Artificial Intelligence · Cross-listed from cs.CL
Training-Time Explainability for Multilingual Hate Speech Detection: Aligning Model Reasoning with Human Rationales
Muhammad Deedahwar Mazhar Qureshi, Sannaan Khan, Muhammad Atif Qureshi et al. (+1 more)
Online hate against Muslim communities often appears in culturally coded,
multilingual forms that evade conventional AI moderation. Such systems, though accurate, remain opaque and risk bias, over-censorship, or under-moderation, particularly when detached from sociocultural context. We propose a training-time explainability framework that aligns model
reasoning with human-annotated rationales, improving both classification performance and interpretability. Our approach is evaluated on HateXplain (English) and BullySent (Hinglish), reflecting the prevalence of anti-Muslim hate across both
languages. Using LIME, Integrated Gradients, Grad X Input, and
attention, we assess accuracy, explanation quality, and cross-method agreement. Results show that gradient- and
attention-based regularization improve F-scores, enhance plausibility and faithfulness, and capture culturally specific cues for detecting implicit anti-Muslim hate, offering a path toward
multilingual, culturally aware content moderation.
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All authors Muhammad Deedahwar Mazhar Qureshi, Sannaan Khan, Muhammad Atif Qureshi, Wael Rashwan
Artificial Intelligence
Toward Safe LLM Agents: A Survey of Specification, Verification, and Enforcement
Pierre Dantas, Lucas Cordeiro, Ehsan Nowroozi et al. (+1 more)
Large
Language Model (LLM)
agents take irreversible real-world actions, yet no system gives formally grounded, task-level safety guarantees for their plans. In a PRISMA 2020 systematic review, we searched six databases (20-29 May 2026) for work on LLM
agent specification, verification, and enforcement published in 2022-2026. We narratively synthesized 38 studies, assessed risk of bias per study, and graded evidence certainty with GRADE. Three authors independently screened a repeated search to validate the single-reviewer screening (agreement: Fleiss' $\kappa = 0.81$). First, specification is the bottleneck: on one
benchmark, only 24.8-34.2% of LLM translations from natural
language into Planning Domain Definition
Language (PDDL) are semantically correct. Second, runtime monitoring is the most mature enforcement strategy; it reduced unsafe actions by 65.37% in one household-
agent study, but its guarantees cover only single steps or short horizons. Third, in one $\tau$-Bench study of transactional tool-use
agents, blocking 94% of unsafe actions still left fewer than 5% of tasks completed both safely and correctly in most settings, because
agents found other unsafe routes (the verifier tax). Fourth, no approach simultaneously achieves soundness, completeness, scalability, semantic grounding, and utility preservation. For seven of the nine principal claims, GRADE gives the lowest certainty rating (Very Low), meaning further studies are likely to produce substantially different figures; each rests on one or two studies in narrow domains. Two of the open problems we rank highest are translating requirements into formal specifications that keep their meaning and enforcing safety over whole plans rather than single actions. We contribute a three-level taxonomy and an agenda of ten open problems.
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All authors Pierre Dantas, Lucas Cordeiro, Ehsan Nowroozi, Norbert Tihanyi
Artificial Intelligence
The Hallucination Snowball: Modeling Error Propagation as State Transitions in Multi-Agent LLM Pipelines
Prabhjot Singh and Bhushan Pawar
Sequential multi-
agent LLM pipelines chain specialized
agents without verification at handoffs, creating a structural flaw with measurable and severe consequences. We show that
hallucinations injected at Stage 1 do not merely persist; they transform: raw numerical facts become derived computations, then narrative prose, then editorially approved conclusions. At each transformation, detectability degrades near-irreversibly. We formalize this as the
hallucination snowball effect, a first-order Markov process over four states (Raw Fact $\to$ Derived $\to$ Narrative $\to$ Invisible) with empirically measured per-boundary escape probabilities of 24.6%, 48.3%, and 89.3%. Across 346 automatically injected
hallucinations in a 4-
agent financial analysis pipeline on FinanceBench, gpt-4o detection drops from 72.0% at Stage 1 to 50.9% at Stage 4, and 23.7% of
hallucinations survive completely undetected in the final output. Even the strongest model tested (Qwen3.5-397B-A17B, 87.0% at Stage 1) faces a structural ceiling; projected Stage 4 detection is only ${\sim}$60–65%. Critically, boundary gates using identical RAG verification tools reduce
hallucination survival from 58.4% to 16.2% versus end-of-pipeline checking (Cohen's $h = -0.911$, $p < 0.000001$), while end-checking alone achieves merely 2.3 pp improvement over no verification. When you verify matters more than whether you verify. Our model predicts survival for $n$-
agent linear pipelines and prescribes optimal verification resource allocation: invest at $S_1{\to}S_2$ first, where 75.4% of
hallucinations are still catchable, not at $S_3{\to}S_4$ where 89.3% have already escaped.
