Artificial Intelligence · Cross-listed from cs.RO
Towards VLA-Dreamer: Refining VLA Behavior Using World Models
Parsa Mastouri Kashani, Jan-Gerrit Habekost and Stefan Wermter
Vision-
Language-Action models (VLAs), while showing strong potential for
robot control, require massive amounts of high-quality imitation learning data. Moreover, the absence of an explicit world model casts further doubt on their control capabilities. In this concept paper, we propose a novel architecture that addresses sample efficiency in VLAs by training a predictive world model on the
embedding space of the VLA's
vision encoder. We hypothesize that these
embeddings are action-relevant and usable for future prediction. To this end, we propose using the suggested architecture to investigate how well these
embeddings predict the future based on actions, as the inability to do so would mark a key limitation of VLA architectures: the lack of a non-lossy implicit world model to simulate real-world dynamics. The proposed architecture differs from the standard world model dynamics as the loss comes from the
embedding space rather than the pixel space, similar to joint
embedding predictive architectures. Furthermore, the trained world model can be utilized for short-term planning tasks by sampling VLA actions given goal images. We intend to examine the richness of
vision embeddings in VLAs and reduce their high data requirements through a world model that can also generate plans during inference.
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Artificial Intelligence · Cross-listed from cs.SE
Beyond Approved Actions: Runtime Validation of Persistent Outcomes in Agent Workflows
Haoran Zhang, Hengtong Zhang, Zhiyu Liang et al. (+3 more)
Large
language model
agents increasingly act on software systems, no longer merely generating text but also changing databases and online services. However, an approved database update may succeed yet leave an unapproved notification because execution can produce persistent effects beyond the requested change. Current safeguards can approve an action or record its aftermath, but without checking the persistent result before continuation, an unapproved outcome can be accepted as success and propagated to later steps. We present EffectMatch, a runtime that collects persistent changes within a controlled execution boundary and compares them with what the application approved for the current state and execution. The comparison governs commit and dependent execution. In comparative evaluation on 206 public business tasks, EffectMatch preserved all clean executions and prevented all tested incorrect commits. Six 20-run ablations exposed the failure caused by each removed mechanism, while 80 task-
topology cases preserved truthful handoffs and blocked invalid continuation. Together, these results show that EffectMatch blocks the silent acceptance and downstream propagation of persistent outcomes inconsistent with application approval.
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All authors Haoran Zhang, Hengtong Zhang, Zhiyu Liang, Yu Yan, Decheng Zuo, Hongzhi Wang
Artificial Intelligence · Cross-listed from cs.CV
UniAR: A Unified Framework for Autism Recognition Enhanced by Multi-View Prompt Learning
Lei Xin, Zeheng Wang, Jiayin Zhu et al. (+6 more)
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder for which early and accurate diagnosis is critical to improving long-term developmental outcomes. However, existing ASD recognition methods are often constrained by the scarcity of diagnostic text data, forcing them to rely mainly on visual analysis and limiting their ability to model clinically meaningful semantic
reasoning. To address this challenge, we propose UniAR, a unified framework enhanced by multi-granularity
prompt learning for robust ASD recognition under heterogeneous data variations. Specifically, UniAR leverages a large
multimodal model to generate hierarchical diagnostic descriptions at the word, phrase, and sentence levels, compensating for the lack of paired clinical reports. To align the generated semantics with visual evidence, we further design a Mixture-of-Experts-based Multi-Scale
Alignment Module, which dynamically matches vector-quantized visual prototypes with semantic representations at corresponding granularities. Extensive experiments on four
benchmarks covering
brain MRI and facial expression scenarios show that UniAR consistently outperforms existing state-of-the-art methods, achieving average accuracies of 75.9% on MRI
benchmarks and 91.6% on facial
benchmarks, while improving average Accuracy on MRI
benchmarks by 1.5 percentage points and average Accuracy on facial
benchmarks by 1.2 percentage points over baselines. These results demonstrate that UniAR offers a robust and interpretable framework for ASD screening under semantic scarcity.
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All authors Lei Xin, Zeheng Wang, Jiayin Zhu, Shihong Huang, Fanhu Zeng, Changjiang Jiang, Dengbo He, Yutao Yue, Zhenglun Kong
Artificial Intelligence · Cross-listed from cs.LG
Softmax Reparameterization for Output-Head Quantization
Asim Kadav, Christian Flores, Chirag Arora et al. (+5 more)
Large vocabularies make output heads a substantial inference cost in small
language models. We introduce softmax reparameterization, a post-training method that searches over functionally equivalent output heads before quantization. The method subtracts a scalar multiple of the vocabulary-row mean from every output row and selects the coefficient by validation KL. For linear-softmax heads, these shifts preserve full-precision predictions exactly and require no decoder retraining; a rank-one correction extends the construction to nonlinear logit paths. Across seven output heads and three quantizers, W4 gains are largest where baseline quantization substantially distorts predictions: test KL falls by 93% on XGLM under RTN and by 73–77% on Phi, BLOOM, and BLOOMZ under activation-weighted MSE. Heads with low baseline error change little; at W2, used as a compression stress test, benefits extend more broadly. On Phi, the gains persist under stronger GPTQ calibration; a separate untouched holdout reproduces the improvements on Phi and BLOOM. Frozen WikiText-selected coefficients also transfer without retuning to C4 and OpenWebMath. Residual analysis on Phi shows how fidelity can improve despite greater total logit error: the selected representative reduces error on likely outputs and lowers its Fisher-weighted cost. For shift-compatible heads, the shift adds no inference operation. With the decoder held in BF16, a packed W4 Phi output head reduces batch-one generation latency by 10.8%, and reparameterization preserves this speedup.
