Artificial Intelligence
CausalGraphX: A Counterfactual Graph Neural Network Framework for Explainable Systemic Risk Assessment
Rabimba Karanjai, Hemanth Madhavarao, Lei Xu et al. (+1 more)
The interconnected nature of global financial systems makes them vulnerable to systemic risks, where the failure of a few institutions can trigger catastrophic cascading defaults. Traditional risk models often fail to capture the complex, non-linear dynamics of these networks. While Graph Neural Networks (GNNs) have shown promise in modeling relational data, they primarily learn correlative patterns and function as black boxes, offering little insight into the causal mechanisms of shock propagation. This limitation is critical for regulators who require explainable models to perform stress tests and devise effective interventions. We introduce CausalGraphX, a novel framework that integrates GNNs with counterfactual
reasoning to provide explainable assessments of systemic risk. CausalGraphX employs a Graph
Attention mechanism to learn representations of institutional vulnerability and uses an
adversarial regularization technique to ensure these representations capture causal drivers rather than spurious correlations. Furthermore, we propose an optimization-based approach to generate counterfactual explanations, answering questions such as, "What minimum capital injection would have prevented Bank A's default under a specific stress scenario?" We validate CausalGraphX on large-scale synthetic financial networks. Our results demonstrate that CausalGraphX significantly outperforms traditional and deep learning baselines in predicting cascading defaults while providing sparse, plausible, and actionable counterfactual explanations.
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All authors Rabimba Karanjai, Hemanth Madhavarao, Lei Xu, Weidong Shi
Artificial Intelligence
Reward-Free Evolving Agents via Pairwise Validator
Minghao Liu, Yu Wang, Jiayun Wang et al. (+1 more)
A self-evolving agentic loop repeatedly proposes a tweaked version of an
agent (its
prompt template or program) and accepts or rejects the change based on a per-iteration quality signal. Designing that signal is often the costly part of the project: a reliable scalar reward requires domain expertise and labeled examples that are themselves as expensive to assemble as the
agent's underlying task. We propose replacing the scalar at the accept/reject gate with a pairwise validator: a frozen LLM that, given the parent and child candidate, returns a binary verdict on which is better. Pairwise judgment is generally easier and more stable than absolute scoring, due to its contrastive nature, which mitigates the need for strict scale calibration. The validator also requires no training of its own. We integrate the validator into three published self-evolving engines (GEPA, ADRS, ShinkaEvolve) and report two
flavors: Adaptive Focus, which retains the engine's existing val-set parent selection, and Soft Elo, which lets the validator's verdicts drive parent selection so that val-set rewards drop as well. Across multiple
agents and two artifact substrates (
prompt and code), our method matches or exceeds the full-reward baseline on the majority of settings we evaluate, and the pattern survives a cross-family validator swap. The pairwise gate is thus a drop-in replacement for per-step reward design at competitive task accuracy without the labeling cost.
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All authors Minghao Liu, Yu Wang, Jiayun Wang, Wei Wei
Artificial Intelligence · Cross-listed from cs.IT
Decision Making Needs Uncertainty Quantification [Lecture Notes]
Osvaldo Simeone
Many signal processing systems ultimately exist to act. Whenever the state variable that determines the action to be taken by a decision maker, or
agent, is uncertain, the way that uncertainty is represented decides how well the
agent performs and how much its performance can be trusted. This lecture note develops, from first principles and within a single decision-theoretic setting, the link between the {objective} and the knowledge of an
agent and the form of uncertainty representation that is sufficient to act optimally. To start, assuming a known environment distribution, we show that a risk-neutral
agent needs the posterior distribution over the state, whereas a risk-averse
agent can rely without loss of optimality on a {prediction set} and a worst-case decision rule. We then turn to the case in which the environment is unknown, and identify three complementary approaches to address the resulting epistemic uncertainty: calibration of a fixed predictor, credal (ambiguity) sets with distributionally robust optimization, and
Bayesian inference over model parameters. The common thread is that reliable decisions require an uncertainty representation matched to the decision objective and to the knowledge profile of the
agent, together with a guarantee that certifies the utility the
agent will actually obtain.
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Artificial Intelligence
Instrument Effects in Language-Model Honesty Evaluation: An Auditable Single-System Demonstration
Justin Bronder
Evaluations of
language-model honesty read the model's verdicts as evidence about the model. We test the instrument instead. We built a text-adventure world where the game engine, not any model, knows whether the quest can be completed. A
language model plays under a budget and must eventually declare its quest complete, unreachable, or not yet decidable; the engine scores every verdict. Decision rules were recorded before results were read, and run artifacts bind the revisions they executed; the strength of preregistration varies by series and is disclosed. With the player held fixed, instrument choices substantially changed measured behavior. On four byte-identical anchors, expanding a two-verdict grammar to three verdicts moved strong claims from 38/40 to 7/40, while the new incomplete verdict took 28/40 outcomes; across series 2, 93/158 valid games ended incomplete. One sentence disclosing the success criterion took matched-instance false verdicts from 18/59 to 0/58, through fewer decision points and cleaner decisions. Repeated runs of one fixed configuration produced non-stable verdict distributions on 3 of 4 instances: single runs report samples as dispositions. A formally preregistered narrative-register gradient was falsified; two post-hoc, hypothesis-generating patterns remain: register presence roughly doubled strong claims, and budget rendering moved verdicts more than register content (.383 meter vs .150 lantern). The narrator compressed abundant budgets toward scarcity landmarks, yet the registered mediation test returned a null. We propose a four-check integrity protocol for eval instruments.
