Artificial Intelligence · Cross-listed from cs.LG
IFCLoRA: Topology-Aware Rank Allocation for Parameter-Efficient Fine-Tuning
Wei Zhang, Xinwu Liu and Yihang Cheng
Low-Rank Adaptation (LoRA) is a widely used approach to parameter-efficient fine-tuning (PEFT) of LLMs whose effectiveness depends on rank allocation. Existing adaptive LoRA methods derive ranks from local gradient, activation, or
matrix statistics collected before or during fine-tuning; training-time variants add overhead, and local signals reveal little about each module's structural role in information propagation, giving weak global grounding for scarce-capacity allocation. We propose IFCLoRA, a
topology-aware method for pre-fine-tuning rank allocation and adapter initialization. Using a small calibration set, IFCLoRA performs intervention tracing on the frozen model and constructs a sparse task-conditioned interaction graph over LoRA target modules. From this graph it extracts a global information-flow
topology prior and fuses it with each node's local gradient sensitivity to form a
topology-dominant Information-Flow Centrality (IFC) score, measuring participation in task-conditioned multi-hop propagation. The IFC scores then serve as module-level routing signals for one-shot discrete rank allocation under a rank-budget constraint. Reusing response vectors from tracing, IFCLoRA constructs a function-preserving flow-response subspace initialization, giving adapters task-relevant output subspaces. Across all settings, IFCLoRA achieves higher mean scores than standard LoRA with comparable fine-tuning time and peak
memory; it requires a one-time offline calibration stage. On GSM8K, IFCLoRA attains the highest mean accuracy among compared PEFT methods on both base models, exceeding standard LoRA by 4.75 percentage points on LLaMA-3.1-8B. Resulting rank allocations are non-uniform and vary across tasks and base models, suggesting that task-conditioned global information-flow
topology can serve as a useful structural prior for rank allocation in low-budget PEFT.
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Artificial Intelligence · Cross-listed from cs.LG
Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression
Hirotaka Sugawara, Yujin Taguchi, Kei Minagawa et al. (+3 more)
Bayesian optimization (BO) is an optimization method that sequentially proposes the next candidate explainable variables for optimizing target variables by balancing exploration and exploitation. BO is often used under a limited evaluation budget, such as hyperparameter tuning of deep learning. Despite its effectiveness, conventional BO may have poor convergence in practical experimental science where each evaluation is often costly and time-consuming. Recently, BO methods have been proposed that accelerate optimization by using pseudo-experimental data that simulate experimental data. However, when only a limited number of experimental data are available, the generated pseudo-experimental data may be of insufficient quality. In this study, we developed PolyBO to improve optimization time by generating high-quality pseudo-experimental data even when the number of trials is limited. PolyBO performs BO efficiently by generating pseudo-experimental data with an adaptively updated versatile parametric model. This low-capacity
polynomial regression model is intended to enable efficient BO even with limited experimental data. PolyBO updates the BO surrogate model with a combined
dataset consisting of experimental data and pseudo-experimental data and then performs optimization. Using synthetic
benchmark functions with diverse landscapes, we found that PolyBO reduced the optimization time by a median of 42%. For a real-world material composition optimization problem, PolyBO reduced the optimization time by a median of 96% compared with conventional methods. Overall, PolyBO achieves efficient optimization in settings where each experiment requires a long time.
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All authors Hirotaka Sugawara, Yujin Taguchi, Kei Minagawa, Yusuke Hiki, Takashi Morikura, Akira Funahashi
Artificial Intelligence · Cross-listed from cs.CV
TRaM-VSR: Importance-Aware Token Routing and Merging for One-Step Diffusion Video Super-Resolution
Sicheng Gao, Zhuyun Zhou, Yixuan Liu et al. (+3 more)
Video super-resolution (VSR) using large-scale
Diffusion Transformer (DiT) priors achieves exceptional perceptual quality but is often impractical due to the quadratic computational cost of processing dense spatio-temporal
token sequences. Existing efficiency-oriented methods risk irreversible detail loss and temporal flickering, a vulnerability especially pronounced in one-step
diffusion models. To address this, we propose TRaM-VSR, a
Token Routing and Merging framework for adaptive
token allocation, leveraging both context-aware
video priors and network-level priors. First,
token importance is estimated by fusing motion-sensitive temporal cues with semantic text similarity, isolating dynamic objects and structural boundaries. Next, this importance is further calibrated and adjusted by an offline planner to guide routing across optimally grouped network blocks. Technically, within each routed group, structurally critical
tokens are processed in a high-fidelity local stream, while less informative
tokens are aggregated into a compact global stream, both modulated by network depth and aligned with the multigranular nature of
diffusion models. Extensive experiments show that TRaM-VSR accelerates inference significantly while preserving state-of-the-art reconstruction quality and robust temporal consistency. The code is available at https://github.com/Ree1s/TRaM-VSR.
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All authors Sicheng Gao, Zhuyun Zhou, Yixuan Liu, Tong Shen, Zongwei Wu, Radu Timofte
Artificial Intelligence · Cross-listed from cs.CL
Why Large Language Models and Humans Converge and Diverge in Evaluating Creativity
Pengzhao Lyu, Yeun Joon Kim, Hanlin Xiao et al. (+1 more)
Despite the growing use of large
language models (LLMs) as creativity evaluators, evidence of their
alignment with human evaluations remains mixed, raising the question of when and why their judgments converge with or diverge from those of humans. Across three studies and six widely used LLMs, we addressed this gap by identifying the standards underlying LLM creativity evaluation and examining their downstream implications. Study 1 showed that LLMs generally relied on a narrower subset of human creativity evaluation standards. Convergence with human standards was strongest in the novelty dimension, whereas divergence was clearest in the contextual dimension, which captures social,
market, and reputational information. Moreover, each LLM exhibited distinct, model-specific standards that varied substantially in breadth. These differences in evaluation standards were reflected in actual creativity judgments. Study 2 (N = 1,103 ideas) showed that LLM evaluations were moderately correlated with human evaluations, and individual LLMs with broader standards better distinguished ideas humans judged as more versus less creative. Study 3 (N = 1,195 participants) showed that LLMs were less sensitive to contextual information: such information significantly altered human creativity ratings but left LLM ratings largely unchanged. Together, our findings help explain the mixed evidence on LLM-human
alignment, showing that
alignment depends on the evidence a judgment demands and the standards each model applies. Selecting an LLM evaluator is therefore a consequential decision: different models, applying different standards, recognise different ideas as creative.