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All authors Asim Kadav, Christian Flores, Chirag Arora, Varun Kotte, Hongbo Zheng, Lan Yan, Priya Shanmugasundaram, Tracy Holloway King
Artificial Intelligence
G2MAF: Test-Time Gradient Guidance for Multi-Agent Flow Policies
Guowei Zou, Haitao Wang, Guoxin Wang et al. (+4 more)
Offline multi-
agent reinforcement learning (MARL) learns cooperative policies from fixed
datasets without further environment interaction and a learned policy is frozen at deployment. Such a frozen policy typically proposes a single joint action and executes it directly at deployment time. However, this one-shot deployment often commits to a suboptimal proposal, even when better nearby alternatives remain consistent with the behavior data. To address this issue, we propose Gradient Guided Multi
Agent Flow (G2MAF), a refinement framework for optimizing joint policies at test-time. G2MAF applies one globally normalized, projected critic gradient to guide and coordinate all
agents' corrections while keeping the action both feasible and close to the frozen policy proposal. Across 24 MPE and SMAC settings, its canonical variant improves 20 frozen settings, with mean relative gains of 9.2% on MPE and 8.9% on SMAC, with model inference latency increased by about 6% only.
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All authors Guowei Zou, Haitao Wang, Guoxin Wang, Zhiquan Chen, Beiwen Zhang, Guojie Wang, Hejun Wu
Artificial Intelligence · Cross-listed from cs.CR
Resource-Optimized and Energy-Aware Agentic AI Framework Anchored on Blockchain for Secure Software Supply Chains
Toqeer Ali Syed and Asadullah Abdullah Khan
This paper proposes a blockchain-backed agentic security framework designed to safeguard the complete software development lifecycle (SDLC) while also securing the agentic AI components responsible for monitoring it. The framework coordinates a set of specialised security
agents, covering source integrity, dependency and SBOM analysis, CI configura tion
auditing, artifact verification, and runtime policy evaluation, each supported by a large
language model (LLM) that interprets artefacts, reasons over tool outputs, and produces structured security reports. To ensure
agent trustworthiness, every
agent generates a cryptographically signed attestation that is recorded in a permissioned blockchain via smart contracts, including an
agent registry, an immutable attestation log, and an enforceable release-policy module. Communication among
agents and with blockchain nodes is secured using a consortium-operated certificate authority, ensuring authenticated and tamper-resistant interactions. A detailed use-case and sequence flow demonstrate how a source code security
agent performs analysis, anchors its attestation on-chain, and triggers a verifiable allow/block deployment decision. The proposed framework of fers decentralised integrity transparent provenance, uninterrupted security assurance and a generalisable architecture to incorporate the agentic AI into the modern software supply chain security.
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Artificial Intelligence
MA-WAM: Multi-Agent World-Action Model for Test-Time Planning
Guowei Zou, Haitao Wang, Guoxin Wang et al. (+4 more)
Multi-
agent cooperative tasks require different
agents to execute a joint action simultaneously, and each
agent's action affects both the observations and responses of the other
agents. Hence, a world model is needed to predict the team return resulting from the joint actions of all
agents. A naive extension directly applies a single-
agent world model to each
agent's action when predicting the team return step by step. However, such an extension fails to capture the dependencies among the simultaneous actions of multiple
agents. We propose Multi-
Agent World-Action Model (MA-WAM), a test-time planning framework that enables a frozen multi-
agent flow policy to evaluate futures of candidate joint actions. To our knowledge, MA-WAM is the first test-time world-model planner for multi-
agent flow policies. MA-WAM predicts the consequences of each joint action according to cross-
agent dependencies and enables efficient candidate scoring. Across 30 offline multi-
agent reinforcement learning (MARL) settings on MAMuJoCo, SMAC, and MPE, MA-WAM achieves mean relative gains of 22.0% over direct execution and 25.6% over uniform action selection. Under the standard evaluation protocol on an A100 GPU, MA-WAM adds 12.1 ms, accounting for 2.5% of the measured generation-and-scoring time.
