Artificial Intelligence · Cross-listed from cs.LG
MORA: Modeling Observed Changes for Drift-Robust Time-Series Anomaly Detection
Xudong Mou, Tiejun Wang, Rui Wang et al. (+5 more)
Time-series anomaly detection (TSAD) identifies deviations from patterns learned from historical data. In non-stationary settings, distribution drift and true anomalies can cause similar local changes, making it difficult to tell whether a deviation reflects abnormality or evolving context. Existing methods typically adapt to detected shifts or learn drift-insensitive representations, but do not resolve this ambiguity. We define this problem as temporal change disambiguation: determining whether a local deviation is explained by broader temporal
evolution. We introduce MORA, a drift-robust TSAD framework that reconstructs the same local target from paired short- and long-term views. The reconstruction gap measures contextual support for a local deviation, and a data-dependent correction mechanism conservatively adjusts the primary local anomaly score. Context can only reduce the score when it improves reconstruction of the same target. MORA needs neither drift annotations nor online adaptation. Experiments on four TSAD
benchmarks show strong robustness to non-stationarity while preserving sensitivity to genuine anomalies.
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All authors Xudong Mou, Tiejun Wang, Rui Wang, Hui Wang, Pin Liu, Tianyu Wo, Xudong Liu, Renyu Yang
Artificial Intelligence · Cross-listed from cs.CR
Secure-CUA: Controlling Untrusted Influence in Computer-Use Agents
Sarthak Choudhary, Mihai Christodorescu, Ashish Hooda et al. (+3 more)
Computer-use
agents (CUAs) perform tasks across applications (such as desktops, mobile apps, and web browsers) by observing graphical interfaces and issuing commands such as clicks and keystrokes. These interfaces combine trusted controls and content with untrusted content needed for legitimate tasks. An adversary controlling this untrusted content can embed instructions or misleading visual cues to change the
agent's intended action or redirect its commands to the wrong interface target. We formalize security requirements for both the
agent's decisions and their execution through GUI commands. In an ideal execution model, we show that enforcing both requirements at each step protects execution traces. We instantiate this model in Secure-CUA, our system for secure CUA execution. Its key idea is to commit to an explicit per-action program, called an $\textit{action transaction}$, before accessing untrusted content. Each transaction fixes its queries to untrusted content and the permitted uses of their responses. The system masks untrusted regions and evaluates each transaction to produce the next action, using an isolated query model to answer its queries. It then locates the intended interface target using the masked interface. Under the model's assumptions, Secure-CUA is secure by design, while generating a new transaction at each step helps maintain high task utility by adapting to changing interfaces. We evaluate Secure-CUA under benign conditions on 400 WebArena tasks using three frontier models across $5$ seeds, yielding $6,000$ execution traces. Secure-CUA achieves an average task success rate of $53.55\%$, compared with $55.12\%$ for Vanilla-CUA and $13.17\%$ for CaMeL-CUA.
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All authors Sarthak Choudhary, Mihai Christodorescu, Ashish Hooda, Somesh Jha, Tongxin Li, Damien Octeau
Artificial Intelligence
LLM-Enabled UAV Dispatch: A System-Level Survey and Taxonomy
Xiao Han, Aoyang Quan, Xiangyu Zhao et al. (+2 more)
Unmanned aerial vehicle (
UAV) dispatch is beginning to move beyond isolated path planning and optimization-driven resource allocation toward system-level coordination supported by semantic
reasoning and LLM-based interfaces. This survey provides a unified characterization of LLM-enabled
UAV dispatch systems that bridges semantic intent, symbolic decision-making, and physical
UAV execution. Rather than treating LLMs as standalone add-ons, we conceptualize them as a cross-layer semantic orchestration layer connecting human instructions, external solvers, and distributed control modules. We organize the literature into four representative dispatch paradigms: pipeline dispatch, global assignment dispatch, decentralized agentic dispatch, and divide-and-conquer dispatch. For each paradigm, we analyze its decision logic, system structure, control flow, representative methods, and potential LLM roles. We further examine how LLMs support semantic parsing,
retrieval-grounded planning, solver orchestration, local
agent reasoning, multi-
agent coordination, safety assessment, and human-facing explanation. We discuss the implications of these paradigms for scalability, robustness, coordination burden, and verification requirements, and identify open challenges including latency-aware
reasoning, grounding reliability, physical feasibility guarantees, edge deployment,
privacy protection, and distributed consistency. This survey provides a system-level taxonomy and design perspective for integrating LLMs into safety-critical
UAV dispatch systems.
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All authors Xiao Han, Aoyang Quan, Xiangyu Zhao, Xiangjie Kong, Guojiang Shen
Artificial Intelligence · Cross-listed from cs.LG
DSReg: Provably Recovering Individual World Latents without Reconstruction
Yujia Zheng, David Klindt, Randall Balestriero et al. (+1 more)
Methods that recover individual latent variables of the world, from nonlinear ICA to dictionary learning and causal representation learning, anchor the latents to observations through reconstruction, auxiliary supervision, or distributional asymmetries such as non-Gaussianity. Methods without these anchors, including joint-
embedding predictive architectures (JEPAs), identify the latent state only up to a linear transformation, so individual latents remain mixed. We close this gap: individual world latents can be provably recovered with no reconstruction, no decoder, and no labels. The key condition is Structural Diversity: different latents leave distinct dependency footprints on observations, just as no two snowflakes are alike. Building on the linear identifiability that LeJEPA provides, we prove that under Structural Diversity, DSReg (Dependency-Sparsity Regularization) recovers individual world latents up to signed permutation, without reconstruction or a decoder. It applies post hoc to any linearly identified representation, reusing trained checkpoints at no loss over joint training, and establishes the first fully identifiable JEPA that recovers every world latent. Moreover, as a condition on dependency footprints, Structural Diversity is strictly weaker than all structural conditions of prior identifiable latent variable models. Across synthetic regimes, world model probes, learned visual encoders, and external renderers, DSReg preserves dense prediction while improving individual-latent recovery and downstream use with scales.