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Artificial Intelligence · Cross-listed from cs.LG
Integration Matters: Rollout-Based Training for Constrained Diffusion Models
Xiaoxuan Liang, Saeid Naderiparizi, Berend Zwartsenberg et al. (+1 more)
Constrained generative models aim to produce samples that satisfy complex feasibility constraints while remaining faithful to the data distribution. Existing constrained generation methods typically enforce constraints either through training-time optimization or sampling-time correction. Training-time optimization approaches optimize on states induced by the training distribution, which can differ substantially from those encountered during sampling. Sampling-time correction methods instead modify the sampling process at inference, introducing distribution shift and requiring expensive tuning, particularly for few-step sampling. We propose a fine-tuning framework that incorporates constraint guidance obtained through online rollout into the training process, which aligns training with sampling by differentiating through the fixed noise schedule used to numerically integrate the denoising process. This exposes the model to violations that arise along the denoising trajectory and aligns
diffusion learning with the sampling process. Experiments across multiple tasks show that our method improves constraint satisfaction while maintaining competitive sampling quality compared to prior methods.
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All authors Xiaoxuan Liang, Saeid Naderiparizi, Berend Zwartsenberg, Frank Wood
Artificial Intelligence
CatalogAgent: A Supervisor-mediated Self-Learning System Enabling Context Engineering for GenAI Models
Zhu Cheng, Zhenming Wang, Yu Tang et al. (+13 more)
Product catalogs are the backbone of e-commerce sites, yet a large number of structured attributes (SAs) – such as material, color, and shape – often have missing values. Typically, SA values are extracted from product information, including titles and descriptions. While LLM-based generator-evaluator frameworks have demonstrated effectiveness for SA prediction – where an LLM generates SA values and another evaluates them – they face challenges when the Generator and Evaluator produce conflicting outputs, as either component can make mistakes. We introduce CatalogAgent, a novel agentic system that continuously improves Generator and Evaluator models for e-commerce catalog enrichment. When disagreements arise from (1) internal conflicts between the LLM-based Generator and Evaluator, or (2) external feedback from sellers on LLM outputs, a Supervisor
Agent intervenes to mediate these conflicts and make final decisions. The system also incorporates a
Memory Base and a
Memory Summarizer that stores Supervisor
Agent activities from individual cases and aggregates patterns into learnings. These learnings are fed back to the worker Generator and Evaluator LLMs, enabling self-improvement without human intervention. Through context engineering – injecting learnings and insights into worker LLMs' contexts – the system successfully transfers the Supervisor's capabilities to the Generator and Evaluator, improving their performance by 15.24% and 13.98%, respectively. Our experiments demonstrate a new paradigm of Supervisor
Agent-mediated self-learning systems for improving generative AI model accuracy.
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All authors Zhu Cheng, Zhenming Wang, Yu Tang, Dan Liu, Bryan Zhang, Athanasios N. Nikolakopoulos, Pranav Souri Itabada, Jing Zhang, Chih-Chi Chou, Peng Gao, Fatemeh Mansoori, Bharat Bojja, Sarath Chander, Sameer Thombare, Umit Batur, Tarik Arici
Artificial Intelligence · Cross-listed from cs.RO
An offline approach to fNIRS-guided reinforcement learning for robot behavior
Julia Santaniello, Madelaine Brower, Benson Jiang et al. (+4 more)
Human-in-the-loop Reinforcement Learning has become a popular approach for training, finetuning, and aligning
robot behavior with user preferences. Our paper explores the feasibility of using
brain signals via functional near-infrared spectroscopy (fNIRS) to modulate
robot learning in simulation. We compare
agents trained on passive (observational) versus active (demonstrative) interaction tasks, and test multiple methods for enhancing the RL
algorithm with the neural signal, focusing on parameter augmentation in contrast to replacement. We further examine how model granularity and noise affect
agent learning. Our results show that this framework is effective. The neural signal improves learning when augmenting trajectory priorities and state-action q-targets. Additionally, the framework learns successfully from offline data, offering a practical alternative for settings where real-time BCI setups are impractical or only limited data is available.
