Game Theory
Synthesizing Update Schedules with Game-Based Extension of Bounded Model Checking
Janis Kröger, Paul Kröger and Martin Fränzle
Ensuring safe software updates in safety-critical systems without interrupting operation and without provisioning and activating cold spare hardware poses a fundamental challenge due to the conflict between system availability and update execution. In this paper, we present a bounded SMT encoding for synthesizing fixed global-time update schedules for timed-games with linear update automata and a fixed number of update transitions. We model the interaction between the system and the update as a two-player timed game. Our key contribution is the synthesis of global time points that define a fixed update schedule which guarantees safe and complete deployment of the update independently of the autonomous system behavior. To this end, we reduce the scheduling problem to a reachability and safety objective and encode it as a quantified SMT problem. We demonstrate it on an example system of a trajectory planner for autonomous driving, showing that the synthesized schedule ensures safe deployment under all admissible executions.
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Logic in Computer Science
Formalization of Fragments of the Theory of Hereditarily Finite Sets
Zuzana Haniková and Štěpán Holub
The axiomatization of the theory of hereditarily finite sets in first-order classical logic is systematically explored and formalized in Isabelle/HOL. The formalization uses a hierarchy of locales, each corresponding to a fragment of the theory given by a particular collection of axioms. An inductive definition of first-order definable predicates is introduced and used to formalize axiom schemata. Special
attention is paid to several equivalent axioms of finiteness, as well as to several equivalent ways of expressing regularity. The work also formalizes several facts about the independence of an axiom from a system of axioms by defining appropriate models.
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Computer Vision
SPOC-Net: Single-Primitive Online Composition Network for GNSS Jamming Set Recognition
Zhihan Zeng, Kaihe Wang, José A. López-Salcedo et al. (+2 more)
Reliable positioning,
navigation, and timing support intelligent transportation, autonomous systems, and space-air-ground integrated networks. However, global
navigation satellite system (GNSS) jamming recognizers that treat each mixture as a separate class are difficult to extend to new combinations. Therefore, this paper proposes SPOC-Net, which decomposes the recognition problem into identifying a set of basic jamming components. Multi-resolution time-frequency features and learned component queries provide evidence for each component type. A high-resolution branch estimates the number of active types, and a structured decoder combines this estimate with component evidence to select a valid set. For training, measured single-component records are the only physical samples used in gradient optimization. Their associated clean in-phase and quadrature (IQ) sequences are combined on demand during training to produce labeled mixtures with different relative powers and jamming-to-noise ratios. Separate measured mixtures from ten training-listed compositions support model selection and decoder calibration; six other compositions are reserved for final testing. Evaluation on 14,220 independently generated, conductively combined, and recorded
radio frequency mixtures yields 80.69% exact-set accuracy and a 92.84% micro-averaged F1 score. On combinations excluded from model development, SPOC-Net achieves 80.89% exact-set accuracy, exceeding the strongest comparison method by 18.77 percentage points under the reported protocols.
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All authors Zhihan Zeng, Kaihe Wang, José A. López-Salcedo, Gonzalo Seco-Granados, Zhongpei Zhang
Software Engineering
Verifying Graceful Degradation in a Distributed Malware-Detection System with SPIN
Andrei Aldea and Dumitru-Bogdan Prelipcean
Modern endpoint malware detection is distributed: a lightweight
agent on each endpoint collects features from a scanned file or process, sends them to a remote server for analysis, and then enforces the returned verdict locally by blocking, quarantining, or disinfecting. Because the endpoint acts on the verdict, the distributed machinery surrounding detection must never turn a transient server failure into a wrong action. We present a formal model, in Promela, of the endpoint decision pipeline of such a system, abstracted from a production architecture at Bitdefender. The model captures the system's graceful-degradation fallback chain: when the primary analysis server times out, the endpoint falls back to an older legacy-protocol server, and failing that to a reduced-signature local scan, before enforcing a verdict. Assuming detection signatures are sound, we specify six safety and liveness properties in linear temporal logic (LTL) and verify them exhaustively with the
SPIN model checker. We prove that the fallback machinery never causes a false positive (an enforcement action against a benign file), commits to exactly one verdict per scan even when timed-out responses arrive late, weakens detection strength only in an explicit and ordered way, and always terminates in an enforcement decision, so the pipeline is deadlock-free. Each property is checked to hold non-vacuously, and we report how the state space grows with concurrent scans and endpoints. The work shows how model checking can give strong correctness guarantees for the failure-handling logic of a production security system, a layer that has received little direct formal
attention.
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Software Engineering
Certified Compilation in the TELEPERM XS Nuclear Safety I&C Platform
Alexandre Berard and Richard B. Kreckel
The large safety instrumentation & control (I&C) systems in civil
nuclear power plants (NPPs) are mainly safe-shutdown systems (reactor protection) or limitation and control systems. Framatome's established TELEPERM XS (TXS Core) product family is a digital I&C system platform to cover all these applications. We illustrate the role of verification in the different stages of the software production toolchain, focus on the formal compilation process, and discuss the contribution of the CompCert certified compiler to the safety case of the product. Scrutinizing the object code produced by this compiler has exhibited suboptimal run-time performance in a certain simple but recurring generated code pattern. We explain how formal methods allow us to address this issue in the compiler while simultaneously reducing its trusted computing base (TCB), thereby strengthening the safety case rather than merely preserving it.
