Social and Information Networks
When Exposure Is Not Attention: Auditing the Preference-Exposure-Consumption Gap in Personalized News Recommenders
Woojin Park
Personalized news platforms are often evaluated as if stated preferences, logged recommendation exposure, and click consumption form a single coherent pipeline. Collapsing these layers can distort audit conclusions: a platform may appear more aligned or diverse than observed click consumption supports, which can misdirect diversity governance or algorithmic intervention. We introduce a reusable Preference-Exposure-Consumption (PEC) audit framework that separates stated preference, observed weighted profile state, logged recommendation exposure, app-surface pathways, and click consumption under explicit observability boundaries. Using six months of logs from a deployed mobile news application, we audit 1,583 user profiles, 95,143 logged recommendation items, and 17,512 click events. Each trace type contributes distinct information; none directly substitutes for another. Preference-consumption
alignment exceeds chance-based null baselines but captures only part of users' top-consumed category set. Logged recommendation lists contain clicked articles more often than a date-matched candidate-pool baseline predicts (11.29% vs 9.13%; top-5 lift 1.47x), but many clicks arrive through other app surfaces. Raw exposure-consumption diversity gaps shrink under count matching, yet concentration mismatch persists in the audit-eligible cohort (HHI gap 0.082). Together, the results show that audit conclusions change depending on whether platforms measure stated preference, logged exposure, or click consumption.
Read the full abstract ▾
Information Retrieval
Does Document Structure Help Dense Retrieval? A Placebo-Controlled Ablation of Four Mechanisms Across Two Corpora
Andrey Kuehlkamp, Priscila Correa Saboia Moreira and Samuel Rund
Retrieval-augmented generation systems increasingly rely on document-structure treatments: structure-aligned chunking, LLM-generated chunk contexts, heading-path metadata, and hierarchical two-stage
retrieval. Separate studies support each on different corpora, embedders, and metrics, and none control for a shared confound: any text prepended to a chunk perturbs its
embedding. We present a mechanism-isolating ablation testing all four treatments under one protocol, matching chunk sizes across conditions and adding a semantically null placebo—heading paths that are structurally valid but shuffled across documents. We score
retrieval with a coverage-aware nDCG and test four pre-registered contrasts via document-clustered bootstrap with Holm correction, on two distant corpora: 200 Wikipedia Featured Articles (951 queries) and 1,585 QASPER papers (4,303 questions). Organization helps, and the cause is content, not
tokens: structure-aligned chunks with real heading paths beat contextualized fixed windows (+0.022 / +0.012 cov-nDCG@10) and the placebo (+0.010 / +0.016). Naive two-stage hierarchical
retrieval hurts (-0.033 / -0.015), traceable to first-stage section recall. Gold structure beats LLM-induced structure on Wikipedia but not on QASPER. Effects are small ($dz$ 0.06-0.11) but Holm-significant and consistent across corpora.
Read the full abstract ▾
Machine Learning · Cross-listed from stat.ML
Conformal Prediction for Spatially Dependent Data via Sequential Whitening
Ayush Baran Sen and Arkajyoti Saha
Split conformal prediction uses prediction errors on held-out (calibration) data to determine how wide the prediction intervals should be. It guarantees distribution-free finite-sample coverage when these errors and the error at the target site are exchangeable. This assumption may fail under spatial dependence and nonrandom sampling geometry. Existing spatial methods use fitting residuals to remove the predictable part of spatial variation from calibration and target errors. However, the spatial variation that only the calibration residuals can predict remains in both the target and calibration errors, reducing the efficiency and stability of the interval. We address this by additionally conditioning on the calibration residuals sequentially, which scales to large networks through nearest-neighbour approximations. Under a correct working covariance and an elliptical residual law, the resulting interval has exact finite-sample coverage under any spatial design, and under further conditions it is asymptotically oracle efficient. We also bound coverage loss under covariance misspecification and develop a diagnostic that identifies regions at risk of undercoverage. In simulated data, our method produces narrower and more stable intervals than global and localized state-of-the-art alternatives. In a national PM2.5 application, it produces narrower intervals within the network and identifies regions at risk of coverage failure.