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All authors Yujia Zheng, David Klindt, Randall Balestriero, Bernhard Schölkopf
Artificial Intelligence · Cross-listed from cs.LG
GeoPrior-Mamba: Structured Process Priors with Mamba for Fine-Resolution XCO2 Reconstruction
Zhao Meng, Yinan Cai, Siru Zhong et al. (+2 more)
Reconstructing fine-resolution column-averaged dry-air CO2 (XCO2) fields from sparse satellite observations requires models to infer spatial structure that is only weakly constrained by direct measurements. Existing learning-based methods typically treat environmental
covariates as ordinary numerical inputs and must therefore learn heterogeneous source-sink relationships largely from sparse supervision. We introduce GeoPrior-Mamba, a multi-directional Mamba framework augmented with offline
language-model-induced structured process priors. Rather than using a
language model to predict XCO2, we use it before training to organize relative process knowledge for biospheric uptake, ecosystem respiration, and anthropogenic emissions into deterministic prior tables. These priors are spatially instantiated using geographic, ecological, emission-related, and seasonal information and are adaptively injected into the reconstruction backbone through a lightweight knowledge adapter. Using OCO-2 observations from 2018-2020, GeoPrior-Mamba achieves an RMSE of 0.81 ppm and an R2 of 0.93 on held-out observations, reducing RMSE by 48.2% relative to CAMS background interpolation and by 3.1% relative to Trans-XCO2 under the same evaluation protocol. Ablation experiments show a measurable contribution from the knowledge-prior branch and substantially faster convergence than the knowledge-free Mamba backbone. Independent TCCON evaluation further supports the consistency of the reconstructed fields with ground-based column CO2 measurements. These results suggest that
language models can provide a practical mechanism for constructing structured process priors when globally consistent process-response representations are difficult to obtain directly, while remaining outside the numerical prediction loop.
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All authors Zhao Meng, Yinan Cai, Siru Zhong, Juepeng Zheng, Haohuan Fu
Artificial Intelligence · Cross-listed from cs.CV
Iris-3B: Going Beyond the Latent with Pixel-Space Diffusion Training, Conversion and Fine-Tuning
Hanqiu Li Cai and Chema Garabito
Pixel-space
diffusion models avoid the lossy VAE of latent models, which suggests an advantage on downstream tasks where fine-grained detail matters. We test this claim along both routes to a pixel-space backbone. We pretrain Iris-3B, a 3B-parameter pixel-space text-to-image
transformer, from scratch through a $256\to512\to1024$ curriculum, after first ablating the prediction target and representation
alignment at $256^2$ to decide what to scale. We also convert a pretrained latent model, FLUX.2 Klein base 4B, to pixel space. We fine-tune both families for monocular depth estimation and for image restoration/super-resolution. We find no significant improvement from using a pixel-space generative prior. Fine-tuned for depth with one matched direct-regression recipe, Iris-3B is level with the latent FLUX.2 Klein and the converted pixel FLUX.2 Klein falls behind it, and on $4\times$ DIV2K restoration neither pixel model beats a latent FLUX.2 Klein fine-tune, the converted one trailing it slightly. We document the recipes, the failure modes and the remaining confounds behind this negative result. Nevertheless, Iris-3B shows that pixel-space pretraining with the pixel-
transformer (PiT) head of PixelDiT scales to 3B parameters and to text-to-image quality competitive with latent models, matching Qwen-Image on OneIG under the official evaluators at $1024^2$. We release its weights and training code in the hope that they help pave the way for further work on pixel-space generation.
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Artificial Intelligence · Cross-listed from cs.CL
Arctic Questions, Missing Answers: A Dataset and Benchmark for LLM Abstention in Arctic Science
Benjamin Wilcox, Dawei Gao, Pradeeban Kathiravelu et al. (+3 more)
Large
language models (LLMs) should abstain from scientific multiple-choice questions when no option is valid, but frequent abstention alone does not demonstrate sensitivity to answer availability. We introduce ArcticQA, a
dataset of 194 questions derived from primary Arctic research, with automated checks of answer support and distractor contradiction against source evidence. We further develop ArcticAbstain, a paired
benchmark comparing answer-present and answer-absent conditions, with the correct answer replaced by a distractor in the latter and an explicit abstention option in both. We evaluate eight models from the Gemini, Claude, and ChatGPT families at high
reasoning effort, with three trials per condition, yielding 9,312 recorded responses. Answer-present abstention rates range from 0.0% to 63.0%, whereas replacing the correct answer increases abstention by 5.05 percentage points on average. These findings highlight substantial baseline differences and the need to evaluate abstention frequency and responsiveness jointly. The
dataset and
benchmark are available at https://github.com/BenWilcox8/arctic-qa.
