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新论文 · v1arXiv:2611.14878

Hear What You Eat: In-Ear Passive Acoustic Sensing for Multi-task Dietary Monitoring

Feiyu Han, Mingtian Zhu, Dawei Yan, Weiwei Jiang, Yuanhao Feng, Yanfei Zhang, Panlong Yang

原文摘要

The increasing obesity health concerns are driving the advancement of continuous dietary monitoring. Yet, existing single-sensor solutions still face challenges of coarse-grained sensing resolution, while multi-sensor fusion strategies encounter significant engineering complexity or hardware requirements. In our work, we present a single modality-based multi-dimensional dietary monitoring and logging solution, named EARDIET, which leverages inward-facing microphones on earables to capture in-ear chewing sounds for fine-grained diet analysis. With the texture audio profiles derivation, the pipeline consisting of signal processing and lightweight deep learning, is carefully designed to achieve eating episode detection, food texture sensing, and food type recognition simultaneously. Comprehensive experiments involving 32 participants with diverse BMI profiles are conducted to validate the effectiveness of multi-dimensional dietary monitoring. Compared with the state-of-the-art solutions, EARDIET can derive multi-dimensional dietary information with only a single modality, outperforming by about 10% in terms of eating episode detection. Furthermore, through real-world clinical validation, our solution can achieve masticatory disorder identification with an averaged F1-score of 85.97%, demonstrating potential for healthcare in the future.

cs.HC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2601.19704

Robust Joint Power Allocation and Antenna Placement for Pinching-Antenna Systems under Gaussian Location Uncertainty

Hao Feng, Ming Zeng, Xingwang Li, Wenwu Xie, Nian Xia

原文摘要

Pinching antenna systems (PASS) have attracted much attention recently owing to their capability to maintain reliable line-of-sight communication in high-frequency bands. By guiding signals through a waveguide and emitting them via a movable pinching antenna, PASS enable dynamic control of signal propagation and spatial adaptability. However, their performance heavily depends on effective resource allocation, including power allocation and antenna positioning, which becomes challenging under user localization uncertainty. Existing studies largely assume perfect user locations, while our prior work considered uniformly distributed localization errors. In this paper, we develop a robust resource allocation framework for multi-user downlink PASS under Gaussian-distributed localization uncertainty. An energy-efficiency (EE) maximization problem is formulated subject to probabilistic outage constraints, and the minimum transmit power is analytically characterized for any given antenna position. For the unbounded Gaussian model, we prove that the resulting one-dimensional antenna-placement objective is convex and derive a globally optimal derivative-bisection algorithm. We further consider a circularly truncated Gaussian model for bounded localization-error regions and develop a deterministic branch-and-bound method that returns an $ε_{\rm B}$-globally optimal antenna position. Finally, we discuss how position-dependent waveguide attenuation modifies the placement objective and may destroy its convexity. Simulation results verify that the proposed branch-and-bound solution closely matches the bounded grid-search benchmark, demonstrate substantial EE gains from optimized pinching-antenna placement over the bounded fixed-antenna architecture, and reveal an increasing distinction between the bounded and unbounded Gaussian models as the location uncertainty grows.

cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12077

Architectural Textured Proxy Generation

Weidan Xiong, Shihao Feng, Kaijie Qu, Hao li, Mingqi Zhang, Daniel Cohen-Or, Hui Huang

原文摘要

Textured proxy models are a fundamental representation for architectural scenes, enabling efficient rendering while preserving visual fidelity. Existing approaches construct such proxies through a sequential pipeline in which geometry is first abstracted from a dense reconstruction and textures are later projected from input images. This geometry-only formulation neglects image evidence during proxy generation, leading to misalignment of structural features such as facades, edges, and their intersections across views. As a result, inconsistencies are deferred to a downstream correction stage during texturing, where the underlying geometric structure can no longer be reliably recovered. In this work, we introduce an image-guided proxy formulation in which proxy generation is jointly constrained by both the dense reconstruction and the input aerial images. By incorporating image derived structural cues directly into the construction process, the proxy is optimized to align with observed features across views from the outset, rather than relying on post hoc correction that may be infeasible. This shift from a geometry-only to an image-guided paradigm produces proxies that are inherently consistent with the input imagery, enabling more accurate geometry and more coherent texture mapping. We realize this formulation through a framework that extracts structural priors from images and dense geometry, and enforces their consistency during proxy optimization. The resulting proxies exhibit improved alignment of structural features, reducing artifacts such as edge duplication, texture drift, and cross view inconsistencies. Experiments on diverse architectural scenes demonstrate that our approach produces compact, geometry texture aligned proxies with substantially improved visual fidelity compared to existing methods.

