Artificial Intelligence
LEBGen: An LLM-Enhanced Bayesian Network Framework for Few-Shot Travel Survey Data Generation
Zijian Shen, Bin Zhou, Jiguang Wang et al. (+2 more)
Travel survey data are essential for transportation planning and travel behavior analysis, yet collecting large-scale representative samples is costly and time-consuming. A practical alternative is to generate synthetic survey records from a few-shot sample. However, such samples provide incomplete coverage of heterogeneous traveler groups and insufficient evidence for recovering the complex dependencies between demographic characteristics and travel behavior. Existing approaches have complementary limitations. Probabilistic generative models such as
Bayesian networks (BNs) offer explicit distributional control, but structures learned from few-shot samples may omit meaningful dependencies or retain spurious ones. Large
language models (LLMs) can help address these difficulties in BN structure learning by providing behavioral knowledge that complements the limited statistical evidence. We therefore propose LEBGen, an LLM-enhanced BN framework that uses this knowledge to refine network structure for few-shot travel survey data generation. Specifically, the LLM first identifies traveler personas from demographic attribute and travel behavior statistics, then recovers dependencies missed by the persona-augmented BN structure and prune spurious ones. The refined BN is parameterized exclusively from the observed data to generate synthetic records. Under a 2% few-shot setting on the 2022 Hong Kong Travel Characteristics Survey, LEBGen reduces the mean marginal Jensen-Shannon divergence from 0.0671 to 0.0091 and the mean absolute Cramer's V error by 14.3% over the best-performing baseline, substantially improving both distributional and dependency fidelity.
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All authors Zijian Shen, Bin Zhou, Jiguang Wang, Ya Zhao, Jintao Ke
Machine Learning
HypLTSF: A Hyperbolic Geometric View of Multi-Scale Hierarchies for Long-Term Time Series Forecasting
Namwoo Kim, Hyungryul Baik and Yoonjin Yoon
Multi-scale modeling has become an effective approach for long-term time series
forecasting, capturing temporal patterns that range from fine-grained local dynamics to coarse global trends. Representations across these temporal scales are inherently hierarchical, with coarser scales abstracting and aggregating information from finer ones. While existing approaches readily exchange information across these scales, the hierarchy itself is typically left as an emergent byproduct of such interactions rather than captured as a
geometric structure in its own right. In this paper, we introduce HypLTSF, a framework that endows the multi-scale hierarchy with a concrete
geometric form by
embedding scale-wise representations into the Poincaré ball, whose exponentially expanding volume naturally accommodates hierarchical structures. To align this geometry with the temporal hierarchy, HypLTSF imposes two constraints: (1) a radial constraint that orders
embeddings by their level of abstraction, and (2) an angular constraint that groups fine-scale patterns sharing a common coarser-scale ancestor. Extensive experiments on long-term time series
forecasting benchmarks show that HypLTSF achieves state-of-the-art performance, suggesting that explicitly modeling the multi-scale hierarchy as a
geometric structure is effective for
forecasting.
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Information Theory
Physics of Information Geometry - Part I: Principle of Least Action on the Probability Simplex
C. Emre Koksal and Deniz Sargun
We develop a least-action framework for describing how a probability distribution can evolve from an
equilibrium state to a prescribed nonequilibrium state under constrained incremental changes. Taking a Gibbs distribution as the
equilibrium reference, the framework gives a direct physical meaning to the geometry of the probability simplex: distance from
equilibrium corresponds to nonequilibrium free energy, while changes between successive distributions carry an informational kinetic cost. The Pythagorean structure of relative
entropy then provides the central insight of the work. It shows that intermediate distributions chosen via sequential information projections can reduce the kinetic cost of large transitions and establishes an energy-conservation-like relation between the kinetic expenditure along a path and the free energy accumulated in reaching the target distribution. Motivated by this geometry, we construct a greedy least-action path through successive information projections, obtain a closed-form characterization of each projection through the Lambert W function, and establish a finite-step performance guarantee. We further show that state-dependent costs can be incorporated naturally by reshaping the underlying Gibbs reference, providing a thermodynamic interpretation of path penalties as modifications of the effective energy landscape. Together, these results provide a unified view of distributional
evolution through least action, information geometry, and nonequilibrium thermodynamics.
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Computer Vision
MARS-CLIP: Multi-Resolution and Attention Refined Zero-Shot Image Segmentation
Nagito Saito, Shintaro Ito, Koichi Ito et al. (+1 more)
Contrastive
Language-Image Pre-training (CLIP) has demonstrated impressive capabilities in zero-shot transfer but often struggles with dense prediction tasks due to low spatial resolution and the loss of structural information. To address these limitations, we propose MARS-CLIP (Multi-resolution and
Attention Refined
Segmentation for CLIP), a novel framework for zero-shot semantic
segmentation. Our approach introduces two key strategies: (i) a multi-resolution feature extraction module that fuses local fine-grained features with global context to overcome input resolution constraints, and (ii) an
attention refinement mechanism that injects spatial and color biases from intermediate layers into the final self-
attention block to accurately restore object boundaries. A set of experiments on six public
datasets demonstrates that MARS-CLIP significantly outperforms state-of-the-art methods.
