Computers and Society
On Being Prepared: Automated Vehicle Incident Management Exercise Practices
Laura Fraade-Blanar, Jackson Graves, Aaron Clark-Ginsberg et al. (+4 more)
Incident management (IM) has evolved over recent decades to cover an ever-expanding array of hazards and systems. Barring real-life experience, exercises are a key tool in developing an effective IM program. As part of enterprise resilience and operational readiness, IM practitioners design exercises to understand and build capabilities for efficiently and effectively responding to incidents. Combining research and policy on an emerging transportation technology, automated vehicles (AVs), with established practices for IM, we describe what makes exercises effective and how they can be used to identify gaps and develop capacity. A range of exercise types exist, from workshops, to tabletops, to drills, and to full-scale exercises. Every stage of an exercise - preparing, setting up, facilitating, closing, and assessing - is interconnected and should further the exercise's objectives. AV IM practitioners can maximize the utility of a mature exercise program through exercise quality, diversity, and volume. The need to respond to AV incidents, from collisions to natural hazards, is inevitable. This paper tailors existing practices to a series of frameworks, predicated on the unique socio-technical nature of AVs to provide first-of-its-kind guidance for AV IM exercises. Additionally, these frameworks are company-neutral to enable intercompany and inter-organization collaboration.
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All authors Laura Fraade-Blanar, Jackson Graves, Aaron Clark-Ginsberg, Sam Cooper, Jeremy Kaufman, Jackie Lindsey, Piet van Os
Computer Vision
LUMIN: Lifecycle-Utility-aware and Metadata-Informed Network for Visual Anomaly Detection
Pengfei Yang
Industrial anomaly detection faces two engineering bottlenecks:
memory bank construction latency and inference efficiency. Traditional sampling
algorithms (Farthest Point Sampling, K-Means, etc.) rely on numerous backbone forward passes and iterative distance computations, with construction times ranging from minutes to hours; heavy computation components such as multi-scale feature extraction struggle to meet the millisecond-level real-time requirements of production lines. This paper focuses on sampling efficiency and inference optimization for industrial deployment with two core contributions: (1) PSP (Plugin Sampler Pipeline)—a four-stage adaptive
memory bank sampling pipeline based on 18-dimensional pixel metadata and five complementary visual plugins. PSP completes all sampling with zero backbone forward passes; coarse filtering is sub-second numerical sorting, and metadata extraction is a one-time offline cost. PSP supports progressive deployment and incremental updates. (2) Two engineering optimization strategies—parallel
memory bank similarity computation (reducing inference
memory and latency by over 95%) and stratified pixel sampling for large-scale evaluation (reducing computation time by 20$\times$ while keeping metrics stable). As a vehicle for validation, we introduce LUMIN (Lightweight Universal Manufacturing Inspection Network) with extreme
segmentation-head compression, systematically exploring the accuracy-efficiency frontier against strong baselines. Experiments on five
benchmarks demonstrate that PSP matches state-of-the-art sampling accuracy at near-random construction cost (341$\times$ faster than FPS), while inference optimizations reduce evaluation time by 20$\times$ with negligible accuracy loss.
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Operating Systems
Adaptive Context Parallelism for Production LLM Serving
Jiarui Guo, Rongle Wang, Peijun Huang et al. (+7 more)
As LLM context windows expand and input sequences grow longer, serving systems face increasing computational and
memory demands. Context parallelism (CP), which partitions the input sequence across multiple ranks to parallelize the computation, has therefore become increasingly important for efficient LLM serving. However, existing CP-enabled systems either rely on static CP configurations or adjust the CP degree only for active requests or batches. In this paper, we present Vertumnus, an adaptive CP serving system designed for heterogeneous and evolving workloads. At the request level, Vertumnus routes requests among workers with different CP degrees using a placement cost that combines predicted queuing delay, cache-aware prefill time, and GPU-time cost. At the cluster level, Vertumnus adapts the worker composition through seconds-scale split and merge operations as workload demand changes. Vertumnus further introduces a global prefix-cache management policy that coordinates cache placement and replication among workers with the same or different CP degrees, preserving cache locality as request assignments and worker composition change. Experiments on a 64-GPU cluster with public and production workloads show that, under the highest evaluated loads, Vertumnus reduces mean TTFT by up to 28.1% and improves
token-weighted SLO attainment by up to 13.3 percentage points over the strongest baseline.
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All authors Jiarui Guo, Rongle Wang, Peijun Huang, Zongwei Lv, Ziqing Wang, Kan Liu, Tao Lan, Lin Qu, Xiaolin Wang, Tong Yang
Machine Learning
Persistent Teacher Anchoring for Tool-Using Agents
Hyun Bin Park, Kyungho Song, Sangmin Lee et al. (+1 more)
Distillation is common in LLM post-training, where on-policy knowledge
distillation (OPKD) uses student-generated trajectories to prepare the student for downstream RL. At each state, the student matches a next-
token distribution supplied by the teacher. As the rollout enters states the teacher would not visit, the teacher-student distribution gap can accumulate. In tool use, this gap becomes consequential because student-written calls execute before supervision and their observations shape later prefixes. Proposer-verifier generation addresses this drift by letting the teacher decide which student-proposed text is retained during generation. Existing formulations govern text but leave tool execution outside their scope. We propose Persistent Teacher Anchoring (PTA), a student-induced but teacher-committed rollout construction. PTA retains chunk-level verification and adds turn-level commitment, allowing a call to reach the environment only after the teacher has verified the entire turn. Treating verified chunks as atomic generation units, we introduce persistent lookahead, which fills idle rollout capacity by advancing future samples and carrying unfinished ones across student updates under the fixed verifier. Across Search-R1-style
retrieval and DeepEyes-style perception RL, applying PTA before downstream RL improves macro best@4 by 2.5 and 2.8 points over OPKD under the same downstream RL budget, while lookahead improves throughput by 24%.
