used as a case study to evaluate the performance of the proposed concurrent FIFO queue, with this multi-GPU implementation being the first known instance of its kind. Experimental results demonstrate that the multi-GPU queue implementation achieves a maximum speedup of 3.92x and an average speedup of 3.04x over the singleGPU baseline on four NVIDIA A100 GPUs. When applied to the Bellman-Ford Single-Source Shortest Path (SSSP)
algorithm, the multi-GPU system achieves a maximum speedup of 3.03x and an average speedup of 2.65x compared to the single-GPU implementation, tested on 10 graphs of different kinds taken from the SuiteSparse
Matrix Collection.
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Artificial Intelligence
VisAdj: Learning Adjacency Matrices from Node-Link Images
Jiahao Xie and Guangmo Tong
Learning adjacency
matrices from node-link images is a fundamental problem for recovering structured graph information from visual observations. Existing methods typically rely on fixed KNN-based heuristics for candidate edge selection and fail to capture dependencies among edges. To overcome these limitations, we propose VisAdj, a new framework for
topology-aware adjacency prediction. VisAdj introduces an
attention-sparse neighbor sampler to adaptively select a high-recall set of candidate node pairs and performs joint edge inference using a line-graph
transformer that treats candidate edges as
tokens and explicitly models dependencies among incident edges. Extensive experiments on synthetic graphs, road networks, and vessel images demonstrate that VisAdj consistently outperforms existing baselines by clear margins.
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Computation and Language
Convergence in Science, Divergence in Religion: Calibrated Framing Differences Across Wikipedia's Language Editions
Hung-Hsuan Chen
When Wikipedia's
language editions describe the same concept, how differently do they frame it? Prior work measures coverage gaps between editions; we measure framing distance for matched concepts. We analyze 2,799 valid articles from 3,000 possible concept-
language observations, spanning 150 Wikidata-anchored concepts, 20
language editions, 4 domains, and a calibration set. Raw
embedding distances reflect both content differences and how well the encoder aligns each
language pair. Even among calibration concepts with stable cross-cultural denotations (e.g., chemical elements, numbers, colors), the largest
language-pair mean distance is 3.6 times the smallest, and distances are typically smaller within
language families. We define a baseline-adjusted distance (calibrated distance): the distance between two
language versions of a concept minus the mean distance for calibration concepts in the same
language pair. This adjustment substantially reduces pair-specific
alignment differences and the
language-family pattern. Across three
multilingual encoders (LaBSE,
multilingual MPNet, and CMLM), scientific articles align more closely than calibration articles, and all three rank religion first and science/technology last. Concept-level rankings are highly consistent across encoders (Spearman rho=0.75-0.79 for MPNet and CMLM relative to LaBSE). Religion lies significantly above the calibration baseline under LaBSE. Within politics, divergence concentrates on concepts such as censorship and refugee, while democracy and human rights are among the most aligned. Code, data, and per-
language-pair calibration baselines are released.\footnote{https://github.com/hhchen1105/cross-linqual-concept}
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Computer Vision
PatchGate: Narrowing the Verbalization Gap with Intrinsic Object Inventories in Frozen Vision-Language Models
Jihyung Ko, Eunji Jung, Hyeongsub Kim et al. (+4 more)
Reliable image captioning in
Vision-
Language Models (VLMs) requires captions to be both precise and complete, avoiding unsupported object mentions while covering visible objects. Existing training-free methods primarily address the former requirement, suppressing unsupported object words by intervening on model-predicted mentions during generation. Because they operate only on objects the model is already likely to mention, visible objects omitted from the output remain difficult to recover. We propose PatchGate, a training-free framework that extracts
prompt-free object evidence intrinsic to a frozen VLM before generation and uses it to narrow the gap between an intrinsic object set and final object mentions. In the first stage, Visual Evidence eXtraction (VEX) reads patch-level lexical evidence from the latter half of LM decoder layers and constructs an image-conditioned object set without any task
prompt. In the second stage, Visual-Evidence Inclusion-Exclusion Decoding (VIED) uses this object evidence to calibrate decoding logits, promoting evidence-supported but under-verbalized objects and suppressing weakly supported but over-verbalized objects. On AMBER, PatchGate improves both sides of object-level reliability, increasing visible-object coverage from 49.4 to 56.0 (+13.4%) and reducing object
hallucination by lowering CHAIR from 7.5 to 6.6 (-12.0%), without external detectors or fine-tuning and with one extra forward pass.
