Artificial Intelligence · Cross-listed from cs.CV
Real-Time Musculoskeletal Surrogates for Pediatric Cerebral Palsy: a Credibility Pilot
Mohammad Arif Ul Alam
Real-time musculoskeletal (MSK) surrogates could support personalized rehabilitation for children with cerebral palsy (CP), but their credibility depends on subject-wise evaluation, low inference latency, and calibrated uncertainty. We develop a subject-conditioned causal neural surrogate using OpenSim-derived static parameters, temporal joint kinematics, true muscle capacities, and training-only perturbations. On a real pediatric CP gait
dataset comprising nine children, we use leave-one-subject-out validation on six development subjects and evaluate a frozen configuration once on three locked test subjects. The surrogate accurately reproduces musculotendon lengths (R-square = 0.92 in development validation and approximately 0.95 on locked subjects; nRMSE < 8%) while requiring only sub-millisecond to few-millisecond neural inference, well below a 100 ms interactive-rehabilitation target. In contrast, direct muscle-force estimation remains unstable at this small, heterogeneous scale: pooled metrics can overstate within-subject, per-muscle accuracy. A Monte Carlo credibility pilot further shows that propagating only +/-5% anthropometry and muscle-capacity variation produces severely overconfident nominal 90% intervals (approximately 4% force coverage and below 1% MT-length coverage). These results establish a leakage-free evaluation and credibility framework for pediatric MSK surrogates, while identifying force modeling and epistemic uncertainty as the central next challenges for clinically credible digital twins.
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Artificial Intelligence
EvoUndo: Recoverability-Constrained Self-Evolution for LLM Agent Harnesses
Tanmay Sah, Dolly Sah, Harshul Jain et al. (+1 more)
LLM
agents increasingly modify their own
prompts, tools, middleware, resources, and execution harnesses at runtime. Such self-
evolution can improve capability, but a successful mutation may leave persistent effects that cannot be safely reversed in states different from the one in which it was created. We introduce EvoUndo, a framework for representing, synthesizing, diagnosing, and independently verifying recoverability of model-generated self-modifications across counterfactual states. Across 600 unseen one-shot self-
evolution tasks, we identify 197 capability-improving mutations that fail recoverability verification. Under the original recovery representation, conventional repair strategies recover 0/197 of these natural failures. Deterministic oracle analysis recovers 48/197 under the original recovery
language L0, while the extended recovery calculus increases empirical oracle recovery to 191/197. A protocol-locked 2x2 grounding-by-expressivity intervention then separates two bottlenecks: exact state-address grounding increases successful recovery from 0/48 to 38/48 (79.2%) when the original
language is sufficient, while extending the recovery
language enables recovery on 142/143 (99.3%) failures in the oracle-defined S1 stratum. On the primary gpt-oss-120b backbone, adding exact-address diagnostics to the richer
language reduces recovery to 133/143 (93.0%); a Qwen3.8-27B replication preserves the grounding and expressivity effects but not this negative interaction, indicating that the latter is model-dependent. These results indicate that reliable
agent self-
evolution requires co-designing verification, state grounding, witness semantics, and recovery-
language expressivity rather than relying on iterative prompting alone.
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All authors Tanmay Sah, Dolly Sah, Harshul Jain, Tanya Sah
Artificial Intelligence · Cross-listed from cs.CR
Optimal Adversarial Testing: Extracting Honest Test Results from Dishonest Test Takers
Owen Cox, April Xu and Weiyu Xu
In applications, it is often required to test objects or people to determine their qualities in terms of certain metrics. However, besides being naturally noisy, the test results can be corrupted by
adversarial behaviors of objects or people being tested (test takers). For example, dishonest test takers can cheat in the exams to distort the test results. With the development of AI technologies, such distortions driven by cheating using AI technologies are becoming more commonplace and severe. In this paper, we propose optimal testing strategies which can still recover needed test results even if there are cheaters polluting the results. The proposed testing strategies will optimally re-test selected group of test takers using different testing security measures. We determine the optimal testing strategies using a dynamic programming method.
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Artificial Intelligence
GRACE:Gradient-guided Coreset Selection for LLM Unlearning
Praveen Bushipaka, Andrea D'Angelo, Lucia Passaro et al. (+1 more)
Machine Unlearning methods for Large
Language Models typically assume pre-specified forget and retain sets. In realistic settings, however, requests may provide only a few examples of undesired behavior, requiring forget and retain sets to be inferred from heterogeneous corpora. We study this data-selection problem and propose GRACE , a gradient-guided coreset selection method that constructs both forget and retain sets for LLM unlearning. GRACE first computes a forget direction from seed examples that elicit the undesired behavior, then selects a compact forget coreset whose gradients approximate this direction using non-negative orthogonal matching pursuit. To preserve model utility, it selects retain examples after projecting out the forget direction and applying clustered orthogonal matching pursuit in the remaining gradient space. Across two target domains, two model families, and four unlearning
algorithms, GRACE improves model utility while maintaining comparable forget quality, with particularly consistent gains over prior gradient-based selection methods.
