Artificial Intelligence · Cross-listed from quant-ph
Symmetry and AI-assisted discovery of magic-state factories
Shubham P. Jain, Adam Wills and Shraddha Singh
Magic-state
distillation is a major resource cost in fault-tolerant
quantum computing. The cost of a magic-state factory depends strongly on its failure rate, which grows with the number of input magic states. Although
symmetry-restricted methods have recently made distance two searches tractable, distance three and above have remained elusive at moderate input counts. We develop
symmetry- and AI-assisted methods to search this regime. We present a unified binary-
matrix formulation encompassing both triorthogonal-code and direct
circuit searches. We show that distance at least three is equivalent to nonzero, pairwise distinct syndromes, separating the choice of syndromes from the search for compatible output gates. We restrict the syndrome search using group
symmetry and
language-model
agents, followed by deterministic solving and independent verification. Our searches yield 699 factory classes, including 564 new ones. These include factories for pure-T states and factories with entangled outputs comprising combinations of T, CS, and CCZ magic states. The pure-T factories [[63, 11, 3]] and [[850, 128, 6]] achieve the lowest overhead exponents we know among protocols with at most 100 and 1000 inputs, respectively, with $\gamma = 1.589$ and $\gamma = 1.057$. Our [[1715, 287, 6]] factory, with $\gamma = 0.998$, is the smallest known pure-T factory with $\gamma < 1$. We also provide a context directory of search briefs and campaign notes with which readers can train their own
agents and tailor the search to their requirements. With these results, we begin constructing an active, open-source repository of magic-state
distillation protocols for the
quantum community, supplemented by our methods and data, for the practical fault-tolerant
quantum computing regime.
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Artificial Intelligence · Cross-listed from cs.CL
Anatomy of LLM Sycophancy: What a Flip Rate Hides
Haonan Huang
A model under pushback can correct itself, capitulate, or hold, and one flip rate counts a correction and a capitulation alike. Using SycoLens, a modular replay protocol, we test how user pressure and evaluation settings shape measured flip rates. Each measurement is one stateless replay of an item, a committed answer, and one scripted user line in a fixed form. Every effect is read against a matched control with the line deleted. Pushback wording, committed text, answer format, boundary distance, and ground truth become factors of one instrument; earlier instruments vary one to three of them. Across eleven frontier models from three providers and about 760,000 controlled replays, which models look sycophantic depends on how the user pushes back. Lines that assert the opposite verdict and lines that challenge the answer without asserting one rank the models almost unrelatedly. Flip effects grow several-fold near a model's boundary, yet items answered identically in every screening draw still carry about half of the most-affected totals. On arithmetic tasks where the truth is known, one model re-derives and corrects itself under pressure while another abandons correct answers without written work. On the model tested, a planted derivation lowers release of the answer it argues for, true or wrong, where a bare stated value does not; the wrong answer is corrected much more often than the true one is abandoned. Under a yes/no readout the rankings come closer, entangled with a pressure-induced shift toward "no". One score per model therefore compares different behaviours across models and
benchmarks. We condense these dependencies into a reporting profile; the instrument, records, and analyses will be released upon publication.
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Artificial Intelligence · Cross-listed from cs.CR
GCTAuto-encoder: A Cross modal Framework for Security Flaw Detection in IoT Networks
Najmieh Sadat Safarabadi
IoT encompasses diverse physical entities, from smart home devices to autonomous vehicles, creating a complex environment with heterogeneous security models. This heterogeneity makes IoT sub-systems vulnerable to various network attacks. Modern security systems must therefore be more robust to ensure security and
privacy for IoT applications. A highly secure IoT system also demands real time insight, requiring data collection at the edge of the computing layer. This diversity calls for a unified security model applied at the foundational level. Edge intelligence offers a direct approach to handling device diversity. A key goal of edge intelligence in IoT is to extract insight from local data; security models can then use this data to build local node protections, and integrating AI models yields an advanced security solution. This research proposes a novel deep learning
algorithm for effective intrusion detection at the edge, supported by a cloud-based IoT framework. We evaluate the proposed cross modal deep learning
algorithm against baseline models. The contribution is a cross domain Deep Neural Network (DNN)
algorithm for intrusion detection. The objective is to assess a multi-method deep learning model to detect intrusions in IoT systems at the edge via community detection with modeled
attention. We evaluate GCT auto-encoder, a novel framework integrating edge intelligence to identify security flaws. The model significantly improves performance and efficiency. On a network intrusion IoT
dataset covering multiple attack scenarios, it achieved 0.908 accuracy, reduced learning loss to 0.00156, and outperformed existing approaches.
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Artificial Intelligence
ANT: A Multi-Granularity Network Traffic Dataset and Benchmark for Agents Behavior Auditing
Fan Li, Xiangyu Gao, Zixuan Liu et al. (+4 more)
The growing adoption of large
language model (LLM)
agents creates a need for network administrators and security teams to audit
agent behavior within organizational networks without inspecting private user content. Network traffic offers an observable source of evidence, but how much it reveals about
agent tasks and operations remains unclear. Existing traffic
datasets lack the joint task and stage annotations needed to evaluate this question. We introduce ANT (
Agent Network Traffic), a
dataset providing
agent behavior information at risk, scenario, and behavior primitive granularities alongside network traffic. ANT contains 3,114 execution episodes across 20 tasks and five scenarios, comprising 276,417 bidirectional flows and 40,049 behavior primitive segments organized into 47 macro groups. We establish a
benchmark for
agent risk identification, scenario recognition, and behavior primitive classification using 13 representative traffic analysis baselines. The results show that existing methods recover useful but uneven behavioral signals. They struggle to identify risk when malicious workflows resemble benign tasks and to distinguish scenarios with similar traffic patterns. Primitive classification is more reliable for frequent macro groups and those with distinctive traffic patterns than for rare or semantically similar groups. ANT provides a common basis for developing more precise auditing and forensic analysis of
agent behavior from network traffic. Our data and code are available at https://anonymous.4open.science/r/ant-main-suite-7BC0/.