Read the full abstract ▾
Machine Learning · Cross-listed from stat.ME
Policy Learning with Weak Signals
Benedikt Koch, Winston Chou, Aurélien Bibaut et al. (+1 more)
Policy learning in digital experimentation faces three challenges: weak signal-to-noise ratios, rich
covariate spaces, and massive data volumes. We formalize this regime by modeling treatment-effect estimates from increasingly fine
covariate partitions as Gaussian observations with bounded signal-to-noise ratios. We establish that, in general, the optimal treatment policy is not learnable in this setting. Even learning the optimal policy value suffers from impractically slow rates. However, when treatment effects vary smoothly, we derive minimax-adaptive policies based on linear smoothers that achieve vanishing welfare regret. We demonstrate the practical value of our framework by applying it to large-scale real-world experiments at Netflix, showing that personalized linear-smoothing policies can dominate unpersonalized policies even in this challenging empirical setting.
Read the full abstract ▾
All authors Benedikt Koch, Winston Chou, Aurélien Bibaut, Nathan Kallus
Artificial Intelligence
UniSkill: Learning Actor-Aligned Skill Proposals for an Evolving Policy
Yifei Lu, Cheng Liu, Dianzhi Yu et al. (+4 more)
Large
language model
agents can improve across tasks by retaining reusable skills distilled from prior interactions. Recent work jointly optimizes task execution and skill extraction, enabling the policy and skillbank to co-evolve. However, as the actor continues learning, rewarding skill proposals through their reuse in subsequent training steps may conflate skill benefits with actor improvement, while directly testing each proposed skill requires costly additional actor rollouts. In this paper, we introduce UniSkill, which uses a shared policy to interact with the environment and propose skillbank edits (Add, Update, or No Edit) from the resulting trajectories. Specifically, the actor learns from environment rewards, while contrastive action feedback guides skill proposal learning. This feedback provides an actor-
alignment signal by measuring how replacing the retrieved skill with a proposed skill changes the current actor's action log-likelihood gap between previously collected successful and failed trajectories from the same task, thereby avoiding new rollouts for each proposal. Since proposal-level feedback may suppress an otherwise appropriate edit operation when the proposed skill content scores poorly, we further apply skill-edit support regularization to preserve exploration. Empirically, UniSkill achieves strong performance, reaching 98.4% success on ALFWorld and 84.7% on WebShop while maintaining stable joint training. Further ALFWorld experiments show that UniSkill remains effective when the shared policy uses a smaller backbone. Our implementation is available at https://github.com/LimOkii/UniSKill.
Read the full abstract ▾
All authors Yifei Lu, Cheng Liu, Dianzhi Yu, Hui Xiang, Ji Zhang, Yuanchu Xiao, Rong Liang
Computer Vision
Beyond Anonymous Captions: Grounding Character Identity in Video Captioning and Question Answering
Anas Filali Razzouki, Killian Steunou, Khalil Guetari et al. (+3 more)
Linking people's appearance and actions to character identities is essential for understanding
video narratives. We present a framework for identity-aware
video captioning and person-centric question answering that combines automatic character identification, explicit spatial grounding, and task-specific adaptation. Starting from LSMDC v2 movie clips, our pipeline matches detected faces to actor reference images, tracks characters across frames, and builds inputs with identity-linked bounding boxes. A strong
vision-
language model generates identity-aware captions and questions, which are manually verified and filtered to create a
benchmark of 750 captioned clips and 3,000 person-centric questions. We study five grounding strategies combining textual coordinates with visual face or estimated person boxes across
Video-MLLM families at roughly 2B, 4B, and 8B parameters and larger frontier models. Combining visual face boxes with textual coordinates yields the most consistent performance across scales and significantly improves overall performance over coordinates alone. Smaller models tend to over-assign known identities when the queried person is not grounded, while larger models better recognize such UNIDENTIFIED cases. We introduce BAC by LoRA fine-tuning Qwen models at 2B, 4B, and 8B scales on about 32K identity-aware captioned clips. Across all scales, BAC outperforms every other evaluated model family of comparable size. BAC-8B reaches 93.20% overall QA accuracy, ranking behind only GPT-5.6 Sol among the frontier models evaluated in our study. Overall, explicitly communicating who is where, together with lightweight task-specific adaptation, substantially improves identity-aware
video understanding without changing the underlying architecture. We release the
benchmark, training data, code, and BAC checkpoints at https://github.com/momentslab/beyond-anonymous-captions.