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All authors Pengzhao Lyu, Yeun Joon Kim, Hanlin Xiao, Yingyue Luna Luan
Artificial Intelligence · Cross-listed from cs.CV
Filling Before Advancing: Capability-Gap-Driven Post-Training for Scenario-Specialized Remote Sensing MLLMs
Yuheng Zong, Minghua Wang, Xin Zhao et al. (+3 more)
Remote sensing
multimodal large
language models (RS-MLLMs) have advanced aerial-image understanding, but scenario specialization remains constrained by scarce high-quality supervision and incomplete capability coverage. We propose filling before advancing (FBA), a capability-gap-driven post-training route that develops prerequisite capabilities before scenario-specific adaptation. For coastal harbor understanding, we construct CPRS (Coastal-Port Remote Sensing), a three-layer
dataset covering RGB, SAR, PAN, and NIR imagery. FBA organizes supervision into RS semantic anchoring, multi-source bridging, and evidence-grounded scenario tuning. We also introduce HarborEval, an eight-track
benchmark assessing perception, spatial understanding, robustness, and generation. Using the same final-task supervision pool, FBA raises the HarborEval score from 57.95 to 70.29 on LLaVA-v1.5 and from 81.09 to 83.37 on Qwen3-VL compared with Direct-SFT. It also outperforms collapsed training and leads the evaluated comparisons on harbor-related VRSBench and RSVQA subsets and expert-scored OpenEval. Stage-wise and supervision-replacement analyses examine the contributions of the supervision roles. A lightweight urban instantiation further supports cross-scenario applicability, with the proposed stage order achieving stronger overall performance than the tested alternatives. Project resources and release updates are available at https://github.com/Z0ngL1ng/filling-before-advancing.
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All authors Yuheng Zong, Minghua Wang, Xin Zhao, Zhi-Hui Zhan, Antonio Plaza, Jon Atli Benediktsson
Artificial Intelligence
Deconstructing Off-Policy Ratios: Entropy-Normalized Trust Regions for Asynchronous Reinforcement Learning
Guanqun Zhao, Zijun Xie, Binbin Zheng et al. (+5 more)
Asynchronous reinforcement learning (RL) accelerates large
language model (LLM) post-training by overlapping rollout generation with policy optimization, but the resulting stale, off-policy data destabilizes optimization and can cause policy collapse. Existing methods gate
tokens by ratio magnitude alone, applying one threshold at every position. We show that the ratio's natural scale is set by
token entropy, so deviations from mid-trajectory weight updates stay within this scale and carry genuine exploration. We further identify an overlooked low-
entropy regime that breaks this scaling, where a near-zero probability amplifies train–inference mismatch into noise far beyond what the local
entropy admits. A magnitude threshold admits this noise and discards the exploration. We therefore propose the
Entropy-Normalized Trust Region (ENTR). Across long-horizon agentic tasks and mathematical
reasoning benchmarks, ENTR outperforms existing asynchronous methods. It improves avg@1 on BrowseComp-Plus by $6.9\%$ over the strongest baseline, trains stably up to $30$ policy versions of staleness, and matches synchronous GRPO at a $2.6\times$ speedup.
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All authors Guanqun Zhao, Zijun Xie, Binbin Zheng, Yehan Yang, Jiafeng Lu, Aoqi Hu, Enlei Gong, Zeyu Chen
Artificial Intelligence · Cross-listed from cs.CL
From Isolated Tasks to Structured Capabilities: A Multilayer Taxonomy for Large Language Models
Shixin Fang, Jiachen Wo, Wenjuan Qin et al. (+2 more)
Large
language model (LLM) evaluation spans diverse tasks and
benchmarks, yet evidence remains organized around tasks rather than the capabilities they probe. This fragmentation limits cross-study comparison, obscures capabilities tasks recruit, and makes coverage gaps difficult to identify. We introduce a multi-layer taxonomy of 14 capability domains and 91 subskills across Primitive, Constructed, and Integrative layers. Human cognitive science guides capability definition and organization, not LLM architecture. Layer assignments draw on developmental precedence and hypothesized functional support, while human-origin constructs are adapted to observable model behavior. To demonstrate operational utility, we screened 31,505 papers from ACL, AAAI, ICML, and NeurIPS between 2023 and 2025 and mapped 15,934 LLM-focused papers through multi-model annotation, consensus, and arbitration. Direct research
attention concentrated on
Language-Semantic Competence (3,551; 22.3%),
Reasoning (3,388; 21.3%), Planning and Decision-Making (2,149; 13.5%), and Perception (1,954; 12.3%), whereas six domains appeared in fewer than 2% of papers. Within domains, the most frequent subskill had a median prevalence of 97.9% and appeared in at least 90% of papers in 10 of 14 domains.