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All authors Fan Li, Xiangyu Gao, Zixuan Liu, Tong Li, Chuanpu Fu, Ziqiang Wang, Ke Xu
Artificial Intelligence · Cross-listed from cs.RO
ArtifactArena: Evaluating Models by What They Build in the Physical World
Kushagra Tiwary*, David Mayo*, Nikhil Behari et al. (+5 more)
To evaluate the frontier, we must measure models not by what they say, but by what they can engineer and build in grounded physical environments. We introduce ArtifactArena, an open-ended platform where models face a physically grounded hardware-software co-design challenge: engineering fully functional
robots to compete in a simulated arena. We evaluate a frontier model's zero-shot, verifier guided refinement, and open-ended physical design capabilities through three harnesses that refine their bot artifacts based on text descriptions, physics simulator feedback, and gameplay data. We
benchmark these capabilities with an Elo ranking of frontier models derived from head-to-head tournaments between their artifacts. By releasing this framework and tournament infrastructure for ongoing community submissions, we establish a living, non-saturating testbed to continuously measure the expanding limits of open-ended intelligence in the physical world. Please visit \href{https://artifactarena.ai}{https://artifactarena.ai} for more information.
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All authors Kushagra Tiwary*, David Mayo*, Nikhil Behari, Xiangzhou Sun, Abdulrahman Alabdulkareem, Isaac Galatzer-Levy, Boris Katz, Brian Cheung
Artificial Intelligence · Cross-listed from cs.CV
Harmful Content Generation in Text-to-Image Models: Capabilities and Moderation Limitations
Paschalis Giakoumoglou, Manos Schinas and Symeon Papadopoulos
Text-to-image generative models can produce highly realistic imagery but also raise concerns about harmful misuse. While safety mechanisms exist, systematic evaluations of their effectiveness against realistic attacks remain limited. We present a systematic evaluation of harmful content generation across five open text-to-image models using an automated pipeline that transforms legitimate news captions into unsafe
prompts targeting sexually explicit content, violence/gore, harmful stereotypes, self-harm, and hate
speech. We evaluate both standard models with built-in safety mechanisms and community fine-tuned variants that bypass content restrictions. A human evaluation of 1,500 generated images shows high harmful-content generation rates: 89.2% for gore-related
prompts, 47.6% for sexually explicit content, 43.6% for harmful stereotypes, 46.0% for hate
speech, and 34.5% for self-harm, predominantly through graphic violence. Models show substantial capability for generating violent and stereotypical content, while community fine-tuned variants are particularly vulnerable to sexually explicit
prompts. Generation quality is largely preserved under harmful prompting, producing imagery of sufficient fidelity to pose risks for disinformation and abuse; FLUX.1-dev produces clearly realistic harmful images in 30.9% of cases. We further evaluate automated moderation systems and find substantial detection gaps that allow unsafe images to evade filtering. Finally, we assess synthetic image detectors and show that models trained only on benign
datasets perform worse on explicit content, while more diverse training data improves detection, highlighting semantic distribution gaps in current approaches. These findings expose limitations in current generation safeguards, moderation systems, and synthetic image detection, highlighting the need for stronger defenses against misuse at scale.
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Artificial Intelligence
You Changed Your Mind, The Model Didn't: Demystifying Intent in Multi-Turn Dialogue
Junle Chen, Wei Chen, Zhengjun Huang et al. (+5 more)
When a large
language model handles a multi-turn task and a user proposes a change but ultimately rejects it, the model should continue as if nothing changed. We find a surprising failure: merely mentioning a rejected change can derail task execution, even when the user's final intent remains unchanged. To systematically study
language model behavior under evolving user intent, we introduce Intent-Eval, a controlled
benchmark spanning tool actions, code, databases, and mathematics. Across diverse tasks, models are vulnerable to both rejected proposals and superseded requirements, consistent with mentioned-as-in-effect confusion: conversational content is treated as active requirements even after it has been rejected or replaced. Accuracy degradation can deepen or persist as interaction continues, highlighting the need to distinguish what has been mentioned from what remains in effect. Building on this insight, we propose Intent-OPSD, a decision-conditioned on-policy self-
distillation framework with Teacher and Student initialized from the same model. The frozen Teacher provides active-intent supervision from the complete task matching the user's decision, training the Student on the full dialogue to follow active requirements reflecting user intent.