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All authors Benjamin Wilcox, Dawei Gao, Pradeeban Kathiravelu, Douglas Causey, Kewei Sha, Yunhe Feng
Artificial Intelligence · Cross-listed from cs.CV
Mixture of Layers: Dynamic Layer Routing for Visual Reasoning
Jeonghwan Kim, Sofia Stoica, Jiwan Chung et al. (+5 more)
Pre-trained
vision encoders contain layer-wise visual representations that differ in spatial granularity, semantic abstraction, and sensitivity to local details. However, most
Multimodal Large
Language Models (MLLMs) rely on only the final or penultimate
vision encoder representations or fixed aggregation rules, making visual abstraction largely query-agnostic and limiting access to fine-grained cues such as small objects, spatial details, text, and subtle visual attributes. In this work, we propose Mixture of Layers (MoL), an instruction-conditioned layer routing approach at the visual patch level that dynamically aggregates query-relevant latent representations from intermediate
vision encoder layers. Given a text query, MoL predicts routing probabilities over
vision encoder layers and performs a top-k sparse aggregation over selected hidden states at either the image level, patch level, or through a hybrid routing mechanism. In doing so, MoL enables query-adaptive access to layer-specific visual features for fine-grained visual
reasoning. Our experiments across 7 fine-grained visual
reasoning tasks demonstrate substantial performance improvements, especially across fine-grained visual grounding and understanding tasks such as +18.9% improvement on V* in overall accuracy, +4.5% on HRBench4K, and +16.3% on CharXiv compared to the baseline MLLMs, without resorting to multi-resolution inputs, simple interleaving of multiple
vision encoders, or increasing the number of patch
tokens. We study
vision encoders' receptive field scales across different layers and their sampling behaviors to provide an in-depth analysis of why layer-wise sampling is helpful, demonstrating that conditional visual representations are a key step towards better visual perception and
reasoning in MLLMs. Our project page is available at https://wjdghks950.github.io/mol.github.io/.
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All authors Jeonghwan Kim, Sofia Stoica, Jiwan Chung, Ansel Blume, Hyeonjeong Ha, Zhenhailong Wang, Xin Luna Dong, Heng Ji
Artificial Intelligence
RSI-Forge: From Research Papers to Environments for Recursive Self-Improvement
Renxiong Wang, Darvin Yi, Abril Herrlein et al. (+16 more)
Environments are the foundation of recursive self-improvement: they provide the problems
agents work on and the feedback used to evaluate progress. Yet constructing challenging research environments with reliable evaluation still depends on domain experts, limiting their scale and disciplinary coverage. We introduce RSI-Forge, a multi-
agent pipeline that turns published papers into executable environments for self-improvement. Three
agents coordinate construction, reproduction, and review to produce tasks with automated evaluators; each paper's method is independently reimplemented to establish a baseline score. We present 210 environments across 18 fields, including 90 reviewed by independent human domain experts. Both experts and
agent judges give high ratings to the potential for improving the provided starting solutions and the evaluators' ability to distinguish solution quality, whereas experts are more critical of shortcut resistance, faithfulness to the source paper, and whether a single idea can exhaust a task. To validate their use for repeated improvement, we evaluate four models over 3 successive attempts on 120 environments, with each attempt inheriting prior code and notes while model weights remain fixed. At least one model improves after the first attempt in 84% of environments. Models also outperform the reproduced paper methods in 68 of the 120 environments, demonstrating room for gains beyond these baselines. Transcript analysis identifies work beyond parameter tuning in 95% of these successful attempts. Analysis of the resulting trajectories shows that models scoring lower on these tasks explore less, more often accept gains smaller than the reported standard error, and rely more heavily on tuning to the development set. RSI-Forge provides a scalable approach to constructing research environments for training and evaluating self-improving
agents.
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All authors Renxiong Wang, Darvin Yi, Abril Herrlein, Anas Mahmoud, Advait Gosai, Lisiman Hua, MohammadHossein Rezaei, Xingang Guo, Anisha Gunjal, Utkarsh Tyagi, David J. Lee, Minglai Yang, Haris Riaz, Chenguang Wang, Huaxiu Yao, Daniel Yue Zhang, Aakash Sabharwal, Tong Zhao, Yunzhong He
Artificial Intelligence
Hanru Bai, Faissal Izermine, Oscar Davis et al. (+1 more)
Flow
Language Models (FLMs) have emerged as a continuous-state alternative to discrete
diffusion language models, yet the role of their continuous representations in
reasoning remains unclear. We investigate this question by comparing the
reasoning efficiency of FLMs and discrete
diffusion models, measured by solution accuracy under matched denoising steps. Unlike discrete
diffusion, which passes categorical states between denoising steps, FLMs evolve a continuous sequence representation throughout denoising and decodes it into discrete
tokens only at the end. Our theoretical analysis shows, from a superposition perspective, how information retained in these continuous states can benefit
reasoning. Intermediate-state interventions provide further empirical support for this theoretical account, showing that removing information about alternative candidates reduces subsequent solution recovery. Together, these findings show that FLMs allow evidence for multiple candidates to persist and inform subsequent
reasoning before a discrete answer is produced. Furthermore, our experiments on maze planning and Sudoku tasks show that FLMs achieve greater
reasoning efficiency in the few-step regime: FLMs achieves higher sequence accuracy than discrete
diffusion baselines at matched model sizes and small denoising steps. On maze planning tasks, FLMs can also achieve comparable accuracy with smaller models. For example, on Maze15, FLM reaches the 95% accuracy target at 64 denoising steps with 36.5% fewer parameters than MDLM. These findings point to continuous state spaces as a promising foundation for
reasoning models that require fewer refinement steps.
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All authors Hanru Bai, Faissal Izermine, Oscar Davis, T. Konstantin Rusch
Artificial Intelligence · Cross-listed from cs.LG
Shared Geometry As A Rosetta Stone: Cross-Modal Alignment Without Paired Data
Dominik Schnaus, Thomas Dagès, Daniel Cremers et al. (+2 more)
Multimodal representations enable zero-shot classification and
retrieval, but aligning independently trained models usually requires large amounts of paired data. Yet, the Platonic Representation Hypothesis suggests that models trained on different modalities may converge spontaneously toward a shared representation geometry. But then, do we even need paired examples for cross-modal
alignment? Remarkably, we show that paired examples are unnecessary for coarse cross-modal
alignment. Our simple Wasserstein Procrustes method with a coarse
geometric initialization aligns two disjoint
embedding sets by estimating a single orthogonal map without seeing any pairs. Across
datasets, modalities, and unimodal models, we show that we can consistently align independently trained representations without pairs, and standard
geometric alignment metrics accurately predict when this is possible. Nevertheless, we can naturally benefit from paired examples. In the very few-pair regime, our method substantially outperforms existing ones, while staying competitive with pair-based methods with more added examples. Finally, we demonstrate that the resulting
alignments can enable text-to-image generation without paired examples. These results show that independently trained models often share enough geometry to establish cross-modal correspondence with little or no paired data.
