Logic in Computer Science
Dynamic Proof Theory for Dynamic Epistemic Logic
Clara Lerouvillois and Francesca Poggiolesi
This paper proposes a calculus for Dynamic Epistemic Logic (DEL) based on dynamic hypersequents, a new framework for capturing the dynamics of epistemic models in a purely structural and natural way. We enrich dynamic hypersequents with
agent indices and an event store, yielding a uniform representation of epistemic events. The resulting calculus enjoys a set of logical rules that are provably invertible. Moreover, all structural rules, including the contraction rules, as well as the cut-rule, are shown to be admissible. Finally, the calculus constitutes, to the best of our knowledge, the only proof-theoretic calculus for DEL that is both grounded in a classical S5 framework and inherently dynamic.
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Machine Learning
Variational Augmented Invertible Koopman Autoencoder for probabilistic time series forecasting
Anthony Frion, Lucas Drumetz, Guillaume Tochon et al. (+3 more)
Neural Koopman autoencoder models have been shown to successfully build a latent
embedding with linear dynamics for arbitrary dynamical systems, enabling strong performance in long-term time series
forecasting. However, these models usually work in a deterministic setting, which does not allow the quantification of the uncertainty of their predictions. Thus, we propose the new Variational Augmented Invertible Koopman AutoEncoder (VAIKAE), in which the latent
embedding follows a Gaussian distribution instead of being deterministic. A key property of the VAIKAE architecture is that it leverages normalizing flow models, enabling the use of likelihood computations in the state space of dynamical systems for training a model. We further propose new strategies for uncertainty-aware latent data assimilation with a trained VAIKAE model. The effectiveness of our methods is demonstrated in a series of experiments on long-term time series
forecasting benchmarks.
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All authors Anthony Frion, Lucas Drumetz, Guillaume Tochon, Mauro Dalla Mura, Ali Can Bekar, Abdeldjalil Aïssa El Bey
Robotics
FP2: Equipping Robotic Foundation Models with Force Control
Hongjie Fang, Shirun Tang, Junjian Hu et al. (+8 more)
Robotic foundation models (RFMs) are increasingly capable of general-purpose manipulation, yet reliable physical interaction remains challenging in contact-rich settings. We present FP2, a lightweight downstream interface that equips task-adapted RFMs with explicit force control while preserving their action-generation capability. FP2 adopts an action-regulation decomposition: the task-adapted RFM serves as a foundation policy responsible for task-level action generation, while a high-frequency force control policy focuses solely on interaction regulation. To condition force regulation on the ongoing manipulation, FP2 compresses foundation-policy contextual representations and combines them with wrench and proprioceptive histories to predict structured force-control parameters. We evaluate FP2 with four RFM backbones across four real-world contact-rich manipulation tasks. FP2 consistently improves task performance and force regulation quality over the corresponding foundation policies, while comparing favorably with representative force-aware and force-control baselines. Ablations further show that foundation-policy context and physical feedback are complementary for effective force regulation, while preserving foundation-policy action generation improves both efficiency and novel-object generalization. Project website: http://force-policy.github.io/fp2
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All authors Hongjie Fang, Shirun Tang, Junjian Hu, Shidong Zhang, Derek Zhang, Linhao Chen, Dehai Li, Mingyu Mei, Wanxi Liu, Cewu Lu, Shiquan Wang
Machine Learning
Looped Actor: Depth-Recurrent Reasoning Models for Reinforcement Learning
T. Konstantin Rusch, Tim Seyde, Jared Boyer et al. (+2 more)
Looped
reasoning models repeatedly apply a shared set of parameters, enabling more computation without increasing the model size. These models also support input-dependent computation by dynamically deciding when to stop looping. Motivated by the recent success of looped
transformers in
language modeling and
reasoning, we investigate whether dynamic looping can similarly benefit sequential decision-making. We provide a complexity-theoretic motivation for this approach by showing that there exist Markov decision processes in which a state-adaptive policy achieves the optimal return with asymptotically less expected computation than any optimal fixed-runtime policy. To learn compute-adaptive policies in practice, we introduce Looped Actor, a
transformer-based policy that repeatedly refines a latent representation toward a fixed point using a shared computational block. This allows the model to allocate computation adaptively by varying the number of loops based on the current state. We evaluate Looped Actor on 22 tasks across six environments, ranging from combinatorial puzzles to robotic manipulation and spanning online and offline reinforcement learning (RL) with discrete and continuous actions. Looped Actor matches or exceeds the performance of an untied baseline with 16$\times$ more parameters, with the largest gains in environments where action selection requires substantial multistep planning. For the Boxoban environment, we find that the computation allocation is structured: the number of loops increases with the number of remaining pushes and future optimal pushes become increasingly predictable from the latent state over successive loops. Together, these results highlight actor looping as a simple and efficient way to equip RL
agents with adaptive computation and improve their planning capabilities. Code is available at https://github.com/camail-official/LoopedActor
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All authors T. Konstantin Rusch, Tim Seyde, Jared Boyer, Zach J. Patterson, Daniela Rus
Data Structures and Algorithms
Query Complexity of Testing Structured Parenthesis Languages
Tim Jackman, Diptaksho Palit and Sofya Raskhodnikova
We study the query complexity of testing membership in structured string
languages, focusing on Dyck
languages and natural generalizations. A tester receives query access to a word and must distinguish valid inputs from words that are far in Hamming distance, while inspecting only a sublinear number of positions. Our results sharpen the boundary between constant-query testability and
polynomial query complexity. First, we prove an $\Omega(n^{2/5})$ lower bound for testing Dyck
languages $D_m$ with any fixed number $m\ge2$ of parenthesis types, improving the previous $\Omega(n^{1/5})$ lower bound of Fischer, Magniez, and Starikovskaya (SODA `18) and nearly matching their upper bound of $O(n^{2/5+o(1)})$. Furthermore, we show that all nonadaptive
algorithms for these problems require $\Omega(n^{1/2})$ queries. Our lower bounds use a Pólya-urn process to construct the hard distribution; Second, we identify a broad class of weighted-parenthesis
languages, which we call excursion
languages, that remain constant-query testable. These
languages encode bounded-step walks that stay nonnegative and return to zero. For every fixed excursion
language, we give a nonadaptive tester with query complexity $O(1/\varepsilon^2)$, and we prove this dependence on $\varepsilon$ is optimal, even for adaptive
algorithms. As a special case, we obtain the tight $\Theta(1/\varepsilon^2)$ query complexity of testing $D_1$, improving the previous $O(\log(1/\varepsilon)/\varepsilon^2)$ upper bound and giving the first matching two-sided-error lower bound. Third, we construct a simple hard
language, Hidden String, that is generated by a deterministic linear grammar but nevertheless requires $\Omega(n^{2/5})$ adaptive queries and $\Omega(n^{1/2})$ nonadaptive queries to test. This shows that
polynomial query complexity appears even for highly restricted string
languages.