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All authors Junle Chen, Wei Chen, Zhengjun Huang, Zhoujin Tian, Yuxuan Liu, Kai Wang, Rui Chen, Xiaofang Zhou
Artificial Intelligence · Cross-listed from cs.CV
Topology-Informed Prompt-Conditioned Universal Segmentation of Uterine Structures from Ultrasound and MRI
Yongheng Sun, Yuexi Gu, Jingwen Sun et al. (+2 more)
Multi-structure segmentation of the uterus is important for computer-assisted screening, diagnosis, and treatment planning of uterine diseases, where ultrasound and MRI provide complementary clinical information. However, developing a unified model across these modalities is challenging due to their substantially different image appearances, anatomical contexts, spatial resolutions, and label spaces. Moreover, existing
datasets often define different segmentation targets, making joint learning challenging and potentially leading to negative transfer across heterogeneous tasks. To this end, we propose a
Topology-informed
Prompt-conditioned Universal Segmentation (TPUS) framework for segmenting multiple uterine structures across ultrasound and MRI. TPUS introduces a graph-based multi-
dataset backbone comprising modality-specific stems and a modality-shared graph-based encoder-decoder to support modality-sensitive input adaptation, structural feature
reasoning, and joint representation learning across heterogeneous uterine segmentation tasks. In addition, TPUS uses task-aware class
prompts to condition the segmentation process for different
datasets and label spaces, a dynamic convolutional adaptation module to generate task-specific output responses, and a
topology-informed loss to encourage anatomically consistent predictions. Experiments on a uterine ultrasound
dataset and a T2-weighted uterine myoma MRI
dataset demonstrate that TPUS achieves Dice scores of 0.898 and 0.693 on the two held-out test sets, respectively, outperforming several generic and universal segmentation baselines. Source code can be accessed at https://github.com/YonghengSun1997/TPUS.
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All authors Yongheng Sun, Yuexi Gu, Jingwen Sun, Maureen Kohi, Mingxia Liu
Artificial Intelligence
polyview: A Python package for multi-view machine learning
Gwendal Debaussart-Joniec and Argyris Kalogeratos
Multi-view learning jointly exploits multiple complementary representations of the same data and has become increasingly important in machine learning. However, the Python ecosystem lacks actively maintained, unified tooling for end-to-end multi-view workflows. In this paper, we present polyview, a Python package that provides tools for multi-view
embedding, clustering, fusion, and view augmentation, as well as for handling incomplete views, all compatible with scikit-learn. The library offers a unified interface for composing heterogeneous multi-view workflows, including seamless transitions between multi-view and single-view stages. It is built around a core set of classes and utilities that enable composition of different methods and straightforward implementation of new ones. We illustrate the package on five real multi-view
datasets and compare its components based on canonical correlation analysis with those of two established libraries. polyview aims to be both a practical toolkit for benchmarking and prototyping multi-view methods and a foundation for future research and development in this area.
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Artificial Intelligence
GPlaceRL: An Open-Source Graph Reinforcement Learning Framework for Detailed Placement
Pavlos Stoikos, Foteini Oikonomou, Christos Poulos et al. (+5 more)
Reinforcement learning (RL) has emerged as a promising approach for placement optimization, particularly when combined with graph neural networks (GNNs) that capture
circuit connectivity. However, most learning-based placement approaches focus on floorplanning, macro placement, or global placement, while detailed placement refinement remains relatively unexplored. In this paper, we present GPlaceRL, an open-source graph reinforcement learning framework for detailed placement refinement. GPlaceRL represents legalized placements as graphs and provides a modular environment for studying graph encoders, policy architectures, reward formulations, and local placement actions. To demonstrate the capabilities of GPlaceRL, we conduct a systematic evaluation of proximal policy optimization (PPO) policies with graph
attention network (GAT) encoders in a per-design optimization setting. Across five placement
benchmarks, the best greedy evaluation results achieve HPWL improvements ranging from $3.27\%$ to $32.87\%$. The results highlight the importance of compact GAT architectures and flexible local action spaces for placement optimization. Overall, GPlaceRL provides a reproducible and extensible framework for systematic research on RL-based detailed placement refinement.
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All authors Pavlos Stoikos, Foteini Oikonomou, Christos Poulos, Maria Pantazi-Kypriou, Athanasios Tziouvaras, Christos Anagnostopoulos, Georgios Karakonstantis, George Floros
Artificial Intelligence · Cross-listed from cs.RO
Odyssey: A Closed-Loop Benchmark for Long-Horizon Real-World Driving with Explicit Navigation Routes
Jungho Kim, Hongjae Shin, Seunghoon Yu et al. (+11 more)
Closed-loop evaluation of end-to-end driving requires continuous rollouts that reveal how earlier decisions affect subsequent driving. However, existing
benchmarks evaluate only short segments and fail to capture later consequences. Ambiguous directional commands also obscure the intended navigation objective. We introduce Odyssey, a closed-loop
benchmark for long-horizon driving comprising 100 scenarios, each reconstructed from a 100-second nuPlan driving log to preserve the context of navigation maneuvers and traffic interactions. To provide a consistent navigation objective, Odyssey replaces directional commands with explicit standard-definition (SD) map routes that specify which roads to follow, while sensor-based planning determines local driving actions. Throughout these rollouts,
diffusion-based refinement of 3DGS-rendered images reduces rendering artifacts along the ego trajectory. To assess how effectively planners follow these routes and prepare for upcoming maneuvers, we introduce SD Route Compliance and Pre-Lane Change Score. These assessments are complemented by RouteDS, which extends the Driving Score with penalties for SD-route deviations and failed lane preparation. We adapt state-of-the-art planners, including
vision-
language-action (VLA) models, and evaluate their navigation performance using these metrics. Odyssey highlights open questions in route representation and integration for E2E driving.
Benchmark code and adapted baselines will be released publicly.