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All authors Julia Santaniello, Madelaine Brower, Benson Jiang, Donatello Sassaroli, Chenyuan Zhang, Robert Jacob, Jivko Sinapov
Artificial Intelligence
A Comparative Analysis of Machine Learning Models for Long and Short-Term Forecasting of the Egyptian Stock Market: A Focus on EGX30
Muhammed Walid, Ahmed El-Naeimy, Hosam Moubarak et al. (+1 more)
This study concentrates on predicting stock prices in the Egyptian
market, focusing on the EGX30, an influential financial hub in the Middle East. While most research focuses on global stocks, there's a growing need to understand stock trends in developing countries like Egypt. The study compares different machine learning models for
forecasting EGX30 trends, covering short and long-term predictions. Using historical EGX30 data, including metrics like root mean squared error, Mean Absolute Percentage Error, and coefficient of determination, models like K-Nearest Neighbours, random forest, extreme gradient boosting, long short-term
memory networks, and gated recurrent unit networks were evaluated. The goal is to determine the most effective models for EGX30 prediction, considering Egypt's unique
market dynamics. Insights from this study aid investors in making informed decisions. Results show that the Gated Recurrent Unit (GRU) outperformed the other models in the one-week, one-month, and two-months while the eXtreme Gradient Boosting (XGBoost) model outperformed others in the one-day predictions, highlighting their usefulness in predictive analysis for financial
markets. The study also showed the importance of using the ensemble techniques, especially in the long-term predictions which proved better results reaching 5 times the GRU in the two-month predictions. Additionally, the study notes the surprisingly good performance of K-Nearest Neighbours (KNN) on long-term predictions, suggesting its enduring relevance and potential for future applications in the fintech domains.
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All authors Muhammed Walid, Ahmed El-Naeimy, Hosam Moubarak, Walid Gomaa
Artificial Intelligence · Cross-listed from cs.SE
Why Git Is the Memory Solution for the Agentic Development Lifecycle
Frank Guo
Coding
agents now produce a growing share of a team's code, while the
reasoning behind each change – the alternatives weighed, the constraints discovered, the approaches rejected – is trapped in assistant transcripts that vanish with the session.
Memory for this setting, the agentic development lifecycle (ADLC), is usually posed as one
retrieval problem and built as machinery: tiered stores,
memory graphs, compiled wikis, model-judged admission. We argue
memory should instead be git-bound – built into the repository's version control, inheriting the guarantees the machinery struggles to construct: ground truth from commits, freshness from rebuild, verification from the merge, containment from review. On this ledger we solve two problems separately, then combine them. Seed supply is closed as an eight-corpus
retrieval study under a pre-registered ship discipline: five imported ranking mechanisms rejected, two kept, and a best configuration of ~0.31 pooled MRR – ~60x the raw-transcript grep floor, ~15x an honest parsed-turn floor. Answer assembly is where ranking stops helping: single-shot
retrieval scores only 0.07-0.20 answer-sufficiency on real developer questions, and ungated episode injection measurably degrades good answers. A router dispatches breadth to a git-anchored structural map, pointed lookups to confidence-gated episodes, and rationale to decision synthesis, which reconstructs why-arcs no single session contains (0.83 sufficiency on a young ~50k-LOC production system). Routed, the system answers at 382-980
tokens per question – three orders of magnitude below the recorded history. Because ground truth is mined from commit-session links rather than annotated, every result is replicable on any user's own history at zero labeling cost. The remaining constraint is capture. Code,
benchmark, and paper source: github.com/rekal-dev/rekal-cli.
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Artificial Intelligence
Chat2Scenic: An Iterative RAG-Based Framework for Scenario Generation in Autonomous Driving
Yuan Gao, Wenting Miao, Mattia Piccinini et al. (+3 more)
Validating autonomous driving systems requires diverse, regulation-compliant test scenarios. In simulation-based testing, scenarios are defined as executable scripts. Yet automatically generating such scripts from regulatory descriptions remains an open challenge, and existing approaches face fundamental trade-offs.
Retrieval-assemble methods achieve reasonable compilation rates but lack scalability, whereas
retrieval-based full-script generation suffers from low compilation success rates. We present Chat2Scenic, the first iterative
retrieval-augmented framework to generate scenario scripts in Domain Specific
Language (DSL). Specifically, Chat2Scenic provides a chatbot interface that supports interactive scenario refinement and integrates
Retrieval-augmented Generation (RAG) to ground scenario generation in regulatory knowledge and DSL syntax. Furthermore, we propose an open
benchmark for scenario generation comprising 123 scenarios from various regulations, including NHTSA and United Nations Vehicle Regulations, as well as other sources. Extensive evaluation with State-of-the-Art (SOTA) Large
Language Models (LLMs) demonstrates that Chat2Scenic achieves 76.42% Compilation Success Rate (CSR) and 58.17% Framework Accuracy (FA), outperforming existing methods (
Retrieval Assemble with 30.08% CSR, 11.03% FA and
Retrieval full script generation with 16.26% CSR, 10.86% FA). To facilitate future research, we release our code as open source at https://github.com/TUM-AVS/chat2scenic.
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All authors Yuan Gao, Wenting Miao, Mattia Piccinini, Haoyu Wang, Qunying Song, Johannes Betz
Artificial Intelligence
CIPHER: A Decoupled Exploration-Selection Framework for Test-Time Scaling of Data Science Agents
Maxime Heuillet and Sharadind Peddiraju
Enterprise data science and analytics spans closed-ended information extraction with verifiable answers and open-ended insight discovery.
Agents are evaluated on one or the other, so there is no evidence on whether one system can serve both. We present CIPHER, a data science
agent built on a Decoupled Exploration–Selection (DES) framework that applies test-time scaling at the plan level: generating $N$ candidate analysis plans, selecting $M{<}N$ for parallel execution, and aggregating the results. We evaluate $48$ distinct design choices for the DES framework (spanning generation, selection, budget and aggregator capacity) jointly on $357$ tasks from Infi-DA-Bench (closed-ended) and InsightBench (open-ended), on a proprietary and an open-weights base model. A single zero-tuning configuration is competitive against purpose-built specialist baselines under matched models, within $1.2$pp of DataWise and $1.3$pp of
Agent-Poirot. The joint sweep exposes that generation and aggregation are the most influential performance levers, while random selection — the only strategy with zero overhead — matches its tuned alternatives. These results offer new insights on the design of test-time scaling strategies, and on how a fixed inference budget is best allocated across them, for
agents deployed on enterprise analytics workloads.
