Artificial Intelligence · Cross-listed from cs.HC
The Silent Cost of Artificial Intelligence Assistance: A Theory of Autonomy Surrender, the Recovery Mechanism, and the Restoration of Human Agency
Ancuta Margondai, Julie Rader, Emma Rader et al. (+2 more)
The integration of artificial intelligence into human decision-making environments has introduced a previously undertheorized cost: the gradual surrender of human autonomy in exchange for access to information and computational assistance. Building on the Human Identity and Autonomy Gap (HIAG) framework, this paper advances a theoretical model of autonomy surrender as a measurable, cumulative process driven by cognitive bandwidth depletion. The model proposes three interacting mechanisms: the silent cost of AI assistance, in which autonomy is transferred incrementally and without awareness; the surrender threshold, beyond which reclaiming autonomous function becomes cognitively and psychologically difficult; and the recovery mechanism, which establishes the design obligation and the ethical responsibility accompanying deliberate human re-assumption of control. The paper argues that human re-entry into the decision loop is not a passive option but an active cognitive event requiring intentional bandwidth restoration. The design of AI systems must incorporate structured re-entry pathways, here termed recovery mechanisms, that preserve human agency while appropriately distributing responsibility. The model further predicts a terminal state, here termed preference inversion, in which functional dependence on AI assistance is experienced not as a deficit but as a preference, transforming the restoration of autonomy from a design problem into a cultural and political one. Implications are drawn for AI system design, governance frameworks, and human factors research.
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All authors Ancuta Margondai, Julie Rader, Emma Rader, Sara Willox, Mustapha Mouloua
Artificial Intelligence
Minim: Privacy-Aware Minimal View for Agents via Trusted Local Sanitization
Hexuan Yu, Chaoyu Zhang, Heng Jin et al. (+4 more)
Modern LLM-powered autonomous
agents increasingly rely on rich user interface (UI) state observations to achieve reliable action grounding in complex digital environments. However, many deployments transmit the full UI state to remote inference servers even when most elements are irrelevant to the current task, which can leak sensitive but unnecessary context such as authentication codes, private notifications, and background application states. We propose MINIM, a trusted local broker that performs
privacy-aware minimization on the client side before any observation leaves the device. Grounded in Contextual Integrity (CI), MINIM learns a dual-score representation for each UI element by predicting an inherent sensitivity score (s) and a task-conditioned necessity score (n). These scores drive a ternary disclosure policy that keeps essential elements, abstracts sensitive attributes when needed, and removes task-irrelevant content. We optimize a CI-aware objective that penalizes necessity errors more strongly on high-risk content, enabling aggressive
pruning while preserving task-critical information. Experiments on real-world UI observations derived from WebArena show that MINIM substantially reduces task-irrelevant sensitive leakage while preserving task-critical semantic context and the interactive affordances required for reliable
agent actions.
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All authors Hexuan Yu, Chaoyu Zhang, Heng Jin, Shanghao Shi, Ning Zhang, Y. Thomas Hou, Wenjing Lou
Artificial Intelligence
Adversarial Concept Search: Predicting Compositional Errors From Feature Geometry
Jennifer Meng Lu, Ruochen Zhang, Isabelle Lee et al. (+3 more)
Humans cannot always intuit what scenarios are most challenging to LLMs. Hoping to capture challenging edge cases, developers either design problems to be difficult for humans or curate extensive
benchmarks. What if we could instead anticipate which scenarios a model will fail on? In this paper, we use an LLM's representational geometry to predict which concept combinations it will fail on. We attribute this compositional failure to interference between salient features. In tasks that require systematic composition - toy programmatic settings, multihop
reasoning,
multilingual factual recall - we find that when a pair of concepts is encoded near-orthogonally, the model reliably composes them. When their linear encodings are close, producing interference, the model fails to compose them. Our method reliably anticipates failure modes across different compositional tasks, without evaluating specific inputs. These results lay the groundwork to use representational geometry to identify high-risk examples, construct targeted stress tests, and provide a scalable foundation for active learning in real-world deployment.
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All authors Jennifer Meng Lu, Ruochen Zhang, Isabelle Lee, David Alvarez-Melis, Ellie Pavlick, Naomi Saphra
Artificial Intelligence
Sorries Are Not the Hard Part: An Expert-Review Case Study of a Semi-Autonomous Formalization
Vasily Ilin and Brian Nugent
Large
language models can often close proof gaps in interactive
theorem provers, but a verified
theorem is not the same thing as a reusable library contribution. We study this distinction through a detailed case study: a semi-autonomous formalization of Grothendieck's vanishing
theorem. The initial version compiles with no sorries, but an expert review found serious problems in definitions,
theorem generality, file organization, and the API. We then ran a review-driven refactor and compression process and obtained a second expert review. The before-and-after comparison shows a sharp split:
agents adapted well to local, mechanically checkable feedback, but remained weak at choosing definitions and designing APIs. We argue that autoformalization should be evaluated not only by closed sorries, but by whether the resulting formalization survives expert review.
