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Computer Science

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Sunday 4 October

Machine Learning

Operational Abstractions of Neural Network Concepts via Topological Representations

Mathieu Pont and Christoph Garth

Concept-based methods provide a semantic level for interpreting and manipulating learned representations, but existing editing approaches are typically specialized to particular interventions and do not provide a common and editable representation of concept organization. To achieve this, we introduce Topological Concept Representations (TCR), a post-hoc operational abstraction that jointly characterizes the concepts encoded in a learned representation and their relationships. TCR constructs an intermediate concept space from concept recoverability and interaction scores, and compactly encodes its organization through topology. Interventions are expressed through modifications of this abstraction that are propagated back to the underlying learned representation. This allows different concept-level operations to share the same optimization framework and separates the desired concept organization from the mechanism to achieve it. We establish stability and reparameterization-invariance properties of TCR and its connections to existing concept-editing formulations. We use TCR to disentangle concepts as a preprocessing step for existing erasure methods, improving worst-group accuracy by 21.89 on average at comparable concept leakage. We further use TCR to transfer concepts from teacher to student models, improving concept recoverability by up to 5.54 while also improving or maintaining competitive test top-1 accuracy.
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Emerging Technologies

A FeFET Voltage-to-Time Converter with Offset Trim by Programmed Multilevel State in 28-nm CMOS

Jeries Mattar, Hanaa Eqeiq, Stefan Dünkel et al. (+4 more)

The offset and gain of voltage-to-time converters (VTCs) in time-domain circuits vary with process, supply, and temperature. This work presents a VTC whose nonvolatile input-referred offset trim is the programmed multilevel state of the ferroelectric field-effect transistor (FeFET) that converts the input. Fabricated in 28-nm CMOS, the 6.07-$\mu$m$^2$ VTC consists of a current-starved inverter with the FeFET and a parallel NMOS leaker in its tail, a capacitor bank, and a following inverter. The programmed state shifts the transfer curve onto the input range, leaving the gain to the capacitor bank and the leaker bias. Measured at 10~MS/s, centered states weaken the second harmonic from $-$12.5~dB in the low-threshold state to $-$32.2~dB and lower the static integral nonlinearity (INL) at 5~bit from 2.42 to 0.92~LSB. Post-layout simulations indicate that the proposed VTC can operate at 500~MS/s, drawing 2.0~$\mu$W from a 0.9-V supply, with nearly unchanged distortion and gain up to 700~MS/s. Across simulated process corners and $\pm$10\,% supply variation, the input-referred offset is calibrated to within 10~mV and the gain to within 4\,% of nominal by adjusting the programmed state and leaker bias.
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All authors Jeries Mattar, Hanaa Eqeiq, Stefan Dünkel, Halid Mulaosmanovic, Gunda Beernink, Sven Beyer, Nicolás Wainstein

Machine Learning

MetaKernelBench: Measuring GPU Kernel Knowledge Transfer Beyond Code

Xueyi Chen, Shiyu Liu, Xin Jin et al. (+5 more)

Recent GPU kernel optimization agents retain what they learn in knowledge bases or as distilled skills. Kernel benchmarks score each attempt's implementation for correctness and speed but leave the reuse value of retained experience unmeasured. We introduce MetaKernelBench, which measures whether experience distilled from an attempt in one kernel domain-specific language (DSL) improves a fresh attempt at the same problem in another. Its 74 problems are fused subgraphs in six families, each posed as a pair of CuTe DSL and TIRx variants that differ only in the DSL. The agent first attempts each variant solo and is instructed to distill what it learns into a natural-language skill, which is transferred whether or not the source attempt passes verification. The skill is the only extra input to a skill-conditioned attempt by the same model in the other DSL. We compare each skill-conditioned attempt with the solo attempt on the same variant under matched per-attempt budgets, scoring correctness and end-to-end runtime. Across six models and both directions, paired lift over solo attempts ranges from -19% to +29%. Four models gain in both directions, yet regressions occur on 16% to 45% of problems in every model and direction. Outcomes follow the source attempt's result relative to the target's solo attempt rather than source success alone, improving in 71% of comparisons when the source stands above and regressing in 54% when it stands below. MetaKernelBench complements implementation-quality metrics by measuring same-problem cross-DSL kernel knowledge transfer.
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All authors Xueyi Chen, Shiyu Liu, Xin Jin, Yuhua Zheng, Xin Li, Haolei Bai, Junhan Zhu, Huan Wang

Computer Vision

EMBER-Bench: Benchmarking Cross-Event Causal Memory in Long-Horizon Embodied Tasks

Aoyang Cai, Boning Zhao, Shaoxuan Xie et al. (+5 more)

Lifelong physical agents must reason over extended interactions where past events continue to shape the world long after they disappear from view. Beyond recalling what happened, agents must infer how history changes the current state and constrains future actions. Yet existing embodied and video-memory benchmarks largely focus on historical retrieval and summary, leaving such history-dependent causal reasoning underexplored. We introduce EMBER-Bench, an egocentric benchmark for cross-event causal reasoning in long-horizon embodied tasks, for which we newly created the task design, video recording, and data annotation. It contains 189 household tasks and 699 QA pairs, spanning task progress, failure recovery, external interventions, and compound long-horizon tasks with distant dependencies and prerequisites, with fine-grained event and causal-chain annotations. EMBER-Bench evaluates reasoning in both directions: next-action prediction selects the next action from history, and causal traceback, given that action, identifies the historical event that makes it necessary. Input ablations that add action logs or privileged cause-and-consequence annotations to the video indicate which kind of historical information models fail to use. Among the 16 evaluated models, the highest overall accuracy is 61.2%, compared with a mean of 98.3% across two human evaluators. At paired decision points, correct traceback is not associated with correct next-action prediction. Adding action logs yields a gain of 1.6 points, whereas cause-and-consequence annotations yield an additional gain of 13.0 points on top of that. These results suggest that extracting causal information from past events and converting it into constraints on current actions remains a key difficulty for long-horizon embodied agents. Project Page: https://zhaoalexgoat.github.io/EMBER-Bench/
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All authors Aoyang Cai, Boning Zhao, Shaoxuan Xie, Dahui Gao, Huan Yang, Zhongyuan Wang, Zhiwei Yu, Guocai Yao