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All authors Praveen Bushipaka, Andrea D'Angelo, Lucia Passaro, Tommaso Cucinotta
Artificial Intelligence
Propagating construction-time knowledge quality into medical question answering: A framework grounded in clinical guidelines
Jie Hu, Junjie Wang, Shan Lu et al. (+3 more)
Large
language models have facilitated knowledge graph (KG) construction from clinical guidelines, but extracted triples vary in structural validity and evidential support. Meanwhile, graph-augmented question answering (QA) systems typically optimize query relevance during
retrieval, with limited reuse of quality information produced during KG construction. This creates a disconnect between construction-time quality control and inference-time evidence use. We investigate whether construction-time triple quality can serve as a persistent signal for downstream evidence selection and presentation. We propose a quality-aware framework that models structural conformance (SchemaConf) and evidential support (EvidScore) as complementary dimensions and fuses them into a per-triple quality signal, Q(t). Rather than using quality solely for filtering, the framework retains Q(t) and derived quality tiers as graph attributes and propagates them into quality-weighted subgraph
retrieval and tier-conditioned evidence prompting, while preserving passage-level provenance. Experiments on Chinese diabetes clinical guidelines show that the utility of the quality signal is distribution dependent. Under cross-version and cross-model shift, the fused Q(t) provides stronger triple-quality discrimination than either component alone (AUC 0.748 vs. 0.703 for EvidScore and 0.645 for SchemaConf). In guideline-grounded QA, propagating construction-time quality reduces required-knowledge omission from 16.3% to 5.3% and conflicting outputs from 16.3% to 2.7%, with an evidence-grounded precision of 81.6% and near-zero invalid citations. Blinded clinician ratings favor the full framework over no
retrieval (4.68 vs. 4.21 on a five-point scale) and approach the oracle condition (4.80), while cross-generator experiments show consistent trends.
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All authors Jie Hu, Junjie Wang, Shan Lu, Yifang Hu, Gong Cheng, Yun Liu
Artificial Intelligence
AGENT-O: A Semantic Agent Card Framework for Interoperable and Governed Healthcare AI Agents
Pengze Li and Cui Tao
AGENT-O is a modular ontology framework that defines a semantic
Agent Card for representing health-oriented AI
agent systems and supports assessment of reporting completeness in scientific publications.
AGENT-O was developed as an OWL 2/RDF ontology covering runtime, models, workflow, tools, clinical use, evaluation, provenance, governance, and reporting assessment. Evaluation included ontology inventory, OWL-RL
reasoning, three SHACL suites, 12 SPARQL competency queries, three cases, and model-assisted reporting-completeness assessment of 279 papers across five dimensions. The ontology contained 1,962 RDF triples and 1,922 Protege axioms, with 252 active classes, 198 active object properties, and 51 datatype properties. All SHACL suites conformed on example graphs, all competency queries returned prespecified evidence, and all 279 papers were scored. Incomplete reporting was highest for runtime/architecture (84.6%), governance/safety (82.8%), and provenance/reproducibility (78.1%), compared with evaluation (25.8%) and
benchmark-process
alignment (29.8%).
AGENT-O supported semantic
Agent Card representation and reporting assessment while revealing an evaluation-specification gap: evaluation and
benchmark procedures were reported more consistently than runtime architecture, governance, and reproducibility.
AGENT-O provides a reusable ontology, semantic
Agent Card profile, and reporting-completeness workflow for structured reporting and gap identification, but does not assess
agent quality or deployment readiness.
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Artificial Intelligence · Cross-listed from cs.CV
Cross-Spectral Dense Correspondence for Multimodal Spectral Medical Imaging
Eric L. Wisotzky, Jost Triller, Simon W. Härtl et al. (+3 more)
Precise dense correspondence is a fundamental prerequisite for
multimodal spectral imaging systems that fuse disparate wavelength ranges for subsequent analysis in medical and scientific imaging. Corresponding image points are often observed with non-overlapping spectral sensitivities, leading to wavelength-dependent contrast changes, intensity inversions, and appearance shifts for which dense ground truth is difficult to obtain and conventional RGB-based training data provides only limited supervision. We address this data gap by introducing a sensor-agnostic cross-spectral modulation protocol on established correspondence
benchmarks with intensity input projection, and by proposing a synthetic cross-spectral correspondence
benchmark simulating physically plausible radiometric differences. Evaluation on several modern dense correspondence backbones trained with our unified cross-spectral protocol showed substantial improvements under severe spectral mismatch while maintaining performance on standard RGB
benchmarks. Ablation experiments show that view-dependent channel selection and nonlinear radiometric transformations provide complementary robustness, indicating that the primary limitation of existing models is not their structural matching capacity but the mismatch between training distribution and spectral characteristics of the target image pair. Qualitative evaluations on heterogeneous medical spectral acquisition systems demonstrate the practical relevance of the proposed training data augmentation protocol as an enabler for spatially coherent spectral fusion in HSI workflows.
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All authors Eric L. Wisotzky, Jost Triller, Simon W. Härtl, Oliver T. Bruns, Peter Eisert, Anna Hilsmann
Artificial Intelligence
Real-Valued Hyperdimensional Sequence Representations with Hadamard Product Binding and Shift Equivariance
Kenny Schlegel, Dmitri A. Rachkovskij, Denis Kleyko et al. (+3 more)
Encoding temporal order is a fundamental requirement for sequence representations in Hyperdimensional Computing. Fractional Power Encoding provides similarity-preserving position vectors whose inner products approximate shift-invariant kernels, and it supports shift-equivariant transformations of encoded sequence representations. However, standard formulations of Fractional Power Encoding are primarily designed for binding operations such as circular convolution or complex-valued multiplication, which limits their compatibility with Hadamard product binding of real-valued vectors. This paper develops real-valued position encodings motivated by Random
Fourier Features, aiming to retain the desirable properties of Fractional Power Encoding while supporting Hadamard-based operations. We propose three real-valued position-encoding variants: a real-valued baseline based on the inverse
Fourier transform, and Sinusoid and Cosine-only representations derived from Random
Fourier Features. Among them, the Sinusoid variant provides an explicit algebraic shift operator, allowing temporal shifts to be applied directly to the vector-encoded sequence representation without re-encoding the shifted sequence. Experiments on time-series classification
datasets show that the proposed real-valued representations achieve performance comparable to standard Fractional Power Encoding while enabling computationally efficient Hadamard product binding. The Sinusoid variant offers the most favorable trade-off, combining efficient real-valued implementation with exact shift-equivariant transformations.