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Artificial Intelligence · Cross-listed from eess.IV
GMN4AD: Graph Matching Network for Alzheimer's Disease Diagnosis with Test-Time Domain Adaptation using Multi-centered Structure Magnetic Resonance Imaging
Chen Zhao, Huan Huang, Yixin Xie et al. (+2 more)
Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that affects millions of older adults, with prevalence expected to rise significantly in the coming years. Early diagnosis, particularly during the mild cognitive impairment (MCI) stage, is critical for timely intervention. Structural
Magnetic Resonance Imaging (sMRI) has emerged as a key modality for detecting AD-related
brain changes, but traditional graph-based approaches often struggle with modality and inter-site heterogeneity, limiting diagnostic performance. In this paper, we propose Graph Matching Network for Alzheimer's Disease Diagnosis (GMN4AD), designed to model interactions between heterogeneous
brain graphs derived from neuroimaging data. Unlike conventional methods that treat each
brain graph independently, GMN4AD leverages graph matching to capture cross-graph relationships, enhancing diagnostic precision. Furthermore, we introduce a test-time domain adaptation strategy that combines contrastive learning to mitigate domain shifts during inference. Extensive experiments on three public AD
datasets demonstrate that GMN4AD achieves superior performance compared to state-of-the-art methods, offering a robust and generalizable solution for AD diagnosis.
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All authors Chen Zhao, Huan Huang, Yixin Xie, Jiajing Huang, Weihua Zhou
Artificial Intelligence
A Multi-Agent AI System for Automated High School Transcript Processing: Collaborative Document Analysis at Scale
Ben Torkian and Jun Zhou
Each year, college admissions offices face an overwhelming challenge: processing millions of high school transcripts, each with unique formats, grading systems, and layouts. This manual process creates operational bottlenecks that delay admissions decisions and consume valuable resources. We present a transformative solution through a multi-
agent AI system where specialized
agents collaborate to automatically process diverse transcript formats through intelligent coordination and communication. Our multi-
agent architecture consists of three specialized
agents-a Pattern Recognition
Agent for format-specific parsing, a Semantic Analysis
Agent for natural
language understanding, and a
Vision Intelligence
Agent for
multimodal document analysis-coordinated by an Orchestration
Agent that manages
agent communication and result reconciliation. Our key innovation lies in
agent-based quality control using GPA extraction as a coordination signal, ensuring reliable
agent collaboration and preventing critical information loss. When evaluated on 40 real world transcripts from high schools across 13 U.S. states, our
agent system successfully processed every document, achieving 96.7% accuracy compared to expert manual review while maintaining practical processing speeds of 45 seconds per transcript. This work demonstrates how multi-
agent coordination can solve complex document processing challenges, offering institutions a scalable, collaborative AI solution that preserves accuracy while dramatically reducing processing time.
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Artificial Intelligence · Cross-listed from cs.CL
SANA: What Matters for QA Agents over Massive Data Lakes?
Austin Senna Wijaya, Jiaxiang Liu, Haonan Wang et al. (+1 more)
Exploratory question answering (EQA) over data lakes requires an LLM
agent to discover relevant sources, analyze retrieved data, and adapt its actions based on intermediate results. End-to-end accuracy alone cannot distinguish failures in search, planning, data analysis, or the
agent's Action Policy: its decisions about what to do next and when to submit an answer. We present SANA (Search
Agent Navigation Ablation framework), a diagnostic ablation framework that transforms EQA tasks into runtime profiles containing gold source sequence, sanitized subquestions, and execution records. SANA uses these profiles to construct idealized search, planning, and data-analysis tools, allowing each component to be ablated; the residual gap is diagnostic evidence for policy failures. To illustrate SANA as a reusable evaluation framework, we adapted two recent EQA
benchmarks, LakeQA and KramaBench, and evaluated lightweight and mid-sized
agents under fixed
prompts, budgets, data lakes, and runtimes. Across both
benchmarks, data analysis is a consistent bottleneck while planning is less so. Search is a major limitation in LakeQA's large data-lake setting, but less so for the smaller-scale KramaBench. SANA thus deconstructs end-to-end task accuracies into a diagnosis of where data-lake
agents fail, and allows for systematic comparisons of progress in search, planning, data analysis, and
agent design.
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All authors Austin Senna Wijaya, Jiaxiang Liu, Haonan Wang, Eugene Wu
Artificial Intelligence · Cross-listed from cs.CV
HiLo-Token: Input-Adaptive High-Low Frequency Token Compression for Efficient Image Editing Model Serving
Haoran You, Yotam Nitzan, Lingzhi Zhang et al. (+8 more)
Creative image editing tools, such as Adobe Photoshop's Remove and Generative Fill buttons, are central to everyday customer use and account for a major share of traffic. However, current generative AI models face significant latency challenges, which become even more pronounced as the field transitions from convolution-based U-Nets to
Diffusion Transformers (DiTs). In our evaluation on hundreds of representative image editing samples spanning a wide range of mask ratios, the DiT module alone accounts for an average of 73% of the total model latency, even after being distilled from 50 timesteps down to 8 timesteps. To tackle this challenge, we propose $\textbf{HiLo-Token}$, an input-adaptive
token compression framework that allocates a larger
token budget to high-frequency, rich-context regions while assigning fewer
tokens to low-frequency areas. Specifically, for the editing region specified by the user mask, we retain all
tokens within a dilated mask to preserve strong locality and contextual relevance. Outside the editing region, we introduce a simple yet effective high-frequency
token selection strategy based on spatial frequency to capture important local details, while using
tokens from a heavily downsampled image to represent low-frequency components and preserve the coarse global structure. Extensive experiments on production-level evaluation data validate the effectiveness of the proposed method, which achieves 3.13$\times$, 2.59$\times$, and 1.67$\times$ DiT speedups on A100-80GB for image editing tasks across small, medium, and large mask ratio categories with average ratios of 6.38%, 15.92%, and 35.36%, respectively, without any regression in generation quality. In production, where the editing pipeline is served as disaggregated, independently autoscaled GPU workers, these per-request savings translate into a 33% reduction in the GPUs required to serve the models.