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All authors Hyun Bin Park, Kyungho Song, Sangmin Lee, Du-Seong Chang
Machine Learning
A Robust Watermark-based Fingerprint Framework for GNNs Ownership Verification
Han Zhang, Yan Wang, Guanfeng Liu et al. (+3 more)
The high training cost of Graph Neural Networks (GNNs) has raised growing concerns regarding model ownership infringement, such as model stealing and unauthorized misuse. To verify model ownership and prevent significant economic losses, two groups of GNN Ownership Verification (OV) methods have been proposed: watermark-based methods and fingerprint-based methods. However, these methods typically face three limitations: (1) the performance degradation of protected models caused by out-of-distribution (OOD) watermark graphs with respect to the training set; (2) the unrealistic assumption that surrogate models have been trained on a watermark-containing training set; and (3) over-reliance on specific output levels for fingerprint extraction. In this paper, we propose a Robust watErMArk-based fingeRprint frameworK for GNNs, named REMARK. REMARK first generates carefully crafted in-distribution watermark graphs that maximize output differences between GNN models, thus mitigating OOD-induced performance degradation. REMARK then extracts robust fingerprints from these output differences to verify GNN ownership, thereby removing the assumptions that surrogate models must be trained on a watermark-containing
dataset or expose specific output levels. Extensive experiments across widely used real-world
datasets and GNN architectures demonstrate that REMARK achieves state-of-the-art OV accuracy and robustness while preserving the utility of protected models.
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All authors Han Zhang, Yan Wang, Guanfeng Liu, Pengfei Ding, Huaxiong Wang, Kwok-Yan Lam
Robotics
Continuous Cognitive Coverage for Autonomous Robots via Event-Dependent Cognitive Treatment and Learning
Hong Su
Autonomous
robots continuously encounter objects, changes, and situations, and every event admitted into cognition should receive an appropriate cognitive treatment rather than remain untreated until an explicit task requires
attention. However, existing task-driven, reactive, or fixed-
reasoning approaches generally process only selected events or apply predefined
reasoning procedures, making it difficult to provide continuous cognitive coverage with differentiated treatment. This paper proposes a continuous cognitive coverage framework in which every cognitively admitted event is assigned an event-dependent cognitive treatment according to its state, context, and history. Different events may therefore invoke description,
memory, risk prediction, planning, diagnosis, analogy, or other learned treatments. Familiar events can be processed automatically by learned mechanisms, whereas unfamiliar or uncertain events invoke explicit deliberation or fallback
reasoning. Multiple cognitive processes can be suspended, resumed, and interleaved so that cognitive processing continues as new events arrive or existing events await evidence. Validated experiences are continuously learned to automate, refine, and revise event-specific treatments. Experiments achieve 96.76% structured treatment accuracy with 93.66% automatic processing, 92.64% cognitive coverage under bursty-delayed workloads, and 79.53% continual-learning joint accuracy, with novel-event reuse reaching 100% automatic processing.
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Cryptography and Security
Memory-Efficient Designs for Word-Wise Universal Fully Homomorphic Encryption
Ardhi Wiratama Baskara Yudha, Erwin Eko Wahyudi, Rian Adam Rajagede et al. (+2 more)
Fully Homomorphic Encryption (FHE) enables computation on encrypted data, preserving
privacy throughout analysis. While its
privacy is very strong, FHE is much slower to execute than the original computation. In particular, due to the recent success in accelerating its compute, the performance bottleneck shifts to the
memory, especially considering that FHE magnifies the data size by orders of magnitude, resulting in a low arithmetic intensity. We propose BXT, an FHE optimization framework that mitigates the
memory bottleneck through four techniques: (1) ciphertext compression, which regenerates ciphertext components from seeds during execution; (2) ciphertext serialization, which packs coefficients as bit arrays and unpacks them during L2-to-L1 transfer; (3) delayed seed generation, which defers PRNG-heavy offline work across aggregated operations; and (4) ciphertext digit
pruning guided by fault-aware training tailored for Universal FHE. On CNN inference, the BXT-CSO50 configuration effectively achieves up to 3.8$\times$ speedup over the 100x GPU baseline with less than 1% accuracy loss at 50% comparison precision.