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Artificial Intelligence · Cross-listed from cs.CY
The Easy Trap: Why LLMs Underestimate Misconception-Driven Difficulty
Amanda La Hadi, Muhammad Johan Alibasa, Guanliang Chen et al. (+1 more)
Large
language models (LLMs) are increasingly used for estimating item difficulty in educational assessment. However, it remains unclear whether such estimates reflect how learners actually experience difficulty. This study investigates the
alignment between LLM-generated difficulty ratings and empirical student performance on basic mathematics tasks. Four widely used LLM-based systems generated difficulty ratings on a 1-100 scale for 32 arithmetic items across multiple runs (N = 640 ratings). These were compared with empirical difficulty derived from responses of 770 Indonesian undergraduates using Classical Test Theory (CTT) and Item Response Theory (2PL). Results show moderate rank correlations (Spearman's rho = 0.52-0.70), indicating that LLMs capture coarse ordering of item difficulty. However, substantial and systematic misalignment emerges in fraction items. Several items consistently rated as easy by LLMs were among the most difficult for students, such as an item with only 34.16% correct for 100 : 1/2. We argue that LLMs approximate curricular difficulty, or what should be easy based on instructional sequencing, rather than cognitive difficulty driven by learner misconceptions. This leads to systematic underestimation of misconception-driven items, a phenomenon we term the Easy Trap. These findings highlight a critical limitation of LLM-based difficulty estimation and suggest that relying on such estimates without empirical grounding may introduce bias in assessment design and adaptive systems.
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All authors Amanda La Hadi, Muhammad Johan Alibasa, Guanliang Chen, A. Taufiq Asyhari
Artificial Intelligence · Cross-listed from cs.IR
Unlocking Spatial Grounding in Large Audio-Visual Retrieval models
Hugo Malard, Michel Olvera, Sanjeel Parekh et al. (+3 more)
Weak supervision sets a practical regime for audio-visual sound source localization as dense spatial annotations are costly to obtain at scale. The task, however, remains challenging, as models must locate sound sources from temporally aligned audio-visual data without pixel-level supervision. Recent large-scale audio-visual
retrieval models, trained at unprecedented scale, encode rich
multimodal structure. We show their latent representations, though optimized for global
alignment, can nonetheless enable fine-grained spatial grounding. While spatial detail is progressively lost in the upper layers of
retrieval backbones due to global pooling, intermediate visual
tokens retain highly structured spatial information. To exploit this, we introduce LAIP (Localization via Audio-Informed Pooling), a framework that employs a lightweight Audio-informed Spatial Pooling (AiSP) to replace the standard global aggregation module. By querying intermediate visual
tokens with audio aligned at the frame level, LAIP recovers localized spatial information that is otherwise discarded by the
retrieval pipeline, with the largest gains observed for PE-AV, a stack with underlying temporal aggregation. Our approach achieves state-of-the-art performance on AVSBench and AVATAR, nearly doubling previous results on the latter, improving average CIoU from 13.21 to 26.22.
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All authors Hugo Malard, Michel Olvera, Sanjeel Parekh, Gaël Richard, Slim Essid, Stéphane Lathuilière
Artificial Intelligence · Cross-listed from physics.app-ph
DDSNet: Dual-domain Symmetry-aware Network for PCSEL Property Prediction
Cen Chen, Haitao Huang, Jiazhi Mao et al. (+3 more)
Efficient exploration of the photonic
crystal (PhC)
lattice design space is essential for developing photonic
crystal surface-emitting
lasers. While coupled-wave theory (CWT) provides an effective physical framework, its computational cost remains prohibitive for large-scale exploration, driving the demand for neural surrogates. However, existing AI models underexploit two key factors of PhC unit-
cell dielectric patterns indicated by CWT: spectral components and asymmetric structures, which largely govern devices' physical properties. This mismatch weakens surrogate accuracy and screening reliability, especially in structure-sensitive regions. To address this, we propose the Dual-Domain
Symmetry-Aware Network (DDSNet). It integrates translation-equivariant spectral filtering with a
symmetry-induced structural prior. The spectral filtering injects a spectral inductive bias into
vision model while preserving translation equivariance on
lattices. Meanwhile, the structural prior decomposes
lattice features into irreducible representation-associated,
symmetry-resolved components and processes them in separate branches. Experiments demonstrate that DDSNet significantly outperforms existing AI baselines in property prediction and high-throughput screening, exhibiting superior reliability in structure-sensitive regions. Crucially, component masking analyses reveal that the network successfully learns property-specific dependencies aligned with physical priors. These results indicate that DDSNet effectively captures physically meaningful structure-property relationships, establishing a highly reliable neural surrogate for PhC design space exploration.
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All authors Cen Chen, Haitao Huang, Jiazhi Mao, Feifan Xu, Zhe Zhuang, Yuxiang Ren
Artificial Intelligence
On the Use of LLMs for Specialised Terminology: A Good Alternative to Corpora?
