Databases
TSGuard: A Real-Time Framework for Detecting and Imputing Missing Data in Streaming Time Series
Imane Hocine, Asma Abboura, Soror Sahri et al. (+3 more)
Streaming sensor applications routinely suffer from delayed or missing observations caused by faults, communication losses, or environmental interference. Although recent imputation methods exploit temporal and spatial dependencies effectively, most either assume offline access to future observations or prioritize throughput without enforcing domain plausibility. We present TSGuard, a real-time demonstration system for monitoring, validating, and imputing missing values in streaming time series. TSGuard combines a lightweight graph-aware temporal imputation model with constraint-aware validation, fallback estimation, and operator-facing explanations. Rather than treating imputation as an isolated prediction task, TSGuard integrates it into a broader data-quality loop: detect problematic observations, impute missing values, validate estimated against physical and spatial constraints, and either retain the original value as a plausible anomaly or replace it when it violates domain constraints. Using environmental sensing as a motivating setting, the demo enables users to inspect delayed sensors, compare imputers, define constraints, and validate flagged values in real time. The combination of lightweight online spatiotemporal imputation, domain-aware validation, and explicit retain-or-replace decisions is our central contribution, while interactive explanations make these decisions inspectable and actionable. for operators.
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All authors Imane Hocine, Asma Abboura, Soror Sahri, Abhijith Senthilkumar, Yacine Hakimi, Grégoire Danoy
Numerical Analysis
A Physics-Driven Multiscale Method for High-Frequency Helmholtz Equations
Huangxin Chen, Jianhui Chen, Shubin Fu et al. (+1 more)
We develop and analyze a physics-driven multiscale method for model reduction of the Helmholtz equation at high frequency. The method constructs a reduced approximation space from local wave responses within a mixed finite element formulation with velocity elimination. These responses are computed by solving Helmholtz problems with boundary excitations on oversampled regions, giving snapshots that incorporate the oscillatory behavior and propagation characteristics of the underlying medium. Local generalized
eigenvalue problems then select modes from this physically informed space to form a reduced global system with sparse interelement coupling. To establish stability and convergence of this reduction, we relate the local spectral approximation to approximation of the global adjoint problem. Starting from an inf–sup estimate for the fine-grid formulation, this argument yields a multiscale inf–sup condition governed by fine-grid resolution, oversampling, and the first omitted local
eigenvalues. The resulting stability establishes well-posedness and quasi-optimality of the multiscale approximation in a discrete energy norm. Together with local spectral estimates, it gives an a priori error bound that explicitly quantifies the roles of the coarse and fine mesh sizes, oversampling depth, spectral truncation, and wavenumber. This analysis provides a quantitative link between local space reduction and the stability and accuracy of the global approximation. Numerical experiments in two and three dimensions demonstrate accurate wavefield approximation with substantial reductions in global dimension and solution cost for homogeneous and strongly heterogeneous media, including perfectly matched layer truncations.
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All authors Huangxin Chen, Jianhui Chen, Shubin Fu, Zhiyu Tan
Computer Vision
CalCErt: Bin-wise Certification of Confidence Calibration in Medical Image Classification
Leo Fillioux, Stergios Christodoulidis, Stergios Christodoulidis et al. (+2 more)
Deep neural networks remain vulnerable to
adversarial perturbations, which can distort not only predictions but also confidence scores, undermining uncertainty calibration. While existing certification methods focus on preserving the predicted category, providing guarantees on how calibration behaves under
adversarial attacks remains overlooked. In this work, we introduce CalCErt, a simple and efficient post-hoc strategy that certifies bin-wise confidence calibration for any pretrained differentiable classifier. Our approach combines empirical calibration estimates, statistical concentration bounds, and local Lipschitz estimates of the confidence function to derive data-dependent upper bounds on worst-case miscalibration within an $ell_2$-ball of radius R. We evaluate CalCErt across 11 medical image classification tasks and multiple
adversarial perturbations, demonstrating substantially higher certified coverage than baseline strategies while maintaining competitive tightness. Our code is available at https://github.com/leofillioux/calcert.
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All authors Leo Fillioux, Stergios Christodoulidis, Stergios Christodoulidis, Maria Vakalopoulou, Jose Dolz
Computer Vision
Behavior Pack Optimization for Video MLLM Post-Training
Zhaolu Kang, Shiyu Liu, Tailong Luo et al. (+12 more)
Video multimodal large
language models (MLLMs) keep climbing
video question answering
benchmarks, yet shuffling the frames, masking the segment that supports the answer, or occluding the target object barely changes their predictions. The accuracy rests on appearance and
language priors, not on the temporal evidence the question asks for. We trace this to the unit of post-training: rewards are computed on a single response to the original clip, so the model is never asked to behave consistently across views. We propose Behavior Pack Optimization (BPO), which replaces the single response with a behavior pack of outputs across counterfactual views chosen by question type, scored jointly. The pack reward asks for stability when the intervention is irrelevant, sensitivity when key evidence is removed, and abstention when no evidence remains. To keep this objective stable at small pack sizes, BPO uses an anchor-relative advantage: the response on the original view serves as a per-
prompt reference instead of a group mean over mixed views. On TempCompass, MVBench, and NExT-QA, BPO improves the macro accuracy of Qwen2.5-VL-7B-Instruct by 4.7 pp, the temporal-hard subset by 7.8 pp, and abstention F1 by 20.0 pp over a budget-matched vanilla GRPO baseline from the same SFT checkpoint. The gains transfer to
Video-MME, LongVideoBench, and to LLaVA-
Video-7B; ablations confirm they follow the view sets, not the rollout count. We hope this pack-level perspective offers a useful starting point for the
video MLLM and
multimodal post-training community as the field moves toward evidence-grounded
video reasoning.