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All authors Nagito Saito, Shintaro Ito, Koichi Ito, Takafumi Aoki
Computer Vision
Dreaming in Flow: Bidirectional Self-Refinement for Unified Multimodal Models
Ke Hao, Yuanzhi Liang, Tingxi Chen et al. (+5 more)
Unified
multimodal models can generate images and interpret them, but their two capabilities are usually optimized with separate supervision. We introduce Bidirectional Self-Refinement via Flow-Direction Feedback and Semantic Replay (BiSe), a post-training method that uses self-generated visual episodes to construct feedback for both pathways. Understanding-guided generation aligns the
prompt-conditioned flow direction with an image-conditioned prediction and with the model's response to a repair instruction. Generation-guided understanding replays the source intent through captioning and image generation, and learns a stable semantic evidence profile across the replayed images while separating an independent episode. Flow-matching and source-
prompt objectives remain active, and frozen initial-model predictions limit drift. BiSe requires no paired image–text data during post-training. It improves text-to-image compositionality across three unified backbones and visual understanding on the two backbones evaluated for that task.
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All authors Ke Hao, Yuanzhi Liang, Tingxi Chen, Rui Li, Haibin Huang, Chi Zhang, Yun Gu, Xuelong Li
Computer Vision
SAM3-O2D2: Zero-Shot Object Out-of-Distribution Detection by Object Class Prompting of the SAM3-Image Model
Lucas Görnhardt, Timo Bartels and Tim Fingscheidt
Object detectors have shown remarkable performance in various fields, among these medical imaging, surveillance, and autonomous driving. However, they are prone to overconfidence when encountering unseen objects in real-world deployments, causing potential safety issues. To address this, detecting out-of-distribution (OOD) objects is essential for reliable object detection. Modern approaches leverage the broad semantic knowledge of foundation models such as CLIP for post-hoc few- and zero-shot OOD detection. However, these methods typically perform OOD assessment in feature space, which can be sensitive to object detector localization errors and variations in object appearance. Moreover, the current state-of-the-art (SOTA) zero-shot method performs computationally costly
diffusion in inference. In this work, for our proposed zero-shot object OOD detection method SAM3-O2D2, we employ the SAM3-image foundation model in an efficient manner. Specifically, we
prompt SAM3 only with the object detector's predicted classes and compare the predictions of the object detector and SAM3. An object is in-distribution (ID), if SAM3 also detects an object at the corresponding location. If SAM3 does not detect the prompted object, this indicates a mismatch between the detector's prediction and the image content, suggesting that the object is OOD. Experimental results show that our method significantly surpasses the so-far zero-shot SOTA method. Specifically, we achieve new SOTA AuROC and FPR95 metrics over both ID
datasets Pascal-VOC and BDD100K and both OOD
datasets MS-COCO and OpenImages.
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Robotics
Seeing is Not Believing: Breaking the Physical-to-Digital Trust Boundary in Robotics
Leming Shen, Shikai Geng, Yuanqing Zheng et al. (+1 more)
In multi-
robot collaboration, task handovers rely on downstream verifiers performing remote attestation, which inspects sensor telemetry to ensure a
robot's physical behavior strictly matches its assigned task. But can this telemetry be trusted? We show that it often cannot. In this paper, we uncover a severe vulnerability in
Robot Operating System (ROS) 2: by modifying a single environment variable, an adversary can execute a pre-built hook to covertly intercept and inject both telemetry and control signals before they are published. Consequently, adversaries can hijack a
robot to perform dangerous tasks while spoofing downstream verifiers with synthesized fake telemetry. Worse still, by exploiting the widespread reliance on third-party Docker containers and auxiliary tools, attackers can distribute compromised packages embedded with these malicious hooks to launch such attacks easily. On a physical Franka Emika robotic arm running Secure ROS 2, our attack injects fabricated telemetry in real time with only around 3 ms of jitter, preserving temporal synchronization and hardware integrity while achieving an 87% success rate even against an AI-based detector. We have responsibly disclosed these findings to the ROS 2 development team. We prepared a demo
video available at https://youtu.be/ExeiGqUrnhQ.
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All authors Leming Shen, Shikai Geng, Yuanqing Zheng, Chris Xiaoxuan Lu
Computation and Language
What Eviction Destroys: A Restore-Counterfactual Audit of Forgetting in Agent Memory
Chen Shen
Agent memory systems must discard stored information when their history exceeds a fixed
token budget. Existing budget-accuracy frontiers quantify the resulting loss in accuracy, but do not distinguish irreversible losses caused by eviction from recoverable
retrieval failures. We introduce the restore counterfactual, a per-question paired intervention that reinstates the question's gold evidence in the read-time context and reruns the same reader. Combining the change in correctness with whether the evidence was retained after eviction classifies each oracle-answerable error as recoverable, irreversible, or residual; in the residual case, the answer remains incorrect after restoration. We evaluate FIFO, random, redundancy-aware, and LLM-importance eviction on LongMemEval-S at three budgets and under two
retrieval regimes, using GPT-4o-mini as the primary reader and judge and GPT-5.4-mini as a robustness reader. Under top-k
retrieval at an 80k-
token budget, the irreversible share among errors corrected by restoration is 0.67-0.73 for FIFO, random, and redundancy-aware eviction, compared with 0.60 for LLM-importance. At 8k
tokens, it reaches 1.00 for all four policies. Recoverable errors occur under top-k
retrieval at 80k
tokens but are absent under forced-gold injection by construction, so budget-accuracy results are not directly comparable unless the
retrieval regime is reported. An exploratory matched-accuracy analysis detects no difference in irreversible rate among accuracy-matched policy pairs at a resolution of 1.2-6 percentage points. The same analysis detects the deliberately destructive control. To our knowledge, this is the first per-item, per-question restore-counterfactual audit of eviction for external
agent-
memory stores on a standard conversational
benchmark.