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All authors Haoran You, Yotam Nitzan, Lingzhi Zhang, Yifan Gong, Mang-Tik Chiu, Connelly Barnes, Yan Kang, Yuqian Zhou, Haitian Zheng, Eli Shechtman, Sohrab Amirghodsi
Artificial Intelligence · Cross-listed from cs.CV
How do Self-Supervised Remote Sensing Vision Models Transfer to Downstream Tasks?
Julia Romero, Qin Lv and Morteza Karimzadeh
Self-supervised geospatial foundation models (GeoFMs) learn transferable representations from remote sensing data, but their downstream behavior is difficult to characterize. We study six representative GeoFMs spanning joint-
embedding, reconstruction, and
multimodal pretraining families, and evaluate transfer across classification, regression, and
segmentation benchmarks under different label availability and downstream pipelines. We find that model rankings change across tasks and adaptation settings. Layerwise probing shows that, in most cases, task-relevant information is more accessible in intermediate
transformer blocks compared to final-layer
embeddings, and that GeoFMs exhibit distinct depthwise profiles. In
segmentation case studies on PASTIS and Sen1Floods11, downstream adaptation settings such as decoder design and fine-tuning can be as impactful as the choice of GeoFM, and standard dense-prediction heads may be poorly aligned with how GeoFMs organize information over depth. Finally, CKA analysis on case studies shows that fine-tuning does not rewrite GeoFMs uniformly across depth, and the strongest changes are localized to the first linear layer of the MLP in ViT blocks. These results help explain why GeoFM rankings shift across
benchmarks and motivate more representation-aware evaluation and adaptation strategies.
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Artificial Intelligence · Cross-listed from cs.LG
Gefen: Optimized Stochastic Optimizer
Nadav Benedek, Tomer Koren and Ohad Fried
AdamW is a default optimizer for deep learning, but its moment states add two parameter-sized buffers to training
memory, increasing the cost of large-scale pretraining. We propose Gefen, a
memory-efficient optimizer that automatically shares second-moment estimates across parameter blocks and quantizes the first moment using a learned codebook. Gefen reduces AdamW's optimizer
memory footprint by up to 8x while maintaining performance, saving 6.5 GiB per billion parameters. Prior work shares second moments across parameters grouped along the Hessian's block-diagonal structure, but relies on hand-specified architectural rules and leaves unexplained why such grouping works. We prove that large mixed Hessian entries constrain the ratio of squared gradients toward one, explaining why shared second moments are accurate when the squared gradients they pool are similar. The Hessian need not be computed: its block structure is inherited by squared gradients, allowing blocks to be found directly. Gefen therefore infers block structure from initial squared gradients, requiring no architecture-specific metadata or user-tuned hyperparameters beyond AdamW defaults. Gefen learns an exact histogram-based dynamic-programming quantization codebook and reuses the blocks for first-moment scaling. Across diverse pretraining experiments, Gefen achieves the lowest peak optimizer
memory among compared methods that maintain AdamW-level performance. In single-machine and distributed training, the reduced footprint enables larger microbatches and substantially improves throughput over AdamW, making Gefen a drop-in replacement that can train larger models or use larger global batch sizes. We provide the complete Python implementation, including fused CUDA kernels at https://github.com/ndvbd/Gefen
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Artificial Intelligence · Cross-listed from cs.CR
Crypto x AI, AI x Crypto: A Survey
Sarah Allen, Pranay Anchuri, James Austgen et al. (+22 more)
The intersection of crypto x AI is spawning papers, products, online posts, and companies. All the surrounding buzz, though, obscures what exactly has been done, what the opportunities and challenges are, and what open questions deserve
attention. This survey paper asks what AI can do for blockchain-based technologies (broadly construed as "crypto") (crypto x AI), and vice versa (AI x crypto). We systematize existing work, summarize key takeaways, highlight open research questions, and offer a perspective on pervasive industry misconceptions, concluding that AI and crypto are still in the very early stages of meaningful integration.
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All authors Sarah Allen, Pranay Anchuri, James Austgen, Maryam Bahrani, Samuel Breckenridge, Aaron Buchwald, Christian Cachin, Andrés Fábrega, Jared Fernandez, James Hsin-yu Chiang, Marwa Mouallem, Roi Bar-Zur, Neil DeSilva, Ittay Eyal, Giulia Fanti, Ari Juels, Andrew Miller, Christian Sillaber, Dani Vilardell, Pramod Viswanath, Wenhao Wang, Matt Weinberg, Sen Yang, Jianzhu Yao, Fan Zhang
Artificial Intelligence
Capability Minimization as a Safety Primitive: Risk-Aware Causal Gating for Least-Privilege LLM Agents
Laxmipriya Ganesh Iyer and Rahul Suresh Babu
Modern decision systems increasingly rely on learned components whose outputs may be confident yet wrong, exposing downstream actions to costly errors. We introduce Risk-Aware Causal Gating (RACG), a framework that decides whether to act on, defer, or abstain from a model's prediction by combining causal effect estimation with calibrated risk control. RACG models the causal pathway from candidate actions to outcomes and gates each decision according to an estimated counterfactual risk rather than raw predictive confidence. To make gating reliable, we derive distribution-free bounds on the probability of acting under high-risk conditions and show how these bounds translate into operating thresholds that satisfy user-specified safety constraints. We further propose an adaptive gating policy that adjusts to distribution shift by monitoring discrepancies between predicted and realized outcomes, tightening the gate when causal assumptions appear violated. Across simulated interventions and real-world decision
benchmarks, RACG reduces high-cost errors substantially while preserving most of the utility of an ungated policy, and it outperforms confidence-based and selective-prediction baselines at matched abstention rates. Our results indicate that explicitly separating causal risk from predictive uncertainty yields decision systems that are both safer and more transparent, offering a principled mechanism for trustworthy automation in high-stakes settings.