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Computer Vision
P4Q: Co-designing Token Pruning and Quantization for Vision-Language Model Acceleration
Haizhao Jing, Zhenhao Shang, Haokui Zhang et al. (+2 more)
Vision language models have achieved strong performance across a wide range of
multimodal applications, yet their substantial computational and
memory costs hinder efficient deployment. Visual
token pruning and post-training quantization reduce inference overhead along two complementary dimensions, namely sequence length and numerical precision. Existing workflows typically optimize these techniques independently or apply them sequentially. Their distinct optimization objectives leave critical interactions unaddressed and constrain the achievable compression performance. We revisit these designs and present P4Q, a practical co-design framework that jointly optimizes visual
token pruning and low-bit quantization for efficient VLM inference. First, P4Q introduces a quantization-aware visual
token selection strategy before the LLM. It applies fake quantization to copies of the features produced by the projector and selects visual
tokens using statistics computed from these fake-quantized features, thereby conditioning the selector's feature-based decisions on simulated low-bit perturbations. Second, P4Q introduces a
pruning-aware quantization calibration strategy. It uses the same selection strategy as
pruning to calibrate the quantized model on the retained-
token distribution, thereby aligning the calibration process with the pruned execution path used during deployment. By coupling these two components, P4Q achieves substantial inference speedups while maintaining comparable task performance, resulting in a better efficiency-accuracy trade-off than independently optimized pipelines. For instance, on LLaVA-NeXT, P4Q achieves an average end-to-end inference speedup of 2.8x across eight distinct test sets, while retaining higher accuracy than prior compression and quantization methods.
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All authors Haizhao Jing, Zhenhao Shang, Haokui Zhang, Rong Xiao, Peng Wang
Machine Learning
From Attention Sensitivity to Layer Role: Revisiting Mixed-Precision Quantization of Transformers
Nafiseh HosseinpourFardi, Negar Alihadi, Mahmoudreza Babaei et al. (+2 more)
Most post-training quantization pipelines fit each weight
matrix to its pretrained counterpart, one
matrix at a time. Whether that proxy tracks what an
attention block actually computes, or how errors in the Q, K and V projections compound inside the softmax, is rarely checked. We write the objective on the
attention output instead, over all three projections at once, and reuse it throughout the pipeline. JAB defines one scalar loss over the joint Q, K, V weights of a block, evaluated against the block's real causally-masked
attention output, and uses it twice: to fit the quantized weights (GPTQ warm start, then STE with learnable scales), and to score the block for a multiple-choice knapsack allocation. On
attention-only quantization of Mistral-7B this works. At 3 bits JAB recovers 77-90% of the gap between uniform GPTQ and full precision, and its sensitivity estimate tracks an oracle costing 73 forward passes to within a fraction of a point. It stops working once MLP layers enter the allocation. A role-aware offset rule needing no sensitivity estimate at all beats JAB on GPT-2's MLP and on the full Mistral-7B model: with a 3-bit floor it quantizes 96.4% of the weights to 4.5 bits per parameter at 6.933 perplexity, within 4.4% of full precision (6.643) at 3.56x compression, against 7.158 for JAB at the same budget. Which
matrix a weight sits in matters more than any sensitivity estimate we computed. Two things came out sideways. Block-local reconstruction is an unreliable proxy for end-to-end perplexity: one run improved a block's own objective 4.6x while perplexity rose 32x, which is why every allocation here is validated end-to-end. And on
attention-only quantization, fine-tuning moved weights farther from their pretrained values while pulling
attention outputs closer, with net gains. Post-training seems to recover
attention behavior, not weights.
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All authors Nafiseh HosseinpourFardi, Negar Alihadi, Mahmoudreza Babaei, Milad Hosseini, Adrian Weller
Numerical Analysis
A priori error analysis for one-parameter Lagrange and POD reduced order methods
Antti Hannukainen and Vigdis Toresen
This work deals with the numerical solution of parametric linear
elliptic partial differential equations using the finite element method for spatial discretization and a proper orthogonal decomposition or Lagrange subspace method based ROM for treating the parameter dependency. We study a simple one-parameter model problem and prove an error estimate for these ROMs as a function of the snapshot point set. Sub-exponential convergence with respect to the number of snapshot points is established under very mild assumptions on the point set. Exponential convergence is proven for structured point sets. In contrast to existing error analysis of Lagrange subspace methods, our estimates are not based on interpolation or perturbation arguments. Rather, we define a new approximation operator from the snapshot subspace and prove error and stability estimates for it.
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Artificial Intelligence
On the Limits of Metacognitive Monitoring in LLMs
Dongqi Han, Yifan Yang and Dongsheng Li
Reliable decisions depend on recognizing when an answer may be wrong. In biological cognition, metacognitive monitoring can dissociate from task performance, raising the question of how closely solving and judging are linked in
language models. Here we study the confidence reports of four frontier models across 15
benchmarks. High task accuracy can coexist with weak error discrimination: a model solves 97% of competition mathematics problems while its answer-time confidence ranks correct answers above errors barely better than chance. Confidence separates correct answers from errors more effectively on questions solved by a separate reference model, while review brings limited improvement on reference-hard questions. Aggregate discrimination also rewards ranking correct answers on easy questions above errors on hard ones, which question-only forecasts already do well. Cross-evaluation helps most where the evaluator answered correctly, and errors shared by the two models usually retain high confidence. Hard questions and shared errors remain difficult targets for prompted self-review and peer oversight, even in models with strong problem-solving performance.