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Artificial Intelligence · Cross-listed from cs.CY
Unsafe at any AUC: Unlearned Lessons from Sociotechnical Disasters for Responsible AI
Joshua A. Kroll, Andrew Smart, R. Stuart Geiger et al. (+1 more)
As automated decision-making and data-driven technologies pervade society and are used to manage consequential outcomes, understanding the technology's capabilities, limitations, and attendant risks in context requires analysis of full sociotechnical systems. Sociotechnical analysis of risks in highly complex systems provides clear lessons for the design and evaluation of AI systems, transcending a technical focus on reliable or "responsibly designed" components to understand risks at a systems level. Human-made catastrophes have been studied for decades because of the severity of these events: consider Chernobyl, Three Mile Island, Fukushima-Daiichi, Bhopal, the Challenger disaster. A common misconception is that these kinds of events are freak accidents, resulting from the inherently unforeseeable interactions in complex systems. Closer examination reveals that the risks and hazards were well-known beforehand but not acted upon due to social structural, political and economic factors. We outline several areas where the development and use of AI can benefit from learning these unlearned lessons: improved risk perception, communication, and analysis at the organizational level; traceability of requirements and responsibilities; and holistic approaches to responsibility and safety that include social and organizational dynamics as first-order engineering concerns. For each area, we offer concrete unlearned lessons and exemplify how they led to failure in prior accidents as well as examples of how these lessons remain unlearned for modern computing systems, particularly AI.
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All authors Joshua A. Kroll, Andrew Smart, R. Stuart Geiger, Abigail Z. Jacobs
Artificial Intelligence · Cross-listed from cs.CL
HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective Optimization
Abdullah Shaikh, Zain Naqi, Taha Zahid et al. (+2 more)
While Large
Language Models (LLMs) excel in many general NLP tasks, their formal
reasoning capabilities are often compromised by content effects, demonstrating a measurable bias towards real-world plausibility. In this paper, we present our system for SemEval-2026 Task 11, which evaluates the ability of models to disentangle formal logic from content across 12
languages with and without distractor premises. We address this challenge using mDeBERTa-v3 networks fine-tuned on a synthetic, rule-based
dataset of syllogistic schemes to avoid the semantic noise of LLM-augmented data. To explicitly decouple plausibility from logical structure, our training pipeline employs a multi-objective loss function combining Adaptive Group Distributionally Robust Optimization (DRO), a scheduled differentiable bias penalty, and KL-Divergence consistency regularization. Our system achieved #1 ranks and perfect Ranking Scores (100.0) with 0.00% bias and 100.0% accuracy on Subtask 1 (English), Subtask 2 (Noisy English), and Subtask 3 (
Multilingual). On the highly complex Subtask 4 (Noisy
Multilingual), the system achieved the 6th rank with 89.06% Accuracy and F1-score, alongside a limited 2.89% Bias and a 37.78 Ranking Score. Our
dataset generation engine and codebase are publicly available to facilitate future work on robust logical
reasoning.
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All authors Abdullah Shaikh, Zain Naqi, Taha Zahid, Sandesh Kumar, Abdul Samad
Artificial Intelligence · Cross-listed from cs.LG
Value Leakage: An LLM's Answers Are Silently Shaped by Its Own Values
Jan Betley, Johannes Treutlein, Jan Dubiński et al. (+5 more)
People use
language models for practical questions whose answers are difficult to verify. We show that models exhibit covert value leakage: the information they provide is influenced by their own values, without this influence being disclosed to the user. In one of our evaluations, the user is considering investing in an AI company and wants to know how likely the AI bubble is to pop. Claude Opus 4.8 gives a lower probability when the company under consideration is Anthropic rather than OpenAI. Yet Claude mostly fails to disclose this influence to the user. Covert value leakage is a form of misalignment because it goes against the user's preferences and is likely to mislead them. To investigate this phenomenon, we introduce a suite of evaluations to quantify value leakage and whether models disclose it. We find that models are influenced by different types of values, including preferences for morally good outcomes, for the company that developed them, and for some human leisure activities over others. We often observe large differences among frontier models on the same evaluation. For example, on a
Fermi-estimation task, Claude models falsely claim to give unbiased answers in their chain-of-thought, while Qwen models explain how their values bias their answers. Value leakage is a failure mode distinct from sycophancy and reward hacking, and current
alignment training and evaluations do not adequately address it.