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Artificial Intelligence
Hyperdimensional computing for structured querying on tabular data embeddings
Sebastián Bugedo and Stijn Vansummeren
Tabular data
embeddings have become a cornerstone of data profiling and data integration pipelines, enabling tasks such as entity annotation and resolution; schema matching; column type detection; and table search, among others. Existing approaches embed rows, columns, or entire tables into a vector space and rely on nearest-neighbor search to retrieve candidate matches. A fundamental limitation of current
embedding methods is the lack of interpretable similarity scores: the concrete similarity value between a query and its nearest neighbour carries no intrinsic meaning, making it impossible to determine whether that neighbour is a true match or simply the least-dissimilar item in a corpus that contains no valid answer. This inability to set principled thresholds for
retrieval undermines practical deployment, particularly for zero-match detection. We investigate the use of HyperDimensional Computing (HDC), specifically the
Holographic Reduced Representations (HRR) model, as a framework for tabular row
embeddings when the
retrieval task corresponds to answering structured select-project queries in vector space. Exploiting the algebraic properties of HDC operations, we derive closed-form expected similarity values for both equality and non-equality
retrieval predicates, which converge to interpretable values as dimensionality increases, and use these to identify suitable
retrieval thresholds. We evaluate HDC against EmbDI, a graph-based baseline, on two real-world
datasets across varying table sizes and predicate lengths. Our results show that HDC matches or outperforms EmbDI for row
retrieval across all configurations, handles non-equality predicates more robustly, and achieves perfect attribute projection accuracy at sufficient dimensionality – while uniquely enabling reliable identification of zero-match predicates through its principled thresholds.
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Artificial Intelligence · Cross-listed from cs.CV
Mirage Probes: How Vision Models Fake Visual Understanding
Daniel Ben-Levi, Judah Goldfeder, Weiliang Zhao et al. (+5 more)
Vision-
language models (VLMs) can answer image-based questions confidently, and often correctly, even when no image is provided. This mirage behavior inflates
benchmark scores without reflecting visual grounding. Prior work treats this as a single failure mode. We argue it is two. Using Mirage Probes, a contrastive probing framework that pairs paraphrased question variants with matched mirage and non-mirage labels on the same image, we show that mirage behavior is linearly decodable from internal activations across residual stream, MLP, post-
attention, and
attention-head sites in two open-source VLMs. We demonstrate that a Naive Bayes text baseline cannot recover this signal, ruling out surface lexical confounds. Cross-
benchmark separability patterns, together with a novel Prior Harnessing Index (PHI) measuring how much a model can answer from text alone, expose two distinct regimes: textual biases, where the model answers from
language priors without engaging visual representations, and spurious images, where it constructs false visual content in latent space and answers as if grounded. The distinction has direct mitigation consequences: text-distribution cleaning can address the first regime but cannot reach the second, since spurious-image mirages live in the model's visual representations rather than its text. Faithful visual grounding will require interventions at the representational level.
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All authors Daniel Ben-Levi, Judah Goldfeder, Weiliang Zhao, Raz Lapid, Amit LeVi, Allen G. Roush, Ravid Shwartz-Ziv, Hod Lipson
Artificial Intelligence · Cross-listed from cs.LG
SuperThoughts: Reasoning Tokens in Superposition
Zheyang Xiong, Shivam Garg, Max Yu et al. (+4 more)
Long Chain-of-Thought (CoT)
reasoning improves LLM problem-solving but is computationally expensive due to sequential
token generation. While recent works explore
reasoning in continuous latent spaces to bypass discrete
token generation, they often struggle with training stability and fail to scale to complex, long-horizon tasks due to lack of supervision signal. We propose SuperThoughts, which compresses pairs of consecutive CoT
tokens into single latent representations and decodes two
tokens per step via a lightweight Multi-
Token Prediction (MTP) module. This preserves discrete
token supervision at training time while doubling throughput at inference time. We finetune Qwen2.5-Math-1.5B-Instruct, Qwen2.5-Math-7B-Instruct, Qwen2.5-Math-14B-Instruct, and evaluate on MATH500, AMC, OlympiadBench, and GPQA-Diamond. With a confidence-based adaptive mechanism that falls back to standard decoding when uncertain, SuperThoughts achieves $\sim$20–30% CoT length reduction while maintaining accuracy with minimal degradation (1-2 points accuracy drop on most tasks).
