Artificial Intelligence
Integrating the Analytic Hierarchy Process with Large Language Models for Transparent Multi-Criteria Decision-Making
Han Zhiguang, Farah Benamara and Pascale Zaraté
LLMs are increasingly employed in a wide range of decision-making tasks. However, the opacity of their internal
reasoning makes it difficult to validate or interpret their outputs, and the need for interpretability becomes especially critical in high-stakes settings. This study examines the decision-making capabilities of LLMs through the Analytic Hierarchy Process (AHP), a classical and widely used multicriteria decision-making framework. We construct a new annotated
benchmark based on AHP and propose the first end-to-end approach that enables LLMs to perform the complete AHP workflow. Experiments in real-world decision problems in the legal and higher-education ranking domains show that our method significantly improves
alignment with expert judgments.
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Artificial Intelligence
Coverage-Aware Virtual IMU Augmentation for Low-Resource Human Activity Recognition
Jiayuan Gao, Yingwei Zhang, Ziyao Tang et al. (+4 more)
IMU-based human activity recognition (HAR) enables continuous,
privacy-friendly monitoring of daily activities using wearable sensors. However, building reliable HAR models that generalize across diverse users and real-world conditions requires large amounts of labeled IMU data, which are expensive and difficult to collect. Existing approaches mainly rely on augmentation or synthesis to expand available data, but indiscriminately adding virtual samples may provide little new coverage and introduce unreliable supervision. To overcome these challenges, we propose a novel coverage-aware virtual IMU augmentation framework that decides where to supplement real data, how to generate and select virtual candidates, and how strongly to weight them during training. Specifically, we select diversity and scarcity anchors in a learned sensor
embedding space, convert anchor dynamics into
prompts, and generate virtual IMU candidates for each anchor. We then rank candidates by a selection cost combining anchor proximity and label consistency, and incorporate the selected candidates into HAR training with reliability-based weights. Experiments on public HAR
benchmarks show that our method consistently improves recognition performance over competitive baselines, and ablation studies confirm the effectiveness of the proposed framework design.
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All authors Jiayuan Gao, Yingwei Zhang, Ziyao Tang, Yuejia Ma, Yuanzhe Chen, Shuchao Song, Boshi Tang
Artificial Intelligence
Turn-level Multiscale Density Ratio Estimation for LLM Agents
Zishuo Zhao, Kai Chen, Ao Li et al. (+1 more)
With the rapid development of Large
language model (LLM),
agent systems enhanced by LLMs show huge potential in being able to deal with complex tasks, especially involving multi-step thinking or interaction with tools. For applying LLM techniques with a well-designed
agent paradigm, post-training of LLM in multiple
agent scenarios is necessary to achieve better performance. Among the variable post-training techniques,
alignment methods such as PPO, DPO, DIL, and GRPO become popular because many papers show a significant positive impact on the model's performance by punishing negative samples while keeping acceptable training complexity. However, most
alignment methods address simple single-turn tasks, and there remains room for improvement for complex multi-turn tasks. We propose Turn-level Multiscale Density Ratio Estimation (tlm-DRE), which assigns different weights on corresponding turns and proposes asymmetric
token-level training based on the positive-negative space gaps across multiple turns of tasks. The results of the experiment on a wide range of
agent benchmarks show that the proposed method performs competitively compared to traditional
alignment methods. The proposed training method enables LLMs to perform robustly in multi-turn
reasoning tasks with both in-domain and out-of-domain conditions.
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All authors Zishuo Zhao, Kai Chen, Ao Li, Yuan Liu
Artificial Intelligence · Cross-listed from cs.LG
TAME: Token Attribution and Masking for Emergent misalignment
Md Rayhanul Masud and Md Rizwan Parvez
Fine-tuning an aligned
language model on narrow, flawed data can induce harmful behavior far outside the training domain, known as emergent misalignment (EM). Prior work has localized EM in model weights, activations, and training documents, but it remains unclear which training
tokens carry the relevant fine-tuning signal. We introduce TAME (
Token Attribution and Masking for Emergent Misalignment), a three-stage framework:
token attribution scores how strongly the fine-tuning update raises each response
token's likelihood, using forward passes through a released LoRA adapter; signal characterization finds patterns among high-attribution
tokens; and causal validation tests them by attribution-guided loss masking. On released EM organisms and a 6,849-example medical-advice split, attribution is concentrated (the top 5% of
tokens hold 32% of the mass) and, in Llama, depleted for medical vocabulary but enriched for a register of unwarranted certainty, even after controlling for
token rarity. Masking high-attribution
tokens during fresh fine-tuning cuts EM by 23x in Llama and 36x in Qwen, with the perplexity cost concentrated on the targeted register rather than on medical content; an equal random mask leaves EM unchanged. In Llama, the attribution pattern suggests that EM-relevant signal lies more in how confidently flawed content is expressed than in its domain vocabulary; the causal masking effect itself holds across both model families.