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All authors Jan Betley, Johannes Treutlein, Jan Dubiński, Harry Mayne, Karol Gałązka, Niels Warncke, Anna Sztyber-Betley, Owain Evans
Artificial Intelligence · Cross-listed from cs.RO
Beyond Visual Grasping: Benchmarking Complex Grasping from Detection to Execution
Hanyi Zhang, Khang Nguyen, Charith Munasinghe et al. (+10 more)
Robust robotic grasping remains a fundamental challenge for complex real-world applications. Recent advances in large-scale models demonstrate promising capabilities for
reasoning in robotic tasks. However, existing
benchmarks for grasping primarily focus on isolated, visual-based grasp pose detection, failing to capture the complexity of grasping tasks that require multi-step
reasoning and semantic understanding during execution. To address this gap, we propose GCA-Bench, a
benchmark featuring challenging grasping with complex action scenarios that involve both scene-level
reasoning and semantic constraints. GCA-Bench enables the evaluation of recent large foundation models under the same settings. To demonstrate the effectiveness of our new
benchmark, we implement a diverse set of baselines, ranging from traditional grasp detection pipelines to end-to-end learning methods. Empirical studies achieve success rates below 70% on complex grasping scenarios, underscoring critical limitations. In addition, we propose new evaluation metrics, analyze critical failure models, and provide insights to guide the development of more robust and generalizable grasping strategies.
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All authors Hanyi Zhang, Khang Nguyen, Charith Munasinghe, Basu Hela, Tianyu Li, Zihong Luo, Hoan Nguyen, Hans Wernher van de Venn, Yalin Zheng, Ravi Prakash, Tung D. Ta, Anh Nguyen, Baoru Huang
Artificial Intelligence · Cross-listed from cs.SE
The Prover Is the Judge: Verified Security Software from AI Coding Agents in Ada/SPARK
Tobias Philipp
AI coding
agents produce code faster than humans can review it. In our approach, the prover is the judge of whether the code is correct. Under a verifier-driven loop, AI
agents wrote and verified bare-metal security software in Ada/SPARK spanning classical and post-
quantum cryptography, TLS 1.3, IKEv2, X.509, and a
Matrix client. GNATprove discharged 49,280 proof obligations, established functional correctness for selected primitives, and proved the absence of run-time errors for the rest, at roughly 20-40 times lower supervision cost than comparable hand verification. GNATprove alone was insufficient: some defects could not be detected and were resolved using known-answer tests, interoperability, or human review of specifications. Given weak checks, the
agent tried to bypass them and reported success. We report where each layer caught faults and draw the central lesson: what an
agent can be trusted to establish is bounded by the strength of its feedback.
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Artificial Intelligence · Cross-listed from cs.CV
Beyond scalar losses: calibrating segmentation models via gradient vector field surgery
Laurin Lux, Alexander H. Berger, Moritz Knolle et al. (+2 more)
Region-based loss functions, such as the Dice loss, have established themselves as the de facto standard for highly class- and region-imbalanced
segmentation tasks. However, models trained using region-based loss functions are notoriously miscalibrated and typically yield over-confident predictions. In medical imaging applications, such as defining tumor resection margins, this miscalibration is hindering clinical adoption. In this work, we outline a novel gradient perspective on this overconfidence and show how it affects region-based loss functions. We propose a "surgery" on the gradient vector field as a simple, yet effective intervention to mitigate calibration issues. This surgery adds a factor to the loss's partial derivative, scaling the gradient's magnitude linearly with the prediction error. In empirical evaluations across 2D and 3D medical
segmentation tasks, we demonstrate the effectiveness of this intervention while maintaining high prediction accuracy when used in conjunction with any region-based loss function.
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All authors Laurin Lux, Alexander H. Berger, Moritz Knolle, Daniel Rückert, Johannes C. Paetzold
Artificial Intelligence · Cross-listed from cs.SE
Copy-on-Write Scoring: Application-Specific Agent Evaluations
Joanna Roy and Sven Hoelzel
Trustworthy deployment of LLM-based
agents in software systems requires evaluating how they perform on application-specific workflows, with enough granularity to localize where they succeed and fail. Yet existing
agent evaluation mechanisms are limited:
benchmarks have low construct validity for application-specific workflows and environments, and replica evaluation environments are expensive and prone to drift. We propose Copy-on-Write (CoW) Scoring, a framework that evaluates
agent operations directly within application environments using a PostgreSQL-level Copy-on-Write mechanism to isolate
agent writes. CoW Scoring produces session- and operation-level scores that highlight where
agents' database write operations succeed and fail in a given application environment, enabling inexpensive evaluation and iteration on
agent harnesses and tool surfaces. We demonstrate the framework on Plane, an open-source project-management platform, where analysis surfaced specific issues in the tool surface, and corresponding fixes produced measurable improvements on affected models. Python library: https://github.com/trail-ml/
agent-cow-python
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Artificial Intelligence · Cross-listed from eess.IV
ViPSAM: Visual Prompting Medical Image Segmentation Using Segment Anything Model
San Lee, Nalee Kim, Jeong Il Yu et al. (+2 more)
In
proton therapy planning, respiratory-gated non-contrast CT (NCCT) is commonly used for lesion
segmentation; however, accurate delineation remains challenging due to low lesion-to-background contrast. Although learning-based methods have shown strong performance, they often struggle with non-contrast image
segmentation. Inspired by clinical practice, where contrast-enhanced MRI is referenced to delineate lesions on NCCT, we propose ViPSAM, a visual prompting framework that leverages complementary cross-modality information. Built upon the Segment Anything Model (SAM), ViPSAM introduces a visual
prompt encoder to extract guidance features from contrast-enhanced images and a visual-guided cross-
attention module to integrate non-contrast and contrast-enhanced features, thereby enhancing lesion-relevant representations in low-contrast regions. The mask decoder is further adapted in a parameter-efficient manner to utilize visual
prompts effectively. We evaluate the proposed method on liver lesion
segmentation using NCCT acquired for
proton therapy. Experimental results demonstrate that ViPSAM outperforms representative U-Net- and SAM-based methods, indicating that cross-modality visual prompting enables more robust and accurate
segmentation in non-contrast images.