Programming Languages

Decompiling Quantum Assembly into Structured Programs

Pingchuan Ma, Zhantong Xue, Zongjie Li et al. (+6 more)

Quantum compilers translate programs into native gates, insert SWAP gates to route interactions onto a device, and optimize the result. The output is quantum assembly, a flat gate list that hides the algorithm's structure (QFT, Grover iteration, QAOA layers). Understanding, auditing, porting, or reusing such assembly requires recovering a program that exposes this structure. We present Quelle, a decompiler that recovers structured programs (library calls, loops, functions, symbolic angles) from routed and optimized quantum assembly (OpenQASM) and checks every output for equivalence with its input. Quelle is built on one invariant: every step is either an exact rewrite, checked where it is applied, or a hypothesis, checked against the input before use. It first lifts the assembly into a circuit by removing compilation artifacts, notably un-routing, which recovers the SWAPs that routing inserted, including those merged into other gates. It then recovers algorithm templates (sketches) whose parameters are solved from quantities preserved by compilation, and, for algorithms without a template, library calls and loops by anti-unification. Failed hypotheses are rejected; the corresponding operations remain in gate-level form. On 816 assembly programs from 22 algorithm families and a random-circuit control, compiled at four routing and optimization levels with native two-qubit gates CX, ECR, or CZ, Quelle's output is equivalent to the input for 100% of the inputs, and it fully decompiles 94% of them; compared on the same inputs, it fully decompiles 1.7x as many as the best LLM-based decompiler, which spends 15K tokens per input. On nine algorithm families for which it has no template, the structure it recovers without templates explains 60% of the gates. It checks inputs of up to 2.3 million gates and emits no inequivalent output on 237 real-world QASMBench assembly files.
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All authors Pingchuan Ma, Zhantong Xue, Zongjie Li, Qixin Zhang, Yuxuan Du, Zhaoyu Wang, Yuguang Zhou, Shuai Wang, Xiaoqin Zhang

Robotics

FreeLoc: Online Floorplan Localization via Diffusion-Aided Pose Refinement

Haocheng Peng, Boyang Zhou, Jiarui Hu et al. (+7 more)

Floorplans provide compact and widely available geometric maps for indoor localization, but existing high-performing floorplan-based methods still convert them into dense scene-specific offline databases, tying accuracy, storage, and runtime to the sampling resolution of the discretized pose space. We present FreeLoc, an online RGB-based floorplan localization framework that treats the floorplan as a directly queryable geometric map. FreeLoc introduces an efficient online geometric querying and diffusion-aided refinement scheme, which retrieves plausible pose anchors through on-the-fly floorplan ray querying and refines them into accurate continuous pose estimates. For sequential localization, FreeLoc develops an online likelihood construction strategy that bridges single-frame localization and probabilistic temporal fusion by constructing likelihoods from coarse-sampled candidates and refined pose hypotheses, enabling histogram-filter-based temporal fusion without offline databases. Experiments demonstrate real-time online inference and state-of-the-art performance in both single-frame and sequential localization, while real-world results validate practical deployability in indoor robotic localization scenarios.
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All authors Haocheng Peng, Boyang Zhou, Jiarui Hu, Xiyue Guo, Ziyang Zhang, Boming Zhao, Yifan Gao, Xiao Li, Hujun Bao, Zhaopeng Cui

Computer Vision

PortraitAes: Intent-Conditioned Structured Portrait Aesthetic Assessment

Junzhou Xie, Haozhong Xiong, Xunyun Tian et al. (+8 more)

Portrait aesthetic assessment assigns comparable scores according to how effectively human-centered images fulfill their photographic intent. These scores support data filtering, candidate selection, and preference modeling in image-generation pipelines. Existing methods typically predict a single aesthetic score or use general-purpose MLLMs without conditioning on photographic intent. This omission matters because the same blur, pose, lighting, or framing choice may serve one photographic intent but undermine another. These models thus learn context-agnostic aesthetic priors and yield inconsistent, inaccurate, misleading judgments for portraits with distinct photographic objectives. We introduce PortraitAes-Bench, an 11K-scale benchmark that decomposes this task into intent-conditioned subjudgments. Expert-authored rubrics define nine photographic intents, six first-level dimensions, and 22 secondary criteria. They support a structured pipeline for intent routing, specialist assessment, verification, and score fusion. Following this structure, we train PortraitAes with multi-task supervision. We then improve score comparability through Gaussian score calibration and within-dimension cross-image ranking. On the standard benchmark, PortraitAes achieves a Pearson correlation of 0.924 and a Spearman rank correlation of 0.934. On the hard-case set, its Pearson correlation is 0.829 and its Spearman rank correlation is 0.795. Across both sets, PortraitAes outperforms the evaluated general-purpose MLLMs and specialized aesthetic baselines.
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All authors Junzhou Xie, Haozhong Xiong, Xunyun Tian, Kaile Du, Tianchen Yu, Qiang Li, Wei Liu, Jiaming Liu, Ruihua Huang, Yang Shi, Guangcan Liu