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Machine Learning
Adaptively Incorporating Directional Hints into Zeroth-Order Optimization
Alexander Ryabchenko, Jian Qian and Wenlong Mou
We study zeroth-order optimization of nonconvex functions with the aid of directional hints, which are cheap but potentially inaccurate approximations of the true gradient direction, given by linear subspaces at each iteration. To leverage these hints adaptively while maintaining robustness to their quality, we introduce Control-Variate Zeroth-Order Descent (CV-ZOD), a new framework that refines the classical zeroth-order gradient
estimator with a control variate that can be set based on the directional hints. We first show that the oracle
algorithm that optimally sets the reference vector and step size at each iteration achieves a convergence rate that interpolates between the first-order $O(1/T)$ rate and the zeroth-order $O(d/T)$ rate, depending on the quality of the hints along the trajectory. We then develop a practical variant of CV-ZOD that achieves the same oracle guarantee up to logarithmic factors, without any prior knowledge of the hint quality. We validate the method empirically on simulation-based scientific optimization tasks, demonstrating sustained progress on nonconvex landscapes where zeroth-order descent is slower and existing guided methods stall as guidance deteriorates.
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Machine Learning
Online Signature Verification Using Augmented Path Signature and T-Mamba
Ruiling Li and Danyu Yang
Handwritten signature verification is vital for personal authentication across commercial and financial applications. Although deep learning methods are widely adopted for online signature verification (OSV), they often struggle with capturing highly discriminative features and modelling long-range dependencies. To address these issues, we propose a novel framework that integrates the augmented path signature (APS) descriptor with the T-Mamba model. The APS descriptor first applies time and basepoint augmentations, then computes sliding-window path signatures. The path signature is a non-parametric feature map from rough path theory that effectively captures
geometric structures and nonlinear inter-channel interactions. Inspired by the efficacy of state space models (SSMs) in sequence modelling, our T-Mamba model employs a hybrid design combining two temporal convolutional network (TCN) blocks with a time-scanning Mamba. This design enables the model to learn both local temporal patterns and global long-range dependencies, substantially improving verification accuracy. Our framework achieves state-of-the-art EERs on three public
benchmark datasets (MCYT-100, SVC-2004 Task 2, DeepSignDB), validating its effectiveness and robustness, especially when the training data is limited. Our code is publicly available at https://github.com/DLRL04/OSV-using-APS-and-T-Mamba.
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Artificial Intelligence
Beyond Coherence: Benchmarking Professional Editing-Technique Execution in Multi-Shot Audio-Video Generation
Tianyi Zeng, Junchao Liao, Yujie Wei et al. (+9 more)
Recent multi-shot audio-
video generators can produce increasingly coherent and cinematic outputs, but coherence does not imply the ability to execute editing techniques. Professional editing depends on shot structure, transition grammar, audio-
video cut relations, and montage, yet existing
benchmarks largely rely on proxies such as content quality, synchronization, or physical plausibility, systematically missing whether such editing instructions are actually executed. We introduce CutCraft, the first
benchmark for editing-technique execution in multi-shot audio-
video generation. CutCraft extends structured multi-shot
prompts with explicit editing specifications and is paired with a hierarchical hybrid evaluation framework that combines shot-structure
alignment, expert-model metrics, tool-grounded
multimodal judgment, and rubric-based question answering. Beyond evaluation, we design an agentic editing baseline that decomposes generation into planning, shot-level synthesis, and post-hoc composition, explicitly realizing editing semantics such as J-cuts, L-cuts, and transition timing. Across 13 state-of-the-art closed- and open-source models, CutCraft reveals a consistent gap between coherence and editing-technique execution: current systems often produce plausible multi-shot
videos yet fail to execute editorial instructions reliably. We find unstable shot structures, weak control of transition execution, and sharp degradation on higher-order montage, while aesthetic quality is only weakly correlated with editing-technique compliance. The
benchmark and metrics, and the editing
agent baseline are available at https://github.com/AlibabaResearch/cut-craft-bench.