Joachim Minder, Guillaume Wisniewski and Natalie Kübler
Specialised translation relies on the use of documentary and terminological resources, including corpora. These resources are particularly useful for terminology. However, their compilation and exploitation have several limitations: they require time, technical skills and access to data that can be difficult to collect. This study examines the extent to which LLMs can assist specialised translators in finding equivalents from English to French. We evaluate four proprietary models, GPT-4o, GPT-5.2, Claude Sonnet 4.5 and DeepSeek, in two specialised domains, Earth, Environmental and Planetary Sciences (EEPS) and Natural
Language Processing (NLP). The experiment is based on 80 terms per domain and compares two prompting strategies: a terminology and a translation mode. The results highlight clear differences between models, prompting strategies and, to a lesser extent, domains. Claude Sonnet 4.5 achieves the best results in the most favourable configuration, while DeepSeek stands out for its greater stability. Analysis of confidence estimates also shows that they are only a partial indicator of terminological accuracy. Overall, the findings suggest that LLMs can be useful tools for specialised translators, but cannot, at this stage, replace specialised corpora. This research therefore paves the way for future work on the real practical usefulness of LLMs for specialised translators in work and educational contexts.
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Artificial Intelligence
Unified Semantic Modeling Framework for Large-Scale Job Understanding at LinkedIn
Dan Xu, Baofen Zheng, Jianqiang Shen et al. (+11 more)
Job understanding is critical to LinkedIn's mission of connecting talent with opportunity. This task involves transforming unstructured and noisy job postings into standardized or derived job attributes that power numerous LinkedIn products. However, building a scalable, cost-efficient, and high-performing job understanding system remains challenging. In this paper, we present a unified semantic modeling framework powered by a small
language model (SLM) to address the challenges. We begin by fine-tuning an open-source SLM using a suite of carefully curated synthetic tasks augmented with
reasoning traces. These tasks jointly target taxonomy-guided classification and taxonomy-agnostic entity extraction. This allows the resulting model to acquire robust zero-shot generalization for job understanding in structured and unstructured contexts. Building upon this foundation, we introduce a multi-adapter architecture with attribute grouping to facilitate efficient task-specific adaptation while streamlining model management across diverse downstream attributes. Offline evaluations and online A/B tests demonstrate significant performance improvement while reducing operational complexity. Our work provides practical insights into building industry-scale text understanding systems.
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All authors Dan Xu, Baofen Zheng, Jianqiang Shen, Qi Xiao, Benjamin Hoan Le, Wen Pu, Saurabh Gupta, Ran Zhou, Neha Saraf, Alice Leung, Qianqi Shen, Liangjie Hong, Jingwei Wu, Wenjing Zhang
Artificial Intelligence
TRACE: Business Rule-Grounded Reasoning Curriculum for Knowledge-Preserving Parametric Tool Retrieval in Enterprise LLMs
Sai Shruthi Sistla, Ashutosh Hathidara, Christopher Toukmaji et al. (+2 more)
Parametric
retrieval enables LLMs to retrieve tools implicitly by assigning each API a unique virtual
token and training the model to generate it via constrained beam search. Toolsense shows that this regime has two critical drawbacks: it destroys parametric tool knowledge during training, and its beam-search decoding is too slow for real-time deployment. We introduce TRACE (Tool
Retrieval via Augmented Chain-of-thought and Enterprise rules), a two-stage curriculum that resolves this dissociation. Stage 1 reuses the multi-format memorization SFT from ToolSense to seed tool knowledge with LoRA. Stage 2 is our core contribution: the model is trained to emit a thinking trace before producing a JSON list of tool
tokens, using two data sources – RRB pairs from ToolSense and queries synthesized to target business rules curated by domain experts – both augmented with
reasoning traces. This training objective preserves Stage 1 MCQ and QA probing accuracy while enabling single-beam greedy decoding at production latency. Evaluated on a combined enterprise catalog of 8,300+ tools across two enterprise product lines, TRACE training for Stage 2 not only preserves but improves tool understanding: MCQ accuracy gains +3.2 pp and QA probing gains +9 pp over Stage 1. On
retrieval, TRACE achieves ~86% recall on Domain A and ~60% on Domain B – compared to
embedding baseline performance of ~27% & ~52% – both with single-beam greedy decoding, making it directly deployable at production latency.
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All authors Sai Shruthi Sistla, Ashutosh Hathidara, Christopher Toukmaji, Mayank Shrivastava, Karthikeyan Asokkumar
Artificial Intelligence
Fast Cross-Scenario Adaptation of CSI Models via Channel Conditional Parameter Generation
Xudong Zou, Siyu Wu, Zunlei Feng et al. (+4 more)
Deep learning has shown strong potential for massive multiple-input multiple-output (Massive
MIMO) physical-layer tasks, including channel state information (CSI) feedback and channel estimation. However, environmental heterogeneity can severely degrade CSI models in unseen scenarios, while conventional adaptation requires target-domain data and substantial computation. This paper proposes Channel Conditional Parameter Generation (CCPG), an end-to-end pipeline for rapid deployment of CSI models in dynamic wireless environments. CCPG identifies scene-sensitive adaptation bottlenecks through component-freezing experiments and generates only lightweight LoRA weights instead of full model parameters. It compresses high-dimensional channel features into compact latent conditions using cascaded SVD and a Perceiver Resampler. An energy-based canonicalization mechanism mitigates permutation and sign ambiguities in LoRA weights, while a
diffusion-based generator incorporates structural information and an asymmetric size-aware loss for
topology-aware parameter generation. Experiments on DeepMIMO and WAIR-D for CSI feedback and channel estimation show that CCPG adapts to new scenarios in about 3 seconds with a single forward pass, without target-scenario training or fine-tuning, and achieves cross-domain recovery performance comparable to costly online adaptation. These results demonstrate that CCPG enables efficient deployment of CSI models in large-scale dynamic wireless scenarios for intelligent 6G communications.