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All authors Jihyung Ko, Eunji Jung, Hyeongsub Kim, Ziseok Lee, Jae Won Cho, Sanghyun Jo, Kyungsu Kim
Machine Learning
A Physics-informed Neural Network Approach for Robust Buckling Load Prediction and Reliability-Based Design of Thin Truncated Conical Shells
Devasmit Dutta, Budhaditya De, Rohan Majumder et al. (+1 more)
Thin-walled truncated conical shells are widely used in aerospace, marine, offshore, and lightweight infrastructure systems due to their high strength-to-weight ratio and
geometric efficiency. Their buckling resistance under axial compression, however, is highly sensitive to
geometric imperfections, manufacturing tolerances, material variability, and nonlinear instability effects. Conventional design procedures rely on conservative knockdown factors (KDFs), such as those recommended in NASA SP-8019, which do not explicitly account for shell geometry, fabrication quality, data uncertainty, or target reliability. This study develops a physics-informed neural network (PiNN) framework for predicting critical buckling loads of thin truncated conical shells and integrates the trained surrogate within a reliability-based design (RBD) formulation. The model combines
geometric and material descriptors with mechanics-informed features derived from shell stability theory and the localized reduced stiffness method (LRSM). A physics-informed loss function penalizes mechanically inadmissible predictions exceeding the theoretical elastic buckling load. The framework is trained and evaluated using 133 experimental Mylar conical shell tests under axial compression. Compared with a conventional deep neural network (DNN), the PiNN improves predictive accuracy, reduces mean absolute error, and enhances physical consistency. The trained PiNN is then used to evaluate reliability indices and calibrate safety-consistent KDFs for prescribed target reliability levels. Results demonstrate that the PiNN-RBD framework provides an efficient approach for uncertainty-aware design of imperfection-sensitive shell structures.
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All authors Devasmit Dutta, Budhaditya De, Rohan Majumder, Sudip Kumar Mishra
Machine Learning
Resilient Concurrent Causal Discovery for Topological Event Sequences
Jiyu Tian, Junhao Dong, Mingchu Li et al. (+5 more)
Causal discovery on topological event sequences is crucial for ensuring the reliability of networks. However, existing methods struggle to capture the complex causal relationships arising from concurrent events and lack robustness to incomplete event sequences. To address these issues, we propose a resilient concurrent causal discovery method, termed RCCD, enabling robust learning of causal graphs from topological event sequences. Specifically, we first introduce an influence-aware hyperedge causal
attention mechanism, which incorporates event duration into the
embedding representation, aggregates concurrent event features via hyperedge causal convolution, and injects network prior knowledge to capture the complex many-to-one causal interactions. Furthermore, we design a masked-based alternating causal optimization framework, which forces the model to recover masked event types based on context through self-supervised mask reconstruction, thereby enhancing the resilience of the predictor to missing data. To validate the effectiveness of our method, we conduct extensive experiments on both simulated and real-world telecommunication network
datasets. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art methods in both accuracy and robustness, making it more suitable for real-world telecommunication network environments.
