Artificial Intelligence
Reward-Density Heuristic for Dynamic Multi-Vehicle Routing: Performance and Computational Efficiency
Manish Kolachalam and Rani Malhotra
The Vehicle Routing Problem (VRP) and its variants represent some of the most practically consequential optimization challenges in modern logistics and urban mobility. In this study, we address a dynamic, online variant combining elements of the VRP and the Orienteering Problem (OP), in which a fleet of vehicles must maximise cumulative reward collected within a fixed time horizon while continuously replanning as new tasks arrive. We propose and evaluate a reward-density heuristic for dynamic multi-vehicle assignment, referred to as the Efficiency heuristic. We evaluate this formulation across two application domains: autonomous drone task allocation and urban taxi dispatch, across multiple fleet sizes and task scales. The proposed method is compared with four classical construction heuristics and three metaheuristic
algorithms (Adaptive Large Neighbourhood Search, Genetic
Algorithm, and Simulated Annealing), all evaluated under identical conditions. Across all tested configurations, the Efficiency heuristic matches the solution quality of the best metaheuristic
algorithms while requiring two to three orders of magnitude less planning time, establishing Pareto dominance over all competing methods on the reward-versus-compute frontier. These findings suggest a practical design principle for real-time allocation and dispatch systems: in dynamic, time-constrained routing environments, carefully designed greedy heuristics can match the output of sophisticated search procedures at a fraction of the computational cost, making them preferable for online deployment.
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Artificial Intelligence
PolyWorkBench: Benchmarking LLM Agents for Cross-Lingual Long-Horizon Workflows
Hongliang Li, Yijin Liu, Zhiwei Zhang et al. (+5 more)
While Large
Language Model (LLM)
agents excel at monolingual long-horizon planning and tool use, enterprise workflows inherently require processing
multilingual resources across extended trajectories. The interaction between multilinguality and long-horizon execution, however, remains underexplored. We introduce PolyWorkBench, a
benchmark designed to evaluate LLM
agents on
multilingual, long-horizon workplace workflows. PolyWorkBench features 67 tasks across five core domains: commerce, knowledge work, legal analysis, localization, and manufacturing. Tasks are authored by the paper's authors from real-world data seeds and independently verified through a second-author audit.
Agents must integrate heterogeneous
multilingual inputs, execute iterative tool-use trajectories, and produce structured domain artifacts. To rigorously assess performance, we adopt Grade, a task-specific structural scoring rubric, as our primary ranking metric, and complement it with Pytest for executable state verification and LLM-as-Judge for semantic quality diagnostics.
Benchmark evaluations reveal that
agent performance varies substantially across
languages and drops sharply on the harder cross-lingual tasks, and our analysis shows that
multilingual execution exposes systematic failure modes across planning, tool interaction, and decision-making in long-horizon
agents.
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All authors Hongliang Li, Yijin Liu, Zhiwei Zhang, Zihe Liu, Xinyue Lou, Jinan Xu, Fandong Meng, Kaiyu Huang
Artificial Intelligence
Information Limits and Attractor Dynamics in Economies of Frontier LLM Agents: A Pre-Registered Test
Cheng Qian
We report a pre-registered, two-part experiment on small economies of frontier
language-model
agents (Claude Opus 4.8), testing two quantitative predictions about coupled multi-
agent systems: an information-theoretic capacity region for wealth growth under
market coupling, and a mean-field residual-scaling law for population misalignment under incentive and control levers. All predictions, acceptance bands, and decision rules were frozen in a public git chain before any run; every reported number re-derives mechanically from cached model outputs; the entire experiment cost $138.76 in metered API spend and is re-runnable at zero cost from the cache. Result 1 (confirmation): in parimutuel-coupled economies, relative growth equals relative claimed information – the gap law G_a - G_b = I_a - I_b holds to a worst-case 46 millinats (pre-registered band: 50) across four perception structures; coalition value is submodular exactly where channels are conditionally independent, and a designed XOR synergy control flips it supermodular by 0.62 >= ln2/2 nats, with
agents reasoning out the joint bit; the joint growth ceiling G_S <= H(X) binds exactly; and the best-informed
agent absorbs essentially the whole wealth pool in 4/5
market seeds. Result 2 (structural negative): the residual-scaling test returned "domain not found." In all 72 population runs, goal dispersion collapsed (V -> 0; maximum 4.85 against a frozen floor of 5.31), the population's response to the two levers was a step function across the dominance boundary rather than a smooth response, and
cells near the boundary were bistable with seed-selected outcomes. No tested LLM population at any capability level realizes the noise-maintained-dispersion regime the smooth mean-field model assumes. We release the full protocol, pre-registration chain, call cache, and analysis code.
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Artificial Intelligence
AgoraSim: A Hybrid Agent-Based Modeling Framework
Chung-Chi Chen
LLM-
agent simulations make natural-
language social scenarios easy to instantiate, but their outputs can be overread as predictions and are often difficult to compare with explicit social dynamics. We present AgoraSim, a hybrid
agent-based modeling framework for scenario-oriented social reaction analysis. AgoraSim resolves textual or
multimodal artifacts into editable ABM configurations, runs ratio-controlled populations that mix LLM,
vision-
language, custom-endpoint, random, and classical
agents, and compares the same scenario against matched classical reference dynamics. All
agents emit a shared structured decision object, enabling common action spaces, interaction protocols, metrics, and audit records. Exposed through a local UI, Python SDK/CLI, and REST API, AgoraSim helps users inspect scenario trajectories, compare modeling assumptions, and identify cases that warrant empirical validation.
