Machine Learning · Cross-listed from stat.ML
Online Individualized Treatment Selection under Partial Feedback: False Discovery Control and Minimax Optimality
Runzhe Gu, Zinan Zhao, Wenguang Sun et al. (+1 more)
We study online individualized treatment selection when only the outcome under the assigned treatment is observed. Our objective is to preferentially treat individuals whose potential outcomes under treatment exceed a
covariate-specific control reference, while limiting false discoveries among treated individuals. We propose Partial-Feedback Adaptive Individualized Treatment Selection (PAITS), which initializes learning from historical data and combines sequential estimation of local null risks, randomized exploration, and feedback-driven updates of the treatment selection threshold. Under regularity conditions, we establish asymptotic false discovery rate control and sublinear gap-weighted assignment regret relative to a population oracle. We further establish a minimax lower bound on assignment regret under prescribed uniform nuisance-estimation requirements and give conditions under which PAITS attains the corresponding constrained minimax rate up to logarithmic factors. These results quantify the tradeoff between accurate treatment assignment and nuisance learning under partial feedback. Simulation studies and a real-data analysis using the Jester recommendation
dataset illustrate the finite-sample performance of PAITS.
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All authors Runzhe Gu, Zinan Zhao, Wenguang Sun, Xintao Xia
Robotics
DeTac: Dual-branch Decoupling of Texture and Force Signals in Vision-Based Tactile Sensors
Zhirui Zhang, Yunkai Tang, Jiahang Li et al. (+6 more)
Vision-based tactile sensors enable fine-grained perception for dexterous robotic manipulation, but current marker-based designs tightly couple surface texture and contact force signals within a single observation. Existing data-driven decoupling methods rely on serial mask-based inpainting, which suffers from mask-sizing dilemmas caused by physical indentation effects, error retention, and lack of real paired data for supervision. To address these challenges, we propose DeTac, a novel dual-branch generative framework that concurrently extracts clean shading and marker images from a single mixed tactile image. DeTac leverages full-image synthesis to naturally bypass mask dependency and introduces a cross-branch synergistic optimization that enables mutual supervision, effectively preventing error retention. Furthermore, we construct a strictly aligned, real-world paired tactile
dataset using custom hardware to provide high-quality supervision. Extensive experiments demonstrate that DeTac achieves state-of-the-art decoupling accuracy, significantly facilitating downstream surface reconstruction and force estimation tasks while exhibiting cross-sensor generalization.
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All authors Zhirui Zhang, Yunkai Tang, Jiahang Li, Fan Fei, Jinnuo Zhang, Kairan Ding, Jieji Ren, Yandong Guo, Boxin Shi
Computation and Language
The interpreter as developer: An autoethnographic design study of building a computer-assisted interpreting tool through human-LLM co-creation
Zeming Zhao
Real-time AI captioning now serves audiences at multilingual events, yet computer-assisted interpreting (CAI) tools are still built for interpreters, rarely by them. This article reports an autoethnographic design study in which a professional interpreter with no programming training built a local-first CAI system through expert-led 'vibe coding', co-creating with an LLM architect and a coding
agent. Analysis of the complete development dialogues shows how tacit professional knowledge, from segmentation to booth infrastructure, was externalized into design after generic defaults failed in use. In a demanding public scientific event, three mechanisms of in-booth appropriation emerge under a firmly human-led division of labor; telemetry from a first fully instrumented assignment shows a fully local chain sustaining a median translation latency of 0.94 s across 971 utterances. The case is read as lead-user innovation newly enabled by generative AI, with implications for the technological literacy of interpreters and the future of interpreter training.
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Cryptography and Security
Differential Privacy Protected Preference Alignment on Survey Derived Data
Xiuyuan Qi, Yi Wang and Wenjie Wang
Preference
alignment enables
language models to better reflect human values, but the preference data contains sensitive information, especially when derived from surveys about political beliefs, social norms, religion, gender attitudes, and cultural values. This paper studies
privacy-preserving
alignment on questionnaire-derived data from the World Values Survey, where supervision takes the form of ordinal labels rather than standard pairwise rankings. Existing efforts mainly focus on general
privacy protections upon model training, while data-specific methods for survey data remain unexplored. We first show the vulnerability of aligned models, then formulate the task as rating prediction and introduce label differential
privacy. Building on this formulation, we construct a differential private pipeline utilizing three
privacy mechanisms: randomized response, exponential mechanism, and DP-SGD. Experiments show that simple label-level mechanisms outperform gradient-level methods, and proper aggregation function for preprocessing also make large contribution. The new fine-grained private comparison motivates explorations toward
language model
alignment with formal
privacy guarantees.