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All authors Ardhi Wiratama Baskara Yudha, Erwin Eko Wahyudi, Rian Adam Rajagede, Qian Lou, Yan Solihin
Artificial Intelligence
Shadow Queries for Private Retrieval in Vector Databases
Xinguo Feng, Zhongkui Ma, Zihan Wang et al. (+4 more)
Large
language models (LLMs) increasingly rely on information
retrieval (IR) systems, such as
Retrieval-Augmented Generation (RAG), to incorporate domain-specific knowledge without costly re-training. These systems often store pre-computed document
embeddings in cloud-based vector databases. However, such
embeddings are vulnerable to
embedding inversion attacks (EIAs), which can reconstruct their underlying text. Existing defenses, such as adding noise or scaling
embeddings, often provide limited
privacy or significantly reduce
retrieval utility. We propose SHAQ (shadow query generation), a semantic-decomposition and
embedding-decoupling defense against EIAs. SHAQ is based on the insight that EIAs rely on the strong coupling between an
embedding and its original text. Instead of storing document
embeddings directly, SHAQ uses a generative
language model to create diverse shadow queries that capture different semantic aspects of each document. These queries are then encoded and stored in place of the original document
embeddings, thereby decomposing document semantics and decoupling stored
embeddings from the source text. Experiments across diverse IR
datasets show that SHAQ substantially improves
privacy while preserving
retrieval utility, achieving a recovery rate as low as 0.2104, defending up to 19.50% more
tokens than baseline defenses, and reaching up to 0.7967 MAP@10 with up to 5.53% utility improvement. These results demonstrate that semantic decomposition and
embedding decoupling provide an effective alternative to directly modifying
embeddings for defending against EIAs.
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All authors Xinguo Feng, Zhongkui Ma, Zihan Wang, Chuan Yan, Guowei Yang, Alsharif Abuadbba, Guangdong Bai
Computers and Society
When Does an Interpretation Count as Established? The Formation, Evaluation, and Responsibility of Interpretation in Generative AI
Deyu Jing
Generative AI research has increasingly evaluated factuality, citation, coverage, and report structure. Yet passing such local checks does not by itself show that a humanistic interpretation has been established. This paper asks how an interpretation comes to be recognized within sociotechnical processes. It introduces three connected concepts. Interpretive appearance names the gap between the finished form of an output and the publicly traceable process through which materials, counterevidence, and revisions constrained the judgment. The evaluation contract names the bounded materials, tasks, criteria, permitted inferences, and failure conditions within which a local judgment is valid. Standing substitution names the unwarranted conversion of a genuine local pass into a stronger claim that an interpretation, result, or research capability has been established, without commensurate new evidence or bridging arguments. The paper then examines responsibility for judgment: a text may acquire recognition while no public structure remains for stating reasons, answering objections, revising, downgrading, or withdrawing the conclusion. Humanistic scholarship provides a revealing test because new materials and conceptual distinctions can alter both the question and the criteria of evaluation. The paper therefore develops delayed closure as a practice of keeping recognized interpretations revisable and proposes five public requirements concerning materials and versions, evidential roles, failure, contract revision, and responsibility. The argument is conceptual and normative: it does not claim to offer a
benchmark or to determine whether models possess understanding. It instead explains why local evaluation, finished textual form, and public recognition must not be treated as sufficient evidence that an interpretation has been formed.
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Machine Learning
Resilience Beyond Stationary Client Unavailability: Unlocking Efficient and Unbiased Federated Learning
Ming Xiang, Stratis Ioannidis, Edmund Yeh et al. (+2 more)
Due to resource constraints or external and internal uncertainties, clients in real-world
federated learning systems are often intermittently available edge devices. In highly dynamic environments, the parameter server lacks prior real-time knowledge of clients' availability, making it challenging to adapt traditional
federated learning
algorithms to be resilient to uncertainties in client availability. If not carefully addressed, complex client availability can introduce significant bias, potentially harming the performance of the trained model. Most prior work either fails to account for non-stationary client availability dynamics or demands significant
memory and computational overhead. This paper aims to develop efficient
federated learning
algorithms that are provably resilient to heterogeneous and non-stationary
stochastic client availability. We propose FedSWE, which admits novel algorithmic structures to (i) compensate for missed computations, (ii) stabilize and diffuse the global updates over rounds, and (iii) evenly mix the local updates through implicit gossiping, despite being agnostic to non-stationary dynamics. Compared with the standard FedAvg, FedSWE introduces light additional
memory and computation overhead. We show that FedSWE converges to a stationary point of non-convex objectives while achieving the desired linear speedup property in certain special cases. We corroborate our analysis with numerical experiments over diversified client unavailability dynamics on real-world data sets.
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All authors Ming Xiang, Stratis Ioannidis, Edmund Yeh, Carlee Joe-Wong, Lili Su
Systems and Control
On the differential of the exponential map
Debanjan Mallik
We study the time derivative of the
matrix exponential $B(t)=\mathrm{exp}(A(t))$, where $A(t)$ is a time-parametrized curve in $\mathrm{Mat}(n)$. Starting from the Taylor series expansion, we derive a nested summation formula, which is then reformulated into a double summation. This expression is converted into an integral representation using Euler's beta function, and further expressed in terms of Lie brackets and the adjoint representation. Later, we extend this to a family of problems and verify a well-known result. Finally, we establish connections to the Gateaux and Fréchet derivatives, proving the latter's existence. These results offer a unified and explicit framework for understanding the derivative of the exponential map.
