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版本更新 · v2arXiv:2611.11262

Improving the Trifference Lower Bound via Non-Perfect Hashing

Chen Yuan, Ruiqi Zhu

原文摘要

We strictly improve the Körner-Marton lower bound $\frac14\log \frac{9}{5}$ on the asymptotic rate of trifferent codes. We obtain this improvement by constructing non-perfect $(9,3)$-hash codes with rate above $\log(9/5)$ and then applying the Tetra-code construction, which gives $R_3\ge\frac14R_{9,3}$. More generally, for every $q\ge4$, we prove $R_{q,3}>B_{q,3}+\frac1{250000}$. We also extend the method to $(p,2,3)$-list recovery. For every $q\ge4$ and $0\le p<\frac{q-2}{12(q-1)}$, we prove $R_q^{\mathrm{LR}}(p)>B_q^{\mathrm{LR}}(p)$. Here $B_{q,3}$ and $B_q^{\mathrm{LR}}(p)$ denote the respective probabilistic lower bounds. For every fixed $0\le p<1/18$, the rate improvement is bounded below by a positive constant independent of $q\ge4$. For every fixed $0\le p<1/12$, we also obtain a positive rate improvement independent of $q$ for all sufficiently large $q$.

cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16447

A $Θ(n)$-Multiplicative Improvement of the Gilbert-Varshamov Bound for Linear and Quantum Codes

Chen Yuan, Ruiqi Zhu

原文摘要

The Gilbert-Varshamov (GV) bound is a fundamental benchmark for the existence of error-correcting codes. For non-linear $q$-ary codes of code length $n$, its finite length form is known to admit a multiplicative improvement of order $Θ(n)$. In contrast, for $q$-ary linear codes of arbitrary rates, the best previously known general improvement was only of order $Θ(\sqrt n)$. This leaves open whether the algebraic constraint of linearity inherently prevents a linear-order refinement. We show that it does not. For every fixed prime power $q$ and relative distance $0<δ<1-1/q$, we obtain a $Θ(n)$-multiplicative improvement of the GV bound for $q$-ary linear codes of arbitrary rate, matching the corresponding result for nonlinear codes. Our proof is based on a simple change of viewpoint. Rather than requiring the initial random linear construction to contain no low-weight vectors, we allow $O(n)$ low-weight points, show that they are independent in the underlying projective space, and then eliminate all of them simultaneously at the cost of a single dimension. The key probabilistic ingredient is an exponential bound on nontrivial linear dependencies among low-weight vectors. We further show that this approach can be extended to quantum codes despite the symplectic self-orthogonality constraint. We obtain a $Θ(n)$-multiplicative improvement of the quantum GV bound for every fixed $0<δ<1-1/q^2$ satisfying $H_{q^2}(δ)<1/2$. Thus, the same $Θ(n)$ finite length improvement is applicable to both general linear structure and symplectic self-orthogonality.

cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14700

Probe What-If Worlds Before You Ship: Persona Simulation for Real-World Decision Making

Julie Zhu, Jianheng Hou, Zhixu Silvia Tao, Shi Bo, Yilan Fan, Yixuan He, Zhiyuan Hu, Shirley Huang 等 24 位作者

原文摘要

User research and system testing reveal whether products and services meet user needs, yet recruiting specialized participants, designing controlled what-if comparisons, and repeating studies as designs evolve often bottleneck design iteration and data-driven decision-making. We present a framework and execution infrastructure that translates real-world industry inquiries into controlled experiments with simulated users. Our methodology formally defines the target cohort, experimental stimuli, and decision-relevant metrics, while the pipeline supports large-scale concurrent runs over cohort-stimulus-environment combinations (survey, web, chatbot, and OS app), auditable trial-level logging, cohort-level outcome aggregation, and cross-cohort and cross-variant statistical reports. Across twelve industry partner cases spanning five domains, we show how constructing hard-to-reach or what-if persona cohorts supports exploratory studies and distills simulated responses into testable design hypotheses. Cross-model comparisons on selected cases largely preserve primary conclusions, whereas perturbing or ablating persona attributes alters service outcomes, effect sizes, and inter-group differences, indicating that grounding target cohorts in detailed personas materially shapes simulation results. Together, these studies show that persona-enabled agents can test specified cohorts at design speed, diagnose systems under test, and deliver directional evidence that focuses problem definition, steers design and strategic decisions, and guides further study.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13968