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All authors Guowei Zou, Haitao Wang, Guoxin Wang, Beiwen Zhang, Zhiquan Chen, Guojie Wang, Hejun Wu
Artificial Intelligence · Cross-listed from cs.HC
Cognitive Skills in the Age of AI: Computing Students and Experts Perceptions
Neha Rani, Vu Minh Anh Le, Austin M. Spangler et al. (+1 more)
AI is becoming increasingly integrated into daily workflows, especially in computing. We are gradually shifting towards an AI-rich future, an impending yet unknown one. One important emerging concern is whether we are accordingly preparing our future computing workforce. Further, we need to know what the important cognitive skills are to remain relevant in the computing workforce and if there are changes in cognitive skill importance. To investigate this direction, we conducted a mixed-methods study, collecting perceptions from computing students and computing experts regarding the importance of cognitive skills in the past, present, and future. We report that the perceived importance of most cognitive skills will decrease in the future, with an AI-rich environment, but critical thinking skills remain important. Further, we report reasons collected through interviews on why the importance of cognitive skills will change and how future computing students can prepare for it.
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All authors Neha Rani, Vu Minh Anh Le, Austin M. Spangler, Erta Cenko
Artificial Intelligence · Cross-listed from cs.CL
MoSAR: Mixture of Semantic Attention Regimes for Learning Adaptive and Approximable Attention Geometries
Michele Paolicelli, Alessandro Petruzzelli, Alessandro Franceso Maria Martina et al. (+2 more)
The quadratic complexity of dense self-
attention remains a central bottleneck for long-context
language modeling. Many efficient alternatives address this cost by deciding in advance where
attention should be sparse or local. We argue that
attention approximation should instead be approached as a
geometric problem, with the relevant interaction geometry learned from data: natural-
language dependencies are input-dependent and difficult to prescribe in advance, so the model should learn where positional relevance can decay and where broader interactions must be preserved. We introduce Mixture of Semantic
Attention Regimes (MoSAR), which learns such an adaptive, controlled-decay geometry over query–key interactions. Input-conditioned query and key routers, applied after positional encoding, select mixtures over short, medium, and global regimes, inducing a continuous distance-dependent
attention field rather than a fixed sparsity pattern. This geometry is learned during training and can subsequently be discretized through top-1 routing. In controlled pre-training experiments with matched 500M-parameter models, MoSAR learns a substantially lower-reach
attention geometry without degrading
language-modeling quality, improving perplexity over dense RoPE at the training context length. Under length extrapolation, MoSAR achieves the best perplexity among all evaluated variants, including strong baselines such as ALiBi. Moreover, the learned geometry remains stable under deterministic top-1 discretization, suggesting that it is not only adaptive, but also amenable to low-cost approximation at inference time.
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All authors Michele Paolicelli, Alessandro Petruzzelli, Alessandro Franceso Maria Martina, Cataldo Musto, Giovanni Semeraro
Artificial Intelligence · Cross-listed from q-fin.TR
Agentic Limit Order Books: Phase Transitions and Market Impact
Jan Rosenzweig
We investigate the systemic macroscopic dynamics emerging from Limit Order Books (LOBs) populated exclusively by autonomous reinforcement-learning agentic traders. By formalizing
agent interactions within a microscopic order-matching engine, we examine two fundamental quantitative phenomena:
equilibrium phase transitions in order flow regime shifts, and the structural dynamics of
market impact. We show that agentic LOBs exhibit distinct phase boundaries separating orderly price discovery from hyper-volatile cascade states, governed by critical thresholds in the number of
agents and observable
market depth. Furthermore, we demonstrate that
market impact under agentic liquidity provision deviates from classical square-root dynamics, exhibiting distinct dissipative, balanced, and non-dissipative regimes under non-linear feedback loops.
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Artificial Intelligence · Cross-listed from cs.CV
Geometric Inconsistency Localization in Multi-View Image Sets
Xander Staelens, Albéric Loos, Bert Ramlot et al. (+3 more)
Novel view synthesis (NVS) models can produce realistic new views of the same scene from different viewpoints. However, these generated views are not always geometrically consistent with one another. Multi-view (MV) consistency has shown promise as a tool for evaluating these NVS models. Its potential for multimedia forensics, however, remains largely unexplored, particularly for localizing
geometric inconsistencies across wide-baseline image pairs. To enable research in this direction, we introduce DeformView, a wide-baseline MV
dataset with pixel-level annotations of
geometric inconsistencies. Using DeformView, we evaluate state-of-the-art MV consistency-scoring methods and show that approaches developed for NVS evaluation transfer poorly to the forensic task of
geometric inconsistency localization. To address this limitation, we propose DEFECt3R, a lightweight learning-based classifier that uses cross-view feature relationships to localize
geometric inconsistencies at the pixel level. By learning from explicit supervision, including hard negatives from geometrically consistent yet deformed views, DEFECt3R improves localization performance and substantially reduces false positives compared to existing consistency-scoring methods. Ablation experiments further show that both feature representations and correspondence quality contribute to localization performance. Overall, our findings demonstrate that MV
geometric consistency is a promising yet underexplored signal for multimedia forensics and establish a
benchmark and baseline for
geometric inconsistency localization in wide-baseline MV image pairs. Code and
dataset are available at https://github.com/IDLabMedia/DeformView-DEFECt3R
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All authors Xander Staelens, Albéric Loos, Bert Ramlot, Hannes Mareen, Peter Lambert, Glenn Van Wallendael
Artificial Intelligence
Purin: A Biology-inspired Mechanism for Artificial Neural Networks
Zishu Liu, Chunbo Luo and Christos Grecos
Artificial neural networks (ANNs) usually represent neural transmission with fixed trainable weights during a training batch, which omits short-term changes in synaptic efficacy. In addition, the discrete time-step simulation requires additional temporal processing that many conventional ANN architectures do not use. To overcome these challenges, we propose Purin, a biology-inspired and ANN-compatible mechanism, that introduces synaptic efficacy modulation into conventional convolutional neural networks. Purin uses a time-interval-based abstraction for neural activities, which allows Purin to introduce short- and long-term synaptic efficacy changes without using discrete time-steps. Purin introduces a bounded factor to represent temporary synaptic efficacy changes, together with two weight
matrices that represent input-side and output-side efficacy. The weight
matrices are updated by backpropagation and interpreted as the long-term synaptic efficacy changes. Experimental results show that after removing the confounding factors in the AlexNet, VGG11, and GoogLeNet architectures, Purin improves the classification accuracies in all three models across the evaluated
datasets.