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Computer Vision
Cilin Yan, Yilun Qiu, Wanyang Zhang et al. (+4 more)
Multimodal large
language models (MLLMs) have approached image
segmentation by
reasoning about visual content and predicting target locations. Their generated responses contain
reasoning traces and localization proposals that can serve as working
memory when revisiting the same image and query. Our exploration reveals that MLLMs benefit from using this self-generated working
memory as context, leading to enhanced
reasoning segmentation. Motivated by this finding, we seek to strengthen the backbone model's
reasoning segmentation capabilities by distilling the guidance gained from revisiting prior attempts, enabling it to benefit with or without working
memory at inference time. To this end, we propose
Reasoning Segmenter with Working
Memory (SWiM), a working-
memory distillation framework for
reasoning segmentation. Specifically, SWiM selects rollouts based on
segmentation quality to construct working
memory and uses the
memory-conditioned model as a teacher. The teacher provides
token-level distributional supervision along student-generated trajectories, while the student receives only the original image and query. Joint optimization of on-policy self-
distillation and outcome-based reinforcement learning combines working-
memory guidance with direct feedback on
segmentation quality. Extensive experiments on
reasoning segmentation benchmarks demonstrate that SWiM achieves state-of-the-art performance, validating the effectiveness of working-
memory distillation.
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All authors Cilin Yan, Yilun Qiu, Wanyang Zhang, Rui Zu, Xiaolong Jiang, Jiayin Cai, Yao Hu
Cryptography and Security
JEV as a Judge for Agent Trace Security: An Empirical Comparison with Generative LLM Judges
Zhiqiang Wang and Yichao Gao
Security evaluation of tool-using
agents requires judging actions in context, yet generative judges add latency, explanation overhead, and output-validation failures. We study whether JEV, a typed decision model, offers a useful alternative for retrospective trace classification. We evaluate JEV and four generative judges on four
benchmark collections totaling 5,219 trajectories, using a common risk rubric and behavior-level labels. JEV attains a
benchmark-averaged positive-class F1 of 77.8, compared with 74.1 for the strongest generative configuration, GLM-5.2, with valid-result coverage of 95.5% and 94.4%, respectively. Performance varies across
datasets, with JEV leading on ATBench500 and MCPHunt and GLM leading on R-Judge and TraceSafe. Across the four
benchmarks, JEV's median successful-call latency is 0.99 seconds; estimated
token cost averages \$0.000195 per valid judgment. These results support JEV as an economical screening signal, with trade-offs in precision and recall.
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Computer Vision
When Text Matters: Design Principles for Visual Token Pruning in Vision-Language Model
Minchan Kang, Kyeonghye Park, Seoyoung Cho et al. (+2 more)
Visual
token pruning has been widely studied as a practical approach to reducing the computational cost of large
vision-
language models. However, it struggles to preserve essential visual information, which can lead to substantial performance degradation. In particular, image-based
token selection can overlook task-relevant details, while text-guided
token selection may fail to capture the text–visual relationships needed for complex
reasoning. We find that applying textual guidance too early can limit its ability to identify answer-relevant visual regions, whereas text-to-visual
attention becomes more informative at intermediate decoder depths. This finding motivates our training-free method, which separates early
vision-guided
pruning from deferred text-guided reselection. We first prune visual
tokens using
vision-encoder
attention, retain additional candidates until the decoder midpoint, and then use text-to-visual
attention to determine the final visual-
token set. Across eight
benchmarks and three models, our method outperforms the best-performing baselines by an average of 11.10 and 16.84 percentage points in performance recovery at 80% and 90%
pruning, respectively, with comparable or lower LLM-prefill latency than most baselines. The source code is publicly available at https://github.com/kmc3661/DeFT
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All authors Minchan Kang, Kyeonghye Park, Seoyoung Cho, Daeshik Kim, Yucheol Cho
Machine Learning
Attention-based Hierarchical Variational Information Bottleneck for Robust Multi-Agent Communication under Variable Bandwidth
Lukas Koch Vindbjerg, Qi Zhang, Yury Brodskiy et al. (+1 more)
Learning-based multi-
agent communication under limited bandwidth does not only require deciding what to communicate, but also structuring messages so that partial transmissions remain useful. We study this problem under prefix truncation, where only the first part of each message is received. To address it, we propose AH-VIB, an
attention-based autoregressive variational communication model that combines a variational information bottleneck (VIB) with sequential message generation and a hierarchical robustness loss. We evaluate AH-VIB on a custom cooperative object-inspection and occupancy-mapping task, where
agents equipped with a limited field-of-view sensor coordinate to scan inspection objects in an occupancy-grid world, under variable and fixed bandwidth conditions, and compare it against MADDPG, CommNet, a flat VIB baseline, and an autoregressive MLP ablation. AH-VIB achieves competitive mean return while improving performance reliability under the most constrained bandwidth conditions. These results indicate that AH-VIB improves the reliability and graceful degradation of learned communication under bandwidth constraints.
