Bin Han, Xintong Ling, Muxia Sun, Hans D. Schotten
A 6G service crosses a chain of stages from different vendors, and its contract binds class-conditioned guarantees at the terminal interface: in the packet relay chain studied here, a priority class is owed a timely-delivery rate and a loss ceiling. We define trustworthiness as conformance of the delivered per-class distribution of outcomes to the contract and develop the measurement structure this forces. Two results fix its form: a discrepancy that treats outcomes interchangeably cannot express contract preferences, and no single expectation-valued score that determines the contracted terminal expectations can be carried from stage to stage once two or more contracted indicators remain independent at some stage. The minimal record is one signed number per independent contracted indicator, which the downstream design data carry to the terminal exactly; classical scalar trust models are the single-indicator case. The terminal deviation splits exactly into per-stage contributions. Transition logs at a stage estimate its contribution without reconstructing its full deployed behavior, a stage at its design behavior is flagged at most at a declared rate whatever traffic it receives, and the estimates bracket each stage's share of the terminal deviation at explicit confidence. In the three-relay chain, a relay that either deprioritizes premium traffic or has degraded capacity violates the same clause with identical contracted evidence: the transition audit localizes the relay in 300 of 300 windows and brackets its share of the deviation, and a diagnostic separates the causes.
Xintong Li, Yufen Lin, Yuhua Wu, Fengran Wang, Minxiao Wang, Rebecca A. Gary, Jacob Kariuki, James Smith 等 12 位作者
Translating population-level exercise guidance into individualized prescriptions requires integrating clinical and wearable data, including diagnoses, medications, functional limitations, and baseline activity, with condition-specific recommendations, a process difficult to scale beyond specialist rehabilitation settings. We developed FITTER, a multi-agent framework that integrates these data with condition-specific guidance to generate personalized FITT-VP prescriptions and applies deterministic safety audit and repair to correct rule-checkable violations. We evaluated FITTER using data from 400 All of Us participants (300 cancer, 100 stroke) against five LLM comparators through computational evaluation, ablation, and structured clinician review. FITTER achieved 98.5% safety-constraint compliance versus 75.8-85.8% for comparators and showed greater quantitative dosing precision and guideline alignment. Cancer-cohort ablation identified safety audit and repair as the largest contributor to safety-constraint compliance. Clinicians reviewed 80 prescriptions across safety, personalization, instruction concreteness, anticipated effectiveness, and clinical acceptability. FITTER provides a clinician-facing approach integrating clinical and wearable data, guidance, and explicit safety constraints.
Chuanrui Hu Qihoo 360, Xingze Gao, Zuyi Zhou Institute of automation, Chinese academy of science, Chinese Academy of Sciences, Dannong Xu Institute for Computer Science, Artificial Intelligence and Technology, Yi Bai, Xintong Li Harbin Engineering University, Hui Zhang, Tong Li Shanda Group 等 11 位作者
Large Language Models (LLMs) are increasingly deployed as long-term interactive agents, yet their limited context windows make it difficult to sustain coherent behavior over extended interactions. Existing memory systems for LLMs often store isolated records and retrieve fragments, limiting their ability to consolidate evolving experience and resolve conflicts. We introduce EverMemOS, a self-organizing memory operating system that implements an engram-inspired lifecycle for computational memory. First, Episodic Trace Formation converts dialogue streams into MemCells that capture episodic traces, atomic facts, and time-bounded foresight. Second, Semantic Consolidation organizes MemCells into thematic MemScenes, distilling stable semantic structures and updating user profiles. Finally, Reconstructive Recollection performs MemScene-guided agentic retrieval to compose the necessary and sufficient context for downstream reasoning. Experiments on LoCoMo, LongMemEval, and PersonaMem-v2 show that EverMemOS significantly outperforms state-of-the-art methods on memory-augmented reasoning tasks.
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cs.CL · cs.AI
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Rajen Chatterjee Apple, Xintong Li Apple, Paisarn Charoenpornsawat, Allen Lee Apple
Hallucinations in machine translation (MT)—outputs that may be fluent yet unfaithful to the source content—remain a critical obstacle. They hinder the reliable deployment of MT systems in real-world applications. Despite growing attention to this phenomenon, progress has been constrained by the lack of large-scale, high-quality benchmarks dedicated to hallucination detection. We introduce HAT (Hallucination Annotation for Translation), a novel dataset designed to advance research on this problem. HAT comprises 350,959 span-level annotated samples across 38 language pairs, with approximately 8,000–10,000 samples per pair partitioned into training, development, and test sets. Annotations were produced by professional translators under rigorous quality control protocols to ensure reliability. We provide a detailed analysis of hallucination distributions and establish benchmark performance using a diverse set of baselines, including automatic MT evaluation metrics as well as large language models. By providing the first large-scale, systematically annotated resource for hallucination detection in MT, HAT enables the development of more faithful translation models and lays the groundwork for future research on building trustworthy machine translation systems.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Chuhan Wang University of Notre Dame, Xintong Li, Jennifer Yuntong Zhang, Junda Wu University of California, San Diego, Chengkai Huang, Lina Yao University of New South Wales, Julian McAuley University of California, San Diego, University of California, San Diego, Jingbo Shang University of California, San Diego
Multimodal large language models often struggle with faithful reasoning in complex visual scenes, where intricate entities and relations require precise visual grounding at each step. This reasoning unfaithfulness frequently manifests as hallucinated entities, mis-grounded relations, skipped steps, and over-specified reasoning. Existing preference-based approaches, typically relying on textual perturbations or answer-conditioned rationales, fail to address this challenge as they allow models to exploit language priors to bypass visual grounding. To address this, we propose SceneAlign, a framework that leverages scene graphs as structured visual information to perform controllable structural interventions. By identifying reasoning-critical nodes and perturbing them through four targeted strategies that mimic typical grounding failures, SceneAlign constructs hard negative rationales that remain linguistically plausible but are grounded in inaccurate visual facts. These contrastive pairs are used in Direct Preference Optimization to steer models toward fine-grained, structure-faithful reasoning. Across seven visual reasoning benchmarks, SceneAlign consistently improves answer accuracy and reasoning faithfulness, highlighting the effectiveness of grounding-aware alignment for multimodal reasoning.
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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