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Artificial Intelligence · Cross-listed from cs.SD
Acoustic-to-Text KV Compression for Full-Duplex Speech Models
Yejin Lee, Seungbeom Kim, Yongha Lee et al. (+1 more)
Full-duplex
speech language models continuously accumulate acoustic key-value (KV) states, making long-running interactions
memory-intensive. During listening, the model can finish processing an audio unit before the next arrives; we term the remaining interval listening-time slack. We propose acoustic-to-text KV compression, which introduces a transcription side channel to convert incoming
speech into compact textual
memory within this interval. When the cache exceeds a target budget during inference, older acoustic states are evicted while transcripts and recent acoustic context remain. We train the side channel with LoRA using cross-
entropy on transcription segments. To preserve listening and speaking behavior, we apply knowledge
distillation to the original model's
token-level output distributions at native prediction positions. On ten-minute LongSpeech sessions, our MiniCPM-o 4.5 implementation reduces peak streaming KV-cache size by 64.6% compared with the same model without eviction. The proposed method also improves transcription, temporal question answering, and summarization over the baseline. Full-Duplex-Bench evaluations further show comparable pause-handling, turn-taking, and interruption performance.
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All authors Yejin Lee, Seungbeom Kim, Yongha Lee, Kyuhong Shim
Artificial Intelligence
DIAL: Position-Debiased LLM Judges with Adaptive Human Preference Calibration
Zesheng Cai, Yingqi Fan, Sichang Chen et al. (+1 more)
Large
language models (LLMs) as a judge enable scalable evaluation, but their judgments can be sensitive to response order and, even after removing such position effects, can still diverge systematically from human preferences.We introduce DIAL, a unified framework that combines abundant LLM comparisons with limited human comparisons to separate judge-specific position effects, learn shared structure in position-debiased LLM preferences, and adaptively calibrate that structure toward the human preference target. Theoretically, we study three aspects of DIAL: (i) identification of latent LLM preferences, position effects, and human calibration; (ii) adaptive estimation that balances LLM anchoring against limited human evidence; and (iii) fixed-weight uncertainty quantification for the calibrated human preference. Empirically, we evaluate position debiasing and human
alignment separately in controlled simulations and on three human-preference
benchmarks, showing that DIAL remains robust to unbalanced response order, achieves strong human-aligned rankings with limited labels, and adapts toward human evidence when LLM information is imperfect. Our real-data study collects over 410K judgments from 21 LLM judges in both display orders, providing a resource for future studies of LLM-judge bias, heterogeneity, and human
alignment.
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All authors Zesheng Cai, Yingqi Fan, Sichang Chen, Jin-Hong Du
Artificial Intelligence
Which Influence Are We Estimating? The Role of Counterfactual Specifications in Data Attribution
Zhe Li, Wei Zhao, Peixin Zhang et al. (+1 more)
Estimating the influence of training examples on model behavior is essential for data debugging, valuation, and attribution. Existing influence
estimators often produce incompatible rankings, which are commonly ascribed to approximation error. We argue that a more fundamental source of disagreement is specification mismatch: influence depends on the behavior being attributed, the intervention applied to each training example, and the counterfactual training process that maps the intervention to a model response. These choices are especially important when the target behavior requires a tractable surrogate, such as query loss, a logit, or a margin. We formalize influence as a counterfactual estimand, distinguish specification mismatch across estimands from approximation error in estimating a fixed estimand, and organize representative
estimators by their implied specifications. We further derive a local decomposition that exposes how behavior signals, training signals, and counterfactual parameter responses interact. Controlled experiments show that exact estimands under different specifications can induce different rankings, whereas approximation error grows as perturbations move farther from their linearization points. Experiments on noisy label detection and LLM attribution show that specification choices significantly affect attribution quality, especially for the choice of behavior surrogate. Behavior-aligned specifications can identify target-specific training examples obscured by default loss-based or similarity-based specifications. These results establish specification analysis as a necessary first step for interpreting and comparing data influence
estimators.