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All authors Lukas Koch Vindbjerg, Qi Zhang, Yury Brodskiy, Lukas Esterle
Machine Learning
Beyond Verbalized Confidence: Calibrating Reasoners with Differentiable Readouts
Chenxiao Fan, Chongming Gao, Gangyi Zhang et al. (+8 more)
Reinforcement learning with verifiable rewards (RLVR) trains
reasoning models to produce correct answers, but does not ensure that their stated confidence is calibrated. The resulting models are systematically overconfident. Recent methods train calibration inside the RLVR loop by having the model state a numerical confidence alongside its answer, but they all obtain the confidence by sampling it as text. This choice imposes two costs: a sampled confidence introduces variance and in practice collapses to a handful of distinct values, and sampling makes the confidence non-differentiable, forcing the calibration loss through a scalar reward. We propose CREDO (Confidence REaDOut) to replace sampling with a deterministic readout. While RLVR optimizes correctness, CREDO reads the confidence from a dedicated
token pair in the model's output distribution and trains it by differentiable regression. CREDO further turns the trained confidence into a signal for accuracy, weighting rollouts by how far confidence and outcome disagree, so that accuracy and calibration improve together. Across mathematical and code
reasoning, CREDO attains the best accuracy and calibration, and the gains extend to abstention and selective prediction.
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All authors Chenxiao Fan, Chongming Gao, Gangyi Zhang, Leyang Shen, Yaxin Gong, Jiamin Wang, Jiakai Wang, Dong Wang, Yang Liu, Fuli Feng, Xiangnan He
Artificial Intelligence
AUV-Bench: Aesthetic Understanding and Generation Evaluation for User Interfaces
Zhijie Deng, Ling Li, Junhao Ji et al. (+7 more)
Multimodal foundation models are increasingly used for evaluating and generating user interfaces (UIs), often producing seemingly reasonable aesthetic judgments and visually plausible pages. However, under professional design scrutiny, their behavior can differ substantially from that of human designers. In professional design practice, designers rely on a systematic set of aesthetic principles that consistently guide judgment, diagnosis, repair, and creation. A coherent aesthetic capability should therefore connect aesthetic judgment with design actions. Existing evaluations, however, typically assess these abilities in isolation, making it difficult to determine whether task-level success reflects a shared aesthetic understanding or merely fragmented task-specific competence. To address this gap, we introduce AUV-Bench, developed in collaboration with professional UI designers around 1,395 executable web interfaces and four tasks: aesthetic scoring, diagnosis, repair, and text-to-UI generation. The tasks share a pool of UIs and aesthetic principles, with diagnosis and repair further aligned on 660 controlled-degradation instances to enable instance-level analysis of judgment and action. Evaluation of 12 models reveals a capability imbalance: models show moderate agreement with professional designers in holistic aesthetic scoring, yet exact diagnosis-chain success peaks at only 24.7%. On the aligned diagnosis-repair cases, correct judgments and successful repairs do not consistently coincide, exposing a Judgment-Action Gap between identifying aesthetic problems and successfully acting on them. In open-ended generation, even leading models achieve only moderate aesthetic quality under human-calibrated evaluation. Overall, current models exhibit partial aesthetic competence, but still lack the fine-grained understanding and judgment-action coherence required for reliable UI design.
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All authors Zhijie Deng, Ling Li, Junhao Ji, Siwei Lyu, Zhipeng Xu, Zulong Chen, Rongyao Fang, Shuai Bai, Xuming Hu, Jiaheng Wei
Computer Vision
EviSplat: Preserving Multi-View Evidence in 3D Gaussian Splatting for Open-Vocabulary Segmentation
Sungho Moon, Kota Shimomura, Junwoo Park et al. (+4 more)
Open-vocabulary 3D scene understanding enables object localization and
segmentation from free-form text queries without a fixed category vocabulary. Many recent methods build on 3D Gaussian Splatting and consolidate multi-view observations, such as masked crops from individual views, into
language features or compact object descriptors before the query is known. However, observations of the same object vary across viewpoints and are not equally informative: some reveal cues relevant to a particular query, whereas others provide incomplete or misleading evidence. Pre-query consolidation can therefore suppress cues on which a later query depends. We introduce EviSplat, which preserves individual observation features as evidence for later text queries. EviSplat retains individual observation features within class-agnostic 3D instances that represent objects, object parts, or background regions. It also learns, for each Gaussian, a distribution describing which visual appearances its observations support. Given a text query, EviSplat scores each instance using its most relevant observations. It then computes a score for each Gaussian by combining instance-level relevance with locally supported evidence, weighted by how often and how unambiguously that Gaussian was observed. Different queries can thus draw on different visual cues from the same preserved evidence. Experiments across diverse
datasets and evaluation protocols demonstrate state-of-the-art performance, supporting the benefit of preserving multi-view evidence until query time and aggregating it according to the query.