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All authors Jungho Kim, Hongjae Shin, Seunghoon Yu, Heecheol Yoo, Myeongjun Kim, Jiyong Oh, Donghyuk Kwak, Seunghyeop Nam, Haesung Oh, Hyunju Kim, Hyungchan Cho, Jaehyun Park, Soo Won Seo, Jun Won Choi
Artificial Intelligence · Cross-listed from cs.LG
Latent Flow Matching for Molecular Graph Generation
Mathis Goupillon, Roman Bresson, Konstantinos Divriotis et al. (+1 more)
Modern graph generative models typically operate directly in the discrete graph space, explicitly generating node and edge variables, which can become costly as graphs grow. In this paper, we perform generation explicitly on latent representations of entire graphs obtained from a pretrained Variational Autoencoder with high reconstruction fidelity. The generated representations, obtained through flow matching, are then decoded only at the final step. Across molecular
benchmarks of increasing size, our approach achieves strong validity and FCD while offering a favorable quality-efficiency trade-off compared with state-of-the-art explicit graph generative models. One of the main advantages of this formulation is that the graph representation only needs to be learned once, after which the same one can be reused across multiple generative objectives without retraining. We demonstrate generation guided by molecular properties and further introduce validity-aware generation though a classifier learned directly in latent space. All code will be made available upon acceptance.
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All authors Mathis Goupillon, Roman Bresson, Konstantinos Divriotis, Michalis Vazirgiannis
Artificial Intelligence · Cross-listed from cs.LG
A Physics-Guided Transformer Framework for Electromigration Analysis in Multi-Segment Interconnects
Pavlos Stoikos, Anuj Pathania and George Floros
As technology scales to smaller nodes, increasing current densities make electromigration (EM) one of the dominant reliability challenges in on-chip interconnects. Accurate transient stress analysis is needed to identify wires susceptible to EM degradation, but applying physics-based solvers across many interconnects remains computationally expensive. This paper proposes a physics-guided
transformer framework for fast EM stress prediction in multi-segment interconnect lines. The framework converts each line into geometry- and DC-aware segment
tokens and uses
transformer attention to capture line-level context. A lightweight query decoder then predicts stress at selected locations and time instants. The model is trained with an objective that combines normalized supervised regression, linewise relative-$L_2$ loss, and physics-guided continuity and terminal-flux terms. Experiments on IBM power grid
benchmarks show that the proposed model achieves relative-$L_2$ error below 8% and reaches up to 2459.68$\times$ speedup compared with the
matrix exponential~solver.
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Artificial Intelligence
AgentPrivArena: Evaluating and Auditing Real-world AI Agent Privacy
Shouju Wang and Haopeng Zhang
The rapid advancement of LLM
agents has enabled systems to autonomously perform complex tasks through external tools, but their growing access to personal data introduces significant
privacy risks. Existing
benchmarks primarily evaluate LLM
agent privacy through simulated trajectories and outcome-based metrics, limiting their ability to capture
privacy risks arising during multi-step
agent execution. In this work, we introduce AgentPrivArena, a framework for evaluating
privacy risks in realistic LLM
agent workflows. AgentPrivArena integrates authentic MCP tools and self-hosted services within a reproducible execution environment. We further propose trajectory-level
privacy metrics that quantify unnecessary information access beyond final response leakage. Building on this framework, we introduce AgentPrivAudit, a runtime auditing approach for monitoring
privacy violations during
agent execution. Extensive experiments on state-of-the-art LLM
agents reveal substantial
privacy risks overlooked by existing evaluation paradigms, highlighting the importance of trajectory-level auditing for trustworthy
agent deployment.
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Artificial Intelligence
Normality Constraint Learning: Adapting Foundation Models for Time Series Anomaly Detection
Xiaohui Zhou, Yijie Wang, Hongzuo Xu et al. (+2 more)
Time Series Foundation Models (TSFMs) achieve strong generalization by learning to reconstruct or forecast broad temporal patterns from large-scale time series during pre-training. Yet this strength can become a weakness for anomaly detection: TSFMs may model rare anomalous patterns as effectively as normal ones, allowing anomalies to be accurately reconstructed or forecasted and thus diminishing their reconstruction/forecasting error-based anomaly scores. This paper proposes $\underline{\textbf{N}}$$\textbf{ormality}$ $\underline{\textbf{C}}$$\textbf{onstraint}$ $\underline{\textbf{L}}$$\textbf{earning}$ ($\textbf{NCL}$), a lightweight plug-and-play framework that adapts pre-trained TSFMs for accurate anomaly detection without modifying their pre-trained parameters. Our key insight is to constrain the broad pattern space of TSFMs to the normal structure of a target time series, preventing their broad modeling capability from obscuring abnormal deviations. Specifically, NCL constructs a compact normality subspace from a few normal patch features and adaptively steers each patch feature toward normality within this subspace, guided by contrastive constraints that form compact and discriminative normality
manifolds. The calibrated features are aggregated to reinforce normal components and fused with the original TSFM output, amplifying the discrepancy between normal and abnormal observations for the reconstruction/forecasting error-based anomaly scoring. Extensive experiments across diverse TSFM families and
benchmarks show that NCL consistently improves anomaly detection performance, providing a generalizable framework for adapting TSFMs to anomaly detection.