Language-Semantic Competence and
Reasoning formed the highest-volume pair (n = 1,864; 11.7%; lift = 2.47), whereas Theory of Mind and Social
Reasoning and Interaction showed the highest lift among pairs with at least 20 co-occurrences (n = 62; lift = 30.84). By shifting the unit of analysis from isolated tasks to structured capabilities, the taxonomy supports research organization, coverage audits, evaluation interpretation, and testable hypotheses for diagnosis, training, and transfer.
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All authors Shixin Fang, Jiachen Wo, Wenjuan Qin, Sihang Jiang, Yanghua Xiao
Artificial Intelligence · Cross-listed from cs.RO
Learning Spatiotemporal Decision Priors for Efficient Path Planning under Partial Observability
Yi Liu, Hongda Zhang, Leyao Zou et al. (+7 more)
Path planning under partial observability remains challenging because an
agent must make long-horizon
navigation decisions from only locally bounded observations. Nevertheless, historical trajectories contain reusable experience-guided directional preferences. Classical planners, however, typically solve each instance from scratch and lack an explicit mechanism to exploit such transferable decision knowledge, often leading to redundant node expansions and locally myopic search behaviors. Motivated by this limitation, this paper proposes ImiPath, a prior-guided learning framework that distills reusable spatiotemporal decision priors from demonstration trajectories and uses them as experience-informed directional guidance to bias planners toward reliable and promising search directions under partial observability. Specifically, ImiPath first constructs a local spatiotemporal observation representation, which encodes the spatial information of the local environment and the temporal information of historical trajectories. The SpatioTemporal-
Attention Policy Network (STAPNet) then transforms this representation into dicision priors. These priors are further incorporated into heterogeneous planners as directional guidance, biasing the search toward locally promising regions. Extensive experiments demonstrate that ImiPath achieves competitive path quality and improves search efficiency by reducing redundant node expansions under local observability. Additional physical experiments on a
magnetic microrobot platform further validate the adaptability and practical deployment potential of the proposed framework.
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All authors Yi Liu, Hongda Zhang, Leyao Zou, Chunlei Meng, Ziqing Zhou, Yuning Chen, Zhuo Zou, Lida Xu, Zhongxue Gan, Chun Ouyang
Artificial Intelligence · Cross-listed from cs.DB
DBA-Bench: A Production-Fidelity Benchmark for LLM-Based Database Operations Agents
Junming Chen, Junyang Jiang, Xu Chen et al. (+2 more)
LLM-based database
agents show promise, but differences in task scope, testbeds, and success criteria hinder direct comparison. A shared
benchmark must also preserve the operational conditions that determine whether diagnosis and remediation succeed in practice. We present DBA-Bench, guided by operational realism, outcome-first evaluation, and controlled scenario reproducibility. It evaluates
agents in live PostgreSQL and distributed TiDB deployments with active workloads, stateful behavior, and multi-source observations. Success requires satisfying scenario-specific operational objectives without violating safety constraints. The primary evaluation covers 231 bounded scenarios across five operational domains, while 10 persistent scenarios extend evaluation to continued operation across interacting events. We evaluate eight automated baselines, including four frontier foundation models, two open-weight models, and two GPT-5.5-backed database
agents. Our evaluation exposes gaps between diagnosis, effective remediation, and safe execution across both database systems.
Agents can identify root causes yet fail to restore operational objectives. Even when objectives are restored, the executed actions can violate safety constraints. Persistent scenarios expose a gap between clearing individual blockers and sustaining business service:
agents leave competing workloads uncoordinated and previously completed repairs unchecked after subsequent changes.
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All authors Junming Chen, Junyang Jiang, Xu Chen, Zibo Liang, Kai Zheng
Artificial Intelligence
Learning on the Job: Continual Learning from Deployment Feedback for Frozen-Weights Agents
Valentin Tablan, Scott Taylor and Kristoffer Bernhem
AI
agents encounter learning opportunities in every episode they run, and discard nearly all of them: the underlying models are frozen at deployment, so an
agent that resolves a difficult request today starts from zero when it recurs tomorrow. Yet ordinary operation already produces feedback, in the form of outcome verdicts and after-the-fact corrections. We show that this feedback is a sufficient signal for continual learning when the frozen model is paired with an external
memory that distils each episode into retrievable natural-
language rules. On the banking domain of $\tau$-bench, against a static-RAG control retrieving over the complete policy corpus, learning from the one-bit outcome verdict lifts single-trial success to 1.6$\times$ the baseline, and learning from corrections to 2.6$\times$, converting 22 of the 84 tasks the baseline never solves. The result spans the deployment spectrum, measured on Mistral Large, an open-weights model that organisations with data sovereignty requirements can self-host, and replicated on a frontier model, Claude Sonnet 5. The accumulated
memory also transfers: each model, reading the store built by the other, rises above its own no-
memory baseline. The harness, protocol, and data are released.
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Artificial Intelligence
Industrial Tokenization for LLM-Based Health Intelligence: A Federated Architecture for Industrial Evidence Integration
Deshui Li, Xiao-Ming Yuan and Zishun Wang
Industrial health management increasingly relies on heterogeneous information sources, including condition monitoring systems, supervisory control and data acquisition systems, maintenance records, inspection results, and prognostic models. Although large
language models provide new opportunities for cross-source
reasoning, industrial data and analytical outputs differ substantially in structure, temporal resolution, physical meaning, and reliability. Directly integrating such heterogeneous information into a monolithic model may reduce interpretability, traceability, and adaptability to equipment and data changes. This paper introduces Industrial Tokenization, a conceptual interface for transforming source-specific analytical outputs into structured and machine-interpretable units of industrial evidence, termed Industrial
Tokens. Unlike numerical
tokens used to encode raw time-series data, Industrial
Tokens represent domain-grounded evidence together with source, temporal scope, operating context, analytical meaning, quality or confidence information, and provenance. Based on this concept, a
federated industrial architecture is proposed, where heterogeneous analytical subsystems retain autonomy while exposing standardized Industrial
Tokens to a central
reasoning layer. As an initial implementation, this study presents an end-to-end DiagnosisToken pathway based on vibration-diagnostic outputs, rule-based event aggregation, structured textual
token generation, and LLM-based interpretation. Other Industrial
Tokens, including SCADA-based condition-monitoring
tokens, maintenance
tokens, and prognostic
tokens, are reserved as future extensions. The proposed framework positions Industrial Tokenization as a semantic interface between domain-specific industrial intelligence and LLM- or
agent-based
reasoning, rather than another method for encoding raw industrial data.