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All authors Xudong Zou, Siyu Wu, Zunlei Feng, Jie Song, Yuanyu Wan, Mingli Song, Jiacong Hu
Artificial Intelligence
Answering Path Queries under Linear and Guarded Existential Rules
Jean-François Baget, Meghyn Bienvenu, Marie-Laure Mugnier et al. (+1 more)
Ontology-mediated query answering is concerned with the problem of answering queries over knowledge bases consisting of a database instance and an ontology. While most work in the area focuses on conjunctive queries (CQs), navigational queries have gained increasing
attention. In this paper, we investigate the complexity of answering two-way (conjunctive) regular path queries ((C)RPQs) over knowledge bases whose ontology is given by a set of guarded existential rules. We first consider the subclass of linear existential rules and show that (C)RPQ answering is NL-complete in data complexity, which matches the data complexity of answering RPQs over plain graph databases (i.e., without an ontology). In combined complexity, both tasks are ExpTime-complete in the general case, but RPQ and CRPQ answering drop to PTime-complete and PSpace-complete respectively if there is a bound on predicate arity. For guarded rules, we provide a non-trivial reduction to the linear case, which allows us to show that the complexity of (C)RPQ answering is the same as for CQs, namely 2ExpTime-complete in combined complexity (ExpTime-complete in the bounded-arity case) and PTime-complete in data complexity.
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All authors Jean-François Baget, Meghyn Bienvenu, Marie-Laure Mugnier, Michaël Thomazo
Artificial Intelligence
CallBench: A Benchmark for Dual-Goal Coordination in Phone Call Assistants
Xuzhao Geng, Haozhao Wang, Xuelian Li et al. (+4 more)
Target-oriented dialogue systems have demonstrated strong capabilities in completing user goals through interactive conversations. However, existing studies are primarily designed for single, explicit goal completion, while phone call assistants face a proxy setting that requires coordinating the device owner's explicit preset goal with the caller's implicit and dynamic goal. We introduce CallBench, a Chinese
benchmark for evaluating dual-goal coordination in phone call assistants. CallBench contains 50,000 complete multi-turn phone call dialogues across six scenarios: takeout, delivery, taxi, work, life, and harassment. It covers regular presets, emergent presets, and no-preset cases, and includes diverse relations between owner-side and caller-side goals, such as
alignment, complementarity, irrelevance, and conflict. We further design a preset-aware turn-level evaluation protocol covering semantic understanding, context use, active guidance, response quality, preset compliance, dialogue rhythm, and safety. Experiments on representative dialogue methods show that existing approaches still struggle with this task, highlighting the need for phone call assistants that can make reliable turn-level decisions between two independent goals under proxy constraints.
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All authors Xuzhao Geng, Haozhao Wang, Xuelian Li, Zhenyu Yang, Haonan Lu, Rui Zhang, Ruixuan Li
Artificial Intelligence
YUKTI: From Natural-Language Situations to Robust, Verifiable Decisions An Uncertainty-Typed Proposition IR, Assumption-Robust Pareto Frontiers, and a Regret Certificate
Suyash Mishra
Language models turn a worded situation into a numeric plan, and the dominant pipelines (NL4Opt, OptiMUS, ORLM, OR-LLM-
Agent) commit to a single objective and point-valued coefficients, then solve once. For decisions that allocate real budget, effort, or clinical
attention, that confidence is the failure mode: every objectified number is an assumption, and a plan optimal only if the guesses are exactly right is fragile – mimicry of computation. YUKTI changes the target of autoformulation. Its representation is a typed-proposition graph whose relationships carry shape priors, coefficient uncertainty, and provenance. YUKTI routes each stage to an exact, nonlinear, or evolutionary solver; couples stages by a distributional Pareto hand-off; and introduces Assumption-Robust Pareto Frontiers (ARPF), resampling assumptions (including structural epsilon-contamination) to score how often each action survives (rho). We prove a bound making rho an exact factor of decision regret, add auditable traceability, and synthesize a
benchmark-faithful data foundation when none exists (SRJANA). We validate three ways: under controlled misspecification the robust compromise cuts mean and tail regret by over 90% versus a naive point plan; on a regulated commercial decision we optimize inside a lawful action space and price the downside in euros; and on a real public
dataset of 41,188 decisions an out-of-sample backtest beats the logged status quo by 34% and a naive point rule by 4% while reducing the optimizer's curse. The solvers are standard; we claim no
benchmark-SOTA win. A head-to-head shows an LLM given the correct numbers, and single-objective optimization, both incur about 47x the held-out regret of YUKTI – an LLM is a formulator, not a solver. Under long-range causal coupling, the forward hand-off becomes unsound, locating where it must become a backward-induction causal policy.