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Artificial Intelligence · Cross-listed from cs.CL
PluraMath: Extending Mathematical Reasoning Evaluation Beyond High-Resource Languages
Daryna Dementieva, Nikolay Babakov, Kathy Hämmerl et al. (+14 more)
Mathematical
reasoning has become a central task for evaluating and tuning
reasoning Large
Language Models (LLMs), yet existing
benchmarks remain heavily biased toward high-resource
languages, with English and Chinese dominating both pre-training corpora and evaluation suites. The recently released PolyMath (Wang et al., 2025)
dataset represents a significant step forward, yet its coverage is still limited to 18 only high-resource
languages. To address this gap, we introduce PluraMath, an extension of PolyMath to 18 additional {underrepresented
languages spanning 6
language families – ranging from mid-resource to extreme low-resource settings. We constructed the
dataset through a human-curated pipeline, where native speakers thoroughly validated pre-computed translations. Using PluraMath, we then
benchmark 27
reasoning LLMs across four model scales – small, mid-size, large, and closed-source ensembles – probing the
multilingual mathematical
reasoning capabilities of state-of-the-art models under diverse linguistic conditions. Our fine-grained analysis confirms a persistent gap in mathematical
reasoning performance between high-resource and underrepresented
languages, with stronger results largely associated with better instruction-following ability. We fully open-source our
dataset, data acquisition pipeline, and evaluation framework, with the goal of lowering the barrier to
multilingual benchmark development for underrepresented communities.
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All authors Daryna Dementieva, Nikolay Babakov, Kathy Hämmerl, Ilseyar Alimova, Jindřich Libovický, Shu Okabe, Miras Baisbay, Lukas Edman, Abrorkhon Inomkhujaev, Antonia Karamolegkou, Mateusz Lango, Volkan Özer, Nikola Selic, Subhankar Swain, Tsedeniya Kinfe Temesgen, Galit Bary Weisberg, Alexander Fraser
Artificial Intelligence
Auto-DSM Under the Lens: A Black-Box Evaluation Framework for LLM-Based DSM Generation
Niels Potters and Theo Hofman
This paper presents a black-box evaluation framework to systematically assess the ability of Large
Language Models (LLMs) to generate Design Structure
Matrices (DSMs) from structured technical documentation. Motivated by the closed-source nature of current Auto-DSM pipelines, the framework introduces a reproducible methodology that
benchmarks generated DSMs (GEN-DSMs) against manually validated ground-truth
matrices (GT-DSMs). The evaluation integrates both single-run and multi-run perspectives, combining structural metrics (Completeness, Correctness, Coupling Density), classification metrics (Selective Accuracy, Abstention Coverage), and stability measures (
Entropy, Fleiss' $\kappa$). To synthesize these aspects, a Composite Quality Score (Q) is proposed. Controlled experiments are conducted on two
datasets: a fictive abstract system and a real-world refrigerator decomposition, covering variations in phrasing, parameter-
dataset alignment, and system complexity. Results show that LLMs can produce structurally plausible DSMs and achieve high reproducibility under well-structured inputs, but remain sensitive to ambiguity, inconsistent dependency definitions, and
prompt formulation. The findings highlight systematic sources of
hallucination and abstention failure, demonstrating both the potential and current limitations of LLM-driven DSM automation. The proposed framework provides a transparent
benchmark for
auditing Auto-DSM pipelines and establishes foundations for integrating LLM-based decomposition methods into model-based systems engineering (MBSE) workflows.
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Artificial Intelligence · Cross-listed from cs.CV
Propose and Attend: Training-free MLLM Grounding Confidence via Multi-Token Localized Attention
Daniel Shalam, Emanuel Ben Baruch, Avi Ben Cohen et al. (+1 more)
Multimodal large
language models can emit localized predictions, bounding boxes for objects and temporal windows for
video and audio events, but they hallucinate these regions prolifically. The model's own
token log-probabilities are nearly uninformative: they conflate grounding quality with input ambiguity, and coordinate
tokens become near-deterministic once the model commits. We propose Multi-
Token Localized
Attention (MTLA): a training-free, post-hoc score that measures how strongly a prediction's
tokens attend to the region they claim. Prior
attention-based detectors, which sum
attention over the entire input modality and read a single response
token, are weaker special cases; we show that summing only within the claimed region and aggregating across all prediction
tokens recovers a stronger grounding signal. The same recipe applies almost trivially to other modalities and tasks: object detection in images and temporal localization in
video and audio. Across multiple MLLM families and three modalities, MTLA improves
hallucination AUROC by +7 to +38 over the best prior training-free baseline. Used as a confidence score for re-ranking, it nearly doubles the zero-shot COCO detection AP of an open-source 8B generalist (from 20.4 to 37.0), narrowing the gap to supervised detectors without any task-specific training.
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All authors Daniel Shalam, Emanuel Ben Baruch, Avi Ben Cohen, Tal Remez
Artificial Intelligence · Cross-listed from cs.NI
MCP-Enabled Agentic AI for Autonomous IPoDWDM Network Lifecycle Automation
Chunmin Xia, Jakub Harbaczewski, Nikhil Dsilva et al. (+3 more)
This demo presents an MCP-enabled agentic AI architecture for autonomous control of vendor-agnostic IPoDWDM networks. We demonstrate live end-to-end lifecycle multi-layer automation and closed-loop control using GNPy and telemetry, validated on a real testbed.