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All authors Jiyu Tian, Junhao Dong, Mingchu Li, Lingling Fang, Liming Chen, Andreas Holzinger, Zheng Yan, Yew Soon Ong
Computer Vision
Through the Schrödinger Bridge: Benchmarking Antemortem Image Restoration from Postmortem Autolysis to Enhance Forensic Diagnostics
Shuang Hao, Jiacheng Yue, Yaxuan Zhao et al. (+4 more)
Forensic histopathology, essential for determining cause of death and disease diagnosis, is severely impeded by postmortem autolysis, i.e., an irreversible,
stochastic degradation process that distorts tissue morphology and introduces diagnostic subjectivity, thereby underscoring the value of restoring autolyzed images to a diagnostically plausible, pre-autolysis state for improving objectivity in forensic practice. This restoration task is fundamentally challenging due to the large, non-deterministic morphological changes caused by autolysis and the infeasibility of pixel-wise paired data, which invalidates assumptions underlying supervised and cycle/structure-consistent unpaired translation methods. To address this, we formalize forensic histopathology autolysis restoration as a new task: under unpaired supervision, transform postmortem images with severe autolysis into diagnostically meaningful “antemortem” representations. We contribute AutoPath, the first homologous yet unpaired
dataset for this problem, constructed by splitting specimens into adjacent tissue blocks—one processed immediately, the other exposed to induce autolysis—yielding nearly ten thousand $10\times$ patches from 69 cases with varying liver conditions. We further frame the problem as a Schrödinger Bridge between the autolyzed and non-autolyzed distributions, offering a principled approach to modeling
stochastic, severe morphological degradation. Critically, we demonstrate the misalignment of generic image-level generative metrics (e.g., FID) with diagnostic utility and propose a forensically grounded, slide-level diagnostic distribution consistency evaluation. Overall, this work establishes a reproducible
benchmark (encompassing task definition, a real-world
dataset, and an evaluation methodology) toward rigorous and practically meaningful progress in autolysis restoration for forensic pathology.
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All authors Shuang Hao, Jiacheng Yue, Yaxuan Zhao, Fan Wang, Jianhua Ma, Erwen Huang, Chunfeng Lian
Artificial Intelligence
Hints, Critics, and Teachers: Prior Injection for Sparse-Reward RL in Vision-Language Math Reasoning
Qiqian Fu
Reinforcement learning for
vision-
language math
reasoning starves under sparse reward: on a pool of 20,830 visual-math problems where Qwen2-VL-2B answers 3.6% of rollouts correctly, 85-97% of GRPO rollout groups are entirely wrong and contribute zero gradient. We train eleven methods under identical conditions in this regime, each injecting a different prior: text (reference-solution hints), distribution (on-policy
distillation from a 7B teacher), and value (a value-pretrained critic with an MSE or HL-Gauss categorical loss). A prior helps exactly when it is delivered: the six arms whose prior effectively reaches the policy separate with no overlap from the remaining five – the no-prior baseline and four arms whose prior is teacher-capped, gated away, or lost to a mis-parameterized critic – both on the pooled in-domain metric and on cross-domain transfer (DynaMath). The central finding, however, concerns evaluation: one slice of the in-domain pool – long used as this project's general-distribution check – anti-correlates with genuine cross-domain transfer (Spearman rho = -0.74, n = 11 arms, permutation p = 0.011), while the hardest in-domain slice predicts it closely (rho = +0.89, p < 0.001). We attribute the inversion to a near-chance multiple-choice subset that rewards models for not having changed; read through it, the best cross-domain method looked mediocre and the worst looked like the champion. Among the methods, hint-guided exploration – not UFT's auxiliary loss – drives hint gains, and replacing the critic's MSE loss with HL-Gauss cross-
entropy is worth +14.4 points in-domain. All accuracies are blind-judged, with paired exact tests.
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Machine Learning
MSM-Mem: A Universal Medical Structured Multimodal Memory Framework for Medical AI Agents
Md Asaduzzaman Jabin, Khoa Le, Lin Zhao et al. (+1 more)
Clinical decision-making is inherently experience-driven: physicians progressively refine their
reasoning by synthesizing patient history,
multimodal observations, and prior diagnostic experiences across interactions. In contrast, current
multimodal large
language model (MLLM)-based medical AI
agents largely operate as stateless inference systems, generating decisions independently for each interaction without retaining or internalizing experiential knowledge. This discrepancy limits their ability to progressively improve
reasoning reliability through usage and adapt to longitudinal patient contexts in real-world clinical workflows. In this study, we propose Medical Structured
Multimodal Memory (MSM-Mem), an agentic
memory framework that enables medical AI
agents to evolve through accumulated clinical experiences. MSM-Mem organizes heterogeneous clinical experiences into semantic, episodic, and visual
memory and incrementally updates them during inference, allowing the
agent to retrieve prior experiences to inform current
reasoning and progressively refine decision-making over time. Evaluations on MoE-LLaVA backbones demonstrate consistent performance improve- ments with further gains observed through continued usage. In general, MSM-Mem offers a viable pathway toward medical AI
agents capable of evolving their
reasoning competence in a manner analogous to the way clinicians learn from practice over time.