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All authors Dominik Schnaus, Thomas Dagès, Daniel Cremers, Xi Wang, Phillip Isola
Artificial Intelligence
Let the Library Speak: Self-Advertised Method Selection for Formal Proving
Xiaopeng Yuan, Suijin Wang, Yanli Wang et al. (+5 more)
LLM-based formal provers can retrieve relevant lemmas and prior proofs, but relevance alone does not say whether a mathematical method can be used on the current
theorem. A method has prerequisites, a target, an intended action, and obligations that its use leaves to prove. Methods that look equally related to a
theorem may therefore differ substantially in whether they offer a plausible next step. We formulate this as an applicability-aware method-selection problem and introduce self-advertisement: before candidates are ranked, a model generates a problem-specific proposal for each one, stating what part of the goal it targets, what action it would take, and what conditions that action requires. We organize 82 reusable methods from Putnam 2000-2014 as Method Contracts, which pair applicability descriptions with Mathlib anchors, a checked example or scaffold, and expected proof obligations. A single batched call elicits proposals across the library; vague or unsupported proposals are demoted, yielding a ranked shortlist accompanied by inspectable claims about each candidate's use. We analyze when similarity-based representations cannot distinguish methods with different applicability, how errors in applicability estimates affect shortlist quality, and what a checked scaffold guarantees under its stated assumptions. Against lexical,
embedding, and
embedding-plus-LLM reranking baselines, self-advertisement achieves 95.0% hit@5 on Putnam 2015-2025, compared with 84.2% for the strongest reranker. On IMO ProofBench, it achieves 91.7% compared with 88.3%. These results indicate improved coverage of annotated methods in the retrieved shortlists, particularly on Putnam.
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All authors Xiaopeng Yuan, Suijin Wang, Yanli Wang, Haibo Jin, Peng Kuang, Jerry Wang, Lijun Yu, Haohan Wang
Artificial Intelligence · Cross-listed from cs.HC
TutorLoop: Regulating Student Learning Behaviors via Sensor-in-the-Loop Generative Feedback
Songlin Xu and Xinyu Zhang
We present TutorLoop, a sensor-in-the-loop system that regulates student learning behaviors by delivering adaptive feedback based on real-time cognitive states. Unlike prior large
language model (LLM) tutors that directly depend on scenario-specific content, TutorLoop operates on sensor-derived signals captured via webcams. Moreover, unlike direct cognitive-to-feedback mappings that are short-sighted, the system employs a deep reinforcement learning (DRL)
agent to optimize the feedback type across the entire learning process. Finally, another LLM tutor refines feedback into human-like, context-aware messages. We evaluate TutorLoop in a large-scale user study (N=187), where a model trained offline is directly applied to a new learning task without retraining. Results show that TutorLoop provides less frequent yet more effective interventions, improving
attention, reducing workload, increasing engagement, and ultimately enhancing learning outcomes. These findings highlight the potential of closed-loop, sensor-driven feedback for scalable human-AI integrated systems to support learning.
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Artificial Intelligence
From Plausible Hierarchies to Useful Taxonomies: Evaluating Agentic Harnesses on Customer Feedback
Prabhath Chellingi, Raviraja G and Viraj Bagal
Taxonomies are the symbolic representations through which AI systems organize evidence, aggregate patterns, and answer questions over large document collections. Over customer feedback, the category tree decides how every record is counted and routed, which problems get seen, and which team owns them. Agentic harnesses now make it easy to generate a plausible-looking hierarchy, and such trees are checked today with generic, individually scoped checks: each name fits its description, sits under the right parent, and stays distinct from its siblings. We ask a more operational question: when is a generated hierarchy actually useful as a production taxonomy? We build six taxonomies over two proprietary feedback corpora (1,940 and 5,000 records): for each corpus, a production reference and two repeated runs of the same harness under identical inputs. All six pass every generic naming and structure check, and a deeper product-coverage check even prefers the generated trees. Yet in every generated tree at least 97.7% of leaf names merely restate an ancestor's name (13.9% and 2.9% in the references), and in one, three of every four records fall under multiple top-level categories. We introduce two families of whole-tree metrics: structural discriminators test whether a tree's shape was learned from the data or imposed by its generator; team partitionability tests whether branches split feedback into groups teams can own. Trees the generic checks rate as equally correct differ by 27 percentage points in cross-branch leakage, and only one of the two beats a random split. Surface plausibility is an insufficient measure of taxonomy quality: evaluation must measure the whole tree as well as each node.
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Artificial Intelligence
DUDA-Bench: Benchmarking LLM Agents on Multimodal Data-Driven Urban Diagnosis
Yizhi Song, Hang Ni, Weijia Zhang et al. (+1 more)
Urban diagnosis integrates heterogeneous observations to identify urban problems, localize affected areas, and investigate contributing factors, informing evidence-based urban planning and management. However, its reliance on labor-intensive, case-specific expert workflows limits scalability and reuse, motivating the exploration of
agent-based execution. To evaluate this capability, we introduce DUDA-Bench, a hierarchical and interactive
benchmark that formalizes data-driven urban diagnosis as a multi-stage
agent workflow. It comprises 86 atomic and 22 workflow tasks spanning four analytical stages, grounded in
multimodal data from 12 cities covering five urban problem types. Evaluations of seven backbone models and five
agent systems reveal a substantial gap between isolated analytical competence and end-to-end diagnosis, with system benefits varying across backbones. Trajectory analysis shows that unresolved evidence gaps propagate across stages, while successful recovery involves revising assumptions and actions using feedback. These findings highlight limitations in coordinating analytical capabilities across stages, particularly adaptive planning, evidence integration, and verification. More broadly, DUDA-Bench provides a framework for translating expert analytical workflows into hierarchical
agent tasks and process-aware evaluation, supporting systematic assessment of end-to-end analytical capabilities.