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All authors Sungho Moon, Kota Shimomura, Junwoo Park, Wonhyeok Choi, Seunghun Lee, Takayoshi Yamashita, Sunghoon Im
Machine Learning
Learning High-Risk High-Precision Motion Control
Nam Hee Kim, Markus Kirjonen and Perttu Hämäläinen
Deep reinforcement learning (DRL)
algorithms for movement control are typically evaluated and benchmarked on sequential decision tasks where imprecise actions may be corrected with later actions, thus allowing high returns with noisy actions. In contrast, we focus on an under-researched class of high-risk, high-precision motion control problems where actions carry irreversible outcomes, driving sharp peaks and ridges to plague the state-action reward landscape. Using computational pool as a representative example of such problems, we propose and evaluate State-Conditioned Shooting (SCOOT), a novel DRL
algorithm that builds on advantage-weighted regression (AWR) with three key modifications: 1) Performing policy optimization only using elite samples, allowing the policy to better latch on to the rare high-reward action samples; 2) Utilizing a mixture-of-experts (MoE) policy, to allow switching between reward landscape modes depending on the state; 3) Adding a distance regularization term and a learning curriculum to encourage exploring diverse strategies before adapting to the most advantageous samples. We showcase our features' performance in learning physically-based billiard shots demonstrating high action precision and discovering multiple shot strategies for a given ball configuration.
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Artificial Intelligence
From Soft Targets to Reward Signals: How Assignment and Reward Objectives Interact
Jiangtao Lin, Bangyang Wei, Siyi Liu et al. (+2 more)
Soft preference targets specify supervision strength, and reward objectives convert that strength into learned reward signals. A central design question remains: how does assigning a fixed set of preference strengths to different response pairs change the rewards produced by different objectives? We introduce assignment geometry to study this interaction. Mean-matched smoothing controls target dispersion, while within-stratum reassignment changes correspondence and preserves the complete target distribution. Across five reward objectives, intact correspondence retains the largest clean preference margins among the compared soft targets within a common accuracy-equivalence budget. Attenuation orderings change with the reward objective, revealing different responses to the same target assignments. Independent reassignments and a related source construction reproduce the retention direction. An attenuation-retention profile compares these combinations through margin magnitude, edit response, and accuracy. Against independently calibrated scaling, APLOT uniform targets deliver additional attenuation on both aggregate and presentation edits. These findings establish a joint design space in which target placement and reward objective shape reward properties beyond preference accuracy.
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All authors Jiangtao Lin, Bangyang Wei, Siyi Liu, Yihang Ding, Yuhan Dong
Machine Learning
When Sparse Reward Meets Dense Distillation: Training Dynamics of On-Policy Distillation
Xinke Jiang, Tao Feng, Zhibang Yang et al. (+4 more)
Reinforcement learning with verifiable rewards provides a sparse post-training signal: a single binary outcome evaluates the entire rollout, and every
token receives the same sequence-level advantage regardless of its individual contribution. To complement this sparse supervision, a growing family of methods adds a scalar-weighted teacher KL term to the policy-gradient objective, providing dense
token-level guidance that may be unreliable at some positions. Despite the benefits of combining these signals, their interaction during optimization can destabilize joint training. To understand how this instability develops, we study the learning dynamics of hybrid reward–
distillation training through a neural tangent kernel (NTK) analysis. We introduce the cross-signal NTK $K_{DR}(n)$, a
token-level statistic that measures the
alignment between reward and
distillation gradients at position n. Through this analysis, we identify two failure modes: 1 Magnitude drowning, where the reward gradient exceeds the
distillation gradient by orders of magnitude, so that even weak directional conflict can cause the
distillation loss to rise despite its explicit inclusion in the training objective; and 2 Localized directional conflict, where the sequence-level advantage and the teacher's position-specific distribution induce opposing updates at the same
token ($K_{DR}(n)\!<\!0$). The severity of these effects depends on the optimization regime: the gradient-norm ratio $\kappa\!=\!\|\nabla\mathcal{L}_R\|/\|\nabla\mathcal{L}_D\|$ varies by roughly an order of magnitude across tasks, and our experiments reveal an empirical threshold beyond which naive mixing can lead to persistent training collapse. Motivated by these findings, we introduce the M3 family, which combines magnitude normalization with three strategies...
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All authors Xinke Jiang, Tao Feng, Zhibang Yang, Zhixin Zhang, Weixuan Xu, Haoyu Zhang, Xu Chu
Artificial Intelligence
Can We Trust the Teacher? Decoupled Credit Direction-Magnitude for Self-Distillation
Yugu Li, Zehong Cao, Peizhen Li et al. (+3 more)
RLVR provides reliable trajectory-level credit, while OPSD offers dense supervision for
token-level credit. This exposes a fundamental coupling when updating step-level credit direction and magnitude with teacher supervision, preventing steps from receiving reliable credit directions and contribution magnitudes, while making both vulnerable to teacher judgment errors and preference variance, as supported by our theoretical analysis. To separate credit direction from its contribution magnitude, we introduce Decoupled Credit Self-
Distillation (DCSD), which theoretically decouples credit direction and magnitude into two reliable signals and uses them to calibrate privileged teacher supervision. Specifically, we design belief-margin probing to determine credit direction and marginal information gain to quantify credit magnitude, enabling step-to-
token credit assignment for policy optimization. Across 11
benchmarks, DCSD achieves the best overall scores against GRPO, OPSD, RLSD, and RLCSD. Compared with base models, DCSD improves the overall score by 8.45 points on mathematical
reasoning and 7.01 points on
multimodal reasoning, while correcting the credit direction for 6% of
tokens and yielding a 1.5$\times$ reduction in
token credit magnitude.
