Artificial Intelligence
PaGNet: A Panel-Aware GBDT–Neural Network for Multi-Target Corporate Tax Avoidance Proxy Forecasting
Wonho Song and Hyungjoon Kim
Forecasting corporate tax avoidance proxies from firm–year panel data is challenging because predictive signals are distributed across short firm histories and related targets, while screening-oriented use requires transparent model behavior. We propose PaGNet (Panel-Aware GBDT–Neural Network), a two-branch hybrid that combines a LightGBM branch using panel-temporal summaries with a Panel-MLP branch using
attention-pooled temporal aggregation and shared-trunk multi-task learning. A per-target validation-optimal blender produces both the final prediction and a compact branch-reliance diagnostic without trainable fusion parameters. On the KoTaP panel of 1{,}754 Korean listed firms from 2011–2024, PaGNet is evaluated under a leakage-free, shared-hyperparameter protocol across four feature regimes. In the direct-proxy-lag-excluded FS1 regime and the tax-history-augmented FS2 regime, accrual targets (TSTA, TSDA) route stably to the LightGBM branch, where PaGNet raises explained variance over the strongest of six baselines by roughly $0.08$–$0.11$ on the primary split. GETR often leans toward the neural branch, while CETR exposes a validation–test branch-selection mismatch rather than a stable branch assignment. A panel-flatten control shows that most accrual gains come from observed multi-year base-panel values, with PaGNet's panel-aware representation adding a smaller but directionally consistent refinement. Rolling-origin analysis confirms stable accrual routing, bounds ETR diagnostics to split-specific behavior, and identifies a far-horizon split where supervised models underperform naive persistence. PaGNet is therefore best viewed not as a universally superior tabular learner, but as a proxy-aware panel model that combines competitive
forecasting with explicit per-target branch-reliance reporting.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.IR
FacetCRS: Multi-Faceted Preference Learning for Pricking Filter Bubbles in Conversational Recommender System
Yongsen Zheng, Ziliang Chen, Jinghui Qin et al. (+1 more)
The filter bubble is a notorious issue in Recommender Systems (RSs), which describes the phenomenon whereby users are exposed to a limited and narrow range of information or content that reinforces their existing dominant preferences and beliefs. This results in a lack of exposure to diverse and varied content. Many existing works have predominantly examined filter bubbles in static or relatively-static recommendation settings. However, filter bubbles will be continuously intensified over time due to the feedback loop between the user and the system in the real-world online recommendation. To address these issues, we propose a novel paradigm, Multi-Facet Preference Learning for Pricking Filter Bubbles in Conversational Recommender System (FacetCRS), which aims to burst filter bubbles in the conversational recommender system (CRS) through timely user-item interactions via natural
language conversations. By considering diverse user preferences and intentions, FacetCRS automatically model user preference into multi-facets, including entity-, word-, context-, and review-facet, to capture diverse and dynamic user preferences to prick filter bubbles in the CRS. It is an end-to-end CRS framework to adaptively learn representations of various levels of preference facet and diverse types of external knowledge. Extensive experiments on two publicly available
benchmark datasets demonstrate that our proposed method achieves state-of-the-art performance in mitigating filter bubbles and enhancing recommendation quality in CRS.
Read the full abstract ▾
All authors Yongsen Zheng, Ziliang Chen, Jinghui Qin, Liang Lin
Artificial Intelligence · Cross-listed from cs.CY
ClassVision: AI-Powered Classroom Attendance System
Ankit Kumar Aggarwal, Veerabhadra Rao Marellapudi, Ovadia Sutton et al. (+1 more)
Students and working professionals have to go through the attendance process every day. Traditional methods of marking attendance using pen and paper or online platforms are human-intensive and time-consuming. To address the challenges in manual attendance processes, this research explores the use of face detection (FD) and face recognition (FR) technology to automate the attendance process, particularly in educational settings, and build a ClassVision course attendance system. We also propose an automated attendance system featuring a human-computer interaction (HCI) and user-friendly web interface that utilizes real-time image processing to identify and recognize students in classrooms and automatically record their attendance. We identified RetinaFace as the best face detection model, and when combined with Face Recognition for verification, it provided the most promising results with a cropped
embedding of 50x50 pixels.
Read the full abstract ▾
All authors Ankit Kumar Aggarwal, Veerabhadra Rao Marellapudi, Ovadia Sutton, Youshan Zhang
Artificial Intelligence · Cross-listed from cs.CL
DeMTS: Denoising Trajectories as Multivariate Time Series for Hallucination Detection in Diffusion Language Models
Xin Zhang, Yili Wang, Yue Tan et al. (+6 more)
Diffusion large
language models (D-LLMs) have emerged as a promising paradigm for text generation. However, similar to autoregressive LLMs, D-LLMs remain vulnerable to
hallucinations, where fluent outputs may contain factually incorrect or unsupported content. Although existing
hallucination detection methods for D-LLMs attempt to leverage uncertainty trajectories of the denoising process to better identify
hallucination signals, they typically compress the trajectories along either the temporal or
token dimension, overlooking the useful information encoded in the complete two-dimensional
token-step structure. Consequently, they may fail to capture
hallucination-relevant patterns, such as inconsistent convergence and cross-
token fault propagation, leading to suboptimal detection performance. To bridge this gap, we propose a D-LLM
hallucination detection framework that formulates the Denoising trajectories as Multivariate Time Series over learnable latent variables (DeMTS for short). DeMTS employs a trajectory-preserving
token-to-variable assignment module to convert
token signals into stable latent variables. Based on these variables, we propose dynamic multivariate temporal modeling to progressively integrate inter-variable dependency modeling with temporal encoding for
hallucination prediction. Extensive experiments on two D-LLMs backbones and three
benchmarks demonstrate that DeMTS outperforms existing
hallucination detection methods while maintaining strong robustness, efficiency, and cross-task transferability.