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All authors Zhaolu Kang, Shiyu Liu, Tailong Luo, Wei Zhang, Yingjie He, Lei Wei, Guansu Wang, Liang He, Siheng Wang, Guangyuan Dong, Jiaqi Su, Shuang Chen, Haoyu Ji, Qishi Zhan, Kaiyue Zhou
Databases
Tracking State Footprints: How Agents Can Transact
Oto Mraz, Rares Şerban, Kyriakos Psarakis et al. (+2 more)
Can AI
agents transact? We argue that they must: as multi-
agent systems (MASs) increasingly write code, deploy infrastructure, modify databases, and call web services, lost updates or stale reads can be catastrophic. Although MASs increasingly execute plans in parallel, current orchestrators do not track the state that
agents read and write. As a result, concurrency anomalies manifest even in simple coding tasks. We frame MAS coordination as a data management problem and propose to describe
agents by their state footprint: the state they read and write across their own local context and state, as well as the state of the orchestrator and external systems. We posit that MASs require guarantees similar to those of databases, but providing them raises new challenges and opportunities: unlike database transactions,
agents do not read from a fixed schema or an isolated snapshot, and cannot be replayed deterministically upon failure. They can, however, resolve conflicts semantically instead of aborting, enabling new forms of concurrency control and conflict resolution. Towards
agents that can transact, we outline a
vision for next-generation
agent orchestrators and transactional interfaces for external systems to participate in agentic transactions.
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All authors Oto Mraz, Rares Şerban, Kyriakos Psarakis, Burcu Kulahcioglu Ozkan, Asterios Katsifodimos
Artificial Intelligence
Keeping JEPA World Models Plannable When Little of the Frame Moves
Florian Strohm, Patrick Wagner, Jannik Schwab et al. (+1 more)
Specifying a goal in
language rather than as a goal frame is a natural interface for planning with a latent world model, but testing it needs scenes in which
language must discriminate between several objects. We build SLIM, a pushing
benchmark with several small objects and paired visual and
language goals on identical scenes. On SLIM a LeWM world model that solves PushT succeeds on under 1% of trials, although a scripted controller with simulator state solves every tier. Probes locate the failure in the encoder: its latent is nearly action-insensitive, neither pusher nor object positions can be decoded from it, and rollouts are no better than copying the current latent forward. One inverse-dynamics auxiliary loss, applied to encoder latents and to predicted latents through a shared head discarded at test time, restores every probe and raises success from 0.003 to 0.35 (0.16 on the hard pushing tier, where a goal-agnostic policy scores zero), and improves PushT at twice the trained horizon. Controls attribute the repair to the gradient into the encoder, and a response sweep shows that the vanilla model plans once enough of the frame responds to actions. A cheap action-sensitivity probe, computable without environment access, acts as an empirical necessary condition: all configurations below its threshold failed to plan. On the repaired latent, a small
language-goal head plans from sentences without retraining the world model: it reaches 0.84 on
navigation (visual-goal oracle 1.00), follows the named zone when it is swapped with a decoy, and degrades gracefully to unseen nouns. A single goal sentence rarely completes a push, but given the push as a sequence of stage sentences the head raises success on the medium and hard pushing tiers from 0.04 to 0.25, on par with the goal-frame oracle, also when the switch between stages is read from the latent alone.
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All authors Florian Strohm, Patrick Wagner, Jannik Schwab, Marco Huber
Computation and Language
Oliver Hauck, Mario Sanz-Guerrero and Katharina von der Wense
Reasoning traces improve large
language models (LLMs), but current models are trained to reason mostly in English. It has been shown that forcing a model to reason in another
language degrades accuracy, even when the
reasoning language matches the
language of the
prompt – but only for a setting where the model reasons over a short
prompt. Here, we ask whether the same holds for
retrieval-augmented generation (RAG), where the model must read and integrate a large amount of retrieved evidence in the target
language. To study this, we build a fully monolingual German RAG question-answering testbed over the fictional world of the tabletop role-playing game The Dark Eye, a domain that is richly documented in German but too niche for the model to answer from
memory, so that it has to rely on
retrieval. Varying the forced
reasoning language of an agentic RAG system on this testbed, we find that aligning the
reasoning language with the
language of the query and the retrieved documents helps. Forced German
reasoning outperforms forced French, although the model
benchmarks higher in French, so the benefit comes from
alignment and not from
language proficiency. The advantage grows when the retrieved context is richer and structure-aware. However, forced German only reaches the level of the model's native, unconstrained English
reasoning without surpassing it, showing that native
multilingual reasoning is needed. We publicly release the testbed and QA
benchmark.