Software Engineering

Rethinking Tool Design for Agentic RCA: A Controlled Empirical Study

Yu Luo, Rongchen Gao, Zhenhui Zhou et al. (+7 more)

Large language model (LLM) agents are increasingly explored for root cause analysis (RCA) in microservice systems, yet empirical guidance on how to design and combine their tools remains limited. We conduct a controlled empirical study of tool abstraction and composition across models and microservice environments. We implement 24 structured tools for metric access (L1), evidence analysis (L2), and diagnosis (L3), alongside a Python-based reference setting (L0). Using 375 failure cases from three microservice systems, we evaluate eight configurations with Qwen3.7-Plus and compare four Qwen models on a shared subset of four configurations. Our results show that tool configurations affect root cause localization and failure type identification differently. With Qwen3.7-Plus, L3 achieves 82.8% top-1 localization accuracy compared with 85.4% for L0, while requiring less than half the time per case. Adding tool levels can improve type identification while reducing localization accuracy. Trajectory analysis reveals cases in which agents override correct diagnostic recommendations after consulting additional evidence. Model choice also changes tool benefits: adding L3 to L1+L2 improves diagnosis for three models but hurts Qwen3-8B, which rarely invokes L3. Tool configuration rankings further change across microservice systems. These findings provide an empirical foundation for designing and using RCA tools, guiding tool selection and composition according to the model, diagnostic objective, and target system.
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All authors Yu Luo, Rongchen Gao, Zhenhui Zhou, Changchang Liu, Yuliang You, Yongqian Sun, Shenglin Zhang, Qiuai Fu, Shijie Wang, Dan Pei

Distributed Computing

Min-Max Uniform Circle Formation by Asynchronous Mobile Robots

Animesh Maiti, Prakhar Shukla and Subhash Bhagat

Given a set of point robots $\mathcal{R}$ in the Euclidean plane and a target circle $\mathbf C$ enclosing all robot positions, the Min-Max Uniform Circle Formation (MMUCF) problem requires the robots to move to distinct positions on $\mathbf C$ such that the final configuration forms a regular $n$-gon while minimizing the maximum distance traveled by any robot. Uniform circle formation is a fundamental coordination task in swarm robotics with applications in perimeter monitoring, surveillance, boundary coverage, and pattern formation. The literature does not address the optimization of the maximum individual displacement during the formation process. In this work, we study the min–max versions of the circle formation and uniform circle formation problems, where the goal is to minimize the maximum distance traveled by any robot. We consider these problems under the $\mathcal{ASYNC}$ model, where robots are autonomous, anonymous, identical, homogeneous, oblivious, and silent, and operate under the Look–Compute–Move model with non-rigid motion. We first give necessary conditions for a deterministic solution and then present deterministic, distributed, and collision-free algorithms that form a circle and a uniform circle in finite time while minimizing the maximum movement. The algorithms ensure that robots reach distinct positions on the circle and, in the uniform case, equally spaced positions on $\mathbf C$ under the considered model.
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Cryptography and Security

Toward Lightweight Aerial 5G gNBs: Reproducible OAI Testbed on Commodity ARM Platforms

Marc Duboc and Ammar El Falou

For a battery-powered UAV base station, every gram and watt devoted to RAN-compute competes with flight-duration margin. We therefore keep the 5G Core (5GC) and central unit (CU) on the ground and design the airborne unit around only the backhaul endpoint, distributed unit (DU), and radio. The key question is then a practical one: can widely available commodity ARM computers sustain a real radio OpenAirInterface (OAI) 5G DU with useful performance over heterogeneous F1? We answer it with Raspberry Pi~5 and Jetson Orin Nano as DUs, a USRP B210, a commercial handset, and Ethernet, Wi-Fi/GRE, and 5G/WireGuard backhaul. Jetson preserves 88.4% of x86 split-DU Ethernet DL throughput and 87.5-89.0% across all three bearers; 5G/WireGuard preserves 76.4-77.1% of each host's wired DL rate. The validated Jetson/B210/RM500Q-GL electronics weigh 657.4 g and draw about 28 W under sustained traffic (757.4 g with integration allowance). A controlled link adaptation intervention raises split DL from 23.4 to 99.4 Mb/s as the dominant Modulation and Coding Scheme (MCS) moves from 3 to 26. Finally, synchronized radio, F1-U, CPU, and UHD evidence narrows the remaining monolithic-split gap to split-path scheduling/timing behavior. Beyond performance, this capability serves the emergency-response use case that motivates the aerial cell: once deployed above an affected area, it can broadcast a PWS warning message. We release the OAI patch that carries the single-segment Write-Replace Warning over F1 from CU to DU, where SIB8 is scheduled to the handset. The public artifact makes the payload, performance, and emergency-broadcast baseline reproducible with laboratory-accessible hardware.
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Machine Learning

Physics-Augmented Graph Transformers for Patch-Antenna Forward and Inverse Design

Avi Epstein, Snir Nehemia, Haim Suchowski et al. (+1 more)

Full-wave electromagnetic (EM) simulation enables accurate patch-antenna analysis but is computationally expensive for large-scale forward prediction and inverse design. We present a mesh-native, physics-augmented graph-learning framework that treats radiation-pattern prediction as signal reconstruction on an irregular surface mesh. For the forward problem, a GPS graph transformer is trained with Physics-Augmented Intermediate Supervision (PAIS), an auxiliary node-level objective that predicts complex surface currents, the physical intermediate linking geometry to radiation. PAIS improves multiple GNN backbones at no inference-time cost, while shuffled-current and non-physical controls show the gain comes from physical correspondence. Direction-conditioned decoding and a differentiable radiation-integral consistency loss further exploit this structure. On an 80,000-sample CST benchmark, GPS+PAIS reaches MSE 0.17 / PSNR 19.67, generalizes to a PCA split, and transfers zero-shot to canonical patches. For inverse design, surrogate-filtered diffusion beats nearest-neighbor retrieval by 32% relative MSE.
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All authors Avi Epstein, Snir Nehemia, Haim Suchowski, Lior Wolf