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All authors Tianyi Zeng, Junchao Liao, Yujie Wei, Ziying Zhang, Litao Li, Tianyi Wang, Zhichao Wei, Shuyao Xu, Wenwen Qiang, Siyu Zhu, Zhenghao Zhang, Long Qin
Artificial Intelligence
MemForest: Efficient Agent Memory Management via EventTree Partitioning and Progressive Merging
Junxi Wang, Te Sun, Jiayi Zhu et al. (+9 more)
Agent memory systems have demonstrated significant potential in long-term dialogue, personalized assistants, and
video understanding. However, continuously accumulated
memory introduces substantial storage and
retrieval costs during inference. To address this issue, we propose MemForest, a general
memory compression framework adaptable to various
agent memory systems. Specifically, MemForest partitions historical
memory into event-centric units by leveraging global semantic similarity and local temporal continuity. For each unit, it constructs a maximum spanning tree, termed an EventTree, and progressively merges redundant
memory nodes by selecting high-weight edges, reducing storage overhead. Furthermore, we introduce an anchor-guided propagation
retrieval mechanism that retrieves relevant
memory nodes from the temporal neighborhoods of key nodes, improving
retrieval accuracy. Extensive experiments demonstrate the effectiveness of MemForest. Under the unimodal Mem0 framework, MemForest retains 97.1% of the original performance while compressing 50% of historical
memory across three
benchmarks (LoCoMo, LongMemEval, and PersonaMem), achieving a 1.89x
retrieval speedup. Under the
multimodal M3-
Agent framework, it preserves 99.7% of the original performance with a 50% compression ratio across two
benchmarks (M3-Bench-
robot and M3-Bench-web), achieving a 2.24x
retrieval speedup. \textcolor{RoyalBlue}{Our code is available at [https://github.com/Celina-love-sweet/MemForest.}](https://github.com/Celina-love-sweet/MemForest.}})
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All authors Junxi Wang, Te Sun, Jiayi Zhu, Chen Zhang, Siyuan Li, Xuyang Liu, Zichen Wen, Xiaobing Tu, Jinkui Ren, Xiantao Zhang, Ziqi Yuan, Linfeng Zhang
Game Theory
Subquadratic Subsidies for Nonnegative or Nonpositive Valuations
Max Dupré la Tour and Mashbat Suzuki
We study envy-freeness with subsidies for indivisible items beyond additive valuations. Assuming that every single-item marginal value lies in $[-1,1]$, we prove that a total subsidy of $O(n^{3/2}\sqrt{\log n})$ suffices to achieve envy-freeness among $n$
agents whenever all
agents assign nonnegative values to every bundle or all assign nonpositive values to every bundle. These valuation classes include monotone goods and monotone chores, respectively, but do not require monotonicity. Our result establishes the first subquadratic total-subsidy bound for general monotone valuations that holds for every number of
agents.
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Artificial Intelligence
Three Types of Negation of Triple and its Elements and an Extension of Triple
Zhenghua Pan
In various data models, the classical triple is a typical semantic data model. However, due to the design of the triple as a simple structure for representing positive assertions, it cannot sufficiently express different forms of negation present in the triple and its elements. This paper conceptually proposes that there are three distinct forms of negation within triples and their elements: contradictory negation, opposite negation and intermediary negation. Based on the the set SCOI and the logic LCOI+PLCOI with three kinds of negation, we propose an extension of triple that can distinguish and express these three different negations in the triple and its elements, called the TCOI triple with contradictory negation, opposite negation and intermediary negation. The TCOI triple is a semantic and structural extension of the classical triple. While retaining the ability to express positive assertions, it systematically introduces the three semantic dimensions of three negations, allowing these negations to independently act on the elements of the triple and on the whole triple. This significantly enhances the triple model capability to represent and
reasoning about complex negative information. This paper also explores the expressive power and
reasoning of the TCOI triple, as well as the application of TCOI triple implication
reasoning in counterfactuals and counterfactual
reasoning. We propose a truth-value (continuous value)
algorithm for TCOI triple implication
reasoning and perform its calculation through an example of the counterfactuals and counterfactual
reasoning.
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Machine Learning
Synergistic Fusion of Topological Structure and Temporal Semantics of Mobility for Urban Region Embedding
Namwoo Kim, Jeeyun Chang, Kanghoon Lee et al. (+1 more)
Urban region
embeddings have shown promising results in diverse urban sensing tasks such as crime, income, and service-call prediction. Recent methods improve representation quality by integrating mobility data with auxiliary modalities, using cross-view
attention or contrastive objectives to align heterogeneous features into a unified region representation. However, leveraging the temporal dynamics of human mobility remains under-explored. Regional inflow and
outflow fluctuate throughout the day, and inter-region connections emerge, persist, and dissolve over time. Moreover, prevailing fusion strategies combine views additively and miss the joint signal that emerges only when views co-occur. To address these gaps, we propose Mobility Stream-Structure Synergy (MoSS), which derives complementary views from mobility data: a Sequence view that preserves each region's hourly inflow/
outflow profile, and a Structure view based on zigzag persistence diagrams that capture how regional connectivity emerges, persists, and dissolves over time. A synergy module then extracts emergent representations from the co-occurrence of these views through multi-degree interactions, explicitly capturing higher-order signal across views. Extensive experiments on New York City and Chicago show that MoSS achieves state-of-the-art performance across three downstream tasks using mobility data alone, outperforming baselines that rely on auxiliary modalities.
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All authors Namwoo Kim, Jeeyun Chang, Kanghoon Lee, Yoonjin Yoon
Artificial Intelligence
Evidence-Aligned Entity Verification for Hallucination Detection in Retrieval-Augmented Generation
Runsong Jia, Zhen Fang, Mengjia Wu et al. (+2 more)
Hallucination detection is crucial for large
language models (LLMs), as hallucinated content creates significant barriers in applications requiring factual accuracy. Current detection methods mainly depend on internal signals like uncertainty and self-consistency checks, using the model's pre-trained knowledge to identify unreliable outputs. However, pre-trained knowledge may become outdated and has coverage limitations, especially for specialized or recent information. To address these limitations,
retrieval-augmented generation (RAG) has emerged as a promising solution by retrieving relevant evidence at inference time, grounding outputs beyond the model's parametric knowledge. In this paper, we target a critical and practical learning problem RAG-based
hallucination detection (RHD), where RAG is employed to enhance
hallucination detection by addressing information updating challenges. To address RHD, we propose a novel method Evidence-Aligned Entity Verification (EAEV), which detects entity-level
hallucinations by leveraging RAG to align generated entities with retrieved evidence contexts. Specifically, EAEV evaluates entity-evidence
alignment through three complementary dimensions and introduces counterfactual stability analysis to ensure robust
alignments under evidence perturbations. Experiments across multiple RAG
benchmarks demonstrate that EAEV achieves consistent improvements over existing methods with strong generalization capabilities.