Advancing a Global Framework for AI Safety and Governance for the Well-being of Humanity

Zeng Yi, Seán Ó hÉigeartaigh, Wang Zhengqi, Lu Enmeng, Chen Yu, Cao Gongce, Guo Xiaoyang, Kang Yanrong 等 76 位作者

原文摘要

The Report "Advancing a Global Framework for AI Safety and Governance for the Well-being of Humanity" is an important outcome of the AI Safety and Governance Program of World Internet Conference Specialized Committee on Artificial Intelligence in 2025. This report provides a systematic review of current global explorations and practices in AI safety and governance, identifies key issues that need to be addressed, and draws upon multilateral governance experiences from other domains. Centered on mechanism design, it explores how to build a global AI safety and governance system that ensures safety and security, promotes inclusiveness, clarifies responsibilities and accountabilities, strengthens effective coordination, and ensures authoritative and efficient governance. Guided by the principles of multilateralism and the vision of building a community with a shared future for humankind, the report proposes institutional measures and policy recommendations to establish a United Nations-centered global AI safety and governance framework. It aims to serve as a reference for governments, international organizations, enterprises, and research institutions. The report is to contribute insights and momentum to advancing people-centered and beneficial AI governance worldwide, ensuring that AI technologies truly serve the well-being of all humanity.

cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13615

Synapse-Control: Making Agent Experience Actionable with Lightweight Runtime Control

Zhiyuan Huang, Shengan Zheng, Chenghu Yang, Song Zhang, Lingyun Li, Chen Yu, Weiquan Huang, Linpeng Huang

原文摘要

Large language model agents reuse execution experience to improve reliability on complex tool-based tasks. Systems such as Sentry call large models to generate failure diagnoses before retrieving advice, adding inference cost to experience reuse. We present Synapse-Control, an agent runtime whose lightweight control plane selects advice and determines when to send it to the task-executing model. Rules shortlist relevant advice, and a lightweight 1.7B classifier fine-tuned on teacher-labeled state-suggestion pairs scores those candidates instead of generating a diagnosis. Micro-contracts encode reusable interface checks, such as required arguments or valid options, without prescribing complete task programs. A Prompt Value Table records earlier guidance and feedback to limit repeated suggestions through cooldown and token-budget checks. Across 1,308 attempts per method in AppWorld and WebShop, Synapse-Control achieves 270 successes (29.8% more than Base), replaces 4,987 generative diagnosis calls, and reduces auxiliary control time relative to Sentry by 50.2% and 51.0%, respectively.

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12762

Sage-Taiyi：一种面向科学发现的元认知架构

Sage-Taiyi: A Metacognitive Architecture for Scientific Discovery

Weida Wang, Benteng Chen, Yigeng Jiang, Shuchen Pu, Weiping Shen, Zichao Liu, Yulin Su, Zitong Wang 等 90 位作者

AI 速读 基于摘要

作者提出一种受元认知启发的AI科学家架构，通过显式监控与控制来调整研究目标并选择可复用推理方法，并在自建基准与外部基准及若干专家协作案例中报告了结果。

cs.AI

arXiv ↗PDF ↗
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We introduce Sage-Taiyi, a metacognition-inspired architecture that uses accumulated scientific understanding to guide what to investigate next and how to investigate it. Through explicit monitoring and control, the system revises its understanding of evidence, assumptions, and unresolved questions, then uses these assessments to adapt research targets and select reusable reasoning methods. On Taiyi-Bench, an AI co-scientist ideation benchmark comprising 265 tasks from 50 papers across ten disciplines, Sage-Taiyi ranks first on all eight metrics of historical rediscovery and open-ended ideation, achieving an Overall score of 54.0 compared with 47.9 for the strongest baseline, AI Scientist-v2. It also performs well on two external scientific ideation benchmarks. Five expert-guided collaborations further connect its proposals to experimental and computational validation across chemistry, materials science, and engineering. In chemistry, reaction-condition optimization raises the yield of cobalt-catalyzed carbonylation from trace amounts to 84% and photocatalytic allylic amination from 13% to 69%. These results support metacognitive regulation as an approach to scientific ideation and its progression toward practical validation.