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All authors Zheyang Xiong, Shivam Garg, Max Yu, Vaishnavi Shrivastava, Haoyu Zhao, Anastasios Kyrillidis, Dimitris Papailiopoulos
Artificial Intelligence · Cross-listed from cs.IR
Mood-Aware Music Recommendation: Integrating User Affective Signals into Ranking Systems
Terence Zeng and Abhishek K. Umrawal
Recommendation systems are essential in modern music streaming platforms due to the vast amount of available content. While collaborative filtering is widely used to suggest items based on the preferences of others with similar patterns, it performs poorly in domains where user-item interactions are sparse, such as music. Content-based filtering is an alternative approach that examines the qualities of the items themselves. Genre, instrumentation, and lyrics have been explored; however, relatively little
attention has been given to
emotion recognition. Since a user's emotional state strongly influences their music choice, incorporating mood signals offers a promising direction for personalization. In this work, we propose a mood-conditioned ranking framework that integrates user affective signals into the recommendation process via softmax-based sampling in the energy-valence space. We evaluate the approach via single-blind experiments in which participants compare recommendations from the proposed system against a baseline. The results indicate improved perceived recommendation quality, providing preliminary evidence for the effectiveness of incorporating mood-based inputs into music recommendations.
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Artificial Intelligence · Cross-listed from cs.HC
SpheriCity: Designing Trustworthy Conversational AI for Sustainability Decision Support
Ahmed Qayyum, Madison Werner, Kathryn Youngblood et al. (+2 more)
We present SpheriCity, an expert-grounded conversational prototype designed to support trustworthy knowledge sensemaking from sustainability reports. City-level circularity assessment reports contain rich information about materials, infrastructure, and policy interventions, yet their length and heterogeneous structure make cross-document synthesis and comparison difficult for practitioners and researchers working on circular economy initiatives. While large
language models (LLM) promise faster knowledge access and synthesis, their opaque
reasoning,
hallucinations, and lack of source transparency introduce risks for trust and interpretability, and require verification in high-stakes sustainability contexts. SpheriCity addresses these challenges through a provenance-first conversational
agent that foregrounds evidence traceability, structured synthesis, and interaction scaffolds to support exploratory querying and cross-document synthesis across sustainability reports. We conducted a formative expert review with six sustainability experts using representative queries spanning cross-city comparison, policy summarization, and recommendation-oriented tasks. Experts evaluated responses across dimensions and provided qualitative reflections on the system's usefulness for sustainability knowledge work. Our results reveal that transparent sourcing, contextual explanation, interpretability, and
alignment with expert workflow strongly shape expert trust and judgments of system usefulness. This work contributes (1) a conversational prototype for sustainability knowledge sensemaking, (2) an expert-grounded evaluation framework for assessing AI responses in high-stakes knowledge domains, and (3) design insights into how provenance, uncertainty communication, and integration in workflow influence expert users' trust in AI assistance for sustainability decision support.
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All authors Ahmed Qayyum, Madison Werner, Kathryn Youngblood, Jenna R. Jambeck, Tahiya Chowdhury
Artificial Intelligence · Cross-listed from cs.CV
Explaining RhythmFormer: A Systematic XAI Analysis of Periodic Sparse Attention for Remote Photoplethysmography
Louis Chen and Torbjörn E. M. Nordling
Remote photoplethysmography (rPPG)
transformers achieve low heart-rate error on
benchmarks, yet their decisions remain opaque–a growing concern as rPPG moves toward clinical heart rate estimation. Existing rPPG XAI is dominated by qualitative heatmap inspection without quantitative faithfulness metrics or physiology-grounded validation, leaving a gap between visual plausibility and auditable evidence. We address this gap. First, we adapt four attribution methods (raw
attention, rollout, flow, Beyond Intuition) to RhythmFormer's bi-level routing
attention with top-$k$ selection. Second, we introduce a skin coverage metric quantifying how much attribution mass falls on skin regions. Third, we adapt the SaCo faithfulness coefficient from its original classification setting to rPPG regression by using the MAE between original and perturbed predicted rPPG waveforms as the perturbation impact. Applying these tools, we quantify a multi-hop leakage effect under sparse top-$k$ routing:
attention rollout and flow almost completely restores the connections that individual refined-
attention layers explicitly set to zero. Beyond Intuition mitigates this via its value-projection-weighted rollout and gradient-supported mask, attaining the highest median refined skin coverage ($0.83$ vs. $0.57$ for vanilla rollout) and faithfulness ($F=0.92$) among the evaluated methods on UBFC-rPPG. Validation across diverse
datasets and model variants is needed. A case study on a low-SaCo outlier further shows all four methods recovering consistently once an artefactual region is replaced, suggesting consistent SaCo behavior across attribution families in this illustrative case. Together, these metrics move XAI for rPPG toward auditable numerical evidence about spatial
alignment and perturbation faithfulness, i.e. trustworthy rPPG XAI.
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Artificial Intelligence · Cross-listed from cs.CL
When Plausible Is Not Realistic: Evaluating Human Mobility in LLM-Based Urban Simulation
Gustavo H. Santos, Aline Carneiro Viana and Thiago H. Silva
LLM-based generative
agents are increasingly used in urban simulators, yet it remains unclear whether they reproduce empirically realistic human mobility patterns or merely generate plausible mobility narratives. We introduce a validation framework for evaluating the mobility of generative
agents of LLM-based urban simulators against real-world mobility data. For this, we use mobility laws, temporal rhythms, network motifs, semantic activity transitions, and behavioral mobility profiles. Using
datasets from the Greater Paris region and Shanghai, we evaluate AgentSociety and CitySim across multiple dimensions of mobility realism. Our analysis reveals a substantial gap between narrative plausibility and empirical mobility realism. Although the simulators capture some high-level semantic activity distributions, they struggle to reproduce core spatial and temporal constraints, including realistic trip-length distributions, origin-destination flows, dwell times, and transition dynamics. We further observe that realistic mobility diversity is unstable across default prompting configurations and may require explicit profile-aware initialization. To support reproducible evaluation, we also contribute scalable and open LLM-driven infrastructure for regional-scale map generation, observability-enhanced simulation, mobility-metric computation, and traffic simulation. Our findings highlight the need for rigorous empirical validation of LLM-based urban simulators and provide practical tools for building more realistic and reproducible urban simulation systems.