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Machine Learning
Page-EntroKV: Hardware-Aligned, Entropy-Weighted KV-Cache Eviction under Grouped-Query Attention
Inbasekaran S
Serving long-context autoregressive
language models is constrained by the key-value (KV) cache. Most dynamic eviction methods score
token importance per query head and choose
tokens independently. This fits poorly with grouped-query
attention (GQA), where several query heads share one physical KV buffer: divergent per-head selections force the serving engine to retain the union of their choices - inflating the cache by up to the group ratio r - while arithmetic mean pooling dilutes the specialized
retrieval heads that carry factual recall. We introduce Page-EntroKV, a formal framework for KV-cache eviction operating at the granularity GQA serving actually allocates. Heads within each physical group are pooled by weights derived from sink-isolated collision (Renyi-2)
entropy - one inner product per head, computed once at prefill with no calibration - so sink heads cannot masquerade as
retrieval heads. Pooled scores are projected onto PagedAttention page frames, and eviction executes at the hardware tuple (layer, group, page). We formalize the union overhead ratio (UOR) and intra-group disagreement, prove an exact identity linking them for two-head groups alongside two-sided bounds at every group ratio, prove strict budget preservation and a finite-context needle-retention bound that arithmetic mean pooling provably violates, and give exact per-layer page accounting. On a pilot architecture (Qwen2.5-1.5B-Instruct, r=6), head-independent replay over 2,240 group measurements yields union overhead up to 4.75x at a 2% budget, while Page-EntroKV holds UOR exactly 1.000; sink isolation removes a 13x sink masquerade; needle recall is 100% versus 0% for mean pooling at a 20% budget; retained cardinality is exact for every page size; and QA and code tasks remain solvable at 20% retention.
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Robotics
Safe Streaming Flow Planning by Aligning Sampling Dynamics with Execution Dynamics
Seunghwan Jang, Jeongyong Yang, Siddharth Ancha et al. (+1 more)
Generative planners based on
diffusion/flow matching can learn to synthesize long-horizon trajectories from demonstrations. However, real-world deployment requires (i) enforcing safety constraints during execution and (ii) tight online replanning at fast execution rates. Prior safe
diffusion/flow planners generate the
agent's full trajectory at once, while repeatedly perturbing intermediate states to satisfy safety constraints. This approach is not only computationally intensive, but also introduces distribution shift since the learned sampling dynamics is distinct from the system's execution dynamics. We propose SafeStreamingFlow, a goal-conditioned planner that aligns flow sampling dynamics with execution dynamics by sequentially integrating a learned state vector field with hierarchical state prediction. Importantly, we need to enforce safety constraints only for the executed step via high order control barrier functions. Across
navigation, racing, and locomotion
benchmarks, SafeStreamingFlow reduces planning latency and improves safety compared to existing methods, while maintaining competitive goal-reaching success.
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All authors Seunghwan Jang, Jeongyong Yang, Siddharth Ancha, SooJean Han
Information Retrieval
Benchmarking Literature Retrieval for a Model Organism: A Dictyostelium Case Study
Yun Wang, Gad Shaulsky, Tomaž Curk et al. (+1 more)
Biological literature
retrieval systems are often developed and evaluated using broad biomedical corpora and general-purpose search tasks. However, many curated knowledge bases operate in narrower model-organism domains, where the literature is sparse and terminology is organism-specific. We introduce a
retrieval benchmark from dictyBase for Dictyostelium, a model organism in
cell and developmental biology. The
benchmark consists of curator-generated biological queries linked to PubMed-indexed articles, together with structured gene annotations. Using this
benchmark, we study three factors in niche biological
retrieval: cross-encoder reranking, gene-aware query expansion, and abstract-only versus full-text
retrieval. We report that reranking and gene-aware query expansion improve
retrieval selectively: reranking is most useful when the model is well suited to biological evidence matching, whereas curated annotations help clarify compact biological queries by reducing vocabulary mismatch. Full-text chunks substantially improve
retrieval when abstracts omit supporting evidence, increasing both candidate recall and top-rank performance, although these cases are harder than queries supported by abstracts. Data and code are publicly available at https://github.com/fulaibaowang/dictycite, and the
benchmark dataset is additionally archived on Zenodo.
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All authors Yun Wang, Gad Shaulsky, Tomaž Curk, Blaž Zupan
Artificial Intelligence
Trading Strategy Optimization via Textual Gradient
Chaoqun Yang, Qian Wang, Fengbin Zhu et al. (+4 more)
Quantitative trading strategy design aims to discover trading programs from historical data that remain effective in future
markets, which can be viewed as a black-box program optimization problem. LLM-based textual gradients offer a promising approach by providing explicit optimization directions for iterative strategy refinement. However, directly applying textual gradients faces two challenges: (1) optimization is myopic, underutilizing experience from previous evaluations; and (2) aggregate backtest feedback overlooks temporal robustness, potentially favoring strategies that perform well only in specific
market periods. To address these challenges, we propose TradeGrad, an experience-guided textual-gradient framework for robust trading strategy optimization. TradeGrad leverages accumulated optimization experience to estimate textual gradients and employs multi-scale revisions for both strategy exploration and refinement. It further introduces the Cross-Period Robust Objective (CPRO), which emphasizes performance in unfavorable historical periods to promote temporal robustness. Experiments on cross-sectional and time-series strategy design in Chinese A-share and U.S. equity
markets show that TradeGrad achieves the best in-sample and out-of-sample performance across all four settings. Notably, its Chinese cross-sectional strategy achieves 27.99% annualized return, 12.19% maximum drawdown, and a Sharpe ratio of 1.63, approximately 68% higher than the CSI 300
benchmark. Further analyses validate the proposed components and show consistent improvements in both in-sample and out-of-sample performance throughout optimization. The code is available at https://github.com/transcend-0/TradeGrad.