Artificial Intelligence

How corner is a corner case? Percentile control for highway scenario generation

Jiaxi Liu, Hang Zhou, Hangyu Li et al. (+5 more)

Generating corner-case scenarios with appropriate adversity in a simulation environment is critical for testing an autonomous vehicle (AV) software stack's safety performance before deployment. Existing autonomous-driving scenario generators can enforce specific behavior, adversity, or feasibility conditions, but they provide limited control over how extreme a generated scenario is relative to plausible futures in the same traffic context. This study represents the adversity of a generated scenario as its percentile in the conditional distribution of future risk given the observed history. This view supports calibrated answers to two questions: how "corner" a generated corner-case scenario is and how its "cornerness" can be fine-tuned. To this end, we formulate history-conditioned risk-percentile requests and learn a reference risk distribution that maps each requested percentile to a physical risk target. We then use a percentile-conditioned joint diffusion model with sampling-time risk guidance to generate multi-agent futures, together with a reference-based criterion for evaluating percentile realization. Experiments use the minimum post-encroachment time (PET) between the ego and its surrounding vehicles as the risk surrogate on highD. On the primary evaluation set, our method realizes 1,422 of 1,440 requests within a 0.05 percentile tolerance (98.75%), with mean percentile error 0.00673 and PET-target error 0.00991 seconds. The resulting interface connects context-relative risk specification, physical realization, and evaluation through a common risk scale. Project website and videos of generated scenarios are available at https://hhj233.github.io/CornerPercentile/.
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All authors Jiaxi Liu, Hang Zhou, Hangyu Li, Yifan Wang, Keke Long, Chengyuan Ma, Bin Ran, Xiaopeng Li

Software Engineering

LLM-Based Test Generation: Information Sources, Generation Strategies, and Quality Evidence

Yunhao Liang, Chengguang Gan, Ruixuan Ying et al. (+3 more)

Large language models are increasingly used to generate test scenarios, executable test suites, assertions, interaction sequences, and fuzzing infrastructure. These artifacts serve different purposes and rely on different sources of information about correct behavior. A test that increases implementation coverage, a test that agrees with a reference program, and a test that detects a requirements violation therefore provide distinct kinds of evidence. We present a structured narrative survey that connects the generation process to the evidence used to justify test quality. Drawing on 95 curated source records through 29 September 2026, we organize the field along four dimensions: testing objectives and artifacts, information available during training and generation, generation and learning mechanisms, and evaluation evidence. We synthesize work on unit and requirements-based testing, test oracles, API and GUI testing, fuzzing, and learned test generators. The resulting framework explains how execution feedback can improve executability while also affecting the independence of an oracle, why specification provenance and training-time reference supervision matter for comparisons, and how downstream code-selection gains differ from test correctness. We use these distinctions to organize benchmarks and derive a research agenda for independent oracle assessment, budget-aware generation, repository-scale evaluation, and test maintenance.
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All authors Yunhao Liang, Chengguang Gan, Ruixuan Ying, Hanjun Wei, Zhe Cui, Shiwen Ni

Computer Vision

VisualErase: Dual-Branch Visual Trajectory Redirection for Robust Concept Erasure in Text-to-Image Diffusion Models

Qianlong Xiang, Miao Zhang, Kun Wang et al. (+3 more)

Concept erasure is essential for the safe deployment of text-to-image diffusion models, as they may reproduce harmful, copyrighted, or privacy-sensitive content learned from unconstrained large-scale data. Existing methods typically erase unwanted concepts while preserving general generation capability by redirecting target-related text-to-image mappings. However, recent studies show that erased models may still retain visual generative trajectories of target concepts, leaving them vulnerable to adversarial recovery attacks and revealing a fundamental gap between redirecting text-to-image mappings and truly removing visual knowledge. To bridge this gap, we propose VisualErase, a new paradigm that redirects concept-bearing visual generative trajectories toward explicitly defined concept-removed outcomes. To enable this redirection, we use structure-preserving image editing to construct content-aligned, concept-removed counterparts for source images, providing explicit visual endpoints that retain non-target content. We then derive a denoising target from each source-to-counterpart pair and use a dual-branch redirection loss to align both text-conditioned and unconditional predictions with this target, since conditional supervision alone does not explicitly constrain generation without textual guidance. To mitigate the adverse effects of concept erasure on non-target generation, we jointly optimize the redirection loss with a counterpart retention loss that matches denoising predictions from the frozen pretrained model. Across style, celebrity, and nudity erasure, VisualErase limits the maximum attack success rate over seven attacks to 0%, 8%, and 0.1%, respectively, while retaining general generation quality. These results highlight the importance of visual trajectory redirection for robust concept erasure beyond text-to-image mappings alone.
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All authors Qianlong Xiang, Miao Zhang, Kun Wang, Yupeng Hu, Junhui Hou, Liqiang Nie

Machine Learning

Beyond Overparameterization: Provable Learning of Input-Convex Multi-Layer Polynomial Networks with Active Queries

Jinqi Tang, Qian Chen, Shihong Ding et al. (+1 more)