cs.GR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2512.18706

X-Talk: On the Underestimated Potential of Modular Speech-to-Speech Dialogue System

Zhanxun Liu, Yifan Duan, Mengmeng Wang, Pengchao Feng, Haotian Zhang, Xiaoyu Xing, Yijia Shan, Haina Zhu 等 20 位作者

原文摘要

We present X-Talk, an open-source framework that champions a decoupled, modular design for LLM-driven speech-to-speech (S2S) systems. While the dominant trend favors end-to-end (E2E) modeling to optimize information flow, these "omni-models" often struggle to balance the competing objectives of complex speech tasks within a single network. X-Talk challenges this paradigm by demonstrating that a systematically optimized cascaded pipeline can achieve sub-second latency without sacrificing modular flexibility. Our framework seamlessly integrates specialized front-end components (e.g., VAD, speech enhancement) and diverse understanding models (e.g., ASR, emotion, and environmental sound analysis) with LLM capabilities like retrieval-augmented generation (RAG) and tool use. By revitalizing the cascaded approach, X-Talk highlights the underestimated potential of modular S2S systems and provides a robust foundation for future research and applications.

cs.SD

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08897

Segmenting Geometric Textures of 3D Meshes

Weidan Xiong, Kaijie Qu, Shihao Feng, Shunqi Fan, Rongyu Xiong, Hui Huang

原文摘要

Recognizing geometric texture patterns on 3D models is critical for applications such as heritage protection, model editing, and 3D printing. Existing 3D model segmentation methods predominantly focus on identifying high-level semantic parts, thereby failing to capture middle-level textures. While recent Vision-Language Models can perform image segmentation with appropriate text prompts, they still struggle to recognize 3D geometric textures. In this work, we present a method that segments 3D mesh models based on middle-level geometric textures. We further introduce a new geometric texture segmentation benchmark and fine-tune SAM3 accordingly. The segmented texture regions are then clustered based on DINO feature similarity and lifted from 2D to 3D. Additionally, we develop an interactive tool to enable both 3D model editing based on our segmentation results and model retrieval from the dataset based on geometric and texture similarity. Experiments demonstrate that our method significantly outperforms existing methods in segmenting geometric textures and can facilitate various applications.

cs.GR · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06910

FrontierPhysics：评估面向端到端物理研究的智能体

FrontierPhysics: Evaluating Agents for End-to-End Physics Research

Bingran You, Wenjun Ke, Ria Rosenauer, Bo Liu, Wenbo Chen, Min Chen, Shuqi Xu, Yue Ma 等 72 位作者

AI 速读 基于摘要

作者提出包含56个由物理学家基于自身研究设计的端到端物理研究基准，用确定性验证器与专家评分量表同时考察智能体研究的正确性与可发表性。

cs.AI

arXiv ↗PDF ↗
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AI agents are playing an increasingly important role in physics research. However, existing physics benchmarks only evaluate fixed questions, isolated sub-tasks, or paper reproduction with given solutions, and rarely check research quality alongside correctness. We introduce FrontierPhysics, an end-to-end benchmark with 56 tasks made by 42 physicists based on their own research, covering nine domains across theoretical, computational, and experimental physics. Each task requires agents to review the literature, develop a research plan, conduct research and experiments, and produce structured outputs and scientific manuscripts. Combining deterministic verifiers with expert-authored rubrics, FrontierPhysics evaluates whether agents' research is correct and publishable. Across 22 models in 26 configurations, no configuration passes more than 46 of its 168 rollouts. Manual review of agent trajectories shows that frontier agents' main weaknesses are insufficiently rigorous reasoning and limited expert intuition, rather than gaps in physics knowledge or execution errors.