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All authors Chaoqun Yang, Qian Wang, Fengbin Zhu, Xinyu Lin, Bingsheng He, Roger Zimmermann, Tat-Seng Chua
Machine Learning
Coverage You Can Steer: Online Conformal Calibration for RL-Driven Hardware-Aware NAS
Pedro Brandimarte, Nerea Aranjuelo, Marcos Nieto et al. (+1 more)
Hardware-aware neural architecture search (NAS) is dominated by evaluation cost: every architecture must be trained before its reward is known. Conformal-prediction filters cut this cost by
pruning candidates whose predicted-reward upper bound misses a threshold, with a distribution-free guarantee that at most a fraction $\delta$ are wrongly discarded. That guarantee assumes exchangeability between calibration and test candidates, which the surrounding reinforcement-learning (RL) loop violates: the policy's proposals improve as search proceeds and, in layer-by-layer construction, shift within every episode. We replace one-shot
quantile estimation with online feedback control (Adaptive Conformal Inference, with tuning-free, locally-adaptive, and group-conditional variants), restoring steerable coverage: dialing the target delivers it, monotonically and reproducibly, for arbitrary sequences. Across three neural-network architecture families and both single-step and sequential search (three seeds), it tracks every requested level to within ${\sim}10^{-3}$ while
pruning 25-50% of evaluations at no measured accuracy cost, whereas static calibration loses control of its coverage and a Gaussian-process baseline stays conservative regardless of the request. Finally, used as an acquisition function on one constrained testbed, the same optimistic bound beats random search, a gain that fixed optimism already carries and online calibration sharpens. The source code is available at https://github.com/Vicomtech/rl-hw-nas.
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All authors Pedro Brandimarte, Nerea Aranjuelo, Marcos Nieto, Oihana Otaegui
Computational Geometry
The Reeb Transform
Erin Chambers, Shankha Shubhra Mukherjee and Katharine Turner
Reeb Transforms offer a compact representation of how shapes in $\mathbb{R}^n$ change when sliced along varying directions. We define the Reeb Transform as the family of Reeb graphs induced by height functions along all directions in the unit sphere. In this paper, we develop a rigorous treatment of Reeb Transforms for o-minimal definable sets, with particular emphasis on one-dimensional stratified spaces embedded in $\mathbb{R}^d$ and on surfaces in $\mathbb{R}^3$. We establish injectivity of the Reeb Transform in multiple settings, including compact surfaces in $\mathbb{R}^3$, capturing the essential topological features needed to uniquely reconstruct such surfaces. However, in dimensions above three, the Reeb Transform ceases to be injective, indicating the limitations of this descriptor in higher-dimensional settings.
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Sound
ParaGeo: Decomposing Paralinguistic Variation into a Shared Latent Geometry
Yuhan Liu, Yuxuan Ou, Ruoxi Su et al. (+2 more)
Speech delivery varies with both the requested paralinguistic attribute and the linguistic content. We introduce ParaGeo, a matched-content decomposition of paralinguistic variation in a frozen
speech language model. Synthesized audio
tokens are replayed with a fixed listening
prompt; pooled key/value (K/V) representations are centered and projected into a shared low-dimensional space. Our GLM-4-Voice probe spans 80 requested controls from 12
benchmark families across eight sentences. With a globally fitted calibration basis, content-held-out centroid accuracy using this basis is 9.49% versus a 1.25% permutation baseline; same-label cross-content cosine similarity is 0.285 versus 0.017, and both conditional permutation tests yield p = 0.001. A separate ten-scenario, six-style probe reveals reproducible contrast directions across scenarios. Static, additive, and temporal interventions produce attribute-, layer-, and schedule-dependent response profiles. These results provide a shared coordinate representation for measuring paralinguistic structure and an empirical starting point for latent
speech control. Code is available at https://github.com/yuhanlydia/ParaGeo.
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All authors Yuhan Liu, Yuxuan Ou, Ruoxi Su, Mohamed Ahmed Zaki, Yunbo Long
Cryptography and Security
The Fragility of Trigger-Tag Mechanisms for Misuse Detection in Open-Weight LLMs
Toluwani Aremu, Manit Baser, Mohan Gurusamy et al. (+2 more)
Open-weight
language models can be downloaded, modified, and deployed beyond their developers' control, limiting the effectiveness of centrally enforced safeguards. Recent work has therefore proposed trigger-tag mechanisms that produce a detectable signal when a model is used under a target condition, such as generating phishing contents. Although these mechanisms borrow from established techniques, their use for conditional misuse detection in open-weight LLMs is relatively new. Therefore, existing research works have not systematically studied the robustness of trigger-tag mechanisms under
adversarial attacks. To close this gap, (i)~we formalize trigger-tags and distinguish
token-level trigger-tags, which introduce watermark-inspired signals during decoding, from weight-level trigger-tags, which learn
backdoor-inspired associations between target conditions and detectable model behavior. Furthermore, (ii)~we introduce \Untag, a unified attack framework that organizes their mechanism-specific attack surfaces into a common taxonomy. We evaluate representative
token-level and weight-level trigger-tags using phishing as a case study. We find that while trigger-tags may provide useful evidence in controlled settings, our attacks render the existing trigger-tag mechanisms to be entirely ineffective. Consequently, we argue that these mechanisms should not be treated as robust misuse detectors when attackers can transform outputs or modify open weights.