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Cryptography and Security · Cross-listed from quant-ph
Succinct Arguments for QMA in the Quantum Random Oracle Model
Alessandro Chiesa and Zihan Hu
Succinct arguments are a fundamental cryptographic primitive for verifying computational claims with small communication. In the classical setting, succinct arguments for NP can be constructed from unstructured hardness alone (e.g., hash functions) by compiling probabilistically checkable proofs (PCPs) or interactive oracle proofs (IOPs) for NP via the commit-and-open paradigm. In contrast, known succinct arguments for QMA rely on “structured” cryptographic primitives, or on the
quantum PCP
conjecture. We construct the first succinct argument for QMA in the
quantum random oracle model (QROM) without relying on additional cryptographic assumptions or unproven
conjectures. This yields succinct arguments for QMA from unstructured hardness alone, showing that ideal hash functions not only suffice for succinct arguments for NP but also for QMA. Underlying our result is an efficiency-preserving transformation that compiles
quantum interactive oracle proofs (QIOPs), a recently introduced interactive generalization of
quantum PCPs, into
quantum arguments for the same
language, via a natural
quantum commit-and-open paradigm. Our transformation applies to every QIOP with public-query soundness, a notion that we formalize to capture a natural requirement of the commit-and-open paradigm and is satisfied by a known QIOP for QMA. As a key ingredient in our transformation, we formalize and construct extractable vector commitments for
quantum states with local openings in the QROM, which may be of independent interest.
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Computer Vision
LazySloth: Bounded LLM-based Lazy Tree Search for Fast Long Video Comprehension
Arka Mukherjee, Kaleen Shrestha, Larissa Zhu et al. (+1 more)
Modern
vision-
language models (VLMs) have shown promising results in long-
video understanding due to the rich semantic information they can capture. However, most methods focus on coarse captioning of extracted image frames that are computationally inefficient and require models with large context windows. While past work has explored efficient methods through
multimodal retrieval-augmented generation (RAG), they rely on lossy
embeddings that lose temporal context and fine-grained detail. Few works to date have investigated how VLM-based query-relevant information
retrieval can be optimized. We introduce LazySloth, an efficient tree-based search method that speeds up
video comprehension and
retrieval tasks 2.9-8.3x (compared to existing agentic methods) through bounded captioning of portions of the
video considered irrelevant by a VLM of the
video. Compared to contemporary specialized
video-understanding VLMs and RAG-based methods, LazySloth achieved similar or better final task accuracy across two recent open-source base VLMs–Gemma 4 31B and Qwen3.6 27B–across four
benchmarks. LazySloth reduced the gap between the base open-source model and a closed-source model, GPT-4o. Ablations showed that replacing VLM scene understanding with CLIP-based
retrieval cost 8.8-19.9% in accuracy, while lazy tree construction matches eager construction at a fraction of the captioning cost. With LazySloth, we demonstrate the possibility of faster long-
video comprehension without substantial loss in performance.
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All authors Arka Mukherjee, Kaleen Shrestha, Larissa Zhu, Maja Matarić
Machine Learning
Simultaneous Neural Optimal Transport
Milena Gazdieva, Kirill Sokolov, Jiawei Chen et al. (+2 more)
Optimal Transport (OT) provides a principled framework for learning transformations between probability distributions from unpaired samples. In many applications, however, a single transformation must map several source distributions to a common target distribution. For example, image restoration might require handling different types of degradation without knowing the degradation of each input at inference time. Simple approaches of pooling the source distributions only encourage
alignment with the target at the aggregate level and may leave individual sources misaligned. In our paper, we consider the simultaneous OT problem which formalizes the task of learning a shared transport map that minimizes the average transport cost while aligning each source distribution with a prescribed target. We propose a neural method for solving the simultaneous OT problem by learning a shared transport map that minimizes the average transport cost while aligning each source distribution with a prescribed target. We derive a max-min formulation for learning this map. We illustrate its application to image restoration, where a single model handles multiple degradation types using a common collection of clean target images.