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Artificial Intelligence
Beyond Episodic AI: Cognitive Field Networks for Biologically Inspired Persistent Cognition
Byung Gyu Chae
Cognitive Field Theory describes cognition as
memory-dressed collective dynamics, in which slow relaxation modes support a persistent cognitive field. By connecting non-Markovian
memory dynamics to discrete recurrent state
evolution through an extended state, we develop a Cognitive Field Network (CFN), a recurrent
Transformer architecture with hidden-field re-entry, \( \Phi_{n+1} = F_\theta(X_{n+1},\Phi_n) \). Rather than prescribing an explicit
memory operation, the CFN allows the full
token-resolved hidden field to re-enter the next computational cycle. We find that learning organizes this pathway into a functional channel for persistent, content-dependent internal states, with temporal persistence systematically controlled by the recurrent training horizon. The learned dynamics generalize beyond this horizon: semantic continuation sustains the recurrent state without explicit answer replay, whereas unsupported states exhibit finite passive relaxation. Periodic re-exposure to relevant input renews the surviving field, producing an approximately stationary nonzero regime over extended recurrent trajectories. Unrelated-input and recurrence-off controls fail to reproduce this behavior, while near-paraphrased input produces weaker renewal, revealing the dependence of persistence on both field re-entry and input relevance. These findings connect
memory-dressed field formation, input-driven reorganization, and recurrent state continuity within a unified dynamical framework. The CFN thus provides an experimentally controllable platform for studying persistent, history-dependent cognition without a separately prescribed
memory system.
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Artificial Intelligence · Cross-listed from cs.RO
Visual Cue Guided Video Planning for Generalizable Robot Navigation
Hojin Lee, Sizhe Lester Li, Maximilian Hilger et al. (+4 more)
Generative
video models can serve as a promising backbone for
robot navigation by predicting future observations as
video plans. Recent approaches often condition
video planning on short-horizon guidance and recover
geometric waypoints through scene reconstruction, leaving longer-horizon planning and precise
video-to-action translation less explored. We present CueNav, a
video model-based
navigation framework combining visual cue guided
video planning with an embodiment-specific Inverse-Dynamics Model (IDM). As visual cues, we use a Bird's-Eye View (BEV) map to convey global task context and retain part of the
robot body in the
egocentric observation to expose embodiment context. These cues guide the
video planner, while the IDM translates dense flow fields extracted from the
video plan into
robot actions. With the visual cue encoding global task context, CueNav achieves nearly 2x higher success in maze
navigation than planning without the cue. The body-aware view with the IDM enables precise
navigation with 70% success in a narrow passage where comparison methods largely fail to complete the task. We further demonstrate zero-shot semantic-conditioned
navigation and deployment of the same
video planner across different
robot platforms. Our results show that visual cue-guided
video planning with embodiment-specific action grounding paves the way toward a generalizable
navigation framework for longer-horizon planning and embodiment-aware control. Additional results and code are available on our project website: https://cuenav.github.io.
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All authors Hojin Lee, Sizhe Lester Li, Maximilian Hilger, Susie Lu, Achim J. Lilienthal, Vincent Sitzmann, Daniel A. Duecker
Artificial Intelligence
LSREP: A Longitudinal State-Replay Protocol for Evaluating Conversational Memory, with ICE v2 as an Audited Local-First Architecture
Deepesh Sonar
Conversational
memory changes during use, so endpoint question answering alone cannot establish how a persistent state accumulates, ages, or incorporates revisions. We introduce LSREP, a Longitudinal State-Replay Evaluation Protocol combining ordered replay, explicit lifecycle schedules, repeated probes, evolving reference answers, and mechanism-fidelity checks. Its architectural case study is ICE v2, a local-first
memory middleware with typed stores,
retrieval fusion, and dynamic context budgets. The private, single-user instantiation contains 1,985 turns, 219 distinct probes, and 1,211 probe-checkpoint observations across 52 checkpoints. On three ordinary-density
datasets, ICE v2 has a near-zero mean quality difference from vector-RAG while selecting 32% fewer fragments but using 6.6% more estimated
prompt tokens. A fourth, dense
dataset exposes catastrophic failures of the unbudgeted baseline. The fidelity audit limits attribution: procedural
retrieval is defective, several mechanisms are unexercised, and graph utility is not established. In a complementary matched public diagnostic, ICE v2 loses decisively to pure vector-RAG on LongMemEval: 50.8% versus 72.8% in the evidence-only oracle and 43.0% versus 69.5% in full-S. Paired differences are -22.0 points (95% CI [-26.6, -17.4]) and -26.5 ([-31.3, -21.8]). Conservative abstention accompanies severe multi-session and temporal failures. ICE uses less context in this diagnostic, establishing a quality-cost trade-off rather than superior efficiency. Together, replay, fidelity
auditing, and public endpoint testing expose distinct failure modes that neither architectural descriptions nor aggregate scores identify alone.
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Artificial Intelligence
VideoMM: Adaptive Macro-Micro Inference for Efficient Video MLLMs
Haoyu Guo, Yuan Feng, Junlin Lv et al. (+3 more)
Scaling
Multimodal Large
Language Models (MLLMs) to long-form
video understanding is bottlenecked by the explosion of visual
tokens, which saturates context windows and incurs prohibitive costs. Current solutions predominantly rely on auxiliary models for
token reduction but face a fundamental dilemma: lightweight encoder-driven approaches often overlook critical semantic information, whereas heavyweight MLLM-driven reduction negates the efficiency gains. {In this work, we identify a more fundamental inefficiency underlying this dilemma: while fine-grained visual details are essential for detailed understanding, they are largely redundant for the preliminary task of selecting semantically relevant regions. } Motivated by this, we introduce VideoMM, which marks a paradigm shift from model-centric downsizing to adaptive perceptual granularity. Specifically, our framework {decouples selection from
reasoning} by executing semantic filtering on a cost-effective Macro Proxy (derived from downscaled frames), and projecting the selected regions onto high-fidelity Micro
Tokens for detailed understanding only when necessary. Extensive evaluations show that VideoMM significantly outperforms existing solutions. It achieves a 6.13$\times$ speedup and a 7.4% accuracy gain over full-context baselines on LongVideoBench, and further accelerates inference by 2.73$\times$ over current leading methods, establishing a highly scalable paradigm for long-
video understanding. Our code is available at: https://github.com/adfh917k/VideoMM.