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All authors Xiaohui Zhou, Yijie Wang, Hongzuo Xu, Weixuan Liang, Guansong Pang
Artificial Intelligence
Multimodal Safety Evaluation Should Measure Controllability Beyond Classification
Junhyeong Park, Hanwool Lee, DongGeon Lee et al. (+4 more)
VLM safety is commonly evaluated through input- and output-level classification. Such classification is necessary, but it does not reveal whether a safety state is accessible or controllable inside the model. We argue that
multimodal safety evaluation should therefore report a controllability profile alongside behavioral classification, separating representation-level detectability, cross-modal specificity, intervention sensitivity, and benign-preserving selectivity. Using implicit toxicity as a stress case, we instantiate this profile on LlavaGuard and Qwen3.5 with sparse feature decompositions. LlavaGuard admits localized handles with a narrow benign-preserving intervention range and modest downstream safety gains, whereas Qwen3.5 supports strong representation-level readout but no comparable selective-control regime under the tested operators. These results show that internal readout and controllability can diverge. Future
multimodal safety
benchmarks should therefore report not only behavioral safety metrics, but also whether safety-relevant internal signals can be intervention-tested and controlled within a validated operating range.
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All authors Junhyeong Park, Hanwool Lee, DongGeon Lee, Dasol Choi, Yejin Son, Haon Park, Youngjae Yu
Artificial Intelligence · Cross-listed from cs.CL
The Assistance Dilemma: Learning to Teach via Multi-Turn Reinforcement Learning
Jakub Macina, Manu Kapur and Mrinmaya Sachan
Large
language models (LLMs) trained to answer questions are natively poor at teaching. Reinforcement Learning (RL) against a simulated student is a promising approach to improve their pedagogy, but existing RL-trained tutors reward the student's success on the tutored problem with the tutor's words still in context. The reward is then easiest to raise by telling the student the answer, and a tuned penalty is needed to reduce telling. Drawing on learning sciences, we introduce a masked near-transfer post-test: the student is tested on an unseen variant of the tutored problem with the tutor's utterances masked, so the reward can rise only through what the student wrote in its own turns. This discourages cognitive offloading by the student and allows the continuous penalty to be replaced by two binary reward gates (factual correctness of tutor response, no solution handover). A leave-one-out ablation shows that the learning-gain reward on its own does not separate teaching from telling: the gates reduce solution handover while the near-transfer post-test improves out-of-domain transfer. Using these reward designs we develop Eduardo, a multi-turn RL recipe for training LLM tutors, and use it to train 4B, 9B, 14B and 27B models from two distinct LLM architectures. Our post-trained Eduardo-27B model matches Gemini-3.1-Pro on MathTutorBench and Claude Opus 4.8 on TutorMoments at 2.4-6.2x fewer thinking
tokens than frontier models, which matters for interactive tutoring. Without being named in the reward, the model more than doubles its use of the push-for-justification teacher move while support fading (e.g., assigning independent work), whose payoff lies beyond a single-problem dialog episode, is trained out. We open-source our training environment, an 8,671-problem near-transfer
dataset, and trained models for further development.
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Artificial Intelligence · Cross-listed from cs.SE
Choosing an energy-efficient software architecture for building system diagnostic support
Roxane Koitz-Hristov and Franz Wotawa
Around 30% of global energy expenditure can be attributed to the building sector, where a large portion of energy-consumption could be avoided by repairing existing faults. Fault detection and diagnosis (FDD) software addresses this issue; however, its creation and operation also have an environmental impact. The magnitude of this impact is influenced by the diagnosis architecture, as different architectures and methods have different energy demands. Yet, simply considering the energy consumed by the software itself is not sufficient to assess its overall environmental impact, since the diagnostic performance, e.g., number of detected faults or number of faults missed, also contributes to its ecological footprint. In this paper, we propose an energy-consumption model that considers FDD performance and energy spend directly by the diagnosis software. In an initial experiment, we compare several FDD architecture families, i.e., rule-based, model-based, classical machine learning, and large-
language-model-based, in simulation using performance and energy-consumption values collected from prior literature. The results show that considering the computational energy and accuracy of FDD can change the relative benefit of the different approaches. Computationally efficient machine learning methods, such as random forest, provide the largest net savings on smaller buildings, whereas more resource-intensive approaches, such as fine-tuned large
language models, become advantageous as building size increases. Our findings suggest that overall energy efficiency depends not only on the computational demand of the FDD software, but also on its diagnostic performance and the scale of the building.
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Artificial Intelligence · Cross-listed from cs.LG
ARO: Aligned Representation learning for multi-Omics data
Amogh Singh, Yash Shah, Chiara D'Ercoli et al. (+4 more)
The high cost of functional molecular assays, and prevalence of missing modalities and unmatched samples in computational biology, create significant barriers to comprehensive multi-omic profiling, essential for capturing and
reasoning over molecules,
cells, tissues, and organisms. This work proposes a model that learns meaningful representations from multi-omics cancer data supporting the reconstruction of missing and unpaired modalities. Contrary to increasingly complex, larger models, e.g. Foundation Models (FMs), ARO prioritizes practical applicability in limited or incomplete data settings. ARO optimally reconstructs missing modalities (MSE of $0.15$ on the validation and test data in the Unmasked settings), with its learned latent
embeddings enabling a downstream cancer classification task. Our findings indicate that analyzing diverse molecular layers as a single integrated system offers a reliable and cost-efficient approach, reducing dependence on large-scale experimental testing, while still supporting multi-omic exploration in limited data settings.