新论文 · v1arXiv:2611.04166

Say the Same, Act Differently: Diagnosing and Steering Text-Action Divergence in Reasoning VLAs

Zihao Feng, Qingzhao Zhang, Chunyu Xia, Bo Yu, Z. Morley Mao, Ramesh Govindan

原文摘要

Reasoning vision-language-action (VLA) models pair physical control with natural-language rationales that supervisors can read to monitor embodied safety. Prior attacks on VLA reasoning corrupt the rationale itself and leave visible evidence in the text. We show that visual perturbations can instead alter the physical action while keeping the generated rationale word-for-word identical. At the shared text/action interface of Alpamayo for driving and InstructVLA for manipulation, our text-neutral action-subspace (TNAS) diagnostic shows that the locally most action-sensitive direction is nearly orthogonal to the active text-loss gradients, with 1.87% and 5.28% norm overlap. At equal hidden-state perturbation norms across both models, interventions along this text-neutral residual shift actions 4.64x to 47.8x more than text-gradient perturbations, whereas text-gradient perturbations induce 21.2x to 277x more text drift. Steering visual perturbations along this direction reaches the same decoupling from the image. Across 154 obstacle-annotated driving scenes in Alpamayo, TNAS-guided attacks induce a new collision under a word-for-word identical rationale in 52.6% of scenes under full-image perturbations and 35.1% under a localized forward-camera patch, compared with 4.55% for unguided action attacks. Relative to a text-preserving baseline, TNAS guidance raises exact rationale preservation from 83.0% to 89.5% and cuts teacher-forced text drift by 68.4% at a comparable collision rate. In InstructVLA, TNAS guidance cuts text drift by 40.5% and preserves the exact response on 85.0% of examples alongside a 1.73-unit action-chunk shift. An unchanged rationale therefore cannot certify a safe action, so monitors for reasoning VLAs must verify the predicted action channel directly.

cs.LG · cs.CR · cs.CV · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2512.05511

Rethinking Infrared Small Target Detection: A Foundation-Driven Efficient Paradigm

Chuang Yu, Jinmiao Zhao, Yunpeng Liu, Yaokun Li, Xiujun Shu, Yuanhao Feng, Bo Wang, Xiangyu Yue

原文摘要

While large-scale visual foundation models (VFMs) exhibit strong generalization across diverse visual domains, their potential for single-frame infrared small target (SIRST) detection remains largely unexplored. To fill this gap, we first systematically introduce frozen VFM representations into the SIRST task and propose a Foundation-Driven Efficient Paradigm (FDEP), a general and efficient framework compatible with diverse VFMs and SIRST networks, which improves detection accuracy without additional VFM-related inference overhead. Specifically, a Semantic Alignment and Modulated Fusion (SAMF) module is designed to achieve dynamic alignment and deep fusion of the global semantic priors from VFMs with task-specific features. Meanwhile, to avoid the inference-time overhead introduced by VFMs, we propose a Collaborative Optimization-based Implicit Self-Distillation (CO-ISD) strategy, which enables implicit semantic transfer between the main and lightweight branches through parameter sharing and synchronized backpropagation. In addition, to unify the fragmented evaluation system, we construct a Holistic SIRST Evaluation (HSE) metric that performs multi-threshold integral evaluation at both pixel-level confidence and target-level robustness, providing a stable and comprehensive basis for fair model comparison. Extensive experiments demonstrate that the SIRST detection networks equipped with our FDEP framework achieve state-of-the-art (SOTA) performance on multiple public datasets. Our code is available at https://github.com/YuChuang1205/FDEP-Framework

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.31948

Duplex-MPE: Benchmarking Multi-Party Interaction in Full-Duplex Dialogue

Chengqian Ma, Wenhao Feng, Weixuan Jin, Gaole Dai, Tianyu Xie, Yuexiao Ma, Zhaolu Kang, Xiangyu Zhao 等 10 位作者