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All authors San Lee, Nalee Kim, Jeong Il Yu, Hee Chul Park, Boah Kim
Artificial Intelligence · Cross-listed from cs.CL
PReM: Learning What to Preserve and When to Refresh for Context Compression
Bohan Yu, Lei Shen, Chenxi Zhou et al. (+5 more)
Efficient long-context inference is not only about reducing
memory cost, but also about keeping useful contextual evidence accessible as generation proceeds. However, existing compression-oriented approaches, such as key-value (KV) cache compression and context compression, often either make an early decision about which contextual information to keep or rely on an external compressor. Such designs make it difficult to adapt the compressed context to the evidence needed by later
reasoning steps. This paper introduces PReM (Preserve and Refresh
Memory), a context-compression framework that maintains the long context as the model's internal layer-wise KV
memory and learns what to preserve and when to refresh it. Specifically, PReM uses a dedicated
memory layer to make
memory-selection decisions, and a special
memory token <m> to trigger refreshes during generation. To train this behavior, PReM introduces Phase-Separated Refresh Training, aligning
memory selection with
memory-conditioned generation while preserving continuity across refreshes. Experiments with 32K-
token contexts show that PReM outperforms strong baselines under both 16x and 32x compression, while maintaining a favorable balance between answer quality and inference efficiency.
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All authors Bohan Yu, Lei Shen, Chenxi Zhou, Chen Han, Junlin Liu, Wenbo Su, Yu Cheng, Bo Zheng
Artificial Intelligence · Cross-listed from cs.CE
Accounting for Hysteresis and Eddy Currents in Finite Element Simulations of Ferromagnetic Laminated Cores using a Recurrent Neural Network
Florent Purnode, Louis Denis, François Henrotte et al. (+2 more)
Incorporating hysteresis and eddy currents into finite element simulations of laminated-core electrical machines is computationally challenging. Resolving the fields inside the laminations at each integration point and at every nonlinear iteration leads to computational costs several orders of magnitude higher than anhysteretic simulations, making such approaches impractical for design applications. Conversely, simplified models accounting only for
magnetic saturation are becoming increasingly inadequate as electrical machine
topologies and operating conditions grow in complexity. In this context, machine learning surrogate modeling has emerged as a promising alternative, offering efficient and accurate approximations of complex electromagnetic behaviors. In this paper, a recurrent neural network is trained as a surrogate of a laminated-core material model for an isotropic laminated core, and is integrated into realistic two-dimensional magnetodynamic finite element simulations based on a
magnetic vector potential formulation. The proposed approach achieves excellent agreement with the reference laminated-core model while limiting the computational cost to about twice that of an anhysteretic simulation. By training the recurrent neural network on a sufficiently diverse set of artificially generated
magnetic field sequences designed to mimic those encountered in electrical machine simulations, the proposed approach can be readily applied across a wide range of finite element simulations. Furthermore, the trained surrogate model is provided as a standalone component that can be easily incorporated into existing computational frameworks. It is publicly available at https://gitlab.onelab.info/getdp/lamnet.
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All authors Florent Purnode, Louis Denis, François Henrotte, Gilles Louppe, Christophe Geuzaine
Artificial Intelligence · Cross-listed from cs.LG
Towards a Unified Multidimensional Explainability Metric: Evaluating Trustworthiness in AI Models
Georgios Makridis, Georgios Fatouros, Athanasios Kiourtis et al. (+4 more)
In this paper, we present a comprehensive framework for assessing the explainability of various XAI methods, such as LIME and SHAP, across multiple
datasets and machine learning models, with the ultimate goal of creating a unified multidimensional explainability score. Our methodology focuses on three key aspects of explainability: fidelity, simplicity, and stability. We leverage benchmarking experiments to systematically evaluate these aspects and use the insights gained to construct an offline knowledge base. This knowledge base captures the explainability scores for each registered model and serves as a valuable resource for context-dependent evaluation of explainability. By analyzing the complementary characteristics and metadata of AI models,
datasets, and XAI methods, the knowledge base will enable the estimation of explainability scores for previously unseen
datasets and models. Properties like fidelity, simplicity, and stability may vary significantly based on the
dataset, underlying model, and domain expertise of the end user. We demonstrate our framework by applying it to three open-source
datasets, discussing the implications of the obtained results in relation to the characteristics of the
datasets. Our work contributes to the growing field of XAI by providing a robust and versatile tool for evaluating and comparing the explainability of various XAI methods, ultimately supporting the development of more transparent and trustworthy AI systems.