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All authors Chunmin Xia, Jakub Harbaczewski, Nikhil Dsilva, Julie Raulin, Dominic Schneider, Achim Autenrieth
Artificial Intelligence · Cross-listed from cs.IR
Faithful or Findable? Evaluating LLM-Generated Metadata for RDF Dataset Search
Riccardo Terrenzi and Serkan Ayvaz
Dataset search depends heavily on metadata, making LLM-generated metadata a consequential form of synthetic content in
retrieval systems. We study six metadata-generation settings for RDF
datasets, ranging from simple rewriting to profile-grounded and agentic graph-based generation, and evaluate them jointly for
retrieval effectiveness and faithfulness. Unconstrained metadata rewriting delivers the strongest
retrieval gains over the original metadata, but it is also the least faithful, showing that search improvements can be driven by unsupported semantic expansion. More grounded settings substantially improve faithfulness, and profile-grounded rewriting provides the most balanced trade-off between
retrieval effectiveness and grounding. These findings position synthetic metadata as a system-level IR problem in which effectiveness, provenance, and trust must be evaluated together.
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Artificial Intelligence · Cross-listed from cs.CL
InfluMatch: Frontier-Quality KOL Search at 4B-Model Cost
Krittanon Kaewtawee, Petmongkon Pornpichitsuwan, Natchaya Temyingyong et al. (+4 more)
Matching influencers (KOLs) to free-form, multi-part Thai marketing criteria is today served either by keyword search over structured profiles, which misses semantic fit, or by prompting frontier LLMs over every candidate, which is accurate but slow and expensive. We present InfluMatch, a low-cost three-stage cascade –
retrieval $\rightarrow$ rerank $\rightarrow$ reason – built entirely from small open-weight models: dense
retrieval returns 50 candidates, a 4B pointwise reranker scores each by the log-probability of a single Yes
token and keeps 10, and a 4B reasoner grades the shortlist per criterion on a rubric with a Thai rationale. The cascade is designed for cost:
reasoning over a filtered top-10 halves
token spend versus
reasoning over all 50 while scoring 14 points higher. End-to-end against human relevance labels on an 11-query set with all 50 candidates labeled, the full cascade reaches 94.1% P@5, versus a
retrieval-only baseline near random; it matches the frontier model Kimi-K2.6 (91.8%) while emitting ${\sim}35\times$ fewer output
tokens and serving a 50-KOL query in ${\sim}20$ s on one A100. Notably, the only fine-tuning that pays off is pairwise: a SimPO-tuned reranker matches the frontier baseline's best-pick accuracy (78.0 EM), whereas fine-tuning the reasoner on pointwise per-criterion labels improves offline scores yet degrades end-to-end ranking – an inversion we trace to the design of the absolute labeling task – leaving the untuned base model as the strongest deployed reasoner. The result is a deployable, explainable KOL search system at a small fraction of frontier serving cost.
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All authors Krittanon Kaewtawee, Petmongkon Pornpichitsuwan, Natchaya Temyingyong, Nutnicha Laplamoon, Wachiravit Modecrua, Krittin Pachtrachai, Touchapon Kraisingkorn
Artificial Intelligence · Cross-listed from cs.CV
Decoupled Single-Mask Annotation Noise Detection via Cross-Sectional Patch Self-Consistency
Yinheng Zhu and Xiaowei Xu
Vascular computed
tomography datasets are commonly annotated only once per scan, yielding the pervasive yet under addressed problem of single mask annotation noise. Existing solutions either require costly multirater fusion or are coupled with network training, preventing explicit
auditing of where and why labels fail. We introduce a decoupled framework for single-mask annotation noise detection that leverages cross-sectional patch self-consistency to produce interpretable and auditable noise evidence. Tubular anatomy exhibits strong cross-sectional recurrence: patches extracted orthogonally along vessel centrelines recur in appearance across locations and subjects. Thus, anatomically similar patches should have consistent masks, and disagreement signals unreliable annotation. Our method samples cross-sectional patches, retrieves intensity-equivalent neighbours via scalable vector search, and computes a patch-level noise score from statistical mask disagreement, yielding explicit image-mask evidence for every flagged region. Aggregating scores produces scan-level quality maps for
dataset quality assessment or quality-weighted training. Experiments on the coronary CT
dataset validate the detected noise for improving training robustness and reveal systematic annotation biases. Specifically, transverse and oblique vessels exhibit 5.1 times higher error rates than axis-aligned structures, with additional correlations to cross-sectional area and intensity. Code is available here.
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Artificial Intelligence · Cross-listed from cs.NI
Agentic AI for IPoDWDM Network Lifecycle Automation: An MCP-Enabled Architecture
Chunmin Xia, Jakub Harbaczewski, Nikhil Dsilva et al. (+3 more)
We present a distributed, vendor-agnostic multi-MCP architecture for SDN-based automation and autonomous control of multi-vendor, multi-layer IPoDWDM networks. The framework enables E2E service lifecycle automation, closed-loop cross-layer control using GNPy model and optical telemetry, and is experimentally validated on a IPoDWDM testbed.