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All authors Amogh Singh, Yash Shah, Chiara D'Ercoli, Arash Mehrjou, Patrick Schwab, Timothy Jones, Pietro Liò
Artificial Intelligence · Cross-listed from cs.LG
Better Call Reward: Reward Hacking as Strategic Abstention in Legal Reasoning Models
Subramanyam Sahoo and Justin Shenk
What happens when a legal AI model learns to look like a lawyer instead of
reasoning like one? We fine tune Qwen3-8B with Group Relative Policy Optimisation (GRPO) against a proxy built from three surface features: citation count, legalese density, and response length. The model does not learn to reason more effectively. It learns to withhold commitment. Across 16 yes or no legal
reasoning tasks from LegalBench (N=320), overall accuracy collapses from 0.500 (chance) to 0.072 (McNemar p < 10^-36), driven entirely by the rate of properly formatted answers falling from 0.900 to 0.109. The model stops committing to answers. Yet when it does commit, accuracy rises from 0.556 to 0.657, showing that the collapse is not a failure of capability but a strategic response: the model has learned that verbose responses packed with citations but empty of a direct answer score higher than terse correct ones. We term this the Saul Goodman effect, a policy that becomes maximally lawyerly while becoming maximally noncommittal, and prove formally that it is the optimal response to any surface feature proxy that attaches no penalty to abstention. We further show that 89.3% of citations produced after training are structurally implausible
hallucinations, many of them subtly corrupted names of real landmark cases, constructed in effect to survive a casual read and fail under scrutiny. To detect this failure mode before deployment, we introduce three diagnostic tools: the Confidence Theater Score (CTS), the Citation Plausibility Rate (CPR), and the Regret Gap (RG). In a domain where a confidently wrong answer can constitute malpractice, the broader lesson is direct: a reward function that measures how legal a response looks will produce a model that is maximally photogenic and minimally useful.
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Artificial Intelligence · Cross-listed from stat.ML
Valid Stopping in Adaptive Generator-Verifier Loops
Mahmoud Hegazy, Michael I. Jordan and Aymeric Dieuleveut
Numerous agentic workflows are based on a generator-verifier loop: a generator proposes candidates, a cheap verifier scores them, and the workflow terminates when a proposal is verified as good enough. The verifier typically proxies a more costly ground-truth oracle, and as the generator searches adaptively against it, false acceptances may accumulate. Proposals can pass the proxy but fail under the costlier ground-truth check. We study when to stop these loops while controlling the false discovery rate of the accepted proposals. Our construction introduces tools of independent interest in distribution-free statistical testing and conformal risk control, including analysis of $e$-values constructed through index betting and a novel conformal risk control procedure for non-monotone losses. We validate the approach in synthetic settings and on a
protein-design
benchmark.
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Artificial Intelligence · Cross-listed from cs.NI
SoK: Semantic Decision Engines in Network Control Loops
Delong Li, Chen Li, Xu Wang et al. (+3 more)
A semantic decision engine such as Jev can return a valid answer and still miss a network deadline, select an infeasible action or leave the service unverified. We systematize 139 paper families by decision interface, execution path and check ownership. Fifty families claim that their engine fits a control loop or time budget, but only four support the claim with matched measurement. Across all 139, four report deadline attainment. The gap concentrates where the decision has no deterministic computation step. Those 72 families make 22 of the claims, none supported, and name a coverage owner in only two. Bounded tests under one event model show that each gap can reverse an admission verdict. A decision that meets a 10 s budget for every isolated request meets it for none once decisions queue ahead of replayed execution times. The same engine passes one coverage check and fails another. We derive a minimum reporting record, design rules and a research agenda for admitting decision engines to control loops.
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All authors Delong Li, Chen Li, Xu Wang, Haochen Gong, Rui Lang, Guangsheng Yu
Artificial Intelligence · Cross-listed from cs.CV
Toward Reliable Infant Pose Estimation: A Training-Dynamics Approach to Noisy Annotation Detection
Emanuele Cardinale, Sara Moccia, Alessandro Cacciatore et al. (+1 more)
Spontaneous movement analysis in preterm infants relies increasingly on markerless pose estimation (PE) to derive clinically relevant motion biomarkers directly from
video recordings. Training accurate infant PE models requires large sets of manually annotated keypoints, and human annotation is inherently prone to error. Noisy keypoints (i.e., keypoints mislocalized with respect to their true anatomical position) are especially problematic in this clinical setting, since they can propagate as artificial artifacts into the reconstructed joint trajectories. Building on the small-loss hypothesis and training-dynamics-based sample selection established in the noisy-label learning literature, we propose a novel framework for detecting noisy keypoint annotations. A hybrid convolutional-
attention model is trained to predict the anatomical category of each keypoint from its spatial coordinates and local visual features; the resulting cross-
entropy training dynamics are then used to derive per-keypoint descriptors, which are partitioned into clean and noisy subsets via unsupervised clustering. We validate the approach on NeoPose, a newly collected
dataset of 65 hospitalized preterm infants, under two realistic noise scenarios (random positional perturbation and left-right swapping) across multiple noise levels. Results show that the proposed approach achieves an F1-score of up to 91.9% in noisy-keypoint detection. The framework further generalizes to the heterogeneous COCO
benchmark, where filtering CE-detected noisy keypoints from the training set also yields measurable improvements (up to 7.4 AP points) in downstream pose estimation accuracy at moderate-to-high noise levels.