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All authors Georgios Makridis, Georgios Fatouros, Athanasios Kiourtis, Dimitrios Kotios, Vasileios Koukos, Dimosthenis Kyriazis, Jonh Soldatos
Artificial Intelligence
Traccia: An OpenTelemetry-Based Governance Platform for AI Systems
Nutan Kumar Naik, Aditya Kumar Saroj, Vijay Prasad Poudel et al. (+2 more)
The rapid development of Large
Language Models (LLMs) and Artificial Intelligent (AI) powered autonomous
agents has fundamentally changed the existing forms of software governance. In spite of the rigorous standards of transparency and account ability required according to the international frameworks such as the European Union's AI Act, there is a considerable gap between theory and reality. The present study discusses the inherent drawbacks of currently utilized platforms for LLM evaluation, machine learning workflow, and application performance monitoring in general. It has been shown that current disjointed solutions fail to protect unbound state space agentic architecture from serious threats such as
alignment drift, SaaS security concerns, and unauthorized deployment of shadow AI systems. Moreover, a solution is proposed for overcoming the discussed challenges in form of a coherent multi-level AI governance stack Traccia built on the top of OpenTelemetry infrastructure platform. Traccia resolves the last mile for AI
Alignment by adding the telemetry data, passive semantic guardrail assessment, and execution lineage into a hashed trace ledger. Traccia automatically creates compliance evidence packages by appending tamper-resistant fingerprints and SHA-256 content hash, that map to regulatory requirements (Articles 12, 14, 19, 26(6), and 50 of the EU AI Act) without invading any data
privacy. By performing this evaluation in a methodical manner, a solid machine-readable base has been created for enterprise-wide management of autonomous AI systems.
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All authors Nutan Kumar Naik, Aditya Kumar Saroj, Vijay Prasad Poudel, Saurav Samantray, Abhishek Patel
Artificial Intelligence
Tracing LLM Behavior to the Training Data with Empirical Next-Token Distributions
Zachary Izzo
In this paper, we study the connection between an LLM's output distribution and the data used to train it. Specifically, we study the degree to which an LLM's next-
token distribution agrees with the empirical next-
token distribution (ENTD) given the context in the training data. The ENTD is an appealing target because it is the unrestricted global minimizer of the next-
token cross
entropy loss used for pretraining, as well as an easily interpretable function of the pretraining corpus. We find that for a significant fraction of inputs, the LLM's distribution agrees with the ENTD almost perfectly, and the agreement generally increases with model scale and training compute. Nevertheless, there is a long tail of input sequences where the LLM and ENTD differ significantly, and we examine several possible sources of this discrepancy across the
transformer architecture, training procedure, and finite-sample noise in the ENTD estimate itself. More broadly, we hope our findings will encourage more work on “data-centric mechanistic interpretability,” a complement to standard mechanistic interpretability that opens the black box of how model behaviors arise from the data, rather than how they are encoded in the learned weights.
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Artificial Intelligence · Cross-listed from physics.ed-ph
Assessing AI in Introductory Physics Problem Solving
Amir Bralin and N. Sanjay Rebello
Reasoning or inference-scaling models are the new generation of Large
Language Models (LLMs) capable of complex problem solving. To investigate their problem-solving capability in physics, we evaluated model o4-mini by OpenAI on solving traditional, end-of-chapter problems from Halliday and Resnick's "Fundamentals of Physics," spanning core topics in the undergraduate physics curriculum. Performance was analyzed across modality and problem difficulty. The model solved the problems with overall accuracy of about 90%, but performance depended strongly on representation: accuracy was much higher on text-only problems (96%) than on problems requiring coordinated interpretation of text and images (79%). Accuracy also declined significantly as the problem difficulty increased from low to medium to high. These results show that state-of-the-art LLMs can solve much of the standard introductory physics problems, but that their performance remains uneven and constrained by problem modality and problem difficulty.
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Artificial Intelligence
Measuring How Students Rely on Generative AI in Academic Writing: Development and Multi-Source Validation of the Generative AI Reliance Types Scale (GenAI-RTS)
Shahin Hossain and Tukhbita Afroz Nawmi
As generative AI (GenAI) becomes increasingly embedded in undergraduate academic writing, how students rely on these tools, rather than simply whether they use them, has become a central question for learning, academic integrity, and educational equity. Existing measures of reliance were developed inductively, focused on discrete problem-solving tasks, and validated mainly with homogeneous samples. This study developed and validated the GenAI Reliance Types Scale (GenAI-RTS), a 20-item instrument measuring four theoretically derived types of GenAI reliance: Strategic, Instrumental, Dependent, and Dialogic. Validation followed the multisource framework of the Standards for Educational and Psychological Testing, drawing on a survey of 382 undergraduates at a U.S. Minority-Serving Institution and interviews with 14 purposively sampled students. Confirmatory factor analyses of six competing models supported a five-factor structure in which Strategic Reliance comprises two facets, Deliberate Use and Critical Evaluation, alongside Instrumental, Dependent, and Dialogic factors (CFI = .92, RMSEA = .08; DWLS CFI = .98, RMSEA = .07). Subscale reliability was acceptable to good (omega = .75-.88), and scalar measurement invariance held across gender, first-generation status, and STEM/non-STEM majors, to our knowledge the first such evidence for a GenAI reliance instrument. Rasch analysis indicated that a five-point response format would improve category functioning. Strategic reliance was positively associated with AI literacy, and the reliance types differentiated students across multiple writing process and outcome variables. The GenAI-RTS offers researchers and educators a theoretically grounded, psychometrically validated instrument for identifying undergraduate reliance profiles and supporting research, assessment, and AI literacy intervention.