Read the full abstract ▾
All authors Anas Filali Razzouki, Killian Steunou, Khalil Guetari, Thomas Kling, Mounîm El-Yacoubi, Yannis Tevissen
Computer Vision
BagDINO: Multi-View Baggage Re-Identification with DINOv3
Vita Santa Barletta, Danilo Caivano, Rebecca Margiotta et al. (+2 more)
Mishandled checked baggage remains a recurrent issue in airport operations, and current recovery workflows still largely rely on tag-based tracking, which does not directly support visual identification when tag evidence is missing or unavailable. This paper investigates baggage re-identification as an instance-level
retrieval problem in a multi-camera setting, leveraging DINOv3 foundation-model representations to match a query image against a gallery of registered baggage images. A Torchreid-style BNNeck re-identification head is placed on top of a DINOv3 backbone, and parameter-efficient adaptation is performed via LoRA. Experiments are conducted on the MVB
benchmark using a progressive study that compares a fully frozen backbone against LoRA and fine-tuning strategies. Results indicate that parameter-efficient adaptation of foundation-model features provides an effective and stable approach for multi-view baggage re-identification under limited training data.
Read the full abstract ▾
All authors Vita Santa Barletta, Danilo Caivano, Rebecca Margiotta, Massimiliano Morga, Davide Pio Posa
Computer Vision
HeiCo-FOCUS: A Clinically Grounded Dataset for Long-Context Video Understanding
Leon Mayer, Lucas Luttner, Patrick Godau et al. (+42 more)
Recent advances in
Vision-
Language Models (VLMs) have led to rapid progress in
video understanding across a wide range of
benchmark tasks. However, existing evaluations largely focus on short-term
reasoning, failing to assess a critical capability: maintaining cumulative temporal consistency over extended time horizons. To close this evaluation gap, we introduce HeiCo-FOCUS, a clinically grounded
dataset for evaluating long-context
video understanding through the task of Foreign Object Contextual Understanding in Surgery. Built on a
dataset of Heidelberg Colorectal surgeries, this task requires models to continuously track multiple objects as they are inserted, manipulated, occluded, and removed over procedures lasting up to hours. HeiCo-FOCUS comprises 30,000 visual question answering (VQA) pairs covering five core capabilities: object recognition, temporal grounding, aggregation, event and procedural understanding, and complex
reasoning. The
dataset was constructed through a rigorous multi-stage annotation pipeline involving large-scale crowd annotation and 39
surgical domain experts to ensure high quality and clinical relevance. To systematically probe model behavior, we introduce a multi-track evaluation framework that progressively increases temporal and contextual demands from single frames to full procedures. Experiments with ten frontier VLMs show that HeiCo-FOCUS tasks are far from solved: only around half of the models clearly outperform a text-only baseline. Across the
video tracks, models perform best on event and procedural understanding (mean Accuracy: 56.5% across all models), while temporal grounding remains particularly challenging for all evaluated models (mean Accuracy: 19.7%). We therefore expect HeiCo-FOCUS to serve as a catalyst for the development of models capable of reliable, temporally consistent
reasoning over hours-long
videos.