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Robotics
Dressing in Motion: A Human Motion-Aware Diffusion Policy for Robot-Assisted Dressing
Haoxiang Sun, Fangyuan Wang, Songhao Huang et al. (+4 more)
Robotic dressing assistance is a promising solution for supporting older adults with physical impairments in daily living. However, dressing under human motion remains challenging, as complex garment–human contact and occlusions make it difficult to generate actions aligned with arm movements. In this letter, we propose a visuomotor policy that learns dressing skills from static expert demonstrations and generalizes to dynamic user-motion scenarios. A
diffusion policy tailored to garment–human interaction geometry learns from partially observed point clouds with varied arm postures. We then introduce an object-centric representation based on PDE
diffusion to capture the axial distribution of the arm. By sampling motion-relevant regions and registering them across consecutive observations, the proposed method approximates arm motion and reactively adapts the executed trajectory. We evaluate our method in simulation and a real-world human study involving nine participants, three garment types, and six arm-motion patterns. Results show that our method outperforms baselines in dressing progress, freedom of movement, and user comfort. The project website is https://anonymous.4open.science/w/dressing-in-motion.
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All authors Haoxiang Sun, Fangyuan Wang, Songhao Huang, Justina Y. W. Liu, Jihong Zhu, Peng Zhou, David Navarro-Alarcon
Sound · Cross-listed from eess.SP
What Selects, What Reconstructs: Repairing Exemplar-Based Complex-Spectrum Separation
Maxime Baelde
Exemplar methods separate a mixture by picking one learned spectrum per source and deforming it until it explains the observation, making one deformation class both reconstructor and selector. We show that the second role is empty as soon as the class can interpolate: the rule then ranks candidates on its regulariser, a choice made before the data, and the estimates sum back to the mixture whichever candidate wins. The condition is a parameter count, so the diagnosis runs before any experiment. On free per-bin deformation of complex spectra it explains the observed pathologies at once: a criterion that ranks candidates by their loudness, and half an output that is a mask on the mixture rather than an exemplar. The same
theorem prescribes the repair, a selection class poorer than the reconstruction class: one complex gain and one pure delay rank the candidates, and a local combination of the best-aligned atoms, fitted jointly in closed form, rebuilds them. On MUSDB18 against the exact ceiling of the masking class, the distance between the criterion and an oracle inside its own candidate pool falls under the rigid selector from 6.2-7.7 to 0.5-2.8 dB, though only 0.9-1.2 dB of that reaches the output, and the per-frame latency of the deployed rule by a factor of 47 to 806. One lock remains, quantified: atoms are scored against the mixture, so the score carries a term for the other source that absorbs the capacity the reconstruction class gains, leaving the output 10.0 dB under the ceiling. Ranking hypotheses by the residual of a fit free enough to interpolate ranks them on the regulariser alone.
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Computation and Language
Vectorizing Classical Tamil: Representation Learning for Verse-Commentary Pairs
Amrit Gopinath and Sangeetha Sivanesan
We construct a corpus of 1,262 verse–commentary (urai) pairs from five Classical Tamil source sections, ranging from technical grammatical prose to modern paraphrase, and ask what information representation learning can recover. We train recurrent and
Transformer encoders, a Siamese-style pair-matching network, an mBART-style encoder–decoder, and a decoder-only
language model. Each analysis is interpreted against an appropriate control on the same data. TF-IDF provides a strong no-training lexical
retrieval baseline, alongside representation analyses and generation controls for the learned models. A fixed string containing the 25 most frequent commentary words scores higher on generation overlap than the decoder-only model. Canonical correlation reaches 1.000 on Gaussian noise at these sample sizes,
token-F1 spans only about 0.02–0.20 on this corpus, and the encoder–decoder continues to lower training loss for sixteen epochs after validation loss has begun to rise. One narrow result remains: the decoder-only model prefers authentic word order in 107 of 112 minimal-pair comparisons (95.5%), but does not reproduce held-out commentary content. We release the extraction and evaluation protocol; redistribution of the source commentaries remains subject to permission.
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Machine Learning
A Fairness Audit of the Duckworth-Lewis-Stern Method: Format-Specific and Gender-Differential Bias, with an Interpretable Calibration Layer for Cricket Target Revision
Soumyadeep Roy
The Duckworth-Lewis-Stern (DLS) method has been the international standard for revising target scores in rain-interrupted limited-overs cricket since 1999. Despite over two decades of operational use, no large-scale empirical audit of its prediction bias has been published. We conduct such an audit on 8,150 international matches (3,095 ODIs, 5,055 T20Is) from Cricsheet, generating 233,550 synthetic interruption scenarios with temporal splits. We document two structured biases. First, DLS prediction error spans a 137-run range across (overs-remaining, wickets-lost) match-state buckets. Second, DLS exhibits a gender-differential bias on ODIs that has not previously been quantified: on the training split, mean over-prediction is +1.51 runs for men but +7.63 runs for women, a gap of +6.13 runs (F = 195.16, p < 10^-43). We
benchmark DLS against five modern alternatives: Bi-LSTM, XGBoost, an enriched XGBoost variant, a deep context-aware model, and a stacking ensemble, and propose DLS-Cal, a lightweight interpretable calibration layer (27K parameters) outputting a state-conditioned correction added to DLS. DLS-Cal reduces absolute bias by 31% on ODI and 19% on T20I, and a gender-aware variant reduces women's ODI residual bias from +6.19 to +0.65 runs while leaving men's calibration unchanged. We release code, models, and data.