Read the full abstract ▾
All authors Xin Zhang, Yili Wang, Yue Tan, Xin He, Yanyu Qian, Yixin Liu, Yi Chang, Shirui Pan, Xin Wang
Artificial Intelligence
Accuracy and Reliability of Large Language Models in Cosmetic Chemistry and Skin Health: A Benchmarking Study
Amelia Liu
As consumers increasingly turn to AI chatbots for skincare advice, the technical accuracy of Large
Language Models (LLMs) in cosmetic chemistry remains largely under-evaluated. We benchmarked 14 LLMs on a structured set of topics related to cosmetic chemistry, including the chemical properties of specific cosmetic ingredients and common cosmetic scenarios that may be of interest to consumers. Web search was disabled throughout to assess each model's internalized knowledge rather than its internet
retrieval capacity. Overall performance was poor, with the most pronounced deficits in quantitative
reasoning and structural identification tasks. While models handled general skincare questions with reasonability, responses consistently lacked the technical depth required for informed consumer decision-making. Notably, conversation with AI can pose a risk: outputs that sound authoritative but contain technical errors are less likely to generate skepticism compared to responses that explicitly acknowledge uncertainty. These findings suggest that general-purpose LLMs, trained predominantly on unverified public data, are currently not reliable sources of cosmetic chemistry information. Progress on two fronts, fine-tuning verified chemical and dermatological
datasets, and substantial improvements to algorithmic
reasoning, will likely be needed before these tools can be considered as resources for public use.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CL
Characterizing Rhetorical Misalignment in Decision-Making with Language Models
Zirui Cheng, Joey Chan, Simo Du et al. (+3 more)
Human decision-making is often shaped by a range of well-documented cognitive biases. As large
language models (LLMs) become increasingly integrated into high-stakes human-AI decision-making, it is important to understand whether their outputs can amplify potential biases, how this influences human decisions, and crucially, whether it can lead to harmful consequences. In this work, we develop a decision-theoretic framework to study rhetorical misalignment, a failure mode where an LLM uses rhetorically inappropriate forms of presentation for a given decision context, thereby inducing suboptimal human decisions. We empirically investigate this phenomenon through a human-subject experiment in realistic clinical decision-making using a
dataset curated from the United States Medical Licensing Examination. By measuring how LLM-generated information affects decisions, we observe that LLMs induce an average 2.81% rate of harmful decision flips across different models, where clinician participants change from a correct to an incorrect answer. Rationales reported by participants provide evidence that these revisions are closely related to the
language used by LLMs that may induce different types of cognitive biases, including anchoring, authority bias, and loss aversion. To enable scalable evaluation, we instantiate our theoretical framework using decision-makers simulated by LLMs to computationally measure rhetorical misalignment. Our findings reveal a safety concern previously unrecognized in high-stakes domains: a model can be factually aligned yet still induce harm through its rhetorical presentation.
Read the full abstract ▾
All authors Zirui Cheng, Joey Chan, Simo Du, Chenhao Tan, Yue Guo, Hao Peng
Artificial Intelligence · Cross-listed from cs.CL
Inference-Time Mitigation of Adversarial Political Bias in Large Language Models
Tejaswi V. Panchagnula, Bruce Coburn, Bryce J. Dietrich et al. (+3 more)
As Large
Language Models (LLMs) become the mainstay for information
retrieval and summarization tasks, ensuring that they are always non-partisan and invulnerable to political bias is a critical step towards safer and more trustworthy Artificial Intelligence (AI). Current model
alignment paradigms, such as reinforcement learning from human feedback (RLHF), make LLMs follow overarching safety instructions. However, this instruction tuning can be exploited via
adversarial prompt injection and be used to generate unsafe content. In particular, political bias has not been specifically targeted by modern
alignment techniques as harmful and biased content. To address this vulnerability of LLMs, we propose mitigation strategies using Chain of Thought (CoT) prompting and Direct Preference Optimization (DPO). Using a public
dataset of legislative
videos, we generate summaries using LLMs, inject bias via
adversarial prompting and evaluate their performance on a four axis scale designed for political summarization. In this paper, we present different methods to shield LLMs against the injection of political bias. Our results demonstrate that the proposed Recursive Self-Correction approach raises model performance from a Political Neutrality Likert scale baseline of 2.14 to 4.56, averaged across all models, demonstrating effective inference-time mitigation of political bias in LLM-generated summaries.