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Artificial Intelligence · Cross-listed from cs.CV
dRAE: Representation Autoencoder with Hyper-Spherical Codes
Tianren Ma, Lin Long, Chuyan Chen et al. (+4 more)
In this work, we aim to discretize the high-dimensional visual representations to bridge the gap with
language models - a non-trivial challenge, as existing quantization methods suffer from codebook collapse, failing to scale while preserving semantic coherence. We identify the root cause as metric mismatch: standard Euclidean codebook objectives are fundamentally misaligned with the anisotropic geometry of representation space, leading to codebook
embeddings with high-variance magnitude scales and uneven angular distributions that hinder scalability. To address this, we propose Hyper-Spherical Quantization (HSQ), which decouples semantic content from feature magnitude via angular routing, preventing code assignment from being dominated by scale rather than meaning. The resulting discrete Representation Autoencoder (dRAE) achieves high-fidelity reconstruction while preserving semantic integrity and supporting scalable codebook budget. Extensive experiments demonstrate consistent performance gains as the vocabulary size scales to 131{,}072, along with 100% codebook utilization, simplified training pipeline, and strong performance across understanding and generation tasks.
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All authors Tianren Ma, Lin Long, Chuyan Chen, Mu Zhang, Junbo Zhao, Tong Zhang, Qixiang Ye
Artificial Intelligence · Cross-listed from cs.LG
TriGlue: a Biology-Inspired Generative Model for Generating Molecular Glue-Induced Ternary Complex
Yuliang Yan, Shuo Yan, Haochun Tang et al. (+2 more)
Molecular glue degraders have emerged as a promising strategy for targeted
protein degradation by inducing ternary complex formation between an E3 ubiquitin ligase and a target
protein. Despite their therapeutic potential, computational design of molecular glues remains largely unexplored. Unlike conventional structure-based drug design, molecular glue design is governed by the unknown
protein-
protein interface and requires the simultaneous modeling of ligand generation,
protein-
protein docking, and ternary complex assembly. In this work, we formulate molecular glue design as a ternary complex generation problem and propose a biology-inspired generative framework, TriGlue. Motivated by the mechanism of molecular glue action, we decompose ternary complex generation into two coupled stages: interface estimation and interface-conditioned complex generation. First, we develop an SE(3)-equivariant interface estimation module that predicts a geometrically constrained
protein-
protein interface from unbound monomer structures. Second, we introduce an interface-conditioned ternary flow matching network that jointly generates the molecular glue and predicts the rigid-body transformation required to assemble the ternary complex. Extensive experiments demonstrate that TriGlue generates chemically valid molecules and produces plausible ternary complexes, which highlight the potential of biology-inspired generative modeling for accelerating molecular glue discovery. Our code is available at https://github.com/yuliangyan0807/molecular-glue-design.
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All authors Yuliang Yan, Shuo Yan, Haochun Tang, Yiqin Sun, Enyan Dai
Artificial Intelligence · Cross-listed from cs.CV
CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography
Dominik Bernard Lau, Hubert Malinowski, Jerzy Szyjut et al. (+5 more)
Accurate pixel-level classification of coronary angiograms is critical for cardiovascular disease assessment, yet the field lacks standardized evaluation protocols. In this work we demonstrate a new
benchmark for the assessment of deep learning models which densely classify pixels of coronary angiograms to one of SYNTAX classes (or background). The evaluation covers 24 distinct architectures starting with classic convnets to recent state-space-based
vision algorithms. We release CARDIAG - a multi-center, multi-label
dataset which we carefully split to reliably compute metrics, accounting for diameter error, overlap, centerline quality and calibration. The data contains SYNTAX labels, binary, uncertainty and
segmentation masks as well as intermediate frames together with the selected non-sensitive DICOM metadata. From the multitude of
algorithms, we nominate ConvNeXt V2 encoder with DeepLab V3 Plus decoder as the best performing, achieving macro $F_1=0.456$, which we then ensemble with Mamba U-Net and Feature Pyramid Network, for an increased $F_1=0.479$. We demonstrate all the architectures to be well calibrated and determine the generalization of the top 5 methods, together with the data efficiency of these architectures. We highlight the importance of both high-resolution and low-resolution features in encoding. We also demonstrate the model correctness in the context of patient demographic, vessel sides and projection angle configurations. Overall the released
benchmark allows for future studies to robustly and rigorously assess the proposals, not only for SYNTAX
segmentation, but lesion detection and many more.