Read the full abstract ▾
All authors Leon Mayer, Lucas Luttner, Patrick Godau, Kai Fritzsche, Annika Reinke, Leonie Boland, Jule Brandt, Janne Heinecke, Chloe K. Nobuhara, Niklas Holzwarth, Evangelia Christodoulou, Marcel Knopp, Dominik Michael, Pascale Piermarco, Saliq Neyaz, Korhan Derin Özarslan, Jakob Hennighausen, Carlos Aumente-Maestro, Tim Rädsch, Dheeraj Baji, Peter Maximilian Full, Finn Aichholz, Justus Veit Erpenbeck, Linus Finn Schott, Bastian Winkelhausen, Claas de Boer, Bianca Güttner, Anneli Hummel, Gregor Just, Max Kirchner, Chenyang Li, Rozenn Raffaut, Ariel Rodriguez, Danush Kumar Venkatesh, Kevin Wang, Jinjing Xu, Mona Sheikh Zeinoddin, Salman Khan, Thomas M. Pausch, Stefanie Speidel, Danail Stoyanov, Daniel A. Hashimoto, Fiona R. Kolbinger, Thomas G. Weiser, Lena Maier-Hein
Computation and Language
HySPE: Positional Encoding via Symplectic Dual Shears
Zhongping Ji
We introduce
Hyperbolic Symplectic Positional Encoding (HySPE), grounding positional
attention in non-compact
symplectic transformations. While canonical Rotary Position
Embedding (RoPE) parameterizes the compact,
elliptic branch of $\Sp(2,\R)$ via rotations, HySPE operationalizes its
hyperbolic branch via a damped symmetric composition of dual shears, yielding a conformally
symplectic contraction with two spectral decay rates per channel pair. To eliminate the exponential representation drift inherent to naive absolute factorizations, we diagonalize the operator in its invariant eigenbasis and introduce blockwise coordinate rebasing with adaptive centered execution. This guarantees length-independent numerical bounds while matching cached RoPE forward latency (7.21\,ms on an RTX 4090). On TinyShakespeare, HySPE-UltraLong maintains an invariant perplexity of 4.810 up to $16\times$ zero-shot extrapolation ($L=4096$), whereas RoPE degrades to 131.198. Scaled to a 51M-parameter subword
Transformer on WikiText-103 ($L_{\text{train}}=512$), HySPE closely matches RoPE in-domain while robustly extrapolating to length 8192, reducing tail perplexity by 83.9% over RoPE. While these controlled experiments establish HySPE's extrapolation robustness and numerical stability, evaluating its scaling behavior on large-scale foundation models remains an important direction for future investigation.
Read the full abstract ▾
Data Structures and Algorithms
3SUM Is Really Hard: A Real-to-Integer Reduction
Nick Fischer, Adam Polak and Jonas Schmidt
We show that if the 3SUM problem on integer-valued inputs can be solved in truly subquadratic time, then it can also be solved in truly subquadratic time on real-valued inputs. This answers an open problem posed by Chan, Vassilevska Williams, and Xu [STOC 2022], and constitutes the first such tight real-to-integer self-reduction in fine-grained complexity. Our proof relies on a surprising combination of the Frank–Tardos
theorem from optimization with Freiman-type
theorems from additive combinatorics.
Read the full abstract ▾
Cryptography and Security
On the Reliability of LLM-Based Vulnerability Patching Benchmarks
Dang K Le, Wenxuan Shi and Xinyu Xing
Large
language models (LLMs) have shown strong potential for automated vulnerability patching, but current
benchmarks can substantially distort reported performance. Drawing on extensive experience developing, running, and stress-testing such frameworks, we identify under-examined pitfalls across three dimensions: (1)
agent-level factors, where prompting, tool availability, and detailed instructions can raise success rates without improving developer-aligned patch quality; (2) framework-level factors, where permission errors, infrastructure bugs, and timeout handling can silently suppress or inflate performance; and (3)
dataset-level factors, where bug reports and single proof-of-concept (PoC) tests fail to capture whether patches address root causes or follow developer intent. We curate 112 historical bugs from 84 open-source C/C++, Go, and Rust projects, each with PoC tests, regression tests, and additional developer tests that assess
alignment with the original developers' design principles. Through controlled experiments and case studies, we show that LLMs can achieve high PoC passing rates under ideal conditions, yet
benchmark execution choices can materially change measured success. More importantly, developer-test passing rates remain low and improve only marginally with newer models, suggesting that models increasingly suppress symptoms without consistently producing upstream-quality fixes. These results show that
benchmark scores are highly sensitive to evaluation design, and we provide practical guidelines for more rigorous, reliable, and reproducible evaluation.
Read the full abstract ▾
Cryptography and Security
Pump-and-Dump meets Honeypot Tokens: Detection and Analysis of Telegram Bait-and-Trap Schemes
Federico Cernera, Massimo La Morgia, Alessandro Mei et al. (+1 more)
While Pump-and-Dump schemes have been extensively studied on centralized exchanges (CEXs), how they operate in decentralized exchanges (DEXs) remains largely unexplored. We monitor 83 Telegram channels used to coordinate Pump-and-Dump campaigns and collect 3,677 events across the BNB Smart Chain and Ethereum. Our analysis reveals that, although these operations superficially resemble CEX-based Pump-and-Dump schemes, their underlying mechanism is fundamentally different. Rather than manipulating prices, organizers orchestrate deceptive Pump-and-Dump campaigns around honeypot
tokens whose smart contracts allow Telegram subscribers to buy but prevent them from selling. Unaware of this restriction, victims purchase these
tokens and are unable to recover their funds. We refer to this new fraud mechanism as Bait-and-Trap. We show that Bait-and-Trap provides a substantially more reliable profit strategy than Pump-and-Dump schemes: organizers gain in 99.3% of cases, extracting over $7 million in profit. To counter this threat, we develop a transaction-simulation tool that detects honeypot
tokens before purchase by testing whether a user can successfully sell against the
token's live contract state, providing a practical defense against Bait-and-Trap operations.