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All authors Milena Gazdieva, Kirill Sokolov, Jiawei Chen, Evgeny Burnaev, Alexander Korotin
Robotics
Towards Spatial Perception for Heterogeneous Robot Collaboration in Subterranean Mining Environments
Mario Alberto Valdes Saucedo, Akash Patel, Christoforos Kanellakis et al. (+1 more)
The autonomous extraction of deep mineral deposits in abandoned underground mines is fundamentally a multi-
agent integration problem. No single platform simultaneously offers the mobility to traverse kilometers of degraded drifts and the sensing payload required to characterize an ore body. This article presents the onboard perception pipeline that bridges two heterogeneous
agents within the PERSEPHONE autonomous mining mission. Which consist of a lightweight Explorer
robot that maps an unknown mine and generates a 3D scene graph of inspection targets, by running a zero-shot,
vision-
language semantic
segmentation stack that detects mineral deposits directly from natural-
language prompts. The map and the graph are then handed to a second Inspector
robot, which carries an advanced sensing payload and uses them to plan close-range inspection viewpoints. We detail the complete pipeline, with emphasis on the
geometric abstraction that turns raw detections into actionable inspection targets, spanning per-view bounding-box generation, cross-view box merging, plane fitting, and polygon extraction, and we report an extensive field validation in a subterranean test facility and in an active magnesite mine, covering both iron-vein and magnesite mineralization under realistic, perceptually degraded conditions.
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All authors Mario Alberto Valdes Saucedo, Akash Patel, Christoforos Kanellakis, George Nikolakopoulos
Computational Complexity
Robust Approximation and the Arity Barrier at Width Two
Nathan Benedetto Proença, Koppány István Encz and Monaldo Mastrolilli
Zwick's
algorithm for Horn satisfiability shows that constraints of unbounded arity can admit a robust approximation guarantee independent of the arity. For finite constraint
languages, Barto and Kozik proved that robust approximability is characterized by bounded width. We ask whether arity-independent robustness persists beyond width one and show that it fails already at width two. For Majority-closed Boolean linear constraints of maximum arity $k \ge 2$, we give a randomized
polynomial-time
algorithm that, without knowing $\varepsilon$, violates an expected $O(\sqrt{\varepsilon \log k})$ fraction of the constraint weight on $(1-\varepsilon)$-satisfiable instances. Under the Unique Games
Conjecture (UGC), a matching NP-hardness lower bound of $\Omega(\sqrt{\varepsilon \log k})$ holds in an explicit parameter regime. Under this assumption, the bounded-width characterization therefore does not extend uniformly to constraints of unbounded arity. The
algorithm rounds a degree-eight Sum-of-Squares (SoS) relaxation with a single Gaussian threshold. Since
polynomial size alone does not make SoS solvable in
polynomial bit complexity, we construct complete truncated Groebner bases for the soft Majority ideal and show that, at every fixed degree $2d$, the relaxation can be optimized to any rational accuracy in
polynomial time with exactly feasible solutions, and degree-$2d$ SoS proofs can be found after an additive perturbation at degree at most $4d+10$. The lower bound combines Raghavendra's gap-to-hardness
theorem with an integrality gap on a Gaussian
star, analyzed via the Isaksson-Mossel
theorem that parallel halfspaces maximize the joint membership probability of exchangeable Gaussians.
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Machine Learning
Scale Sensitivity in Low-Bit Post-Training Quantization: Curvature of the Quantization Error Landscape
Jonas von Berg, Massimiliano Datres, Carlo Kneißl et al. (+1 more)
Post-training quantization (PTQ) methods in the GPTQ family minimize a layer-wise reconstruction error on a uniform grid whose scale must be chosen; the common max-based choice degrades sharply at low bit-widths. We study how sensitive this objective is to the scale. For a layer with i.i.d. Gaussian weights and calibration activations of sufficiently large effective rank, we prove that, as the width grows, the normalized round-to-nearest loss converges with high probability, uniformly over all scales, to the mean-squared error of a uniform quantizer applied to a standard Gaussian; we verify the effective-rank condition for wide, randomly initialized MLPs with odd Lipschitz activations and isotropic Gaussian calibration data. The limiting objective has a unique nondegenerate minimizer, whose scale decreases strictly with the number of levels and whose
curvature with respect to relative scale errors decays approximately exponentially with the bit-width. GPTQ experiments on five LLMs show the same trend: the scale rule changes perplexity substantially at 2–3 bits and negligibly from 6 bits on, and a local measure of GPTQ scale sensitivity decreases with bit-width in line with the Gaussian
curvature. The Gaussian-optimal scale fails on raw weights; after Hadamard incoherence processing it matches the best searched rule at 3 bits and above without any search, but remains clearly worse at 2 bits.
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All authors Jonas von Berg, Massimiliano Datres, Carlo Kneißl, Gitta Kutyniok
Computation and Language
Look What You Made Us Cluster: Hate Narrative Extraction from Reddit Discourse
Annabelle K. L. Chua, Forster J. Khoo, Joel C. R. Tan et al. (+8 more)
Narrative extraction allows us to identify online hate narratives, supporting the construction of rigorous detection systems. Existing computational approaches, however, are limited in precision as they rely on semantic representations, which tend to capture only surface-level meaning. To detect more precise and interpretable narratives, we present an extraction pipeline that represents narratives as entity-evaluation pairs. Narratives are extracted using a Large
Language Model (LLM)
reasoning process that extends Aspect-Based Sentiment Analysis, identifying the aspect, classifying its judgement type as the basis for evaluation, and deriving the evaluation accordingly. Extracted narratives are then clustered using Leiden, following which clusters are resolved to an intended level of granularity through an LLM-guided refinement process. We illustrate this narrative pipeline with English Reddit comments from 2024 that criticize Taylor Swift, analyzing a representative cluster that exhibits hate
speech patterns to demonstrate its interpretive value.