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All authors Kenny Schlegel, Dmitri A. Rachkovskij, Denis Kleyko, Amy Loutfi, Stefan Streif, Evgeny Osipov
Artificial Intelligence · Cross-listed from cs.CL
BanglaMed-QA: A Question Answering System for Healthcare Support in Bangla
Rowzatul Zannat, Abdullah Al Shafi, K. M. Azharul Hasan et al. (+1 more)
Medical question answering (QA) systems have become crucial tools for providing reliable health information. But they remain very unexplored for low-resource
languages like Bangla due to limited
datasets and systems tailored to these
languages. To address this, we introduce BanglaMed-QA, a robust QA system specifically designed for the Bangla medical domain. The process begins with building a structured medical knowledge base that includes 4,493 QA pairs in 9 categories under 506 diseases. To improve semantic comprehension, domain-specific root word dictionaries and synonym sets are proposed, in addition to part-of-
speech tagging for anaphora resolution. We adopt supervised machine learning models in which SVM is found to be the best model to categorize questions. Multiple similarity metrics, including cosine, Jaccard, BM25, and Levenshtein, are applied with soft and hard voting methods for query matching. The performance of the QA system has been evaluated in two aspects, with a 95% F1 score in an automated evaluation and an average human satisfaction rating of 0.9 out of 1.0. This validates the real-world application of BanglaMed-QA in closing the healthcare information gap for Bangla speakers.
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All authors Rowzatul Zannat, Abdullah Al Shafi, K. M. Azharul Hasan, Atia Shahnaz Ipa
Artificial Intelligence · Cross-listed from cs.CR
Layered LLM Defenses as an Ensemble: Access Tiers, Inference Cost, and the Measured Failure Correlation Between Defense Layers
Abrar Alotaibi, Muhammad Shahid Jabbar, Sadam Al-Azani et al. (+1 more)
Practitioners defend large
language models (LLMs) by stacking defenses, assuming the layers compound. A stack is an ensemble, and ensembles compound only under a condition the LLM security literature recommends but never measures: the members must fail on different inputs. Two instruments make that measurable. The Adversary Access-Tier Model (AATM) grades an adversary by the access it holds, from system-only (A0) to influence over training data (A4). A cost model sorts defenses into five classes of inference-time overhead; because two classes require training weights or reading activations, they tier the defender as AATM tiers the adversary. From these we derive how a stack behaves, and the quantities a defender cares about diverge: coverage saturates within a tier, cost rises by class, false refusals accumulate as a union, and residual attack success falls multiplicatively only under independence. We measure that independence. Running one adaptive adversary against a seven-layer stack, failure correlation is positive in all fifteen measurable pairs ($\phi$ from $0.30$ to $0.75$), and the joint residual exceeds the multiplicative prediction by up to $0.172$. Stratifying on behavior difficulty dissolves most of the association, so the dependence is predominantly common-cause, but it survives permutation inference, majority-vote grader labels, and externally calibrated thresholds. The same stack refuses four in five benign
prompts while remaining statistically indistinguishable from its strongest single layer. The dependence is architectural rather than sampling-based: members correlate through the model they all wrap, so no wider member pool weakens it. Diversity therefore selects stack members but does not predict what an assembled stack delivers, which has to be measured end to end.
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All authors Abrar Alotaibi, Muhammad Shahid Jabbar, Sadam Al-Azani, Moataz Ahmed
Artificial Intelligence
MAIL: Memory-driven, Adaptive, Incremental, and Literature-grounded Framework for Hypothesis Generation in Chemistry
Mahdi Babaei, Xueshen Li, Yutao Kuang et al. (+2 more)
The ever-expanding volume of the chemical literature offers unprecedented opportunities to generate novel and impactful hypotheses. However, the bottleneck lies in efficiently navigating this vast knowledge base to formulate high-quality, experimentally meaningful insights. While Large
Language Models (LLMs) show promise for this task, existing methods often rely on static inspiration corpora, predefined heuristics, or laborious human-in-the-loop pipelines and decision-support frameworks that limit scalability and novelty. In this work, we propose an automated approach, a
Memory-augmented, Adaptive, Incremental, and Literature-grounded (MAIL) framework for hypothesis generation in chemistry. Our MAIL method formulates hypothesis generation as a temporally grounded,
memory-driven
reasoning process, where hypotheses emerge from an evolving conceptual path that continuously accumulates and reinterprets prior knowledge. We evaluated the MAIL framework on a public TOMATO-Chem
dataset and a newly curated and disseminated high-novelty nature/science challenge (HN-NS)
dataset. Across both
datasets, MAIL generates structurally coherent and mechanistically plausible hypotheses, achieves the highest MIOS and MPOS by more effectively recovering the central ideas and methodological elements of the historical target hypotheses, and obtains the highest overall expert-evaluation scores for scientific quality. These results demonstrate the potential of LLMs to autonomously explore chemical domains and generate hypotheses that are both innovative and chemically plausible.