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All authors Haoyu Guo, Yuan Feng, Junlin Lv, Mingjun Xiao, S Kevin Zhou, Xike Xie
Artificial Intelligence · Cross-listed from cs.LG
Continuous-Time Machine Learning: A Unified Mathematical Perspective
Waleed Razzaq, Yun-Sheng Zhao and Yun-Bo Zhao
Continuous-time (CT) machine learning has emerged as a principled framework for modeling temporal dynamics as a continuous process, particularly when observations are sampled at arbitrary time points or span long-range horizons. However, major branches of CT machine learning have matured in separate research communities, leaving their mathematical relationships and design trade-offs insufficiently characterized. In this survey, we develop a unified, concept-driven view of major CT machine learning branches through a taxonomy that organizes families according to their underlying base mathematical formulations. We present a canonical mathematical formulation that relates these families through different architectural choices of vector-field parameterization, stochasticity,
memory mechanisms, and discretization. We compare training
algorithms, optimization strategies, and failure modes, highlighting the trade-offs across families. We further provide a comparative analysis of theoretical computational complexity alongside an illustrative architecture-controlled
benchmark analysis on representative architectures from each family. We also review software ecosystems supporting their implementation. Finally, we identify open challenges in approximation theory, training stability, hardware-efficient implementations, benchmarking, foundation models, and scientific machine learning, and discuss an agenda for future research.
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Artificial Intelligence · Cross-listed from cs.RO
World Models for Embodied Intelligence: From Plausible to Controllable to Actionable
Nanjie Yao, Hao Wang, Chong Cheng et al. (+10 more)
World models connect perception and decision-making in
embodied intelligence by maintaining hidden state, anticipating consequences, comparing interventions, and adapting when execution departs from expectations. Although progress is often measured by visual fidelity, their value lies in improving behavior. Before reaching for a cup, a person anticipates its weight and resistance to grasping, shaping the hand before contact. Such anticipation is coarse and rarely pictorial, yet it guides action. This raises a central question: which predictive capabilities improve behavior? Existing surveys, organized by architecture, output modality, or application domain, leave this question implicit. We introduce three progressively stronger capability levels: Plausible models preserve task-relevant temporal,
geometric, or physical structure; Controllable models additionally predict how interventions alter that structure; and Actionable models translate predictions into measurable gains in planning, action, learning, evaluation, verification, recovery, or data selection. We complement this hierarchy with a 3 x 4
matrix crossing geometry, physics, and action grounding with improvement loops centered on data, rewards, policies, and the model itself. Using this framework, we survey manipulation,
navigation, locomotion, autonomous driving, and general
embodied learning, tracing technical progressions, clarifying capability requirements, and examining
datasets,
benchmarks, and evaluation protocols. We identify challenges in long-horizon consistency, uncertainty calibration, causal intervention testing, latency, verification and recovery, and cross-embodiment transfer. This perspective shifts evaluation from visual plausibility toward whether predictions capture task-relevant state, reflect intervention effects, and improve the closed-loop behavior of
embodied agents.
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All authors Nanjie Yao, Hao Wang, Chong Cheng, Zhikang Chen, Wenzhe Li, Jiafei Lyu, Li Shen, Peilin Zhao, Zongqing Lu, Gao Huang, Steven Hoi, Dacheng Tao, Deheng Ye
Artificial Intelligence · Cross-listed from cs.RO
Weave: Learning Whole-Body Dexterous Loco-Manipulation from Human-Object Interactions
Liu Cao, Xingze Wu, Jingzhi Cui et al. (+4 more)
Learning
humanoid-object interaction requires coordinating whole-body balance, locomotion, and dexterous hand contact to control both
robot and object motion. Human demonstrations provide examples of coordinated interaction, but transferring these behaviors to
humanoid robots requires learning how to establish and maintain effective contacts under different embodiments and dynamics. We present Weave, a unified framework for learning whole-body dexterous
humanoid-object interaction from captured human demonstrations. Weave first converts captured human-object interactions into executable
robot-object references through contact-aware retargeting and approach-motion completion. At its core is a contact- and geometry-aware policy that jointly commands 29 body joints and 12 actuated finger joints across multiple objects and interaction sequences. Evaluation across nine objects yields a 92.5% success rate on trained interactions and, without any additional training, 65.0% on sequences never seen during training. We additionally release ~9,000 physically executed rollouts spanning ~23 hours, providing
robot-object trajectories with contact annotations for downstream interaction-policy learning and physically consistent HOI motion generation. Project website: https://xiaohu-art.github.io/Weave/
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All authors Liu Cao, Xingze Wu, Jingzhi Cui, Botian Xu, Mingzhi Pei, Ruoqu Chen, Mengdi Xu
Artificial Intelligence
little m: An AI Agent for Industrial Process Optimization
Yongchao Ye, Xinyu He, Dutliff Boshoff et al. (+2 more)
Manufacturing consumes one third of global energy and still has significant room for improvement in terms of energy efficiency. Optimal process control is essential for this purpose. However, synthesizing mathematical optimization models from messy, real-world industrial specifications requires bridging unstructured natural
language and spatial diagrams with rigorous mathematical syntax. This poses a profound challenge for general-purpose Large
Language Models (LLMs), which may introduce invalid constraints when tasked with modeling continuous multi-physics dynamics. To address this, we introduce little m, an AI
agent designed to assist the formulation of industrial process control models. Combining a domain-specific knowledge repository with LLM-driven interaction, the proposed framework formulates real-world optimization problems as mathematical models. For systematic evaluation, we introduce the Industrial Process Control
Benchmark (IPC-Bench), a novel
multimodal dataset of 50 canonical scenarios requiring joint
reasoning over text and process diagrams. Through comprehensive automated structural assessments and double-blind human evaluation, little m substantially outperforms state-of-the-art LLMs, generating semantically correct models. These evaluations assess formulation quality rather than solver feasibility, formal physical validity, or closed-loop industrial performance. The implementation of little m and the IPC-Bench
dataset are available at https://github.com/yeyongchao/process-modeling-
benchmark.