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All authors Toluwani Aremu, Manit Baser, Mohan Gurusamy, Nils Lukas, Dinil Mon Divakaran
Computer Vision
Foresight: planning future perception in streaming VLMs without retraining
Ashok Prasad Neupane, Dipan Bartaula, Ankit Belbase et al. (+5 more)
Existing streaming
vision-
language models (VLMs) continuously perceive and reason over visual streams, but their computational pathways remain fixed throughout inference. Consequently, they cannot adapt computation to evolving scene dynamics, where different future events demand different levels and forms of perception. We show that streaming VLMs inherently possess the ability to anticipate the immediate future, and leverage this capability to dynamically configure future computation in a training-free manner. Realizing such anticipatory computation, however, is very challenging: future anticipation must be sufficiently reliable to guide computation, planning must run concurrently with streaming inference, and online reconfiguration must incur negligible overhead. To address these challenges, we introduce FORESIGHT, a dual-stream architecture comprising two Siamese LLMs with shared weights, input encoders, and KV cache. The first LLM continuously processes incoming
tokens, while the second runs ahead of the stream to anticipate future context, plan future computation, and generate task responses without interrupting streaming inference. Each plan decides when to reason next, what to check then, and how densely to sample, keeping transient evidence separate from persistent control. The resulting computation plan is executed online through an efficient reconfiguration protocol with schemaguided decoding and lightweight diff-based updates, enabling dynamic adaptation with low overhead. With a frozen Qwen3-VL-8B backbone, FORESIGHT achieves 23.0 mean joint F1 on OmniPro Online evaluation beating strongest trained baseline by 9.5%, while improving the backbone by 6.7 on StreamingBench and 15.4 on OVO-Bench, with the largest gain of 18.7 when evidence arrives later in the
video stream. Our source code will be made publicly available.
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All authors Ashok Prasad Neupane, Dipan Bartaula, Ankit Belbase, Saugat Adhikari, Samip Ghimire, Saroj Poudel, Binod Bhattarai, Danda Pani Paudel
Computer Vision
In-Distribution Forcing for Long Video Generation at Test Time
Jeongwoo Shin, Youngyoon Choi, Sangwoo Jo et al. (+5 more)
Modern autoregressive (AR)
video diffusion models excel at short-horizon
video generation, yet generating long
videos remains challenging due to drifting, where colors and textures shift, and motion dynamics decay. Existing works primarily rely on KV conditioning, which selects or modifies cached key-value (KV) entries to mitigate drifting. However, we observe that KV conditioning alone is insufficient as it assumes cached KV entries remain in-distribution. This assumption fails beyond the training horizon: nothing constrains the construction of KV entries during rollout, giving rise to the KV-provenance problem where cached entries themselves become out-of-distribution (OOD). To address this, we propose In-Distribution Forcing (ID-Forcing), a test-time framework that aligns both KV caching and KV conditioning with training configurations. Its key mechanism, self-caching, prevents OOD KV entries at their source. Each chunk is cached without attending to prior KV entry, keeping the rolling window exactly in-distribution. Consequently, ID-Forcing seamlessly extends short-horizon models to minute-scale
video generation. Extensive evaluations show that our method remains competitive on standard
video generation
benchmark while substantially outperforming prior work in mitigating drifting, as validated by both our drift metrics and a user study.
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All authors Jeongwoo Shin, Youngyoon Choi, Sangwoo Jo, Hyunmog Kim, Sungjoon Choi, Joonseok Lee, Jaewoong Choi, Jaemoo Choi
Machine Learning
How to Find and Reuse Policies for Continuous Adaptation in Lifelong Reinforcement Learning
Saptarshi Nath, Inish M. D'Souza, Antonio Carta et al. (+2 more)
In lifelong reinforcement learning, retaining previously learned policies is not sufficient for effective transfer to a new task. Useful knowledge may be distributed across several prior policies, and its relevance may change as the learner acquires experience. One hypothesis is that task similarity can be effectively used in a continual learning setting to find and combine previously learned policies. To test it, Adaptive Mask Selection and Composition (AMSC) is designed to estimate similarity from online experience via non-parametric Wasserstein task
embeddings from state-action-reward samples. The z-score-normalized sparsemax of the similarity scores are used to derive a variable-size support to periodically choose and weight policies to form a prior when learning a new task. On CT-graph and MiniGrid, AMSC achieves higher mean performance and forward transfer than the evaluated modular composition baselines while exhibiting no forgetting. Results on Continual World suggest that identifying relevant prior knowledge and determining its layer-specific composition may require additional layer-specific tuning. Ablations show that selecting relevant sources and determining how strongly to reuse them are central to these gains. Independently measured pairwise transfer is also positively associated with task-
embedding similarity. These results indicate that task similarity can be an effective criterion to select and weight specific knowledge for reuse in lifelong reinforcement learning.