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All authors Mahdi Babaei, Xueshen Li, Yutao Kuang, Jolene P. Reid, Yu Gan
Artificial Intelligence · Cross-listed from cs.LG
Deriving Scaling Laws for OpenEuroLLM Models: Learning Rate, Batch Size and Loss
Niccolò Ajroldi, Diana Alexandra Onutu, Haider Al-Tahan et al. (+4 more)
We study the scaling behavior of learning rate and batch size in pretraining dense large
language models on English-prevalent corpora. Beyond scaling jointly optimal learning rates and batch sizes, we investigate their marginal
evolution with model capacity and data scale and develop a model that captures these relationships. As we employ a Warmup-Stable-Decay learning rate schedule, we further investigate the gains from learning rate annealing over a broad range of hyperparameters settings, models and data budgets, and whether the optimal learning rate and batch size transfer between the stable and decay phases. Finally, we characterize the dependence of loss on model capacity and
dataset size, evaluating recently proposed scaling forms that explicitly model their interaction. We find these approaches particularly effective at capturing both undertraining and overtraining regimes across our experiments. This study establishes a first baseline and scaling procedure for the development of future OpenEuroLLM models. We open-source the complete collection of pretraining runs used in this study.
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All authors Niccolò Ajroldi, Diana Alexandra Onutu, Haider Al-Tahan, Jörg Franke, Sampo Pyysalo, Jenia Jitsev, Aaron Klein
Artificial Intelligence · Cross-listed from cs.LG
VISTA: Verifier-Informed Student-to-Teacher Adaptation for On-Policy Self-Distillation
Zewen Ding, Zezhong Wu, Zhou Tao et al. (+5 more)
On-policy self-
distillation (OPSD) improves
reasoning by training a problem-only student on its own rollouts using dense
token-level supervision from a privileged teacher that also sees a reference solution. However, standard OPSD treats the teacher distribution as a fixed target along the student's rollout and updates only the student %, although – even though privileged conditioning does not guarantee that the teacher always provides the most appropriate target for problem-only
reasoning. This one-way supervision can therefore misdirect the student when the teacher distribution is misaligned with valid student
reasoning. We therefore introduce Verifier-Informed Student-to-Teacher Adaptation (VISTA), which preserves the standard OPSD student update while using outcome-verified rollouts to adapt the teacher toward the student distribution. Within each verified rollout, VISTA further restricts this adaptation to the top-$k$ positions with the largest teacher–student KL divergence. Notably, VISTA reuses the rollout and loss function from standard OPSD, introducing no additional sampling or separate reward objective. Across AIME24, AIME25, and HMMT25 with Qwen3 models at 1.7B, 4B, and 8B, VISTA achieves the highest Avg@12 at every scale, improving over OPSD by $0.6$, $0.7$, and $2.1$ points, respectively. These results demonstrate the value of student supervision from outcome-verified rollouts and highlight student-to-teacher adaptation as a promising direction for OPSD.
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All authors Zewen Ding, Zezhong Wu, Zhou Tao, Shida Wang, Shizhuo Hou, YongXiang Hua, Haoyu Cao, Linli Xu
Artificial Intelligence · Cross-listed from cs.RO
PanelShield: Verifiable Closed-Loop Safe Planning for Robotic Industrial Panel Operation
Guipeng Xin, Jiahe Xu, Chenhui Wan et al. (+3 more)
Industrial panel operation is knowledge-intensive and safety-critical. Beyond control recognition and action generation, execution must satisfy constraints in operation manuals and safety regulations. While foundation-model-based planners show strong semantic capability, they typically lack computable, localizable, and reproducible mechanisms for violation detection and repair. To address this, we propose PanelShield, a verifiable closed-loop safety planning framework for manual-guided industrial panel operation. The framework generates parameterized action primitive sequences from task-relevant manual evidence and applies dual formal verification with LTL and a Safety FSM to enforce cross-step temporal correctness and local transition legality. When violations occur, it outputs a structured counterexample with the earliest violating step and cause, enabling targeted repair and re-verification. We build a multi-level long-horizon planning
benchmark covering three representative industrial device panels, and evaluate the framework in simulation and real-world robotic experiments. Results show that PanelShield improves complex safety-constrained task performance over foundation-model-only planning baselines while reducing the violation rate to 2.7%, with 4.1 s total latency. Real-world experiments demonstrate end-toend feasibility. Overall, PanelShield offers a verifiable approach to robotic panel operation that balances flexibility, safety, and auditability.
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All authors Guipeng Xin, Jiahe Xu, Chenhui Wan, Jie Liu, Youmin Hu, Zhongxu Hu
Artificial Intelligence · Cross-listed from cs.RO
MaCoPlanner: LLM-Assisted Manual-Compiled Task Planning with Proactive Safety Verification for Robotic Industrial Panel Operation
Guipeng Xin, Jiahe Xua, Mohammad Deghat et al. (+4 more)
Robotic industrial panel operation requires not only accurate control localization but also compliance with operating procedures, safety rules, and device-state constraints distributed across heterogeneous manuals. This study presents MaCoPlanner, a task-planning framework built on knowledge compiled from equipment manuals that converts equipment manuals into a typed intermediate representation, retrieves task- and state-relevant evidence, and uses it to support plan generation. Before actuation, candidate plans are symbolically rolled out and checked against procedural and state-transition constraints; detected violations are localized and returned for targeted repair, while unresolved plans are rejected. A separate execution interface grounds verified symbolic actions to physical controls and updates the device state. Under an independent evaluation oracle, MaCoPlanner achieves a final violation rate of 2.7%, and 26.3% of the runs in the repair analysis are rejected after exhausting the refinement budget. Compared with Raw-Manual, task success increases from 62.8% to 84.4% on Level-2 tasks and from 25.9% to 43.2% on Level-3 tasks. Experiments on a controller-panel simulator without an attached industrial load further demonstrate integrated execution feasibility under representative interaction conditions, without claiming industrial deployment readiness.