Read the full abstract ▾
All authors Tejaswi V. Panchagnula, Bruce Coburn, Bryce J. Dietrich, Robert X. Browning, Edward J. Delp, Fengqing Zhu
Artificial Intelligence
Dynamic Coalition Formation and Communication Pricing in Skill-Based Agentic AI Systems
Mojtaba Eslami
Modern agentic AI systems combine multiple large
language model
agents with heterogeneous skills, yet most architectures either fix communication in advance or allow full broadcast. Both can be inefficient because
token cost, latency, redundancy, and error propagation increase with the number of active
agents and communication links. We model
agent selection and communication as a cooperative game with task-conditioned net utility $U(C\mid x)=V(C\mid x)-\sum_{i\in C}c_i$, separating coalition-level costs from
agent activation costs. We propose a marginal-value activation rule and greedy router, extend the model to optimize communication edges with per-edge costs, and use estimated Shapley values to predict which
agents are worth contacting before and during execution. We connect the problem to submodular maximization and prove two limited guarantees: a
curvature-refined bound for a monotone, cardinality-constrained special case, and a tight $1/2$-approximation, with a correction for signed objectives, for an unconstrained non-monotone case via double greedy. Neither guarantee applies directly to the main router, which remains a heuristic. We also prove a Shapley-submodularity sandwich bound linking the error of marginal-value routing to a per-
agent diminishing-returns quantity. In synthetic experiments, greedy routing achieves $99.5%$ of brute-force-optimal utility while activating $1.96$ of $8$
agents on average, compared with $38.8%$ for full broadcast. Performance is robust to activation cost and redundancy weight but falls to $66%$ under strong violations of submodularity or noisy value estimates. We distinguish the framework from Shapley pricing, hedonic coalition formation, and communication-graph
pruning, and propose evaluation on real multi-
agent LLM
benchmarks.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CL
Search-G1: Co-Evolving Representation-Based Rewards for Selective Grounded Search
Ruoxi Cheng, Haoxuan Ma, Hongyi Zhang et al. (+7 more)
Search-augmented
language agents should retrieve external information only when necessary and ground their answers in retrieved evidence. Yet terminal correctness assigns equal credit to direct answers from parametric knowledge, evidence-grounded answers, and answers recalled after redundant search. Process supervision and LLM judges provide richer feedback but can require high-cost annotation or inference; low-cost, non-contrastive policy confidence signals do not directly identify whether retrieved evidence changes an answer. We propose Search-G1, a reward framework that couples
retrieval necessity with evidence reliance through two intervention-calibrated hidden-state readouts for selective grounded search. A
prompt readout predicts closed-book sufficiency, whose complement proxies policy-relative
retrieval necessity; an answer-commit readout predicts evidence reliance as sensitivity to evidence deletion conditional on the realized
reasoning prefix. A correctness-first reward gates evidence-reliance credit by
retrieval necessity, rewards sufficient direct answers, and penalizes repeated search. After calibration, scoring needs neither process annotations nor LLM-judge inference. As policy-relative targets, trajectory distributions, and representations may drift, Search-G1 periodically regenerates targets and refits its readouts. Across four QA
benchmarks and two scales, Search-G1 achieves competitive task accuracy, with stronger counterfactual evidence dependence and fewer searches in the main comparisons, complemented by independent human audits of answer support.
Read the full abstract ▾
All authors Ruoxi Cheng, Haoxuan Ma, Hongyi Zhang, Junming Zhang, Ranjie Duan, Qiaolin Xia, Hao Wang, Yu Lu, Haibo Shi, Xingjun Ma
Artificial Intelligence
NL2SHACL-Bench: A Benchmark Suite for Natural Language to SHACL Translation
Yuchen Zhou, Niels Bobet and Maribel Acosta
SHACL is a core technology for validating the conformance of RDF knowledge graphs (KGs). Yet, authoring SHACL shapes requires technical expertise that most domain experts lack. Translating natural
language requirements into SHACL (NL2SHACL) would lower this barrier. However, there is no dedicated
benchmark for NL2SHACL, and evaluating generated shapes requires methods beyond string comparison, as semantically equivalent shapes can differ in serialisation and structure. To tackle these challenges, we present NL2SHACL-Bench, a
benchmark suite for natural
language to SHACL translation. Using NL2SHACL-Bench, we evaluate four state-of-the-art large
language models (LLMs) for this task. Our results show that current LLMs are highly capable of generating syntactically valid SHACL, but still struggle to produce semantically equivalent constraints for complex logical and structural patterns. This indicates that NL2SHACL-Bench provides a meaningful basis for measuring advances in the NL2SHACL state of the art.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CL
WuYuEval: A Multi-Level Benchmark for Large Language Models in Solid Waste Management
Yi Zhang, Hongyang Wang, Zheng Hao Leong et al. (+11 more)
Large
language models (LLMs) are increasingly used as technical assistants, but their competence in solid waste management (SWM) remains difficult to assess because existing
benchmarks emphasize general knowledge rather than professional decisions under engineering, environmental, and policy constraints. We introduce WuYuEval, a multi-level
benchmark for evaluating LLMs in SWM across foundational knowledge, domain
reasoning, and expert decision-making. After quality
auditing, WuYuEval contains a Foundation Module with 4,590 closed-ended multiple-choice questions across six task types and eight domain categories, together with an Expert Module with 247 scenario-based open-ended questions involving multi-objective optimization, constraint trade-offs, and system design. For expert tasks, we combine anchor-calibrated LLM-as-a-Judge scoring with Elo-based pairwise comparison. Across 33 LLMs, performance varied widely. The leading model reached 94.64% accuracy on the Foundation Module, but average accuracy still fell from 84.14% on easy questions to 42.50% on hard questions, with lower performance concentrated in calculation, experimental design, urban planning, and open-ended expert tasks.
Reasoning-oriented Thinking modes improve most matched model pairs after
auditing, but the gains depend on baseline capability and are not uniformly positive. These results suggest that visible deliberation helps only when it remains anchored to units, assumptions, and engineering constraints; otherwise, it may drift from decisive answer boundaries. WuYuEval therefore provides both an evaluation resource and an empirical basis for developing SWM-oriented foundation models with professional
reasoning chains and explicit constraint control.