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Computation and Language
Beneath the Surface of Chains-of-Thought: A Mechanistic Interpretation of Reasoning Operations in LLMs
Seogyeong Jeong, Jaehui Hwang, Dongyoon Han et al. (+3 more)
Reasoning in large
language models unfolds through diverse functional operations, such as problem formulation, goal decomposition, and deduction. Although these operations are explicitly distinguished in text, little is known about how they are geometrically organized in representation spaces. To this end, we investigate whether distinct
reasoning operations exhibit corresponding
geometric structure in hidden representations. We find that operations are separable in held-out representations, with separability peaking in middle layers, and verify that this structure is not explained by lexical or positional confounds. Across layers,
token-wise operation-
alignment becomes more distributed over spans, while identical surface
tokens are represented differently depending on the operation of its surrounding chunk.
Attention-masking interventions further show that operation-aligned representations at chunk onset depend on preceding
reasoning context. Consequently, our work demonstrates that
language models maintain representational correspondence between linguistic
reasoning expressions and their internal
geometric structures. Code and project materials are available at https://github.com/naver-ai/beneath-cot.
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All authors Seogyeong Jeong, Jaehui Hwang, Dongyoon Han, Geonmo Gu, Alice Oh, Taekyung Kim
Computer Vision
SeamFlow: Structure-Aware Flow Matching on Edge Probabilities for Artist-Like UV Unwrapping
Yuming Zhao, Zangyueyang Xian, Qijian Zhang et al. (+4 more)
3D surface cutting and UV unwrapping are fundamental problems in computer graphics. Traditional
geometric optimization methods mainly focus on reducing parameterization distortion, but they often overlook visual semantic coherence in seam layouts. Recent autoregressive generative methods improve semantic coherence, yet limited perception of mesh
topology often causes inaccurate local cuts. To address these limitations, we introduce SeamFlow, a novel generative framework for 3D surface cutting. We reformulate the discrete mesh-cutting problem as continuous flow matching in a high-dimensional edge-probability space. Through continuous relaxation, SeamFlow learns a deterministic mapping from a Gaussian prior to a target seam-probability distribution. An
evolution network couples local topological
tokens with global shape priors and guides smooth probability flow through Ordinary Differential Equation solving. Compared with existing autoregressive generative frameworks, SeamFlow improves
topology awareness through edge tokenization while eliminating both 3D spatial projection errors and artificial sequential-order bias. Extensive experiments demonstrate that SeamFlow achieves exceptional semantic coherence and remarkably low parameterization distortion. The project page is https://meshy-dev.github.io/seamflow.
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All authors Yuming Zhao, Zangyueyang Xian, Qijian Zhang, Rendong Liang, Qin Jia, Ying He, Junhui Hou
Computers and Society
Controversy and Group Certainty Jointly Shape Everyday Moral Judgments
Ziyu Chen, Minjeong Shin, Tuan Dung Nguyen et al. (+6 more)
Everyday moral life rarely resembles a trolley problem. It involves disputes about families, relationships, work, money, and care, situations in which people often encounter the judgments of others. We examined how judgments about nuanced interpersonal dilemmas respond to social information that conveys collective opinion without revealing the arguments behind it. Specifically, we studied two signals: controversy, the extent to which community judgments are divided between two opposing verdicts; and group certainty, the confidence expressed by each side. We derived these signals from 54,827 judgments on 135 dilemmas posted to Reddit's r/AmItheAsshole and presented them separately or together in a preregistered randomized experiment (N = 2,159). Relative to the control condition, all three treatments increased both weakening, a changed verdict or reduced confidence, and strengthening, increased confidence without a verdict change. Thus, aggregated social information can reshape moral judgments even without arguments. Controversy alone tripled the weakening rate, from 5.3% to 15.9%. Shown alone, certainty on either side was associated with movement toward that side. Shown alongside controversy, only like-minded certainty remained clearly associated with less weakening. This asymmetry of influence is double-edged: the confidence of like-minded others may help minorities resist majority pressure, but it may also insulate mistaken judgments from correction.
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All authors Ziyu Chen, Minjeong Shin, Tuan Dung Nguyen, Colin Klein, Chenhao Tan, Nick Schuster, Nicholas George Carroll, Alasdair Tran, Lexing Xie
Artificial Intelligence
DCFA: Dual-view Causal-inspired Attribution for Failure Reasoning in LLM-based Multi-agent Systems
Zehao Wang, Lanjun Wang, Shilong Jin et al. (+2 more)
Large
language model (LLM)-based multi-
agent systems have experienced rapid growth in recent years. Despite their promise, such systems remain fragile, frequently exhibiting
reasoning and coordination errors that can lead to system-level failures. Failure attribution in such systems relies on tracing natural
language interactions among
agents to identify the decisive error, which refers to the earliest action whose correction can reverse system failure. There are two key challenges: 1) Shallow attribution: Existing methods often capture only minor deviations, such as incomplete
retrievals or formatting errors, which verification mechanisms can correct, while missing the decisive cause of system failure. 2) Contextual degradation: As the length of the system traces increases, the model's
reasoning ability rapidly deteriorates. To address these challenges, we propose DCFA, a training-free framework for failure attribution. DCFA integrates a global module that constructs structured causal-inspired dependency graphs from system traces to identify the initial decisive error, and a local module that applies local counterfactual-inspired
reasoning to refine causal-inspired attribution. Experiments on the Who&When
benchmark across six LLMs show that DCFA improves step-level accuracy by up to 8.27% over state-of-the-art baselines.