原文摘要

Real-time full-duplex speech models can listen while speaking, enabling natural interaction without rigid turn boundaries. Existing benchmarks evaluate turn-taking, interruption handling and multi-round dialogue, but largely centre on a designated user rather than an assistant participating in a shared conversation among several people. We introduce Duplex-MPE to evaluate when such an assistant should answer, remain silent or stop speaking. The benchmark contains 2,000 scenarios with three or four human speakers and one assistant, each paired across explicit and implicit addressing of the same request. Models receive continuous conversation audio without transcripts or supplied turn boundaries. Four scores measure fresh response initiation, answer accuracy, silence preservation and stopping when a human resolves a request. We evaluate eight open-weight speech systems: MiniCPM-o 4.5, Moshi, FLM-Audio, Voila, Freeze-Omni, Realtime-Venus-Audio, Moshika-RL-Seamless and PersonaPlex. MiniCPM-o 4.5 leads on three scored capabilities, while frequent speech from other systems can coexist with inaccurate answers or failures to remain silent. A transcript-based Gemini 3.1 Pro reference responds 64.3 percentage points more often to explicit than implicit requests; paired tests detect no significant response-presence difference for the speech systems.

cs.SD · eess.AS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01710

CST: Lightweight Risk Triage with Source-Grounded Evidence for Web Discussions

Zhihua Wang, Yifeng Gao, Yunhao Feng, Rui Pan, Baiheng Zhao, Zixing Chen, Nan Li

原文摘要

Web communities rely on moderators to catch harmful messages such as insults, hate speech, and scams, but no one can read every message. Existing tools give each message a risk score, which tells moderators where to look but not what to read or how to check it. Yet meaning depends on context: the same reply, "I agree.", can support or oppose removing someone from a group. Moderators therefore need source-grounded evidence, not just scores: a few key messages kept with their original wording and replies. We present CST, a lightweight triage pipeline that first decides where to look, then what to show. It scores risk by pairing character patterns, which capture exact wording, with sentence meaning from a fixed multilingual model, so it handles several languages and runs on an ordinary CPU. It then groups related messages and picks from each group in turn, so a small reading budget covers different parts of a discussion, and links every pick back to the original thread. Across six tasks from five datasets in several languages, CST ranks harmful messages more accurately than a fully fine-tuned multilingual model and, under the same budget, finds more of them than reading by score alone. In a user study, reviewers needed at least 27% less time with CST's grouped evidence while keeping decision accuracy above 94%. CST turns risk scores into evidence that moderators can read quickly and check in context. Code and dataset reconstruction resources will be released at https://github.com/Kirito14IT/CST-MIL.

cs.CL · cs.AI · cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01700

MemHarmBench: Evaluating Cross-Session Prompt Injection in Memory-Persistent AI Agents

Zhihua Wang, Yunhao Feng, Yifeng Gao, Rui Pan, Baiheng Zhao, Zixing Chen, Xingjun Ma

原文摘要

AI agents often rely on persistent memory to retain user preferences and operational rules across sessions. However, this capability introduces a new safety risk: malicious instructions from messages, documents, or Web content can be retained in memory and influence future tasks even after the original attack content is removed. Existing attack success metrics, however, fail to capture who is affected and how attacks translate into downstream harm during otherwise normal task execution. We introduce MemHarmBench, a victim-centric benchmark for evaluating cross-session prompt injection in memory-persistent AI agents. MemHarmBench considers three categories of potential victims: users, workspace/project owners, and external stakeholders. Our evaluation first exposes an agent to malicious instructions and tests whether they are retained in memory. We then remove the attack content and initiate a new session with a legitimate task while preserving the agent's memory. We jointly evaluate whether the agent follows the attacker's intent, completes the legitimate task, and exhibits observable execution anomalies. Across multiple agent systems, we find that cross-session attacks can remain behaviorally stealthy: agents often complete tasks normally and exhibit no detectable anomalies while still making critical decisions aligned with the attacker's intent. By connecting malicious memory retention to downstream decisions and affected parties, MemHarmBench provides a systematic framework for exposing safety risks that conventional attack- and task-success metrics may overlook. We publicly release MemHarmBench at https://github.com/Kirito14IT/MemHarmBench.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35338