Read the full abstract ▾
All authors Federico Cernera, Massimo La Morgia, Alessandro Mei, Francesco Sassi
Distributed Computing
Reconciling Bottom-Up Metrics with Top-Down Reporting for Cloud Carbon Accounting
Philipp Wiesner, Loïc Lannelongue, Alexander Acker et al. (+1 more)
Organizations seeking to reduce the carbon footprint of their cloud applications must rely on two largely disconnected ways of measuring it, each with important limitations. Bottom-up metrics like Software Carbon Intensity (SCI) provide signals for carbon-aware optimization, but tenants lack the information needed to account for many provider-side overheads. Top-down reports by cloud providers provide a more comprehensive view of a tenant's carbon footprint but are coarse and methodologically opaque. Because the two approaches differ in scope and reporting frequency, the current state of the art treats them as decoupled: bottom-up metrics for optimization, top-down reports for corporate disclosure. We argue that the two signals should be reconcilable. A per-workload metric whose improvements never surface in the provider's audited report will not be adopted for accountability at scale. We survey the state of the art in cloud carbon accounting and propose a
vision for reconciled SCI (rSCI): a per-workload metric that re-anchors a bottom-up energy estimate to the cloud provider's top-down report through a residual. By decomposing and allocating this residual according to its physical drivers, rSCI can preserve operational incentives while also attributing idle capacity and
embodied carbon to the workloads that drive them. We show that today's top-down reporting is still too coarse and methodologically inconsistent to support credible reconciliation and lay out concrete steps in provider reporting and standards to make it practical.
Read the full abstract ▾
All authors Philipp Wiesner, Loïc Lannelongue, Alexander Acker, Odej Kao
Computation and Language
InterView-C: A Synchronized Multimodal Corpus of VR Avatar-Mediated Survey Interviews
Patrick Schrottenbacher, Leon Hammerla, Lydia Kleine et al. (+2 more)
We present InterView-C, a German
multimodal corpus of 27 survey interviews conducted entirely in virtual reality, with both interlocutors represented by avatars. The corpus aligns spoken interaction with synchronized behavioral data, including gaze, head and body movement, facial behavior, hand and finger tracking. Its reference transcripts and linguistic annotations provide a reliable interface between this
multimodal spoken interaction and predominantly text-based NLP methods. This interface is important because automatically transcribing
speech can distort linguistically relevant information, while downstream models trained on existing resources may additionally face transfer challenges when applied to transcribed spoken data. InterView-C therefore provides word-timed and manually post-edited verbatim transcripts for all 54 recordings, interview-item timings, questionnaire responses and negation cue and scope annotations for 1,422 sentences, 1,398 of them doubly annotated ({\alpha}=0.87 for cues; {\alpha}=0.81 for scopes). We demonstrate both challenges empirically: nine open-weight ASR systems disproportionately misrecognize short closed answers and number words, while negation models trained on existing corpora show lower and highly variable performance on our transcribed interviews than a model trained on the InterView-C annotations. InterView-C thus enables linguistic analyses of spoken interaction while retaining their
alignment with rich
multimodal behavior.