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All authors Annabelle K. L. Chua, Forster J. Khoo, Joel C. R. Tan, Huey Ting Ang, Kheng Hwee Tan, Joel Y. A. Sim, Shirley W. H. Ow, Ria Mundhra, Elsie C. K. Toh, Youfeng Xu, Lynnette H. X. Ng
Computer Vision
Complementary Retrieval-Augmented Prompting for Consistent Long-Form Video Generation
Xianghan Wei, Xiaoda Yang, Zhi Wang et al. (+7 more)
While recent
video foundation models excel at generating high-quality short
videos, long-form
video generation remains a critical challenge, where a major bottleneck lies in conditioning independently generated shots to preserve consistent characters, scenes, and objects throughout a story. Existing training-free approaches typically condition target shots using retrieved historical visuals. However, these references often suffer from severe informational mismatch, either introducing irrelevant contextual redundancy or failing to provide the full combination of required elements for the target shot. To resolve this, we present Complementary
Retrieval-Augmented Prompting, an agentic framework that strategically aggregates a compact set of mutually supportive historical references to achieve complete and targeted conditioning for long-form
video generation without retraining or modifying the underlying generator. Specifically, our framework explicitly models the visual elements required by each target shot by parsing the narrative script into a text-grounded visual element registry that tracks characters, objects, scenes, and their shot-level states. A VLM-annotated keyframe library further maps these elements to past visual observations. Guided by the required elements, our
agent retrieves complementary references that maximize target-element coverage while minimizing historical noise. Finally, the retrieved references, structured element states, and grounding instructions are assembled into a unified
prompt for the frozen
video generator. This element-aware process provides comprehensive conditioning while remaining fully interpretable. Quantitative and qualitative evaluations on multi-shot story generation demonstrate that our method consistently outperforms recent-frame,
memory-based, and entity-level
retrieval baselines in cross-shot consistency and text-controllability.
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All authors Xianghan Wei, Xiaoda Yang, Zhi Wang, An Pan, Daoan Zhang, Huayi Zhang, Yan Zhang, Wei Xu, Zishun Liao, Jianwen Lou
Software Engineering
Complexity-Aware Evaluation of LLM Comprehension
Ali Mohammadi Esfahani, Nafiseh Kahani and Samuel A.Ajila
Large
language models (LLMs) are increasingly used for software engineering tasks that require understanding existing source code, including behavior prediction, function explanation, debugging, and code review. However, aggregate
benchmark accuracy can conceal how model reliability changes as source code becomes structurally more complex. This paper presents a complexity-aware framework for evaluating LLM code comprehension using cyclomatic complexity, nesting depth, branching factor, and Halstead volume. We evaluate DeepSeek-Coder-V2 and Llama through two complementary tasks: automatic input-output prediction over 300 Python functions and manually assessed semantic comprehension over a balanced subset of 60 functions. The functions are grouped into Low-, Medium-, and High-complexity bands. DeepSeek-Coder-V2 achieves an overall automatic accuracy of 78.33%, compared with 70.33% for Llama. However, accuracy decreases substantially from Low to High complexity, from 93.52% to 52.78% for DeepSeek-Coder-V2 and from 87.04% to 47.22% for Llama. Incorrect predictions are consistently associated with higher values of all four complexity metrics, and correlation and logistic-regression analyses confirm broadly comparable negative associations between structural complexity and correctness. Manual semantic comprehension shows the same degradation pattern, with accuracy decreasing from 100.00% to 75.00% for DeepSeek-Coder-V2 and from 90.00% to 60.00% for Llama. These findings demonstrate that complexity-aware evaluation provides a more diagnostic assessment of LLM code-comprehension reliability than aggregate accuracy alone.
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Artificial Intelligence
Routing Should Pay for Itself: Sparse Supervision for Economical LLM Routing
Guannan Lai, Gelin Bian, Hao-Xuan Ma et al. (+5 more)
Large
language model (LLM) routing reduces serving cost by assigning each query to an appropriate model while preserving response quality. Learning such a router, however, often requires executing multiple candidate models on historical queries to collect query–model quality feedback, creating a nontrivial supervision cost before deployment. Existing work largely focuses on serving-time efficiency, overlooking whether the resulting savings are sufficient to recover this upfront expenditure. We further observe that routing quality often saturates well before all query–model feedback is collected, suggesting that dense supervision can be economically over-provisioned. We propose SaveRouter, a sparse-supervision routing framework that selectively acquires informative model feedback and shares capability information across related queries, while retaining query-level refinement for fine-grained routing. We evaluate routing by jointly accounting for supervision expenditure and subsequent serving-time savings. Across four routing
benchmarks, the main setting uses only about 33–41% of available training feedback while maintaining competitive or better routing quality, and reduces the break-even deployment volume by approximately 1.9–9.5 times compared with the fastest conventional router. Further analysis shows that acquiring more supervision is not always economically preferable: the supervision level that minimizes serving cost can differ from the one that achieves the earliest payback. Our code is publicly available at https://github.com/LAMDA-Model-Reuse/SaveRouter.