新论文 · v1arXiv:2611.12434

Frontier AI Safety and Governance 2026: Risk Understanding and Coordinated Governance

Zeng Yi, Seán Ó hÉigeartaigh, Cao Gongce, Lu Enmeng, Chen Yu, Han Kaiyu, Li Kai, Wang Zhengqi 等 110 位作者

原文摘要

Frontier AI Safety and Governance 2026: Risk Understanding and Coordinated Governance is a key outcome of the AI Safety and Governance Program of World Internet Conference Specialized Committee on AI in 2026. Drawing on the latest developments in frontier AI capabilities and applications, the report systematically examines the evolving risk landscape, analyzing key risk areas, their severity and response priorities. Building on a review of existing global governance practices and an assessment of frontier AI governance readiness, it identifies key governance gaps and weaknesses and proposes directions and practical mechanisms for coordinated responses. The report provides a reference for government departments, international organizations, businesses, research institutions and other stakeholders. It aims to strengthen the international community's understanding of and consensus on frontier AI risks, enhance preparedness and collective response capacity, and advance the safe, reliable and responsible development of frontier AI to better serve the common well-being of humanity.

cs.CY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.20981

CaLR: Causal Latent Revision for Robust Diffusion Reasoning

Wei Cai, Jian Zhao, Yuchen Yuan, Xuelong Li

原文摘要

Autoregressive (AR) models suffer from local greediness, while diffusion language models (DLMs) often lack the strict causal structure required for reasoning. To combine the advantages and overcome the drawbacks of the dual, we propose Causal Latent Revision (CaLR), a framework that reformulates reasoning as constrained latent optimization. By adopting a causal topology matrix (CTM) from an expert model and implicit differentiation, CaLR performs gradient-guided ``thought revision" to enforce logical consistency, enabling dynamic self-correction of intermediate steps during parallel generation. Empirically, CaLR achieves SOTA DLM performance on complex benchmarks, surpassing strong AR baselines and demonstrating superior robustness in constrained tasks like Sudoku.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2603.02999

OneRanker: Unified Generation and Ranking with One Model in Industrial Advertising Recommendation

Dekai Sun, Yiming Liu, Jiafan Zhou, Xun Liu, Chenchen Yu, Yi Li, Jun Zhang, Huan Yu 等 9 位作者

原文摘要

The end-to-end generative paradigm is revolutionizing advertising recommendation systems, driving a shift from traditional cascaded architectures towards unified modeling. However, practical deployment faces three core challenges: the misalignment between interest objectives and business value, the target-agnostic limitation of generative processes, and the disconnection between generation and ranking stages. Existing solutions often fall into a dilemma where single-stage fusion induces optimization tension, while stage decoupling causes irreversible information loss. To address this, we propose OneRanker, achieving architectural-level deep integration of generation and ranking. First, we design a value-aware multi-task decoupling architecture. By leveraging task token sequences and causal mask, we separate interest coverage and value optimization spaces within shared representations, effectively alleviating target conflicts. Second, we construct a coarse-to-fine collaborative target awareness mechanism, utilizing Fake Item Tokens for implicit awareness during generation and a ranking decoder for explicit value alignment at the candidate level. Finally, we propose input-output dual-side consistency guarantees. Through Key/Value pass-through mechanisms and Distribution Consistency (DC) Constraint Loss, we achieve end-to-end collaborative optimization between generation and ranking. The full deployment on Tencent's WeiXin channels advertising system has shown a significant improvement in key business metrics (GMV - Normal +1.34\%), providing a new paradigm with industrial feasibility for generative advertising recommendations.

cs.IR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.06157

Neural Stochastic Forecasting for Lateral Molecular Diffusion Simulations

Jacob Ede Levine, Mario Hidalgo-Soria, Chen Yun Wen, Yun Lyna Luo, Sai Chandra Kosaraju