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All authors Saptarshi Nath, Inish M. D'Souza, Antonio Carta, Soheil Kolouri, Andrea Soltoggio
Machine Learning
Exploring the Trade-Off Between Structured Pruning and Fault Tolerance in Deep Neural Networks for Space Applications
Toon Vinck, Naïn Jonckers, Jaro De Roose et al. (+2 more)
Deep Neural Networks (DNNs) inherently exhibit a degree of robustness to bit-level faults due to their distributed representation of information. As a model increases in width, this information becomes more dispersed, theoretically reducing the impact of any single bit fault. In this paper, we empirically investigate the relationship between model width and robustness to Single Event Upsets (SEUs). We conduct a comprehensive experiment in which baseline models undergo iterative structured
pruning to reduce their width while preserving task performance as much as possible. At each
pruning stage, we run a targeted fault-injection campaign to evaluate the model's performance under simulated bit-flip scenarios. Our results show that, although structured
pruning increases per-inference sensitivity to faults by reducing redundancy, this effect is effectively counterbalanced by shorter execution time, which lowers the probability of encountering an SEU. These findings suggest that structured
pruning can yield significant energy and latency savings without compromising overall reliability, providing useful guidance for designing robust AI systems for space applications.
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All authors Toon Vinck, Naïn Jonckers, Jaro De Roose, Jeffrey Prinzie, Peter Karsmakers
Computation and Language
Building Interpretable Feature Representations for Resume-Vacancy Matching by Distilling Production LLM Signals
Ilya Chekin, Vyacheslav Malyugin, Vladimir Chirkov et al. (+1 more)
Matching candidates to vacancies is central to recruitment, and a recruiter needs to see why a candidate fits, not only a single opaque relevance score. We provide this evidence as named, interpretable matching dimensions recruiters can act on - eight in our current deployment. We propose a two-part approach. The first is an LLM-based labeler whose
prompts and feature definitions were refined from recruiter feedback while it served as an earlier production matching stage. In the current architecture, it is used only for offline labeling and is not called on online requests. The second is a feature bi-encoder distilled from it: a LoRA-adapted
embedding backbone with compact per-dimension heads that runs on CPU and serves all online requests. Both parts keep improving:
prompts are revised as feedback arrives, and the bi-encoder is retrained on the updated labels. The model is trained on 168,772 labeled vacancy-resume pairs (17,921 vacancies and 180,030 resumes). Recruiters using the service can confirm or revise surfaced feature predictions. On 927 recruiter-recorded values from this selected production-feedback subset, the deployed student agrees with the recorded decisions in 888 cases (95.79%). This is operational, non-blinded agreement rather than an independent human evaluation.
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All authors Ilya Chekin, Vyacheslav Malyugin, Vladimir Chirkov, Mikhail Yurushkin
Computation and Language
Ontological Instability and Statistical Amplification: The Paradox of "Humanizing" LLM-Generated Text
Claudiu Creanga and Liviu Dinu
Supervised AI-text detectors report high
benchmark accuracy, but it is not clear what their decisions are based on. We analyze a RoBERTa-based detector under semantic, structural, and tokenizer-level perturbations, using the M4
dataset (N = 10,000) and controlled generations (N = 300). When Mistral-7B-Instruct was asked to make machine text sound more human, Verb Diversity rose from 0.77 to 0.92 and the outputs became easier to detect. Detection scores appear to track statistical complexity, which also leads to a 76.3% false-positive rate on formal human writing. As a control, we evaluate event-based Latent Space detection. Paraphrasing changed 87% of its event sequences (Jaccard = 0.067), and homoglyphs altered 70% of the extracted verbs even though extraction still ran (Jaccard = 0.30). Its best domain AUC was 0.577. RoBERTa's robustness seems specific to the features it uses, and structural abstraction did not make detection more robust.
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Computation and Language
Emergent Structure in the Marginal Attention Space of Language Models
Valentino Maiorca, Walter Nelson and Francesco Locatello
While representation similarity across independently trained
language models is well-documented, how internal mechanics such as
attention behave across models remains far less characterized. Inspired by this gap, we examine the structure of post-softmax
attention weights by marginalizing over query positions, mapping them into a joint
token-head "marginal
attention space". Evaluating across 60+ diverse LLMs, we find that different properties emerge when reducing this space along its
token and head axes. When reduced
token-wise, marginal
attention yields a text-intrinsic signal robustly conserved across models. To explain this property, we empirically connect marginal
attention to the input-output Jacobian of the network, and prove theoretically that under a smoothness assumption, models with similar next-
token distributions are guaranteed to have similar input-output Jacobian statistics. When reduced head-wise, it forms a model-private signature conserved across documents. Practically, this provides a natural way to estimate a per-head budget for key-value (KV) cache eviction, effectively decoupling model-specific budget allocation from text-intrinsic
token scoring. On standard eviction
benchmarks, a per-head budget precomputed offline on pretraining text, combined with a training-free
token score, shows competitive performance with methods that recompute the budget on every document or train it per target. Code available at https://github.com/Flegyas/marginal-
attention
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Artificial Intelligence · Cross-listed from physics.ao-ph
S2S-JEPA: Predicting the Predictable at Subseasonal-to-Seasonal Timescales
Chenyu Dong and Gianmarco Mengaldo
The subseasonal-to-seasonal (S2S) timescale, roughly from two weeks to two months ahead, is a critical forecast window for sectors such as agriculture, energy, and water management. Yet, it is widely known as the `predictability desert'. Recent AI weather models excel up to two weeks ahead but deteriorate beyond, largely because they are trained to predict fine-scale details that are neither predictable nor essential at S2S timescales. We argue that a more physically grounded objective is to forecast only the slowly varying components that remain predictable. Computer
vision reached the same conclusion with the Joint-
Embedding Predictive Architecture (JEPA), which predicts in latent space, discarding unpredictable details. In this work, we introduce S2S-JEPA, which brings the JEPA paradigm to S2S
forecasting. It is tailored to this task through design elements from state-of-the-art AI weather models. S2S-JEPA achieves comparable skill to the gold-standard ECMWF physics-based ensemble and surpasses it on multiple metrics at weeks 5 to 6.