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All authors Zhe Li, Wei Zhao, Peixin Zhang, Jun Sun
Artificial Intelligence
Samples, Sources, Space: Decomposing Data Scale in Spatially Structured Representation Learning of Human Brain Microarchitecture
Christian Schiffer, Mathis Bode, Thomas Lippert et al. (+2 more)
Scaling studies typically represent training data by a single count of samples. For hierarchically and spatially structured data, however, the same number of samples can be drawn from few or many sources and distributed differently across the underlying domain. We therefore study data scaling as an allocation problem, separating unique sample count, source diversity, and spatial coverage. We study this decomposition in microscopic whole-
brain histology, where a source is an individual
brain, and a sample is an image patch at a specific spatial location. Across 93 controlled pretraining runs of a contrastive model that uses spatial proximity for supervision, we vary data allocation, compute, and model capacity over 11.6 million spatially anchored image patches from 21 human
brains. Performance improves with more unique samples, broader spatial coverage, additional compute, and larger model capacity. At fixed sample count, distributing samples across one to 18 subjects produces no detectable improvement, even though representations generalize substantially better to subjects encountered during pretraining. Inter-subject variation therefore strongly affects generalization, but additional subjects provide no benefit when a fixed sample budget is distributed across more sources. These results establish sample count, source diversity, and spatial coverage as distinct axes of data scaling in spatially structured representation learning.
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All authors Christian Schiffer, Mathis Bode, Thomas Lippert, Katrin Amunts, Timo Dickscheid
Artificial Intelligence · Cross-listed from cs.SE
Rethinking Data Quality for AI-Driven Systems: Evidence from Practitioner Interviews
Hariharan Gopinath, Jan Bosch and Helena Holmström Olsson
Data quality research has usually treated data as an input that is stored, processed, and validated. In AI-driven software-intensive systems, data also shapes model behavior, evaluation, and lawful use. Empirical evidence remains limited on how practitioners define, assess, and manage quality under these conditions. We interviewed 16 practitioners from nine organizations and analyzed the transcripts using reflexive thematic analysis and developed six themes from participants' accounts. In AI systems, traceability shifted from modular debugging to attributing model behavior, while using models as quality assessors introduced circularity.
Agent context and
memory became data objects, and synthetic and pseudo-labeled data made authenticity a quality concern. In foundation-model development, lawfulness became a gate for training data, while representativeness was judged through coverage of situations in which the system must behave safely. Prior ML research examines many of these problems separately. Our study provides a practitioner-grounded account of how they are encountered together as an engineering and organizational concern. We also interpret five recurring conditions as helping explain how the themes relate to reduced trust in data and AI outcomes. We synthesize these findings through lifecycle assurance: a conceptual framing focused on producing evidence that data can support a specific AI claim when its influence may be embedded in model behavior, model-based judgments, or
agent actions.
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Artificial Intelligence
Evolutionary Safety of Recursive Self-Improving AI: Taxonomy, Risk Discovery, and Evaluation
Chang Gong, Jingping Bi, Di Yao et al. (+6 more)
Rapid advances in AI are bringing recursive self-improvement (RSI) closer to practice, with growing progress in model self-training, self-evolving
agents, and automated AI development. Yet the very mechanisms that enable continuous improvement also introduce new challenges for AI safety. Unlike isolated failures, safety risks can persist across updates, accumulate through repeated adaptation, and propagate to successor systems. More importantly, the processes that generate and select future improvements may themselves evolve, making safety increasingly difficult to assess and maintain. This raises a fundamental question, i.e., how can safety be maintained when both AI systems and the processes that improve them continue to change? Therefore, we introduce Evolutionary Safety to study how safety properties are preserved, weakened, or restored throughout persistent and recursive self-improvement. We characterize six recurring manifestations of evolutionary risk and develop a five-domain taxonomy that captures how safety-relevant changes arise, persist, and propagate across evolving systems. Building on this foundation, we establish a framework for risk discovery and evaluation across states, updates, trajectories, and lineages, and examine governance principles for controlling modifications, verifying and authorizing updates, and ensuring traceability and recovery. We further identify open problems in maintaining safety as both AI systems and their improvement mechanisms continue to evolve.
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All authors Chang Gong, Jingping Bi, Di Yao, Ran Cheng, Yaodong Yang, Kaile Wang, Xinjian Liang, Chao Xiang, Ruijie Guo
Artificial Intelligence
Accounting for Bias Enables Sustainable LLM Evaluation
Harshita Katoch, David Antony Selby, Gerrit Großmann et al. (+1 more)
LLM-as-a-judge has become the de facto standard for scalable, subjective evaluation, yet current leaderboards compensate for systematic measurement bias by running ever more comparisons, an approach that is both statistically unsound and computationally wasteful. The root cause is an incomplete measurement model, treating LLM judges as neutral, interchangeable instruments ignores documented biases like position bias, verbosity bias, judge severity, and self-enhancement, that no volume of additional data can eliminate. We propose a unified latent variable framework that jointly models pairwise and ordinal data while explicitly correcting for these confounders, recovering reliable rankings from substantially fewer comparisons. Because fitting this model costs negligible compute relative to a single round of LLM inference, bias correction is not only more statistically rigorous but also a more sustainable approach to trustworthy evaluation.