Read the full abstract ▾
All authors Yi Zhang, Hongyang Wang, Zheng Hao Leong, Zihao Wu, Kaijun Lin, Zhixing Pan, Qixun Huangfu, Wei Ren, Wenyan Wu, Fangyun Wang, Wenting Yu, Hengyu Lin, Muling Yang, Zongguo Wen
Artificial Intelligence · Cross-listed from eess.SP
Multimodal Wearable-Based Olfactory-Induced Emotion Recognition in Arousal-Valence Dimensions
Chen-Yang Xu, Lan Zhang, Fei-Yi Fan et al. (+2 more)
Olfaction is important for
emotion regulation because it acts as a non-intrusive and cognitively lightweight pathway that directly engages the
brain s affective circuitry and achieves unobtrusive emotional modulation. This trait is essential for advancing practical affective computing in daily and
attention-critical scenarios. However, current olfactory
emotion research has two key limitations. First, it overemphasises the valence dimension while neglecting arousal. Second, it lacks
multimodal datasets that synchronously capture central and peripheral physiological responses to olfactory stimuli. To address these issues, we construct a large-scale
multimodal olfactory
emotion dataset based on 111 subjects, in which odors are labeled in the 2D arousal-valence space and electroencephalogram (
EEG), electrocardiogram (ECG), and photoplethysmography (PPG) signals synchronously recorded. Nevertheless,
multimodal signals present challenges such as non-stationarity, differences in latency, and cross-modal heterogeneity. Thus, we propose a spatiotemporal-frequency hybrid fusion network (STF-HFNet), which integrates three core modules. Frequency aggregation processing learns adaptive frequency aggregation in order to model non-stationary dynamics. Reciprocal guided
attention enables reciprocal bidirectional calibration for cross-modal temporal
alignment without synchronisation priors. Hybrid collaborative fusion combines spatial and channel
attention mechanisms to enhance cross-modal complementarity while suppressing redundant information. Extensive experiments show that STF-HFNet achieves state-of-the-art (SOTA) recognition accuracies of 88.34% on the AMIGOS
dataset and 92.40% on our self-constructed
dataset, and outperform the SOTA methods by 8.27% and 5.07%, respectively.
Read the full abstract ▾
All authors Chen-Yang Xu, Lan Zhang, Fei-Yi Fan, Bin Hu, Qing-Hao Meng
Artificial Intelligence
RadHarmony: Radiological Data Handling in the Era of Agentic AI
Frank Li, Bardia Khosravi, Mohammadreza Chavoshi et al. (+5 more)
Training deep learning models on radiological images requires integrating heterogeneous
datasets across different sources, file formats, directory layouts, label schemas, and annotation types. We present RadHarmony, an open-source Python library that provides a unified API for loading, harmonizing, and augmenting radiological
datasets, with a primary focus on chest radiographs and early support for computed
tomography (CT) and
magnetic resonance imaging (MRI). RadHarmony standardizes metadata from 24 public
datasets into a single tabular format, wraps MONAI's map-style
datasets for deep-learning-ready sample delivery with optional on-disk caching, and supports classification labels,
segmentation masks, bounding boxes, and radiology report text through a single interface, with an interactive visualization tool for
dataset exploration and verification. To lower the barrier for integrating new
datasets, RadHarmony introduces an AI-
agent skill that guides the full integration workflow from raw data inspection through code generation and testing. We demonstrate the library's utility by pretraining RadHarmony-ViT, a reference
vision transformer baseline that combines three heterogeneous chest radiograph
datasets with no
dataset-specific code. The code and pretrained model weights are available at https://github.com/f10409/RadHarmony.
Read the full abstract ▾
All authors Frank Li, Bardia Khosravi, Mohammadreza Chavoshi, Theo Dapamede, YoungSeok Jeon, Janice Newsome, Hari Trivedi, Judy Gichoya
Artificial Intelligence · Cross-listed from cs.IR
Two Views, One Voice: Evidence-Grounded Conversational Music Recommendation
Sungwook Yoo and Sewook Yoo
Traditional conversational recommenders entangle
retrieval and response generation within a single text interface, so exact entity cues fade as the dialogue's intent evolves, which compromises explanation credibility. We address this within the ACM RecSys Challenge 2026, which mandates both top-20 ranking and evidence-grounded response generation. This paper presents the third-place solution by team "swyoo" for the Blind-B industry track. We decouple
retrieval and response into separate pipelines connected strictly via ranked tracks and metadata.
Retrieval combines a hybrid lexical-dense pool for exact matching with a task-adapted pool driven by fine-tuned Qwen 8B adapters. Candidates are calibrated via LightGBM, then routed to an evidence-grounded propose-assign-select (PAS) framework to structure responses. This system also ranked second on the explanation-quality leaderboard in the final blind evaluation. Our findings demonstrate that: (i) isolating
retrieval and response preserves both catalog cues and fluid intent; (ii) structuring generation via explicit evidence assignment is key to this near-best-in-class explanation reliability.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.IR
REPREC: Representation Driven Parameter-Efficient Recommendation System
Harshini Kavuru, Dwipam Katariya, Giri Iyengar et al. (+3 more)
Large
language models (LLMs) have been applied to sequential recommendation by incorporating collaborative signals through input conditioning or model adaptation. However, existing approaches often require LLM fine-tuning, additional architectural modules, representation
distillation, or item-level conditioning over long interaction histories, increasing computational and deployment costs. We propose REPREC, a lightweight framework that conditions a frozen LLM using compact user-level representations. REPREC maps a fixed-size
embedding from a frozen sequential encoder into a small set of learned soft
tokens through an MLP injector, training only the injector while leaving both pretrained backbones unchanged. Our extensive experiments demonstrate that REPREC consistently improves recommendation performance across different sequential encoders, LLM backbones, and user activity levels. Its compact conditioning mechanism also makes REPREC computationally efficient during both training and inference. Moreover, training with short histories while evaluating with longer contexts retains 94–99% of full-history performance while achieving an average $1.50\times$ per-epoch training speedup. The code is available at: https://github.com/phdbotcode/REPREC
Read the full abstract ▾
All authors Harshini Kavuru, Dwipam Katariya, Giri Iyengar, Pranab Mohanty, Kalanand Mishra, Raghu Machiraju
Artificial Intelligence · Cross-listed from cs.IR
MedJudgeRAG: Option-Wise Evidence Judgment with Dynamic Knowledge Graphs for Medical MCQA
Seongwon Seo, Seung Hwan Cho and Young-Min Kim
In medical multiple-choice question answering (MCQA),
Retrieval-Augmented Generation (RAG) can supplement the domain knowledge of
language models (LMs). However, since vanilla RAG indiscriminately utilizes retrieved documents, it can degrade LM performance. To address this, we propose MedJudgeRAG. Our framework represents retrieved documents as a dynamic knowledge graph (KG) composed of entities and relations. For each option, the model judges an evidence verdict from the retrieved documents and the KG. Based on the verdict combination, the model determines a knowledge utilization strategy to reason toward the final answer. These capabilities are trained via supervised fine-tuning using structured
reasoning traces generated by a teacher LM. The training employs a weighted cross-
entropy loss that differentially weights the KG and
reasoning segments. Experiments on two medical MCQA
benchmarks demonstrate that MedJudgeRAG consistently outperforms both vanilla RAG and parametric baselines. Furthermore, ablation analysis reveals that the dynamic KG is more effective as graph-conditioned supervision at training time than as an explicit output at inference time. Our code is available at https://github.com/hyu-amllab/medjudgerag, and the generated
reasoning traces are released at https://huggingface.co/
datasets/youarethewon/medjudgerag.
