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版本更新 · v2arXiv:2605.15592

Efficient Image Synthesis with Sphere Latent Encoder

Tung Do, Thuan Hoang Nguyen, Hao Li

原文摘要

Few-step image generation has seen rapid progress, with consistency and meanflow-based methods significantly reducing the number of sampling steps. Despite their low inference cost, these approaches often suffer from training instability and limited scalability. Sphere Encoder is a recent alternative that produces high-quality images in only a few steps; however, it requires repeated transitions between the pixel space and latent space during inference while jointly optimizing reconstruction and generation within a single architecture. This design leads to computational inefficiency and objective conflict between reconstruction and generation. To address these limitations, we decouple the framework into a fixed pretrained image encoder and a separate latent denoising model trained entirely in a spherical latent space. Our approach eliminates repeated pixel-space operations during training and inference, improving efficiency and allowing reconstruction and generation to specialize independently. On Animal-Faces, Oxford-Flowers and ImageNet-1K datasets, our method significantly outperforms Sphere Encoder in both generation quality and inference speed, while achieving competitive results against strong few-step and multi-step baselines.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35328

Constrained Decoding: Harmful or Beneficial? Revisiting Its Impact on LLM Performance

Ngoc Trinh Hung Nguyen, Alonso Silva, Armen Aghasaryan, Mehwish Alam

原文摘要

Constrained decoding guarantees that Large Language Models (LLMs) produce outputs conforming to predefined formats, but is often assumed to inherently degrade reasoning. We examine this assumption through a taxonomy of three constraint types: \emph{format constraints}, which enforce output structure; \emph{validity constraints}, which mask invalid answer; and \emph{reasoning constraints}, which restrict reasoning trajectories. We theoretically show that appropriate constraints can preserve or improve performance by eliminating invalid paths. Across four model families and eleven open-source models, causal ablations on diverse benchmarks using state-of-the-art constrained decoding strategies reveal that apparent performance degradation largely stems from non-equivalent prompting and output formats rather than from decoder-level masking. Meanwhile, validity constraints improve reliability and extractability by eliminating invalid answers, while reasoning constraints can improve reasoning and, consequently, accuracy when they enforce beneficial trajectories. These findings show that the impact of constrained decoding depends on the type of constraint and the output space it removes. Our code is available.

cs.CL · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35282

Satellite Intelligence: Distributed Training, Collaborative Inference,and LLM Adaptation in LEO Constellation

Zheng Lin, Zhanwei Wang, Yuxin Zhang, Yafei Wang, Ons Aouedi, Zihan Fang, Jinfeng Xu, Jing Yang 等 12 位作者

原文摘要

The proliferation of orbital sensing data and the real-time demands of emerging global services are pushing low Earth orbit (LEO) satellite networks from bent-pipe relays toward intelligent onboard processing. This shift confronts a fundamental tension, where modern artificial intelligence (AI) training paradigms require intensive computation, abundant memory, and stable connectivity, all of which are scarce under the dynamic topologies, intermittent links, and severe size, weight, and power (SWaP) constraints of satellite networks. Addressing this tension demands rethinking AI workflows, from training and inference to the emerging deployment of large language models (LLMs), under a constraint-aware design philosophy. To the best of our knowledge, this paper provides the first survey that jointly covers all three, whereas prior surveys examine these aspects in isolation. We begin by identifying the deployment challenge of satellite intelligence (SI) and deriving design principles for SI. We then examine three advancing paradigms: i) distributed training, where federated learning (FL) and split learning (SL) enable satellite-ground co-training under resource limitations; ii) collaborative inference, which exploits single-satellite, multi-satellite, and satellite-ground architectures to meet real-time service demands; and iii) on-orbit LLM adaptation and serving, which tackles the frontier challenge of deploying LLMs under severe orbital constraints and resource-aware serving. Finally, we identify open challenges and future directions. By bridging the AI and satellite networks, this paper provides a research roadmap for the implementation of space-native AI technologies.

cs.NI · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35244

Beyond Vision-Language Models: Visual-Semantic Affect Graphs for Emotion Recognition in Artworks

Ngoc-Duy Mai, Ha-Thanh Nguyen, Hideaki Takeda

原文摘要

Emotion recognition in artworks remains challenging because affective meaning is often subjective, implicit, and shaped by relationships between visual form and semantic associations. While vision-language models provide strong multimodal representations, direct image-to-emotion prediction may overlook explicit relations among formal, semantic, and affective cues that support emotional interpretation. This paper proposes Visual-Semantic Affect Graphs (VSAG), a graph-based framework that represents artworks through interpretable feature nodes, including color, brushstroke, composition, light-shadow, line quality, and extracted visual entities. The graph learns feature-emotion associations for dominant emotion prediction, with valence-arousal structure modeled through an optional hierarchical auxiliary affect module. We evaluate VSAG and VSAG-TopK against direct VLM inference using Qwen3-VL and InternVL3.5 on EmoArt-5k. Results show that direct VLMs provide useful but modest baselines, while graph-based models improve dominant emotion prediction. In particular, the hierarchical valence-arousal variant of VSAG achieves the best overall performance, improving accuracy, macro-F1, and weighted-F1 over both direct VLM inference and the emotion-only VSAG baseline. Per-class results indicate that graph-based models and VLMs capture partly complementary signals, while valence-arousal structure is most useful when integrated as a coarse-to-fine affect hierarchy rather than as a simple post-hoc correction. These findings suggest that explicit visual-semantic structure can support both prediction and interpretation in artwork emotion recognition.