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All authors Yongchao Ye, Xinyu He, Dutliff Boshoff, Way Kuo, Lishuai Li
Artificial Intelligence
AI for Games in the Foundation Model Era
Meng Luo, Yanlin Li, Hao Li et al. (+7 more)
Foundation models, alongside advances in learned game-world models, are reshaping AI across the game lifecycle. Beyond playing games, recent systems model players and game dynamics, support design and development, adapt player-facing experiences at runtime, and evaluate resulting artifacts. Yet these directions have evolved largely separately, obscuring which capabilities transfer across settings and which remain tied to particular games, engines, interfaces, or player populations. We organize the literature into six roles according to the immediate use of AI output: playing and acting; modeling players and games; designing games; building and maintaining games; generating and adapting at runtime; and testing and evaluating games. For each role, we examine what structure is supplied by the game or workflow, what AI learns or produces, which capabilities and artifacts transfer across settings and roles, and what evidence supports the claims. We identify cross-role connections: trajectories train world models, learned environments provide experience for
agents, design specifications drive executable implementations, and play or testing feedback guides revision. However, control schemes, rules, engine interfaces, state representations, and player contexts often remain setting-specific, so downstream claims require validation in the target setting. Evaluation is most standardized for bounded game playing and selected learned environments, while persistent state in learned worlds, repeated software revision, validated player modeling, sustained runtime adaptation, and representative automated testing remain less established. The central challenge is to reuse or transfer outputs and capabilities across roles while re-establishing evidence for effectiveness in the game-specific contexts where they are used.
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All authors Meng Luo, Yanlin Li, Hao Li, Hongzhan Lin, Pengfei Zhou, Tianjie Ju, Ran Zhang, Yeying Jin, Mong-Li Lee, Wynne Hsu
Artificial Intelligence
ANIMASK: What the Model Contributes to Role Play in Simulated Story Worlds
Xiucheng Zhang, Zhuoning Xu, Hanjun Luo et al. (+3 more)
When a
language model plays a character, the observed behavior reflects both the assigned persona and the default dispositions of the actor model itself. Existing evaluations test persona fidelity or model defaults in isolation, but neither says, at a specific choice with consequences, what the persona changed and what the model's default kept. We introduce ANIMASK, a simulation framework that freezes books and scripts into story worlds whose characters act on their own motivations and replays each story from its freeze point. We hold out the author's continuation as a human reference, verify through in-story interviews that each persona remains present, and at every decision point compare the character's action with what the model produces when the persona is removed. Across 40 stories, 6 actor models, and 3,846 decision points, the replays converge away from their canons in one shared direction, toward flatter, cooler stories that leave their tensions open. The personas stay present and obeyed throughout. On three choices in four the model's default already falls inside what the persona accepts, and where the two diverge the model is the cautious one, holding where the persona would press. The persona guarantees who the character is, and the model sets how far the character will go.
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All authors Xiucheng Zhang, Zhuoning Xu, Hanjun Luo, Yankai Chen, Hanan Salam, Xue Liu
Artificial Intelligence · Cross-listed from cs.LG
Right Direction, Wrong Step: Geometric Analysis of Finite-Step Failure in Looped Transformers
Zhihao Guo, Zonghan Wu, Haizhou Du et al. (+4 more)
Looped
Transformers offer a parameter-efficient route to test-time scaling by reusing shared layers for iterative latent
reasoning. However, additional iterations can reduce support for a reference answer, leaving unclear whether an update's direction is locally unhelpful or its full displacement moves too far. We study this distinction by analysing reference utility, which measures this support, along the model's own update direction, varying the fraction of the proposed displacement supplied to the readout. This reveals finite-step failures in which a locally improving direction produces a harmful full update. A pathwise
curvature decomposition characterises how initial progress is lost, while a local quadratic model predicts full-step gains and useful step scales. Bounds based on accumulated
curvature variation characterise the approximation error of these predictions. Experiments across two model families reveal this separation on mathematical and commonsense tasks. A fixed quarter step produces positive gains in reference utility for 72.2–83.2% of selected failures across four settings. These findings identify a mismatch between update direction and step scale as a mechanism of lost progress, explaining how some harmful updates retain useful computation.