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All authors Zehao Wang, Lanjun Wang, Shilong Jin, Junjie Chen, Yanghua Xiao
Software Engineering
Same Request, Different Answer: Quantization Amplifies Cache-Induced Divergence in LLM Serving
Aditi Patodiya
Major open-source serving stacks reuse the key-value tensors of a shared prefix by default. We measure what this prefix reuse costs in reproducibility. Holding model, decoding, seed and request order fixed at batch size one, we ran an eighty-episode multi-turn agentic tool-use workload with prefix reuse on and off across two engines and four weight formats. Without prefix reuse, repeated runs reproduced every
token and log-probability in all ten configurations. With it, cache-hit and recompute paths diverged on both engines. On llama.cpp with GGUF weights this path divergence rises with quantization: for Qwen2.5-7B over ten independent server histories, it reached 37.4 percent of episodes at 16-bit and 82.5 percent at four-bit. Near-tie decisions became only about 1.2 times as common, while the cache-path change in top-two margins grew 4.9-fold from 16-bit to three-bit. Serving four-bit weights through 16-bit kernels left divergence not detectably different from 16-bit, placing the amplification mainly in the quantized execution path. At Q4_K_M, run-to-run divergence came mainly from llama.cpp's server-level
prompt cache: under the canonical episode order it moved divergence by 37.5 points, and at its default, divergence spanned 20.0 to 28.7 percent under five random orders. With cache state restored, each path reproduced exactly while the two still differed. In the tested stacks, cached serving is irreproducible because the cache state is absent from the request and not reset by default. A single-turn bridge shows path divergence reaching task outcomes with no detectable net change in accuracy. We release the harness, logs, and analysis pipeline.
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Computer Vision · Cross-listed from physics.med-ph
BEAM3R: Beam's-eye-view architecture with Mamba-3 for implicit dose reconstruction
Chen Cheng, Michael Ferraro, James Grover et al. (+2 more)
To enable accurate and rapid
photon control point and
proton beamlet dose calculation in the DoseRAD2026 challenge, we present BEAM3R, a dose estimation framework operating in beam's-eye-view (BEV). Our core innovation combines a Mamba-3 state-space depth-sequence core with physics-based transport conditioning to model long-range depth transport without expensive 3D convolutions. BEAM3R shares a 2D CNN encoder-decoder architecture for
photon and
proton dose tasks, processing per-plane BEV slices.
Proton beamlets are conditioned on water equivalent thickness and remaining range, encoding the parameters determining Bragg peak position.
Photon models use a bidirectional Mamba-3 core to capture dose contributions from materials downstream of the calculation point, while the
proton model uses a forward core with learned energy-prefix
tokens and a Bragg-peak refinement module. To reduce interpolation artifacts and support high spatial resolution, we introduce axial grid
alignment of BEV
lattices with CT slices and an implicit super-resolution representation via sub-pixel phase packing, evaluated by a differentiable Triton-accelerated resampler that reconstructs packed cubic B-spline coefficients directly in CT space. For MRI-based tasks, synthetic CTs (sCT) are generated by a patch-based conditional GAN with a SwinUNETR backbone. On the preliminary DoseRAD2026 test set, CT-to-
photon and CT-to-
proton models achieved 1%/1 mm local gamma pass rates of 96.8% and 96.0%, with stratified plan-level MAEs of 0.0041 and 0.0079. Substituting sCT reduced gamma pass rates to 89.7% for
photon and 75.4%
proton plan level doses, with stratified plan-level MAEs of 0.0093 and 0.0336. Standardised runtimes were 23.4 s and 18.4 s for CT-to-
photon and CT-to-
proton prediction, increasing to 39.7 s and 42.8 s for the corresponding MRI-based pipelines.
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All authors Chen Cheng, Michael Ferraro, James Grover, David E J Waddington, Emily Hewson
Machine Learning · Cross-listed from nucl-th
FUSION: a skill-based research agent for publicly obtainable nuclear-physics codes
Jin Lei
Running an unfamiliar
nuclear-physics code is rarely difficult because of the physics alone. One must find and build the program, learn its input conventions, and decide whether a plausible output is actually correct. A general-purpose coding
agent helps with the first two tasks but may make the last one harder: it can write an input file that runs with the wrong physical convention. FUSION addresses this problem with code-specific skills. A skill obtains the code from its public source, starts from a verified input, runs and parses the calculation, records known failure modes, and must reproduce a stated
benchmark to a stated tolerance before reporting a result. The current release covers twenty codes, spanning optical models and reactions,
nuclear structure, fission and statistical models, astrophysics and R-
matrix analysis, and heavy-ion transport. It also includes an offline, searchable collection of 61 167 pages derived from the nucl-th literature. User notes and credentials remain outside the public repository. FUSION is available under the MIT license at https://github.com/jinleiphys/FUSION; documentation is at https://vibeinscience.com. Here I describe the design, the checks behind the current release, and one complete calculation from input to comparison with measured data.