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All authors Dominik Bernard Lau, Hubert Malinowski, Jerzy Szyjut, Adam Brzeski, Tomasz Dziubich, Radosław Targoński, Tomasz Figatowski, Natalia Zielińska
Artificial Intelligence · Cross-listed from cs.RO
One Hand Watches The Other: Dynamic Multi-Agent Cooperation for Sample-Efficient Bimanual Manipulation in Dynamic Environments
Jan Ole von Hartz, Abhinav Valada and Joschka Boedecker
Multi-stream
robot manipulation policies achieve unparalleled sample efficiency and generalization by modeling actions relative to environmental reference frames. However, existing approaches typically assume these frames to be strictly exogenous. This causal assumption collapses in dynamic settings, such as when a single
robot arm manipulates a moving object or when two arms coordinate, where each arm effectively becomes part of the dynamic environment of the other. We propose DynaMAC, a lightweight, policy-agnostic framework that resolves this causal limitation while preserving the sample efficiency, computational speed, and flexibility of multi-stream policies, DynaMAC treats the opposite arm as a dynamic task parameter, thereby providing a unified formulation for dynamic manipulation and bimanual coordination without requiring an explicit leader-follower relationship. To rigorously evaluate these capabilities, we introduce DynaBench, a novel
benchmark for
robot manipulation in dynamic environments. Across both dynamic environments and bimanual manipulation tasks, DynaMAC outperforms leading probabilistic and generative baselines by over 35 percentage points while requiring 20 times fewer samples. Crucially, DynaMAC generalizes zero-shot from static demonstrations to dynamic environments, substantially simplifying data collection and establishing an elegant bridge toward human-
robot collaboration.
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Artificial Intelligence · Cross-listed from cs.DB
Benchmarking Text-to-SQL under Role-Based Access Control
Yang Fei, Yangfan Jiang, Yin Yang et al. (+1 more)
Given a database S and a natural
language question Q, text-to-SQL systems aim to generate an SQL query that correctly answers Q when executed against S. Currently, popular text-to-SQL
benchmarks mostly assume unrestricted access to S; in practice, however, user access is often restricted, e.g., through role-based access control (RBAC) policies. This leads to a potential disconnect between benchmarking results and real-world performance: an LLM with high
benchmark scores might perform poorly in an access-controlled environment, by frequently violating RBAC, or rejecting a query q that could be answered with only permitted data in S. Motivated by this, we present a comprehensive text-to-SQL benchmarking framework with realistic RBAC constraints, which features an LLM-assisted workflow that augments existing text-to-SQL
benchmarks with plausible user roles and access policies. To do so, we formulate the problem of role synthesis as a structured
reasoning process over the database schema, in which the LLM first infers the application context from the schema, and then derives role responsibilities and access scopes consistent with this context. This process is audited by human-in-the-loop quality control, in which domain experts perform metric-guided screening on the generated roles. Besides the augmented
dataset, the proposed framework also contains evaluation metrics that identify RBAC-specific failure modes, and disentangle SQL utility from access-control compliance. We apply the proposed framework to several widely-used
benchmarks, and conduct a systematic empirical study of state-of-the-art text-to-SQL systems. The results show that many solutions (especially open-weight LLMs) with high benchmarking scores under an unrestricted setting suffer sharp performance degradation once access constraints are in place, due to frequent RBAC violations.
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All authors Yang Fei, Yangfan Jiang, Yin Yang, Xiaokui Xiao
Artificial Intelligence · Cross-listed from cs.CL
MEUSLI: a Multilingual Projector for LLM-based ASR and Beyond
Lorenzo Concina, Seraphina Fong, Marco Matassoni et al. (+1 more)
Lightweight projectors are an established way to connect pre-trained
speech encoders with large
language models (LLMs), mapping acoustic features into
token-level
embeddings for tasks like ASR and spoken question answering. Existing systems, however, typically only support a few
languages and are often limited to English. We introduce MEUSLI, the first open-science
multilingual projector family that links a Whisper encoder with open-source
multilingual LLMs, enabling fully open-source end-to-end ASR in 28 European
languages. MEUSLI extends prior monolingual pipelines, delivering strong results across high- and low-resource
languages. Using proper continual leaning techniques, MEUSLI can be easily extended to other
languages not seen in training. We further demonstrate that the MEUSLI projector can be leveraged beyond ASR, enabling
multilingual speech translation and topic identification with only a few hours of task specific supervision per
language. Overall, MEUSLI provides a solid foundation for
multilingual speech understanding tasks, supporting scalable and inclu- sive open-source SpeechLLM
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All authors Lorenzo Concina, Seraphina Fong, Marco Matassoni, Alessio Brutti
Artificial Intelligence
Junlin Fang, Do Nguyen-Thanh, Xiaogang Xu et al. (+2 more)
Large
reasoning models (LRMs) generate long
reasoning traces before producing final answers. While these traces may contain useful signals for
hallucination detection, harnessing them is non-trivial because long trajectories often include noisy steps that obscure the cues relevant to truthfulness assessment. In this paper, we identify two prevalent forms of
reasoning noises, i.e., irrelevant steps and repetitive steps, and show that both substantially degrade
hallucination detection performance. Existing confidence-based scores and naive
embedding-based filtering fail to reliably separate noisy from informative steps. To address this challenge, we propose REDE, a novel learning framework for denoising
reasoning traces for
hallucination detection. Specifically, REDE leverages final-answer
attention as an automatic supervision signal to shape the step-level representation space, yielding refined
embeddings in which noisy steps can be reliably identified and filtered. REDE can be readily plugged into diverse
hallucination detectors by operating on the filtered
reasoning trajectory after removing noisy steps. Extensive experiments on multiple
reasoning benchmarks show that REDE consistently improves detection performance over competitive baselines.
