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新论文 · v1arXiv:2611.01396

SpecUltra: A Unified Foundation Model for Heterogeneous Spectral Images

Weili Kong, Baisen Liu, Xiaojun Bi, Jiaming Pei

原文摘要

Spectral imagery, including hyperspectral image (HSI) and multispectral image (MSI), exhibits substantial heterogeneity in band numbers, wavelengths, spectral resolutions, hindering unified modeling and cross-sensor transfer. From the perspective of imaging physics, these heterogeneous observations can be regarded as discrete measurements of an underlying continuous spectrum acquired through different sampling processes. Motivated by this perspective, we propose SpecUltra, a unified foundation model for heterogeneous spectral images. Specifically, SpecUltra employs 3D convolutional tokenization and wavelength-aware 3D RoPE to encode relative wavelength and spatial relationships, enabling fine-grained spatial-spectral modeling within a shared coordinate system. We further construct MultiHyper-200K, a large-scale multi-sensor HSI dataset, and pretrain SpecUltra using auxiliary-token-assisted mask image modeling (MIM), where a level token indicates the radiometric product type and register tokens absorb sensor- and acquisition-dependent variations. To bridge the spectral-granularity gap between HSI and MSI, we introduce Spectral Kernel Transformation (SKT), which transfers pretrained knowledge in parameter space by constructing MSI embedding kernels from fine-grained HSI-pretrained kernel slices. This design preserves pretrained spectral priors while maintaining compatibility with the pretrained backbone. Experiments on HSI and MSI classification and segmentation benchmarks demonstrate state-of-the-art or competitive performance across disparate sensors and downstream tasks.

cs.CV

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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1077ACL Anthology · ACL 2026

TED-TTS: Training-Free Intra-Utterance Emotion and Duration Control for Text-to-Speech Synthesis

Qifan Liang, Yuansen Liu, Ruixin Wei national university of singaore, National University of Singapore, Nan Lu, Junchuan Zhao, Ye Wang

原文摘要

While controllable Text-to-Speech (TTS) has achieved notable progress, most existing methods remain limited to inter-utterance-level control, making fine-grained intra-utterance expression challenging due to their reliance on non-public datasets or complex multi-stage training. In this paper, we propose TED-TTS, a training-free controllable framework for pretrained zero-shot TTS to enable intra-utterance emotion and duration expression. Specifically, we propose a segment-aware emotion conditioning strategy that combines causal masking with monotonic stream alignment filtering to isolate emotion conditioning and schedule mask transitions, enabling smooth intra-utterance emotion shifts while preserving global semantic coherence. Based on this, we further propose a segment-aware duration steering strategy to combine local duration embedding steering with global EOS logit modulation, allowing local duration adjustment while ensuring globally consistent termination. To eliminate the need for segment-level manual prompt engineering, we construct a 30,000-sample multi-emotion and duration-annotated text dataset to enable LLM-based automatic prompt construction. Extensive experiments demonstrate that our training-free method not only achieves state-of-the-art intra-utterance consistency in multi-emotion and duration control, but also maintains baseline-level speech quality of the underlying TTS model. Audio samples are available at https://aclanonymous111.github.io/TED-TTS-DemoPage/.

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.1476ACL Anthology · ACL 2026

GQLBench: A Large-Scale Cross-Domain, Cross-Dialect Benchmark for NL2GQL

Yanning Su Fudan University, Yuhang Zhou, Yang Fang, Sen Liu Fudan University, Guangnan Ye Fudan University, Hongfeng Chai Fudan University

原文摘要

Despite growing interest in NL2GQL, benchmarking progress has been constrained by the lack of resources that are simultaneously large-scale, cross-domain, and cross-dialect. To address this gap, we present GQLBench, a new benchmark built through an automated and scalable framework that integrates NL2SQL-to-NL2GQL conversion with graph-native data generation. GQLBench supports execution-based evaluation on both Cypher and ISO-GQL, covering hundreds of graph databases and over 20k natural language questions for each dialect. By combining converted data from mature NL2SQL resources with synthetic graph-specific queries, it captures both schema diversity from real-world relational sources and graph-native reasoning challenges, including long paths and cycles. Beyond overall performance comparison, GQLBench also enables fine-grained evaluation across dialects, graph patterns, and query complexity. Experiments on advanced LLMs show that even strong proprietary models struggle on GQLBench, with gemini-3-flash achieving only 35.40% average execution accuracy across the two dialects. Our data and code are available at https://github.com/qxssadf/GQLBench.

cs.CL

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

记录更新 · 记录 #2ACL Anthology · ACL 2026:acl:2026.acl-long.1698ACL Anthology · ACL 2026

Reasoning Hijacking: The Fragility of Reasoning Alignment in Large Language Models

Yuansen Liu, Yixuan Tang National University of Singapore, Anthony Kum Hoe Tung National University of Singapore

原文摘要

Current LLM safety research predominantly focuses on mitigating Goal Hijacking, preventing attackers from redirecting a model’s high-level objective (e.g., from “summarizing emails” to “phishing users”). In this paper, we argue that this perspective is incomplete and highlight a critical vulnerability in Reasoning Alignment. We expose the inherent fragility of current alignment techniques by proposing a new adversarial prompt attack paradigm: Reasoning Hijacking. To demonstrate this vulnerability, we instantiate it via the Criteria Attack, which subverts model judgments by injecting spurious decision criteria without altering the high-level task goal. Unlike Goal Hijacking, which attempts to override the system prompt, Reasoning Hijacking keeps the task goal intact but manipulates the model’s decision-making logic by injecting spurious reasoning shortcuts. Through extensive experiments on three different tasks (toxic comment, negative review, and spam detection), we demonstrate that even state-of-the-art models are highly fragile, consistently prioritizing injected heuristic shortcuts over rigorous semantic analysis. Crucially, because the model’s explicit intent remains aligned with the user’s instructions, these attacks can bypass defenses designed to detect goal deviation (e.g., SecAlign, StruQ), revealing a fundamental blind spot in the current safety landscape. Data and code are available at https://github.com/Yuan-Hou/criteria_attack.

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.1722ACL Anthology · ACL 2026

Glyph: Scaling Context Windows via Visual-Text Compression

Jiale Cheng, Yusen Liu, Xinyu Zhang, Yulin Fei The Hong Kong University of Science and Technology, Wenyi Hong Department of Computer Science and Technology, Tsinghua University, Ruiliang Lyu Zhipu AI, Weihan Wang, Zhe Su 等 14 位作者

原文摘要

Large language models (LLMs) conventionally represent text as sequences of discrete tokens, making long-context scaling largely a matter of processing more tokens more efficiently.We instead explore a complementary direction: increasing how much original context each token represents.To this end, we introduce Glyph, a framework that renders long texts into compact visual pages and processes them with a vision-language model (VLM), allowing a fixed context window to cover substantially more text.To make visual compression practical, Glyph combines continual pre-training on rendered long-text data, an LLM-driven genetic search to identify rendering configurations that balance compression and task performance, and post-training with supervised fine-tuning and reinforcement learning.Across multiple long-context benchmarks, Glyph achieves 3–4× token compression while maintaining performance comparable to strong text-only LLMs such as Qwen3-8B, with over 4× faster prefilling and decoding and 2× faster supervised fine-tuning.Under more aggressive compression, a VLM with a 128K context window can handle tasks that would otherwise require up to 1M input tokens.Our code and model are released at https://github.com/thu-coai/Glyph.

cs.CL · cs.CV

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