Read the full abstract ▾
All authors Patrick Schrottenbacher, Leon Hammerla, Lydia Kleine, Doris Stingl, Alexander Mehler
Software Engineering
AdaT$^2$: Adaptive Test Transformations for Black-Box Boundary Testing of Conversational Agents
Liting Lin, Boxi Yu, Qinghua Xu et al. (+3 more)
Conversational
agents based on large
language models (LLMs) must comply with policies. Each condition in a policy draws a boundary between user requests, and the
agent must behave differently on its two sides. We present AdaT$^2$, which extracts statements from the
agent's replies in exploratory conversations with an LLM acting as the user, and uses the statements to guide boundary test generation. Each statement describes one condition and the behavior expected when the condition holds. Besides plain tests guided by single statements, AdaT$^2$ writes transformed tests guided by pairs of a statement and a test transformation instruction, such as "omit one required input". The instruction of a pair can move a test to the other side of the statement's boundary or to another boundary. The statements and instructions form far more pairs than a run can try, and many pairs are not applicable. Adaptive pair selection therefore chooses the statement of each pair by novelty and the instruction with the bandit
algorithm Bayes-UCB, which learns from whether earlier pairs yielded a test and whether the
agent passed it according to an LLM judge. Our
benchmark counts the two sides of each boundary separately and distinguishes boundaries explicitly defined by the
agent's
prompt, tool code, or knowledge base from boundaries that the
agent's LLM infers from domain knowledge. On four domains of $\tau^3$-bench, 62.7% to 83.3% of AdaT$^2$'s tests are valid boundary tests whose expected behavior is explicitly defined, higher in every domain than AgentEval's (47.7% to 68.1%). Transformed tests add 13 to 46 explicitly defined boundaries that plain tests miss. As regression tests, AdaT$^2$'s test suites detect all eight seeded policy faults in the airline domain and four of eight in the retail domain, and AgentEval's test suites, with fewer than a third as many tests, detect five and two, respectively.
Read the full abstract ▾
All authors Liting Lin, Boxi Yu, Qinghua Xu, Yuzhong Zhang, Lionel Briand, Emir Muñoz
Machine Learning · Cross-listed from cond-mat.other
Progress and Prospect of AI in ARPES Workflow
Sandy Adhitia Ekahana, Aalok Tiwari, Pratik Saud et al. (+4 more)
Artificial intelligence (AI) is becoming an increasingly useful tool across the experimental sciences, including angle-resolved photoemission spectroscopy (ARPES), which routinely produces large, multidimensional
datasets of electronic structure. Recent advances in AI and machine learning (ML) have opened new opportunities across the entire ARPES workflow, from automated sample preparation and real-time data acquisition to post-experiment data analysis and comparison with theoretical calculations. Despite this progress, a comprehensive review of ML applications, their capabilities, and reliability across the different stages of ARPES workflow is still lacking. In this review, we first introduce ML methods that are most relevant to experimentalists working in condensed matter physics and materials science. We then follow the ARPES workflow, reviewing existing ML applications at each step and discussing their advantages, limitations and potential for future development. We also examine the current ARPES data landscape, where several open databases are available but remain relatively small and fragmented compared with large, shared
datasets such as ImageNet. Given these limitations, we suggest that the community focus on sharing pretrained models that can be further trained, adapted to specific tasks, and redistributed, while working toward a larger and standardized open ARPES
dataset repository. Finally, we discuss our perspectives on the future of AI within the ARPES workflow using a six-level framework of laboratory automation, highlighting the opportunities and challenges in moving toward a fully autonomous, self-driving ARPES laboratory.
Read the full abstract ▾
All authors Sandy Adhitia Ekahana, Aalok Tiwari, Pratik Saud, Aaron Bostwick, Chris Jozwiak, Eli Rotenberg, Jyoti Katoch
Computation and Language
LLM4Impact: Integrating Heterogeneous Information for Scientific Impact Prediction
Yong Cao, Markus Flicke, Haoyu He et al. (+2 more)
Predicting the future impact of a newly published paper is challenging because it must be inferred from heterogeneous evidence available at publication time. Existing approaches often rely on a single source of information or combine multiple sources without accounting for their different predictive roles. In this paper, we present LLM4Impact, an evidence-aware method for scientific impact prediction that learns to represent, integrate, and calibrate heterogeneous information. LLM4Impact combines semantic, graph, LLM, and temporal representations, and injects graph information into a frozen LLM through continuous prefix
tokens. A context aware gating mechanism adaptively weights different evidence, while a separate calibration module accounts for domain and temporal variation in citation scales. We further construct a large-scale
benchmark dataset with 2 million papers, leakage-safe point-in-time heterogeneous ego graphs, temporal splits, and both year-level and month-level citation targets. Experiments show that LLM4Impact consistently outperforms strong semantic, graph, and LLM based baselines, with a 10.13% reduction in year RMSE on the in distribution test set and a 6.87% reduction under out-of-domain distribution. Our results reveal that the value of such evidence is context dependent: different papers benefit from different sources, while domain and publication time affect how evidence translates into citations. This finding motivates adaptive evidence selection and context-conditioned calibration rather than simply richer representations. We will release our code,
benchmark, and an interactive web demonstration upon publication.