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All authors Yizhi Song, Hang Ni, Weijia Zhang, Hao Liu
Artificial Intelligence · Cross-listed from cs.GT
The Confidence Game: Strategic Miscalibration in Human-AI Delegation
Raghu Arghal, Saswati Sarkar and Shirin Saeedi Bidokhti
Calibrated uncertainty quantification is essential to ensuring AI
agents are trustworthy and reliable. However, when
agents seek to maximize user engagement or revenue, confidence reports may be strategically distorted, detracting from their informativeness. We formalize this problem in the Confidence Game: a repeated signaling game with imperfect monitoring in which an
agent of unknown honesty and ability reports its confidence, and a user decides whether to delegate the task or complete it herself. The
agent manages the tradeoff between manipulating signals and maintaining its reputation. We characterize the Markov Perfect
Bayesian Equilibria of the two-period game and show that honest reporting is not an
equilibrium, inflation is the unique best response once the
agent is sufficiently myopic, and under-reporting requires that the user believe honesty to be a minority. We then place an LLM in the
agent role, supplying it with its true probability of success so that any gap between what it knows and what it reports is attributable to incentives rather than to miscalibration. The model claims high confidence on 56% of tasks it has been told it will probably fail. This persists on real tasks, where it must estimate its own accuracy and causes miscalibration to increase while the
agent's signal becomes less informative. Furthermore, we find that the LLM
agent's decisions are coherent, but it systematically underestimates both how likely the user is to delegate and how secure its reputation is, resulting in less extreme behavior. Pricing the
agent's reporting rule, we find that it destroys 68% of the gains from delegation, of which 71% is information the report no longer carries and no amount of user sophistication recovers. Overall, we establish confidence reporting under delegation as a strategic problem and provide a tractable basis for modeling, analyzing, and testing
agent behavior.
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Artificial Intelligence · Cross-listed from cs.IR
From Chunks to Functional Evidence: Function-Aware Retrieval for EDA Documentation QA
Xiaotian Qiu, Kairui Liu, Shi Chenyi et al. (+3 more)
Retrieval-Augmented Generation (RAG) is widely used to ground answers in documents. For complex technical documentation, however, the primary bottleneck is often not model
reasoning but a mismatch between a query and the way knowledge is organized for
retrieval. This mismatch is pronounced in Electronic Design Automation (EDA) documentation, where the information needed for an answer is scattered across heterogeneous yet tightly coupled artifacts. We therefore redesign the basic
retrieval unit of RAG. Instead of operating on isolated chunks or binary relations, we collect typed artifacts into EDA functional units. Each unit is recorded as a hyperedge with links to its source chunks. We then train an encoder to align queries with functional units and combine unit
retrieval with direct chunk
retrieval. After mapping the selected units back to their sources, a unified reranker chooses the evidence given to the generator. On the newly constructed EDADocEval-QA
dataset, our method improves ROUGE-L by 37.1% over Chunk RAG and 55.6% over the strongest graph baseline. On the public ORD-MMBench
benchmark, it improves ROUGE-L by 30.0% over the strongest baseline. These results support function-aware evidence organization in the evaluated EDA documentation settings.
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All authors Xiaotian Qiu, Kairui Liu, Shi Chenyi, Jinyuan Deng, Qi Sun, Cheng Zhuo
Artificial Intelligence
TopoGraphRAG-Bench: Evaluating Multimodal GraphRAG on Layout-Grounded Evidence Reasoning
Ruochi Li, Jianzhe Lin, Haoxuan Zhang et al. (+4 more)
Real-world documents distribute evidence across text, tables, figures, and captions within complex page layouts. Answering complex questions over such documents therefore requires more than retrieving relevant passages: systems must recover the evidence
topology that connects heterogeneous evidence units. Existing GraphRAG evaluations remain largely text-centered, while
multimodal document RAG
benchmarks assess cross-modal
retrieval and generation without directly evaluating recovery of the intended evidence
topology. We introduce TOPOGRAPHRAG-BENCH, a layout-grounded
benchmark for
multimodal evidence
reasoning in GraphRAG, comprising 2,024 questions over 201 long, visually rich documents. Questions are constructed bottom-up from text, figure, and table evidence units under three controlled
topologies: single-hop
retrieval, bridge-chain
reasoning, and multi-source synthesis. To ensure that questions preserve their intended structure, we apply counterfactual validation for shortcut resistance, modality necessity, and evidence necessity. We evaluate text-only GraphRAG, page-level visual
retrieval, and
multimodal GraphRAG systems using
retrieval, generation, and
topology-aware
reasoning metrics.
Multimodal GraphRAG systems achieve the strongest overall performance, but still fail when visual-textual evidence
alignment or multi-unit composition is incomplete. Text-only GraphRAG struggles when key dependencies are grounded in figures or tables, while page-level visual
retrieval lacks the fine-grained structure needed for
topology recovery. These findings motivate GraphRAG systems that move beyond text-derived entity relation graphs to explicitly model document layouts, cross-modal evidence
alignment, and the
reasoning roles of evidence units. Code and data are available at https://richardlrc.github.io/TopoGraphRAG-Bench/.