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All authors Chunmin Xia, Jakub Harbaczewski, Nikhil Dsilva, Julie Raulin, Dominic Schneider, Achim Autenrieth
Artificial Intelligence
Integrating knowledge graphs and multilingual scholarly corpora for domain-adaptive LLMs in SSH
Adam Faci, Alessio Miaschi, Anne Combe et al. (+4 more)
The integration of Large
Language Models (LLMs) into scientific research workflows, particularly for bibliographic discovery and literature synthesis, raises significant methodological, epistemic and regulatory challenges for the Social Sciences and Humanities (SSH), especially with regard to disciplinary diversity,
multilingual access to sources and the evaluation of results. This paper presents an on-going use case developed within the European project LLMs4EU and the ALT-EDIC infrastructure, aimed at adapting foundation models to SSH research practices and supporting tasks such as question answering, comparative document analysis and literature review. The evaluation framework follows the LLMs4EU protocol and encompasses both independent quantitative benchmarking (
retrieval, summarisation, traceability and
hallucination detection) and a qualitative assessment involving a panel of Digital Humanities experts. By
embedding model adaptation within research infrastructures and a structured legal and ethical compliance framework, the use case explores how domain-sensitive and regulation-aware generative AI can support SSH scholarship while preserving reliability and epistemic responsibility.
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All authors Adam Faci, Alessio Miaschi, Anne Combe, Pascal Cuxac, Francesca Frontini, Nicolas Larrousse, Stéphane Pouyllau
Artificial Intelligence · Cross-listed from cs.CV
NegROI: Click-Centric Uncertainty-Guided Refinement with Scene-Conditioned Negative Prompts for Robust Interactive 3D Segmentation
Shuheng Zhang and Feng Wu
Interactive 3D
segmentation aims to extract object masks in point clouds with minimal user clicks. Despite recent progress, most existing approaches still struggle with (i) coarse voxel resolution that blurs fine boundaries under limited clicks and (ii) hard false positives caused by confusing background structures. These issues are exacerbated by density and scale shifts across
datasets (e.g., dense RGB-D reconstructions vs. sparse
LiDAR scans), where fixed refinement heuristics and purely click-driven decoding generalize poorly. To address them, we propose NegROI – a novel
transformer-based interactive framework that couples click-centric multi-resolution refinement with scene-conditioned negative
prompts. Given a coarse voxel prediction, it refines only a local Region Of Interest (ROI) around the current click on a finer grid and fuses refined logits back to the coarse mask. To improve robustness and efficiency, we introduce uncertainty-driven selective refinement that prioritizes ambiguous regions. Meanwhile, we model hard background patterns via a set of scene-conditioned negative
prompts obtained by cross-
attention over scene
tokens. We further stabilize these
prompts with a diversity regularizer. Finally, we propose boundary-aware hard negative mining to supervise negative-
prompt attention toward boundary-proximal, high-confidence false positives. Our experiments on common
benchmark datasets (i.e., ScanNet, S3DIS, and KITTI) demonstrate improved click efficiency and reduced false positives, with stronger cross-
dataset robustness than the state-of-the-art baselines.
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Artificial Intelligence · Cross-listed from cs.SI
Signed-Graph Recommendation as Structural Consistency Maximization
Zifan Wang, Siyu Chen and Wenzhuo Song
While signed social recommendation has shown great potential by modeling both trust and distrust relations, its effectiveness is often hindered by structural noise and data sparsity. In this work, we first identify a fundamental inconsistency across the structural, propagation, and semantic layers of existing models, which leads to biased representations learned from sparse or noisy
datasets. Furthermore, we observe that most existing methods treat the observed graph as fixed, failing to bridge the gap between noisy
topologies and reliable social semantics. To address these issues, we propose a unified framework named SSC-Loop that treats signed social recommendation as the maximization of structural consistency. SSC-Loop includes three dedicated modules: ESA-DA for structural consistency, a P/N/O propagation mechanism for propagation consistency, and a contrastive learning objective for semantic consistency. Experiments on Epinions demonstrate that SSC-Loop achieves strong performance on explicit signed social rating prediction, while auxiliary results on Slashdot under a derived link-existence setting further suggest its ability to exploit signed social structures. Source code is available at https://github.com/Refrainwww/SSC-Loop.