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All authors Guannan Lai, Gelin Bian, Hao-Xuan Ma, Jun-Peng Jiang, Long Chen, Jian-Dong Liu, Zhi-Hao Tan, Han-Jia Ye
Computer Vision
BeatDance: Generating Beat-Consistent 3D Dance with Hierarchical Spatial-Temporal Modeling
Xiaojian Shen, Dahu Shi, Jianrong Zhang et al. (+7 more)
Generating realistic 3D dance from music is a challenging task that requires accurate synchronization with musical rhythms while capturing the spatial complexity of human motion. Although existing methods can generate physically plausible dance motions, they often struggle to achieve precise
alignment with music, such as the beat. To address this limitation, we propose a novel
diffusion-based framework, BeatDance, with two components: 1) We present a Hierarchical Decoupled
Attention (HDA) module, which first disentangles the learning of human pose and temporal dynamics. A hierarchical structure is then employed to capture both short-term and long-term dependencies, thereby enhancing spatial-temporal modeling. 2) We adopt cycle-consistent learning by introducing an auxiliary dance-to-music module. During training, discrepancies between the reconstructed and original music induce a stronger loss signal, effectively encouraging the consistency property between the music and dance motion. Extensive experimental results demonstrate that our proposed approach outperforms recent competitive methods on two
benchmark datasets.
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All authors Xiaojian Shen, Dahu Shi, Jianrong Zhang, Hai Li, Hongwei Zhao, Dawei Zhang, Yunzhi Zhuge, Zhiliang Wu, Guanghui Yue, Wei Zhou
Hardware Architecture
MEDEM: Multi-Engine DL Accelerator Design Methodology
Fareed Qararyah, Mohammad Ali Maleki and Pedro Trancoso
Multi-engine deep learning (DL) accelerators are becoming increasingly prevalent as they address the heterogeneity and growing complexity of modern DL workloads. To efficiently process diverse DL workloads, these accelerators must incorporate combinations of engines with complementary capabilities to match the distinct computational characteristics of these workloads' heterogeneous kernels. However, existing multi-engine DL accelerator design approaches lack a systematic methodology, leaving fundamental questions unresolved. These include how to co-design engines for workloads with diverse computational characteristics and which engine combinations minimize aggregate execution costs (such as time or energy) across such workloads. Addressing these questions requires efficient exploration of exponentially large design spaces. To address these questions systematically, this work proposes Multi-Engine DL Accelerator Design Methodology (MEDEM). MEDEM defines generic engine abstractions, co-designs candidate instances (engines), and selects a combination of co-designed engines to minimize aggregate execution cost given diverse DL workloads and a resource budget. MEDEM encompasses a set of design strategies that efficiently navigate the exponentially large design spaces of engine co-design and combination selection, identifying highly optimized multi-engine accelerators. A comprehensive evaluation demonstrates that MEDEM identifies accelerators that outperform state-of-the-art designs, delivering
geometric-mean improvements of up to 4.84x in energy-delay product (EDP) and 1.59x in throughput. The improvements are achieved using different resource budgets, demonstrating MEDEM's scalability, and using 51 single- and multi-model DL workloads, demonstrating its generalizability.
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Artificial Intelligence
Direct Experience World-Model Optimization: Learning the World Beyond Action Imitation
Xiangcheng Zhan, Zirui Chen, Yicheng Zhao et al. (+2 more)
World-Action Models (WAMs) couple action generation with predictions of how physical interactions unfold. However, current post-deployment learning paradigms typically improve behavior without requiring better world predictions. Especially in dexterous manipulation, small execution errors can compound in high-dimensional action spaces, hindering policy improvement and pushing interactions beyond the world model's training distribution. Motivated by this, we propose Direct Experience World-Model Optimization (DEWO), a post-deployment learning paradigm for WAMs that, alongside action imitation, refines world representations through visual experience to better condition action generation. Specifically, it identifies interaction turning points and learns from successful and failed futures to support classifier-free guidance. An additional value head estimates task progress from
video representations and activates guidance when progress stalls during inference. Across five DexJoCo tasks, DEWO improves average success across all three WAM formulations. Ablations show that visual supervision from successful and failed continuations improves both prediction and control beyond action supervision alone. On four real-world tasks across Wuji and Sharpa, 3 x 3 grid evaluations show that two rounds of deployment learning increase success from 51.0% to 71.7% in
cells with at least one initial success, a gain of 20.7 percentage points. These findings support continued predictive learning for improving control through deployment experience, making world modeling an active part of WAM adaptation.
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All authors Xiangcheng Zhan, Zirui Chen, Yicheng Zhao, Ziteng Gao, Shuo Yang
Cryptography and Security
Confidence-Guided Protocol IR for LLM-Aided Security Protocol Modeling
Siqi Li, Yufan Cai, Hongshu Wang et al. (+3 more)
Large
language models offer a promising interface for translating natural-
language protocol descriptions into formal security models, but their outputs remain difficult to trust without expert validation. In this paper, we present a human-in-the-loop framework for generating Tamarin-verifiable formal models of security protocols. Our key observation is that the main correctness bottleneck is the semantic accuracy rather than the syntactic validity of the intermediate protocol representation. To address this problem, we introduce a protocol intermediate representation (IR) that serves as a human-auditable semantic checkpoint between natural-
language parsing and formal model generation. The IR explicitly captures protocol participants, message flows, value provenance, cryptographic operations, proof targets, and compromise assumptions. We further design an interactive interface that highlights uncertain fields and guides users to inspect the most critical semantic decisions based on model confidence before model generation. Rather than replacing formal-methods experts, our approach uses LLMs to produce auditable semantic drafts while leveraging verification tools to check the resulting formal models. Code and verification artifacts are available at https://github.com/laplace1002/TamarinAgent.git.