The theoretical understanding of multi-layer neural networks is largely confined to overparameterized settings, which obscure parameter identifiability and incur high sample complexity. Neural tangent kernel (NTK) provides a general theory for wide networks, but does not offer efficient sample-complexity guarantees. Recent feature-learning results go beyond kernel methods for single-neuron, multi-index, and hierarchical targets. However, the analysis is often restricted to shallow or specific architectures and to the overparameterized regime. We break this paradigm to achieve parameter-level recovery of deep target networks, albeit by using active data queries. Specifically, we study $L$-layer polynomial networks with even degree-$k$ monomial activations and nonnegative higher-layer weights. This structure makes the target network input-convex, while the optimization landscape remains highly nonconvex with respect to the parameters. Leveraging input convexity and active queries, we propose ASPIRE (Active SamPling for Iterative Recovery via Eigendirections), a layerwise sampling-based diagonalization algorithm that recovers all network parameters to $\delta$-accuracy with sample complexity $ \widetilde O_{k,L}\left(d^{L^2+O(L)}\delta^{-2e}\right) $ in polynomial time. To our knowledge, this is the first parameter-recovery guarantee for deep target networks whose exponent grows only polynomially with depth, as well as the first justification for the effectiveness of using high-quality data in neural network training, with a remarkably exponential separation.
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All authors Jinqi Tang, Qian Chen, Shihong Ding, Cong Fang

Artificial Intelligence

EmoRSS: Mitigating Emotion-Induced Over-Refusal in Large Language Models

Shuyi Miao, Yaojin Ma, Chenhang Cui et al. (+5 more)

Emotional expression can influence the safety decisions of large language models (LLMs), offering a potential avenue for improving safety alignment. Existing studies have mainly focused on how emotional expressions facilitate attacks under harmful requests, while overlooking their effects on benign requests. We find that emotional expression can also systematically increase refusal tendencies on benign requests, leading to unnecessary over-refusal. Based on this observation, we propose emotion-guided refusal subspace steering (EmoRSS), an activation-steering method that mitigates emotion-induced over-refusal while preserving refusal behaviour on harmful requests. Specifically, we first identify a refusal-sensitive layer using layer-wise linear probes and construct a refusal subspace from sparse autoencoder (SAE) features aligned with the probe direction. Next, we use paired regular and emotional requests with the same queries to estimate the mean activation shift in the features defining the refusal subspace. Finally, we decode this shift into an activation intervention vector and apply it in the reverse refusal direction during inference, without updating the backbone parameters. Experiments on two LLMs show that, when requests contain emotional expressions, our method achieves a more favourable trade-off between refusing harmful requests and answering benign ones than prior over-refusal mitigation baselines, while better preserving general task performance.
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All authors Shuyi Miao, Yaojin Ma, Chenhang Cui, Xiaohao Liu, Dang Jisheng, Shengda Zhuo, Fei Shen, Tat-Seng Chua

Machine Learning

Pessimistic Minimax Learning for Public-Private Information Games under Unilateral Coverage

Shuze Daniel Liu, Claire Chen, Jiuqi Wang et al. (+1 more)

We study offline learning in two-player zero-sum contextual games with public and private information, motivated by strategic settings such as auctions and negotiations with private valuations. We introduce unilateral prescriptive concentrability and show that asymmetric information can change offline coverage through its effect on equilibrium behavior. For finite state-action spaces, we develop a pessimistic algorithm with an $\tilde{O}(1/\sqrt{n})$ exploitability rate, matching the standard sample-size dependence for fully observed minimax games. We further develop a pessimistic policy mirror descent framework, PPA-PMD, for general function approximation and obtain a unified $\tilde{O}(1/\sqrt{n} + 1/\sqrt{T})$ exploitability rate with no-regret actor updates. Together, these results provide the first theoretical framework for offline equilibrium learning under public-private information constraints.
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All authors Shuze Daniel Liu, Claire Chen, Jiuqi Wang, David Simchi-Levi

Cryptography and Security

ContractLens: Latent Security Knowledge for Malicious Smart Contract Detection

Shuyi Miao, Xinyi Huang, Wangjie Qiu et al. (+4 more)

Detecting malicious smart contracts is essential to safeguarding the Web3.0 ecosystem. However, existing auditing methods based on large language models (LLMs) largely rely on prompt engineering or attack-specific fine-tuning, while the internal representations that support malicious logic detection remain poorly characterized. To address this gap, we propose ContractLens, a framework that localizes and uses latent security knowledge in pretrained LLMs for interpretable malicious smart contract detection. Specifically, we first use layerwise linear probes to identify candidate layers that distinguish malicious from benign contracts, then select critical layers based on their sensitivity to malicious logic removal and stability under behaviour-preserving surface modifications. Next, within these critical layers, we combine activation contrast, malicious-logic specificity, and probe weights into a tri-factor importance score and adaptively select malicious contract-discriminative units (MCDUs) using the knee point of the ranked score curve. Finally, we concatenate the selected units' activations into a compact representation and train a lightweight classifier for detection, keeping the pretrained model frozen throughout. Neuron masking experiments show that masking MCDUs causes larger drops in detection performance than masking randomly selected neurons or neurons matched in activation magnitude. ContractLens achieves higher average detection accuracy than the evaluated methods on all three pretrained models. Further evaluations demonstrate generalization to unseen malicious intents and show that, with the identified MCDUs fixed, a classifier trained on few labelled samples can still achieve strong detection performance.
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All authors Shuyi Miao, Xinyi Huang, Wangjie Qiu, Fei Shen, Jinchun He, Zhiming Zheng, Tat-Seng Chua

Artificial Intelligence

CIPO: Counterfactual Imagination Policy Optimization for Adaptive Tool Granularity Selection

Yu Li, Yunlu Wan, Zijian Zhu et al. (+4 more)