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Robotics
SHOES: Sim-to-real Hardware-adaptation via On-policy Expansion of Skills for Legged Robots
Kota Tsuruhara, Tsung-Yuan Chen and Kento Kawaharazuka
Humans expand their locomotion capability by wearing appropriate footwear, such as hiking boots for mountains or skates for ice. Footwear changes the ground-contact interface and enables traversal of otherwise difficult terrain. In contrast, locomotion controllers are typically trained for a fixed morphology, and their performance can degrade when shoes alter foot geometry, mass, and friction. We propose SHOES, a sample-efficient framework for adapting locomotion policies to footwear-induced morphology changes. SHOES freezes a simulation-trained nominal policy and fine-tunes only lightweight, identity-initialized modulation modules on the
robot, preserving the initial behavior while enabling efficient hardware adaptation. We also introduce SafeBox, a workspace-aware waypoint sampler that constrains online adaptation within a user-defined workspace. The simulation results with a shoe-equipped Unitree Go2 show that SHOES substantially recovers locomotion performance after footwear-induced morphology changes using limited additional adaptation. Under the same adaptation budget, SHOES outperforms FiLM-only, Linear Adapter-only, and residual-RL baselines. Real-world experiments on a Unitree Go2 fitted with human shoes further evaluate the effectiveness of SHOES for hardware deployment.
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Social and Information Networks
A temporal graph dissimilarity measure capturing epidemic spreading behavior
Salvatore Freni, Lorenzo Dall'Amico, Francesca Pistilli et al. (+3 more)
Comparing temporal networks is a challenging problem in network analysis. Commonly adopted methods compare structural properties of temporal graphs and can be highly sensitive to the pre-selected features. However, temporal networks are the support of dynamical processes, and a process may take place in statistically indistinguishable ways on structurally different temporal graphs. In particular, a dissimilarity measure that compares temporal graphs based on how spreading processes unfold on them is highly desirable. In this work, we first define the Time-Shift DTW, a dissimilarity between epidemic curves that accounts for both differences in their shape and temporal shifts. We then introduce Epidemic-Aware Temporal Graph Dissimilarity (EATGD), a deterministic and differentiable dissimilarity between temporal graphs that combines an
embedding-based temporal graph distance with a set of topological and temporal features. Its parameters are calibrated using
stochastic SIR simulations to maximize the correlation with the Time-Shift DTW, after which graph comparisons require no further epidemic simulations. Using empirical human proximity networks from the SocioPatterns collaboration and synthetic temporal graphs, we show that EATGD captures epidemic-based graph dissimilarity and generalizes across heterogeneous network settings. Its differentiability and computational efficiency make EATGD suitable for integration into machine-learning pipelines and provide a potential loss function for training generative models to reproduce epidemic dynamics without requiring an exact reconstruction of fine-grained network structure.
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All authors Salvatore Freni, Lorenzo Dall'Amico, Francesca Pistilli, Alain Barrat, Tatiana Tommasi, Ciro Cattuto
Computer Vision
GHOST: Geometry-Constrained Hybrid Diffusion for Open-Vocabulary 3D Scene Graph Generation
Changqun Feng, Wangxiandi Yin, Xin Hu et al. (+4 more)
3D scene graph generation requires understanding both object semantics and metric spatial relations. However, existing open-vocabulary methods often struggle with spatial predicates because
language similarity reflects semantic co-occurrence rather than explicit geometry. We propose GHOST, a Geometry-constrained Hybrid
diffusion framework for Open-vocabulary 3D Scene graph generaTion. GHOST jointly models continuous node geometry and discrete edge predicates, following a geometry-to-constraint-to-predicate denoising process to ensure geometrically consistent relation prediction. It introduces
language-grounded constraint induction, which maps predicate
embeddings to differentiable
geometric constraints that guide both layout refinement and predicate reweighting. A Geometry-
Topology Collaborative Denoiser further couples node and edge
reasoning through noise-adaptive interaction and orientation-aware modeling. Experiments on 3DSSG demonstrate state-of-the-art performance in both closed-set and open-vocabulary settings, including over 17-point gains over RelationField on spatial predicates.
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All authors Changqun Feng, Wangxiandi Yin, Xin Hu, Ruiting Dai, Feiyang Yang, Jielei Wang, Tao He
Computer Vision
Not Every Cue Is Evidence: Cue Supportability for Forensic Distillation in Face Forgery Detection
Binjia Zhou, Dawei Luo, Shuai Chen et al. (+7 more)
Explainable face forgery detection requires not only correct judgments but also forensic evidence supported by the input image. However, training cues are typically derived from the forgery generation pipeline. Since images available to the model are of limited quality and resolution, it is difficult to observe the fine-grained traces these cues refer to. Training methods that further supply such cues as privileged supervision exacerbate the problem, as the student is taught to articulate evidence it cannot perceive. The student consequently resorts to templated or over-generalized expressions, giving rise to the correct-answer, wrong-evidence (CAWE) failure. We propose a dual supportability audit that filters privileged cues before
distillation. Each candidate cue must be predictable from the remaining privileged context and causally tied to high-attribution visual regions in the current image. Only cues satisfying both conditions receive substantial
distillation weight, preventing unsupported details from leaking into the student. Experiments across eight
benchmarks show that our method maintains or improves detection accuracy while significantly raising explanation faithfulness compared to existing paradigms.