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All authors Harshita Katoch, David Antony Selby, Gerrit Großmann, Sebastian Vollmer
Artificial Intelligence · Cross-listed from cs.SD
BAT-CLIP: Trimodal Alignment of Brain, Audio and Text
Suhyun Kim, Jinmo Han, Danny Dongyeop Han et al. (+9 more)
Decoding and interpreting naturalistic
speech from the
brain increasingly relies on
alignment to pretrained
speech and
language representation spaces. However, current CLIP-style
brain-
speech alignment ground neural activity to a single anchor modality-audio or text-despite the
brain's inherently
multimodal speech processing. This induces a trade-off: audio anchoring preserves temporal structure but weakens linguistic separability, while text anchoring captures semantics yet discards acoustic detail. We propose BAT-CLIP, the first CLIP-style trimodal
alignment framework for iEEG that jointly aligns neural
embeddings to both pretrained audio and text anchors in a shared, frozen audio-text
manifold. On the naturalistic Podcast
benchmark, BAT-CLIP yields more robust representations than bimodal CLIP baselines. We also highlight the importance of using self-supervised foundation models for CLIP training.
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All authors Suhyun Kim, Jinmo Han, Danny Dongyeop Han, Ahhyun Lucy Lee, Jewoon Lee, Yonghyeon Gwon, Zach Paris, Chun Kee Chung, Saewoong Bahk, Nam Soo Kim, Seong Jae Hwang, Jiook Cha
Artificial Intelligence
SPO: Discovering Adaptive Large Neighborhood Search Operators via Stackelberg Program Optimization
Xinyi Ke, Kai Li, Junliang Xing et al. (+2 more)
Large neighborhood search (LNS) relies critically on destroy and repair operators, whose effectiveness depends on both adaptation to the evolving LNS state and interaction between the two roles. We introduce Stackelberg Program Optimization (SPO), an LLM-based framework for discovering adaptive executable destroy-repair programs. SPO conditions operator decisions on a compact LNS state, allowing state-dependent behavior to emerge through program discovery, and organizes destroy-repair discovery as a Stackelberg interaction over program space that reflects their asymmetric dependency. Role-specific credits evaluate destroy programs as leaders and repair programs as conditional follower responses, guiding a coupled optimization process that combines LLM generator learning with population-based evolutionary search over programs. Experiments on the traveling salesperson problem and capacitated vehicle routing problem show that SPO outperforms strong baselines across a broad range of settings and generalizes beyond the discovery scale to larger instances and
benchmark sets. Behavioral analyses further demonstrate state-dependent operator behavior and coupled destroy-repair improvement during discovery.
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All authors Xinyi Ke, Kai Li, Junliang Xing, Yifan Zhang, Jian Cheng
Artificial Intelligence
Semantic Navigation for Issue Localization in Code Repository
Yunxiang Wei, Zhenyu Lei and Jundong Li
Repository-level issue localization aims to identify and rank the files and functions relevant to resolving a reported issue. LLM
agents approach this task iteratively: they identify a set of potentially relevant locations, inspect the corresponding code, and revise their judgments about these candidates as new evidence is acquired. Existing environments, however, provide limited support for this loop:
agents must search for unresolved relation targets, reconstruct entity semantics from raw source code, and revise candidates without evidential basis. To address these limitations, we present SemNav, a framework that leverages deterministic
retrieval to seed a broad candidate set and an LLM
agent to continually refine that set, thereby combining initial coverage with evidence-guided revision. SemNav supports this process through three key components. A Semantic
Navigation Graph resolves program relations on demand through a
language server, enabling direct
navigation to related entities across files. Issue-conditioned Semantic Cards provide compact, source-grounded interpretations of each entity's role and relevance to the issue. A persistent Candidate Workspace records each candidate together with its evidential basis, enabling grounded verification, revision, and ranking. Across SWE-bench Lite and PLocBench, SemNav outperforms existing baselines, improving File Hit@10 from 68.33% to 82.67% with Gemma 4B. Component ablations and trajectory analysis support the complementary roles of all three components, while Semantic Cards reduce working-context load by 48.2% relative to full-source reading. SemNav further ranks first on all seven evidence-quality metrics on SWE-Explore and improves downstream issue resolution from 44.00% to 52.33%.
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Artificial Intelligence · Cross-listed from cs.CL
Improving Visual Sensitivity of LLMs on Multimodal Machine Translation with Metric-based Loss Weighting
Paweł Mąka, Piotr Andruszkiewicz, Yusuf Can Semerci et al. (+2 more)
Multimodal Machine Translation aims to incorporate additional signal from non-textual modalities to improve translations by resolving ambiguities. While models, through
multimodal fusion, are able to accept images related to the source text, they can ignore this information. Therefore, increasing their visual sensitivity remains an active research area. In this work, we introduce a training method, Metric-based Loss Weighting, that improves visual grounding of translations by increasing the loss function for
tokens that benefit from the accompanying image. We identify these
tokens using the Point-wise Cross-mutual Information (PCXMI) metric, which compares the model's output probabilities with and without visual context. We introduce a Congruency-based PCXMI metric and experimentally show that both metrics working in combination yield the best results. We evaluate our method by fine-tuning three pretrained
Multimodal Large
Language Models on the task of Image-guided Machine Translation for three
language directions. Metric-based Loss Weighting outperforms other tested methods on the CoMMuTE contrastive
dataset, improving accuracy by up to more than 7 percentage points compared to standard fine-tuning, while maintaining strong general translation performance.