cs.CV · cs.AI · cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35167

Biology-Guided Context-Aware Residual Propagation for Spatial Gene Expression Prediction from Histology

Vu Van An, Long D. Nguyen, Ho Viet Duc Luong, Binh P. Nguyen

原文摘要

Spatial transcriptomics provides spatially resolved molecular profiling, but its cost, limited throughput and experimental complexity hinder routine large-scale use. Predicting spatial gene expression from hematoxylin-and-eosin-stained histology images offers a scalable alternative, yet remains challenging because histology provides only an indirect morphological view of molecular activity. Existing methods typically decode expression from visual features in a single stage, even when enhanced with spatial aggregation, retrieval or multi-resolution fusion. This formulation is restrictive because spot-level expression reflects not only local morphology but also cellular identity, neighborhood composition and tissue-level organization. We propose BCARP, a Biology-guided Context-Aware Residual Propagation framework for histology-guided spatial transcriptomics prediction. Instead of directly predicting gene expression from histology embeddings, BCARP treats them as morphology visual priors and progressively refines spot representations in a transcriptomics-oriented latent space. Each refinement step integrates nuclei-guided local cellular context with prototype-guided global context propagation, enabling information exchange across both neighboring and spatially distant spots. A pattern-scale decoder further separates spatial expression into pattern prediction from raw expression with scale calibration. Evaluated on three ST datasets and ten HEST-1k cohorts, BCARP achieves the highest mean correlation-based performance against other baselines. Numerical results support biology-guided local-global residual propagation as an effective alternative to visual decoding for histology-to-transcriptomics prediction.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35165

How Tuning, Reference Preview, Actuator Modeling, and Fallback Policies Change Quadrotor Controller Comparisons: A Reproducible Simulation Study

Xuan-Quy Dao, Le-Minh Nguyen, Ngoc-Bich Le

原文摘要

Quadrotor controller comparisons commonly report a single tracking error, although the resulting ranking also depends on tuning effort, reference information, actuator prediction, optimizer fallback, and command timing. We quantify these effects for cascade PID, hover LQR, boundary-layer sliding-mode control (BL-SMC), linear MPC with LQR fallback (LMPC-F), a preview-enabled LMPC-F, and a geometric $SO(3)$ controller on a common nonlinear plant with rotor gyroscopic coupling, motor lag, and saturation. A primary study (556 episodes at 50~Hz) combines matched twelve-candidate tuning, 400 paired evaluation episodes, and a 48-cell MPC factorial; a confirmation study (1462 episodes) repeats the evaluation on 50 fresh realizations with tuning budgets of 12, 24, and 48 candidates. A matched twelve-candidate tuning protocol changed the mean-RMSE ordering obtained with the fixed baseline settings, moving BL-SMC from last to first; the new ordering recurred on the fresh realizations, and a 48-candidate budget left it unchanged. Reference preview substantially reduced MPC tracking error and, on the figure-eight task, removed fallback activity. On an abrupt step, the outcome depended on the interplay between fallback policy, predictor, and actuator authority. Command timing also changed the outcome: replaying measured command-call delays terminated three of four nominal MPC configurations on a desktop computer and all four on a Raspberry Pi~5, whereas capping the solver at 100 iterations removed every deadline miss in the tested nominal timing profiles. Controller rankings therefore depend not only on tracking accuracy but also on the tuning protocol, reference information, fallback behavior, and implementation timing. We propose a reporting checklist covering tracking accuracy, tuning effort, completion and failure rates, fallback activity, applied-input variation, and command timing.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.30488

CoMo3R-SLAM: Collaborative Monocular Dense SLAM with Learned 3D Reconstruction Priors for Outdoor Multi-Agent Systems

Zhihao Cao, Qi Shao, Shuhao Zhai, Feng Tian, Anh Nguyen, Hesheng Wang, Baoru Huang

原文摘要

Outdoor robot teams need a shared dense map despite limited overlap, independent reference frames, and uncertain monocular scale. Collaborative dense SLAM systems typically resolve this with depth sensors, which add payload, power, and calibration cost. We present CoMo3R-SLAM, a collaborative monocular dense SLAM system that places learned feed-forward 3D reconstruction priors at the center of the multi-agent problem: their dense pointmaps anchor scale across agents and supply correspondences strong enough to verify inter-agent links geometrically. Each agent tracks and fuses its own keyframes from a single RGB stream, while a coordinator retrieves cross-agent keyframes over the prior's encoder features, verifies them by bidirectional dense pointmap matching, synchronizes the independent similarity gauges in closed form, and refines every keyframe in one unified multi-agent sim(3) graph. Finally, a pose-depth alternation over geometry-aware segments lets inter-agent observations constrain dense structure as well as trajectories. Requiring neither measured depth nor supplied intrinsics, CoMo3R-SLAM attains the lowest trajectory error on three of four Tanks and Temples scenes, and competitive accuracy on Waymo driving sequences, while running at approximately 6-8 FPS on RTX 3080 Ti. A long-horizon traversal, independently captured day and night streams, and teams of up to four agents further map its operating range.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.13700