原文摘要

Predicting the long-time diffusion of membrane proteins is challenging because molecular dynamics (MD) simulations are computationally expensive, while experimental single-particle tracking is often limited by finite observation times. Here, we introduce a neural stochastic (NS) model for forecasting the collective diffusion of membrane proteins from coarse-grained MD trajectories. The model combines a transformer-based representation of protein configurations, recurrent networks operating at multiple temporal scales, and a stochastic latent process with learned memory. A cross-attention decoder then generates correlated protein displacements while accounting for the instantaneous system geometry and periodic boundary conditions. We evaluate the model in two membrane systems: seven interacting PIEZO channels and a mixed system containing seven PIEZO and ten TREK channels. For the seven PIEZO system, the NS model reproduces the anomalous diffusion exponent measured in MD simulations $α\sim 1$, substantially improving on a previously developed variational autoregressive model that exhibits superdiffusive behavior. The model also generates trajectories extending to 40,000 frames and captures the distinct diffusion characteristics of PIEZO and TREK populations in the mixed system. Comparisons of time-averaged mean-square displacements, Van Hove displacement distributions, and non-Gaussian parameters show that the model preserves key statistical features of the simulated dynamics. These results establish neural stochastic modeling as a promising approach for generating long trajectories of multi-protein membrane diffusion and extending the statistical analysis of molecular dynamics simulations.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v7arXiv:2606.03371

See, Foresee, Act: Intent-Aware World Modeling for Proactive Retail Assistance

Honghui Zhang, Anna Min, Chenmeinian Guo, Yujia Zhang, Zezhou Zhang, Yichen Yu, Guanyu Liu, Yongming Qin 等 12 位作者

原文摘要

World models allow agents to anticipate action consequences, but proactive assistance also requires interpreting incomplete observations and deciding whether to intervene before a user asks for help. We propose See-Foresee-Act and instantiate it as the Proactive Intent World Model (PIWM) for retail assistance. See preserves role-relevant observable evidence alongside AIDA-BDI customer states; Foresee predicts one-step consequences for candidate actions; Act explicitly compares those consequences, including non-intervention. GuidanceSalesBench supplies predominantly synthetic supervision linking observations, states, candidate outcomes, and decisions. On 509 Full test examples, consequence-based selection achieves 63.65% decision accuracy, compared with 45.38% for direct decision output, an improvement of 18.27 percentage points. These results support candidate-consequence comparison for decision reasoning in this retail task. Code and data will be released after consent verification and upon publication.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.16867

GoodServe: Globally-coordinated Routing of Agentic LLM Inferences for High Goodput

Boxiao Du, Boning Huangfu, Yizhou Luo, Chen Chen, Zijun Li, Minchen Yu, Xiaoyi Fan, Minyi Guo

原文摘要

Agentic applications consume LLM inferences as complete outputs: a code patch or database query is useful only after its last token arrives. Their performance is thus measured by end-to-end deadline satisfaction and goodput, the number of inferences completed within their deadlines. In heterogeneous GPU pools, existing greedy routers favor the fastest or least-loaded instance, often wasting powerful GPUs on requests that could meet their deadlines on weaker ones. Maximizing goodput instead requires globally coordinated routing, informed by uncertain output lengths and dynamic instance performance. We present GoodServe, a goodput-oriented routing system that addresses these challenges through a predict-and-rectify design. A task-gated mixture-of-experts predictor estimates request output lengths, while an EMA-smoothed black-box estimator tracks queuing delays and per-token speeds, accounting for prefix-cache reuse. A just-enough policy routes each request to the weakest instance capable of meeting its deadline and rejects requests that cannot finish on time. Periodic risk checks trigger migration of endangered requests using token IDs, at 7-15 times lower cost than KV-cache transfer. On a four-GPU heterogeneous testbed with three agentic benchmarks, GoodServe achieves the highest goodput across all evaluated SLO levels, improving over the best existing router by up to 31.0%. Its routing overhead remains 5 ms per request at 10,000 requests per second across 512 simulated instances.

cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01511

OpenULF: A Modular Toolkit Enables Data-Driven Ultra-Low-Field MRI with both Classical and Quantum Sensors