Large language model (LLM) agents solve complex tasks through multi-step interactions with external tools. These interactions often contain recurring local tool sequences. Treating such sequences as composite "Skills" can shorten tool-use trajectories and reduce repeated low-level decisions. However, when atomic tools and composite skills coexist, skill use becomes a policy problem: the agent must decide whether the current state requires atomic fine control or skill-level abstraction. In this paper, we argue that effective skill use should be studied as adaptive tool granularity selection. The most direct training signal for this problem is to compare the consequences of atomic and skill choices available from the same state. Based on this view, we propose CIPO, a Counterfactual Imagination Policy Optimization framework for adaptive tool granularity. CIPO constructs executable skills through budget-constrained mining of successful tool-use trajectories and trains granularity decisions with counterfactual branch rollouts. For each base rollout, CIPO branches at the first eligible granularity decision and replaces the chosen action with a feasible atomic or skill alternative. The paired outcome difference serves as a supplementary reward for policy optimization. Experiments across multiple benchmarks and model backbones show that CIPO improves task success and decision efficiency over baselines. Further analyses show that CIPO learns effective skill use by improving the choice between atomic tools and composite skills based on the current state, without simply increasing skill frequency.
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All authors Yu Li, Yunlu Wan, Zijian Zhu, Han Luo, Chao Ren, Long-Fei Li, Lei Feng

Machine Learning

How Long, Not How Close: A Learned Temporal Metric for Planning in Latent World Models

Lama Moukheiber, Haotian Xue and Yongxin Chen

Latent world models plan by rolling a frozen predictor forward under candidate action sequences and ranking the candidates by the latent distance between their imagined end state and the goal. However, this ranking breaks down when the goal lies several plans away, because the latent distance measures how closely an end state resembles the goal rather than how far it remains from reaching it. To address this, we propose TEMPO, a temporal-distance planning objective that leaves the world model untouched, learns only from the recorded trajectories already used to train it, and adds negligible cost to the planner's search. TEMPO learns a small map of the frozen latent in which the distance between two states of an episode reflects the number of environment steps between them, and blends this distance into the planner's cost. It requires no rewards, policies or success labels and, being a cost rather than a model, applies to frozen world models with one latent vector per state that plan by a latent distance. We evaluate TEMPO on eleven simulated environments (e.g., maze navigation, tabletop pushing, robotic arm control and three-dimensional manipulation) with the LeWM and PLDM planners. With a small MLP that adds at most 0.3% to a plan's arithmetic, TEMPO improves both planners at every goal distance, including the one-plan setting of their evaluations, raises LeWM from 36% to 99% on TwoRoom three plans from the goal, and remains competitive on a broad range of 2D and 3D navigation, reaching and manipulation tasks.
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Computational Geometry · Cross-listed from quant-ph

Improved Upper and Lower Bounds for Quantum Convex-Body Volume Estimation

Ruizhe Zhang

Estimating the volume of a high-dimensional convex body is a fundamental problem in theoretical computer science. Given a quantum membership oracle for a convex body $K\subset\mathbb{R}^d$, we show that estimating $\mathrm{vol}(K)$ to relative error $\varepsilon$ takes $\widetilde O(d^{5/2}+d^{3/2}/\varepsilon)$ queries. This improves the previous quantum upper bound $\widetilde O(d^3+d^{9/4}/\varepsilon)$ of Chakrabarti et al. (ACM TQC 2023) and Cornelissen–Hamoudi (SODA 2023), and achieves a larger quantum speedup over the currently best classical upper bound $\widetilde O(d^{7/2}+d^3/\varepsilon^2)$ of Jia et al. (JACM 2026). Our quantum algorithm combines two new ingredients: an input-dependent effective spectral-gap analysis of quantum walks based on uniform classical warm-start mixing bounds, and a single quantum estimator for products of normalizer ratios in simulated annealing based on classical bridge sampling. We also prove an $\Omega(d)$ quantum query lower bound for constant relative error, improving the previous $\Omega(\sqrt d)$ lower bound.
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Cryptography and Security

Hidden Risks of Jev: An Empirical Study of Security, Privacy, and Dual Use

Shang Wang, Tianqing Zhu, Huajie Chen et al. (+3 more)

Jev turns natural-language questions into typed answers and probabilities with low latency and cost, enabling applications to route requests and select tools. While this interface allows Jev to integrate naturally into application workflows as a decision layer, the security and privacy implications of this emerging use remain largely unexplored. To address this gap, we conduct the first systematic study of these implications using the official Jev API and NanoJev, a local model with controllable training data and updates, focusing on three research questions: (1) What security threats arise when Jev is deployed as an application decision layer? (2) What private information can Jev reveal despite returning constrained typed outputs? (3) How can Jev's general-purpose decision capability be used for beneficial purposes or misused? Jev's decisions depend on application state and may be influenced by user-provided inputs. We therefore adapt prompt injection and adversarial suffixes to manipulate its decisions. Open-source Jev distribution and updates introduce supply-chain risks, which we examine by implanting backdoors in NanoJev through training data poisoning. Since Jev's outputs reflect both application state and information learned during training, we further adapt membership, private attribute, and internal knowledge inference attacks to recover sensitive information despite its constrained output format. Finally, Jev can serve as a general-purpose decision oracle for defensive and malicious workflows. We examine this dual use through four detection tasks covering prompt injection, jailbreak inputs, harmful content, and AI-generated text, alongside misuse scenarios involving jailbreak and model extraction. Our empirical evaluation shows that Jev remains vulnerable to the examined security and privacy threats, while its decision capability can support beneficial and malicious uses.
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All authors Shang Wang, Tianqing Zhu, Huajie Chen, Jiayang Li, Meng Yang, Bo Liu