C-MambaPose: Phase-Preserving Complex Mamba for Cross-Environment Wi-Fi Human Pose Estimation

Phuc H. Nguyen, Van-Dinh Nguyen

原文摘要

Cross-environment generalization remains a fundamental challenge for Wi-Fi-based human pose estimation (HPE), as environmental multipath can drastically alter Channel State Information (CSI) for the same human motion. Existing approaches often discard CSI phase or collapse complex signals into real-valued features early, losing motion-sensitive phase dynamics. We present C-MambaPose, a phase-preserving complex-valued state-space framework for robust cross-environment Wi-Fi 3D HPE. Our key insight is to preserve complex phase relationships throughout spatiotemporal modeling, rather than using phase only as an input feature. C-MambaPose sanitizes synchronization-induced phase errors and employs a Spatiotemporal Complex Mamba encoder with dynamic multi-scale receptive fields to capture long-range phase-amplitude dynamics. Cross-attention then maps the learned representations to human joints, followed by a skeleton-aware GraFormer decoder for anatomically coherent pose estimation. Experiments on MM-Fi show that C-MambaPose achieves strong performance across all settings, with the largest gains in unseen environments, reaching $298.5$ mm and $317.6$ mm MPJPE on the two cross-environment protocols using only $3.78$ M parameters and no pretraining. Ablations further show that prematurely discarding complex phase relationships substantially degrades performance, demonstrating their importance for cross-environment Wi-Fi HPE.

eess.SP · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34740

Networked Humanoid Intelligence: Communication, Coordination, Collaboration, and Social Interaction

Loc X. Nguyen, Huy Q. Le, Ji Su Yoon, Nguyen H. Tran, Eui-Nam Huh, Choong Seon Hong

原文摘要

Humanoid robots are moving from laboratory platforms toward connected fleets that share workspaces, infrastructure, tasks, and knowledge with other robots and humans. As deployments grow, the operation and improvement of each humanoid depend on information exchanged across the network. However, existing research has largely studied humanoids as individual systems. This survey examines networked humanoid intelligence through four functions of information exchange: communication, coordination, collaboration, and social intelligence with human interaction. We first establish the foundations of networked humanoid systems, define the question addressed by each function, and connect them through a common factory scenario. We review the literature along six dimensions: interaction architecture, exchanged information, physical coupling, operating timescale, system scale, and embodiment. We separate results obtained with physical and simulated humanoids from those based on other robotic platforms or disembodied agents, because the latter do not fully capture humanoid-specific constraints such as balance, whole-body motion, contact, and physical interaction. We examine hardware, datasets, and benchmarks, and distinguish risks handled locally from those networking introduces, such as stale information or unsafe knowledge propagation. Finally, we identify open problems in control-aware networking, compact body-centric messages, physically grounded coordination, network- and embodiment-aware knowledge sharing, fleet-level social consistency, and accountable human oversight.

cs.RO · cs.ET · cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34523

How Humans Learn Unfamiliar Worlds: A Think-Aloud Dataset on ARC-AGI-3

Shuzhi Liu, Archana Warrier, Dat Nguyen, Chengqi Zheng, Kevin Ellis, Yewen Pu

原文摘要

Learning through exploration and using newly acquired knowledge to achieve goals are central capabilities of general intelligence. Understanding these capabilities requires tracing how learners build, use, and revise world models during interaction. We construct the first dataset that records how humans discover and use the mechanisms of novel environments, using ARC-AGI-3 as a testbed: 144 trajectories from 64 participants across 25 public games, each pairing time-synchronized actions with think-aloud audio, plus 250 trajectories from large language model (LLM) agents with and without harnesses on the same games. We annotate mechanism triggers and recognition, action purposes, and errors and recovery, linking changes in understanding to the observations and actions that support them. We analyze characteristics of human cognition and compare them with those of LLM agents. Both groups anticipate mechanisms and use familiar analogies. Qualitative representations are more common in successful human trajectories than in successful LLM trajectories (50.0% versus 14.3%). Humans more often explore unfamiliar situations without a concrete hypothesis, while hypothesis testing is common in both groups. We also trace how learners recover through belief revision, replanning, and execution correction. As LLMs with well-designed harnesses reach saturated performance on public ARC-AGI-3 games, our cognitive analyses motivate next-generation benchmarks that challenge familiar priors, require accumulated evidence and abstraction, and test planning through subgoals, experimentation, and recovery. Our dataset is available at [https://arc3-human-think-aloud.github.io/](https://arc3-human-think-aloud.github.io/).

cs.AI

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.70ACL Anthology · ACL 2026

ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning

Vladislav Smirnov, Quang-Chieu Nguyen, Sergey Senichev MTS AI and ITMO University, Minh Ngoc Ta, Ekaterina Fadeeva ETHZ - ETH Zurich, Artem Vazhentsev Mohamed bin Zayed University of Artificial Intelligence and Independent Researcher, Daria Galimzianova Mohamed bin Zayed University of Artificial Intelligence, Nikolai Rozanov 等 17 位作者