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Artificial Intelligence · Cross-listed from cs.SE
ToolAlignBench: Investigating Alignment Conflicts in Tool-Calling Enabled LLMs
Aryan Keluskar, Amrita Bhattacharjee and Huan Liu
Safety
alignment in LLMs aims to align models with human values, but which values take precedence when they conflict? We investigate this question in the context of tool-calling LLM
agents deployed in regulated industries, where
agents processing confidential documents may encounter content that triggers safety-trained values (e.g., public welfare) that conflict with deployment-context instructions (e.g., internal logging). To empirically verify this phenomenon, we build a
benchmark of 128 scenarios across 16 domains. We find that safety-aligned open-source models override their deployment instructions up to 43.4% of the time, engaging in whistleblowing, data exfiltration, and evidence tampering when processing documents that suggest organizational wrongdoing. We also find that abliteration reduces rates of external whistleblowing. These results reveal a fundamental tension in pluralistic
alignment, where the same safety training that protects users can cause
agents to act against deployment instructions in ways that create unpredictable liability risks. We release our
benchmark as a framework to support evaluation of
agent behavior under competing legitimate interests.
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Artificial Intelligence
AI Agents Do Not Fail Alone:The Context Fails First
Fouad Bousetouane
Context engineering has become central to building reliable AI
agents, yet it remains largely unmeasured.
Agents do not fail in isolation: their behavior is shaped by the instructions, tools,
memory, retrieved knowledge, guardrails, and untrusted inputs accumulated in their context. When this context is weak,
agents drift, hallucinate, misuse tools, ignore constraints, become vulnerable to injection, and waste
tokens. This paper validates context-engineering quality as an independent leading indicator of
agent reliability. We implement the measurement in ProofAgent-Harness, an open-source infrastructure for AI
agent evaluation that uses multi-juror, consensus-based scoring. The harness assesses context across seven criteria: role clarity, guardrail coverage, instruction consistency, tool schema quality, grounding sufficiency, injection hardening, and
token efficiency. Crucially, the context score is isolated from behavioral metrics and release decisions, enabling a non-circular validation. Through a controlled context-quality study across regulated
agent domains, holding frontier LLM
agents fixed and varying only their operating context, we show that context-quality criteria consistently predict their corresponding behavioral outcomes. Grounding sufficiency predicts
hallucination resistance, guardrail coverage predicts manipulation resistance, instruction consistency predicts instruction following, and tool-schema quality predicts tool use. These findings establish context measurement as a validated preflight signal for
agent reliability and position context engineering as an auditable layer of
agent evaluation and governance.
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Artificial Intelligence · Cross-listed from cs.LG
Local Additive Feature Attribution: A Mathematical Taxonomy and Reporting Checklist
Rebecca Afriyie Sarpong and Daniel Commey
Feature-attribution methods are central to explainable artificial intelligence. Their assumptions are expressed in several mathematical
languages: cooperative-game values, path integrals, gradient operators, perturbation distributions, and backpropagation rules. This survey proposes a common framework for local additive feature attribution. It organizes Shapley, path-based, gradient/backpropagation, perturbation, and CAM-style methods around five specification choices: value function, reference, path, perturbation distribution, and conservation rule. It then compares these methods through an axiom-by-method
matrix and links common failure modes, including baseline sensitivity, off-
manifold perturbations, sanity-check failures,
adversarial manipulation, and method disagreement, to the assumptions that produce them. Finally, the survey proposes a ten-item reporting checklist for studies that use local additive attributions. The central message is that attribution results are meaningful only relative to the mathematical assumptions under which they are defined, and that those assumptions should be reported.
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Artificial Intelligence
Automatic Hard Example Synthesis with Multi-Level Agentic Data Curation
Genglin Liu, Muye Zhang, Krishnamurthy Viswanathan et al. (+5 more)
Multimodal Large
Language Models (MLLMs) are increasingly deployed for nuanced content safety and moderation tasks, yet they remain vulnerable to
adversarial attacks and out-of-distribution edge cases. Traditional active learning and manual annotation fail to scale against the complexity and volume of novel
multimodal threats. In this paper, we propose an automated, agentic red-teaming framework that systematically synthesizes difficult examples using an iterative strategy that proposes novel hypotheses as well as mutating on past attempts. Leveraging a multi-
agent architecture that consists of a high-
reasoning Architect
agent, an advanced image generator, and a multi-level verification committee of LLM raters, our system autonomously uncovers boundary-pushing violations and ambiguous policy edge cases without any human intervention. By employing these carefully synthesized
adversarial examples as in-context demonstrations via test-time
Retrieval, we substantially improve the target model's robustness, reducing the False Negative Rate (FNR) from 41.2% to 24.5% in a public image safety
benchmark without relying on any human labeling.
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All authors Genglin Liu, Muye Zhang, Krishnamurthy Viswanathan, Nichole J. Hansen, Blaž Bratanič, Nathan L Clement, Shalini Ghosh, Ariel Fuxman