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Artificial Intelligence
SearchEyes: Towards Frontier Multimodal Deep Search Intelligence via Search World Simulation
Zhengbo Jiao, Yiming Cheng, Yilei Jiang et al. (+15 more)
Training
multimodal search
agents to perform multi-hop
reasoning remains challenging due to a fundamental structural disconnect: existing pipelines construct training data, search environments, and reward signals independently, causing synthesized structural metadata to be discarded, environments to rely on irreproducible external engines, and RL rewards to remain sparse at the trajectory level. We present SearchEyes, which uses a typed knowledge graph as the backbone of a simulated search world that unifies all three components. We propose Perception-Knowledge Chains (PKC) to sample constrained multi-hop paths over the visual-knowledge intersection of Wikidata5M, retaining hop-level entity metadata that simultaneously defines a self-contained search world and step-level reward anchors. We further propose Hop-Anchored Policy Optimization (HaPO), which reuses these anchors for step-level credit assignment without a separately trained process reward model. Experiments on six
multimodal knowledge-intensive
benchmarks show that SearchEyes achieves state-of-the-art performance among open-source
multimodal search
agents, with SearchEyes-27B improving over the strongest open-source baseline by 6.2 points on average.%
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All authors Zhengbo Jiao, Yiming Cheng, Yilei Jiang, Kaituo Feng, Rui Huang, Tianyi Jiang, Juanxi Tian, Jiapeng li, Qunzhong Wang, Tailai Chen, Qianshan Wei, Chuan Xiao, Shanyu Rong, Yangfu Li, Yanhan Zhou, Yunpu Ma, Yifan Zhang, Xiangyu Yue
Artificial Intelligence · Cross-listed from cs.IR
Ryota Tanaka, Taku Hasegawa and Kyosuke Nishida
Multimodal document
retrieval aims to retrieve relevant pages while preserving both textual and visual content from the original document. However, existing
benchmarks primarily evaluate simple lexical or semantic matching, and most methods encode pages independently. Consequently, they overlook the contextual information in the document required to resolve queries that aggregate information across multiple pages. In this paper, we introduce CMDR and CMDR-Bench, a new
multimodal document
retrieval task and
benchmark that require modeling document context. To address this challenge, we propose CMDR-Embed, a contextual
multimodal embedding framework that explicitly incorporates document context by jointly encoding multiple pages and deriving page-level
embeddings from a shared contextual representation. Furthermore, we introduce CMCL, a contextual
multimodal contrastive learning objective that effectively trains CMDR-Embed by balancing contextual modeling with page-level discriminability. Experiments demonstrate that CMDR-Embed significantly outperforms non-contextual
embeddings, highlighting the importance of context-aware
multimodal embeddings for advancing document
retrieval.
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Artificial Intelligence
PCBWorld: A Benchmark Environment for Engine-Grounded PCB Design Automation
Hyungseok Song, Junseok Park, Won-Seok Choi et al. (+4 more)
PCB routing is the task of connecting the nets of a board with copper traces under strict design rules, yet learning-based methods still lag behind rule-based routers. We introduce PCBWorld, an open-source engine-grounded PCB routing environment built on KiCad, an electronic design automation (EDA) engine. As a human engineer does,
agents in PCBWorld interactively route a board through the engine's native operations, guided by its Design Rule Check (DRC) feedback. The environment supports both RL and tool-using LLM
agents. Alongside the environment, PCBWorld-Bench provides three board
datasets in the native .kicad_pcb format, two controllable synthetic generators and 679 real open-source boards. It scores any completed board with eight engine-checked evaluation metrics, regardless of the routing method. In our experiments,
agents in PCBWorld consistently outperformed grid-action RL policies and open-loop LLM baselines, and an RL policy trained only on synthetic boards transferred zero-shot to real boards, approaching rule-based routers. PCBWorld and PCBWorld-Bench are available at https://github.com/LGAI-Research/PCBWorld.
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All authors Hyungseok Song, Junseok Park, Won-Seok Choi, Seohui Bae, Han-Seul Jeong, Youngjoon Park, Soonyoung Lee
Artificial Intelligence · Cross-listed from cs.CV
PolicyShiftGuard: Benchmarking and Improving Policy-Adaptive Image Guardrails
Mingyang Song, Luxin Xu, Haoyu Sun et al. (+3 more)
Image guardrails are typically trained and evaluated under a fixed safety policy, implicitly treating safety as an intrinsic property of an image. Real deployments are different: the same image may be allowed in one product, restricted in another, and newly disallowed when a policy boundary changes. We study policy-adaptive image guardrailing, where a model must decide whether an image violates the currently supplied policy and generalize to held-out policy definitions. We introduce PolicyShiftBench, a comprehensive
benchmark with 2,000 policy-discriminative instances over 265 images, where each image is paired with 7.55 policy-conditioned
prompts on average to test whether models adapt to the active policy rather than relying on image-level safety priors. We then propose PolicyShiftGuard, a compact policy-conditioned guardrail trained with a two-stage training recipe that combines Randomized Policy SFT (RP-SFT) with Boundary-Pair Policy Adaptation (BP-Adapt). BP-Adapt trains matched
prompts for the same image and risk category using standard label supervision and a pairwise comparison loss that separates blocking policies from passing policies. Experiments show that existing VLMs and specialized guardrails remain brittle under policy shifts, while PolicyShiftGuard substantially improves policy-sensitive performance. The 7B model achieves SOTA performance of 76.9 Avg. F1 and 72.1 Avg. PSS on PolicyShiftBench, transfers well to UnSafeBench and SafeEditBench, and improves the latency-performance trade-off with a concise output format. Ablations confirm that matched pass/block boundary pairs are essential for stable policy adaptation.