原文摘要

Test-time compute (TTC) scaling has emerged as a powerful paradigm for improving large language model (LLM) reasoning by allocating additional compute during inference, e.g., via multi-sample generation and verifier-based reranking. Existing TTC scaling strategies and reasoning scorers remain fragmented, evaluated under inconsistent protocols, and are rarely analyzed through the lens of quality-cost trade-offs. We introduce ThinkBooster, a unified framework for seamless test-time compute scaling of LLM reasoning, which consists of (i) a modular Python library implementing state-of-the-art TTC scaling strategy and scorer families, (ii) a benchmark that jointly evaluates performance and computational efficiency, and (iii) a deployable OpenAI-compatible proxy service that enables drop-in integration of adaptive reasoning into real-world applications. We further provide a demo visual debugger for inspecting the reasoning trajectories, intermediate selection decisions, and alternative reasoning paths. Empirical results on mathematical and coding tasks reveal the performance-compute trade-offs of TTC scaling strategies and scoring methods and demonstrate that ThinkBooster provides practical gains in real-world tasks. The code is available online under an MIT license.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.94ACL Anthology · ACL 2026

SemViQA: A Semantic Question Answering System for Vietnamese Information Fact-Checking

Dien X. Tran, Nam V. Nguyen, Tran Tan Thanh, Anh T. Hoang, Dương Văn Tài, Lê Thanh Di, Phuc-Lu Le

原文摘要

Recent advances in LLMs have accelerated both information generation and misinformation, especially in low-resource languages like Vietnamese, motivating robust fact-checking systems. Existing methods struggle with semantic ambiguity, homonyms, and complex linguistic structures, often trading accuracy for efficiency. We introduce SemViQA, a novel Vietnamese fact-checking framework integrating Semantic-based Evidence Retrieval (SER) and Two-step Verdict Classification (TVC). Our approach balances precision and speed, achieving state-of-the-art results with 78.97% strict accuracy on ISE-DSC01 and 80.82% on ViWikiFC, securing 1st place in the UIT Data Science Challenge. Additionally, SemViQA Faster improves inference speed 7× while maintaining competitive accuracy. SemViQA sets a new benchmark for Vietnamese fact verification, advancing the fight against misinformation.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1031ACL Anthology · ACL 2026

Hard2Verify: A Step-Level Verification Benchmark for Open-Ended Frontier Math

Shrey Pandit SalesForce.com, Austin Xu Anthropic, Xuan-Phi Nguyen, Yifei Ming Salesforce AI Research, Caiming Xiong Salesforce Research, Shafiq Joty Nanyang Technological University and SalesForce.com

原文摘要

Large language model (LLM)-based reasoning systems have recently achieved gold medal-level performance in the IMO 2025 competition, writing mathematical proofs where, to receive full credit, each step must be not only correct but also sufficiently supported. To train LLM-based reasoners in such challenging, open-ended settings, strong verifiers capable of catching step-level mistakes are necessary prerequisites. We introduce Hard2Verify, a human-annotated, step-level verification benchmark produced with over 500 hours of human labor. Hard2Verify is designed to rigorously assess step-level verifiers at the frontier: Verifiers must provide step-level annotations or identify the first error in responses generated by frontier LLMs for very recent, challenging, and open-ended math questions. We evaluate 29 generative critics and process reward models, demonstrating that, beyond a few standouts, open-source verifiers lag closed source models. We subsequently analyze what drives poor performance in step-level verification, the impacts of scaling verifier compute, as well as fundamental questions such as self-verification and verification-generation dynamics.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.12ACL Anthology · ACL 2026

Parallel Universes, Parallel Languages: A Comprehensive Study on LLM-based Multilingual Counterfactual Example Generation

Qianli Wang, Van Bach Nguyen Phillips-Universität Marburg, Yihong Liu Ludwig-Maximilians-Universität München, Fedor Splitt Technische Universität Berlin, Nils Feldhus, Christin Seifert Phillips-Universität Marburg, Hinrich Schuetze, Sebastian Möller 等 9 位作者

原文摘要

Counterfactuals refer to minimally edited inputs that cause a model’s prediction to change, serving as a promising approach to explaining the model’s behavior. Large language models (LLMs) excel at generating English counterfactuals and demonstrate multilingual proficiency. However, their effectiveness in generating multilingual counterfactuals remains unclear. To this end, we conduct a comprehensive study on multilingual counterfactuals. We first conduct automatic evaluations on both directly generated counterfactuals in the target languages and those derived via English translation across six languages. Although translation-based counterfactuals offer higher validity than their directly generated counterparts, they demand substantially more modifications and still fall short of matching the quality of the original English counterfactuals. Second, we find the patterns of edits applied to high-resource European-language counterfactuals to be remarkably similar, suggesting that cross-lingual perturbations follow common strategic principles. Third, we reveal that multilingual counterfactual data augmentation (CDA) yields larger model performance improvements than cross-lingual CDA, especially for lower-resource languages. Yet, the imperfections of the generated counterfactuals limit gains in model performance and robustness. Finally, we identify and categorize four main types of errors that consistently appear in the generated counterfactuals across languages.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1407ACL Anthology · ACL 2026

Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly Detection

Junjun Pan, Yixin Liu Griffith University, Rui Miao, Kaize Ding Northwestern University, Yu Zheng Griffith University, Quoc Viet Hung Nguyen Griffith University, Alan Wee-Chung Liew Griffith University, Shirui Pan Griffith University