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Computer Vision
A Benchmark for Spatially Grounded Gesture Generation
Anna Deichler, Rishabh Dabral, Fethiye Irmak Dogan et al. (+2 more)
Communication in shared space interweaves verbal and non-verbal signals, and pointing gestures anchor
language to the environment: "put the cup on that one" is uninterpretable without the gesture that fixes the referent. Yet no common framework exists for evaluating whether generated gestures indicate their intended referent; distributional metrics reward a gesture aimed at the wrong object as long as it looks natural. We introduce a
benchmark for spatially grounded gesture generation, comprising ~2K pointing-annotated clips from naturalistic VR dialogue with ground-truth 3D referents, a task in which systems must decide when, how and where to point within conversational
speech, and a protocol that separates temporal
alignment, spatial grounding and perceived naturalness. We also provide a flow-matching baseline, MM-Conv-Flow. Evaluating it alongside an independent
retrieval-based system and captured human motion, we find that
geometric grounding can exceed that of human pointing without any gain in perceived naturalness, showing that referential gesture quality must be measured along separate dimensions.
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All authors Anna Deichler, Rishabh Dabral, Fethiye Irmak Dogan, Anindita Ghosh, Jonas Beskow
Computation and Language
Ask, Relax, or Act? Evaluating Actionable Indeterminacy in LLM Preference Reasoning
Ang Li, Yue Lin, Feifei Kou et al. (+6 more)
An LLM
agent can recognize uncertainty yet still choose the wrong next step: asking when action is already justified, or seeking clarification when the constraints must change. We formalize actionable indeterminacy: act when an accepted action is shared across all admissible preferences or objectives, clarify when each possibility is feasible but no action is shared, and propose a minimum-cost permitted constraint repair when the request is infeasible. We construct a solver-grounded
benchmark spanning object allocation, meeting scheduling, apartment choice, and stable matching. Matched pairs retain the same source while changing whether intervention is necessary, and evaluation separates decision correctness, matched-pair reliability, and fully correct responses. Our findings reveal a recurring difficulty in recognizing when intervention is unnecessary: models can identify situations requiring clarification or repair yet still intervene when a justified action already exists. Correct decision labels also fail to guarantee usable actions, questions, or repairs. Crucially, response requirements shape not only how decisions are expressed but also which decisions are made. Making the required content explicit substantially improves fully correct responses and can change intervention decisions, even when outputs are already parseable. These findings highlight that reliable agency requires more than recognizing uncertainty: it requires intervening only when necessary and translating the chosen next step into a verifiable response.
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All authors Ang Li, Yue Lin, Feifei Kou, Zhan Su, Prayag Tiwari, Wenhao Li, Shuhui Zhu, Hongyuan Zha, Baoxiang Wang
Machine Learning · Cross-listed from stat.ML
Invariance of Clustering Operations in Causal Effect Identification
Jani Nykänen, Otto Tabell, Santtu Tikka et al. (+1 more)
Clustering variables in causal graphs reduces the size of the graph and simplifies causal inference. However, arbitrary clustering can alter crucial causal relations among variables and lead to erroneous conclusions. While the identifiability of a causal effect in the clustered graph implies the identifiability in the original graph under mild conditions, nonidentifiability in clustered graph does not imply nonidentifiability in the original graph without further assumptions. When both identifiability and nonidentifiability are preserved, the clustering operation is called identification invariant. We present a broad class of clustering operations that are identification invariant based on conditions related to the c-components of the original graph. Finally, we demonstrate use of the results in practical settings.
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All authors Jani Nykänen, Otto Tabell, Santtu Tikka, Juha Karvanen
Computer Vision
Beyond Single Videos: Benchmarking and Active Evidence Seeking for E-Commerce Cross-Video Reasoning
Jinghan Zhao, Yiman Hu, Liang Wu et al. (+2 more)
E-commerce
videos are information-dense and frequently compared by consumers evaluating products and merchants assessing marketing strategies. However, existing
multimodal models mainly focus on single-
video understanding and have limited ability to compare information across
videos. We introduce AdsCVR, the first e-commerce cross-
video reasoning benchmark, containing 2,483
videos and 6,110 question-answer pairs across six
reasoning dimensions. Cross-
video reasoning requires models to locate fine-grained evidence among many redundant frames and integrate visual details,
speech, and on-screen text. We therefore propose AdSeek, an agentic framework that dynamically selects visual and audio tools during multi-turn exploration, replacing static uniform sampling with active evidence acquisition. To address the sparse credit assignment of reinforcement learning, we develop an offline trajectory rectification mechanism that identifies
reasoning errors and missing
multimodal evidence in RL-generated trajectories. The corrected trajectories provide supervised fine-tuning signals that reduce biases learned during RL. This mechanism supports a rectified bootstrapping pipeline in which initial RL exposes
reasoning bottlenecks, supervised fine-tuning corrects them, and a final RL stage further improves the policy. AdSeek achieves 74.30 percent accuracy on the AdsCVR test split, outperforming its Qwen3-VL-8B-Instruct backbone by 27.90 percentage points. It also generalizes to the open- domain CrossVid
benchmark, demonstrating effective active evidence gathering.