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All authors Md Asaduzzaman Jabin, Khoa Le, Lin Zhao, Tianming Liu
Computation and Language
MCite-RL: Towards Reliable Multimodal RAG via Citation-enhanced Agentic Reinforcement Learning
Suifeng Zhao, Zida Liu, Xinyu Lei et al. (+3 more)
Multimodal Retrieval-Augmented Generation (RAG) with visual citation is crucial for ensuring the traceability and verifiability of MLLMs. However, current RAG and SFT-based methods struggle to achieve robust cross-modal
reasoning, causing imprecise visual citations or decoupling between the citation and the generated answers. To address these limitations, we propose MCite-RL, a citation-enhanced agentic reinforcement learning framework designed for reliable
multimodal RAG. MCite-RL introduces an Agentic Refinement module for visual citation that employs iterative
retrieval,
reasoning, and recursive cropping to progressively narrow the search space, transforming citation into a dynamic, evidence-driven
reasoning process rather than a static step. Furthermore, we incorporate a Citation-enhanced Reward mechanism that integrates both process-level and outcome-level feedback within a reinforcement learning paradigm to jointly optimize answer accuracy and source traceability. Extensive experiments on
benchmarks such as Wiki-VISA, FinRAGBench-V, and MMLongBench-Doc demonstrate that MCite-RL effectively achieves the joint optimization of citation precision and answer quality.
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All authors Suifeng Zhao, Zida Liu, Xinyu Lei, Lei Sun, Jun Gao, Sujian Li
Computation and Language
More Computational Resources Do Not Ensure Higher Scholarly Impact: Evidence from Leading NLP Conference Papers
Shuai Chen, Tong Bao, Jitong Peng et al. (+1 more)
Computational resources are increasingly central to NLP research, but how closely reported GPU capability aligns with scholarly impact remains unclear. We analyze 13,921 ACL, EMNLP, and NAACL main-conference papers published between 2020 and 2025, using GPU resources as our operational measure of computational resources. From full texts, we extract GPU models and counts, standardize each paper's largest reported configuration into a comparable hardware-capability measure, and link these data to citation, award, topic, and institutional metadata. GPU reporting became more common but remained incomplete, while reported capability increased mainly through newer hardware generations and medium-scale multi-GPU configurations. Resource concentration substantially exceeded impact concentration: the annual top 20% of GPU-quantifiable papers accounted for 83.9%-89.9% of reported GPU capability, but only 27%-32% of citations and 20%-33% of paper awards. In adjusted models, a tenfold increase in aggregate reported GPU capability was associated with a 3.52-percentage-point increase in within-NLP topic-year citation percentile, but increased model R^2 by only 0.0042. GPU count showed more consistent positive associations with citation and award outcomes than newer hardware generation. Overall, reported GPU resources are associated with scholarly impact but provide little standalone explanation of research influence.
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All authors Shuai Chen, Tong Bao, Jitong Peng, Chengzhi Zhang
Computer Vision
FlashReg: GPU-Accelerated 3-Clique Point Cloud Registration for Real-Time Correspondence-to-Pose Estimation
Ziyang Yu, Xiang Li, Qiong Chang et al. (+1 more)
Graph-based point cloud registration achieves high robustness by identifying geometrically consistent correspondence sets, but constructing second-order compatibility graphs and enumerating candidate cliques remain compute- and
memory-intensive. This work presents FlashReg, a GPU-oriented correspondence-to-pose
estimator that avoids materializing the dense scored second-order graph. Its Fast First- and Second-Order Graph (FFSOG) construction builds a capacity-bounded sparse second-order graph directly from the binary first-order graph. A dataflow-optimized three-node clique (3-clique) search then selects pivots from compact per-row candidate pools and enumerates triples through sorted sparse-neighborhood intersections. Across indoor and outdoor
benchmarks, FlashReg reduces correspondence-to-pose latency by 2–3x relative to TurboReg at comparable registration recall, while using about 50% of its peak allocated tensor
memory on an embedded GPU. These results make FlashReg suitable as a high-throughput registration backend within onboard perception pipelines.
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All authors Ziyang Yu, Xiang Li, Qiong Chang, Jun Miyazaki