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Artificial Intelligence · Cross-listed from cs.OH
Model Collapse: On Recursion, Noise, and Uncharted Machine Visions
Violaine Boutet de Monvel
Since 2023, computer scientists have warned against model collapse – the contamination of training sets with AI-generated outputs that progressively degrade model performance. Exemplifying a positive-feedback-driven failure, it produces effects such as word repetition or pixel noise, ultimately leading to a loss of meaning and coherence – at least from an engineering standpoint. From a creative one, however, collapse is not merely a breakdown: it also functions as a recursive mirror that recalls early analog
video feedback experiments, raising once again the question of what happens when a system turns inward and sees itself. In such cases, so-called machine
vision no longer transmits the world (as in tele-
vision) but increasingly generates worlds from within. Drawing on media archaeology through case studies of both historical
video synthesis techniques and contemporary artistic uses of machine learning, this paper examines what recursive training reveals about the dependent nature of AI-generated data. It argues that the potential effects of collapse challenge transhumanist ideals while inviting an aesthetic perspective, positioning noise and recursion as key concepts for understanding both artmaking and the AI ecosystem. Distributing agency across scales and networks, the latter currently remains reliant on new human-produced content, particularly within foundation models trained on massive
datasets.
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Artificial Intelligence · Cross-listed from cs.RO
Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration
Xinghao Zhu, Zixi Liu, Shalin Jain et al. (+18 more)
Dexterous
robot manipulation can benefit from the abundance of human demonstrations, but transferring such demonstrations to
robot policies remains challenging. We present Contact Wrench Guidance from Human Demonstration in Robotic Dexterous Manipulation (CHORD), a framework for long-horizon manipulation of rigid and articulated objects with reinforcement learning. The key idea is object-centric contact wrench space guidance: we represent human and
robot motions by the forces and torques they can induce on the object, enabling similarity to be measured by the induced instantaneous motions. This guidance makes reinforcement learning more scalable for contact-rich dexterous manipulation. We further introduce a large-scale simulation
benchmark with 4,739 bimanual dexterous manipulation tasks, constructed from motion-capture
datasets and reconstructed in-house
videos. Evaluated on 1,831
benchmark tasks, CHORD achieves an average success rate of 82.12%, demonstrating strong scalability. CHORD also generalizes to whole-body manipulation from hand-only and third-person demonstrations, achieving a 90.77% success rate, and the learned policies transfer to the real world in both open-loop and closed-loop settings.
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All authors Xinghao Zhu, Zixi Liu, Shalin Jain, Chenran Li, Milad Noori, Michael Andres Lin, Huihua Zhao, John Welsh, Mrinal Verghese, Wei Liu, Tingwu Wang, Xingye Da, Zhengyi Luo, Vishal Kulkarni, Naema Bhatti, Yuke Zhu, Linxi Fan, Bowen Wen, Danfei Xu, Soha Pouya, Yan Chang
Artificial Intelligence
The MMM Data Model – A Normative Specification for Knowledge Interoperability in a Decentralisable Knowledge Commons
Mathilde Noual
Many information systems are built around documents: self-contained units optimised for print production and linear reading. While effective for large-scale dissemination, the document-centric organisation constrains how knowledge can be structured, updated, shared, and reused. Formal approaches address some of these limitations but struggle to achieve widespread contribution and adoption due to their prioritisation of formal structure over other system properties such as human usability and scope. AI systems are reshaping document production, but without providing a unified portable alternative to traditional documents for humans' expression and exchange of knowledge. This paper presents MMM, a data model for knowledge documentation that emerged from the practical needs of interdisciplinary collaborative research, and positioned here within a comparative analysis of the design space of information systems. MMM combines a small set of normative constraints with the expressive freedom of free-text labels. It is designed for interoperability across disciplines, applications and deployments without requiring semantic convergence. A reference implementation and pilot deployment data demonstrate implementability and early usability.
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Artificial Intelligence · Cross-listed from cs.RO
Memory-Native Non-Terrestrial Networks for Embodied Intelligence
Chengyang Li, Yikun Wang, Jiahui He et al. (+6 more)
Non-terrestrial networks (NTN) provide ubiquitous connectivity for
embodied intelligence (EI), enabling
robots in the wilderness to leverage cloud resources or report critical information to remote centers. However, the synergy is nontrivial due to the highly dynamic, resource-constrained,
topology-varying, and task-oriented environment. Existing memoryless NTN protocols become inefficient, since the decisions are driven by local channel conditions and instantaneous service demands. To address these limitations, this paper proposes the
memory-native NTN (Mem-NTN) paradigm that leverages long-horizon contexts for
memory-augmented system optimization. To realize this paradigm shift, we establish a dual-
memory architecture that distinguishes between physical
memory representing the state of the world and digital
memory encoding historical network experience. We develop
memory acquisition, compression, valuation, update, and utilization mechanisms that facilitate cross-layer,
memory-native decision-making, spanning from the physical and access layers up to the network and application layers. Experiments in satellite
embodied question answering (SEQA) demonstrate that the proposed Mem-NTN consistently outperforms conventional stateless NTN and terrestrial approaches.