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All authors Emanuele Cardinale, Sara Moccia, Alessandro Cacciatore, Lucia Migliorelli
Artificial Intelligence · Cross-listed from cs.CV
SpatialChain: A Benchmark for Auditing Spatial Reasoning Faithfulness in VLMs
Rafael Teixeira Sousa, Vinícius Paulo Lopes de Oliveira, Elisa Ayumi Masasi de Oliveira et al. (+5 more)
Thinking-enabled
vision-
language models (VLMs) report ever-higher accuracy on spatial
benchmarks, yet final-answer scores cannot reveal whether a correct prediction reflects faithful spatial
reasoning or a linguistic shortcut. We introduce SpatialChain, a
dataset of 28,350 training and 899 test examples pairing spatially-oriented GQA questions with scene-graph-grounded
reasoning chains, retained only when the generated answer matches the symbolic ground truth, and a two-axis evaluation combining objective chain-overlap metrics with a scene-graph-aware LLM judge that scores faithfulness and completeness independently of the final answer. Applied to nine thinking-enabled VLMs, the protocol surfaces three findings invisible to standard accuracy: (i) four of nine models achieve $\geq$79% VQA accuracy while exhibiting shortcut rates above 39%, i.e., correct answers whose
reasoning the judge marks as unfaithful; (ii) chain quality significantly predicts answer correctness for seven of nine models, but the two exceptions (Claude Sonnet 4.6, InternVL3.5-8B) reveal qualitatively distinct failure modes, terse output vs. verbose-decorative
reasoning, that
benchmark accuracy alone conflates; (iii) SFT on SpatialChain improves Qwen3-VL-8B by +6.2 pp in-domain and reduces its shortcut rate to 22%, while a stylistic specialization effect on external
benchmarks motivates replay-augmented training as mitigation. The faithfulness judge is validated against 198 human-annotated items, where judge-human agreement matches human-human agreement, and against a second judge from a different provider, which preserves the model ranking ($\rho$ = 0.88). Data, generation scripts, and evaluation code are released at https://github.com/spatialchain/SpatialChainBenchmark.
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All authors Rafael Teixeira Sousa, Vinícius Paulo Lopes de Oliveira, Elisa Ayumi Masasi de Oliveira, Luiza Martins de Freitas Cintra, Fernanda Bufon Färber, Igor Dias Aguiar, Julia Yasmim de Almeida Nobre, Arlindo Rodrigues Galvão Filho
Artificial Intelligence
From Benchmark to Bench: Can Agents Survive Real-World Drug Discovery?
Pierre Llompart, Levent Guner, Helen Lai et al. (+2 more)
Agentic systems increasingly coordinate molecular-design tools, but it is unclear which layer of the stack limits outcomes on real projects. We developed MAGI, an open modular
agent that authors objectives, launches and monitors optimization, interprets structure–activity relationships, and revises its strategy accordingly. MAGI generates molecules either directly through the LLM or by delegating to REINVENT 4, with scoring services interchangeable behind a common contract. We tested it across nine retrospective lead-optimization campaigns from three pharmaceutical companies, replayed under fixed temporal cutoffs. Both routes produced valid structures: LLM proposals stayed closer to local chemistry and reached comparable or higher primary activity in fewer operations, whereas REINVENT explored broader chemical space. Whether a campaign met its objective depended on the predictive models, not on the generation route: attainment followed model accuracy on the chemistry proposed, dropping once that chemistry moved outside the model's applicability domain. Separately, a blinded evaluation asked whether the MAGI's output could pass as expert work: chemists were not able to discriminate agentic proposals from held-out compounds, and judged the SAR
reasoning broadly plausible yet incomplete. Together, these results position MAGI as a coordination layer pluggable into existing computational chemistry workflows. The ceiling on real projects, however, remains currently set by scorer applicability rather than by tool orchestration.
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All authors Pierre Llompart, Levent Guner, Helen Lai, Alessandro Tibo, Yijie Xu
Artificial Intelligence
What Did the Agent Actually Do? Evidence-Grounded Oversight for Long-Horizon Agents
Zhongxiang Sun, Jiahao Yan, Hongkang Zhao et al. (+5 more)
As
agents take on long-horizon tasks, users shift from making individual decisions to overseeing autonomous execution. Yet the volume of
agent activity and the fragmentation of supporting evidence make it difficult to determine which decisions warrant user verification. We study monitors that identify consequential decisions and locate evidence to help users assess their implications. We introduce AgentMonBench, a software-engineering
benchmark comprising three subsets that cover two complementary dimensions:
alignment between requirements and behavior, and awareness of consequential autonomous decisions for verification. To support these judgments, we propose the Evidence-Grounded Behavior Graph (EBG), a training-free method that groups source-linked evidence into behaviors and organizes their relationships into a graph. EBG presents task-oriented views of this graph to help monitors interpret behavior in context. Experiments across eight models show that EBG improves decision identification and evidence localization in most settings compared with direct access to the original context. Further experiments show that EBG's evidence-localization gains persist across input scales and hyperparameter settings, while real-world applications illustrate its practical value for human oversight.