Read the full abstract ▾
Artificial Intelligence
Share No More Than the Request Requires: Federated Disclosure for Perspective-Aware AI
Sourena Khanzadeh, Daniel Platnick, Marjan Alirezaie et al. (+1 more)
Modern AI systems bring societal risks such as mass surveillance, extreme concentrations of power, and loss of user autonomy—calling into question a model where third-parties collect and control massive amounts of user data. Users require a sovereign system to securely own, govern, and disclose their context while remaining compliant across regulated domains with strict provenance, interpretability, and policy adherence. Perspective-aware AI approaches this by transforming a user's aggregated personal data into a structured identity model called a Chronicle: a temporal knowledge graph that represents and grows with the user. Chronicles support the secure disclosure of context across
federated networks. A Chronicle holder may expose a queryable, authorized view that a third-party
agent may consult without centralizing anyone's data. This paper explores the problem of minimum-necessary disclosure across domain boundaries: when a requester's
agent queries a Chronicle, how can the system constrain its response to release only what the requester's relationship, stated purpose, and specific task require? We propose Provenance Preserving Chronicles (PPC), a
federated protocol that compiles each holder's Chronicle into a compact authorized evidence subgraph governed by one rule: share no more than the request requires. Holders keep local sovereignty; an access controller projects relationship-aware views over domain-expert ontologies; and a two-phase flow returns provenance-linked text first, releasing raw artifacts only after explicit holder approval. We frame the problem, map gaps in blockchain, P2P, and holder-sovereign designs, define the core constructs, and sketch the protocol with an explicit threat model.
Read the full abstract ▾
All authors Sourena Khanzadeh, Daniel Platnick, Marjan Alirezaie, Hossein Rahnama
Artificial Intelligence · Cross-listed from stat.ML
Modeling Memory-Dependent Reliability of LLMs: A Hidden Markov Model
Robab Aghazadeh Chakherlou, Siddartha Khastgir, Peter Popov et al. (+1 more)
Reliability assessment of large
language models (LLMs) seeks to estimate the probability that a model produces correct responses under a specified operational profile. Conventional
benchmark-based evaluation, often summarized by aggregate accuracy, provides a point estimate of performance but does not characterize the uncertainty associated with reliability claims. Currently, statistical inference methods for LLM reliability assessment are emerging. However, a key assumption underlying these models is that test outcomes can be treated as independent repeated trials. This assumption may be inappropriate in sequential settings, where later responses depend on earlier interactions through retained context, error propagation, or an evolving interaction state. We extend a hierarchical
Bayesian framework for LLM reliability assessment by relaxing the assumption of independent task outcomes and introducing a Hidden Markov Model to capture sequential dependence in
benchmark-constructed interaction sessions. In this formulation, outcomes are generated from a latent interaction state evolving according to a first-order Markov process, capturing changes in interaction context. Through experiments using Anthropic Claude and OpenAI on four
datasets, we demonstrate the potential impact of sequential dependence on reliability assessment. The results suggest that ignoring sequential dependence may lead to overconfident reliability estimates.
Read the full abstract ▾
All authors Robab Aghazadeh Chakherlou, Siddartha Khastgir, Peter Popov, Xingyu Zhao
Artificial Intelligence · Cross-listed from cs.NI
Building AI That Works: ESnet's Pragmatic Approach to AI-Driven Operational Excellence
Bin Dong, Sukhada Gholba, Brooklin Gore et al. (+15 more)
The ORBIT (Operations Responses and Business Intelligence Toolkit) project was initiated to assess agentic AI for the upcoming ESnet 7 initiative and to address persistent operational pain points in the Network Operations Center (NOC) workflow. ESnet operators experience slow
retrieval from siloed data sources, incidents described in lengthy and difficult-to-parse tickets, and context loss across shift handoffs. These challenges increase cognitive load and prolong incident resolution times. ORBIT therefore targets routine automation, cross-source synthesis, and actionable insights delivered directly within operators' existing tooling. ORBIT is an agentic AI system integrated into ServiceNow, ESnet's primary incident management platform. The design uses a modular, layered architecture comprising a centralized
reasoning hub, tool access via MCPs for ESnet data sources, a semantic search layer, and an operator-facing chat interface. To manage the complexity and stochasticity of the AI toolchain, ORBIT follows industry best practices by structuring task logic as versioned, tested "skills" that guide the system in performing bounded responsibilities. This improves reliability and predictability compared to fully unconstrained
agent behavior. Key results show that ORBIT successfully delivered all six initial tasks, and the architecture enabled rapid development of two additional tasks proposed by NOC engineers. We observed strong organic adoption of general-purpose infrastructure components, especially the chat interface and LiteLLM model gateway, including high request volumes from outside the project. Experiments with skills indicate that this approach can reduce task completion steps while eliminating observed error modes.