Hanhan Wu, Yuchen Yuan, Weiwen Jiang

原文摘要

Ultra-low-field (ULF) Magnetic Resonance Imaging (MRI) offers a portable, low-cost alternative to conventional scanners by operating at field strengths below 10 mT. At these fields, two sensor technologies compete: quantum SQUID (Superconducting Quantum Interference Device) sensors, which provide high sensitivity but require cryogenic cooling, and classical induction coils, which are inexpensive and room-temperature but produce weaker signals. With conventional physics-based imaging, both suffer from low signal-to-noise ratios (SNR). Data-driven reconstruction has overcome similar limits in related inverse problems, but its use for ULF-MRI has been hindered by a lack of open datasets, benchmarks, and flexible simulators. To close this gap, we present OpenULF, a modular, open-source simulation toolkit that generates paired training data and supports end-to-end machine-learning (ML) reconstruction. OpenULF integrates a Bloch-equation-validated physics simulator, calibrated SQUID and coil receiver models, reconstruction ML networks, and an automated design-space exploration engine. Architecturally, a receiver abstract base class, a typed HDF5 contract that decouples physics from learning, and a resumable sweep engine make sensors, models, and datasets independently substitutable. Using OpenULF, we train 774 models across four anatomical datasets and conduct three design-space explorations. The effectiveness of ML reconstruction depends strongly on the dataset and the sensor. We identify a SQUID-to-coil crossover near 450 $μ$T and demonstrate that sensor hardware and ML model capacity can be traded off during system design. Sensor and reconstruction choices must therefore be co-designed: the best sensor is set by the dataset and field strength, not by SNR.

eess.IV · cs.CV · physics.med-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39256

On Proximity Gaps for One-Point Algebraic Geometric Codes

Songsong Li, Chaoping Xing, Chen Yuan, Ruiqi Zhu

原文摘要

Proximity gaps and correlated agreement underpin the soundness of interactive oracle proofs. We establish these properties for general one-point algebraic geometry (AG) codes, extending corresponding results for Reed-Solomon codes. For codes of length $n$ over $\mathbb F_q$ with designed distance $n-m$, write $ρ=m/n$ and fix $ρ\in(0,1)$. For polynomial curves of degree at most $\ell$ and any $0<\varepsilon<1$, we prove correlated agreement with multiplicative slack $\varepsilon$ and soundness $O(\ell(\ell+1)/(\varepsilon q))$ whenever $e\le(1-\varepsilon)(n-m)/2$, with no additional genus penalty. This gives asymptotically good AG code families from Garcia--Stichtenoth tower of unbounded length over a fixed field with soundness bounds independent of $n$, beyond the RS restriction $n\le q$. We also establish mutual correlated agreement up to radius $(1-\sqrtρ-η)n$, with soundness $O(\ell n/(η^3q))$ for $0<η<1-\sqrtρ$. Our half designed distance proof combines auxiliary divisors, simultaneous Berlekamp-Welch interpolation, and error-incidence counting to obtain a common polynomial representation without a Polishchuk-Spielman divisibility lemma. Finally, two counterexamples on the Garcia-Stichtenoth tower show limitations beyond half the minimum distance, at radii below the Johnson bound and with positive slack. The first requires a positive constant soundness lower bound independent of $q$ and $n$, precluding a uniform $O(1/q)$ guarantee throughout the Johnson regime. The second fixes the field and lets the relative gap above half distance tend to zero while ruling out correlated agreement for every soundness parameter $σ<1$, with positive limiting rate and relative error radius.

cs.IT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.31066

Dual Exam: Controlled Evaluation of Multimodal Agents and World Models

Zhefan Rao, Meng Chu, Xuan Billy Zhang, Jingfan Chen, Guohao Cai, Runyi Li, Shiyi Du, Xu Huang 等 92 位作者

原文摘要

World Models (WMs) can support Multimodal Agents (MAs) through simulated experience, while Agent trajectories can inform World Model refinement. Evaluating this interaction requires separate evidence about action selection and state evolution. We introduce Dual Exam, which tests Agents under fixed environment response mechanisms and World Models under fixed action interventions across physical, digital, social, and scientific domains. The Agent track compares 10 configurations on 1,088 items in 15 reporting categories. The World Model track contains 400 cases across eight scenarios; 15 configurations have full coverage and one covers six scenarios. GPT 6 Astra leads the case-weighted Agent Index at 31.3, and GPT-Image-2 leads the full-coverage WM Overall at 21.7. These scores use different tasks and metrics and are not directly comparable. A 150-case pilot of one coupled pairing records zero task successes and motivates component-level diagnosis. Results reveal uneven task performance and failures to preserve task-relevant state. Their value for predicting coupled success or sustained reciprocal learning remains to be established.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2512.05459