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All authors Siqi Li, Yufan Cai, Hongshu Wang, Xinyue Zuo, Zhe Hou, Jin Song Dong
Machine Learning
Learning Macroscopic Dynamics without Reconstructing Microscopic States
Zhichao Han, Yue Zhao and Qianxiao Li
Modeling the temporal
evolution of macroscopic properties of complex systems is an important scientific task. To predict this
evolution without full microscopic simulation, a common approach encodes microstates into compact latent states, learns their
evolution, and reads out macroscopic predictions from the latent trajectory. These latent states are often learned through microstate reconstruction. However, with limited latent capacity, reconstruction can favor high-variance microscopic details over information needed for macroscopic prediction. Yet jointly learning latent states and their transition without reconstruction often fails to obtain latent dynamics that support accurate macroscopic prediction. We show that this failure can arise from latent scale collapse: shrinking the latent state scale reduces training loss while macroscopic
evolution error remains large. Here, we propose a reconstruction-free framework to learn latent states with their dynamics for prescribed macroscopic prediction. Training alternates between updating the latent representation with the transition and next-state latent targets fixed, and updating the transition with the latent representation fixed. At inference, the trained model predicts macroscopic states recursively from an initial microstate. Our theoretical analysis characterizes reconstruction misalignment and scale collapse under joint training, and gives a sufficient condition for local convergence to correct latent dynamics for our method. Experiments on epidemic spreading on a
lattice, mixing of two particle species, and
polymer stretching demonstrate that the proposed method achieves substantially better macroscopic prediction over baselines.
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Machine Learning
Rethinking Soft Tokens for Parallel Decoding in Diffusion Language Models
Kodai Kawamura, Kenji Kawaguchi and Anji Liu
Diffusion language models (DLMs) enable parallel generation by predicting and committing multiple
tokens at each denoising step, yet they can generate individually plausible but mutually inconsistent
tokens. Recent work shows that soft
tokens can mitigate this issue by representing uncertain positions with continuous
embeddings built from the model's predictive distribution at the previous decoding step. However, although soft
tokens are commonly understood as preserving predictive uncertainty, how soft-
token feedback improves parallel decoding has not been systematically examined. In this paper, we investigate this question in frozen pretrained DLMs to examine soft-
token feedback without the effects of additional training. To construct soft-
token inputs in a training-free setting, we identify a
geometric mismatch between conventional soft-
token construction and the pretrained
embedding space. Based on this observation, we propose a training-free, geometry-aware construction of soft
tokens. Our analysis of soft-
token feedback suggests that uncertainty preservation alone does not fully explain how it reshapes subsequent predictions. To better explain how soft-
token feedback improves parallel decoding, we provide empirical evidence that it favors coherent
token sequences. Across four pretrained DLMs and four math and code
benchmarks, our method outperforms standard parallel decoding and a training-free Euclidean soft-
token baseline. Code: https://github.com/kodaikawamura/rethinking-soft-
tokens
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Computer Vision
Multi-task learning for the automatic grading of enlarged perivascular space burden using MRI
Jesse Phitidis, William N. Whiteley, Joanna M. Wardlaw et al. (+11 more)
Enlarged perivascular spaces (PVS) visible in
brain magnetic resonance imaging (MRI) are increasingly thought to be linked to poor
brain health. PVS are elongated structures of less than 3 mm in diameter and can be numerous. To reflect the incidence of PVS, radiologists visually score their burden following a clinical grading scale - a task that would benefit from automation to accelerate analyses and overcome the influence of inter-observer differences. We developed and evaluated methods for training machine learning models to score PVS incidence in the basal ganglia (BG) and centrum semiovale (CSO) leveraging the Potters/Wardlaw scale. The novelty in our work lies in the use of imperfect, semi-automatically generated "silver-standard" PVS
segmentation masks during training, in addition to PVS radiological scores. We comparatively evaluated a conditional convolutional neural network (CNN) which accepts PVS masks as an extra input channel, a multi-task CNN which performs both PVS
segmentation and scoring, and a logistic regression model which utilises features derived from PVS masks to predict PVS scores. Multi-task learning was the most effective method, achieving a mean average precision of 64.08% compared to 60.22% for the conditional CNN, 52.11% for a baseline CNN trained only to predict PVS scores, and 49.32% for the logistic regression model. The multi-task model showed an ability to localise individual PVS not shown by the other CNNs, and behaved in a probabilistically sensible way, predicting with lower confidence on inherently harder classes. Age, sex, hypertension status, white matter hyperintensity volume, and ischaemic stroke lesion status were shown to be associated with the multi-task model's PVS score predictions and the ground truth in a similar way.
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All authors Jesse Phitidis, William N. Whiteley, Joanna M. Wardlaw, Miguel O. Bernabeu, Yajun Cheng, Xiaodi Liu, Junfang Zhang, Una Clancy, Stephen Makin, Roberto Duarte Coello, Susana Muñoz Maniega, Mark E. Bastin, Simon R. Cox, Maria del C. Valdés Hernández
Computer Vision
Anatomy-Aware Prediction of Bronchoscopic Accessibility from 3D CT
Linkai Peng, Cuiling Sun, Bin Wang et al. (+10 more)
Pre-operative planning for bronchoscopy is critical for the diagnosis of lung lesions. Current accessibility assessment relies on subjective manual inspection of CT scans, which is time-consuming and prone to inter-observer variability. In this paper, we formalize bronchoscopy accessibility prediction as a novel supervised learning task and present the first end-to-end framework to address it. We propose an Anatomy-Aware Mixture-of-Experts (MoE) model that integrates specialized modules: a CT Expert for local morphological features, a Lobe Expert for anatomical priors, and a Path Geometry Expert that encodes the sequential constraints of the bronchial tree. To support this task, we curated the first clinical
dataset of 438 cases with pre-operative CT scans and documented procedural outcomes. Experimental results demonstrate that our method achieves an AUROC of 0.8052, significantly outperforming both state-of-the-art baselines and experienced human experts. This work establishes a new
benchmark for computer-aided interventional planning in pulmonary medicine. Our data and code will be publicly available at https://nubagcilab.github.io/BronchoAccess/.