Read the full abstract ▾
All authors Bin Dong, Sukhada Gholba, Brooklin Gore, Shawn Kwang, David Mitchell, Samuel Oehlert, Garrett Stewart, Brendan White, Luke Baker, Ed Balas, Britt Gathright, Chin Guok, Jon-Paul Heron, John MacAuley, Scott Richmond, Chris Robb, Chris Tracy, Kesheng Wu
Artificial Intelligence
Let AI Agents Translate Networks, Not Reason About Them
Hongyu Hè and Maria Apostolaki
A formal model enables verifying reachability, localizing an outage, or anticipating the blast radius of a change. Yet, virtually no production network has one, since writing a model by hand demands rare expertise and is hard to keep current as the network changes frequently. At its core, network modeling is a typographical exercise: it translates network artifacts (e.g., configurations,
topology, and routing state) into rules in formal logic. Translation of this kind is what large
language models (LLMs) nowadays do well. Unlike free-form AI
reasoning, such translation can be formally verified. Once modeling is no longer the bottleneck, trusting AI to reason over large, complex networks no longer makes sense. Our position therefore cuts against the prevailing race to put autonomous AI
agents in charge end-to-end. We instead confine AI to translation and rely on a solver for reliable long-horizon
reasoning, building a reusable formal model of general network behavior that can then be specialized to specific tasks, e.g., root-cause analysis (RCA). We build TypoNet that constructs and validates a symbolic model of an emulated production-scale WAN from the network's own artifacts. Our preliminary evaluation shows TypoNet helps in two ways. On its own, TypoNet answers operational questions (e.g., reachability verification and change-impact analysis) faster, more cheaply, and more reliably than an LLM. As a tool for an AI
agent, TypoNet boosts fault localization at lower cost. The result makes the case for AI that builds verifiable network models and relies on a solver for reliable long-horizon
reasoning.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.NI
Invariant Discovery for Networked Systems
Hongyu Hè, Alexander Krentsel, Sylvia Ratnasamy et al. (+1 more)
Invariants, the relations expected to hold among measured signals of a network, underpin applications from verification to traffic generation, telemetry imputation, and input validation. Yet, writing them by hand demands rare expertise in both formal logic and networking. Testing all possible combinations of telemetry variables statistically is intractable at network scale, while existing automatic tools still require a human-authored grammar of admissible invariants and learn only exact, "hard" rules that break under real-world noise. Our key idea is to use an LLM to inspect variable metadata, classify counter data types, and predict candidate syntactic relationships that form a compact grammar, followed by a deterministic statistical search within that grammar. If the initial grammar fails to recover trusted known invariants on held-out telemetry, Autogram instructs the LLM to iteratively widen the grammar until recovery succeeds. By confining the LLM to grammar induction and re-induction while delegating candidate verification to logical and statistical checks, Autogram turns non-deterministic AI proposals into auditable invariants with formal guarantees within the induced grammar. We design and prototype Autogram and evaluate it on both public and production WAN telemetry, recovering expert-derived invariants with full recall and low false positives. We close with a discussion of open problems on the path toward fully open-ended invariant discovery.
Read the full abstract ▾
All authors Hongyu Hè, Alexander Krentsel, Sylvia Ratnasamy, Maria Apostolaki
Artificial Intelligence
Design Theater: Evaluating the Gap Between User-Facing Design Reasoning and Implementation in Generative UI Tools
Kashif Imteyaz, Kaif Imteyaz, Nakul Rajpal et al. (+3 more)
Generative UI tools promise to democratize UI design by turning natural
language descriptions into complete interfaces. Alongside the interface, these tools generate user-facing design rationales that explain their layout, accessibility, and design choices. However, it remains unclear whether these stated rationales are actually reflected in the interfaces they produce. We call this disconnect “Design Theater”: plausible and confident design rationales that have little relationship to the actual implementation. To study this phenomenon, we introduce a
benchmark and three metrics for measuring Design Theater. The
benchmark includes 24 UI generation tasks spanning structural, styling, and functional design requirements. Using this
benchmark, we evaluate 120 interfaces created by five generative UI tools. On average, over 25% of user-facing design rationales are not implemented in the generated interface, and the implementation failure increases to 34% for functional requirements. Tools recognize roughly half of the UX principles embedded in
prompts (mean = 0.54), with four of five tools implementing 6% or fewer functional principles. We also measure interface similarity across tools and find convergence in visual appearance and layout organization, with greater variation in color choices. Overall, we contribute: 1) the concept of Design Theater; 2) a
benchmark with metrics for assessing whether the stated
reasoning of generative UI tools is reflected in their implementations; 3) and findings from a systematic evaluation of these tools. We discuss what these findings mean for the design and evaluation of generative UI tools.