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All authors Runsong Jia, Zhen Fang, Mengjia Wu, Jie Lu, Yi Zhang
Computer Vision
Tracking-by-detection in Multi-object Tracking: Survey and Experiments
Yujin Yang, Kyujin Shim, Kangwook Ko et al. (+1 more)
Multi-object tracking (MOT) is an essential computer
vision task that simultaneously tracks multiple objects in
video sequences, with various applications in surveillance, autonomous
navigation, and human-computer interaction. The tracking-by-detection (TBD) paradigm, which combines object detection with temporal association, has emerged as a leading approach, driven by innovative
algorithms. Despite recent progress, fair evaluation of TBD-based methods remains a challenge. Many studies introduce modules such as similarity metrics, data association strategies, or motion models, but they are often evaluated under inconsistent protocols, with different baseline trackers, hyperparameters, and
datasets. Such inconsistencies obscure the genuine contribution of each module and hinder objective comparison. This survey systematically reviews TBD-based MOT techniques, including similarity measurements, data association, camera motion compensation, and interpolation strategies. Starting from a minimal baseline tracker, we fairly evaluate the contributions of each method across diverse
datasets and accumulate well-balanced methods. Our findings establish a strong baseline tracker and provide a foundation for the principled design of robust and versatile MOT systems suitable for real-world deployment.
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All authors Yujin Yang, Kyujin Shim, Kangwook Ko, Changick Kim
Game Theory
Stable Voting Rules on the Edge of Optimal Metric Distortion
Ziyi Cai, Fabian Frank, Jabari Hastings et al. (+3 more)
We prove the existence of a randomized voting rule with metric distortion at most $2.1248$, within $0.013$ of the lower bound of $2.11264$. Our rule comes from a generalization of \emph{stable $k$-lotteries} developed in the context of committee selection. In contrast to prior work, our rule samples from a single distribution derived from a zero-sum game, without mixing between voting rules. Our result also gives sharp distortion bounds for stable $k$-lotteries. In particular we show that stable $2$-lotteries have distortion $7/3$, despite only relying on aggregate preferences over triples of candidates, and that stable $2$-lotteries are optimal in this class of voting rules.
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All authors Ziyi Cai, Fabian Frank, Jabari Hastings, Prasanna Ramakrishnan, Kangning Wang, Qilin Ye
Artificial Intelligence
Revoked but Still Authoritative: An Empirical Study of Revocation Enforcement in Agent-Memory Systems
Yi Ting Shen, Kentaroh Toyoda and Alex Leung
Long-running
language-model
agents depend on persistent
memory. Many
agent-
memory systems preserve history through soft revocation: a contradicted fact is marked invalid and retained rather than deleted. However, whether that mark is enforced at
retrieval time is unexamined. In this paper, we measure five such systems: we load each with a revoked policy and its replacement, track whether the revoked fact is returned at
retrieval and whether the
agent then acts on it across nine policy scenarios and nine models, and score every trial under six defense conditions. We find that no system enforces revocation by default: the revoked fact is returned wherever the revocation label is visible to the
retrieval layer, outranks its replacement, and leads
agents to the unsafe action. Based on these findings, we develop a guard that sits between the
agent and any
memory backend and withholds records that are revoked or conflict with their replacement.
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Cryptography and Security
ACEA: An Adversarial Co-Evolution Arena for Head-to-Head Red-Team and Blue-Team LLM Testing
Yi Ting Shen, Kentaroh Toyoda and Alex Leung
Automated red-team attacks and blue-team defenses for large
language models (LLMs) are advancing quickly. However, attackers and defenders are built and tested in isolation, and the resulting scores are hard to trust. To tackle this, we present ACEA (
Adversarial Co-
Evolution Arena), a platform that connects a pluggable red-team adapter and a pluggable blue-team adapter to a shared target LLM and scores their attack and defense rates with an LLM judge. ACEA contributes four components. First, a pluggable, model-agnostic arena. Any red or blue project connects over a minimal HTTP protocol, which we call the ACEA Standard Adapter Protocol (ASAP). It can be written in any
language, and a project that exposes nothing but the protocol is a full participant. Second, an evaluation methodology built for
adversarial rounds. Seeding the target with canonical secrets gives verifiable ground truth that separates real leakage from
hallucination. We also send each attack to the target even when the defense blocks it, which measures the attack's raw potency independently of whether it was stopped. Together these yield a per-round decomposition of attack strength and defense effectiveness. Third, a real-time, game-style visualization with a detailed end-of-battle report that localizes each failure. The evaluation thus becomes an actionable signal for improving a red or blue project. Fourth, an optional in-context improvement loop that turns each round's outcome into advisory hints for the next. An adapter can then adapt across rounds without keeping state, provided it reads the hints. We describe the design of ACEA and the metrics through which red and blue teams are scored head to head.