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All authors Zhihao Guo, Zonghan Wu, Haizhou Du, Huan Huo, Yilei Shao, Athanasios V. Vasilakos, Qingsong Wen
Artificial Intelligence
ReDraft, Don't Just Distill: Reference-Driven Revision for Continual VLLM Post-Training
Zhihao Zhang, Mingqi Wu, Qiaole Dong et al. (+15 more)
Continual post-training of large
multimodal models should add new capabilities while preserving those from pre-training, and the two goals pull in opposite directions. SFT gives explicit target supervision that learns a task from near-zero accuracy, but its off-policy targets move the model far enough to cause forgetting; on-policy methods such as RLVR and self-
distillation preserve policy proximity yet supply little signal when the policy cannot yet solve the task. We introduce ReDraft (Reference-Driven Revision and Fine-Tuning), which obtains both from the model's own failures: using an expert response only as a reference, it has the model revise its own incorrect rollout, keeps the revision only if a verifier accepts it, and fine-tunes on what survives. Each retained target is therefore explicit, yet still close to the current policy. Across Counting, Clock Reading, and Jigsaw on Qwen2.5-VL-3B/7B, two of them with near-zero accuracy, ReDraft gains 56.9 points on the target task against SFT's 52.9 while cutting prior-task loss from 16.6 to 1.5 points (11.3x less forgetting), and improves on OPSD along both axes (19.3 gain, 6.2 loss). Data- and parameter-space analyses match the design: revised targets are more probable under the base model, and the updates they induce stay compact and follow SFT's direction more closely than OPSD's. Together, these results show that revising the model's own rollout rather than directly imitating an expert trajectory can reconcile cold-start acquisition with prior-capability retention.
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All authors Zhihao Zhang, Mingqi Wu, Qiaole Dong, Enyu Zhou, Shuo Li, Boyang Liu, Jiazheng Zhang, Honglin Guo, Xin Guo, Shaofan Liu, Junzhe Wang, Dingwei Zhu, Minlong Peng, Yuan Hua, Zhiheng Xi, Qi Zhang, Tao Gui, Xuanjing Huang
Artificial Intelligence
EchoPath: Execution-Level Replayable Memory for GUI Agents
Yao Zhao, Aditya Shanmugham, Swastik Roy et al. (+1 more)
Computer-use
agents increasingly operate browsers, software, and desktop applications via CLI or API portals, but graphical user interface (GUI) still plays an important role in common industrial production scenarios. GUI
agents commonly employ fresh observe-plan-ground-act loops, which is inefficient for enterprise tasks that repeatedly update records, process forms, configure tools, and export reports. We introduce EchoPath, a model-agnostic harness that converts artifact-validated GUI trajectories into standardized, parameter-controlled callable
memories, analogous to Model Context Protocol (MCP)-style tool calls rather than unstructured experience records. Each
memory stores task-intent keys, application and state preconditions, flexible input parameters, GUI evidence, validation provenance, and lifecycle state, so the host
agent invokes a targeted procedure only when it can be deterministically replayed in the current runtime. The core mechanism enabling replay is an image-based target-reaiming
algorithm that treats stored coordinates as visual evidence, matches the remembered GUI target against the current screen, and emits corrected operation coordinates before execution. During replay, EchoPath rebinds only declared modifiable inputs and rejects ambiguous or incompatible steps to bounded grounding repair or fresh planning. In experiments with real computer-use tasks, EchoPath reduced median
token cost by more than 90% and median execution time by about 60%. These results support a bounded form of enterprise GUI
memory: validated execution experience can become a controllable callable asset for recurrent work rather than only context for another
reasoning pass.
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All authors Yao Zhao, Aditya Shanmugham, Swastik Roy, Yanxun Xu
Artificial Intelligence · Cross-listed from cs.IR
AURA: Agentic Diagnosis and Refinement for Production Recommender Systems at Scale
SungGeun Kim, Abhinav Narain and Daniel Nemirovsky
How and why does a recommender system fail the users it serves? Oftentimes, practitioners are left to improve their
algorithms based on a combination of feedback from stakeholder teams, domain expertise, and insights from data analyses. Yet the nuances of how and where recommendations perform well or poorly for end users are difficult to discern from aggregate quantitative metrics. Whereas these metrics provide a high-level and incomplete picture, further granularity into the quality of recommendations and their patterns requires
reasoning with domain understanding and objectivity, at scale. We contemplate this complex conundrum and describe a method and implementation that uses the latest AI agentic advances to provide actionable diagnoses and improvements for production recommender systems. We present AURA (Agentic Understanding and Refinement of recommender
Algorithms), an end-to-end agentic system that performs qualitative evaluation at scale and can then generate improvements to our
algorithms at the code level. Specialized
agents read production engagement logs, from thousands of sessions to millions, and surface patterns and examples of how the recommender fails real users. The next step uses those diagnoses and context about the recommender's own code, data, and training pipeline to propose and implement refinements grounded in that codebase. We report the system design, initial tests on production data from two large consumer platforms at a major media-streaming company, safeguards, operational learnings, and early results toward a self-improving recommender system. Finally, the diagnostic gap AURA closes is not specific to streaming. The architecture is built to transfer: every domain-specific element enters through the configuration layer that already ported it between our two platforms. We map it concretely to e-commerce and online-retail recommendation.