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All authors Paweł Mąka, Piotr Andruszkiewicz, Yusuf Can Semerci, Jan Scholtes, Gerasimos Spanakis
Artificial Intelligence
Neural State Prediction: Obstructing Shortcut Learning in EEG Foundation Models
Kieren Yu, Ziyang Liu, Chang Huang et al. (+2 more)
EEG foundation models increasingly use masked prediction to learn from unlabeled recordings, but optimizing this objective does not ensure transferable neural representations. A central challenge is that stable positional cues and local correlations can make masked regions predictable without integrating distributed neural context. To reduce this reliance on low-information prediction paths, we introduce Neural State Prediction (NSP), a latent-predictive framework that constrains both the prediction target and the available context. NSP uses a Target Encoder updated by an exponential moving average (EMA) to define latent supervision. Identity residualization removes additive effects associated with channel identity and relative time from the targets, while
topology-separated context excludes their immediate spatial and temporal neighborhood from the visible input. We pretrain NSP on 2.2 million
EEG segments from TUEG and evaluate it across 30 downstream
datasets spanning clinical diagnosis, sleep staging,
emotion recognition, motor imagery, event-related potentials, cognitive-state decoding, and
language retrieval. Under full-parameter multi-task fine-tuning on
EEG-FM-Bench, NSP achieves 63.94 macro balanced accuracy across 14
datasets, exceeding the strongest evaluated baseline by 2.35 percentage points. Controlled component ablations assess the contribution of each mechanism, while matched context controls and held-out interventions characterize the role of context geometry, signal content, and positional information. Jointly designing latent targets and their context offers a promising direction for
EEG foundation models that learn from distributed signal structure.
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All authors Kieren Yu, Ziyang Liu, Chang Huang, Jintai Chen, Kaishun Wu
Artificial Intelligence · Cross-listed from cs.IR
AgentRecommender: LLM Agents Enable Customizable Recommender Systems on the User Side
Ryoma Sato
Recommender systems have traditionally been developed for platforms. However, this has given rise to many phenomena that may be advantageous for platform lock-in but are a nuisance to users, such as clickbait, filter bubbles, and the spread of fake news. Recently, user-side recommender systems have been proposed as a new paradigm for solving this problem. If users deploy their own recommender systems, they are no longer at the mercy of the platform's interests. However, building a user-side recommender system is not trivial; in particular, customizing one for oneself requires additional data. We propose AgentRecommender, a method that leverages the investigation capability and internal knowledge of LLM
agents to flexibly build user-side recommender systems without additional data. AgentRecommender allows users to easily create recommender systems tailored to their own preferences.
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Artificial Intelligence · Cross-listed from cs.IR
SPADE: Escaping the Popularity-Similarity Frontier to Measure Serendipitous Recommendations
Tobias Vente, Maarten Peirsman, Noah Daniëls et al. (+2 more)
Recommender systems engineer serendipity to foster active exploration and break predictable consumption cycles. The problem with existing offline beyond-accuracy metrics is that they often either isolate historical similarity or global popularity. We aim to design an evaluation metric that examines similarity, popularity, and actual user relevance. To achieve this, we introduce SPADE (Serendipitous Pareto Distance Evaluation). SPADE maps all items into a two-dimensional space to directly calculate a user-specific Pareto frontier of maximally popular and historically similar items. The final serendipity score is then computed by averaging the minimum Euclidean distance from this boundary strictly for the correctly recommended test-set items. Evaluating SPADE across five
datasets and five baseline
algorithms confirms its effectiveness; our results show that the metric successfully prevents
algorithms from exploiting beyond-accuracy measures with irrelevant or non-personalized recommendations, reliably isolating serendipitous discoveries.
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All authors Tobias Vente, Maarten Peirsman, Noah Daniëls, Hannu Toivonen, Bart Goethals
Artificial Intelligence · Cross-listed from cs.CV
ReG-SAM: Reference Graph-Driven SAM for 2D Foundational Vessel Segmentation
Donghang Lyu, Zichen Zhang, Oleh Dzyubachyk et al. (+1 more)
Vessel
segmentation in medical images is essential for many clinical tasks, ranging from diagnosis to treatment planning. However, it remains challenging due to complex vascular morphology and diverse imaging conditions. Existing deep learning methods rarely aim at building a generalizable vessel segmentor across anatomies and modalities. While the Seg- ment Anything Model (SAM) has shown promise for med- ical image
segmentation, its original design does not fully exploit vascular morphology and struggles with fine-grained vascular structures, leading to suboptimal performance. In this paper, we propose ReG-SAM, a SAM-based framework tailored to 2D vessel
segmentation that leverages reference graph set for enhancing vascular representations. Specifically, we introduce two modality-aware representations derived from the reference masks: graph
prompt embeddings (GPEs) that encode global spatial features from graphs, and vascu- lar prototype
embeddings (VPEs) that capture fine-grained modality-specific vessel characteristics from multi-scale fea- ture maps and vascular masks. Since both require vascular masks that are unavailable during inference and require robust modality-aware vascular feature representations, we construct a modality-wise vascular database and develop two reference graph-guided representation learning schemes for estimating GPEs and VPEs using samples from the database rather than ground-truth masks. Extensive experiments across 19
datasets demonstrate that ReG-SAM consistently outperforms existing baselines, even those using manual
prompts, particularly on challenging thin vessels.