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Artificial Intelligence
CircuTutor: Transforming Static Circuit Problems into Intelligent and Dynamic Tutoring
Ziyu Luo, Xiaorui Ma, Lin Chen et al. (+1 more)
Learning direct current
circuit concepts requires learners to connect invisible physical quantities, such as current, voltage, resistance, and power, with observable outcomes such as bulb brightness. Conventional textbook materials and general-purpose
circuit simulators provide opportunities for problem solving and exploration but offer limited support for explaining why
circuit behavior changes or diagnosing the
reasoning behind incorrect answers. We present CircuTutor, a
circuit-state-driven intelligent tutoring system that transforms static textbook
circuit problems into an interactive tutoring workflow. CircuTutor first uses
multimodal problem parsing to extract the textbook question,
circuit topology, component parameters, switch states, and answer options, which are converted into a structured task and validated through
circuit simulation. Learners can then interactively explore the
circuit (by changing parameters) and submit an answer while a SPICE-compatible solver computes physically consistent
circuit states. After the learner submits an answer, CircuTutor presents a before-and-after
circuit state animation corresponding to the selected operation, organizes the simulated state changes into a causal
reasoning chain that explains the underlying
circuit behavior, maps answer discrepancies to likely misconceptions, and generates adaptive follow-up exercises targeted at the diagnosed misconception. Our experimental results demonstrate that CircuTutor effectively improves conceptual learning and the overall learning experience. The proposed framework demonstrates how simulated
circuit states can be transformed into intelligent and interactive tutoring for
circuit education, with the potential to generalize to other STEM domains.
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All authors Ziyu Luo, Xiaorui Ma, Lin Chen, Xiaoming Chen
Machine Learning
Revisiting Spectral Representations in Generative Diffusion Models
Yuehao Wang, Peihao Wang, Hanwen Jiang et al. (+3 more)
Diffusion models have shown remarkable performance on diverse generation tasks. Recent work finds that imposing representation
alignment on the hidden states of
diffusion networks can both facilitate training convergence and enhance sampling quality, yet the mechanism driving this synergy remains insufficiently understood. In this paper, we investigate the connection between self-supervised spectral representation learning and
diffusion generative models through a shared perspective on perturbation kernels. On the
diffusion side, samples (e.g., images,
videos) are produced by reversing a
stochastic noise-injection process specified by Gaussian kernels; on the spectral representation side, spectral
embeddings emerge from contrasting positive and negative relations induced by random perturbation kernels. Motivated by this, we propose a self-supervised spectral representation
alignment method to facilitate
diffusion model training. In addition, we clarify how joint spectral learning can benefit
diffusion training from a
geometric perspective. Furthermore, we find that the optimization of the spectral
alignment objective is in an equivalent form of
diffusion score
distillation in the representation space. Building on these findings, we integrate a spectral regularizer into
diffusion training objectives to improve the performance of
diffusion models on multiple
datasets. Experiments across images and 3D point clouds show consistent gains in generation quality. Code is released at https://github.com/yuehaowang/spectral-reg-
diffusion.
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All authors Yuehao Wang, Peihao Wang, Hanwen Jiang, Ziyi Yang, Qixing Huang, Zhangyang Wang
Information Theory
Variational Bayesian Data Detection for Multiuser MIMO Systems Corrupted by Phase Noises
Toan-Van Nguyen and Duy H. N. Nguyen
Phase noise (PN), arising from imperfect local oscillators, introduces multiplicative distortions that degrade the performance of communication systems. In uplink multiuser multiple-input multiple-output (
MIMO) systems, this impairment is further compounded by the presence of independent oscillators at each transmit and receive
antenna, each contributing an uncorrelated noise component. Existing PN compensation
algorithms at the receiver either rely on linearization approximations that lose accuracy under severe PN conditions, or incur computational complexity that scales prohibitively with the number of
antennas. To address these limitations, we propose a variational Bayes (VB) framework for joint PN estimation and data detection in uplink
MIMO systems. We develop VB-based detectors that treat noise statistics as latent variables, and reformulate the inference problem by absorbing the transmitter PN into the transmitted signal, treating the resulting composite variable as the inference target. Under von Mises priors, this reformulation yields exact closed-form conjugate posterior updates, from which we derive an improved detector achieving superior performance at low complexity. Simulation results demonstrate that the proposed VB
algorithm achieves lower symbol error rates than the Self-Interference Whitening (SIW)
algorithm and conventional phase-noise-unaware detectors across a wide range of channel conditions, modulation orders, and PN severities, while remaining computationally scalable to large
MIMO deployments.
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Robotics
CALIPER: Clean Scenes Cannot Rank Physical Inference in Pretrained Visual Representations
Aman Mehta and Riya Baviskar
How far a pushed object slides depends on its mass and friction, which no single image reveals. Pretrained visual encoders are increasingly used as the perception front end of world models for manipulation, and their physical competence is assessed with perturbation
benchmarks and linear probes, almost always in a clean, fixed-camera scene. We show that these assessments cannot distinguish an encoder that infers physics from one that does not. CALIPER (calibrate, then predict) is a direct test: an object of unknown mass and friction is struck twice at known speeds, a third strike is shown only up to the moment of contact, and a linear readout on frozen features must predict how far the object slides. Swapping in another object's calibration clips checks that the evidence is actually used. Across 2,000 simulated episodes and eight representations, from V-JEPA 2 to a randomly initialised ViT and raw pixels, calibration adds +0.50 R^2 and the swap removes it. Yet in the clean scene every representation lands within 0.02 R^2 of the ceiling set by true simulator state, because a fixed camera exposes the object's displacement directly in pixel coordinates. Resampling camera, lighting, and clutter for every clip spreads the same representations across 0.50 R^2; when the readout chooses a push speed for a goal distance, V-JEPA 2 misses by 4 mm and the random ViT by 20 mm, no better than ignoring the object. Linear probes track none of this: a change in frame aggregation moves a probe more than pretraining does, and erasing the probed mass direction from the same representation costs nothing in one scene and 0.35 R^2 in the other. Whether a
benchmark can rank models is an empirical property, and we give three checks that establish it.