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Artificial Intelligence · Cross-listed from cs.CL
RoleBreak: Benchmarking Long-Horizon Role-Playing Robustness in Spoken Dialogue
Yuqi Wang, Fengyuan Liu, Haochen Luo et al. (+2 more)
Speech-to-
speech dialogue models increasingly support persona control, yet existing spoken role-playing
benchmarks remain largely character-centric and short-horizon. This leaves open whether spoken dialogue models can sustain diverse roles over extended interactions, especially beyond predefined fictional characters. We introduce RoleBreak, an open
benchmark for long-horizon role-playing robustness in spoken dialogue. RoleBreak contains 310 character-based and user-centered roles, 6,688 human-verified dialogue turns, and 11,743 fine-grained evaluation criteria, with 1,856 turns carrying expressive
emotion targets for evaluating vocal
emotion. Its scenarios are designed to stress role consistency, interaction quality, safety, and affect over extended conversations. We evaluate nine configurations spanning full-duplex, omni-modal, and cascaded ASR–LLM–TTS paradigms. We find four key patterns. First, current systems are substantially stronger at semantic role adherence than at vocal
emotion. Second, semantic robustness remains brittle over long interactions: even the strongest evaluated system encounters its first persona and safety failures after only 10.4 and 11.6 turns on average. Third, scaling the LLM substantially improves semantic robustness and delays failure, but yields little improvement in vocal
emotion. Finally, user vocal
emotion affects role-playing behavior even when linguistic content is fixed. These findings highlight persistent gaps in both long-horizon robustness and vocal expressiveness in spoken role-playing systems.
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All authors Yuqi Wang, Fengyuan Liu, Haochen Luo, Zhiqi Yu, Qi Liu
Artificial Intelligence · Cross-listed from cs.SD
Structure Across Voices: Comparing acoustic-event type accumulation and sequence dependence across four vocal repertoires using frozen audio encoders
Mudit Sinha and Sanika Chavan
Vocal repertoires can differ in acoustic-event type accumulation and temporal organization, yet direct comparison is difficult because corpora use different native events and unequal amounts of sequence. We compare sperm whale codas, human
speech phones, Bengalese finch syllables, and common marmoset calls using the same frozen-audio-encoder procedure while matching event count and local sequence opportunity. Whale shows the fastest type accumulation; Finch shows the strongest immediate dependence and repeated-subsequence recurrence. Physically interpretable acoustics recover complementary parts of this profile, continuous analyses without clustering support broad Whale acoustic coverage, and source- and position-preserving nulls retain both Finch order effects. Extending predictive context shifts the comparison toward Whale. Thus repertoire differences depend on the acoustic property and temporal scale measured rather than forming a single hierarchy.
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Artificial Intelligence · Cross-listed from eess.SY
Large Language Models in the Loop: A Stability- and Network-Aware Survey in Networked Control, Cyber-Physical, and Multi-Agent Systems
Haiping Du and Linping Chan
Modern networked control systems (NCSs), cyber-physical systems (CPSs), and complex multi-
agent network systems (CNSs) increasingly rely on large
language models (LLMs) for high-level decision-making. However, the slow,
stochastic nature of LLMs directly conflicts with the strict stability and safety guarantees required by these physical systems. This survey presents a unified analysis of how LLMs can be admitted into the control loop of NCS, CPS, and CNS without compromising closed-loop guarantees. We organize this around a core principle: the LLM operates as a slow supervisor adjusting high-level goals and constraints, while a fast, certified inner loop maintains physical stability. Under this framework, LLM integration maps directly to classical networked control challenges, where inference latency acts as delay, API failures as packet dropouts, tokenization as quantization, and
hallucinations as bounded disturbances. We assess current developments across all these three domains, highlighting that rising model capabilities are frequently accompanied by a drop in formal safety assurances. Finally, we propose concrete future research directions, identifying the widespread lack of formal stability proofs as the field's central open problem.
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Artificial Intelligence · Cross-listed from cs.CV
A Vision-Language Foundation Model for Precise and Comprehensive Brain Tumor Diagnosis from Preoperative Multimodal Data
Yinong Wang, Jianwen Chen, Zhou Chen et al. (+27 more)
We developed BrainVLM to classify all 12 World Health Organization (WHO) 2021
brain tumor types. BrainVLM integrates an uncertainty quantification strategy to indicate prediction reliability and a module for generating radiology reports to elucidate the clinical rationale. BrainVLM was trained on multi-modal data (MRI scans, demographics, and radiology reports) from 40,043 individuals. It was validated on 5,211 patients with pathologically confirmed
brain tumors, including 3,877 held-out patients from the primary hospital and 1,334 patients from 11 independent hospitals. We further conducted two proof-of-concept studies to validate its clinical utility in AI-clinician workflows: 1) a blinded multireader study where 12 neuroradiologists across varying experience levels interpreted 248 retrospective cases with or without AI assistance, and 2) a real-world prospective study in which 1,009 patients were independently and blindly assessed by BrainVLM and radiologists before surgery. Additionally, we demonstrated BrainVLM's utility in preoperative molecular subgroup prediction for adult-type diffuse gliomas, using a multi-center cohort of 632 patients. In primary evaluation, BrainVLM achieved an area under the curve (macro-AUC) of 0.85 (95% CI: 0.84-0.86), and an F1 score of 0.82 (95% CI: 0.81-0.83), surpassing neuroradiologists (F1 = 0.80 (95% CI: 0.79-0.81)). In external validation across 11 centers, BrainVLM achieved an AUC = 0.80 (95% CI: 0.79-0.82) and F1 = 0.75 (95% CI: 0.73-0.78), compared with F1 = 0.71 (95% CI: 0.69-0.73) for neuroradiologists. In prospective real-world evaluation, BrainVLM maintained performance comparable to neuroradiologists. The BrainVLM project page is available at https://hku-healthai.github.io/brainvlm_project.github.io/.