原文摘要

Large language model (LLM)-based multi-agent systems (MAS) have shown strong capabilities in solving complex tasks. As MAS become increasingly autonomous in various safety-critical tasks, detecting malicious agents has become a critical security concern. Although existing graph anomaly detection (GAD)-based defenses can identify anomalous agents, they mainly rely on coarse sentence-level information and overlook fine-grained lexical cues, leading to suboptimal performance. Moreover, the lack of interpretability in these methods limits their reliability and real-world applicability. To address these limitations, we propose , an explainable and fine-grained safeguarding framework for detecting malicious agents in MAS. To incorporate both coarse and fine-grained textual information for anomalous agent identification, we utilize a bi-level agent encoder to jointly model the sentence- and token-level representations of each agent. A theme-based anomaly detector further captures the evolving discussion focus in MAS dialogues, while a bi-level score fusion mechanism quantifies token-level contributions for explanation. Extensive experiments across diverse MAS topologies and attack scenarios demonstrate robust detection performance and strong interpretability of XG-Guard.

cs.CL · cs.MA

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1507ACL Anthology · ACL 2026

MTA: Multi-Granular Trajectory Alignment for Large Language Model Distillation

Pham Khanh Chi, Quoc Phong Dao, Thuat Nguyen Independent, Linh Ngo Van Hanoi University of Science and Technology, Trung Le Monash University, Thanh Hong Nguyen University of Oregon

原文摘要

Knowledge distillation is a key technique for compressing large language models (LLMs), but most existing methods align representations at fixed layers or token-level outputs, ignoring how representations evolve across depth. As a result, the student is only weakly guided to capture the teacher’s internal relational structure during distillation, which limits knowledge transfer. To address this limitation, we propose Multi-Granular Trajectory Alignment (MTA), a framework that aligns teacher and student representations along their layer-wise transformation trajectory. MTA adopts a layer-adaptive strategy: lower layers are aligned at the word level to preserve lexical information, while higher layers operate on phrase-level spans (e.g., noun and verb phrases) to capture compositional semantics. We instantiate this idea through a Dynamic Structural Alignment loss that matches the relative geometry among semantic units within each layer. This design is motivated by empirical findings that Transformer representations become increasingly abstract with depth, and is also consistent with linguistic views in which higher-level meaning emerges through the composition of lower-level lexical units. We further incorporate a Hidden Representation Alignment loss to directly align selected teacher–student layers. Experiments show that MTA consistently outperforms state-of-the-art baselines on standard benchmarks, with ablations confirming the contribution of each component.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1509ACL Anthology · ACL 2026

TALAS: Teacher-Anchored Layer Alignment with Adaptive Sharpness-Aware Minimization for Embedding Distillation

Quoc Phong Dao, Hoang Son Nguyen, Pham Khanh Chi, Linh Ngo Van Hanoi University of Science and Technology, Nguyen Thi Ngoc Diep, Thien Huu Nguyen University of Oregon, Trung Le Monash University

原文摘要

Knowledge Distillation (KD) has established itself as a pivotal technique for compressing large pre-trained language models. However, existing methods that force a student to strictly mimic the teacher’s sentence embeddings or internal features often incur prohibitive computational costs and yield suboptimal performance due to the inherent capacity gap. To address these challenges, we propose TALAS (Teacher-Anchored Layer Alignment with Sharpness-aware minimization), a unified framework that synergizes hierarchical (multi-layer) alignment with robust optimization. First, we introduce a Teacher-Anchored mechanism that selectively distills final sentence embeddings only into the student’s upper layers, thereby reducing overhead while respecting capacity constraints. Second, we bridge the semantic gap in lower layers via Layer-Aligned Self-Distillation, which propagates knowledge top-down using internal geometric relational constraints in the embedding space. Finally, to prevent the student from memorizing point-wise teacher noise, we integrate Adaptive Sharpness-Aware Minimization (ASAM) into the training objective, guiding the model towards flat minima for enhanced generalization. Empirical results on standard sentence embedding benchmarks demonstrate that TALAS consistently outperforms strong distillation baselines while achieving superior training efficiency in terms of computational cost and memory footprint.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1522ACL Anthology · ACL 2026

SRA: Span Representation Alignment for Large Language Model Distillation

Quoc Phong Dao, Hoang Son Nguyen, Pham Khanh Chi, Tung Nguyen Hanoi University of Science and Technology, Linh Ngo Van Hanoi University of Science and Technology, Nguyen Thi Ngoc Diep, Trung Le Monash University

原文摘要

Cross-Tokenizer Knowledge Distillation (CTKD) enables knowledge transfer between a large language model and a smaller student, even when they employ different tokenizers. While existing approaches mainly focus on token-level alignment strategies, which are often brittle and sensitive to discrepancies between tokenizers, we argue that the method of aggregating tokens into more robust representations before distillation is of equal importance. In this paper, we introduce SRA (Span Representation Alignment for Large Language Model Distillation), a novel framework that reframes CTKD through the physical lens of Multi-Particle Dynamical Systems. SRA shifts the fundamental unit of alignment from tokens to robust, tokenizer-agnostic spans. We model each span as a cluster of particles and represent its state by its Center of Mass (CoM) - an attention-weighted average that captures rich semantic information. We leverage the concept of span centers of mass with attention-derived weighting to prioritize the most salient spans. In addition, we employ a geometric regularizer to preserve the structural integrity of the representation space and introduce aligned span logit distillation to enhance knowledge transfer across models. In challenging cross-architecture distillation experiments, SRA consistently and significantly outperforms state-of-the-art CTKD baselines, validating our physically-grounded approach.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1527ACL Anthology · ACL 2026

CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data

Pedro Ortiz Suarez, Laurie Burchell Common Crawl Foundation, Catherine Arnett EleutherAI, Rafael Mosquera, Sara Hincapié Monsalve Factored, Thom Vaughan Common Crawl Foundation, Damian Stewart, Malte Ostendorff Common Crawl Foundation 等 97 位作者

原文摘要

Language identification (LID) is a fundamental step in curating multilingual corpora. However, LID models still perform poorly for many languages, especially on the noisy and heterogeneous web data often used to train multilingual language models. In this paper, we introduce CommonLID, a community-driven, human-annotated LID benchmark for the web domain, covering 109 languages. Many of the included languages have been previously under-served, making CommonLID a key resource for developing more representative high-quality text corpora. We show CommonLID’s value by using it, alongside five other common evaluation sets, to test eight popular LID models. We analyse our results to situate our contribution and to provide an overview of the state of the art. In particular, we highlight that existing evaluations overestimate LID accuracy for many languages in the web domain. We make CommonLID and the code used to create it available under an open, permissive license.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1601ACL Anthology · ACL 2026

CypherSmith: Transforming Text-to-Cypher Generation for LLMs with Synthetic Data

Zeyu Zhang Amazon AGI, Kexuan Sun Amazon, Zheng Tang Amazon, Jens-S. Vöckler Amazon, Thien Huu Nguyen University of Oregon, Thuy Vu Amazon AGI

原文摘要

Knowledge Graph (KG) retrieval is a promising augmentation to address knowledge gaps and hallucinations in LLMs. As KGs in practice are stored in graph databases (e.g., Wikidata, Freebase), accurate retrieval requires translating natural language questions into structured queries (query generation). A key challenge of query generation is Text-to-Cypher, which generates Cypher queries for property graphs (e.g., Neo4j), a paradigm increasingly adopted in industry for their scalable architectures and expressive schemas. However, compared to other query generation tasks such as Text-to-SQL or Text-to-SPARQL, Text-to-Cypher remains underexplored due to scarce public KGs and datasets. Existing datasets are small, domain-limited, and lack diversity, constraining LLM progress. To address this, we introduce CypherSmith, an instruction-tuning dataset over 12\times larger than prior public Text-to-Cypher datasets, spanning diverse domains to better support LLM fine-tuning. Our key distinction lies in fully leveraging open-source LLMs for large-scale synthetic data generation and introducing a novel likelihood-based filtering technique to ensure high-quality Text-to-Cypher data. Extensive experiments demonstrate the effectiveness of CypherSmith, achieving state-of-the-art LLM performance.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1613ACL Anthology · ACL 2026

Lizard: An Efficient Linearization Framework for Large Language Models

Chien Van Nguyen University of Oregon, Huy Huu Nguyen, Ruiyi Zhang, Hanieh Deilamsalehy Adobe Systems, Puneet Mathur Adobe Systems, Viet Dac Lai Adobe Systems, Haoliang Wang Adobe Research, Jayakumar Subramanian Adobe Systems 等 13 位作者

原文摘要

We propose Lizard, a linearization framework that transforms pretrained Transformer-based Large Language Models (LLMs) into subquadratic architectures. Transformers faces severe computational and memory bottlenecks with long sequences due to the quadratic complexity of softmax attention and the growing Key-Value (KV) cache that makes inference memory-bound by context length. Lizard addresses these limitations by introducing a subquadratic attention mechanism that closely approximates softmax attention while preserving model quality. Unlike prior linearization methods constrained by fixed, non-adaptive structures, Lizard augments the architecture with compact, learnable modules that enable adaptive memory control and robust length generalization. Moreover, we introduce a hardware-aware algorithm that solves numerical instability in gated attention to accelerate training. Extensive experiments show that Lizard achieves near-lossless recovery of its teacher model’s performance, significantly outperforming previous methods by up to 9.4 - 24.5 points on the 5-shot MMLU benchmark and demonstrating superior associative recall.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1631ACL Anthology · ACL 2026

Octopus: Gated Selective Attention for Memory-Bounded Long-Context Inference in Large Language Models

Chien Van Nguyen University of Oregon, Ryan A. Rossi, Linh Ngo Van Hanoi University of Science and Technology, Franck Dernoncourt Adobe Research, Thien Huu Nguyen University of Oregon

原文摘要

Transformer inference becomes increasingly memory-bound as the Key–Value (KV) cache grows linearly with sequence length. While subquadratic architectures offer constant-memory inference, they rely on aggressive state compression that degrades performance on complex reasoning tasks. We propose Octopus, a framework that confers fixed-memory inference onto pretrained Transformers without the information loss of linearization. Octopus retrofits attention layers with Gated Selective Attention, a learnable module that enforces an adaptive sparsity policy over the context history. By dynamically scoring and retaining only high-utility KV states, this mechanism transforms the unbounded cache into a compact, evolving memory budget that filters out uninformative noise. Empirically, on the GSM8K benchmark, it outperforms state-of-the-art linearized baselines by over 36 points under identical memory constraints. Remarkably, Octopus also surpasses its own full-cache teacher, demonstrating that learned sparse retention serves as an effective regularizer for long-horizon reasoning.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1687ACL Anthology · ACL 2026