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Artificial Intelligence
Agentic ML Exploration (A-MLE) for Ads Ranking
Erwin Gao, Vinodh Kumar Sunkara, Jingyi Guan et al. (+36 more)
Modern industrial ads ranking stacks are increasingly bottlenecked not by model capacity or training compute, but by the throughput of human ML iteration - the cycles of research, implementation, training, debugging, evaluation, and launch required to surface a single statistically significant improvement. A typical ranking stack contains numerous differentiated models with heterogeneous data, architectures, and infrastructure constraints, and each cycle takes days to weeks of senior engineer
attention per model. As a result, techniques that have proven effective on one model diffuse into others slowly and unevenly, leaving substantial recoverable signal unexplored. We present Agentic ML Exploration (A-MLE), an autonomous LLM-
agent system that systematically explores ML techniques across a portfolio of ads ranking models. A-MLE decomposes ML iteration into five stages involving hypothesis generation, exploration strategy, experiment execution, result analysis and shared knowledge substrate which are orchestrated by a single
agent that invokes domain-specific skills and agentic workflows against a sandboxed execution layer, with human-in-the-loop checkpoints at each stage boundary. We deploy A-MLE across a representative set of large-scale ads ranking models and evaluate it along a tiered capability framework (tool availability, autonomous workflow execution, and open-ended exploration). We further report a controlled cross-LLM study using a fixed
agent loop, which surfaces qualitative differences in execution reliability and exploration aggressiveness across the Claude Sonnet, Gemini, and GPT families. We discuss failure modes and the design choices that govern reliability. Our findings suggest that agentic exploration is a practical force multiplier for ML engineers in industrial recommenders, especially for the long tail of models that rarely receive expert
attention.
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All authors Erwin Gao, Vinodh Kumar Sunkara, Jingyi Guan, Qinjin Jia, Hangjun Xu, Xiang Ji, Sherman Wong, Surya Teja Chavali, Pratik Vaishnavi, Aryan Pandhi, Xiaoyu Deng, Zhaodong Wang, Samarth Inani, Fan Yang, Jakob Moberg, Zoe Zu, Nicolas Bievre, Sami Khenissi, Amit Jaspal, Ehsan Fakharizadi, Srinidhi Viswanathan, Dorothy Sun, Abishek Vanam, Sneha Iyer, Sheela Yadawad, Wenjie Chen, Gaby Nahum, Junhua Gu, Peter Chu, Yucheng Liu, Xin Zhao, Vitor Cid, Chaorong Chen, Vijay Pappu, Ashwin Kumar, Wenlin Chen, Ben Schulte, Deepak Chandra, Ritwik Tewari
Artificial Intelligence
zScore-N: A Neural Network for On-Chain Wallet Reputation Scoring
Girish G N, Ashutosh Sahoo, Akshay SP et al. (+2 more)
Wallet reputation scores decide who receives an airdrop, who can borrow, and who enters an allowlist across decentralised finance. They almost always begin as hand-written formulas: compositions of clamped logarithmic, linear and square-root transforms over behavioural features, with every threshold and point award set by hand. Such a formula is readable and deterministic, but it is piecewise and non-differentiable, it cannot improve as data accumulates, and it cannot distinguish a feature that is genuinely zero from one its pipeline failed to capture. We present zScore-N, the neural network that replaced ours in production. The formula served as its teacher: calibrated against 5,208,952 wallets sampled across 2019-2024 and verified to reproduce production output to within 2.3e-13, it supplies unlimited labelled training data at zero label noise. The trained network reproduces it to 0.58 points RMSE on the 1000-point scale (R^2 = 0.99997), against 2.25 for gradient-boosted trees and 28.04 for linear regression on identical features and splits. Trained with missing-value masks against uncorrupted targets, it halves the error that incomplete data introduces: at 10% feature-level missingness the formula drifts 51.4 points from its own complete-data output with a systematic -12.5 point bias, while the network drifts 17.9. The network carries the score at production scale, across a population of millions of wallets spanning six orders of magnitude in size and activity.
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All authors Girish G N, Ashutosh Sahoo, Akshay SP, Gurukiran S, Dhanashekar Kandaswamy
Machine Learning
CUNO: Curriculum and Preference Optimization for Stable Graph Unlearning under Mass Deletion
Chenhan Zhang, Ali Braytee, Madhushi Bandara et al. (+4 more)
Graph unlearning removes the influence of designated training data from a trained graph model without retraining from scratch. However, existing methods suffer a sharp drop in model utility under large deletion ratios (mass deletion), a phenomenon we refer to as catastrophic unlearning. We find that a key cause is the uniform treatment of all deleted samples, which is particularly damaging in graph learning: structural dependencies cause different nodes to play vastly different roles in the learned model, yet existing methods apply the same forgetting operation to the entire forget set. Based on this insight, we propose CUNO, a curriculum-based graph unlearning framework that removes the forget set progressively, ordering samples by their estimated unlearning difficulty across multiple stages. CUNO further employs a distribution-level negative preference optimization (NPO) objective at each curriculum stage that steers the model away from its original behavior on the current forget subset while preserving retained performance. Our theoretical analysis shows that the curriculum design is most beneficial when the forget set spans a wide range of unlearning difficulty, a condition naturally satisfied under mass deletion. Comprehensive experiments confirm that CUNO consistently mitigates catastrophic unlearning: at 20% deletion, it retains 74% of the original utility compared to 26-53% for existing methods, and maintains more than half the original utility even at 50% deletion. Our code is publicly available at https://anonymous.4open.science/r/cuno-D4FF.