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All authors Yugu Li, Zehong Cao, Peizhen Li, Yang Zhang, Siyi Hu, Jianglin Qiao
Machine Learning
Context-dependent time-series prediction via HyperReservoirs
Kohei Tsuchiyama, Takatomo Mihana, Ryoichi Horisaki et al. (+1 more)
Time series prediction is a common application of reservoir computing. When the training and testing time series data contains multiple dynamical regimes, because an underlying parameter is changing, or the data in fact consists of multiple distinct systems, simple application of the reservoir computing principle produces high prediction errors. Here, we propose a HyperReservoir as an extended model of reservoir computing especially designed for such cases. The HyperReservoir combines a main reservoir with a smaller context reservoir, where the latter modulates the output weights of the former. This structure resembles the hypernetworks from deep neural network literature. However, in contrast, HyperReservoirs retain the simple training via linear regression of standard reservoir computing. We compare the proposed architecture with a conventional ESN, in which context acts at the input, and a full-
matrix Conceptor, in which context modulates the reservoir state space. We evaluate all three models on time-series prediction tasks based on Lorenz and Rössler systems, including for varying bifurcation parameters and time sampling scales. We find that the HyperReservoir achieves the lowest mean test error in all three tasks, and particularly outperforms conceptors on data that is sampled from the same attractor but at different time scales.
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All authors Kohei Tsuchiyama, Takatomo Mihana, Ryoichi Horisaki, André Röhm
Computers and Society
Conspiracy and Environment Communities on Reddit Sort Along Different Demographic Axes
Miguel Á. Sánchez-Cortés, Corrado Monti and Gianmarco De Francisci Morales
The impact of online platforms in political mobilization is widely documented, yet it remains unclear how it affects different demographic groups. To shed light on this phenomenon, we examine two contrasting cases: engagement with environmental causes on one side, and with conspiracy theories on the other. Both are well represented on Reddit, allowing us to study the different origins of these groups using a network-based method. By inferring age, gender, affluence, and partisanship of Reddit users, we construct stratified transition networks to compare the entry pathways of different demographic groups toward these communities. We find that demographic differentiation operates differently in these two domains. Entry into conspiracy communities is primarily structured by gender and partisanship: masculine users disproportionately arrive through overtly political and alt-right spaces and share right-wing media sources, whereas feminine users more often pass through esoteric and spiritual subreddits before converging on r/conspiracy. In contrast, environmental communities are differentiated mainly by age and affluence. Affluent users tend to approach environmental subreddits via discussions of individual energy management, technology, and finance, while less affluent users arrive through protest-oriented and
climate movement spaces. By comparing these two issue domains, the study shows that sociodemographic sorting does not operate uniformly across polarized topics. Instead, distinct demographic groups build their own paths, shaping both the routes through which they engage with political content and the informational ecosystems that sustain their engagement. Our approach, based on the
attention-flow graph, refines our understanding of how different groups become embedded in online political communities.
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Computer Vision
ORAV: Benchmarking Audio-Video Generation from Multimodal Contexts
Jiacheng Hua, Xiaokun Feng, Jiaqi Hua et al. (+3 more)
Audio-
video generation using heterogeneous
multimodal references has emerged as a new challenge, requiring both compositional control over generation and grounded understanding of
multimodal context. In this paper, we introduce ORAV Bench for Omni Reference Audio-
Video Generation, comprising 380 task instances with 2-10 references, 9 semantic roles, and 30 role compositions. Instructions specify the relationships among references; the media supply the identities, dynamics, and audio characteristics to be realized. To evaluate these open-ended outputs, we develop a reference-aware pairwise protocol that prepares visual and auditory evidence, compares the intended contribution of each reference, and checks the overall verdict in both presentation orders. On held-out instances, it achieves 86.08% effective agreement with human judgments. Across 5 frontier systems, overall rankings conceal distinct strengths across reference compositions. A recurring failure is to reproduce unintended source content in place of the requested result, despite closely resembling a reference. Reproducible pointwise diagnostics of quality, reference affinity, and
speech reveal distinct dimensions of model behavior. ORAV thus offers a
benchmark for tracking progress toward controllable, compositional, and reference-faithful audio-
video generation.
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All authors Jiacheng Hua, Xiaokun Feng, Jiaqi Hua, Chang Liu, Biao Wang, Miao Liu
Machine Learning
QiYao-M: Multimodal Time Series Foundation Model with Role-Aware Modeling of Endogenous and Exogenous Modalities
Hanyin Cheng, Linfeng Wang, Zhengbo Qu et al. (+7 more)
Existing
multimodal time series foundation models (TSFMs) typically model heterogeneous modalities through largely shared mechanisms, overlooking the distinct
forecasting roles of endogenous and exogenous modalities. In this work, we propose QiYao-M, a role-aware
multimodal TSFM that models the two types of modalities separately. For endogenous modalities, to capture how they evolve along with the underlying temporal dynamics, we introduce an Endo-
Multimodal Predictor and Endo-
Multimodal Supervision to explicitly learn their
evolution from history to the future. For exogenous modalities, to generalize across domains and across various modality types and numbers under the scarcity of exo-
multimodal pretraining data, we propose an Exo-
Multimodal Retrieval Enhancer that enables rapid downstream adaptation without updating the TSFM parameters. We further introduce Endo-Modality Proxy Training to train this
retrieval module without exogenous
multimodal pretraining data. Extensive experiments across unimodal and
multimodal benchmarks demonstrate strong
forecasting performance in scenarios both with and without exogenous modalities.