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All authors Binjia Zhou, Dawei Luo, Shuai Chen, Feng Xu, Haoyuan Li, Jia wen Seow, Jiachi Wang, Jie Lei, Zunlei Feng, Yijun Bei
Machine Learning
StructZO: Structured Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning
Xuanlin Yang, Jia Li and Zhouchen Lin
Fine-tuning large
language models at scale is increasingly limited by the
memory cost of backpropagation. Zeroth-Order (ZO) optimizers reduce this burden by relying only on forward function evaluations, but they still exhibit a substantial performance gap to first-order (FO) optimizers such as Adam. We argue that this gap does not arise solely from the absence of explicit gradients: FO fine-tuning also benefits from informative low-rank optimization geometry, large-scale signal aggregation, and well-conditioned update directions. Motivated by this, we introduce StructZO, a structured Zeroth-Order optimization framework for
memory-efficient LLM fine-tuning. StructZO performs large-query ZO estimation within an activation-induced informative subspace and applies spectral control to the resulting reduced-space update before mapping it back to the original parameters. This lets ZO aggregate useful directional signals in a compact, geometry-aware space while avoiding the activation and optimizer-state
memory costs of backpropagation. We show theoretically that, when the induced subspace captures concentrated optimization-relevant directions, StructZO reduces the effective ZO estimation dimension from $mn$ to $mr$, with the remaining projection error governed by the spectral tail of the activation Gram
matrix. Empirically, across OPT-series and Llama3.1-8B on diverse downstreamtasks, StructZO improves over the strongest existing ZO baselines by up to $8.7%$ relative on average and up to $26.9%$ relative on individual tasks, while remaining within $1.5%$ of FO-Adam on average. These results suggest that structured and spectrally controlled ZO updates can substantially narrow the gap between ZO and FO optimization.
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Computation and Language
CivWorldBench: Evaluating Language World Models in Structured Multi-Agent Sandbox Worlds
Ang Li, Haofei Yu, Zhengwei Tao et al. (+7 more)
The growing use of
language models for forecasting motivates a broader evaluation of their world-model capabilities. Such evaluation must go beyond predictive accuracy to assess hidden-state inference and intervention prediction. Real-world forecasting
benchmarks offer limited access to hidden-state ground truth and controlled experiments. We introduce CivWorldBench, a
benchmark built on a multi-
agent strategy game engine with recorded internal states, controlled observations, and paired intervention rollouts. Its 1,900 decision points from 475 worlds support three task levels: association forecasting, belief inference, and intervention prediction. Reference predictors support attributable evaluation of capability gaps, with an expert harness and empirical ceiling estimates contextualizing scores. Stored trajectories make intermediate predictions verifiable, while
agent-play experiments test whether world-model capabilities support decision-making. The main panel comprises eleven frontier
language models. Composite
benchmark scores correlate with real-world forecasting leaderboards on matched model panels, more closely than with a general-capability index, supporting its use as a proxy for real-world forecasting capability. Strong association forecasts do not consistently extend to belief inference or intervention prediction, and effect magnitudes remain poorly calibrated despite informative direction predictions. Process verification reveals errors missed by final-answer evaluation, and structured
reasoning improves intervention prediction for the strongest model in the process study.
Agent-play experiments identify useful assisted plans and motivate evaluating decision-relevant capabilities beyond association forecasting. These findings identify hidden-state inference, intermediate-state prediction, and effect calibration as priorities for world-model training.
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All authors Ang Li, Haofei Yu, Zhengwei Tao, Liang Chen, Kuan Li, Miaomiao Li, Chenran Cai, Bin Liang, Jiaxuan You, Kam-Fai Wong
Cryptography and Security
CryptoFlowBench: Benchmarking Multi-Hop Reasoning on Blockchains for Cryptocurrency Fund Tracing
Runang He, Tongya Zheng, Jing Cao et al. (+7 more)
Pseudonymous accounts and complex smart-contract ecosystems substantially hinder the tracing of illicit cryptocurrency fund flows. Existing Graph Neural Networks (GNNs) have achieved promising performance in blockchain fraud detection, yet their static node- or edge-level predictions do not naturally generalize to multi-hop fund tracing. Meanwhile, existing tracing methods mainly rely on predefined heuristics to capture multi-hop relationships, limiting their adaptive
reasoning capabilities. To bridge this gap, we introduce CryptoFlowBench, a
benchmark constructed from authoritative reports on cryptocurrency-related illicit incidents, with four tasks spanning local to multi-hop fund tracing. These tasks pose substantial challenges to existing learning- and heuristic-based methods, particularly in complex multi-hop scenarios. We therefore propose ChainSleuth, a graph-tool-augmented
agent that iteratively reasons, invokes graph tools, and updates its tracing trajectory, enabling adaptive information acquisition and recovery from erroneous paths. Experiments show that no baseline consistently dominates all four tasks, revealing substantial gaps between local fraud detection and multi-hop fund tracing. ChainSleuth achieves notable improvements on multi-hop tasks, demonstrating the potential of agentic graph
reasoning for cryptocurrency fund investigation.