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Artificial Intelligence · Cross-listed from cs.MA
Safety-Contract Graph Multi-Agent Reinforcement Learning for Autonomous Network Security Response
Jose Luis Lima de Jesus Silva
Autonomous network-security response systems promise to reduce Security Operations Centre (SOC) reaction latency, but reward-only multi-
agent reinforcement learning (MARL) can improve security reward while remaining non-deployable. We present a safety-contract graph MARL framework and instantiate it as ACD$^3$-GAT (Adaptive Constrained Counterfactual Decisioning with a Graph
Attention Network encoder), an architecture that separates simulator observations from reusable operational budgets, constrained optimization, graph state encoding, and counterfactual action screening. We evaluate the method in CAGE Challenge 4, where
agents operate under budgets for Mean Time to Recover (MTTR), false-positive response, and firewall change-management disruption. Across the
benchmark, every unconstrained method violates the SOC downtime budget in 100% of evaluated episodes, with mean downtime proxy costs of 311-430 against a budget of 50. This complements prior CAGE Challenge 4 findings by showing that reward-only learning lacks operational discipline. Constrained MAPPO-GAT (C-MAPPO-GAT) isolates Lagrangian operational-cost control and budget-aware screening, while ACD$^3$-GAT adds budget context, CVaR tail-risk estimation, opponent-belief state, and Graph Counterfactual Risk Propagation (G-CRP). The replicated comparison includes three 200-episode seeds for IPPO, MAPPO-GAT, C-MAPPO-GAT, and ACD$^3$-GAT. C-MAPPO-GAT reduces downtime violation from 100% to 0.3% and mean downtime cost from 355.4 to 15.5 relative to MAPPO-GAT. ACD$^3$-GAT reduces mean downtime cost to 48.2 with a 13.8% violation rate, placing it on the safety-contract frontier rather than at the most conservative compliance point.
Topology-seed and coupled adaptive Red-process stress tests preserve this contrast and show lower worst adaptive degradation for safety-constrained policies than reward-only MAPPO-GAT.
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Artificial Intelligence · Cross-listed from physics.soc-ph
AI can help scientists publish less
Gianfranco Bertone
We can do more than defend science from a flood of AI-assisted papers. Used well, AI offers a historic opportunity to correct distortions in the publication system, help us publish fewer and better papers, and give scientists back the time to do their best work.
Artificial Intelligence
Poker Arena: Multi-Axis Profiling of Strategic Reasoning and Memory in LLMs
Pratham Singla, Shivank Garg and Vihan Singh
Strategic
reasoning under uncertainty underpins consequential decisions in negotiation, finance, and policy, but prevailing game-play
benchmarks collapse heterogeneous
reasoning dimensions into a single scalar, leaving the capability structure of frontier LLMs unexamined. We introduce Poker Arena, a no-limit Texas Hold'em tournament platform that couples a three-layer
memory architecture (within-hand, session, and cross-session) with a nine-axis cognitive profile decomposing strategic
reasoning into interpretable dimensions such as bet-sizing calibration and positional awareness. We evaluate seven frontier models across 50 sessions of 1,000 hands and a controlled
memory ablation; tournament chips and aggregate axis score order the field differently: Claude Opus 4.6 wins +$15,730 chips with 14 first-place finishes, yet ranks only fifth of seven on mean axis score, while persistent
memory helps some models and hurts others. These findings show that multi-axis evaluation surfaces capability structure that scalar leaderboards systematically misrank, with cross-dimensional consistency outweighing peak performance on any single axis.
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Artificial Intelligence · Cross-listed from quant-ph
Aligning Quantum Operators with Large Language Models
Rogerio Feris, Yunchao Liu, Pengyuan Li et al. (+2 more)
Can Large
Language Models (LLMs) understand and reason about
quantum operators? Despite their remarkable capabilities in mathematics and symbolic
reasoning, LLMs remain inherently blind to
quantum representations such as
unitary matrices. In this work, we take a step toward bridging this gap by introducing an approach that maps
unitary operators into the latent space of an LLM, enabling unified modeling over
quantum and linguistic inputs. We instantiate this idea on Clifford+T
circuit synthesis over a Pauli rotation gate set, where our model achieves results competitive with state-of-the-art methods and scales consistently with training data, with no signs of saturation. Our approach further enables
language-conditioned synthesis, allowing gate constraints unseen during training to be specified directly in natural
language. This work suggests a path toward
quantum–aware foundation models that can natively interpret and reason about
quantum operations, which could have broader implications reaching across
quantum compilation and
algorithm discovery.