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All authors Donghang Lyu, Zichen Zhang, Oleh Dzyubachyk, Marius Staring
Artificial Intelligence
Momentum-Guided Federated Split Distillation for Personalized Temporal Edge Intelligence
Ahmed-Rafik Baahmed, Jean-François Dollinger, Amine Brahmia et al. (+1 more)
We propose a momentum-guided
federated split
distillation framework for personalized, efficient, and autonomous temporal edge intelligence. We introduce TeRR-SAtt, our novel temporal reservoir student
attention design that combines fixed reservoir representations, a lightweight temporal student, and personalized output modules. We also present AMGF, our anticipatory momentum-guided fusion mechanism that clusters clients through learning momentum and derives specialized teacher updates. On real-world smart-building data, TeRR-SAtt reduces edge training latency by 65.50%, inference latency by 44.70%, training
memory usage by 18.40%, and inference CPU usage by 33.10% over the considered baselines. At the same time, AMGF improves local learning by up to 35.31% in RMSE compared to global updates.
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All authors Ahmed-Rafik Baahmed, Jean-François Dollinger, Amine Brahmia, Mourad Zghal
Artificial Intelligence · Cross-listed from cs.LG
Teacher-Anchored Selection of Post-Training Quantized Models under Domain Shift
Alejandro Rodriguez Dominguez, Muhammad Shahzad and Xia Hong
Compressing a trained model yields a family of deployment candidates, and under domain shift the most compressed one need not be the one to deploy. We study selection over such a family, with candidates and teacher fixed and target labels absent or scarce. Two findings organize the label-free case. Minimum teacher distortion behaves almost as a constant rule, selecting the same eight-bit, per-channel, unclipped configuration in every run, which does not minimize empirical target cross-
entropy. Established
estimators divide sharply: in the overconfident-collapse regime of the CNN families, confidence-based
estimators order the family close to backwards, and the diagnostics that identify it need the labels the setting denies, while output-distribution
estimators match the teacher-relative anchor and on one architecture beat it. Distortion is nonetheless stable, so a supervised term can move selection away from it. Combining the two, we give exact quadratic identities for a canonical quadratic analogue of the family. We also show that under symmetric corruption the label-dependent part of a criterion linear in the label indicator is multiplied by one common factor whenever its coefficient sums are candidate-invariant, a class holding teacher contrasts and accuracy but not cross-
entropy. These characterize the score's components without bounding selection regret. Across one hundred and thirty-four candidate families, one per independently trained convolutional or
Vision Transformer teacher, anchoring reduces mean regret at the smallest label budget in every setting, an advantage that fades beyond twenty-five labels.
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Artificial Intelligence · Cross-listed from cs.CV
FedHisto-PAST: Parameter-Efficient Stain-Aware Federated Learning for Cross-Site Lung Histopathology Classification
Muhammad Muhtasim Shahriar, M. M. Golam Hafiz, Saad Aloteibi et al. (+1 more)
Cross-site lung histopathology classification must account for stain variation, non-IID client data, missing classes, and the cost of adapting large pathology encoders. This study evaluates FedHisto-PAST v2 for three-way classification of adenocarcinoma (ACA), Normal, and squamous
cell carcinoma (SCC). FedHisto-PAST v2 combines a frozen HIBOU-B foundation model with parameter-efficient adaptation, stain-conditioned paired-view prediction and feature consistency, reliability-aware prototype learning, and adaptive
federated aggregation. Experiments used a five-client, non-IID, raw-data-local simulation with fixed internal evaluation, client-level analysis, component ablations, communication accounting, and a development-influenced exploratory LungHist700 cohort. All principal methods achieved near- ceiling internal performance, which limited discrimination on the fixed split. On LungHist700, FedHisto- PAST v2 achieved a Macro-F1 of 0.728560 and a balanced accuracy of 0.730454. Higher recognition of Normal and SCC was accompanied by lower ACA recall, and calibration remained imperfect. Prediction-level consistency was the only component with a clearly supported independent contribution in the external ablation analysis. Feature consistency and prototype regularization showed no conclusive independent overall gains in Macro-F1. The framework updated 1.253841% of the model parameters. The results provide exploratory cross-
dataset evidence for stain-aware, parameter-efficient federation; they do not establish formal
privacy, patient-level independence, prospective deployment, or clinical validation.
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All authors Muhammad Muhtasim Shahriar, M. M. Golam Hafiz, Saad Aloteibi, Mohammad Ali Moni