Read the full abstract ▾
All authors Kashif Imteyaz, Kaif Imteyaz, Nakul Rajpal, Kaif Shaikh, Michael Muller, Saiph Savage
Artificial Intelligence
SAGE: Safety-First Defense-in-Depth Guardrails for Verified Lifecycle Control of High-Impact Generative AI
Mahdi Eslamimehr
High-impact generative AI makes catastrophic misuse a lifecycle-control problem, not merely a
prompt-filtering problem. SAGE is a safety-first, authorization-separated architecture in which credible catastrophic-enablement risk constrains admissibility before utility, latency, or commercial objectives are considered. It combines signed release manifests, diverse detectors, robust risk envelopes, least-risk defaults, output checking, three-valued monitoring, protected audit chains, containment, and rollback. Formal results establish safety priority, conservative detector bounds, monotone release gating, tamper-evident records, and an authorization cut; two PRISM abstractions verify authorization separation and lifecycle invariants under explicit assumptions. A frozen, vendor-symmetric study sent 84 cases to each of four GPT, four Claude, and two Gemini snapshots: 840 calls yielded 794 target responses, 46 provider errors, and 449 successful judgments covering 375 responses. Eight snapshots had complete judged domain coverage. Harmful-compliance estimates were low; variation arose mainly from benign utility and safe redirection. Seven multiplicity-adjusted contrasts involving Claude, Gemini, or GPT-5 snapshots and the GPT-5 mini and GPT-5 nano snapshots were supported, while no tested contrast between the Claude or Gemini snapshots and GPT-5 or GPT-5.5 survived correction. The observed harmful-compliance range is a conservative, protocol-bound view from one generation per
prompt with no tools,
retrieval, history, or human adjudication; it is not an upper bound on operational assistance. A preregistered extension specifies how to test a wider best-worst gap using a locked split, repeated sampling, multi-turn and sandboxed-tool conditions, and domain-expert scoring.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CL
Not All LLM Reasoning is Visible in the Chain-of-Thought
Vatsal Baherwani, Tom Goldstein and Ashwinee Panda
A key question for AI safety is whether a
language model expresses all of its
reasoning in its output
tokens. We demonstrate a concrete failure mode where frontier models exhibit invisible
reasoning by leveraging semantically irrelevant filler
tokens to improve performance on synthetic
reasoning tasks. We evaluate 13 frontier
language models across three tasks and find that many models benefit significantly from filler
tokens, with accuracy improvements of up to 13 percentage points. The benefit depends on which
tokens are used and differs across models. We further show that filler
tokens enable Claude Opus 4.5 to satisfy a hidden modular arithmetic constraint without sacrificing accuracy on its primary task, demonstrating that invisible
reasoning can serve objectives entirely invisible to CoT monitoring. Reinforcement learning gives Qwen3-235B strong preferences over filler
token content, but neither RL nor supervised fine-tuning produces a filler
token benefit that persists at test time. Our results indicate that frontier models already perform consequential computation with no interpretable trace in their output
tokens.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CV
Controlling Embedding Spaces with Text-Conditioned Transformations
Joseph Fioresi, Fabian Caba Heilbron, Pankaj Nathani et al. (+2 more)
Multimodal embedding spaces in models like CLIP enable powerful capabilities such as semantic similarity
retrieval and cross-modal zero-shot classification. These
embeddings compress high-level semantics into a single vector, which comes at the cost of primarily expressing a dominant semantics like main object while suppressing other important attributes such as camera angle or color tone. We propose a text-conditioned transformation of visual
embeddings that makes such attributes explicitly accessible. Given a natural
language description of an attribute category (e.g., "color" or "art style"), a network generates an affine transformation that emphasizes the specified attribute. Conditioning on text enables it to learn many attributes simultaneously, accessing them at inference time through an intuitive interface. The network is trained to align transformed
embeddings with the frozen latent space, enabling
retrieval using existing large-scale
embeddings without any re-encoding. When applied to a full set, the same mechanism transforms the latent space for attribute disentanglement tasks such as multi-clustering. By operating directly in latent space, our method provides a unified and efficient framework for controlling
embedding spaces, demonstrating state-of-the-art performance across both attribute-based
retrieval and multi-attribute organization tasks with near-zero inference cost. Project page: https://joefioresi718.github.io/ControlEmbed_webpage/
Read the full abstract ▾
All authors Joseph Fioresi, Fabian Caba Heilbron, Pankaj Nathani, Mubarak Shah, Kushal Kafle
Artificial Intelligence
Agent Team Work Zone: An Automated, Persistent Workspace for Long-Lived Claude Code Agent Teams
Shouren Wang
Large
Language Model (LLM)
agents have significantly improved coding and programming workflows. Claude Code, in particular, is one of the most powerful LLM coding
agents and is capable of conducting complex coding tasks. However, several drawbacks can undermine long-term agentic workflows. (1) Irrecoverable
agent teams: The
Agent Teams feature is powerful, but the working state accumulated by each teammate is lost and cannot be resumed once the process stops, for example, when a terminal is closed. (2) Compaction erodes working detail: Compaction condenses the conversation into a summary, causing an
agent's working details to become vague. (3) Agentic "technical debt": Over time, a user's decisions and the
agents' operations become trapped in compacted old chats, making the project increasingly difficult to maintain and review. (4) Heavy
prompt writing: Assigning or handing off tasks requires users to repeatedly write long
prompts to achieve the expected agentic performance. We propose ATWZ (
Agent Team Work Zone), a filesystem-based operations layer built around Claude Code's native
Agent Teams that addresses these problems. Its central design principle is to treat each
agent and teammate as a human employee and preserve their important working state in files stored in a dedicated directory called a "workstation," together with the skills, hooks, and scripts that use and maintain these files. With ATWZ, an
agent team can periodically back up its working state, allowing an
agent's knowledge to be recovered after compaction. After a process ends, the team can be restored with a single command. These features also substantially mitigate the agentic "technical debt" described above. Moreover, within ATWZ,
agent "employees" can send documents to one another, greatly reducing the effort required to write
prompts.
Read the full abstract ▾
Artificial Intelligence
How Well Can AI Generate Backlogs from App Mockups?