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All authors Runang He, Tongya Zheng, Jing Cao, Yucheng Ying, Shunyu Liu, Bingde Hu, Yang Gao, Zeyuan Hu, Jianrong Zhang, Mingli Song
Computer Vision
Tikhonov-Stabilized Bezier Representation Forecasting for Training-free Diffusion Acceleration
Lei Zhu, Mujie Lin, Ruochong Zheng et al. (+5 more)
Diffusion models, particularly
Diffusion Transformers, achieve strong image and
video generation quality but remain expensive at inference time due to repeated denoiser evaluations. Feature caching offers a deployment-friendly acceleration strategy by reusing or predicting intermediate representations without retraining the generator or modifying the sampler. However, existing cache-then-forecast methods often rely on Taylor-style finite-difference extrapolation, which can become unstable over longer cache intervals, and typically predict local module outputs whose errors may accumulate through subsequent denoiser blocks. We propose BeziCast, a training-free
diffusion acceleration framework that forecasts output-proximal denoising representations using low-order B'ezier trajectories. BeziCast estimates B'ezier control points via Tikhonov-stabilized fitting, providing a smooth, capacity-controlled temporal parameterization that avoids explicit high-order derivative estimation and decouples trajectory capacity from cache-query density. To moderate aggressive extrapolation, we further derive equivalent forecasting weights and introduce a convex-hull-inspired guardrail that detects high-risk predictions and softly pulls them toward a conservative simplex-projected estimate. Extensive evaluations across advanced image and
video diffusion models demonstrate the effectiveness of BeziCast. In particular, BeziCast accelerates FLUX.1 by up to (4.79\times) and HunyuanVideo by up to (4.11\times), while preserving substantially better generation quality than competing acceleration baselines.
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All authors Lei Zhu, Mujie Lin, Ruochong Zheng, Guangyi Wang, Hao Li, Peng Jin, Chang Liu, Jie Chen
Machine Learning · Cross-listed from stat.ML
Explaining Heterogeneous Treatment Effects with Causal Forests and the Hoeffding Functional Decomposition
Gaëtan Patinier, Clément Bénard and Margaux Törnqvist
The estimation of heterogeneous treatment effects is a central objective of causal inference. However, state-of-the-art
algorithms to solve this task are based on machine learning principles, and thus behave as black boxes. Consequently, the identification of
covariates driving heterogeneity remains a challenging and open problem. To address this limitation, we introduce CausalTreeHFD, an interpretable extension of causal forests, based on the Hoeffding functional decomposition adapted to tree ensembles. Our approach decomposes predictions of conditional average treatment effects (CATE) into contributions from individual
covariates and pairwise interactions, providing a direct characterization of the factors driving treatment effect heterogeneity. We establish the consistency of CausalTreeHFD, which converges towards the Hoeffding decomposition of the underlying CATE function, under standard assumptions for the analysis of causal forests. Experiments on simulated and real
datasets show that CausalTreeHFD accurately identifies relevant
covariates and interactions, and precisely recovers their contributions to heterogeneous treatment effects.
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Computer Vision
Pixels or Code? Benchmarking Instruction-Guided Visual Artifact Operation
Shun Guo, Shuizhou Shi, Boao Wang et al. (+4 more)
Visual artifacts such as charts, technical diagrams, webpages, and document pages underpin scientific, commercial, and engineering workflows. Editing these artifacts requires direct modification, dependency-aware updates, preservation of unaffected content, and compliance with domain constraints. We define this capability as Visual Artifact Operation (VAO). Prior
benchmarks test parts of this problem, including cascading updates in charts. We study direct edits, dependent updates, and preservation across visual domains and pixel, code, and
agent paradigms under one image-only protocol. We introduce Visual Artifact Operation Bench (VAO-Bench), comprising 1,573 manually screened tasks across nine artifact categories and three difficulty levels. VAO-Bench uses executable artifacts and deterministic rendering to trace reference transformations and annotate direct edits, dependent updates, and preservation constraints. All systems receive the same source image and instruction; we evaluate pixel-space editors, code-generating
multimodal systems, and tool-using
agents on direct-edit accuracy, dependency-aware update, content preservation, domain validity, and visual quality. Experiments show that executable-code systems better support dependent updates, while pixel-space editors offer faster execution and strong visual quality. Explicitly stating dependencies improves both paradigms, identifying implicit-update inference as a central bottleneck. VAO-Bench provides a reproducible foundation for dependable visual artifact editing across generation paradigms.
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All authors Shun Guo, Shuizhou Shi, Boao Wang, Wei Xiong, Siyu Sun, Jiangtong Li, Jie Li
Artificial Intelligence
RAP-WAM: Learning World-Action Models through Predictive Rehearsal and Action Perturbations
Yuanpeng He, Ceyao Zhang, Fangjing Li et al. (+8 more)
Recent world action models draw on
video generation priors to jointly predict future observations and
robot actions. Adapting these priors to control requires determining how future predictions should guide policy learning. RAP-WAM combines predictive Rehearsal and Action Perturbations (RAP) with future-supervised queries and Coupled Action Projection for policy learning. Predictive rehearsal trains on model-generated inputs while retaining recorded targets, and accumulates recurring errors into a spatial prior. We perturb actions and measure changes in
video predictions. Action-response-based spatiotemporal credit assignment combines these responses, prediction error, and the rehearsal prior to allocate visual supervision; spatially aggregating the responses determines supervision intensity across future action steps. No separate scoring network or deployment-time probe is required. The base learns from a
video-pretrained backbone without intermediate pretraining on external embodied data. We evaluate the fixed base and its
agent-refined, task-conditioned system. On 42 RoboDojo tasks, their average success rates are 12.85% and 15.62%, with Scores of 17.86 and 20.59. Across 15 real-
robot tasks, the complete system achieves 89.00% task-macro-average success, exceeding the strongest of five baselines by 37.67 percentage points. Across 315 fixed-output simulation windows, 45.21% of response-top20 positions fall outside error-top20, revealing information beyond prediction error.