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All authors Ruochi Li, Jianzhe Lin, Haoxuan Zhang, Haihua Chen, Junhua Ding, Edward Gehringer, Yang Zhang
Artificial Intelligence · Cross-listed from cs.LG
Multimodal LLMs Can Learn to Read Brain Signals: A Vision–Language Model for Unified Multi-Task EEG Decoding
Parastoo Azizeddin, Omid Sharafi and Maryam M. Shanechi
Learning
EEG representations that generalize across cognitive tasks, subjects, and recording conditions remains a key challenge in electroencephalography (
EEG) decoding. Recent advances in foundation models have improved
EEG decoding performance, yet a fundamental open question remains: how to effectively interface neural signals with these models to enable multi-task learning across
datasets. To investigate this question, we introduce BraVista, a visual-
language framework that encodes multichannel
EEG signals as structured images and enables multi-task learning through instruction-conditioned
vision-
language models (VLMs). Our approach relies on continued post-training of a general-domain VLM, leveraging its visual and linguistic priors to adapt to neural signals without a separate large-scale
EEG-specific pretraining stage. We evaluate BraVista on four
datasets spanning sleep staging, emotion recognition, cognitive workload classification, and abnormal
EEG detection, showing strong performance across these tasks. Further analyses show that the choice of
EEG-to-image representation is critical to performance. Moreover, through controlled perturbations of the
EEG signal, we observe a gradual performance degradation under increasing noise, suggesting that the model relies on
EEG-relevant information rather than superficial visual patterns. Together, these findings establish structured visual representations as an effective and scalable interface between neural signals and general-domain foundation models for unified multi-task
EEG decoding.
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Artificial Intelligence
Relevance Is Not Sufficiency: What Actually Closes the Evidence Gap in Long-Term Memory QA
Yufeng Li, Shuxin Li, Zhenhua Xu et al. (+7 more)
LLM
agents that interact with a user across many sessions accumulate histories that exceed their context window, so they store past interactions in an external
memory and answer each question from a small set of retrieved records. Existing
memory systems rank records by lexical or
embedding relevance, yet the top-ranked
memories can each be relevant while jointly omitting a complementary fact that the answer requires, especially for multi-session and temporal questions. Drawing on the distinction between relevance and sufficiency in legal evidence scholarship, we recast
memory retrieval as constructing a sufficient
memory set. To operationalize this view, we introduce a blinded LLM judgment over the retrieved set, together with Gold Hit and Turn Hit as evidence-coverage proxies. We then propose Budgeted Flat Reconstruction (BFR), which builds sufficient sets over a fixed flat
memory store in two stages. Specifically, we first apply Formal Concept Analysis for
Memory Selection (FCA-MS) to decompose the question into information requirements and select a compact candidate subset that jointly covers them. Then, we repeatedly acquire unseen records through deeper text search or complementary entity and session views, stopping when the budget is exhausted. Experiments on LoCoMo and LongMemEval-S show that BFR outperforms same-store adaptations of recent
agent-
memory systems in both answer quality and evidence coverage. Specifically, on LongMemEval-S it raises judged accuracy from 72.4% to 82.2% and Turn Hit to 91.4%.
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All authors Yufeng Li, Shuxin Li, Zhenhua Xu, Junxian Li, Peng Zeng, Sheng Yao, Changting Lin, Gaolei Li, Ran Bi, Meng Han
Artificial Intelligence · Cross-listed from cs.CL
OnlineQAT: On-Policy Distillation for Ultra-Low-Bit Large Language Models
Wenjun Wang, Heng Li, Yanggan Gu et al. (+1 more)
Quantization-aware training (QAT) can recover much of the accuracy lost when large
language models are compressed below four bits. Existing re- covery stages, however, are commonly optimized on fixed completions or teacher-generated answers, whereas the deployed quantized model condi- tions on prefixes generated by itself. Quantization errors can therefore move the model into states that are absent from offline recovery data. We introduce OnlineQAT, a two-stage framework that first obtains a usable low-bit initialization through block-wise QAT and then performs on-policy
distillation (OPD) on student-generated responses. At each visited pre- fix, a frozen full-precision teacher provides a sampled reverse-KL training signal. On Qwen3-1.7B, OnlineQAT obtains the best average among the compared quantized methods: 57.28 at W3A16 and 32.52 at W2A16, im- proving over ReasoningQAT by 2.90 and 0.44 points, respectively. The results suggest that student-visited states provide a useful recovery signal beyond fixed-completion training, particularly at three bits.
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All authors Wenjun Wang, Heng Li, Yanggan Gu, Hongxia Yang
Artificial Intelligence · Cross-listed from cs.LG
Shared Low-rank Basis Factorization for Data-free Mixture-of-Experts Compression
Tianxiao Cao, Jiahe Shao, Yuning Qiu et al. (+3 more)
Mixture-of-Experts (MoE) large
language models decouple capacity from compute through sparse routing, but their large parameter count creates storage and serving challenges. We analyze three MoE compression families: expert
pruning, expert merging, and weight reconstruction, and derive structural error bounds showing that
pruning and merging can incur non-vanishing errors tied to routing and expert heterogeneity. In contrast, weight reconstruction avoids these structural costs by preserving expert structure and routing. Motivated by the analysis, we propose Shared Low-rank Basis Factorization (SLBF), a data-free weight reconstruction method that uses rank-$k$ bases shared among experts, enabling richer cross-expert sharing, faster convergence, and lower reconstruction error. A post-hoc gauge fixing removes redundant parameters at no representational cost. Across five MoE architectures spanning 16B to 122B parameters, SLBF consistently outperforms methods from all three compression families.