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All authors Hanyin Cheng, Linfeng Wang, Zhengbo Qu, Yang Shu, Zhongwen Rao, Meng Wang, Yijie Li, Xin Jiang, Bin Yang, Chenjuan Guo
Computation and Language
Adapt Semantics, Not Structure: Few-Instance Schema Calibration for Scientific PDF Extraction
Zixiao Dong, Wei Yang, Zihao Liu et al. (+4 more)
A well-designed extraction schema is not necessarily ready for reliable LLM execution. When only limited verified extractions are available, manually tuning hundreds of field definitions through trial and error is costly. We frame this problem as few-instance schema calibration: adapting the operational semantics of an existing schema from a few annotated documents while preserving its structural contract. We introduce CPSE, a contract-preserving semantic extraction framework that jointly calibrates extraction
prompts and field-level semantic descriptions from a few gold annotations. CPSE decomposes the schema into an invariant structural contract and mutable field semantics, and further separates identity discovery from record completion using manifest-conditioned resolution. On expert-annotated
polymer-science documents, CPSE improves extraction by 9.93 points over an execution-matched baseline, with consistent gains under an independent judge and in a blinded expert audit. These results show that CPSE enables low-resource schema execution while preserving the output structure required downstream.
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All authors Zixiao Dong, Wei Yang, Zihao Liu, Chenshu Li, Longzhang Liu, Tao Tan, Hong Xie
Artificial Intelligence
Nociception as a Control Primitive: Afferent Channels and Nociceptive Memory for Agents Deployed in One Body
Wolfgang Maass
An
agent deployed in a single body cannot learn how fast that body wears, because every trial that would reveal its wear resistance wears the body it would protect. We study this epoch-one setting, in which the parameters of a fixed-weight policy are set before the body is drawn and never updated in life. The
agent carries a load-gated nociceptive channel and a
memory that retains what was felt. We prove that felt cost moves the allocation to the best-paid work not yet felt rather than the gentlest, that an
agent without retention never sees the felt-cost constraint bind, and that the channel pays only where the threat is individually unpredictable, cheap to avoid and expensive to ignore. We measure per body, setting the
agent with channel and
memory against the same individual without them, where neither carries a schedule learned across lives. On $2{,}000$ simulated floor-layer knees, with wear anchored to published loss rates, feeling, retaining and substituting extends the working life from age $55.2$ to $59.6$ and raises career output from $33.7$ to $36.1$. $69.3\%$ of bodies gain and none lose. A body that feels but retains nothing past the day gains one of the $+4.4$ years, and retention carries the rest. A population-trained
agent gains $+0.65$ years from the same channel at $-0.54$ output. The difference is what a species prior already supplies, and a single body has none. The two are related by an identity, the ablation mean reporting $(1-\chi)$ of the per-body value with $\chi$ the share a blind schedule already captures, so we report both. Where the regime map predicts value, a care
robot sextuples its certified service life and a field-anchored fleet writes off $0.15$ of its machines instead of $0.55$. Where it predicts none, a rover gains little over blind caution, so the map holds in both directions.
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Computation and Language
OpenWhistle: A Large-Scale Longitudinal Dataset and Benchmark of Bottlenose Dolphin Vocalizations
Faadil Mustun, Chiara Semenzin, Roberto Dessi et al. (+7 more)
Recent advances in bioacoustics have been driven by large-scale corpora and standardized
benchmarks, yet existing resources are overwhelmingly bird-centric and shallow per species, limiting their use for studying the structure of a single species' communication system. This gap is particularly acute for cetaceans: despite bottlenose dolphins (Tursiops truncatus) being a compelling case of complex vocal communication among non-human mammals, existing dolphin
datasets are small, fragmented, and largely closed. We introduce OpenWhistle, the largest publicly available
dataset of dolphin vocalizations. It comprises approximately 180,000 whistles (114 hours) recorded over five years from a stable pod of five individuals in a semi-natural environment, paired with a curated subset of 8,354 expert-annotated whistles and reproducible evaluation protocols for whistle-type detection and classification. We further release the full processing pipeline for whistle detection,
segmentation, and categorization. To demonstrate its utility, we pretrain a Wav2Vec2.0 model adapted to dolphin acoustics on the OpenWhistle corpus and show that it learns effective representations, outperforming general-purpose bioacoustic models such as AVES and BioLingual on both tasks while leaving meaningful headroom for future work. By releasing the
dataset, pipeline, and evaluation protocol, we provide the first open dolphin whistle
dataset tailored for training self-supervised models, laying the groundwork for advancing dolphin communication research and developing models that capture fine-grained acoustic structure within species.