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All authors Guipeng Xin, Jiahe Xua, Mohammad Deghat, Chenhui Wan, Jie Liu, Youmin Hu, Zhongxu Hu
Artificial Intelligence
Memristive-Friendly Hadamard Reservoir Computing: Structured, Multiplier-Free Recurrences at Scale
Andrea Ceni, Gianluca Milano, Carlo Ricciardi et al. (+1 more)
Reservoir Computing (RC) designs Recurrent Neural Networks around a fixed, i.e., untrained, recurrent layer, and is a natural candidate for neuromorphic hardware. Memristive-friendly reservoirs derive the
neuron dynamics from memristive-device kinetics, but still rely on dense recurrent
matrices, which are expensive to realize physically. In this paper, we replace the dense
matrix with a structured orthogonal operator, built from sign diagonals, a permutation, and a fast Walsh-Hadamard transform. The operator is multiplier-free, requires $O(N)$ parameters and $O(N\log N)$ operations per step, and is never materialized as a
matrix. We instantiate it in a standard and in a memristive-friendly Echo State Network, with one binary input connection per unit. Our mathematical analysis shows that exact orthogonality yields an echo state condition that is tight in the recurrent scaling, and a noise response that is predictable at design time. Moreover, the operator mixes the whole state in a single application. Experiments on twenty classification and seven regression
benchmarks, at reservoir sizes up to $N = 8192$, show that the structured models match dense orthogonal reservoirs, and achieve better mean performance than the cycle reservoir by a margin that widens with size. Furthermore, we time the recurrent step on three hardware platforms, where it is up to $50\times$ faster than a dense product and $10^4\times$ smaller in
memory. Finally, we ablate the operator and measure the response to noise, quantization, device mismatch and discrete faults.
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All authors Andrea Ceni, Gianluca Milano, Carlo Ricciardi, Claudio Gallicchio
Artificial Intelligence · Cross-listed from cs.CL
A Probabilistic Interpretation of KV Cache Eviction
Renato Geh, Alex Chen, Daniel Israel et al. (+2 more)
The premise and promise of KV (cache) eviction is simple: higher throughput can be achieved by evicting some entries from the KV cache, at a negligible cost to quality. This holds empirically for many existing methods, though most rely on creative heuristics for selecting which entries to drop. Despite recent advances, the problem of KV eviction has remained informal in the literature. This paper aims to properly formalize this problem through the lens of probabilistic
reasoning and reveal what can be learned from this perspective. Concretely, we (1) formalize the problem of KV eviction and, unfortunately, prove that it is computationally hard, (2) show that by framing it probabilistically, KV eviction reduces to the problem of expectation estimation, which can be approximated through sampling, (3) show that through this probabilistic interpretation, correcting for evicted entries during decoding—a previously ignored problem—becomes feasible, and (4) reveal that existing methods in the literature are zero-variance biased
estimators that can be easily adapted in order to enable decode time correction. In practice, we show that this probabilistic version of KV eviction coupled with decode time correction is more robust to different tasks compared to existing eviction methods and achieves competitive performance at the same compression budget.
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All authors Renato Geh, Alex Chen, Daniel Israel, Aditya Grover, Guy Van den Broeck
Artificial Intelligence · Cross-listed from cs.CL
Angelo Sparacino, Francesca Toni and Adam Dejl
In computational argumentation, obtaining arguments that explicitly support or attack given claims is a critical precursor to downstream
reasoning tasks. When these supporting and attacking arguments are to be retrieved using semantic search methods, they need to be assessed for topic-relevance to the claims of interest as well as for correctness of their (positive or negative) stance towards the claims. In this paper we explore how dense
embedding models (hereafter, models), powering modern
retrieval pipelines, can serve as the basis of semantic search incorporating this dual assessment. We show experimentally that existing models struggle with asymmetric
reasoning, exhibiting a strong bias toward topical overlap while ignoring instructional stance. We also show that correcting this bias via contrastive training triggers a new failure mode where models over-correct, over-fixating on polarity keywords (e.g., "supports" or "refutes") at the expense of the semantic topic. We thus introduce diagnostic word-ablation metrics to quantify this phenomenon and propose a data-centric solution. By implementing a balanced argument curriculum alongside LLM-augmented, stance-inverted arguments, we force the (
embedding) models to learn deeper directional logic rather than exploiting superficial lexical shortcuts. Our evaluation demonstrates that, for sufficiently powerful models, this approach can alleviate the observed overcorrection, achieving further improvements in stance-aware argument
retrieval.