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All authors Tianxiao Cao, Jiahe Shao, Yuning Qiu, Kyohei Atarashi, Hisashi Kashima, Qibin Zhao
Artificial Intelligence
SearchWorld: Spatial Value-Grounded Imagination for UAV Object Search via World Models
Yatai Ji, Zhengqiu Zhu, Yong Zhao et al. (+5 more)
Autonomous unmanned aerial vehicle (
UAV) object search involves a closed loop of perception, decision-making, and action under partial observability. Urban environments pose several challenges: large search areas and narrow egocentric views limit coverage, dense 3D geometry constrains safe motion, and open-world instructions require identifying a specific target among distractors. Many existing methods mitigate partial observability through explicit maps or
memory representations, yet remain largely reactive,
reasoning over past observations without explicitly predicting future states. World models enable prospective
reasoning through imagined rollouts. However, image-generating world models can incur high inference latency, while spatially grounded planning remains challenging for latent world models. We propose SearchWorld, a recurrent state-space world model that connects explicit spatial
memory with value-guided imagination. The model maintains BEV exploration and obstacle
memory and decodes a task-aware spatial value layer to guide search. A cognition-action network uses this learned spatial value prior to improve the policy through imagined rollouts, without training a separate scalar critic. Training progresses from world-model learning to expert imitation and imagination-based exploration refinement. On
UAV-ON, SearchWorld improves the success rate to 23.8% (19.5% for the strongest published
agent) and raises oracle success to 35.5%, while remaining robust on unseen scenes (19.9% success rate). By grounding imagination in explicit spatial representations, SearchWorld enables
UAV agents to plan prospectively rather than react.
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All authors Yatai Ji, Zhengqiu Zhu, Yong Zhao, Yue Hu, Fanglong Yao, Chen Gao, Pengfei Zhu, Quanjun Yin
Artificial Intelligence · Cross-listed from cs.CV
Visual Jev Rewards: Reference-Bound Verification for Multi-Subject Image Generation
Baoteng Li, Wenzhuo Wu, Kongming Liang et al. (+1 more)
Multi-subject image generation requires rewards that verify whether requested attributes, actions, and relations hold for the specified reference subjects. Subject presence alone does not establish that the correct subjects participate in a requested interaction. We present reference-bound Visual Jev rewards that turn these visual decisions into generator training signals. Each subject-related question receives a positive label only when the requested condition and the relevant reference identities hold jointly. We construct fixed questions offline, train a Qwen3.5-4B verifier with binary supervision, and directly read Yes probabilities from its
language-model head. Their mean supplies a GRPO reward while retaining individual judgments for inspection. Using 200 MICo-150K training tasks and 30 updates, the framework raises a GPT-5.4 composite score from 41.78 to 52.50 on a manually selected 897-task MICo-Bench subset; direct 27B rewards yield 51.84. Each reward is tested in one GRPO run, and offline human evaluation does not establish a statistically significant advantage over direct scoring. The study provides an initial implementation and evaluation of Visual Jev as a reference-bound reward for multi-subject image generation.
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All authors Baoteng Li, Wenzhuo Wu, Kongming Liang, Zhanyu Ma
Artificial Intelligence · Cross-listed from astro-ph.IM
Constrained Diffusion for Data-Scarce Orbital Monte Carlo in Constellation Tasking
Omar Ramadan, Sam Siavoshian, Amir Kashif Saeed et al. (+3 more)
Constellation Monte Carlo results depend on the orbital population used to evaluate a tasking policy. With scarce reference trajectories, replay limits
geometric diversity, while independent orbital-element jitter can violate physical constraints. We study constrained
diffusion for orbital-population augmentation. A force-conditioned
diffusion model learns a 13-dimensional orbital prior, recovering semimajor axis from perigee altitude and eccentricity; Basilisk propagates each sample under one of five force-model tiers. Using 800 reference trajectories, we compare
diffusion with jittered bootstrap, per-tier Gaussian mixtures, and a conditional variational autoencoder, and evaluate distributional fidelity, support shift, classical astrodynamics diagnostics, and 4,000 paired GoDSAT-compatible campaigns. The largest
diffusion model achieves held-out trajectory MMD of 0.0171 +/- 0.0239, similar to bootstrap (0.0170) and the mixture (0.0198), but samples farther from training priors (median nearest-training distance 2.7 versus 0.10 standardized units). All generators fail to match the shifted-blind population (classifier AUC 0.991-0.998). Endpoint-conditioned samples satisfy Lambert boundaries but have greater interior error than the matched Lambert reference (29.2 versus 5.7 km mean RMSE). Residual
diffusion improves selected sparse forecasts and catalog-mode recall but does not outperform classical
estimators on custody ranking. In a fixed 16-satellite configuration,
diffusion yields similar mean custody to replay and bootstrap, while the force-tier mixture shifts custody by about 5.5 percentage points. Constrained
diffusion supports local, in-support augmentation but cannot replace orbital dynamics or serve as an operational posterior. Orbital-population construction is a consequential source of uncertainty in constellation analysis.
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All authors Omar Ramadan, Sam Siavoshian, Amir Kashif Saeed, Benjamin A. Johnson, Amin M. E.-A. Diab, Benjamin M. Rodriguez
Artificial Intelligence · Cross-listed from cs.LG
Denoising Blocks, Not Tokens: Efficient Compressed Continuous Diffusion with Branching Token Realization
Xinsong Feng, Peng Du, Zhizhuo Yang et al. (+3 more)
Diffusion language models (DLMs) generate text through iterative parallel refinement, offering the potential for higher throughput than autoregressive (AR) decoding. However, most DLMs still maintain one generative state per
token, so every denoising step processes a state sequence as long as the output sequence, limiting the throughput gains from parallel generation. Continuous DLMs provide an additional degree of freedom: a single continuous state can represent multiple
tokens, allowing
diffusion to operate on a much shorter latent sequence. We introduce Branching Latent
Diffusion (BLD), which exploits this flexibility by compressing a 1024-
token sequence into only 64 block latents, a $16\times$ reduction. BLD combines latent compression with branching
token realization, where each latent is decoded by a local AR branch and all branches run in parallel. Because strong compression makes joint latent generation difficult, BLD generates the latents in groups, conditioning each group on previously generated latents. In end-to-end evaluation on the same GPU, BLD reduces generation FLOPs by more than $80\times$ and increases throughput by more than $6\times$ relative to the similarly sized ELF-L baseline. Compared with the AR baseline, BLD achieves more than $6\times$ higher throughput and more than $4\times$ lower latency. Despite the compression, BLD maintains competitive local fluency and diversity, although long-range coherence remains challenging. Overall, BLD shows that moving
diffusion from
token-level states to compressed latent sequences can substantially improve the efficiency of long-sequence generation.