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All authors Chengyang Li, Yikun Wang, Jiahui He, Yujie Wan, Shuai Wang, Yuan Wu, Yik-Chung Wu, Chengzhong Xu, Huseyin Arslan
Artificial Intelligence · Cross-listed from cs.NE
Local Pheromone Network: Sparse Local Learning with Multi-Scale Synaptic Trails, Consolidation, and Replay
Xingcheng Fu, Xianjun Chen and Zhihao Li
Backpropagation-trained dense neural networks are powerful function approximators, but they couple learning across many parameters and can overwrite previous associations when tasks conflict. This paper describes Local Pheromone Network, a small research prototype for sparse, local, manually updated neural networks. In Local Pheromone Network, each output unit reads only a fixed local neighborhood of input units subject to
geometric distance and molecular-tag compatibility. Each synapse stores a weight, a short-term pheromone trace, a long-term pheromone trace, and an optional consolidation state. Training does not call automatic differentiation. Instead, every layer performs a pheromone-weighted Hebbian-style update on a budgeted subset of local synapses selected from local error and co-activity. The update budget adapts online: it shrinks when loss improves and expands toward recently active neighborhoods when loss worsens. Optional mechanisms add structural plasticity, local replay, output masks for partitioned learning, and a target-free local contrastive step. We present the implementation, learning rule, and preliminary experiments on synthetic regression, partitioned
memory, conflicting
memory, consolidated conflict, structural plasticity, replay, and a synthetic long-context hybrid
memory task. The prototype learns local linear rules, preserves partitioned
memories through tags and masks, reduces forgetting under consolidation, and uses replay under conflict.
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Artificial Intelligence · Cross-listed from cs.CV
Data Provenance for Image Auto-Regressive Generation
Bihe Zhao, Louis Kerner, Michel Meintz et al. (+3 more)
Image autoregressive models (IARs) have recently demonstrated remarkable capabilities in visual content generation, achieving photorealistic quality and rapid synthesis through the next-
token prediction paradigm adapted from large
language models. As these models become widely accessible, robust data provenance is required to reliably trace IAR-generated images to the source model that synthesized them. This is critical to prevent the spread of misinformation, detect fraud, and attribute harmful content. We find that although IAR-generated images often appear visually identical to real images, their generation process introduces characteristic patterns in their outputs, which serves as a reliable provenance signal for the generated images. Leveraging this, we present a post-hoc framework that enables the robust detection of such patterns for provenance tracing. Notably, our framework does not require modifications of the generative process or outputs. Thereby, it is applicable in contexts where prior watermarking methods cannot be used, such as for generated content that is already published without additional marks and for models that do not integrate watermarking. We demonstrate the effectiveness of our approach across a wide range of IARs, highlighting its high potential for robust data provenance tracing in autoregressive image generation.
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All authors Bihe Zhao, Louis Kerner, Michel Meintz, Tameem Bakr, Franziska Boenisch, Adam Dziedzic
Artificial Intelligence · Cross-listed from cs.RO
RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis
Minh-Loi Nguyen, Nghiem T. Diep, Hung K. Nguyen et al. (+13 more)
Robot world models now generate synthetic manipulation
videos, but evaluating them is hard: realistic-looking outputs often violate physical laws, temporal consistency or task logic, while conventional metrics and monolithic
Vision-
Language Model (VLM) judges return scores that do not say what failed. We present RoboGaze, a training-free multi-
agent VLM evaluator that grounds the task and scene, routes each subtask to dimension-specific specialists and verifies their findings with a critic, producing temporally localized diagnostic reports that state what failed, when, why and how severely, under a schema of 6 dimensions and 30 failure types whose annotation reliability and coverage we measure. To
benchmark it, we introduce RoboGazeBench, 914 human-annotated clips from five generators, conditioned on simulated and real-world data and running from 4.9 to 148.9 s. Across eight open-source and proprietary VLM backbones, RoboGaze outperforms every baseline; even a published agentic detector given the same schema and a slightly larger inference budget trails it by 10.1-12.3 description-F1 points, a margin that blinded human raters confirm. On the strongest backbone it closes 83% of the gap between chain-of-thought prompting and human agreement, and its critic curbs the "cry-wolf" false positives of standard VLMs, lifting mean clean-clip accuracy from 17% to 85%. The advantage is largest on held-out generators and long clips, and carries over to selecting generated demonstrations (80% vs. 71% precision).
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All authors Minh-Loi Nguyen, Nghiem T. Diep, Hung K. Nguyen, Minh Le, Doanh L. Thien, Tuan A. Tran, Hoang H. Tran, Dung D. Le, Vu N. Duong, Khoa D. Doan, Ngan Le, Daniel Sonntag, An T. Le, Vien Anh Ngo, Duy M. H. Nguyen, Tran V. Nhiem
Artificial Intelligence · Cross-listed from cs.RO
A Query-Driven Communication-Efficient Digital Twins Design for Autonomous Driving
Nuocheng Yang, Longyu Zhou, Sihua Wang et al. (+2 more)
Digital twins (DTs) have become a potential technology to perform risk-free simulation of physical entities for deterministic and high-reliability services in diverse scenarios such as autonomous driving and low-altitude economy. In the autonomous driving scenario, traditional DT methods that rely solely on vehicle's real-time state synchronization, however, might lead to unacceptable computing and communication consumption for construction of high-fidelity DT with redundant data. To address this issue, we first propose a query-driven DT architecture to enable the DT to actively request the desired environment data from vehicles based on its simulation result. Then, we formulate an optimization problem whose goal is to minimize autonomous driving position error while accounting for DT fidelity and communication constraints. We also design a cross-time-step progressive query mechanism to further improve communication efficiency. The simulation results show that our proposed method achieves a 24% reduction in planning position error compared to traditional methods, while reducing communication overhead by 40%.