PrivCode: When Code Generation Meets Differential Privacy

Zheng Liu, Chen Gong, Terry Yue Zhuo, Kecen Li, Weichen Yu, Matt Fredrikson, Tianhao Wang

原文摘要

Large language models (LLMs) have presented outstanding performance in code generation and completion. However, fine-tuning these models on private datasets can raise privacy and proprietary concerns, such as the leakage of sensitive personal information. Differentially private (DP) code generation provides theoretical guarantees for protecting sensitive code by generating synthetic datasets that preserve statistical properties while reducing privacy leakage concerns. However, DP code generation faces significant challenges due to the strict syntactic dependencies and the privacy-utility trade-off. We propose PrivCode, the first DP synthesizer specifically designed for code datasets. It incorporates a two-stage framework to improve both privacy and utility. In the first stage, termed "privacy-sanitizing", PrivCode generates DP-compliant synthetic code by training models using DP-SGD while introducing syntactic information to preserve code structure. The second stage, termed "utility-boosting", fine-tunes a larger pre-trained LLM on the synthetic privacy-free code to mitigate the utility loss caused by DP, enhancing the utility of the generated code. Extensive experiments on four LLMs show that PrivCode generates higher-utility code across various testing tasks under four benchmarks. The experiments also confirm its ability to protect sensitive data under varying privacy budgets. We provide the replication package at the code repository: https://github.com/Liuzzyg/PrivCode.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34452

AuRA: From Atoms to Bits through Autonomous Robot Interaction

Xinchen Yu, Yoonkyo Jung, Haoran Chen, Zikui Cai, Aayush Talreja, Seungjae Lee, Hyunjin Kim, Furong Huang

原文摘要

Simulation enables robot-learning experiments at scale, but acquiring physics-informed digital twins often requires human manipulation and manual annotation of recorded interactions. To reduce this reliance on human effort, we present AuRA, a system that automates robot interaction and data preparation for digital-twin reconstruction. AuRA combines execution-aware grasp selection, gripper-motion measurements derived from robot states, and registered pre-interaction geometry and appearance priors. These choices enable autonomous collection, avoid reliance on visual gripper tracking, and recover object information obscured by the robot. Across 13 deformable objects, AuRA reduces active human labor and wall-clock collection time per successfully reconstructed asset by 88.5% and 55.4%, respectively. Relative to the human-collected PhysTwin baseline, AuRA improves seven of nine metrics for future prediction under known gripper motion, including a 28.1% reduction in one-way $L_1$ Chamfer distance. It also achieves similar mean silhouette IoU and Chamfer distance under the evaluated unseen interactions. These results highlight the potential of autonomous robot interaction to support more efficient digital-twin acquisition with comparable reconstruction quality.

cs.RO

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.13ACL Anthology · ACL 2026

TaoType: Predicting Fine-Grained Typing Intent for Faster Search

Yipeng Yu, Yichen Yuan, Chengxiao Feng, Xu Liu

原文摘要

“Is the user’s current query input exactly what they intend to search for?” Our work aims to answer this question by determining, at each typing, whether the current query is complete. If so, a search is implicitly triggered in advance without waiting for user confirmation. This approach reduces response time and enhances the user search experience. Specifically, we propose TaoType, a client-side framework that introduces innovation in data sampling, feature selection, model design and training, and online strategy. Experiments in a leading mobile shopping application named Taobao validate its effectiveness, achieving offline precision/recall/accuracy of 0.7936/0.8196/0.7742, respectively, and decreasing online response time by 640.51±93.65 milliseconds, which is of great benefit to the search system. Unlike prior work that focuses on optimizing server-side engineering pipelines or simplifying ranking models, our method leverages client-side typing behavior for real-time early prediction, utilizing on-device computation to gain response time reducing. To the best of our knowledge, our work is the first to identify and address this problem. This work also introduces App Intelligence, a new paradigm for enhancing mobile applications by integrating on-device AI to boost business value and user experience.