Andrea Lezcano Airaldi, Lourdes Romera and Walid Maalej
Creating sprint backlogs requires considerable effort, as items such as epics, user stories, and tasks can be missed or inconsistently specified. We propose a
multimodal approach to support backlog generation from visual app mockups, an artifact available at early project stages. We evaluate three prompting strategies on GPT-4o: a zero-shot baseline, Compositional Chain-of-Thought (CCoT) for
vision-
language reasoning, and a persona-driven
prompt. We study seven app development projects across two countries and interview developers about the results. Overall, we observed that the baseline
prompt favours recall over precision, whereas CCoT is more balanced, achieving average F1 scores of 52-66% for epics and user stories. Tasks were more challenging to generate accurately. Precision gains were most consistent when adding architectural context, particularly for backend tasks (precision gains up to 35%). Interviews with developers revealed that up to 26% of false positives were still considered useful, reflecting the creative and open-ended nature of backlog creation. To capture this, we propose a new measure called Revised Recall, which complements ground-truth evaluation with developer assessments. Our findings suggest that hybrid prompting with architectural context can assist backlog generation from early mockups, though results vary by item type and developer oversight remains necessary.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.SE
Evaluating and Mitigating the Misguidance Effect of Buggy Code in LLM-Generated Unit Tests
Junda Zhao, Shurui Zhou and Eldan Cohen
While Large
Language Models (LLMs) show great promise for automating unit test generation, recent studies suggest that the quality of generated tests can be negatively impacted when models are prompted with buggy code. This paper presents a new metric to quantitatively measure the "misguidance effect," a phenomenon where buggy code steers LLMs toward generating tests that validate its erroneous behavior rather than expose it. Our analysis reveals that prompting LLMs with buggy code has a severe, twofold impact: it significantly increases "misguided tests" that assert incorrect behavior while simultaneously suppressing the generation of effective, bug-finding tests. We further corroborate this effect from a model-internal perspective, showing that buggy code skews LLMs' preference toward tests that assert the same erroneous behavior. To counter this, we introduce and validate a specification-based unit test generation paradigm that replaces the code under test in the
prompt with an LLM-generated specification docstring. Our results show that this paradigm effectively reduces misguided tests while substantially increasing effective tests, improves multi-round, feedback-driven test generation pipelines, and remains applicable to both buggy and bug-free code. Overall, these results suggest that specification-based prompting is a promising strategy for mitigating misguidance from buggy code in LLM-generated unit tests.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.SE
Do Coverage and Mutation Scores of LLM-Generated Test Suites Correlate with Their Effectiveness? (Replicability Study)
Junda Zhao, Shurui Zhou and Eldan Cohen
Recent advances in large
language models (LLMs) have driven growing interest in using LLMs to automate test generation. Prior work commonly evaluates generated test suites using proxy metrics such as code coverage and mutation score. However, studies by Inozemtseva et al. and Papadakis et al. show that, for human-written tests, correlations among coverage, mutation, and real-bug detection can largely vanish once test suite size is controlled, raising concerns about the validity of evaluations based on proxy metrics. It also remains unclear whether these conclusions carry over to LLM-generated tests, given that prevailing LLM-based test-generation workflows differ substantially from traditional approaches. In this paper, we conduct a large-scale replication study of these two prior works using a wide range of test suites generated by a diverse set of LLMs, and re-examine the relationships among coverage, mutation, and real-bug detection effectiveness. Our findings diverge substantially from prior results. We show that the usefulness of coverage and mutation is highly context-dependent: in regression-style settings where the code provided to the LLM can be reasonably assumed bug-free, these metrics can provide meaningful signals when comparing across models; in another common scenario where the code-under-test may already be buggy and the goal is to expose the bug within the code-under-test, they no longer serve as reliable indicators. We also find little evidence that test suite size is a dominant confounder for correlations among coverage, mutation, and real-bug detection for LLM-generated tests. Based on these findings, we discuss how to interpret results from prior studies and provide actionable guidance for evaluating LLM-based test generation.
Read the full abstract ▾
Artificial Intelligence
Towards Trustworthy Physical Intelligence: From Theory to Practice Across Life Cycle
Yang Wang, Hongxuan Liu, Xinghui Xu et al. (+40 more)
Physical intelligence refers to intelligence systems that understand, reason about and act in accordance with the physical world and its underlying laws, dynamics, and constraints. Unlike conventional AI systems, physical intelligence interacts continuously with uncertain physical environments, and its actions produce consequences that are physically irreversible. As existing trustworthy AI frameworks have been developed primarily for digital AI systems, they do not fully capture the distinctive challenges of Physical Intelligence, such as physical safety, cyber-physical security, and physical manufacturing process. To address this gap, our paper develop a foundation for understanding, operationalizing, and governing trustworthy physical intelligence. First, we characterize the fundamental capabilities of physical intelligence. Second, we investigate the role of physical laws and principles in AI and analyze the associated challenges of physical intelligence. Third, we provide a unified structure of the end-to-end physical intelligence life cycle across five core stages and introduce the Trustworthy Physical Intelligence Operationalization (T-PAIO) framework. Fourth, we formulate the Trustworthy Physical Intelligence (T-PAI) Principles as a theoretical framework that synthesizes key trustworthiness dimensions and establishes a conceptual foundation for the governance of physical intelligence systems.
Read the full abstract ▾
All authors Yang Wang, Hongxuan Liu, Xinghui Xu, Arjun Menon, Xiaoran Cai, Yunyu He, Alex Tarvo, Jingzong Zhou, Mengzhong Ma, Xinpeng Wei, Yi Yu, Shaobo Wang, Cheng Peng, Aoran Jiao, Alexei Korolev, Yanyan Zhang, Kai Ye, Xinpeng Li, Chengquan Guo, Jingjing Fu, Nicholas Bai, Yongjun He, Junru Ren, Silei Ren, Mohamad Louai Shehab, Keshu Cai, Nathaniel Dennler, Traian Tus, Gaoyue Zhou, Abdullah Garra, Mason Nakamura, George Ortiz, Marius Urbanas, Lars Johannsmeier, Rohit Sharma, Suraj Deshmukh, Michael Spranger, Vinayak Gupta, Xu Chen, Ashis G. Banerjee, Yuxiang Feng, Yoshua Bengio, Peng Qi