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All authors Nuocheng Yang, Longyu Zhou, Sihua Wang, Changchuan Yin, Tony Q. S. Quek
Artificial Intelligence · Cross-listed from cs.SD
EmotionAI: A Privacy-Preserving Computational Intelligence Pipeline for Speech-Emotion-Grounded Conversational Analysis
Wai Laam Mak, Isibor Kennedy Ihianle and Pedro Machado
Reviewing recorded interviews for affective cues such as composure and agitation is slow and subjective, and cloud services that could automate the task require sensitive audio to leave the device. EmotionAI is a fully local Computational Intelligence (CI) pipeline that couples
Speech Emotion Recognition (SER) with generative
reasoning. Speaker diarisation, Whisper Automatic
Speech Recognition (ASR) and a wav2vec2
emotion classifier produce per-segment affective evidence, and an
adversarial three-model local Large
Language Model (LLM) panel turns that evidence into timestamp-grounded, citation-constrained answers. Zero-shot evaluation on the RAVDESS four-class English subset (n = 672) measures the cost of cross-corpus transfer: the deployed classifier scores 48.8% accuracy, above random (24.9%) and majority (28.6%) baselines but below an in-domain MFCC + logistic-regression comparator (71.0%). The complete pipeline runs in a mean 157 s on CPU (real-time factor approximately 1.33) with zero external calls. The contribution is not state-of-the-art SER but an auditable,
privacy-preserving integration of imperfect affective evidence into grounded conversational analysis.
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Artificial Intelligence · Cross-listed from q-bio.GN
Stable-Shift: Biologically Structured Prediction of Transcriptional Responses to Unseen Gene Perturbations
Sajib Acharjee Dip and Liqing Zhang
Predicting transcriptional responses to genetic perturbations could reduce the experimental burden of functional genomics, but extrapolation to genes that were never perturbed during training remains difficult. We present Stable-Shift, a structured method for estimating unseen-gene responses. Stable-Shift aggregates single-
cell measurements into perturbation-level expression shifts, fits a low-rank response basis using training perturbations only, and predicts an unseen gene's coordinates in that basis from biological context. The context combines STRING interactions, network structure, control-
cell expression statistics, and Gene Ontology annotations; the evaluated implementation uses graph convolution to integrate these inputs. On the supplied K562 Perturb-seq
benchmark, Stable-Shift obtained 0.592 cosine similarity, compared with 0.569 for GEARS, together with higher Spearman correlation and top-gene precision among the evaluated methods. Its mean cosine similarity over five unseen-gene splits was 0.589 +/- 0.008. The same ordering was observed in the supplied graph-aware, residualized, gene-space, and Norman-
dataset comparisons. These results support further study of biologically structured latent-response prediction, while the lower gene-space accuracy and sensitivity to sparse graph neighborhoods limit the scope of the present conclusions.
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Artificial Intelligence · Cross-listed from q-bio.GN
Privacy-preserving federated tensor decomposition of single-cell immune data: recovering multicellular programs across institutions
Axel Faes, Stephanie M. van den Berg and Maryam Amir Haeri
Tensor decomposition of donor $\times$
cell-type $\times$ gene single-
cell data recovers multicellular programs: coordinated axes of inter-individual transcriptional variation that span
cell types and stratify disease. Yet immune single-
cell atlases are increasingly multi-institution, multi-ancestry, and governed, so patient
cells often cannot be pooled. We present a
federated estimator: each site computes a local program subspace, and a coordinator merges these by stacked SVD under
federated global-mean centering, provably equivalent (up to truncation) to the centralised decomposition. This centering makes the merge robust to site-label confounding (program AUC $0.957$ vs.\ $0.861$ for naive per-site centering). Only program subspaces leave a site, and aggregation is compatible with secure aggregation. On a 261-donor systemic lupus erythematosus atlas it recovers the canonical interferon program (ISG enrichment AUC $0.998$; case–control separation $0.958$; bootstrap $\Delta\text{AUC}=-0.000$, 95% CI $[-0.004,+0.012]$ vs.\ centralised), across institution-scale and multi-ancestry partitions, and across three real COVID-19 sites (subspace correlation $0.989$). It recovers the program when no site observes all
cell types (correlation $1.000$, exact by construction), which fixed-feature
federated PCA cannot. On an interstitial-lung-disease atlas the recovered program predicts disease better than the best single
cell type (AUC $0.96$ vs.\ $0.91$; gap 95% CI excludes zero) and the advantage survives federation; a liver cohort is consistent ($p=0.005$). Membership-inference shows secure aggregation cuts attack AUC from $0.91$ to $0.61$. The method enables cross-institution, cross-ancestry recovery of multicellular immune programs without sharing
cells.