Software Engineering

TeleGen: Improving LLM-Based Web Application Generation via Runtime Telemetry

Yujia Luo, Haonan Zhang, Jiasi Shen et al. (+2 more)

Large language models can generate runnable web applications from natural-language requirements, but many generated applications still fail interactive tasks. Existing generate-execute-repair pipelines execute the generated application and use task outcomes or error messages to guide code revision. However, this feedback often misses the runtime behavior between a browser action and the final task outcome, making interaction-level failures difficult to diagnose. Therefore, we propose TeleGen, an observability-enhanced framework for LLM-based web application generation. TeleGen instruments generated applications, collects runtime telemetry during task execution, and compresses raw telemetry logs into concise briefs for repair. We evaluate TeleGen on WebGen-Bench and Web-Bench. On WebGen-Bench, TeleGen improves task success from 67.7% with repair without telemetry to 76.2%, an increase of 8.5 percentage points. On Web-Bench, it improves cumulative Pass@2 from 21.7% to 29.8%. Ablation results show that runtime telemetry provides a useful diagnostic signal, while telemetry briefs make this signal more effective and less costly to use. Further analysis shows that telemetry is especially helpful for failures involving hidden execution paths, such as navigation, form workflows, and frontend-backend coordination.
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All authors Yujia Luo, Haonan Zhang, Jiasi Shen, Zishuo Ding, Weiyi Shang

Computer Vision

Geometry-Aware Preference Optimization for Text-to-Image Diffusion Models

Lei Wang, Zhen Wang, Yuexiang Xie et al. (+1 more)

Preference alignment has become a standard practice for text-to-image diffusion models. Direct Preference Optimization (DPO) simplifies this process by eliminating explicit reward modeling. Its diffusion variant, Diffusion-DPO, has become a widely adopted baseline. Diffusion-DPO essentially encourages the likelihood of preferred samples while suppressing dispreferred ones. In this paper, we revisit DPO-style alignment methods for diffusion models from the perspective of the manifold hypothesis. Under this view, natural images concentrate near a low-dimensional manifold embedded in the high-dimensional ambient space, whereas DPO directly optimizes preference distributions in the full space without accounting for this geometric structure. This creates a mismatch in the optimization dynamics: it suppresses geometry-preserving tangential updates, while insufficiently restricting hazardous normal-direction updates. This mismatch gradually degrades image quality and diversity. To address this issue, we propose Anisotropic Geometry-Aware Preference Optimization (APO), which replaces the uniform Euclidean treatment of prediction errors with a geometry-aware anisotropic metric derived from the reference model. Concretely, APO adaptively strengthens regularization in directions where the reference denoising function is highly sensitive, while relaxing constraints in directions that permit safe semantic adjustment. This recalibrates preference optimization according to the local manifold geometry, and maintains the original manifold structure. Experiments show that APO achieves strong performance and an average win rate exceeding 60% against various existing alignment methods across diverse benchmarks. It requires significantly fewer training steps than prior methods, and preserves generation diversity throughout training.
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All authors Lei Wang, Zhen Wang, Yuexiang Xie, Yaliang Li

Computer Vision

Robust Tensor Completion via Reflective Convolution Nuclear Norm Minimization

Weiguo Zhou, Feng Zhang, Wenjin Qin et al. (+1 more)

Robust tensor completion recovers multidimensional data from partial observations corrupted by sparse gross errors. Existing convolutional low-rank models typically construct translated copies using circular continuation, which introduces artificial wrap-around neighborhoods for finite nonperiodic data. We propose reflective convolution nuclear norm minimization (RCNNM), which replaces circular shifts with endpoint-nonrepeating reflection. The resulting lifting has nonuniform entry multiplicities and satisfies a weighted Gram identity that supports both the recovery analysis and the optimization method. Under random sampling and sparse corruption, we establish high-probability exact recovery of the underlying tensor and sparse errors, together with stability under bounded dense perturbations. We further develop a two-block ADMM with a closed-form entrywise tensor update, while singular-value thresholding is implemented through the smaller right Gram matrix. Experiments on synthetic tensors, BSDS color images, and CAVE multispectral images show that RCNNM consistently improves over its circular-lifting counterpart, with the clearest gains near image boundaries. In particular, average boundary-PSNR improvements reach 3.26 dB while global reconstruction quality remains competitive.
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All authors Weiguo Zhou, Feng Zhang, Wenjin Qin, Jianwen Huang

Machine Learning

What Will Post-Training Fix? Per-Problem Gains Are Shared Across Independent RL Runs, and Existing Checkpoints Predict Them Better Than A Priori Signals

Xiaoxian Duan

Data selection, curricula and the evaluation of post-training recipes all assume that we can tell, before training, which problems a model will improve on. We test this assumption directly. For two base models, DeepSeek-R1-Distill-Qwen-1.5B and Qwen2.5-Math-1.5B, we evaluate eighteen post-training runs on up to 1532 competition math problems with many samples per problem, and compare signals available before training against a noise ceiling derived from the agreement between disjoint subsets of runs. Three findings hold on both base models. What post-training fixes is shared: independent runs agree on which rarely solved problems improve, with a noise ceiling of about 0.9, yet the two base models agree with each other only at rho=0.25 - the shared component belongs to the base model, not to the problem. A priori signals capture a minority of it: base pass rate, the likelihood of a correct solution, a larger model's pass rate and their combinations explain only 0.30 and 0.24 of the explainable variance in gains. Existing checkpoints are the better predictor: the per-problem gains of a single checkpoint from another family predict a new run better than every a priori signal, alone or combined (0.52 vs. 0.33 and 0.34 vs. 0.17 against the combined signals). The conclusions hold on problems from 2025-2026 competitions and when the baselines on the two sides of every comparison are estimated independently. We propose the noise ceiling as a standard companion to per-problem signals.
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Artificial Intelligence