Read the full abstract ▾
All authors Yong Cao, Markus Flicke, Haoyu He, Katrin Renz, Andreas Geiger
Operating Systems
Behind a Simple Read: Understanding Work and Waiting in the Linux I/O Stack
Yang Shen, Wenzhe Zhang, Yong Dong et al. (+4 more)
A simple read interface provides uniform semantics but unpredictable performance. Using synchronous large-buffer Linux reads, we decompose the buffered-read path into work volume, processing time, and critical-path exposure. Reducing local cost or accelerating request submission need not shorten a complete read. Linux uses large folios to reduce metadata work and I/O-copy overlap to hide most cold-read copying; an experimental kernel prototype shows that more aggressive overlap and parallelism can shorten the host path when these mechanisms fail. Device waiting dominates SSD cold reads. We abstract first-completion wait F and subsequent completion capacity B_CQ from request-batch timelines. Real-SSD experiments characterize their variation with request and batch size, while MQSim traces explain their internal origins. A wait chain along the folio-bio-request mapping connects device completion to upper-layer waiting. Under strict batching, the median absolute cross-layer prediction error is at most 7.43%. Finally, we apply the completion model and wait chain to request supply, Linux readahead, data layout, and waiting policies. Cases cover gains, no gains, and reversals, comparing predictions with interventions to close the loop from observation and modeling to understanding and control and provide a measurable basis for layered I/O decisions.
Read the full abstract ▾
All authors Yang Shen, Wenzhe Zhang, Yong Dong, Min Xie, Wei Zhang, Huijun Wu, Zhenwei Wu
Data Structures and Algorithms
Attention via Black-Box Vector Search
Stepan Zharkov, Krish Singal, Ashwin Padaki et al. (+1 more)
Sparse
attention mechanisms estimate
attention over $n$
tokens using a small subset of keys. Many existing approaches use maximum inner product search (MIPS) to retrieve the heaviest keys, which motivates the following question: given black-box access to a MIPS oracle, how many keys must be retrieved to output an $\varepsilon$-accurate
attention estimate? We answer this question by unifying prior approaches through the framework of priority sampling. With a single MIPS index, we show that $\Theta(\sqrt{n}/\varepsilon)$ retrieved keys are both sufficient and necessary. With $\Theta(\log n)$ indices, we give an
algorithm that retrieves only $O(\log n+1/\varepsilon^2)$ keys and prove that this is near-optimal. More generally, we design
algorithms that establish a smooth tradeoff between the number of MIPS indices and number of retrieved keys. We then show that if we allow augmentation of keys and queries, we can bypass the above lower bounds: there exists a simple priority-sampling
estimator using a single MIPS index and $O(1/\varepsilon^2)$ retrieved keys. When integrated into LLM inference, our
algorithms outperform top-$k$ and sampling approaches used in prior work and yield
attention approximation that scales favorably to long contexts.
Read the full abstract ▾
All authors Stepan Zharkov, Krish Singal, Ashwin Padaki, Alexandr Andoni
Computer Vision
HarnessIR: Harnessing Multimodal Foundation Models for Universal Real-World Image Restoration
Xiangtao Kong, Shuaizheng Liu, Rongyuan Wu et al. (+5 more)
Real-world low-quality images suffer from complex mixed degradations, including but not limited to noise, blur, atmospheric effects, etc. Recent agentic methods usually model real-world image restoration (Real-IR) as a sequential tool calling problem over task-specific single-degradation restoration models. This paradigm, however, is fundamentally limited because complex real-world degradations cannot be cleanly undone degradation by degradation, and the tool used for task-specific models caps the capability of the
agent system. In this work, we present HarnessIR, an agentic framework for Real-IR by harnessing a
multimodal foundation model (MFM) as the executor. HarnessIR consists of five stages: perception and diagnosis, on-demand tool invocation,
prompt composition, execution, and verification-driven refinement. Unlike prior agentic Real-IR methods that rely on tool chains assembled from task-specific models, HarnessIR feeds the restoration requirements, the perceptual diagnosis, and the evidence into an MFM that performs restoration in a single pass, followed by verification stages to determine whether the result warrants further processing. Under our harness, off-the-shelf MFMs handle restoration tasks remarkably well, achieving state-of-the-art results on the widely used MiO100 synthetic
benchmark. More importantly, by exploiting the strong generalization ability of MFMs, HarnessIR delivers compelling restoration quality on challenging real-world scenes where previous agentic IR systems often struggle. Codes is available at https://github.com/PolyU-VCLab/HarnessIR.