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.1781ACL Anthology · ACL 2026

CAMEC: Complexity-Aware Multi-Expert Collaboration for Reliable Chinese Medical Question Answering

Yukang Wu, Xiyuan Jia, Jiayi Wu, HongChen Yu, Yuhan Qiu, Wu Guohua Hangzhou Dianzi University

原文摘要

Large language models (LLMs) are promising for medical question answering (QA) but remain unreliable in Chinese clinical settings due to hallucinations, weak factual grounding, and difficulty handling clinically complex cases. We propose CAMEC (Complexity-Aware Multi-Expert Collaboration), a framework that combines hierarchical medical adaptation with complexity-aware expert routing for reliable Chinese medical QA. We adopt a three-stage LoRA-based supervised fine-tuning pipeline for domain adaptation, instruction following, and clinical reasoning. At inference, CAMEC routes each query by predicted complexity and selectively recruits three experts: an internal chain-of-thought (CoT) expert, a retrieval-augmented expert over a dense medical vector database, and a knowledge graph (KG) expert over a structured medical knowledge base. An LLM-as-a-Judge module evaluates and critiques expert reports, iteratively refining them into a consensus answer. Experiments on four Chinese medical benchmarks show that CAMEC consistentlyoutperforms strong general and medical LLM baselines, achieving 78.86% (CMExam), 84.15% (MedQA-CN), 78.51% (CMMLU-Med), and 74.40% (CMB-exam), with consistent absolute improvements over the previous state-of-the-artHuatuoGPT-o1-7B across all benchmarks. The complexity-aware router reduces expert invocations and inference cost, making CAMEC both highly effective and computationally efficient.

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.1933ACL Anthology · ACL 2026

Where Paths Split: Localized, Calibrated Control of Moral Reasoning in Large Language Models

Chenchen Yuan Technical University of Munich, Zheyu Zhang Technical University of Munich, Gjergji Kasneci Technische Universität München and University of Tuebingen

原文摘要

Large language models often display heterogeneous moral preferences across settings. We study inference-time steering toward a desired ethical framework while preserving general competence. We present Convergent-Divergent Routing, which traces and edits minimal branch points inside transformer blocks where ethical-framework-related pathways first converge and then diverge. Gating non-target branches at these loci blocks the downstream propagation while leaving upstream computations intact. We find that this intervention alone increases targeted ethical-framework reasoning. To achieve fine-grained control, we adapt Common Spatial Patterns to the residual stream and extract, for each branch-point layer, a pair of directions that discriminate between utilitarian and deontological frameworks. We then introduce Dual Logit Calibration, a closed-form, minimum-\ell_2-norm update that moves the residual within this two-dimensional subspace so the resulting directional projections align with user-specified preference weights. Experiments on real-life moral dilemmas show that our method reliably achieves preference calibration and largely preserves general capabilities, outperforming recent baselines while providing an interpretable mechanism.

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.406ACL Anthology · ACL 2026

Visual Attention Reasoning via Hierarchical Search and Self-Verification

Wei Cai, Jian Zhao, Yuchen Yuan China Telecom, Tianle Zhang China Telecom, Ming Zhu China Railway Rolling Stock Corporation Limited, Haichuan Tang CRRC ACADEMY, Xuelong Li China Telecom and Northwestern Polytechnical University

原文摘要

Multimodal Large Language Models (MLLMs) frequently hallucinate due to their reliance on fragile, linear reasoning and weak visual grounding. We propose Visual Attention Reasoning (VAR), a reinforcement learning framework that reformulates reasoning as a hierarchical search with self-verification. VAR enforces traceable evidence grounding by generating explicit bounding boxes, guided by a novel reward function combining geometric precision and semantic sufficiency. Furthermore, it replaces linear Chain-of-Thought with a tree-search policy capable of backtracking to correct logical errors. Theoretical analysis validates the framework’s reliability, and extensive experiments demonstrate that VAR significantly outperforms state-of-the-art methods on complex hallucination and safety benchmarks.

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