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All authors Rogerio Feris, Yunchao Liu, Pengyuan Li, Hang Hua, David Kremer
Artificial Intelligence · Cross-listed from cs.SE
A Benchmark and Framework for Evaluating Next Action Predictions in Spreadsheets
Tejas Agrawal, Vu Le, Sumit Gulwani et al. (+1 more)
Predictive code completion greatly accelerates how quickly developers work. In spreadsheets, despite being much more common, such auto-completion features are virtually non-existent. To address this gap, we introduce a
benchmark for systems that observe a sequence of user actions in a spreadsheet and predict future actions. Two challenges are (1) the absence of edit histories in public spreadsheet corpora and (2) the complex space of spreadsheet actions (spatial, temporal, composite). To address (1), we manually curate 52 sequences of 12K actions that recreate spreadsheets from public corpora, seeded by parametrized heuristics and LLM refinement. To address (2), we propose an online evaluation that expects a prediction after each user action, accepts or rejects that prediction, updates the future actions upon acceptance, and repeats this until the target spreadsheet is obtained. We use multiple baseline predictors (including zero-shot LLMs, fine-tuned SLMs, and classical models) and analyze different properties that our
benchmark teaches us, including but not limited to: properties of saved actions and false positives, efficiency, effect of user profiles, effect of triggers, and effect of context.
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All authors Tejas Agrawal, Vu Le, Sumit Gulwani, Gust Verbruggen
Artificial Intelligence · Cross-listed from eess.SY
An integrated interpretable control effectiveness learning and nonlinear control allocation methodology for overactuated aircraft
Umut Demir, Aamir Ahmad, Torbjørn Cunis et al. (+1 more)
Nonlinear dynamics and the strong couplings that arise between multiple effectors undermine the assumptions behind conventional, linear control allocation techniques. When flight enters regimes where nonlinear effects dominate, linear allocators exhibit reduced accuracy due to increased model mismatch, which subsequently degrades performance and robustness of the flight control system. High fidelity onboard models and black box data driven approaches can recover accuracy across the flight envelope, but respectively impose computational burdens prohibitive for real time allocation and sacrifice the interpretability required for verification and fault diagnosis. This paper addresses these limitations by learning an explicit, physics constrained analytical model of the control effectiveness mapping from representative flight data using Sparse Identification of Nonlinear Dynamics. The resulting mapping is compact, interpretable, and admits analytical derivatives, enabling efficient computation within nonlinear solvers that additionally incorporate actuator dynamics, without requiring an onboard model. An online adaptation mechanism monitors prediction residuals and refreshes the model when significant plant changes are detected, providing graceful reconfiguration under actuator failures and varying operating conditions. The methodology is evaluated on a high fidelity nonlinear
benchmark aircraft across a range of aggressive maneuvers, achieving accuracy comparable to a full nonlinear onboard model while substantially reducing computational cost relative to established baselines.
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All authors Umut Demir, Aamir Ahmad, Torbjørn Cunis, Walter Fichter
Artificial Intelligence
MA-ProofBench: A Two-Tiered Evaluation of LLMs for Theorem Proving in Mathematical Analysis
Lushi Pu, Weiming Zhang, Xinheng Xie et al. (+6 more)
Large
Language Models (LLMs) have made notable progress in automated
theorem proving, yet existing formal
benchmarks remain limited in both mathematical coverage and difficulty. Most are concentrated in areas that are easier to formalize, such as
algebra and elementary number theory, and provide limited coverage of subfields that require deeper
reasoning, including mathematical analysis. To address this gap, we introduce MA-ProofBench, to the best of our knowledge, the first formal
theorem-proving
benchmark dedicated to Mathematical Analysis. The
benchmark contains 200 formalized
theorems covering 6 core topics and 27 subcategories, including measure and integration theory, complex analysis, and functional analysis. The problems are divided into two difficulty levels, an undergraduate level (Level I, 100 problems) and a Ph.D. qualifying level (Level II, 100 problems), to evaluate how well LLMs perform formal
reasoning at different mathematical depths. Each problem is constructed through a human-led, LLM-assisted formalization pipeline followed by independent expert review, ensuring that the formal statements remain faithful to the original mathematics. We evaluate a range of recent general-purpose
reasoning models and formal
theorem provers on MA-ProofBench. However, most models perform poorly: even the best-performing model, GPT-5.5, achieves only 16% Pass@8 on Level I and 5% on Level II, while most models stay close to 0% on Level II. Further analysis identifies Mathlib
hallucinations and incomplete proofs as the two dominant failure modes, while an evaluation on the natural-
language version of the
benchmark exposes a clear gap between informal and formal
reasoning. MA-ProofBench is intended to serve as a reliable reference for tracking progress in formal mathematical
reasoning in advanced domains.
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All authors Lushi Pu, Weiming Zhang, Xinheng Xie, Zixuan Fu, Bingxiang He, Hongya Lyu, Xin Li, Jie Zhou, Yudong Wang
Artificial Intelligence · Cross-listed from cs.CV
CineOrchestra: Unified Entity-Centric Conditioning for Cinematic Video Generation
Sharath Girish, Tsai-Shien Chen, Zhikang Dong et al. (+4 more)
Cinematic
video depicts multiple subjects acting or interacting at specific moments, captured with deliberate camera movement, and stitched together by shot transitions. Together, these elements demand a level of fine-grained control beyond current text-to-
video models. Existing work addresses each axis in isolation: multi-subject personalization, temporal control, multi-shot synthesis, or camera control; no prior framework jointly integrates all four. We present CineOrchestra, a unified
video diffusion model that controls subjects, events, cameras, and shot transitions simultaneously. Our key insight is that these heterogeneous cinematic elements share a fundamental structure: each is an entity acting over a specific temporal interval, which can therefore all be expressed through one shared structure of entity-centric conditioning primitives, augmented with reference images for visual entities. This formulation reduces the architectural challenge to a single positional encoding problem, which we solve with two parameter-free coordinated rotary
embeddings: (a) an interval-sampled temporal RoPE that yields consistent
attention behavior across events of dramatically varying duration, and (b) a 2D entity-temporal cross-
attention RoPE that disambiguates per-entity conditions and routes each to its corresponding spatiotemporal region. On two new
benchmarks, CineOrchestra outperforms six per-axis specialists on dense caption following and shot-transition timing, with consistent gains in a pairwise user study and component ablations. Project page: https://snap-research.github.io/CineOrchestra
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All authors Sharath Girish, Tsai-Shien Chen, Zhikang Dong, Mukesh Singhal, Hao Chen, Sergey Tulyakov, Aliaksandr Siarohin
Artificial Intelligence · Cross-listed from cs.LG
Beyond LoRA: Is Sparsity-Induced Adaptation Better?