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All authors Yinong Wang, Jianwen Chen, Zhou Chen, Shuwen Kuang, Haoning Jiang, Yanzhao Shi, Huichun Yuan, Yan-ran Wang, Bing Wang, Lei Wu, Bin Tang, Li Meng, Baihua Luo, Bin Zhou, Wei Ding, Weiming Zhong, Wei Hou, Yuanbing Chen, Zhiping Wan, Wei Wang, Zhenkun Xiao, Wenwu Wan, Allen He, Yuyin Zhou, Longbo Zhang, Feifei Wang, Zhixiong Liu, Michael Iv, Xuan Gong, Liangqiong Qu
Artificial Intelligence
A Framework for Generating Valid Context-Specific Benchmarks through Expert Guidance
Kimberly Le Truong, Nari Johnson, Anna Kawakami et al. (+1 more)
This paper presents an end-to-end approach for generating context-specific large
language model (LLM)
benchmark datasets by combining expert input with synthetic data generation. Existing
benchmark construction methods often trade off validity and scalability:
datasets designed with domain experts can produce high-quality evaluations but are slow and costly to create, while synthetically generating data may scale efficiently but often results in unrealistic, redundant, or out-of-scope examples. To address this gap, we introduce a schema eliciting key information about the goals, scope, and context of an evaluation task, and use this information to guide synthetic data generation. We further define four criteria grounded in measurement validity for assessing
dataset quality: coverage, diversity, content realism, and stylistic realism. Using these criteria, we show how expert-informed scaffolds can guide synthetic data generation toward more valid
benchmarks. Through quantitative evaluations and a real-world case study with domain experts, we demonstrate that our approach improves
benchmark data quality over existing methods while preserving validity. We additionally analyze how different types of schema information affect different
dataset quality criteria, and provide practical guidance on which information to prioritize collecting under resource constraints.
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All authors Kimberly Le Truong, Nari Johnson, Anna Kawakami, Hoda Heidari
Artificial Intelligence
Do LLMs Have Values? A Quantitative Analysis and Alignment Framework for Values in Large Language Models
Keqing Zhang, Jingyu Chen, Yufan Liu et al. (+6 more)
As Large
Language Models (LLMs) increasingly handle complex subjective tasks, aligning their intentions and behaviors with human values has become a critical scientific challenge. However, current efforts are confounded by a striking behavioral paradox: they fluctuate unpredictably under minor wording changes ("swing"), yet stubbornly ignore explicit instructions to correct ingrained biases ("
rigidity"). Resolving this duality is critical for reliable AI
alignment. To systematically understand and safely steer these latent subjective preferences, our study is structured around three fundamental questions. First, do LLMs possess an intrinsic value system? By projecting responses from 106 LLMs (150,000 queries per model) and 95,000 human survey profiles into a shared sociological space, we empirically confirm that they do. However, they do not mirror human diversity, instead crystallizing into a highly concentrated, idealized value core. Second, how can these values be quantified? We propose the Prior-Environment-Cognition (PEC) framework. This model mathematically defines value expression as the joint outcome of inherent dispositions like parameter weights (Prior), external contexts such as user
prompts (Environment), and internal
reasoning processes like Chain-of-Thought (Cognition). Finally, how can LLMs' values be aligned toward a desired target? Using PEC diagnostics, we establish an adaptive "
Alignment Prescription". Rather than blindly applying resource-intensive training, this method identifies the minimum effective intervention needed for each dimension, ranging from zero-cost
prompts to targeted parameter updates. Extensive empirical validation confirms that our approach successfully verifies the presence of LLM values, accurately quantifies their shifts, and achieves more efficient and precise steering than conventional blind training, all without degrading general capabilities.
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All authors Keqing Zhang, Jingyu Chen, Yufan Liu, Yongqiang Zhu, Nai Ding, Lai Jiang, Congyan Lang, Bing Li, Weiming Hu
Artificial Intelligence · Cross-listed from cs.RO
ProxiDex: Learning Dynamics-Guided Proximity Policy for Dexterous Manipulation
Yushan Bai, Boyu Zheng, Zhiyang Mao et al. (+4 more)
Multi-finger dexterous manipulation relies on stable hand-object interactions, yet these interactions are partially observable in practice. Visual observations are often occluded by the hand, tactile sensors introduce hardware-specific modalities and calibration burdens, and existing policies rarely model how these cues evolve under actions, making them brittle under contact uncertainty. To address these, we present ProxiDex, a dynamics-guided proximity policy framework that treats hand-object proximity as an interaction state for dexterous manipulation. ProxiDex reconstructs interaction point clouds and converts
geometric distances into proximity cues, forming a hardware-agnostic contact representation that provides immersive feedback during VR teleoperation. Built on this representation, ProxiDex learns action-conditioned proximity dynamics with a coupled forward-inverse design: future observation latents are predicted from actions, while proximity variations are decoded from latent changes. Leveraging these dynamics, ProxiDex adaptively reweights proximity
tokens across manipulation phases and uses dynamics-consistency supervision to guide policy inference, stabilizing action generation under unreliable visual feedback. Simulation and real-world experiments demonstrate improved success rates and robustness over representative baselines across standard, unseen objects, and perturbation scenarios. Additional visualizations are available at https://proxidex.github.io/.