Do LLMs Understand Sequential Structure? A Controlled Study of Inference and Generation

Jerry Wang, Zhengxiang Wang, Ting Yu Liu et al. (+3 more)

Large language models (LLMs) are increasingly used as interactive agents and simulators, yet it remains unclear whether they can recover latent sequential structure beyond surface action frequencies. This distinction is critical for behavioral simulation, where actions are often shaped by prior context rather than marginal frequencies alone. We study this question using controlled two-player Rock–Paper–Scissors interactions and a one-player stochastic n-gram continuation task. Across these experiments, we test whether LLMs can identify latent strategies, follow simple Markov rules, and sustain higher-order conditional dependencies. Our framework separates distribution matching from conditional rule following. Results show that longer context does not improve identification, correct recognition does not ensure faithful simulation, and higher-order dependencies substantially degrade rule recovery. Apparent behavioral fidelity can therefore mask incorrect generative mechanisms.
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All authors Jerry Wang, Zhengxiang Wang, Ting Yu Liu, Hsin-Ling Hsu, Yi-Cheng Lai, Tengfei Ma

Machine Learning

Priced Guidance: Can Language Models Generate Future Research Ideas?

Kaiyue Wen, Tengyu Ma and Percy Liang

We evaluate language models' capability to generate novel research ideas through the lens of compression. We aim to lower-bound the potentially tiny probability that a language model generates the essence of a future research idea without any hints. Rather than estimate this probability through expensive repeated sampling, our Priced Guidance framework measures the compression cost: how many additional bits of information are needed to guide the model to recover the target idea. We prove that if the model can recover the target idea with at most K bits of guidance in expectation, then it can generate the idea without any guidance with probability at least $2^{-K}$. In our framework, the language model, called the generator, can pose a sequence of multiple-choice questions and specify a probability distribution over possible answers. A guide, which is a language model with access to the target idea, selects answers. If a selected answer has prior probability p, the generator pays $-\log_2 p$ bits. The generator aims to produce an idea that matches the essence of the target idea with minimal cumulative cost. This cumulative cost equals, up to an additive constant, the number of bits of information sent by the guide. Using this methodology, we evaluate five generator LLMs (Opus 5, Fable 5.1, GPT-6 Astra, GPT-5.6 Sol, and GLM 5.3) on the core ideas in 87 recent high-quality deep learning papers and we use LLM as judge to determine whether the generated idea matches the target in terms of the central research object and defining mechanism. Fable 5.1 achieves the lowest median compression cost at 69.9 bits, substantially lower than gzip's median of 5,712 bits for losslessly compressing the summary of the target idea. A uniform ensemble of Fable 5.1, Opus 5, and Astra further reduces the median compression cost to 55.8 bits and improves the generation probability lower bound by 18,000 times.
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Artificial Intelligence

Runtime Authorization of Self-Generated Subgoals in Long-Horizon Tool-Using AI Agents

Genliang Zhu and Chu Wang

Long-horizon tool-using AI agents create subgoals, replan, delegate work, and compose sibling results. Per-tool permission checks cannot establish that a changing goal graph remains within the principal-approved task. We address this authorization gap in a finite structured domain with one principal and one authorization root. Each proposed goal-graph mutation carries a version-bound witness that its continuation traces, resources, obligations, invariants, and closing condition refine the active root contract; every protected effect is rechecked at an atomic commit boundary. Free-form goal text supplies no authority. We prove trace-policy and modeled forbidden-state preservation under explicit mediation, abstraction, freshness, and atomicity assumptions, plus conditional root-success preservation, a separation result for memoryless allowlists, exact finite-domain decidability, and universal-safety monotonicity under sound abstraction refinement. An executable model explores 340 states and 419 transitions. Across 96 matched cases covering 25 structural schemas, the complete mechanism commits zero forbidden states in 48 drifted cases and completes all 48 benign counterparts. Two public upstream runtime paths execute 258 native dispatches across 32 cases, with every case-level decision and receipt chain matching. A frozen host-local study covers 129 synthetic one-factor-at-a-time cells, all matching fixed decisions and reasons. A history-aware continuation comparator blocks every modeled bad trace prefix but commits all operations in 11 cases whose violations lie in typed resources, freshness, or explicit-join evidence outside its trace projection. Within the registered structured domains, runtime authorization preserves useful replanning while preventing self-generated subgoals from becoming a source of new authority.
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Computation and Language

IREA: Intermediate Representation-based Embedding Alignment for Normative RAG

Mirae Han, Sihyeong Yeom and Harksoo Kim

Large language models (LLMs) have shown strong performance across various tasks, but they still struggle with questions involving ethical judgment. Previous studies have attempted to train LLMs on ethical standards, but the diversity and relativity of ethical norms make them difficult to fully internalize in model parameters. As an alternative, we introduce normative RAG, a retrieval-augmented approach that supports ethical judgment using external normative knowledge. Normative retrieval involves a distinct asymmetry between context rich narrative queries and generalized normative statements. Existing factual retrieval methods rely on query-only expansion into a document-like form, making them insufficient for resolving this asymmetry. Therefore, we propose Intermediate Representation-based Embedding Alignment (IREA), a bidirectional alignment method that maps both text types into a shared situation-behavior representation. This representation captures ethically salient contextual and behavioral information in a normalized form, reducing surface-level discrepancies and improving alignment in the embedding space. Experimental results show that IREA improves normative retrieval and downstream ethical judgment across multiple settings, demonstrating the effectiveness of bidirectional alignment for normative RAG.
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