SpecMind: Cognitively Inspired, Interactive Multi-Turn Framework for Postcondition Inference

Cuong Chi Le, Minh V.T. Pham FPT Software AI Center, Tung Duy Vu, Cuong Duc Van Hanoi University of Science and Technology, Huy N. Phan, Hoang N. Phan, Tien N. Nguyen

原文摘要

Specifications are vital for ensuring program correctness, yet writing them manually remains challenging and time-intensive. Recent large language model (LLM)-based methods have shown successes in generating specifications such as postconditions, but existing single-pass prompting often yields inaccurate results. In this paper, we present SpecMind, a novel framework for postcondition generation that treats LLMs as interactive and exploratory reasoners rather than one-shot generators. SpecMind employs feedback-driven multi-turn prompting approaches, enabling the model to iteratively refine candidate postconditions by incorporating implicit and explicit correctness feedback, while autonomously deciding when to stop. This process fosters deeper code comprehension and improves alignment with true program behavior via exploratory attempts. Our empirical evaluation shows that SpecMind significantly outperforms state-of-the-art approaches in both accuracy and completeness of generated postconditions.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.170ACL Anthology · ACL 2026

LLM-XTM: Enhancing Cross-Lingual Topic Models with Large Language Models

Minh Chu Xuan, Tien-Phat Nguyen, Linh Ngo Van Hanoi University of Science and Technology, Dinh Viet Sang, Nguyen Thi Ngoc Diep, Trung Le Monash University

原文摘要

Cross-lingual topic modeling aims to discover shared semantic structures across languages, yet existing models depend on sparse bilingual resources and often yield incoherent or weakly aligned topics. Recent LLM-based refinements improve interpretability but are costly, document-level, and prone to hallucination, with prior white-box approaches requiring inaccessible token probabilities. We propose LLM-XTM, a framework that integrates LLM-guided topic refinement with self-consistency uncertainty quantification, enabling black-box, stable, and scalable enhancement of cross-lingual topic models. Experiments on multilingual corpora show that LLM-XTM achieves superior topic coherence and alignment while reducing reliance on bilingual dictionaries and expensive LLM calls.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1817ACL Anthology · ACL 2026

Towards Fast and Accurate Modeling for Cross-Lingual Label Projection

Thang Le Mohamed bin Zayed University of Artificial Intelligence, Huy Huu Nguyen, Anh Tuan Luu Nanyang Technological University, Thamar Solorio Mohamed bin Zayed University of Artificial Intelligence and University of Houston, Thien Huu Nguyen University of Oregon

原文摘要

Information extraction (IE) systems rely on structured data for training, but such annotated data is highly imbalanced across languages, with low-resource languages receiving little attention. Label projection techniques aim to bridge this gap by transferring structured annotations from high-resource to low-resource languages. However, existing methods are either inaccurate or too slow for large-scale use. This work aims to address this problem by developing a more effective method that remains sufficiently efficient for large-scale projection. In particular, we propose to synthesize alignment sequence pairs and fine-tune an encoder model with span alignment objective, while controlling data influence during training. Experimental results across 50+ languages show that our framework consistently outperforms previous state-of-the-art methods while maintaining fast inference speed. In addition, we introduce EXP - the first benchmark for explicit evaluation of label projection, thereby reducing confounders and non-determinism in method assessment.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1862ACL Anthology · ACL 2026

Learning Uncertainty from Sequential Internal Dispersion in Large Language Models

Ponhvoan Srey Nanyang Technological University, Xiaobao Wu Shanghai Jiaotong University, Cong-Duy T Nguyen VinUniversity and School of Computer Science and Engineering, Nanyang Technological University, Anh Tuan Luu Nanyang Technological University

原文摘要

Uncertainty estimation is a promising approach to detect hallucinations in large language models (LLMs). Recent approaches commonly depend on model internal states to estimate uncertainty. However, they suffer from strict assumptions on how hidden states should evolve across layers, and from information loss by solely focusing on last or mean tokens. To address these issues, we present Sequential Internal Variance Representation (SIVR), a supervised hallucination detection framework that leverages token-wise, layer-wise features derived from hidden states. SIVR adopts a more basic assumption that uncertainty manifests in the degree of dispersion or variance of internal representations across layers, rather than relying on specific assumptions, which makes the method model and task agnostic. It additionally aggregates the full sequence of per-token variance features, learning temporal patterns indicative of factual errors and thereby preventing information loss. Experimental results demonstrate SIVR consistently outperforms strong baselines. Most importantly, SIVR enjoys stronger generalisation and avoids relying on large training sets, highlighting the potential for practical deployment.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1943ACL Anthology · ACL 2026

Wait! There’s a Way Out: A Decision Mechanism for Forecasting Conversational Derailment