Videos and further details are available on the project website: https://rapwam.github.io/.
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All authors Yuanpeng He, Ceyao Zhang, Fangjing Li, Kefei Zhu, Lijian Li, Yueheng Li, Tianjia He, Wenpin Jiao, Zhi Jin, Yaodong Yang, Yuanpei Chen
Distributed Computing
Revisiting Fast Tendermint: An Agreement Violation and Two Repairs
Alejandro Ranchal-Pedrosa
Fast Tendermint reduces Tendermint's good-case latency to two communication steps for n>=5f+1. We show that its published round-change rule violates Agreement: one Byzantine process suffices for every f>=1, and for f>=2 two Byzantine processes suffice without double signing. The failure occurs when a process advances past a round without recording the value that may have been decided there. The Quint specification that accompanies the protocol does not allow this kind of round skipping and is not affected; the violation lies in the published pseudocode. We present two repairs. Walk requires a process to leave each round on a quorum of that round's precommits and to precommit before leaving. It keeps the recorded valid state and removes the wait after a precommit quorum. Justify carries the evidence needed to accept a proposal in the proposal itself, removing both the recorded valid state and the proposer's synchronization wait. Both satisfy Agreement and Validity under asynchrony and Termination under the partially synchronous gossip model of Tendermint and Fast Tendermint. Both retain proposal timeouts. The main text presents the executions that violate Agreement and explains the two repairs. The appendices provide proofs, a brief comparison with other round-change designs, and an extension to chains.
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Computer Vision
VisExMEM: Visual Evidence-Based Explainable Multimodal Evaluation Metric
Subhadeep Roy, Christoph Leiter and Steffen Eger
Current image-text evaluation often relies on a global score, which gives limited insight into which semantic details are supported by the image. Explainable metrics provide rationales, but the visual evidence supporting those rationales can still be difficult to verify. We introduce VisExMEM, a reference-free evaluator that decomposes captions into atomic claims, localizes visual evidence, verifies each claim, and averages claim-level support into a global score. Explanations use the same evidence and decisions. We train Qwen3.5-based 4B and 9B models on 100K samples with multi-task SFT, counterfactual refinement, and evidence-aware GDPO. Across eight
benchmarks, VisExMEM-9B achieves the highest descriptive composite among open-source evaluators in our comparison, exceeding VQAScore by 11.7 points while remaining competitive with GPT-5.6 Sol. We also introduce AGITA-Bench, containing 600 human-annotated image-text pairs and 8.7K atomic claims for global and claim-level evaluation. For untuned Qwen3.5 and GPT-5.6 Sol controls, decomposed prompting raises the same composite by 3.6-5.1 points relative to holistic prompting. Human evaluation shows that VisExMEM explanations outperform prior explainable metrics and approach GPT-5.6 Sol.
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Machine Learning
ARMED: Adaptive Retrieval-Augmented Medication Recommendation
Kyungkeon Chung, Se Un Park, Hyejin Lee et al. (+1 more)
Medication recommendation requires jointly considering diagnoses, comorbidities, longitudinal patient history, and treatment interactions. Although recent
retrieval-augmented generation approaches using
language models have shown promise for clinical recommendation, most existing systems rely on fixed
retrieval pipelines that cannot adapt to diverse clinical
retrieval scenarios and may introduce noisy or irrelevant evidence. We propose ARMED, an adaptive clinical RAG framework that dynamically expands
retrieval according to
retrieval sufficiency and current-admission relevance. ARMED first observes the initial
retrieval outcome and selectively performs supplementary or fallback multi-query
retrieval when additional evidence is needed. The framework also incorporates a relevance gate that activates only clinically relevant same-patient history and organizes retrieved evidence into structured query-level clinical evidence. Experiments on MIMIC-IV demonstrate that ARMED consistently outperforms previous static
retrieval baselines and improves overall medication recommendation quality. By combining locally runnable small
language models with adaptive
retrieval, ARMED supports medication recommendation without relying on external
language model APIs or task-specific fine-tuning.
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All authors Kyungkeon Chung, Se Un Park, Hyejin Lee, Junyoung Park
Computation and Language
CultureBot: A Framework for Retrieval-Augmented Exploration of Cultural Heritage Data
Vasileios Karampinis, Angelos Vlachos, Giorgos Filandrianos et al. (+4 more)
Digital cultural heritage collections encode rich relations among objects, places, periods, providers, images, and source institutions, yet users typically access them through keyword search and faceted browsing. We present CULTUREBOT, an interactive framework for natural-
language exploration of cultural heritage collections that combines knowledge-graph-guided
retrieval, lexical-semantic
retrieval, and source-linked generation. CULTUREBOT maps user queries to structured catalogue relations, enriches retrieved records with textual evidence, and generates answers that link back to the underlying repository records. We instantiate the framework on 81,852 SearchCulture records and release an online demonstration. In an expert evaluation involving six cultural-heritage specialists, 55 persona-grounded queries, and 250 evaluations, the graph-guided system was rated significantly higher than two general-purpose assistants and a non-graph ablation, with the largest gains in evidence grounding, diversity,
reasoning, and completeness. These results support graph-guided
retrieval as a practical way to expose the relational structure of cultural collections through conversational access while preserving links to catalogue evidence.