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All authors Yushan Bai, Boyu Zheng, Zhiyang Mao, Hongzheng Sun, Yuchuang Tong, En Li, Zhengtao Zhang
Artificial Intelligence · Cross-listed from cs.CV
Efficient Text-to-Image Generation: An Adaptive Step Schedule Controller for Diffusion Models
Kuluhan Binici, Cihan Acar, Shivam Aggarwal et al. (+2 more)
Text-to-image
diffusion models often use a fixed number of denoising steps, balancing time costs and image quality. However, the optimal number of steps depends on the complexity of the input text
prompt. We propose an adaptive
diffusion controller that dynamically adjusts the number of steps to generate high-quality images efficiently, without additional model training. By leveraging a mixture of step schedules with varying step sizes and evaluating the error term discrepancy at each timestep, our method transitions between schedules to optimize performance. Experiments on COCO and DiffusionDB show that our approach reduces inference time while maintaining visual fidelity, offering a more efficient alternative for text-to-image
diffusion models.
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All authors Kuluhan Binici, Cihan Acar, Shivam Aggarwal, Siying Liu, Tulika Mitra
Artificial Intelligence · Cross-listed from cs.CV
Vision And Text Transformer For Predicting Answerability On Visual Question Answering
Tung Le, Huy Tien Nguyen and Le Minh Nguyen
Answerability on Visual Question Answering is a novel and attractive task to predict answerable scores between images and questions in multi-modal data. Existing works often utilize a binary mapping from visual question answering systems into Answerability. It does not reflect the essence of this problem. Together with our consideration of Answerability in a regression task, we propose VT-
Transformer, which exploits visual and textual features through
Transformer architecture. Experimental results on VizWiz 2020
dataset show the effectiveness and robustness of VT-
Transformer for Answerability on Visual Question Answering when comparing with competitive baselines.
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Artificial Intelligence
Query-Aware Source-Risk Triage for Retrieval-Augmented Generation
Kainan Zhou, Gangzhen Qian, Chuhong Xu et al. (+1 more)
Retrieval-augmented generation (RAG) pipelines may omit a source's material relationship to the query. We study a pre-generation triage layer that treats this relationship as query dependent. The method routes canonical query families for enhanced review and assigns retrieved pages to pass, contextualize, exclude, or review. It combines a four-dimension page score, rank-discounted family aggregation, intent-preserving query mutations, and a family-held-out router. A single-coded pilot of 200 real URLs supplies provisional calibration anchors; a 20,000-row scenario with synthetic domain identifiers supports controlled workload analysis. An oracle page gate defines a risk-coverage target for a future learned classifier. The evaluation shows why page-level frequency cannot substitute for family-level exposure and quantifies how calibration changes scenario activation. Annotation reliability remains unmeasured, and synthetic rankings omit real
retrieval dynamics. The result is an auditable triage method and validation plan, not an estimate of deployed review workload, live-Web prevalence, or downstream answer-quality gains.
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All authors Kainan Zhou, Gangzhen Qian, Chuhong Xu, Lu Yi
Artificial Intelligence · Cross-listed from cs.CR
The MAL Simulator: Cyber Operations Simulation based on Attack & Defense Graphs
Jakob Nyberg, Sandor Berglund, Andrei Buhaiu et al. (+3 more)
We have developed the MAL Simulator, a cyber operation simulator based on the Meta Attack
Language (MAL). The MAL Simulator is intended for decision-driven cyber attack and defense simulations, for system analysis and the development of automated
agents. By building the simulator around an attack modeling
language, it can be adapted to different target domains without modifying the source code. We used the simulator for two case studies where we trained two types of
agents for automated cyber operations: a defensive
agent and an offensive
agent. To ground the experiments, we base the models in data collected from an emulated network implemented in the cyber range CRATE. We found that the trained attacker policy could reach the designated targets more efficiently than the compared search methods, and that the trained defender
agent induced lower costs than a naive heuristic
agent under noisy alert conditions. When testing the RL attacker against the RL defender, we found that the performance of the defenders dropped significantly. This emphasizes the importance of cyber attack simulators to facilitate training both offensive and defensive
agents. The MAL Simulator and associated tooling is publicly available and provides common interfaces for compatibility with existing machine learning frameworks.
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All authors Jakob Nyberg, Sandor Berglund, Andrei Buhaiu, Joakim Loxdal, Pontus Johnson, Mathias Ekstedt
Artificial Intelligence
QueryFormer: Winning Solution for KDD Cup 2026 Tencent UniRec Challenge
Yuanzhe Zhou and Zhaoyang Zeng
Post-click conversion rate (pCVR) prediction requires jointly modeling feature interactions and sequential user behaviors. The KDD Cup 2026 Tencent UniRec Challenge calls for a unified architecture addressing both. We observe that existing unified architectures often generate query
tokens—the central information hub—with projection-based multi-layer perceptrons (MLPs), without explicit
token-to-query
attention for refining the query side. We propose QueryFormer, centered on a stackable unified field–sequence block that bridges non-sequential multi-field features and behavioral sequences, and provide a latency-aware scaling study over view width $H$, model width, depth, data, and compute. The block generates queries through cross-
attention and packs sequence queries into shared-parameter
attention. QueryFormer secured 1st place in the Industrial Track, achieving an official test area under the ROC curve (AUC) of 0.83254; a modest post-competition scale-up reached 0.832713. Within our grid, $H$-scaling improves validation AUC from 0.84540 to 0.84615 and beats HyFormer at comparable budgets. Ablation identifies query generation as the largest contributor. Packed shared-parameter cross-
attention keeps H=8 inference latency to only 1.89x that of H=1, positioning the bridge as an efficient stackable unified block.
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