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Computer Vision
Where to Look Matters: Learning Influential Views for VLM-based 3D Visual Grounding
Tsung-Chih Chiang, Hsuan-Kung Yang, Jou-Min Liu et al. (+4 more)
Recent zero-shot 3D visual grounding methods leverage
vision-
language models (VLMs) to localize objects in 3D scenes from natural
language queries. However, these methods typically rely on heuristic rules to select which camera views are provided to the VLM, often prioritizing object visibility rather than grounding relevance. We present IVSGround, a framework that learns Influential View Selection for VLM-based 3D visual grounding. Instead of using fixed heuristics, a lightweight view selector is trained to identify views that provide discriminative evidence for grounding. To obtain supervision signals, we generate training signals using feedback from a
reasoning VLM through a two-stage rejection sampling process. During inference, the learned selector predicts query-conditioned influential views for each candidate object, which are then evaluated by a frozen
reasoning VLM through comparative grounding. Experiments on ScanRefer and NR3D show that IVSGround consistently improves grounding accuracy over existing zero-shot pipelines, demonstrating that selecting where to look is crucial for effective 3D visual grounding. Project page: https://ivsground.github.io/
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All authors Tsung-Chih Chiang, Hsuan-Kung Yang, Jou-Min Liu, Ting-Ru Liu, Chun-Wei Huang, Quan Kong, Chun-Yi Lee
Artificial Intelligence
Aplaud: Adaptive Personalized Low-Rank Decomposition for User-Specific LLM
Xinyu Li, Ruoming Jin, Jianfeng Zhu et al. (+2 more)
In this paper, we study the problem of personalized survey response prediction using fine-tuned large
language models (LLMs). This task poses unique challenges: limited per-user training data, scalability of model storage, and the need to exploit shared structure across survey questions. To address these issues, we propose Aplaud (Adaptive Personalized Low-rank and User-specific Nested Decomposition), a lightweight and scalable framework for LLM personalization. Aplaud extends the LoRA paradigm by separating adaptation into a frozen, shared low-rank basis and a compact user-specific correction, augmented with a rank-one residual for finer personalization. To further reduce per-user parameter cost and mitigate overfitting, the correction
matrix can be factorized into an even lower-rank form. Empirical results demonstrate that Aplaud achieves efficient, scalable personalization across users while outperforming state-of-the-art LoRA-based personalized LLM approaches in both generalization and inference efficiency.
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All authors Xinyu Li, Ruoming Jin, Jianfeng Zhu, Ruixin Guo, Zhi Liu
Machine Learning
Training Large Language Models for Small-Molecule Design with Synthetic Task Scaling
Frank Hu, Shriram Chennakesavalu, Zichen Wang et al. (+5 more)
Designing viable drug candidates requires searching a combinatorially large and rugged chemical space for molecules that satisfy multiple, often competing, objectives. Large
language models (LLMs) provide a useful generative prior for this problem because of their representational capacity,
reasoning ability, and flexibility when incorporating information from the external environment. While reinforcement learning from verifiable rewards (RLVR) can be used to improve the capabilities of LLMs, many chemically relevant scoring functions require hours or even days per evaluation, making them prohibitively expensive to use directly during online training. Here, we investigate whether LLMs can learn molecular design strategies from cheaper synthetic tasks that generalize to expensive molecular lead optimization settings. We find that curriculum-based training recipes that gradually incorporate more challenging synthetic design tasks enable strong performance that surpasses that of much larger frontier models on structure-based lead optimization. Our results suggest that scaling post-training using synthetic tasks is an effective strategy for adapting LLMs to high-cost experimental scenarios that are too expensive to directly train on.
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All authors Frank Hu, Shriram Chennakesavalu, Zichen Wang, Patricia Suriana, Bodhi Vani, Kirill Shmilovich, Kangway Chuang, Colin Grambow
Computer Vision
Bridging Modalities and Tasks: A Unified Hierarchical ViT for SAR-to-Optical Translation and Semantic Segmentation
Siyuan Liu, Xuze Zhang, Yongshun Wang et al. (+3 more)
Synthetic Aperture Radar (SAR) images have all-weather, day-and-night observation capabilities. However, compared with optical images, their speckle noise and non-intuitive scattering mechanism limit the interpretability of the images. Generative models for SAR-to-optical (S2O) conversion can improve visual interpretability, but existing methods often ignore the constraints on semantic structure, which are necessary for downstream tasks, for the sake of visual effects. We propose a unified collaborative dual-task learning framework, termed BMT (Bridging Modalities and Tasks), that jointly optimizes S2O image translation and semantic
segmentation through a shared hierarchical
Vision Transformer. The framework integrates: (1) a LocalViTBlock that fuses global self-
attention with spatial depthwise convolution through a learnable gating mechanism; (2) an enhanced output module combining multi-scale refinement processing, color correction and anti-aliasing, which calibrates channel-level color statistics through feature fusion; (3) a ControlNet-style conditional injection mechanism that encodes SAR wavelet features and
segmentation labels into a multi-scale feature pyramid and injects them at each encoder layer through zero-initialized convolution; (4) a bounded Kendall uncertainty weighting scheme that prevents either task from dominating the shared representation. We evaluate the framework under both paired and unpaired translation settings, on the public WHU-OPT-SAR paired
dataset and a self-constructed unpaired ship
dataset built from HRSID and DIOR, respectively. The experimental results show that the proposed method achieves competitive S2O translation quality and semantic
segmentation performance. The
dataset and source code have been publicly released at https://github.com/Lewisyuaner/BMT-S2O-main.