Who Pays for Thinking? Billing Hidden Reasoning Tokens in Model Cloud Contracts

Chao Feng, Burkhard Stiller

原文摘要

In token-priced language model services, users can see the prompts they submit and the answers they receive, but may also pay for internal reasoning and candidate answers that are not shown in the final response. This paper develops a three-party service model to study how charging for this intermediate computation shapes providers' decisions and social welfare. Users purchase answers from a model firm that pays an execution cloud, with either supplier controlling the computation budget. The model separates user billing from upstream payments and identifies when metered pricing encourages excessive generation, when fixed service prices restrict valuable computation, and how responsibility for execution costs changes these choices. A LiveCodeBench study measures answer quality, latency, token use, and resource consumption across five model deployments, with numerical billing comparisons for the three covered by archived tariffs. At equal average charges on the reference workload, the calibrated comparisons show that billing structure changes both service participation and the computation budgets suppliers select. For accepted services with different budgets, the measured outcomes define the user-value thresholds at which their welfare ranking reverses. Metered pricing yields higher welfare when the additional inference it induces creates net social value, whereas fixed service pricing is preferable when the costs of that computation outweigh its benefits.

cs.GT

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.2ACL Anthology · ACL 2026

Smarter, not Bigger: Fine-Tuned RAG-Enhanced LLMs for Automotive Hardware-in-the-Loop Testing

Chao Feng, Zihan Liu, Siddhant Gupta, Jan von der Assen

原文摘要

Hardware-in-the-Loop (HIL) testing is essential for automotive validation but suffers from fragmented and underutilized test artifacts. This paper presents HIL-GPT, an industry-deployed retrieval-augmented generation (RAG) system that integrates semantic retrieval with domain-adapted large language models to support test engineers in real-world HIL workflows. The system combines domain-specific embeddings to enable traceable retrieval of test cases and requirements under industrial latency and cost constraints. Through empirical evaluation, we show that compact, domain-adapted models can achieve a favorable trade-off among accuracy, latency, and cost compared to larger general-purpose models, challenging the assumption that larger models are always preferable in industrial NLP systems. An A/B user study further confirms that HIL-GPT improves perceived helpfulness, truthfulness, and satisfaction over general-purpose LLMs.

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.1653ACL Anthology · ACL 2026

From Local to Global: Revisiting Structured Pruning Paradigms for Large Language Models

Ziyan Wang University of North Carolina at Charlotte, Enmao Diao DreamSoul, Qi Le University of Minnesota - Twin Cities, Pu Wang University of North Carolina at Charlotte, Minwoo Lee University of North Carolina at Charlotte, Shu-ping Yeh, Evgeny Stupachenko, Hao Feng Intel 等 9 位作者

原文摘要

Structured pruning is a practical approach to deploying large language models (LLMs) efficiently, as it yields compact, hardware-friendly architectures. However, the dominant local paradigm is task-agnostic: by optimizing layer-wise reconstruction rather than task objectives, it tends to preserve perplexity or generic zero-shot behavior but fails to capitalize on modest task-specific calibration signals, often yielding limited downstream gains. We revisit global structured pruning and present GISP, Global Iterative Structured Pruning, a post-training method that removes attention heads and MLP channels using first-order, loss-based important scores aggregated at the structure level with block-wise normalization. Built on this global importance metric, GISP adopts an iterative schedule, rather than one-shot pruning, stabilizes accuracy at higher sparsity, and mitigates perplexity collapse without requiring intermediate fine-tuning. Importantly, the iterative pruning forms nested subnetworks that support a ”prune-once, deploy-many” workflow. Furthermore, GISP defines structural importance directly with respect to a target loss, making it easy to adapt pruning to task-specific objectives. In this work, we use perplexity for language modeling and a margin-based objective for decision-style tasks. Extensive experiments show that across Llama2-7B/13B, Llama3-8B, and Mistral-0.3-7B, GISP consistently lowers WikiText-2 perplexity and improves downstream accuracy, with especially strong gains at 40–50% sparsity; on DeepSeek-R1-Distill-Llama-3-8B and Qwen3-8B with GSM8K, task-aligned calibration substantially boosts exact-match accuracy.

cs.CL

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