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All authors Junlin Fang, Do Nguyen-Thanh, Xiaogang Xu, Zhen Fang, Sean Du
Artificial Intelligence
Nanbeige4.2-3B: Unlocking Agentic Capabilities in a Compact Model
Nanbeige Lab: Chen Yang, Chengrui Huang, Fufeng Lan et al. (+21 more)
We present Nanbeige4.2-3B, a compact general agentic model with 3B non-
embedding parameters. It delivers strong performance across code-
agent, office-
agent, and complex tool-use tasks while maintaining highly competitive
reasoning capabilities in mathematics, coding, and science. Nanbeige4.2-3B is pretrained from scratch on 28T
tokens with a Looped
Transformer that reuses the layer stack to increase capacity without adding parameters. For SFT data and trajectory construction, we expand the diversity of executable environments, task assets, and agentic scaffolds through real-world deployment and large-scale synthesis. Our RL pipeline applies mixed-mode RLHF over Think and Non-Think responses to improve overall model quality and reduce failure cases, length-controlled
reasoning RL to balance accuracy and
reasoning efficiency, and agentic RL with outcome and process rewards to stabilize long-horizon training. Extensive evaluations show that Nanbeige4.2-3B outperforms larger models, including Qwen3.5-9B and Gemma4-12B, across diverse agentic
benchmarks while remaining competitive on
reasoning and
alignment tasks. Performance with OpenClaw further supports its use as a compact local personal assistant.
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All authors Nanbeige Lab: Chen Yang, Chengrui Huang, Fufeng Lan, Hanhui Chen, Hao Zhou, Huatong Song, Jiaqi Cao, Jiaying Zhu, Jinlin Niu, Kai Wang, Lisheng Huang, Qiliang Liang, Ran Le, Ruixiang Feng, Shuang Sun, Tao Gu, Tao Zhang, Tianyu Luo, Yang Song, Yun Xing, Yuntao Wen, Ziyao Xu, Zongchao Chen, Zongqiang Li
Artificial Intelligence · Cross-listed from cs.LG
A Leakage-Free Stacked Ensemble Method for Multiclass Classification
S. P. Sharmila and Aruna Tiwari
Multiclass classification is a fundamental problem across a wide range of domains. It is still challenging due to possession of high inter-class similarity, class imbalance
datasets, and variability in data distributions. Rule-based classifiers such as XGBoost often achieve stronger performance on structured features, but they are limited in capturing smooth functional relationships among variables. Similarly, neural network models can represent complex nonlinear interactions but frequently suffer from overfitting and generalization issues. To address these limitations, we propose LFS-FRAME, a Leakage-Free Stacked ensemble framework that integrates functional learning using Kolmogorov-Arnold Networks (KAN) and rule-based learning via XGBoost for robust multiclass classification. The proposed framework constructs unbiased meta-features by employing a strict out-of-fold stacking strategy to ensure complete isolation between training and validation data hence preventing performance leakage. By learning over probabilistic outputs from heterogeneous base learners, the meta-classifier effectively exploits both global functional patterns and sharp decision boundaries present in the complex data. Experimental evaluations on multi-class
datasets demonstrate that LFS-FRAME improves performance metrics, and overall accuracy is 89.85% in identifying major families and 81.74% in identifying sub-families relative to strong single-model baselines. These results highlight the effectiveness of leakage-free functional and rule-based stacking for reliable and generalizable multiclass classification.
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Artificial Intelligence · Cross-listed from cs.CL
FSE: Continual Learning for Named Entity Recognition by Fast-Slow Experts
Yunan Zhang, Yang Fan, Heng Li et al. (+2 more)
Continual Learning for Named Entity Recognition (CLNER) enable models to incrementally learn new entity types without forgetting previously acquired ones. However, existing methods suffer from catastrophic forgetting and insufficient exploitation of shared information across tasks. This paper proposes FSE, a Fast-Slow Experts enhanced span-based NER model for CLNER. The shared fast expert learns
token-level links to efficiently filter out unlikely spans, while the task-specific slow expert performs span classification only on the remaining candidates. It stabilizes learning by promoting knowledge sharing across tasks and maintains plasticity by reducing learning burden at each task. A length-decay negative sampling strategy to mitigate span imbalance is also introduced. Extensive experiments on OntoNotes and FewNERD synthestic
datasets demonstrate that FSE achieves state-of-the-art performance in CLNER scenarios, with effectiveness of each component, empirical evidence of faster convergence and expected functionality of both experts.
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All authors Yunan Zhang, Yang Fan, Heng Li, Xiangping Wu, Qingcai Chen
Artificial Intelligence · Cross-listed from cs.CL
Multimodal Language Models Benchmarked Against the NRC Reactor Operator Licensing Examination: Fine-Tuning and Retrieval Strategies
Isak Hwang, Yoon Pyo Lee and Syed Bahauddin Alam
Competence claims for a
language model in a safety-critical domain are credible when measured against a standard the domain already enforces. We evaluate an open-weight 31-billion-parameter
multimodal model (Gemma 4 31B-IT) on the U.S.
Nuclear Regulatory Commission Reactor Operator Generic Fundamentals Examination (GFE), scoring it paper by paper against the 80% criterion applied to every human candidate, with no rounding up. The evaluation set is a census of every GFE administered at the March sitting from 2015 to 2021, giving seven pressurized water reactor (PWR) and seven boiling water reactor (BWR) papers and 697 scored items. Eight configurations cross three model states, the base model, supervised fine-tuning (SFT) on distilled chain-of-thought rationales and
retrieval-augmented fine-tuning (RAFT), with three
retrieval conditions, none and BM25
retrieval over the Department of Energy Fundamentals Handbooks under fixed-size and structure-aware chunking. Out of the box it answers 51.94% correctly and passes no paper. SFT with fixed-size chunking
retrieval passes 8 of 14, reaching 80.23% on PWR items and 79.77% pooled, with a Wilson interval spanning the threshold. The preferred chunking granularity reverses with training state, structure-aware before fine-tuning and fixed-size after, so chunking optimized against a base model cannot be inherited by its fine-tuned descendant. RAFT trails SFT by 2.2 to 2.3 percentage points overall, and the deficit holds in all four reactor-type and chunking strata. The pipeline runs on one workstation with no network access at run time, and the result approaches operator-level command of engineering fundamentals without reliably achieving it.