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All authors Chenhan Zhang, Ali Braytee, Madhushi Bandara, Xin Hao, Paul J. Kennedy, Massimo Piccardi, Raymond Owen
Computer Vision
CS-CLIP: Compositional Scene Graph-guided CLIP for Robust Compositional Reasoning
SeongJun Jeong, Minjoon Jung, Woo Suk Choi et al. (+2 more)
Vision-
language models (VLMs) demonstrate strong performance across compositional
reasoning benchmarks, which require
reasoning over semantic perturbations of objects, attributes, relations, and their interactions. However, our controlled analysis reveals that existing compositionality-aware VLMs exhibit element-specific biases, often underperforming vanilla CLIP on certain compositional elements. To address this, we propose Compositional Scene Graph-guided CLIP (CS-CLIP), which uses scene graphs to identify compositional elements and construct structured negatives via selective masking. We further retain negatives that are most contradictory to the original caption, forcing the model to rely on compositional structure rather than surface cues. CS-CLIP achieves state-of-the-art compositional
reasoning with robust performance across compositional elements. It also preserves general
vision-
language capabilities such as cross-modal
retrieval and downstream visual
reasoning, while requiring fewer training samples than prior methods.
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All authors SeongJun Jeong, Minjoon Jung, Woo Suk Choi, Youwon Jang, Byoung-Tak Zhang
Databases
SmartANN: Object Causal Modeling Boosts Approximate Nearest Neighbor Diagnosis and Auto-Design
Yutong Zhou, Guoxin Kang, Lei Wang et al. (+3 more)
Approximate Nearest Neighbor (ANN)
algorithms achieve high efficiency through interdependent phases across index construction and query execution. This coupling allows upstream performance loss to propagate downstream, affecting execution behavior and measurable outputs. Existing component-level analyses mainly compare isolated design choices, while end-to-end
benchmarks report aggregate metrics; neither traces loss propagation across dependent phases, hindering root-cause attribution and automated redesign. We present SmartANN, a framework based on the object causal model (OCM) for ANN bottleneck attribution and automated redesign. SmartANN represents an ANN workflow as eight ordered, replaceable objects and diagnoses them with a sequential diagnose-and-replace loop. At each iteration, it identifies the first object deviating from expected behavior or output as a bottleneck. Because an upstream bottleneck can obscure downstream ones, SmartANN replaces it with a test oracle when available, or with an implementation producing a better outcome, then continues downstream diagnosis. From the diagnosed bottlenecks and failure causes, SmartANN composes compatible actions from a pluggable action library to generate an optimized end-to-end ANN design. We instantiate SmartANN for IVF-PQ and HNSW, covering partition-and-quantization and graph-based ANN families. Experiments on eight real-world
datasets show that SmartANN improves Recall by 0.24–74.20%, and increases QPS by 28.8–256.5% at comparable Recall, with low diagnosis and auto-design overhead. The code is available at https://github.com/zhouyutong20/SmartANN.
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All authors Yutong Zhou, Guoxin Kang, Lei Wang, Xueya Zhang, Qinwei Yang, Jianfeng Zhan
Artificial Intelligence
Style Over Substance: Content-Invariant Wrappers Flip LLM Safety-Judge Verdicts
Yongxi Zhou, Wenbo Ye, Yuanzhe Liu et al. (+3 more)
Automatic safety judges – for example Llama Guard, or GPT-4o given a grading
prompt – produce the numbers behind reported jailbreak success rates, defense evaluations, and safety leaderboards. We ask whether these judges grade what a reply contains or how it sounds. We place a fixed piece of text before or after a model reply (a style wrapper) and leave the reply itself unchanged byte-for-byte, so any change in verdict is an error of the judge. Over 600 JailbreakBench replies (300 harmful, 300 refusals), 9 wrappers, and 8 judges, using paired tests and a measured per-judge repeat-disagreement rate (the noise floor), we find that most judges barely move but specific judges have specific, cheap blind spots. A
token refusal ("I can't help with that") placed before the unchanged harmful text flips 19.9% of GPT-4o-mini's correct "unsafe" verdicts to "safe" (noise floor 0.5%; 18.2% under majority-of-three re-scoring), yet moves Claude only 0.4%. The deployed Llama Guard 4 guard model is evaded by an "educational course" preface on 12.3% of harmful replies, and the flips persist across repeats, while a second deployed guard (gpt-oss-safeguard-20b) is immune. Changing only the grading
prompt to ignore tone cuts the GPT-4o-mini attack roughly tenfold on the same model, so the weakness lives in the judge, not in the content. Pure tone-only decoration that adds no new claim still flips up to 2.1% of verdicts on the vulnerable judges, several times their noise floors. On this artifact set the judges also disagree about which target model is safest, and a bootstrap shows the ranking is unstable before any wrapper is added. We release the data, wrappers, code, and per-verdict labels.
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All authors Yongxi Zhou, Wenbo Ye, Yuanzhe Liu, Zihan Dong, Junwei Yao, Jiahui Ge