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Artificial Intelligence
LoopArena: Benchmarking Models as Runtime Controllers for Loop Engineering
Yi Wang, Haopeng Zhang, Chengxiang Huang et al. (+4 more)
Loop Engineering is emerging as a practice for organizing development work around coding
agents. Instead of writing each
prompt by hand, practitioners design loops that monitor progress, assign work, run checks, and decide what the
agent should do next. Even with a capable coding
agent, a loop may trust a stale progress note, skip needed verification, spend its budget in the wrong direction, or stop before the task is safe to submit. Yet the final outcome of one end-to-end run cannot tell whether success or failure reflects the loop's guidance or the coding
agent's ability to carry out the task. We introduce LoopArena, a
benchmark for evaluating how well one model can guide a separate coding
agent through a long-running task. The model under evaluation is the Controller: after each coding round, it receives a structured summary of the run and instructs a separate, fixed coding
agent, the Worker, on what to do or verify next, or decides whether to stop. LoopArena evaluates this ability in three complementary settings that differ in execution scope and cost. Type I consists of 160 low-cost control questions; its execution cost is incurred once during
benchmark construction, so each new Controller is evaluated through a single four-way choice with no code execution. Type II evaluates Controller guidance on 80 selected task slices, while Type III evaluates the corresponding 80 full tasks from their original states. On full tasks, the best observed Strict Success Rate is 36.25%, leaving substantial room for improvement in long-horizon loop control. Across Controllers, the paired reduction in estimated inference cost averages 60.6%, and Type II produces a positively correlated ordering under the main Core criterion (Spearman's rho = 0.7353). We release the
benchmark data and evaluation code at https://github.com/AMAP-ML/LoopArena.
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All authors Yi Wang, Haopeng Zhang, Chengxiang Huang, Rui Dai, Kaikui Liu, Piotr Koniusz, Xiangxiang Chu
Artificial Intelligence
RECAST: Recent & Context-Aware Sampling for Test-Time Adaptation in Streaming Biosignals
Yong-Yeon Jo, Junho Song and Joon-myoung Kwon
Streaming biosignals vary across subjects and drift over time, so population-trained models lose accuracy during long-term monitoring. Test-time adaptation (TTA) enables online personalization by updating the model on incoming samples. But in a stream, a basic question is left open: which samples should drive each update? Using all buffered samples blurs the update with irrelevant segments. Using only the latest segment makes the update noisy and unstable. The most useful samples are recent, aligned with the current physiological state, and reliable enough to learn from. We propose RECAST (REcent & Context-Aware Sampling for TTA), a lightweight sampling module for buffered TTA frameworks. RECAST builds each adaptation batch from three signals: temporal recency, contextual similarity, and predictive reliability. It changes only which samples are used, leaving the model and the training objective unchanged. On two blood-pressure
datasets, RECAST improves estimation accuracy and trend tracking over baselines and ablations. The per-patient gains are statistically significant on both
datasets, with broad improvement on the regular
benchmark and gains concentrated on the hardest patients in the emergency-department setting. RECAST stays practical, adding only sub-second latency per segment on a single GPU and CPU core.
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Artificial Intelligence · Cross-listed from cs.RO
Spatial-Semantic Reasoning using Large Language Models for Efficient UAV Search Operations
Marin Maletic, Marijana Peti, Tamara Petrovic et al. (+1 more)
We present a real-time semantic
navigation framework for Unmanned Aerial Vehicles (
UAVs) focused on improving time efficiency in the Object Goal
Navigation (ObjectNav) task. Central to our approach is a Large
Language Model (LLM) that interprets user-provided natural
language instructions and performs semantic
reasoning over detected objects and spatial context to prioritize high-probability search regions. The system combines real-time object detection, 3D spatial mapping, and
polynomial spline interpolation for smooth and feasible
UAV trajectory planning. Unlike prior methods that rely on offline
reasoning or simulator-constrained action spaces, our framework can operate in real time, continuously updating semantic relevance based on new observations. Experiments in both simulated and real-world settings demonstrate reductions in mission duration while maintaining high search accuracy, underscoring the effectiveness of LLM-guided
reasoning for time- efficient
UAV-based ObjectNav.
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All authors Marin Maletic, Marijana Peti, Tamara Petrovic, Stjepan Bogdan
Artificial Intelligence
Finding Where the Buck Stops: An Automated Failure Attribution-Based Reflection Framework for Multi-Agent Collaboration
Xiaoqing Wang, Keman Huang, Bin Liang et al. (+3 more)
Multi-
agent systems (MAS) powered by large
language models have shown promise for complex tasks but suffer from high failure rates. Current self-reflection methods for MAS require all
agents to reflect upon failure, overlooking a critical reality: failures typically stem from a specific
agent leading the task astray, namely the decisive error
agent, while others merely fulfill their regular duties. Forcing regular-behaving
agents to reflect contaminates their
memory with wrong insights. Hence, we propose DoCtOR (Diagnose-then-Correct PPO-enhanced Reflection), a novel reflection framework that enhances multi-
agent collaboration. DoCtOR first identifies the decisive error step and decisive error
agent through automated failure attribution, then employs counterfactual
reasoning to generate a corrected decisive error step, and finally engages only the decisive error
agent to produce targeted reflections. Experimental results show DoCtOR achieves 22%, 26%, and 27% improvements over initial success rates on HotPotQA, ChartQAPro, and Mind2Web
datasets, outperforming Reflexion, Retroformer, and COPPER. We further establish the generalizability of our diagnose-then-correct paradigm and demonstrate that in low-resource settings, focusing reflection on
reasoning steps after the decisive error step achieves comparable quality to reflecting on the complete failure trajectory.