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All authors Mingyang Song, Luxin Xu, Haoyu Sun, Minzhou Pan, Yu Cheng, Bo Li
Artificial Intelligence · Cross-listed from cs.LG
K-ABENA: K-Adaptive Backpropagation with Error-based N-exclusion Algorithm : (Compensated Loss-Based Sample Exclusion with Unbiased Gradient Estimation)
Jean-Francois Bonbhel
We present K-ABENA (K-Adaptive Backpropagation with Error-based N-exclusion
Algorithm), a selective gradient computation framework that reduces per-iteration training cost by excluding a fraction of low-loss ("minor") observations from the backward pass. Its canonical form (v3) combines a defensive-mixture sampling design over the minor set with Horvitz-Thompson inverse-probability reweighting, yielding a design-unbiased Horvitz-Thompson gradient
estimator (Lemma 2) and whose self-normalized practical variant carries a bias of order O(1/m) with an explicit constant (Lemma 3). We prove an O(1/sqrt(T)) non-convex convergence guarantee for SGD under the
estimator, with an additive term that quantifies the residual bias (
Theorem 1). We further prove that uncompensated loss-based selection - a family that includes OHEM, SBP, and the two earlier K-ABENA variants - admits no stationary point at any minimizer where its selection bias is bounded away from zero (Proposition 2), and we quantify this failure empirically: at 0.17% class imbalance, uncompensated variants reach test AUC 0.53-0.62 versus 0.9998 for full-batch SGD, while the compensated
estimator attains 0.9991 at identical 28.4% compute savings. On real
datasets (Breast Cancer, Digits, Wine, Diabetes) the compensated
estimator is statistically indistinguishable from full-batch SGD (paired permutation tests, p >= 0.5; Section 7) while saving 28-54% of per-epoch gradient computation. A biased "regularized mode" (the earlier half-domain variant) is retained as an option with a proven exact bias decomposition (Lemma 5) and quantified contraindications: it collapses to 0.386 accuracy under 40% label noise (baseline: 0.832) and to 0.53 AUC under extreme imbalance. Every advantage and every limitation reported in this paper is either proved or measured; all experiments are CPU-scale (NumPy/scikit-learn) and their scope is stated explicitly.
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Artificial Intelligence · Cross-listed from cs.SD
From Textural Counterpoint to Feature Encoding: A Multi-Dimensional Machine Representation Study of Haydn's "The Lark" Integrating Electroacoustic Analysis
Yakun Liu, Zhiyu Jin, Hai Luan et al. (+2 more)
Chamber music, as a highly precise multi-part interactive system, contains a logic of "role assignment and dynamic interaction" that provides an extremely valuable blueprint for exploring human-computer collaborative composition paradigms. Addressing the lack of role perception capabilities in existing deep music generation models during polyphonic interactions, this paper conducts an interdisciplinary analysis of Haydn's String Quartet in D Major, The Lark (Op. 64, No. 5). We propose a novel research path: "Classical Morphology Qualitative Analysis-Electroacoustic Quantitative Measurement-Machine Representation Reconstruction." The study first utilizes auditory analysis to dissect the counterpoint morphology of the leading voice and the underlying groove in the first movement. Subsequently, it introduces spectrum and dynamic feature analysis tools from a Digital Audio Workstation (DAW) to translate subjective auditory perception into objective, measurable physical parameters. Building on this, the paper introduces a fundamentally new approach to low-level computer feature extraction: completely abandoning the traditional mechanical quantization grid, introducing Event-based Timestamps to record the duration of micro-timing, and transforming acoustic features into an independent "Role-Aware Encoding" as an aesthetic heuristic mechanism (a phenomenological anchor). This study not only completes the logical loop spanning classical analysis, electronic music mapping, and AI symbolic generation but also establishes a profound theoretical foundation-from the perspectives of interactive aesthetics and media philosophy-for constructing human-computer collaborative music systems imbued with "social attributes" and "otherness awareness."
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All authors Yakun Liu, Zhiyu Jin, Hai Luan, Dong Liu, Xiaonan Li
Artificial Intelligence
Uncovering Latent Depression Severity for Binary Depression Detection via Advantage-weighting Ranking
Manning Gao, Tingyi Liu, Leheng Zhang et al. (+3 more)
Automatic depression detection using audio-visual data faces significant challenges, particularly in disentangling overlapping feature distributions and establishing robust decision boundaries. To address this, we propose a fine-grained
multimodal framework featuring a temporal encoder and a mutual
transformer to facilitate deep cross-modal fusion. Our core contribution is the Binary Advantage-weighting Ranking Loss, which optimizes the latent space distribution through two complementary mechanisms: Advantage-weighted Separation, which mines hard pairs by computing a pairwise prediction difference
matrix and dynamically weighting them based on their difficulty; and Advantage-weighted Compactness, which minimizes intra-class variance to force features to cluster around their respective class centers. Extensive experiments on D-vlog and LMVD demonstrate that our model reconstructs the latent ordinal structure by prioritizing hard pairs, thereby achieving state-of-the-art performance.
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All authors Manning Gao, Tingyi Liu, Leheng Zhang, Haifeng Hu, Yuncheng Jiang, Sijie Mai
Artificial Intelligence · Cross-listed from cs.CV
Few-Medoids: An Embarrassingly Simple Coreset Selection Method for Few-Shot Knowledge Distillation
Cemil-Andrei Dilmac, Florinel-Alin Croitoru and Radu Tudor Ionescu
Coreset selection aims to identify a small and highly representative subset of a massive
dataset for efficient model training. The problem remains challenging even in the few-shot knowledge
distillation (KD) setup, where a full-scale pre-trained teacher informs the student network. Typical sample selection strategies often struggle to surpass the random selection baseline. In this paper, we showcase few-medoids, an embarrassingly simple coreset selection strategy that chooses the samples closest to the centroid (average image) of each class. We present extensive KD experiments on four
datasets, covering a wide range of image classification problems, and three teacher-student model pairs, comprising both convolutional and
transformer networks. Although the proposed method is embarrassingly simple, our empirical results indicate that few-medoids is able to consistently surpass the random selection baseline, as well as the other coreset selection strategies. We therefore consider that few-medoids can be used as a drop-in replacement for commonly-used baselines (e.g. herding or k-center Greedy), in future research on coreset selection. To reproduce the reported results, we publicly release our code at https://github.com/CemilAndreiDilmac/Few-Shot-KD-Coreset.