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All authors Linkai Peng, Cuiling Sun, Bin Wang, Jamie Rowell, Catherine Gao, Oyku Ikizgul, Eminenur Sen Tasci, Andrea Bejar, Halil Ertugrul Aktas, Gorkem Durak, Momen Wahidi, Christopher Kapp, Ulas Bagci
Machine Learning
MoTIF-X: A Multimodal Tokenized Framework for Interpretable and Extensible Molecular Representation Learning
Linqing Mo, Jiayu Zhou and Bin Chen
Molecular representation learning is central to computer-aided drug discovery. Molecular graphs, SMILES strings, and 3D conformations provide complementary structural information, yet many
multimodal approaches encode these views independently and align them only at a later stage, limiting fine-grained cross-modal interaction and substructure-level interpretability. To address these limitations, we introduce MoTIF-X, a motif-centered framework that uses graph-grounded chemical motifs as shared anchors for
multimodal integration and interpretation. Its first pretraining stage learns motif representations through hierarchical contrastive learning across atomic, motif, and molecular scales. The second stage contextualizes these representations with SMILES and torsion-angle
tokens through
multimodal masked
token modeling. After pretraining on drug-like molecules with multiple conformers, MoTIF-X achieved the lowest mean absolute error on all nine OpenADMET ExpansionRx endpoints and the best overall performance among the evaluated methods. Significance analyses supported its advantage in the vast majority of endpoint-baseline comparisons after multiple-testing correction. Ablation studies supported the complementary contributions of motif-
token contextualization,
multimodal integration, and two-stage pretraining. Beyond molecular properties, the framework extended to drug-target interaction prediction, achieving the best average classification performance across the evaluated
benchmarks and generalizing to an external drug-cold-start
dataset without additional fine-tuning. Its motif-centered design also enabled substructure-level interpretation: higher motif attribution scores were associated with larger experimentally measured activity shifts. Together, these findings support MoTIF-X as a transferable and interpretable framework for molecular modeling.
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Machine Learning
High-Dimensional Simulation-Based Inference in Latent Spaces
Lars Kühmichel, Stefan T. Radev, Bhanu Prasanna Koppolu et al. (+3 more)
Neural simulation-based inference (SBI) has been widely successful in inferring a relatively small number of interpretable parameters from potentially high-dimensional observations, such as images or time series. Accordingly, representation learning in SBI has focused almost exclusively on compressing the observations used to condition the posterior. More recently, however, SBI has begun to target increasingly high-dimensional parameter spaces, raising the complementary question of whether the inference target itself should be compressed. Our answer is a practical merger of SBI and latent generative modeling, which learns a low-dimensional representation of the simulator parameters, performs posterior inference directly in this latent space, and maps posterior samples back to the original parameter space. We characterize the conditions under which latent-space inference recovers the desired target posterior and systematically study its empirical trade-offs. Across four case studies and three generative families, we compare latent and standard
estimators while controlling for network capacity, regularization, optimization, and training compute. At matched training compute, latent-space inference achieves accuracy and marginal calibration comparable to direct target-space inference while sampling up to more than an order of magnitude faster.
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All authors Lars Kühmichel, Stefan T. Radev, Bhanu Prasanna Koppolu, Masoumeh Davoudi, Jerry M. Huang, Paul-Christian Bürkner
Systems and Control · Cross-listed from math.OC
Information Limits of Multistage Inventory Control: Learning, Valuation, and Censoring
Hanzhang Qin, David Simchi-Levi and Ruihao Zhu
Learning how to replenish inventory and estimating the cost of doing so require different information. We establish information-theoretic lower bounds for offline multistage inventory control with independent bounded demand. For fixed additive error in total expected cost, policy learning requires cubically many scalar observations in the horizon in the worst case, even under full observation and adaptive sample allocation. This matches the known empirical-risk upper order. Stationarity permits a quadratic policy-learning guarantee, yet valuation with inherited stock can remain cubically difficult even when the optimal policy is known. Censoring sharpens this distinction: optimal decisions can be learnable while absolute costs remain unidentified. We give a carrysafe coverage condition under which truncation preserves inventory transitions and policy gaps exactly. Combining this reduction with a matching lower bound yields the sharp censored-learning rate, with raw sample requirements inversely proportional to the usable fraction of sales logs. The same qualified observations can check coverage and fit the policy under a joint error guarantee. The results isolate information limits for decisions and valuation under an explicit conditionally independent logging protocol.