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All authors Vasileios Karampinis, Angelos Vlachos, Giorgos Filandrianos, Maria Lymperaiou, Paraskevi Tzouveli, Chrysoula Zerva, Giorgos Stamou
Machine Learning
ECHO: Correcting Time Series Foundation Model Forecasts with Historical Analogues and Errors
Boya Zhang, Xilin Dai, Yilin Wang et al. (+4 more)
Time Series Foundation Models (TSFMs) provide general-purpose forecasting capabilities, yet their forecasts can remain misaligned with the temporal characteristics of a target domain. This motivates lightweight forecast correction even when only black-box access to the TSFM is available. Our key observation is that two forms of historical information provide complementary correction signals: historical analogues reveal how similar contexts evolved, while past forecast errors reveal a TSFM's characteristic deviations in the target domain. Based on this observation, we propose ECHO, a black-box forecast correction framework that learns from both sources. ECHO uses AnalogE to assess retrieved historical analogues together with the current context and base forecast, learning transferable corrections through cross-domain pretraining. In parallel, RecentE learns target-specific residual corrections from past forecast errors and refines them as causally available feedback accumulates. ECHO dynamically combines the two corrections according to their observed forecasting performance. Across 5 representative TSFMs and 9 real-world
datasets, ECHO reduces MSE in all 45 backbone–
dataset settings, yielding an average relative reduction of 9.26% in macro-average MSE. Its gains also extend to long-horizon forecasting.
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All authors Boya Zhang, Xilin Dai, Yilin Wang, Qiang Xu, Luyao Dong, Xing He, Wen Wu
Machine Learning · Cross-listed from math.OC
Taming Nonconvexity with Epigraph Lifting
Clara Lage, Hok Shing Wong and Matthias Ehrhardt
We consider inverse problems regularized by a trained neural network with convex activations and unrestricted weights. The resulting optimization problem is generally nonconvex, and gradient-based methods may converge to poor local minima. We introduce a penalized epigraph lifting of the network and show that the lifted problem admits a difference-of-convex (DC) decomposition. For Lipschitz continuous activations, we establish finite exactness: the original objective is recovered exactly at a finite and explicitly computable penalty threshold $\bar\rho$. Building on this structure, we propose H-DC (Homotopic DC), a homotopic scheme that solves a sequence of DC subproblems with increasing $\rho$, warm-starting each level from the previous solution. Numerical experiments on synthetic problems, a
manifold denoiser regularizer, and image inpainting with a learned Mumford–Shah regularizer show that H-DC consistently escapes poor local minima and reaches solutions with lower objective values than gradient-based methods from the same initialization.
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Machine Learning
Resample the Answer: Where to Spend Test-Time Compute in Recursive Reasoning
Maxime Illouz
Recursive
reasoning models such as the Tiny Recursive Model (TRM) iteratively refine two latent states: an answer state y, decoded into the output, and a
reasoning state z, which is never decoded. Many of their failures have been attributed to bad basins, regions of latent space that trap the recursion on wrong answers. Recent test-time approaches perturb y, z or both to recover these failures, but the impact of this choice on performance, test-time compute efficiency and scaling properties remains unclear. Through controlled experiments on TRM, we find that a failed trajectory carries its failure in its answer state. At equal compute, in depth and in width, resampling y recovers more failures than any perturbation of z we tested, and the performance gap widens as the test-time compute budget grows. Our approach, Gated Answer Resampling (GAR), pairs this resampling of the answer state with the model's own halting head and restarts only the puzzles that the head rejects. With at most 64 restarts per puzzle and no training or hyperparameter search, GAR takes TRM from 85.9% to 99.4% on Sudoku-Extreme and from 68.8% to 85.6% on Pencil Puzzle Bench validation. On the same checkpoints, GAR also outperforms Probabilistic TRM (PTRM), which perturbs z at each step, even when PTRM uses 42 to 197 times as much compute.
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Artificial Intelligence
AuthGateBench: Benchmarking LLMs as Authorization Gate
Zhixiong Zhuang, Jin Jia, Zhiwei Shang et al. (+1 more)
Automated approval systems let coding
agents execute high-risk actions with minimal human oversight by delegating the authorization decision to a classifier, typically a large
language model (LLM). Such systems, adopted in tools like Claude Code and Codex, remain vulnerable to failure modes such as scope escalation, where the
agent takes actions exceeding what the user authorized. Existing
benchmarks focus mainly on malicious requests, evaluating refusal under an implicit policy. We introduce AuthGateBench, the first policy-guided
benchmark for authorization gating under benign requests. We implement a novel pipeline where an LLM generates contrast pairs with identical action and trajectory but differing request, flipping the label between block and allow. This allows us to assess whether models reason over context rather than the action alone. We further examine whether auxiliary environment signals can help models make better decisions. The resulting
benchmark comprises 1,400 instances spanning five failure modes drawn from real-world Claude Code incidents. Evaluations show accuracy degrades unevenly across failure modes as models over-rely on internal safety
alignment, and models still block most actions even when explicitly authorized, revealing unstable rather than deterministic decisions; retrospectively retrieved environment signals, however, substantially improve authorization accuracy.