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All authors Xiaoqing Wang, Keman Huang, Bin Liang, Hongyu Li, Xiaoyong Du, Wuqiong Pan
Artificial Intelligence
Physics-Guided Flow Matching for CT Image Reconstruction
Davide Evangelista
Deep generative models have recently emerged as powerful priors for solving ill-posed inverse problems in CT, with
diffusion-based approaches achieving state-of-the-art reconstruction performance. However,
diffusion models typically rely on
stochastic sampling procedures, long inference trajectories, and carefully tuned noise schedules, which can limit computational efficiency and numerical stability, especially at high spatial resolutions. In this work, we investigate Flow Matching as an alternative generative prior for CT reconstruction. We train a high-resolution Rectified Flow Matching model on 256x256 chest images from the Mayo Clinic Low-Dose CT
dataset. To mitigate overfitting and limited anatomical variability, we employ a two-stage training strategy consisting of an initial phase with strong, anatomically informed data augmentation, followed by a fine-tuning phase with reduced or no augmentation to refine structural fidelity. The resulting model is capable of generating high-quality and anatomically coherent CT-like images, serving as a strong learned prior. We then evaluate multiple reconstruction methods specifically designed for Flow Matching models, including Plug-and-Play Flow, FlowDPS, Flower, and Flow-Priors (ICTM), and compare them against state-of-the-art
diffusion-based reconstruction
algorithms such as DDRM, DPS, and DiffPIR. Experimental results across several CT inverse problem settings show that Flow Matching-based approaches consistently outperform
diffusion-based methods in terms of PSNR, SSIM, and perceptual quality, while requiring fewer sampling steps. Finally, we publicly release the trained Flow Matching model and accompanying code to facilitate reproducibility and future research. Overall, this work demonstrates that Flow Matching provides a stable, efficient, and effective alternative to
diffusion models for high-resolution CT image reconstruction.
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Artificial Intelligence
Regime-Aware Portfolio Management via Retrieval-Augmented LLM-Guided Expert Switching
Ahmad Asadi and Reza Safabakhsh
Financial
markets are inherently non-stationary, making the effectiveness of individual portfolio-management strategies highly dependent on changing
market conditions. This work proposes a
retrieval-augmented expert-switching framework that dynamically selects portfolio management experts based on their historical performance under similar
market situations. A dual-stream variational autoencoder represents asset-level and
market-wide information, while a
retrieval-based knowledge base stores historical situations and expert performance. During inference, an instruction-tuned LLM reasons over the retrieved evidence to identify the most appropriate expert rather than directly generating portfolio actions. We further establish a monotonicity property showing that adding a locally superior expert cannot degrade the switching mechanism's performance. Experiments across cryptocurrency, stock, and foreign-exchange
markets show that the proposed selector achieves the highest cumulative return and Sharpe ratio among the evaluated selection strategies in all three
markets. In the stock
market, for example, cumulative return increases from 26% for the best fixed expert to 34%, while the Sharpe ratio improves from 0.74 to 0.96. Ablation results confirm the importance of both
retrieval and LLM
reasoning, while experiments with different expert-pool sizes demonstrate the value of complementary expertise. Overall, the findings support
retrieval-grounded expert switching as an effective approach to adaptive portfolio management in non-stationary financial environments.
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Artificial Intelligence · Cross-listed from cs.CV
A comprehensive and trustworthy benchmark of AI methods for change detection in Earth observation
Tadej Tomanič, Alice Baudhuin, Jan Sotošek et al. (+4 more)
Change detection in Earth observation (EO) is critical for monitoring land surface transformations, yet recent research in the field is constrained by inconsistent evaluation protocols and a narrow focus on predictive accuracy without regard for computational efficiency. To address this, we present a standardized, open-source
benchmark for evaluating state-of-the-art (SOTA) deep learning methods for Earth observation change detection. We conduct a comprehensive analysis of ten representative model architectures, ranging from convolutional networks (CNNs) to
vision transformers (ViTs), across ten heterogeneous change detection
datasets. We rigorously evaluate these models with identical experimental protocols, comparing models trained from scratch against those utilizing pre-trained weights. Furthermore, we evaluate predictive performance alongside computational efficiency, including parameter counts and inference latency. Our findings reveal that well-optimized classical architectures, such as Siamese U-Nets, frequently outperform more complex contemporary models when computational efficiency is factored in, and that pre-training consistently provides a significant performance boost with no additional inference cost. To ensure complete transparency and reproducibility, all experimental resources, including standardized data splits, training scripts, training logs, and model checkpoints are publicly available and adhere to FAIR principles (Findable, Accessible, Interoperable, and Reusable).
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All authors Tadej Tomanič, Alice Baudhuin, Jan Sotošek, Jure Brence, Panče Panov, Nikola Simidjievski, Dragi Kocev
Artificial Intelligence · Cross-listed from cs.RO
Training-free Suction Grasp Detection for Deformed Aseptic Cartons Using Vision-Language Models and Geometric Surface Scoring
Marin Maletic and Goran Vasiljevic
Robotic sorting of recyclable waste is challenging due to the deformable and geometrically inconsistent nature of target objects. We present a training-free suction grasping system for sorting deformed aseptic beverage cartons, decoupling target identification from grasp-point selection. An open-vocabulary
vision-
language model detects cartons from a text
prompt, SAM2 refines each detection into an instance mask, and a
geometric scoring method selects the suction point by combining surface flatness with normal
alignment. Three
geometric methods are compared: k-nearest-neighbour PCA, Sobel cross-product, and RANSAC plane fitting. Evaluated on a real
robot across three deformation levels and 35 cluttered scenes, single-object grasp success reaches 88.2% and end-to-end
retrieval in clutter is 72.6%.