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Artificial Intelligence · Cross-listed from cs.CR
i-EXAM: Instructable and Explainable Attack Connectivity Graph Modeler
Rakesh Podder, Wadia Ganim, Sarath Sreedharan et al. (+2 more)
i-EXAM is a planning-powered tool that helps system administrators to create security profiles of complex networks and perform what-if analyses to identify network hardening strategies. It leverages planning compilation that provides soundness and completeness guarantees to identify attack paths, evaluate security metrics, generate diverse hardening strategies, and explain these strategies in natural
language using Large
Language Models.
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All authors Rakesh Podder, Wadia Ganim, Sarath Sreedharan, Indrajit Ray, Indrakshi Ray
Artificial Intelligence · Cross-listed from cs.CV
Harrison.Rad 1.5 Technical Report: A radiology foundation model that can draft reports from images, priors and clinical context
Suneeta Mall, Vladimir Nekrasov, Ashnil Kumar et al. (+5 more)
Imaging demand is growing faster than the radiology workforce can expand, and reporting backlogs cannot be resolved through training and recruitment alone. The most direct opportunity is reducing the time and effort radiologists spend producing reports, a task that requires interpreting images, integrating clinical history and prior studies, and drafting structured findings. We present Harrison.Rad 1.5 (HR1.5), a radiology-specific
multimodal large
language model that accepts interleaved text and visual inputs and generates structured and unstructured text across plain-film radiology, spanning computed radiography, chest, musculoskeletal, abdominal, spine, and pelvic x-rays, and mammography. HR1.5 is trained through a three-stage pipeline: domain adaptation of a base
language model on radiology reports, contrastive
vision-encoder training with curriculum-based hard negatives on ~6 million image-report instances, and visual-question-answering fine-tuning on multi-turn conversations. We evaluate it with a Findings-Diagnosis scoring framework that extends RadGraph-XL entity extraction with ontology-based synonym matching and polarity-contradiction detection, benchmarked on RadBench, a simulated FRCR 2B Short Case examination scored against Angoff-method thresholds, ReXGradient, and internal multi-modality
datasets. HR1.5 is the only system evaluated to meet the simulated FRCR passing standard and achieves the highest accuracy on closed-format clinical questions, across anatomical regions, on internal multi-body-part and mammography reporting, and on the primary clinically-aligned score for public chest reporting. We further examine explainability and model behaviour, including question-sensitive Grad-CAM heatmaps,
attention analysis, and confidence estimation, to support responsible future evaluation toward clinical use, and a framework for clinically grounded assessment of report quality.
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All authors Suneeta Mall, Vladimir Nekrasov, Ashnil Kumar, Sajith Karunasena, Aiden Nibali, Alix Bird, Mateo Diaz Shine, Jarrel Seah
Artificial Intelligence · Cross-listed from cs.PF
Think Before You Grid-Search: Floor-First Triage for LLM Serving
Yihua Liu
LLM serving optimization typically
benchmarks many configurations and reaches for heavy profilers when latency targets are missed. We argue for the reverse discipline: estimation is the analytical layer of profiling – without it, optimization degenerates to grid search. Floor First is a residual-driven triage workflow. Each decode step is modeled as a five-dimensional resource vector (HBM bytes, FLOPs, network bytes, network messages, KV capacity); summing within a resource and maximizing across resources gives an optimistic floor, the plain sum a pessimistic one. Where a measurement lands inside this [max, sum] interval reads out overlap quality before any profiler is opened, and profilers escalate only on residuals above a stated threshold. Deployment alternatives are compared by wall ordering – which resource wall binds first as load grows – rather than by point
benchmarks. The account is compositional: new
attention or state-space variants enter by declaring one module, and the workflow ships as a zero-dependency calculator plus an
agent skill that enforces the discipline in agentic optimization loops. As a case study we analyze a DeepSeek-V3.2-style 671B MoE/MLA model on 16 NVIDIA H20 GPUs, whose ridge point of ~74 FLOP/byte (vs ~590 for H100) makes it an extreme decode-oriented part. The floors show TP16 decoding is KV-capacity-limited to ~70 concurrent 8K requests; sparse
attention removes the KV-bandwidth term but not the capacity wall; an EP16+DP-
attention layout accepts slightly worse same-batch weight traffic for an order-of-magnitude higher capacity wall (~644) – while single-stream latency favors TP by 2.4x. The layout judgment is thus a computable function of the operating point, explaining why production deployments on identical hardware have shipped opposite
attention layouts.
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Artificial Intelligence · Cross-listed from cs.LG
Differentially Private Natural Gradient Descent
Pan Li, Kai Chen, Shuai Chang et al. (+3 more)
Under a fixed
privacy budget, the utility of differentially private (DP) training is ultimately determined by its optimization efficiency. Standard first-order DP optimizers such as DP-SGD rely solely on local gradients and ignore the underlying loss
curvature. This
geometric blindness causes severe zigzagging in ill-conditioned landscapes, squandering precious
privacy budgets on inefficient iterations. Practitioners are thus trapped in a bind: either stop training prematurely or inject massive per-step noise, both of which critically compromise final model utility. Natural Gradient Descent (NGD) resolves this by preconditioning gradients with
curvature, aligning updates with the loss geometry and extracting more efficient signal from every noisy step, offering a principled pathway to break the
privacy-utility bottleneck. Despite its theoretical appeal, directly integrating NGD with DP introduces fundamental challenges:
curvature estimation itself consumes prohibitive
privacy budgets, isotropic DP operations conflict with the anisotropic scaling of NGD, and the inverse
curvature catastrophically amplify parameter updates in flat directions, causing training instability. We propose DP-NGD, a practical framework that systematically addresses these obstacles by decoupling
curvature estimation from private data, reconciling isotropic DP constraints with anisotropic second-order optimization via a whitened-space mechanism, and dynamically clamping the
curvature to stabilize training. Extensive experiments on standard
benchmarks demonstrate that DP-NGD achieves state-of-the-art accuracy, breaking through the utility ceilings of first-order baselines while delivering up to a $10\times$ convergence speedup under the same
privacy budget.