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All authors Siyuan Liu, Xuze Zhang, Yongshun Wang, Licong Pan, Hang Liu, Huihui Li
Numerical Analysis · Cross-listed from math.NT
Numerical experiments on the Hardy conjecture for the Gauss circle problem
Satoshi Yamaguchi, Shoichi Fujima, Shigehiko Kuratsubo et al. (+2 more)
The classical unsolved Gauss circle problem concerns estimating the error between the number of
lattice points inside a circle and the area of the circle as its radius tends to infinity. About a century ago, Hardy proposed a
conjecture concerning this problem. In this paper, we attempt to provide numerical evidence in support of the Hardy
conjecture through large-scale numerical computations.
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All authors Satoshi Yamaguchi, Shoichi Fujima, Shigehiko Kuratsubo, Eiichi Nakai, Tsuyoshi Yoneda
Hardware Architecture
FlexPosit: Tunable Fractional Precision for LLM Inference Accelerators
Yimin Gao, Liangtao Dai, Jun Yin et al. (+2 more)
Large
language models (LLMs) offer remarkable capabilities but impose prohibitive compute and energy costs. Quantization governs the trade-offs between accuracy and hardware efficiency across granularity and bit-width. Finer granularity (e.g., group-wise) provides high accuracy but incurs scaling and control overhead, while coarser granularity (e.g., channel-wise) has lower overhead but loses accuracy at low precision. Meanwhile, mixed-precision quantization exposes rich accuracy-efficiency trade-offs algorithmically, but existing LLM accelerators remain limited to discrete precision modes, leaving the fractional design space between them unexplored. FlexPosit bridges these gaps through co-design of Posit-based quantization and a precision-tunable bit-serial architecture. Algorithmically, FlexPosit employs distribution-aware quantization with hardware-aligned, sensitivity-guided mixed-precision allocation, leveraging the Posit format's tapered precision to achieve group-wise-like accuracy with channel-wise-like regularity. Architecturally, FlexPosit is a unified bit-serial systolic array with lightweight per-column decoders, unified Processing Elements (PEs), and a global precision controller, enabling tunable fractional precision while preserving fully regular systolic dataflow. Across diverse LLMs, FlexPosit achieves near-FP16 accuracy with sub-5-bit fractional weights. It achieves 1.8x higher throughput and 1.2x lower energy than BitMoD (group-wise quantization), and 1.5x higher throughput and 2.0x lower energy than OliVe (channel-wise quantization), establishing a new Pareto frontier for precision-tunable LLM acceleration.
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All authors Yimin Gao, Liangtao Dai, Jun Yin, Xinfei Guo, Mircea Stan
Machine Learning
Locating and Steering Refusal Beyond Attention
Preethi Carmel Bosco and Gopalakrishnan Srinivasan
Where inside a
language model does refusal live, and does that place change when the architecture does? In a
transformer, refusal is governed by a single direction in the residual stream, a finding that safety and interpretability tooling now depend on. State-space models (SSMs) route information through a recurrent update instead of
attention, sharing no
token-mixing mechanism with a
transformer. Does the same safety representation survive this shift, or must it be rediscovered per architecture? It survives. A single rigid rotation, which can only reorient a space and not reshape it, aligns one model's representation space with another's, so the two genuinely share the representation. A harm probe trained on a
transformer then flags an SSM's harmful inputs, and removing the aligned direction makes a model answer attacks it would otherwise refuse, while a random direction of the same size does far less. What is architecture-specific is not where the direction is steered but where it must be read. Each layer computes a fresh output that is then added into the residual stream, and harm is cleanly readable at this output, the write site, before the addition. A control that holds the intervention's strength fixed shows that what matters is where the direction is estimated, not where it is applied. Applied through a detector-triggered gate, this direction lowers jailbreak success in all four architecture families we test (SSM,
transformer, recurrent, hybrid), and on the SSM it holds against an attacker that tunes its
prompt against the defense. The gate only matches a trivial rule that returns a fixed refusal whenever the same detector fires, so what transfers across architectures is the direction itself, not defense strength. Safety tooling built on refusal therefore ports to a new architecture by re-estimating the direction at that architecture's write site, not by rebuilding it.
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Computation and Language
Knowing What Not to Answer: Selective Non-Compliance in Vision-Language Models
Minji Kim, Jihyoung Jang and Hyounghun Kim
Vision-
language models (VLMs) are expected to respond helpfully to appropriate requests while withholding compliance with requests that are incorrect, unsafe, infeasible, or unanswerable. However, existing
benchmarks predominantly evaluate non-compliance at the level of the query as a whole, assuming that each request either warrants compliance or requires withholding compliance. In practice, real-world queries can contain a mixture of answerable content and components for which compliance should be withheld. In this paper, we introduce KoNA, a
benchmark for evaluating selective non-compliance in VLMs across five categories: False Premise, Visual Inaccessibility, Universal Unknown, Task Feasibility, and Safety. Each task evaluates two capabilities: query-level non-compliance and component-level non-compliance under paired single and compound queries. Our evaluation across diverse VLMs shows that models often fail to refuse, correct, or abstain appropriately, and these failures become more pronounced when queries require selective non-compliance. To address this challenge, we fine-tune VLMs using KoNA examples that require selective non-compliance, together with a fully answerable set that should receive direct answers. Our fine-tuned models achieve substantial improvements in non-compliance accuracy while largely maintaining performance on fully answerable tasks. These results suggest that the fine-tuned models can distinguish between answerable components and those requiring non-compliance and respond in a task-appropriate manner.
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