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All authors Xinsong Feng, Peng Du, Zhizhuo Yang, Daniel M. Bikel, Jiayun Wang, Haipeng Chen
Artificial Intelligence · Cross-listed from cs.LG
Kuration SDK: Addressing the Virtual2Real Gap via Data Curation
Nirmit Desai, Eric Song, Mayank Sengupta et al. (+4 more)
Benchmarks for measuring the quality of action-conditioned world models are still evolving and shifting away from visual similarity-based metrics to action-semantic and physically-grounded metrics. However, for domain and task-agnostic action-conditioned world model training, existing
benchmarks provide a limited signal. By training and evaluating
diffusion world models on CounterStrike gameplay data, we confirm that qualitative playability does not correspond with metrics such as FVD, LPIPS, and JEDi. We term this the Virtual2Real gap. We posit that, in lieu of reliable
benchmarks, curating raw gameplay data and measuring a variety of diagnostic properties provides a more robust signal to bridge the gap, before the training even begins. We present several curation strategies and a general-purpose kit for physical AI data curation called Kuration SDK, which is being open-sourced with this paper. The SDK was instrumental in uncovering the root cause of the virtual2real gap in a specific case: why two world models trained on identical gameplay map, action and state distribution, behaved very differently when played in spite of having very similar LPIPS and FVD scores. Thus, Kuration SDK has the potential to uncover the root causes of Virtual2Real gap in specific
datasets and accelerate development of sample-efficient training
datasets.
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All authors Nirmit Desai, Eric Song, Mayank Sengupta, Tejal Bedmutha, Siri Reddy, Sahiti Dharmavaram, Kunal Sawarkar
Artificial Intelligence · Cross-listed from cs.LG
Node-level Graph Neural Architecture Search Framework
Lintao Yanga, Sirui Lia, Yaqing Wang et al. (+4 more)
In recent years, Graph Neural Networks (GNNs) and architecture search frameworks have gained extensive application in non-Euclidean data processing, attributable to their superior capacity in managing unstructured data. Nevertheless, traditional approaches typically apply uniform convolution operations to all nodes, regardless of their varying structural and feature characteristics, which can undermine model performance and result in over-smoothing issues as the number of layers increases. To overcome this limitation, in this work, we propose a Node-Level Graph Neural Architecture Search (N-GNAS)
algorithm. It can automatically choose an appropriate network architecture for each subset of nodes when updating node features. N-GNAS also introduces a contrastive learning loss to separate sample features from different categories and vice versa. In experiments conducted on eight
datasets for node and graph classification, our methodology outperforms current leading GNAS techniques and traditional human-designed GNNs. For example, it achieves an accuracy rate of 78.26% on the CiteSeer
dataset.
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All authors Lintao Yanga, Sirui Lia, Yaqing Wang, Pietro Liò, Xu Shen, Baisong Liu, Chengbin Peng
Artificial Intelligence
The AI Evaluation Ecosystem
Yash Dave, Sang T. Truong, Serena Wang et al. (+1 more)
AI evaluation shapes the decisions of model providers, users, funders, and regulators. We argue that designing valid
benchmarks requires contextualizing design choices in the dynamics of this ecosystem of actors. We develop a simulation architecture that combines rule-based
market dynamics with LLM-driven strategic actors, building on advances in Generative
Agent-Based Modeling (GABM). We model
benchmarks, consumer needs, and provider capabilities as vectors over a six-dimensional capability space (
reasoning, coding, knowledge, safety, communication, agentic), with structural information partitions across actors. As a case study, we apply this stylized simulation to explore
benchmark holdout design. We find that moving from public
benchmarks to private holdout
benchmarks shrinks the gap between
benchmark scores and user satisfaction on most
benchmarks but widens it on a few, depending on where holdout weights shift scoring credit. We stress-test our findings at both the instrument and case-study level, drawing on the V&V framework of Sargent (2013) and GABM-specific evidence criteria. Beyond holdout design, our simulation is a hypothesis-generating sandbox for studying how evaluator and policy choices, in turn, reshape the ecosystem.
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All authors Yash Dave, Sang T. Truong, Serena Wang, Sanmi Koyejo
Artificial Intelligence
RT-Safe: Benchmarking Agent Safety in Real-Time Embodied Environment
Tianruo Rose Xu, Jiawei Ren, Yichi Yang et al. (+2 more)
Rapid progress in AI
agents has brought growing
attention to
agent safety, with extensive evaluation focused on digital environments. As
agents move into the physical world, embodied safety becomes increasingly important: failures can cause human injury and costly hardware damage. Beyond selecting safe actions, embodied
agents must also operate under real-time constraints: the physical world does not pause while an
agent reasons. As pedestrians move and vehicles approach during inference, an action that appears safe at observation time may become unsafe before execution. Real-time embodied safety therefore depends on both decision quality and decision latency. We introduce RT-SAFE, a simulated urban
benchmark for evaluating embodied-
agent safety under real-time constraints. RT-SAFE combines navigation tasks with moving actors, environmental hazards, and traffic rules, while allowing the world to evolve throughout inference and action execution. Across eight VLMs,
agents achieve high task completion yet almost never complete safely: in the hardest setting, only 0.7% of episodes finish without a safety event. More strikingly, matched static and real-time evaluations yield task completion rates of 91.3% and 94.1%, respectively, while real-time execution increases collisions by $12.3\times$. These results reveal that standard task success can mask substantial safety failures, and that decision latency itself can become a source of physical risk. Finally, we show that RT-SAFE can support offline RL training and substantially reduce collision rates while achieving strong task completion.
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All authors Tianruo Rose Xu, Jiawei Ren, Yichi Yang, Zhaoxu Zheng, Lianhui Qin