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Artificial Intelligence
The Hitchhiker's Guide to Agentic AI: From Foundations to Systems
Haggai Roitman
The Hitchhiker's Guide to Agentic AI is a comprehensive practitioner's reference for building autonomous AI systems, covering the full stack from first principles to production deployment. The central thesis: building great agentic systems requires understanding every layer of the pipeline, not just one. The book opens with the LLM substrate, covering
transformer architecture, GPU systems, training and fine-tuning (SFT, LoRA, MoE), model compression, and inference optimization, as essential foundations. It then develops the
alignment and
reasoning layer: RLHF, PPO, DPO and its variants, GRPO, reward modeling, and RL for large
reasoning models including chain-of-thought and test-time scaling. The second half is devoted to agentic AI proper: agentic training and trajectory-based RL, RAG and Agentic RAG,
memory systems (in-context, external, episodic, and semantic),
agent harness design, loop engineering, graph-based orchestration, and a taxonomy of
agent design patterns covering security, red teaming, and gateway infrastructure. Inter-
agent coordination is covered in depth: the Model Context Protocol (MCP),
agent skills and tool use, the
Agent-to-
Agent (A2A) protocol, and multi-
agent architectures spanning centralized, decentralized, and hierarchical
topologies. The book concludes with
agent development frameworks, agentic UI design, evaluation methodology (non-deterministic evaluation,
reasoning collapse, LLM-as-Judge), production deployment, and the regulatory environment (EU AI Act, California SB 942) as an engineering requirement. Each chapter pairs theory with implementation guidance, executable notebooks, and references to the primary literature.
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Artificial Intelligence · Cross-listed from quant-ph
Self-Modulating Quantum Fast-Weight Programmers for Efficient Adaptive Sequential Learning
Samuel Yen-Chi Chen, Yifeng Peng, Kuo-Chung Peng et al. (+8 more)
Recent advances in
quantum machine learning have motivated efficient models for sequential data processing. In this paper, we propose Self-Modulating
Quantum Fast Weight Programmers, or Self-Modulating QFWP, which extends
Quantum Fast Weight Programmers by introducing adaptive modulation over both newly generated fast-weight updates and historical fast-weight
memory. Numerical results show that the proposed mechanism improves convergence stability and prediction performance across varying model settings, including different numbers of
qubits and input sequence lengths. We further provide theoretical arguments explaining how self-modulation balances new information injection with
memory retention, thereby enhancing temporal information propagation. These results suggest that Self-Modulating QFWP is a compact and effective framework for
quantum machine learning on time-series data.
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All authors Samuel Yen-Chi Chen, Yifeng Peng, Kuo-Chung Peng, Jiun-Cheng Jiang, Chun-Hua Lin, Junghoon Justin Park, Huan-Hsin Tseng, Hsin-Yi Lin, Kuan-Cheng Chen, Chen-Yu Liu, Shinjae Yoo
Artificial Intelligence · Cross-listed from quant-ph
Recursive QLSTM with Dynamic Variational Quantum Circuit Adaptation
Samuel Yen-Chi Chen, Yifeng Peng, Jiun-Cheng Jiang et al. (+8 more)
Recent advances in
quantum computing and machine learning have motivated the development of
quantum models for sequential data processing. In this paper, we propose a Recursive
Quantum Long Short-Term
Memory model, or Recursive QLSTM, which extends QLSTM through metacore-based recursive constructions. We numerically test the model under different input sequence lengths, metacore designs, and recursive rules, and identify the best-performing architecture among these variants. For this selected model, we further provide theoretical arguments explaining why its recursive structure improves temporal information propagation and enhances learning performance. Our results suggest that Recursive QLSTM offers a flexible and effective framework for
quantum recurrent learning over input time series of various lengths.
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All authors Samuel Yen-Chi Chen, Yifeng Peng, Jiun-Cheng Jiang, Chun-Hua Lin, Kuo-Chung Peng, Junghoon Justin Park, Huan-Hsin Tseng, Hsin-Yi Lin, Kuan-Cheng Chen, Chen-Yu Liu, Shinjae Yoo
Artificial Intelligence
GUI vs. CLI: Execution Bottlenecks in Screen-Only and Skill-Mediated Computer-Use Agents
Xiao Zhou, Siyue Zhang, Yilun Zhao et al. (+4 more)
Computer-use
agents can execute software tasks through either graphical interfaces or programmatic command interfaces, but existing evaluations confound interaction modality with differences in tasks, initial states, verifiers, and permitted actions. We introduce a matched execution-layer
benchmark of 440 desktop tasks across 18 applications and 12 workflow categories, where screen-only GUI
agents and skill-mediated CLI
agents receive identical goals, states, and final-state verifiers while being restricted to modality-native actions. In this controlled setting, the strongest GUI
agent reaches a 59.1% full pass rate, outperforming the strongest original-skill CLI
agent at 48.2%; however, verifier-guided skill augmentation raises CLI success to 69.3%, showing that much of the CLI deficit comes from incomplete skill coverage rather than model capability alone. These results suggest that GUI and CLI expose different execution bottlenecks: GUI
agents are limited by reliable grounded interaction over long-horizon workflows, whereas CLI
agents are limited by the coverage and scalability of their skill interfaces.
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All authors Xiao Zhou, Siyue Zhang, Yilun Zhao, Jinbiao Wei, Tingyu Song, Arman Cohan, Chen Zhao