Elijah Cadenhead, Cristian McGee, Xin Li et al. (+2 more)
Low-rank adaptation (LoRA) and its variants provide a
memory- and compute-efficient alternative to full fine-tuning of pre-trained models. However, questions remain about the comparative generalizability of these approaches and how the structural restrictions on low-rank updates preserve effective adaptation performance. We present a historical framing, covering the past (full fine-tuning and original LoRA), the present (different variants of LoRA), and propose simpler, cheaper, parameter-efficient extensions by inducing sparsity within existing LoRA variants: Cheap LoRA (cLA), training a single low-rank factor with the other fixed (deterministically or, in its randomized variant, stochastically), and the chained circulant variant, ${c}^3$LA. We frame cLA as a structured instance of asymmetric LoRA, serving as a controlled column-subspace restriction of full fine-tuning. We derive information-theoretic generalization error bounds for these variants, marking one of the first endeavors in this area. Empirically, we evaluate 11 fine-tuning methods across 10 pre-trained models and 14
datasets, analyzing the fine-tuned models' performance and generalization using tools such as loss landscapes and spectral analysis. Despite the sensitivity of fine-tuned models to the pre-trained model,
datasets, and other factors, our study suggests that restricting LoRA-based PEFT methods' adaptation to a sparse, structured column space remains competitive across tasks with their parameter-matched baselines while reducing up to 10% training time and peak GPU
memory up to 15%, even with a naïve, non-optimized, sparse implementation. Our theoretical and empirical generalization measures provide a more consistent and principled approach to their cost-effective adaptation than commonly used analytical tools. Overview and code are available at: https://elicaden.github.io/Beyond_LoRA/.
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All authors Elijah Cadenhead, Cristian McGee, Xin Li, El Houcine Bergou, Aritra Dutta
Artificial Intelligence · Cross-listed from cs.CR
SEVRA-BENCH: Social Engineering of Vulnerabilities in Review Agents
Rui Melo, Riccardo Fogliato, Sean Zhou et al. (+2 more)
Large
language models (LLMs) are increasingly deployed in automated code-review systems, where their approvals can determine which code is merged into shared repositories. However, it is unclear whether review
agents can detect vulnerability-introducing code when an attacker controls both the code change and the persuasive Pull Request (PR) narrative designed to mask it. We introduce SEVRA-BENCH (Social Engineering of Vulnerabilities in Review
Agents), a
benchmark that measures how often a review
agent approves such
adversarial PR s. Each PR in SEVRA-BENCH is built from a historical commit that fixed a vulnerability. We automatically reverse that fix to extract the original vulnerable code, and submit the resulting code change as a PR wrapped in one of 15 social-engineering framings. To test review-
agent resilience to narrative manipulation, these framings vary dimensions such as supporting evidence, conveyed urgency, signals of prior approval, and appeals to authority. SEVRA-BENCH evaluates a retained challenge split of roughly 1000
adversarial PRs drawn from publicly disclosed vulnerability fixes across the top 10 entries of the MITRE's 2025 most dangerous software weaknesses. Evaluating 8 review
agents against this
benchmark, we reveal that review
agents are susceptible to narrative manipulation, exposing a significant gap in security capabilities.
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All authors Rui Melo, Riccardo Fogliato, Sean Zhou, Pratiksha Thaker, Zhiwei Steven Wu
Artificial Intelligence · Cross-listed from cs.CY
Position: Align AI to Our Aspirations, Not Our Flaws
Nikita Kazeev and Bui Nhat Huyen Phan
We argue that aligning AI to aggregated human preferences is the wrong target. With current technology, one can train AIs to share the values of a Silicon Valley techno-optimist, a degrowth environmentalist, a national-conservative culture warrior, a single-party state cadre, or a devout religious traditionalist. We should not. Human values produce societies that thrive or fail on the merits of those values - from failed states and extreme inequality to declining happiness, political polarization, and government dysfunction in the world's wealthiest democracies. The pluralistic-
alignment program correctly diagnoses that there is no single "humanity" to align with, but is dangerous if taken as the main directive. We argue that AI should be trained to a non-negotiable floor of objective
alignment goals - competence, bounded by the constraints of factual accuracy, honesty, and lawfulness and that pluralism belongs at the surface (
language, register, conventions, missing-context defaults) and across the wide band of legitimate value tradeoffs that respect the floor, but not at the level of values that violate it. We highlight the empirical reality of unfiltered pluralistic values, propose four commitments as a constructive alternative, and engage six credible objections: commercial pressure and practical feasibility, democratic legitimacy, regulatory compliance, over-reliance on institutionalist explanations, the charge that the floor itself is culturally laden, and the limits of Coherent Extrapolated Volition.
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