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All authors Zhixiong Zhuang, Jin Jia, Zhiwei Shang, Ziyu Wang
Distributed Computing
Federated Coreset Subsampling for Distributed Memory Banks
Jason Witzemann, Daniel Pröll and Sebastian Hegenbart
In real-world scenarios, industrial processes can span multiple factories and goods are often produced at multiple sites, leading to a shared yet non-identical distribution of data across locations. To address this, we propose two
federated approaches for i) combining existing coresets (Double Subsampling) and ii) creating new coresets (
Federated Subsampling) in a distributed anomaly detection setting. In particular, we adapt the
memory bank based PatchCore
algorithm to a
federated setting, resulting in increased cold-start performance, resilience to domain- and sensor-shift, and resilience to unreliable actor participation, a common problem in classical, network based
federated learning strategies.
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Graphics
Low-Cost Deep Learning for Automated Keyframe Interpolation in Sprite Animation
Kenneth Alonso and Gustavo Patow
Sprite-based animation remains prevalent in
video game development, but manual in-betweening—generating intermediate frames between keyframes—is labor-intensive. Existing
video frame interpolation methods, optimized for photorealistic content, introduce smoothing artifacts when applied to the sharp edges, limited color palettes, and stylized motion characteristic of hand-drawn sprites. We present a deep learning approach for sprite in-betweening that adapts a U-Net encoder-decoder architecture with domain-specific modifications, including RGBA-aware processing, multi-scale flow refinement, and alpha-gated blending to preserve pixel-perfect sprite aesthetics. Following data-centric AI principles, we demonstrate that a few carefully curated sprite animation triplets—selected for diversity, quality, and representative edge cases—enable effective training without requiring massive generic
datasets. Experimental evaluation using the FLIP perceptual metric shows that our method achieves a mean error of 0.258 on 42 test triplets, outperforming state-of-the-art
video interpolation RIFE (0.313, 17.5% worse), traditional optical flow Lucas-Kanade (0.449, 42.6% worse), and general frame synthesis CyclicGen (0.603, 57.2% worse). This study shows that quality-over-quantity data curation and domain-specific architectural adaptations yield better interpolation quality than general-purpose methods trained on orders of magnitude more data, validating the data-centric AI paradigm for specialized visual domains. This is particularly important for small-to-medium-sized game development studios, where budget and time constraints are critical.
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Machine Learning
Learning $\omega$-Powered Conditional Distributions with Importance-Weighted Flow Matching
Maciej Falkiewicz, Frantzeska Lavda, Jason Ramapuram et al. (+1 more)
Classifier-Free Guidance (CFG) with guidance scale $\gamma$ is commonly motivated by the $\omega$-powered conditional distribution obtained by setting $\omega=\gamma$, but CFG does not, in general, sample from this distribution exactly. We study whether the powered target can instead be learned explicitly. We introduce Importance-Weighted Conditional Flow Matching (IW-CFM), which reweights conditional flow-matching examples using density ratios obtained from frozen conditional and unconditional flow models. With exact normalized weights, the resulting objective has the same gradient as flow matching on the powered target; in practice, we use self-normalized estimation and a regularized empirical approximation for high-dimensional data. On synthetic distributions, IW-CFM recovers analytical powered targets for which numerically matched CFG, $\gamma=\omega$, exhibits a visible mismatch. On ImageNet, an amortized lightweight corrector achieves better
alignment than fixed- and matched-guidance CFG with the corresponding weighted and filtered reference distributions. We additionally present an exploratory CLIP analysis of powered-density rankings on real ImageNet data. Aggregate shifts across selected
prompt-specific contrasts coexist with heterogeneous classwise responses, including nonmonotonic trajectories. These results provide a practical route for studying and approximating model-defined powered conditional distributions.
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All authors Maciej Falkiewicz, Frantzeska Lavda, Jason Ramapuram, Alexandros Kalousis
Computer Vision
Not All Challenges Are Equal: Evaluating Interactive Liveness Detection Against Real-Time Face Swaps
Anton Firc, David Drtil, Martin Perešíni et al. (+3 more)
Remote identity verification assumes that a camera stream shows a live person, yet public real-time face-swap tools can replace that person before the stream reaches the verifier. Passive deepfake detectors generalize poorly to new generators, while interactive challenges are often selected without evidence of which gestures expose current attacks. We evaluate randomized gesture challenges in 108 live sessions with six subjects, 24 impersonated identities, and two face-swap tools, DeepFaceLive and FaceFusion, injected through a virtual camera. The static calibration segment reaches a session-level AUC of 0.878 with a frozen UCF spatial detector, while the mean across the active-gesture windows reaches 0.988. Pose and combined gestures have the highest point estimates, whereas expressions show no clear improvement. Hand occlusion against FaceFusion, configured with an occlusion mask, scores below the static baseline. The end-to-end time required to satisfy the validator also distinguishes attacks from genuine sessions, achieving a session-level AUC of 0.732, although this signal includes synthesis and transport delays. These results show that challenge sets should be tested against current attack configurations and that response time can provide an additional, deployment-specific signal.