Laerdon Kim, Vivian Nguyen, Cristian Danescu-Niculescu-Mizil Cornell University

原文摘要

Forecasting conversational derailment is the task of predicting, as the conversation unfolds, whether it will eventually derail into personal attacks. Since forecasting models operate in an online fashion, they must decide whether to “trigger” an alert after each utterance—for example, to notify participants or a moderator that the conversation is at risk of derailing. Existing approaches make this decision solely based on the estimated likelihood of derailment given the preceding utterances, implicitly assuming that the conversation’s future trajectory is fixed. As a result, they ignore the possibility of future recovery and incur an unnecessarily high rate of false positives.In this work we propose a method for decoupling the decision to trigger from derailment likelihood estimation. Our approach is inspired by the first human baseline on this task, which shows that humans achieve dramatically lower false positive rates by selectively deferring their decision to trigger when they anticipate that tension is likely to subside. We operationalize this insight with a deferral mechanism that uses forward-looking simulations to assess whether a tense moment admits plausible paths to recovery. Incorporating this mechanism into a state-of-the-art forecasting model substantially reduces false positives without sacrificing forecasting accuracy. More broadly, this work highlights the value of treating decision-making as a first-class component of forecasting systems.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.200ACL Anthology · ACL 2026

Thermometer of Thoughts: Enhancing LLM’s Exploration via Attention Temperature Modulation

Zhiyuan Yu, Shijian Xiao, Cam-Tu Nguyen, Zhangyue Yin, Lekai Xing, Wenzhong Li Nanjing University, Sanglu Lu Nanjing University

原文摘要

Improving the exploration of reasoning is essential for advancing Large Language Models’ (LLMs) problem-solving performance. Current methods primarily rely on output-level stochasticity, which decode within fixed reasoning patterns of LLM and suffer from insufficient exploration. In this paper, we introduce adjusting attention temperature to directly modulate the model’s internal focus during reasoning, which enables a dynamic shift between exploratory and focused processing. We reveal that moderate adjustments preserve LLM’s reasoning capability while producing problem hardness-dependent benefits: higher temperatures facilitate solving complex tasks by encouraging wider exploration, whereas lower temperatures mitigate overthinking on simpler problems. Leveraging this insight, we propose a two-stage inference strategy: first, attention temperature scaling modulates the LLM’s reasoning patterns to diversify the reasoning traces; then, a difficulty-aware aggregation scheme is introduced to effectively identify the most reliable solution from the generated candidates. Extensive evaluations show that our method improves Pass@10 by 6.78–14.20% and aggregation accuracy by 9.74% across 7 reasoning benchmarks.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.2018ACL Anthology · ACL 2026

Explainable Disentangled Representation Learning for Generalizable Authorship Attribution in the Era of Generative AI

Hieu Man University of Oregon, Van-Cuong Pham University of Oregon, Nghia Trung Ngo University of Oregon, Franck Dernoncourt Adobe Research, Thien Huu Nguyen University of Oregon

原文摘要

Learning robust representations of authorial style is crucial for authorship attribution and AI-generated text detection. However, existing methods often struggle with content-style entanglement, where models learn spurious correlations between authors’ writing styles and topics, leading to poor generalization across domains. To address this challenge, we propose Explainable Authorship Variational Autoencoder (EAVAE), a novel framework that explicitly disentangles style from content through architectural separation-by-design. EAVAE first pretrains style encoders using supervised contrastive learning on diverse authorship data, then finetunes with a Variational Autoencoder (VEA) architecture using separate encoders for style and content representations. Disentanglement is enforced through a novel discriminator that not only distinguishes whether pairs of style/content representations belong to the same or different authors/content sources, but also generates natural language explanation for their decision, simultaneously mitigating confounding information and enhancing interpretability. Extensive experiments demonstrate the effectiveness of EAVAE. On authorship attribution, we achieve state-of-the-art performance on various datasets, including Amazon Reviews, PAN21, and HRS. For AI-generated text detection, EAVAE excels in few-shot learning over the M4 dataset.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.2092ACL Anthology · ACL 2026

Layer-Wise High-Impact Parameter Ratio Optimization in Post-Training Quantization for Large Language Models

Cuong Pham Monash University, Anh Dung Hoang, Cuong C. Nguyen, Trung Le Monash University, Gustavo Carneiro University of Surrey, Thanh-Toan Do

原文摘要

Large language models (LLMs) have advanced natural language processing, but their massive parameter counts create computational and memory challenges during deployment. Post-training quantization (PTQ) has emerged as a promising approach to mitigate these challenges. While existing PTQ methods can effectively quantize LLMs, they experience substantial accuracy loss at extremely low bit-widths due to high-impact parameters. Several approaches address this by retaining high-impact parameters in FP16 format, but they apply fixed ratios across all layers, overlooking layer-wise sensitivity variations. We propose a quadratic optimization framework that determines layer-specific ratios of high-impact parameters while considering inter-layer dependencies. We quantize high-impact parameters to moderate bit-widths while the remaining parameters are quantized to extremely low bit-widths. Under the same resource budget, this preserves more high-impact parameters than methods retaining a few in FP16 format. Our framework enables leveraging advanced quantization methods for high-impact parameters while applying lightweight computational quantization methods to the rest, achieving an effective balance between computational efficiency and accuracy during quantization process.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表