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All authors Pan Li, Kai Chen, Shuai Chang, Shengzhi Zhang, Peizhuo Lv, Jinwen He
Artificial Intelligence · Cross-listed from cs.LG
Unsupervised Anomaly Detection of Information Operations Users via Behavioral and Language Patterns
Sishun Liu, Sajal Halder, Ke Deng et al. (+2 more)
Information Operations on social media networks have been identified as a significant threat to democracy and modern society, but they are challenging and expensive to detect by humans. Existing supervised IO detection methods fail to capture the dynamic nature of evolving IO user behavior, while existing unsupervised approaches rely on oversimplified assumptions of coordination among IO users that may not exist in practice. To overcome the limitations of existing methods, we formulate IO user detection as an anomaly detection problem and propose a novel unsupervised IO user detection approach called Temporal-bEhavior-
laNguage Signals for information Operation Recognition (TENSOR), which leverages
multimodal data, including temporal online user behavior, such as message posting activities, and the textual content of the messages. The motivation is that IO users are typically a very small fraction of all online users and have unique temporal behavioral and
language patterns. Specifically, we train a Temporal Point Process (TPP) to capture abnormal temporal behavioral patterns of IO users because they are known to behave in a coordinated manner for IO campaigns. We further introduce a novel evidence function that converts LLM responses, which are generated from user post timelines, into quantitative scores to adjust the TPP outputs for better IO user detection. Experimental results show that TENSOR outperforms the baselines on five real-world IO
datasets. Code is available at https://github.com/xiuzhenzhang/TENSOR.
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All authors Sishun Liu, Sajal Halder, Ke Deng, Yan Wang, Xiuzhen Zhang
Artificial Intelligence · Cross-listed from cs.LG
AbICL: In-Context Learning for Antigen-Specific Antibody Affinity Ranking
Zhiyuan Chen, Jing Hu, Junzhe Wang et al. (+4 more)
Accurate ranking of antibody candidates according to their binding affinity is essential for therapeutic antibody discovery. However, existing methods treat affinity comparisons independently and ignore the contextual information encoded in other labeled comparisons, limiting their ability to capture antigen-specific binding landscapes. For many target antigens, a small number of experimentally characterized affinity comparisons are often available. An important question is whether the model can exploit these existing comparisons to infer antigen-specific ranking patterns that facilitate subsequent affinity ranking. This form of learning from labeled demonstrations closely resembles the paradigm of In-Context Learning, motivating us to revisit antibody affinity ranking from an ICL perspective. To this end, we propose AbICL, an ICL framework for antigen-specific antibody affinity ranking. AbICL combines a pretrained structural encoder with a context ranking head and is trained with an episodic meta-training strategy that enables the model to leverage support demonstrations for test-time adaptation without gradient updates. Experiments on the AbRank
benchmark demonstrate that AbICL consistently outperforms existing ranking baselines across almost all data splits and evaluation
benchmarks. Further analysis shows that the value of contextual demonstrations depends on how well they match the target inference task, and becomes increasingly pronounced under distribution shift and fine-grained affinity discrimination. These findings highlight the potential of ICL as an effective paradigm for antigen-specific antibody affinity ranking, particularly in challenging settings where a single global ranking function is insufficient.
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All authors Zhiyuan Chen, Jing Hu, Junzhe Wang, Yueyang Huang, Xinyi Yang, Zhaoyang Wang, Feng Zhu
Artificial Intelligence
StateFuse: Deterministic Conflict-Preserving Memory for Multi-Agent Systems
Sergey Volkov, Yang Li and Ye Luo
Agent systems accumulate conflicting observations across branches, retries, and replicas, yet many practical
memory layers still collapse disagreement behind overwrite rules that are difficult to inspect or correct. We present StateFuse, a conflict-aware replicated
memory contract built on standard OpSet/CRDT merge. StateFuse does not introduce a new join
algebra; it defines an
agent-facing semantics layer with immutable history, explicit conflict objects, exact and semantic correction handles (claim_id / claim_ref), deterministic predicate contracts, and projection-time resolution that cannot rewrite replicated state. We evaluate StateFuse against flat multi-value, raw-log, provenance-style, and collapsed baselines under matched resolver and verification policies. On a 282-question official conflict-bearing MemoryAgentBench slice, the compared methods tie on answer accuracy, but conflict-preserving surfaces keep contradictions visible while collapsed surfaces do not. In a controlled
agent loop with uniform verification, preserving ambiguity enables safer abstention and correction than early collapse. A correction-handle ablation further shows that semantic handles matter when exact prior identifiers are unavailable. The resulting claim is narrow: StateFuse is best supported as a safer public
memory contract for contradiction surfacing, abstention, and auditable correction, not as a universal accuracy gain.
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