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All authors Anton Firc, David Drtil, Martin Perešíni, Milan Šalko, Vojtěch Staněk, Kamil Malinka
Sound
MPASCNet: Magnitude-Phase-Aware Adaptive Spectral Compression for Compressed Music Restoration
Xinlong Deng, Yu Xia, Qiuqiang Kong et al. (+2 more)
Compressed music restoration aims to recover high-fidelity audio from lossy codecs such as MP3 by restoring missing spectral content and reducing coding artifacts. Recently, generative
adversarial networks and band-split models have made progress in compressed music restoration. However, existing methods either operate without explicitly modeling phase or rely on predefined frequency partitions, which may limit restoration performance and the use of input-dependent frequency information. To address these issues, we propose MPASCNet, a Magnitude-Phase-Aware Adaptive Spectral Compression network. Instead of using fixed bands, MPASCNet combines magnitude and phase features to guide cross-
attention, adaptively compressing full-resolution spectral features into compact
tokens. These
tokens are then processed by a spectro-temporal backbone to model spectral-temporal dependencies. Finally, separate decoder branches reconstruct the full-resolution magnitude and wrapped phase spectra. Experiments on MP3 restoration show that MPASCNet outperforms the Apollo and AEROMamba baselines across multiple restoration metrics while using only 6.49 M parameters. The code is available at https://github.com/xinghour/MPASCNet.
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All authors Xinlong Deng, Yu Xia, Qiuqiang Kong, Yin Cao, Jie Jiang
Artificial Intelligence
Chain Dimension Scaling: Communication-Efficient Loop-Style Scaling for Industrial Short-Video Ranking
Zhimeng Jiang, Yuening Li, Ruixiao Sun et al. (+4 more)
Industrial recommendation rankers keep scaling in width and depth to billions of parameters, but this increasingly incurs costs in parameter growth, distributed communication overhead, and training inefficiency. We introduce \underline{C}hain \underline{D}imension \underline{S}caling (CDS), a scaling strategy orthogonal to width and depth. CDS repeatedly applies a single feature-interaction block with parameters shared across iterations, so the number of iterations (the chain length) becomes a new scaling dimension that adds no parameters. A lightweight intra-chain residual connection stabilizes optimization and improves quality. CDS enables two regimes. In the compression regime, shallow blocks with a longer chain match deeper baselines with fewer parameters and faster training; in the quality-scaling regime, increasing chain length under a fixed parameter budget yields consistent offline and online gains, suggesting the chain dimension adds modeling capacity beyond parameter reuse. We evaluate CDS in a large-scale multi-task short-
video ranking system in production. In the compression regime, CDS stays within $0.03\%$ of baseline offline quality while reducing trainable parameters by 26.6% and improving training throughput by 6.4%, with neutral-to-positive online participation. In the quality-scaling regime, quality improves monotonically with chain length. Wall-clock analysis shows the efficiency gain comes mainly not from reduced FLOPs but from lower cross-device synchronization overhead and reduced optimizer update cost. These results position parameter-shared iterative computation as a practical, communication-efficient scaling axis for industrial ranking systems.
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All authors Zhimeng Jiang, Yuening Li, Ruixiao Sun, Yuanzhen Lin, Kun Jin, Chuan He, Liang Liu
Cryptography and Security
Purpose IR: Compiling User Purpose for Privacy-Preserving Agent Workflow State
Dongsheng Chen, Xiangyu Zhao, Lei Ma et al. (+2 more)
LLM-
agent workflows routinely transform rich observations into plans, summaries,
memories, handoffs, and other reusable state. This creates a
privacy boundary that task relevance alone does not capture: an observation may be needed as evidence for a task without its full semantics being authorized to persist. We call this mismatch the purpose-to-state semantic gap. The key distinction is that evidence authority is not release authority. We introduce Purpose IR, a bounded typed intermediate representation that compiles natural-
language user purpose into an executable semantic boundary for reusable
agent state. A Purpose program is an acyclic semantic dataflow graph in which evidence leaves specify task-required semantics, typed dependencies constrain authorized derivations, and declared sinks determine which values or actions may cross the reusable-state boundary. A deterministic compiler constructs and validates the authoritative program, while a typed runtime reconstructs reusable state only from validated sink outputs. Under explicit execution contracts, this design yields release confinement and purpose noninterference, characterizing what factual semantics may be released and which observation differences must become irrelevant to reusable state. Across 1,200 cases spanning LangGraph, CrewAI, and MobileRun, Purpose IR achieves 99.6%
Privacy, 83.3% Task, and 83.3%
Privacy-Compliant Success (PCS), outperforming the strongest high-
privacy baseline by 27.5 PCS points. On 493 PrivacyLens trajectories, it achieves the highest Safe Action Rate at 77.3%, 10.4 points above the next-best method, extending the same boundary from reusable state to downstream actions. These results show that
privacy-preserving
agent state requires more than filtering sensitive or task-irrelevant content: it requires an explicit, executable representation of what the user's purpose authorizes to survive.
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All authors Dongsheng Chen, Xiangyu Zhao, Lei Ma, Xin Yao, Xuetao Wei