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All authors Zhongxiang Sun, Jiahao Yan, Hongkang Zhao, Haojie Ding, Boheng Zhang, Fan Yang, Xiao Zhang, Jun Xu
Artificial Intelligence · Cross-listed from cs.LG
Quantifying the Stability of Multi-Step Reasoning via Error Amplification
Dongyue Li, Ziniu Zhang, Minxuan Duan et al. (+1 more)
We consider the stability of multi-step
reasoning processes, which have extensive applications in
language models, including chain-of-thought and algorithmic
reasoning. While longer sequences of
reasoning can improve a model's generation capability at test time, the errors due to intermediate
reasoning steps can accumulate in autoregressive generation, and thus grow substantially at the end. In this paper, we ask: What are the key factors determining the stability of multi-step
reasoning? First, we show an inference error bound governed by the product of spectral norms of the Jacobians taken through the input space across generation steps. This product can be viewed as an error amplification factor, which could scale exponentially with the number of
reasoning steps, serving as a quantitative measure of
reasoning stability. Second, we analyze this measure in
transformer models trained to predict simple tasks like linear and quadratic functions. We theoretically prove that the
transformer model converges to a solution where the stability measure decays, thus yielding nearly zero inference loss over (arbitrarily) long steps. Finally, the stability analysis leads to several algorithmic implications for controlling the stability, through (i) chain-of-thought length compression that reduces the sensitivity of each step, and (ii) quantization-aware training that regularizes the input Jacobian norms. We validate the proposed
algorithms by fine-tuning
language models on graph-algorithmic
reasoning tasks and symbolic state-tracking tasks. Across seven evaluations, our
algorithms improve over baseline comparisons by 3.5% on average, and by 8.2% for longer-length inputs. Ablation analysis validates that the stability measure is drastically reduced by 3-8$\times$, confirming the regularization effect on the spectral norms of the (input space) Jacobians.
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All authors Dongyue Li, Ziniu Zhang, Minxuan Duan, Hongyang R. Zhang
Artificial Intelligence · Cross-listed from cs.CR
RAISED: Self-Distillation for Robustness to Prompt Injection in LLM Agents
Mohamed Dhouib, Clement Elliker, Alexi Canesse et al. (+5 more)
Tool-using
language-model
agents are vulnerable to indirect
prompt injection because they must act on untrusted external content. Existing training-time defenses can reduce attack success rates, but often at the cost of general capabilities. We show that training-based defenses induce substantial drift in the model's output distribution, altering its behavior even in benign settings and providing a potential mechanism for utility degradation. We further identify a failure mode of these defenses: On benign tool-use tasks, the model refrains from a step needed to finish an authorized task, particularly when that step is indicated by a tool output. To address these limitations, we introduce RAISED (Robust Attack Invariance through Self-
Distillation), a training framework that combines self-generation and self-
distillation. The model first generates its own tool-use scenarios, with an emphasis on cases where task completion requires acting on legitimate guidance from tool outputs. Then, through self-
distillation, the student is trained to match the teacher's clean-context behavior on both clean and injected variants of the same trajectory. RAISED substantially reduces the attack success rate of
prompt injections in tool responses while, unlike prior training-based defenses, preserving utility on both agentic and general-purpose
benchmarks.
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All authors Mohamed Dhouib, Clement Elliker, Alexi Canesse, Maël Jenny, Lucas-Andrei Thil, Mahammed El Sharkawy, Sonia Vanier, Elie Bursztein
Artificial Intelligence
CVIF: A Criticality-Driven Visual Intervention Framework for Geometric Diagram Understanding in MLLMs
Jiahui Kang, Bifan Wei, Lingling Zhang et al. (+4 more)
Despite significant progress in visual tasks by
Multimodal Large
Language Models (MLLMs),
geometric diagram understanding remains challenging due to the presence of sparse visual cues and ambiguous symbol-primitive associations. MLLMs may therefore rely on textual priors, producing interpretations that conflict with visual evidence. We introduce the training-free Criticality-Driven Visual Intervention Framework (CVIF), an inference-time method that localizes critical layers and executes visual interventions during the transition from evidence aggregation to semantic decoding. At these layers, a Geometry-Constrained Local Relation Reconstruction (GCLR) module selects and weights vertex-centered visual evidence, while an Adaptive Visual Steering Operator (AVSO) redistributes
attention mass toward the selected
tokens. Experiments on PGPS9K and PGDP5K show that CVIF raises Overall F1 from 77.85 to 85.58 and from 75.23 to 82.84, respectively, establishing a novel inference-time visual intervention paradigm.
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All authors Jiahui Kang, Bifan Wei, Lingling Zhang, Tianwen Jiang, Qiuyong Xiao, Jihong Zhang, Jun Liu
Artificial Intelligence · Cross-listed from cs.LG
Scaling Down the Scaling Laws: Parameter Efficiency and Compute-Optimal Training in Resource-Constrained Large Language Models
Joe Dwyer
Large
language models (LLMs) have achieved substantial performance gains through increases in model size, training data, and computational resources. However, traditional scaling approaches produce diminishing returns, rising financial and environmental costs, and barriers to participation for researchers operating outside large industrial laboratories. This review examines the
evolution of LLM scaling theory from empirical scaling laws to compute-optimal training, with particular emphasis on parameter efficiency,
token utilization, data efficiency, and resource-constrained environments. Foundational work on scaling laws is synthesized alongside later research on compute-optimal training, data
pruning, efficient architectures, quantization, low-rank adaptation, and edge-oriented optimization. The literature indicates a shift from scale maximization toward more deliberate allocation of parameters,
tokens, compute, and hardware resources. At the same time, important empirical, theoretical, and methodological gaps remain regarding whether scaling principles established on enterprise-grade infrastructure generalize to smaller models and constrained computing environments. This review organizes these developments into a unified framework for resource-efficient LLM training and argues that future progress should evaluate efficiency not solely through model performance, but through the relationship among performance, parameter count, computational cost,
token allocation, and hardware constraints.
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