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All authors Faadil Mustun, Chiara Semenzin, Roberto Dessi, Pablo Robin Guerrero, Pierre Orhan, Alexis Emanuelli, Emanuele Rossi, Yair Lakretz, Gonzalo de Polavieja, German Sumbre
Machine Learning
DivOPD: Spread Wide, Look Close for Asynchronous On-Policy Distillation of Multi-turn Agents
Hanyang Wang, Zeyuan Liu, Zhengyu Chen et al. (+7 more)
On-policy
distillation (OPD) trains student
agents through teacher supervision on their own interactions with an environment. However, in asynchronous multi-turn training, arrival-order batching can allow a few early or long rollouts to dominate learner updates while other valid rollouts become stale before being used, wasting already-generated experience. To address this problem, we introduce DivOPD, a simple learner-side batch-selection method that spreads a fixed turn budget across more rollouts and, within each rollout, prioritizes turns with larger cumulative teacher-student disagreement. Turns without usable teacher feedback are excluded. The per-turn loss and optimizer remain fixed; selection only changes which student-visited turns receive training weight. For no-progress rollouts, an optional extension briefly hands control to the teacher before returning it to the student. Across six teacher-student settings on the simulated ALFWorld, ScienceWorld, and WebShop
benchmarks, with 1.5B-7B students, DivOPD raises cross-setting mean peak success rate from 77.4 to 84.4 and mean success over the last five evaluations from 71.5 to 78.6. It reaches all reported setting-specific targets with
geometric-mean speedups of 1.84x in training
tokens and 1.87x in learner GPU time relative to vanilla OPD. Teacher intervention further raises this last-five mean to 82.4 while retaining about 1.7x learner-GPU speedup over vanilla OPD. Code will be released at https://github.com/HanyangWang0418-oss/DivOPD.
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All authors Hanyang Wang, Zeyuan Liu, Zhengyu Chen, Jingqing Ruan, Chaoxu Pang, Zhongda Su, Wulin Xie, Zhizhao Zeng, Ke Zeng, Tianxiang Zhao
Machine Learning · Cross-listed from physics.ao-ph
MW-Nowcast: Six-hour ensemble nowcasting of extreme precipitation
Ning Wang, Zuliang Fang, Weixin Jin et al. (+19 more)
Extending reliable nowcasting of extreme precipitation could provide critical additional time for warnings and emergency response during high-impact events such as flash floods. Radar-based generative machine-learning models have enabled skilful hyperlocal precipitation nowcasting, but accurate prediction of intense precipitation remains confined to the first few hours. Because storm-scale structure is predictable for longer than individual
cells, a natural strategy is to predict that structure while generatively modelling only the uncertain local growth, decay, reorganisation and initiation of storms. Here we present Microsoft Weather Nowcast (MW-Nowcast), a six-hour ensemble radar nowcasting model that jointly learns a deterministic predictor to capture organised precipitation structure shared across ensemble members, and a generator to produce diverse local residuals around this shared prediction. Across independent test data from the United States, Europe and China, MW-Nowcast achieves higher detection skill than leading methods for heavy and extreme precipitation throughout the 6 h horizon. For the most intense rainfall, MW-Nowcast doubles the available warning time across all three regions, delivering 6 h forecasts with skill previously limited to 3 h for the leading generative baseline. A cost-loss decision analysis shows that MW-Nowcast retains substantial value for a broad range of applications even at 4-6 h, where alternative methods offer little benefit. These additional hours can give forecasters and emergency managers the time to warn and act before extreme rainfall strikes, helping to protect lives and property.
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All authors Ning Wang, Zuliang Fang, Weixin Jin, Zhongjian Lv, Shuang Qin, Pengcheng Zhao, Siqi Xiang, Jiang Bian, Haoyi Xiong, Nan Guan, Bin Zhang, Liangjie Zhang, Denvy Deng, Qi Zhang, Matt Corey, Jitu Keshri, Sridhar Iyer, Hongyu Sun, Kit Thambiratnam, Jonathan Weyn, Richard E. Turner, Haiyu Dong
Computer Vision
Transform-Aligned Learned Features for Lossy Point Cloud Attribute Compression
Yueru Chen, Pengpeng Yu, Dingquan Li et al. (+3 more)
Transform-based methods provide an effective framework for point cloud attribute compression by representing attributes as transform coefficients. Introducing learned spatial context into this framework requires mapping spatial representations to the transform domain, but this known basis change is often left for the network to learn implicitly. We propose Transform-Aligned Learned Features (TALF) by applying the attribute transform to learned spatial representations, explicitly aligning them with the coding targets. Our analysis shows that the resulting features exactly represent the first-order prediction term of a smooth nonlinear model, with a bounded Taylor remainder. We integrate TALF into a transform-based attribute codec with explicit coefficient prediction and conditional residual
entropy modeling under a unified coefficient-domain rate–distortion objective, while retaining explicit quantization-step control. Extensive experiments across three
benchmark datasets and multiple transform bases demonstrate that TALF improves rate–distortion performance over conventional and learned baselines.
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All authors Yueru Chen, Pengpeng Yu, Dingquan Li, Wei Gao, Wei Zhang, Fei Song