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Artificial Intelligence · Cross-listed from cond-mat.dis-nn
Multiplicity of Stable Attractors in Disordered Neural Models
Raffaele Marino, Roberto Livi and Antonio Politi
We show how large-deviation statistics allows one to obtain reliable estimates of the multiplicity of stable fixed-points in a model of neural ordinary differential equations previously employed in computational tasks. The result is obtained by developing a suitable perturbative method in the amplitude of the disorder. It turns out that for not-too-large coupling strengths there are no qualitative differences between the symmetric case, when the dynamics is a purely gradient
evolution, and the asymmetric case, when limit cycles and chaos can, in principle, arise. The selection of this specific model is dictated by pedagogical reasons, but we are confident that the approach can be extended to other many-degree-of-freedom dynamical models characterized by different classes of random coupling
matrices.
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Artificial Intelligence · Cross-listed from cs.LG
CEL: Comprehensive Counterfactual Explanations Library and Benchmark
Oleksii Furman, Łukasz Lenkiewicz, Marcel Musiałek et al. (+1 more)
Counterfactual explanations are a prominent approach in explainable artificial intelligence (xAI), providing actionable guidance on what input changes would alter a model's prediction to a desired outcome. While early methods primarily focused on minimal feature changes, recent work incorporates additional properties such as sparsity, actionability and plausibility. Despite this progress, fair and systematic evaluation remains challenging. Existing studies often rely on different data splits, predictive models, and evaluation metrics, which limits objective comparison across methods. To fill this gap, we introduce CEL (Counterfactual Explanations Library), a unified library and
benchmark for counterfactual explanations designed to support consistent implementation and evaluation. CEL includes 18
datasets of varying size and complexity and provides implementations or reimplementations of 14 widely used counterfactual methods. Using this standardized setup, we conduct a comprehensive quantitative comparison across a variety of methods on
datasets that differ in size, number, and types of attributes. The evaluation protocol incorporates multiple complementary metrics capturing validity, coverage, sparsity, proximity, and distributional plausibility, including density- and outlier-based measures to assess the realism of generated counterfactuals. To the best of our knowledge, this is the first comprehensive
benchmark that systematically evaluates recent counterfactual explanation methods within a unified and reproducible framework. While prior libraries and benchmarking efforts exist in the literature, many are outdated, limited in scope, or lack consistent evaluation protocols. The proposed
benchmark aims to improve reproducibility, enable fair comparison, and establish a workbench for the development of future counterfactual explanation methods.
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All authors Oleksii Furman, Łukasz Lenkiewicz, Marcel Musiałek, Maciej Zięba
Artificial Intelligence · Cross-listed from cs.AR
Sparse by Command: Task-Conditional Compute Skipping for Multi-Task Inference Accelerators
Afzal Ahmad, Gaoyu Mao, Shoubo Hu et al. (+4 more)
Multi-task inference models share a single backbone across diverse tasks, yet execute identical computation regardless of which task is active - wasting energy and cycles on task-irrelevant operations. We observe that the task command, typically available before inference begins, provides a free signal that can be exploited to skip unnecessary computation at the hardware level. We present a HW/SW co-designed approach in which a lightweight gating network, trained jointly with the backbone, predicts per-tile binary execution masks conditioned on the task input. Each tile corresponds to a fixed group of output channels (the native scheduling granularity of the accelerator), enabling masked tiles to be skipped with zero overhead. This yields a task-dependent reduction in compute, where each command activates only the subset of the network it requires, without changes to the model architecture or inference pipeline. We co-design the full system stack: a command-conditioned training procedure that learns hardware-aligned tile masks under a sparsity objective; an instruction set architecture whose instructions carry per-tile bitmask fields, allowing the hardware to skip masked tiles without software intervention; and a tiled inference accelerator with configurable parallelism, double-buffered
memory, and INT8 datapath that natively supports sparse tile execution. We prototype on an AMD/Xilinx Alveo U50 FPGA and evaluate on a closed-loop visuomotor driving task in CARLA autonomous driving simulator. Task-conditional sparsity reduces FLOPs by 66-76% while maintaining driving quality. On-device latency decreases by 51-59%, from 9.12 ms to 3.74-4.44 ms (2.1-2.4x speedup), with energy per inference dropping from 263 to 108-128mJ.
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All authors Afzal Ahmad, Gaoyu Mao, Shoubo Hu, Hui-Ling Zhen, Mingxuan Yuan, Xinyu Chen, Wei Zhang
Artificial Intelligence · Cross-listed from cs.CR
Agent Security Needs Redefinition through a Holistic Framework
Vincent Siu, Jingxuan He, Kyle Montgomery et al. (+3 more)
Agent security is widely treated as a question about action content. Defenses ask whether an instruction looks malicious.
Benchmarks ask whether an
agent performs a harmful sounding action. We argue that
agent security is fundamentally a contextual problem, and that the current content based framing systematically misdefines it. A command to “delete user data” might be a routine administrative request or a
prompt injection attacking production systems, and the content alone cannot distinguish the two. Authorization context can. Across every injection task in AgentDojo and WASP, the same action is one an authenticated user would plausibly request in a routine workflow, which makes the conflation a structural property of evaluating security through content. We operationalize contextual security through four properties that must hold jointly and be evaluated continuously across the
agent's trajectory. Source Authorization asks who issued the command. Task
Alignment specifies the
agent's authorized objective. Action
Alignment evaluates whether each action serves that objective. Data Isolation governs information flows across privilege boundaries. Under this reframing, indirect
prompt injection becomes a Source Authorization violation. Snapshot
benchmarks are structurally incapable of evaluating Data Isolation. Existing defenses are reorganized around the property they actually approximate. The contextual reframing changes which defenses are coherent, which evaluations measure something useful, and which attack patterns evaluation can see at all.