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Artificial Intelligence
Beyond Task-Only Matching: Personalized Skill Routing with Counterfactual Evaluation
Tianle Wang, Yanghe Zou, Xiang Liu et al. (+3 more)
The rapid expansion of reusable skill repositories makes skill routing a critical capability for large
language model (LLM)
agents. Existing methods treat routing as task-only semantic matching. However, when users with incompatible constraints issue an identical request, this assumption conflates task relevance with skill suitability: a task-only router can select a semantically plausible skill that is unsuitable for the requesting user. To expose this failure mode, we formulate personalized skill routing as profile-conditioned
retrieval, in which relevance depends jointly on the task and the user profile. We first introduce a profile-counterfactual
benchmark, in which the task is held fixed while changes in the user profile induce changes in the reference skill. We further construct paired counterfactual supervision and propose SkillFeed, a progressive retrieve-and-rerank framework that first establishes task–skill
alignment and then learns profile-conditioned discrimination. By retrieving body-level evidence and reranking semantically similar but profile-conflicting candidates, SkillFeed identifies skills that satisfy both task requirements and user constraints. On SkillFeed-Bench, SkillFeed attains 75.1% top-1
retrieval accuracy, a 23.1-point improvement over the corresponding pretrained routing baseline. Adding profile conditioning yields a 35.1-point gain on queries where user profile changes the reference skill. This contrast shows that user profiles are most consequential precisely when they change skill suitability. Our website is publicly available at http://www.aiskillfeed.com .
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All authors Tianle Wang, Yanghe Zou, Xiang Liu, Ziyao Huang, Chenchen Fu, Weiwei Wu
Artificial Intelligence
REINS: Refusal-Enhanced Inhibitory Steering with Sparse Autoencoder Features
Kai-Xuan Ding, Hao-Xiang Xu, Ji-Hua Peng et al. (+3 more)
Steering with Sparse Autoencoders (SAEs) offers a lightweight inference-time path for adapting the behavior of large
language models without retraining. By exposing sparse and interpretable features, SAE steering provides a promising interface for safety control that guides harmful continuations toward refusal. However, we observe that complex wrappers can still undermine existing SAE steering methods on harmful
prompts. To evaluate this failure mode systematically, we construct Generalized Undercover Instruction Safety Evaluation (GUISE), a
dataset of harmful
prompts with complex wrappers. Existing single direction SAE steering methods do not reliably produce refusals on harmful
prompts, suggesting that refusal enhancement alone can be too weak when the harmful continuation path remains active. This motivates us to propose Refusal-Enhanced INhibitory Steering (REINS), which suppresses harmful continuation features and enhances safe refusal features in the same SAE feature space. Experiments on GUISE and other
datasets show that prior methods either intervene too weakly or achieve only apparent safety through collapse, while REINS substantially reduces harmful responses, markedly improves safe refusals and largely preserves general capabilities.
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All authors Kai-Xuan Ding, Hao-Xiang Xu, Ji-Hua Peng, Zi-Qi Chen, Jiaqi Wang, Zhen-Hua Ling
Artificial Intelligence
Stay Within Your Bounds: Distance-Guided Decoding for Guaranteed Context-Free Grammar Compliance
Vincenzo Collura, Karim Tit, Eleonora Giunchiglia et al. (+2 more)
Grammar-constrained decoding helps large
language models produce syntactically valid structured outputs, such as code, JSON, and SQL. For context-free grammars, many practical decoders enforce local prefix feasibility: each
token must keep the current prefix extendable to some valid completion. Yet, under tokenizer-grammar mismatch and finite
token budgets, feasible prefixes may still fail to reach acceptance. We propose a lookahead-guided decoding framework for context-free grammars based on pushdown automata. Offline, we compute bounded pushdown summaries with reachability labels and upper-bound distances to acceptance. Online, these estimates guide horizon-aware
pruning and beam search. The resulting decoder is syntactically sound: every output is accepted by the target grammar. Experiments on JSON, SQL, and Linear Temporal Logic (LTL) show both consistent syntactic validity and improved completion quality over existing baselines.
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All authors Vincenzo Collura, Karim Tit, Eleonora Giunchiglia, Mike Papadakis, Maxime Cordy
Artificial Intelligence
Generative AI Alignment with Hinduism's Theological Plurality and Sacred Representation
Dipto Das, Arpita Kundu, Nusrat Jahan Mim et al. (+2 more)
Generative AI systems are increasingly used to answer personal questions and mediate everyday practices, including religion. However, existing discussions around AI
alignment and ethics have largely centered secular, Western, and Abrahamic assumptions about religion, offering limited
attention to other faith-based traditions. In this paper, we examine how Hindu users engage with generative AI systems in relation to their religious knowledge, belief, and practice. Drawing on 15 semi-structured interviews with Bangladeshi Hindu participants, we analyze how users interpret AI-generated religious representations, scriptural explanations, devotional interactions, and synthetic religious media. We found that AI can be both accessible and ethically troubling. While AI supported scriptural inquiry, devotional visualization, and religious storytelling, our study also identified concerns about theological flattening, cultural misrepresentation, devotional manipulation, and the simulation of sacred presence and authority. We conclude by arguing that religious
alignment in generative AI requires interpretive
alignment: systems that disclose their limits, preserve plurality, and avoid simulating sacred authority and sycophantic personalization.
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All authors Dipto Das, Arpita Kundu, Nusrat Jahan Mim, Shion Guha, Syed Ishtiaque Ahmed