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Machine Learning
Yulong Huang, Chen Jiang, Zhanpeng Zhou et al. (+5 more)
Looped
Transformers provide a parameter-efficient approach to depth scaling by repeatedly applying shared
Transformer blocks. Recent
reasoning models have likewise highlighted the value of scaling test-time computation through longer computation trajectories. However, the principles for designing effective loop transitions remain poorly understood. We view the looped hidden state as a fast weight that is updated throughout the depth. We formulate loop transitions as local gradient-based updates, with recurrent blocks predicting implicit targets at each depth. Our framework derives loop transitions in closed form from a projection, a local objective and an optimizer update rule. Mapping representative loop transitions into this framework reveals mismatches between their transitions and projections. We first align the input maps of existing transitions. We then derive OperLoop, which combines explicit weight decay, adaptive step size and a delta objective. The aligned variants reduce training loss and improve average commonsense accuracy. OperLoop improves average generative performance over the compared looped and non-looped baselines under matched training FLOPs. These results support the framework's usefulness for loop design. We extend the analysis to additional loop models and outline a roadmap for future loop transition design.
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All authors Yulong Huang, Chen Jiang, Zhanpeng Zhou, Hongtao Zhang, Tianyu Li, Tianyu He, Xiangyu Zhang, Bojun Cheng
Computer Vision
Do-JEPA: From Masking to Intervention in Latent World Models
Hossein Resani and Javen Qinfeng Shi
Latent world models are trained to predict what happens next, so nothing in their objective separates what an action caused from what merely co-occurred with it. Object-masking models such as C-JEPA intervene on what the predictor can see; we intervene on what physically happens. From one saved simulator state we run the dynamics under an action $a$ and under a reference action $a_{\varnothing}$, and train the model to predict the difference $\Delta z=z^{a}-z^{a_{\varnothing}}$ between the two latent futures. The resulting objective, Do-JEPA, has an effect loss, a support loss (where the action enters), a propagation loss (where its effect travels) and invariance losses (what must not change). In a synthetic system with object-aligned variables, support supervision finds the directly intervened object in 99.95% of test cases, where a sparse action mask sends the action to a nuisance slot in every case, and response-onset supervision recovers the ring-shaped propagation graph (edge AUROC 0.975 vs. 0.624). From pixels, the effect loss beats a control trained on exactly the same data: it lowers latent effect error by 28.4% on an end-to-end LeWM model and physical effect error by 13.5% when trained and tested on natural action sequences, and on three independently generated CausalWorld
benchmarks it lowers responsive effect error by about 20% under physics shifts and the latent context sensitivity of predicted effects by 66%. Trained from scratch it costs factual accuracy; fine-tuning an existing model with it removes this cost. Together, these results show that intervening on the world, rather than on what the model sees, helps latent world models predict what their actions cause.
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Artificial Intelligence
Beyond Prompt Count: How Data Shapes Transfer in On-Policy Distillation
Jiaxuan Wang, Jiafei Lyu, Yuchen Cai et al. (+8 more)
On-policy
distillation (OPD) trains students using teacher feedback on their own sampled responses, yet how
prompt choice shapes transfer across teacher-student pairs remains poorly understood. We systematically study
prompt quantity, source, and selection across RL- and SFT-continuation pairs and cross-model settings. We find that OPD can be highly
prompt-efficient: a few
prompts can approach large-pool performance, with four DAPO
prompts matching the observed mathematics score of 3,840 DeepMath
prompts. However,
prompt utility is relational rather than intrinsic: changing only the teacher can reverse the relative effectiveness of mathematics and code
prompts. To characterize these transfer differences, we analyze parameter and functional changes across
prompt supports and model pairs. Functional
alignment with the teacher varies across supports and target tasks; in continuation pairs, teacher-aligned prediction changes can coexist with weak parameter
alignment. Continued OPD on effective supports can restore performance after unfavorable transfer. Finally, targeted selection does not consistently outperform uniform random sampling, and filtering out a source that performs poorly alone yields no consistent gain across three paired support draws. Overall, our results distinguish
prompt efficiency from
prompt interchangeability and show that effective data choice depends on the teacher-student pair and target capability, with random sampling providing a competitive baseline in the studied settings.
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All authors Jiaxuan Wang, Jiafei Lyu, Yuchen Cai, Siye Wu, Pengyuan Wang, Jiashun Liu, Xiang Cheng, Kai Yang, Yangkun Chen, Saiyong Yang, Lan-Zhe Guo
Computer Vision
MG-Thinker: Bi-Axial Self-Reflection for Multi-Image Reasoning Grounding
Heyu Huang, Chi Chen, Zonghao Guo et al. (+3 more)
Reinforcement learning (RL) has recently delivered substantial gains in
multimodal reasoning, opening a promising route for fine-grained visual perception. Yet for multi-image
reasoning grounding (MRG),
reasoning over real-world multi-image contexts toward pixel-precise localization, existing RL-based approaches overlook two characteristics intrinsic to this paradigm: a coarse-to-fine hierarchical
reasoning pattern, and heterogeneously distributed task–sample difficulties. In this work, we present MG-Thinker, a post-training RL framework that advances a new MRG paradigm featuring such hierarchical
reasoning, supported by a curated 25K MRG
dataset with task-adaptive Chain-of-Thought (CoT) annotations that elicit multi-perspective evidence before conclusion. To remedy the heterogeneous task–sample difficulties, we further propose Bi-Axial DAPO (BiA-DAPO), which decomposes rollout advantages along an intra-group signal axis and an inter-group competence axis through two complementary mechanisms, both grounded on our defined candidate pool for stable group-level statistics. Extensive experiments show that MG-Thinker achieves state-of-the-art performance on multi-image
reasoning grounding while consistently improving generalization across multi-image understanding and diverse
multimodal benchmarks.
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All authors Heyu Huang, Chi Chen, Zonghao Guo, Yuhua Li, Maosong Sun, Ruixuan Li