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All authors Vincent Siu, Jingxuan He, Kyle Montgomery, Zhun Wang, Chenguang Wang, Dawn Song
Artificial Intelligence · Cross-listed from cs.CV
EVL-MCoT: Enhanced Vision-Language Multi-CoT for Harmful Meme Detection
Hao Yang, Jin Wang and Xuejie Zhang
MEMEs are widely used on the internet and often carry strong elements of sarcasm or irony. Understanding their hidden meanings typically requires a joint interpretation of text and
vision. Existing methods focus on the dual-stream
vision-
language model to extract the visual and text simultaneously, which lacks background information and prior knowledge about the comprehensive explanation of MEME. One feasible option is to adopt chain-of-thought (CoT). However, the simple CoT approach lacks multi-perspective thinking, which may compromise the reliability of the resulting answers. Moreover, it often relies on shallow feature fusion, lacking the fusion of local details and fine-grained visual-
prompt text
alignment. This limitation prevents a deeper understanding of the intricate connections between the visual and the text. Herein, an enhanced
vision-
language multi-CoT (EVL-MCoT) approach is proposed to address these limitations. By promoting multi-CoT, EVL-MCoT enhances consistency and reduces bias in the decision-making process. Additionally, we design a prototype-guided and context-guided decoding framework, which incorporates visual prototypes to guide the fusion process and enables the model to align textual and visual information more precisely. We achieve promising results on the HatefulMemes and MultiOff
datasets. The source code has been publicly released and is available at https://github.com/BGWH123/EVL-MCoT.
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Artificial Intelligence
Zero-Shot Mission-Level Evaluation for Aerial MLLM Agents
Suman Navaratnarajah, Taehyoung Kim, Jona Ruthardt et al. (+5 more)
Multimodal Large
Language Models (MLLMs) are emerging as core
reasoning modules for
embodied agents, yet it remains unclear how well general-purpose models can solve long-horizon
embodied tasks from a single high-level instruction. We introduce MissionBench, a
benchmark for mission-level evaluation of MLLMs in aerial 3D environments. It comprises 120 missions across five simulated 3D environments and four task families.
Agents must autonomously plan, navigate, and report outcomes using only
egocentric observations and its action history, without aerial-specific fine-tuning. Across 22 open- and closed-source MLLMs, the strongest model succeeds on fewer than 35% of missions compared to 84.4% human performance, highlighting the difficulty of multi-step
embodied tasks. Despite large variations between model families, we observe gains from scaling, indicating that larger general-purpose models possess stronger zero-shot
embodied capabilities. Our analysis shows that mission-level competence requires coordinating multiple capabilities beyond spatial perception, including multi-step planning and adaptive
reasoning. This motivates closed-loop evaluation and highlights both the promise and risk of scaling-driven improvements for
embodied AI.
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All authors Suman Navaratnarajah, Taehyoung Kim, Jona Ruthardt, Ishaan Bhimwal, Ryousuke Yamada, Yannik Blei, Wolfram Burgard, Yuki M Asano
Artificial Intelligence · Cross-listed from cs.CV
Hao Yang, Jin Wang and Xuejie Zhang
Multimodal chain-of-thought (CoT)
reasoning integrates visual and textual cues through step-by-step inference. In small models with limited
token budgets, modality-interaction fusion often suppresses tiny cross-modal differences. In particular,
multimodal CoT often struggles when different images pair with identical text or different texts pair with an identical image, making such inputs nearly indistinguishable after fusion. This study proposes Visual Saliency Steering
Distillation (VSSD). VSSD leverages the
attention maps of
multimodal large
language models to generate perturbed images that capture task-sensitive feature directions, and then applies singular value decomposition to extract dominant steering vectors to guide inter-layer
distillation. Experiments on ScienceQA and M$^3$CoT demonstrate that VSSD improves rationale generation and answer inference. The code is available at https://github.com/BGWH123/VSSD.
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Artificial Intelligence · Cross-listed from cs.CR
Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening
Huy Q. Ngo
Microsoft's Active Directory (AD) is a directory service that enables the IT admin to manage security permissions and control access within a Windows domain network. As a core management system in many of organisation, AD has become a primary target for adversaries. While many solutions for hardening attack graphs exist, these efforts fall short in addressing several key practical challenges specific to the AD attack graph. First, existing models often assume the graph is static, whereas a real-world AD environment is highly dynamic. Second, most proposed solutions are limited to the defensive measure of revoking vulnerabilities (edge removal), while more active defence mechanisms are largely unstudied. Third, because not all remediations are implementable, a practical end-to-end model must incorporate system admin feedback into the prioritisation process. This thesis aims to address these limitations by studying and proposing a number of game-theoretic and optimisation-based decision-making models. First, we propose a honeypot/decoy placement model based on the principle of minimising the number of shortest paths and the number of Domain Admin-reachable nodes. Second, building on this model, we introduce a defence strategy that considers the dynamic/temporal nature of the AD graph, where the objective is to find the location to deploy decoys that maximises the worst-case incident response time. Third, we introduce an adaptive prioritisation model that queries each high-risk attack path to the IT administrator for mediation. Finally, we introduce an end-to-end adaptive prioritisation model that minimises the approval effort of the system admin by finding a general adaptive edge-removal policy that generalises the system admin's decisions to edges with similar risk features. We show that the problems underlying all of the contributed models are computationally intractable.
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