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All authors Jinghan Zhao, Yiman Hu, Liang Wu, Jian Xu, Bo Zheng
Artificial Intelligence
Predictor-Guided Latent Space Codon Optimization for Maximizing Protein Expression
Alberto Caron, Tianyu Cui, Dmytro S. Lituiev et al. (+13 more)
Codon optimization, the process of selecting synonymous codons to improve mRNA translation efficiency and
protein expression, is central to therapeutic
protein production and mRNA vaccines, yet it remains a hard problem. The design space is discrete and combinatorially large, precluding gradient-based methods, and existing tools rely on heuristic proxies (e.g., Codon Adaptation Index or GC-content) that poorly capture true expression. We introduce Latent-Space Codon Optimization (LSCO), which recasts this discrete problem as a continuous one by mapping sequences into the latent space of a pretrained mRNA
language model, enabling efficient gradient-based search. LSCO combines four components: a data-driven expression objective from an uncertainty-aware predictor, a Minimum-Free-Energy regularizer for structural stability, a naturalness prior from a
protein-to-codon back-translation model, and constrained decoding for
protein fidelity. On a real-world, wet-lab antibody expression
dataset, LSCO outperforms simple frequency-based, as well as modern deep generative baselines in predicted expression, while retaining suitable biophysical properties.
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All authors Alberto Caron, Tianyu Cui, Dmytro S. Lituiev, Mangal Prakash, Artem Moskalev, Amina Mollaysa, Bo Zhai, Hirsh Nanda, Daniel M. Poole, Zhongyin Liu, Iman Farasat, Robert Davidson, Nikolay V. Manyakov, Tommaso Mansi, Scott Oloff, Rui Liao
Artificial Intelligence
Peer Influence across Heterogeneous AI Models
Frida Nøhr Laustsen, Marie Haahr Petersen, Victoria Popa et al. (+4 more)
When two AI
agents disagree, who persuades whom? As multi-
agent systems increasingly combine
language models of different families and sizes, the answer can determine which judgments survive interaction. Measuring persuasion as the probabilistic shift in an
agent's decision after a single exchange with a dissenting peer, we test seven open-weight models across three
language understanding tasks. We find that persuasion is strong: when models disagree, receivers often abandon their initial judgment after seeing a peer's answer and explanation. Surprisingly, however, neither standalone certainty nor model scale reliably predicts persuasion dynamics. Models producing almost perfectly consistent decisions in isolation can be among the most susceptible to persuasion, and small models can match larger ones as persuaders and resist their influence just as effectively. Furthermore, we show that the size of the shift depends more on the susceptibility of the listener than on the persuasiveness of the speaker. Persuasion patterns are therefore specific to each model pairing, with heterogeneity amplifying persuasion in some combinations and suppressing it in others, allowing a dissenting
agent running a small model to overturn the judgments of a much larger one. These findings show that the behavior of interacting models cannot be inferred from their individual properties but must be evaluated in the combinations in which they will operate.
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All authors Frida Nøhr Laustsen, Marie Haahr Petersen, Victoria Popa, Ariel Flint, Romualdo Pastor-Satorras, Andrea Baronchelli, Luca Maria Aiello
Machine Learning
LS-AR: Future-Predictive Latent Steering in Autoregressive LLMs
Anubha Gupta and Eduardo Pignatelli
Standard autoregressive (AR) models process high-level task instructions, state history, and transient
tokens within a single shared sequence of
tokens. Consequently, they lack the architectural mechanisms needed to isolate macro-objectives from context noise. To overcome this single-channel limitation, we introduce Latent-Steered Autoregressive (LS-AR), a dual-channel architecture that decouples continuous goal steering from discrete
token decoding via FiLM conditioning. We evaluate a Static Goal Encoder (P_0) for persistent macro-objective retention across long rollouts and a Dynamic State Tracker (P_t) for recurrent latent updates during generation. On long-horizon
retrieval past context limits (H=1024, W=500), LS-AR (Static) achieves 100% target recall where parameter-matched baselines collapse (0%), while increasing throughput by ~35% and cutting peak VRAM by 52.8%. In Blocksworld planning under forced perturbations (k=1), LS-AR (Dynamic) sustains an 89.0% completion rate vs. 71.0% for the baseline, though zero-shot entity scaling (N -> N+1) exposes single-vector capacity limits (0%). Finally, dual-channel authority analysis shows that text goal dropout establishes latent-dominant control, offering structural defence against text
prompt injection while introducing a latent vector attack surface.
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