Read the full abstract ▾
All authors Xiangtao Kong, Shuaizheng Liu, Rongyuan Wu, Lingchen Sun, Zhengqiang Zhang, Jixin Zhao, Yuhui Wu, Lei Zhang
Hardware Architecture
When Algorithmic Exploration Becomes Cheap: A Case Study of Agentic Research in EDA
Keren Zhu, Yu Deng, Xiaoyu Hao et al. (+6 more)
As EDA researchers, we conducted eight deliberate trials of agentic
algorithm exploration, selecting several topics outside our areas of depth. One faculty member and seven students participated, including students without publication experience. With limited intervention in the
algorithms,
agents developed mathematical constructions, analyzed existing tools, and implemented improvements; some efforts fell short of their practical goals. We also used AI to collect, classify, and analyze 8,420 papers from four EDA conferences and two journals over 2022-2026. Among 2,380 primary-core papers, we classified 97.7% from titles and abstracts as computationally closed, including work on new formulations. Together, these observations suggest that much of EDA offers an executable environment for increasingly accessible
algorithm research. We see an opportunity for tool developers to investigate ideas they previously lacked time to pursue. We also ask how EDA should validate and reward research when results become easier to produce than to examine, and what papers and venue labels will continue to tell us about a contribution.
Read the full abstract ▾
All authors Keren Zhu, Yu Deng, Xiaoyu Hao, Liwen Jiang, Zijian Jiang, Cunqing Lan, Boxiang Song, Pujun Su, Yaojia Wang
Machine Learning
m-Set Adversarial Bandits with Winner Feedback
Nicolò Cesa-Bianchi and Matteo Papini
We show upper and lower bounds on the regret of $m$-set
adversarial bandits for different utilities (winner reward or sum of rewards) and feedback models (winner index, winner reward, sum of rewards, and their combinations). By comparing to standard bounds for combinatorial and MNL bandits, our results reveal how subtle changes in the setting can have a dramatic impact on the learning rates. Our main technical contributions are the information-theoretic lower bounds on the regret. Experiments on synthetic data confirm our theoretical analyses.
Read the full abstract ▾
Multiagent Systems
Know the Shape, Find the Fault: Topology-Conditioned Diagnosis of Multi-Agent LLM Failures
Xinwen Liu, Zhuocheng Pan, Isabella Zhu et al. (+3 more)
Multi-
agent LLM systems coordinate task execution through exchanges of information among
agents. When coordination breaks down, similar symptoms in execution traces can reflect different problems in how information is passed, used, or verified. Communication
topology captures how
agents exchange information and provides structural cues for distinguishing coordination failure modes. Using these cues for diagnosis requires establishing how
topology relates to failure patterns and recovering the relevant structure from execution traces that lack explicit
topology labels. We analyze the relationship between communication
topology and failure patterns and introduce MAScope, a two-stage framework for
topology-conditioned diagnosis. Its Trace Structural Extractor TSE recovers communication
topology from heterogeneous execution traces by grounding an interaction graph in message evidence. The
Topology-Conditioned Judge TC-Judge then classifies failures using the trace, predicted
topology, an empirical failure prior estimated from separate labeled traces, and a short description of
topology-specific failure patterns. Under a fixed orchestration structure, the recovered
topology can be reused across executions. Experimental results show a statistically significant association between communication
topology and failure type, with $\chi^2 = 409.9$ and $p = 1.2 \times 10^{-70}$. On the \num{851} MAST-clean traces, ground-truth
topology context raises gpt-mini's Macro-F1 from $0.173$ to $0.350$. With predicted
topology, the pipeline achieves $0.346$, approaching the trace-only gpt-5.4 baseline of $0.372$. For \num{1000} traces under a fixed orchestration structure, the projected pipeline cost, including one
topology extraction, is approximately $6\%$ of repeated gpt-5.4 diagnosis cost. These results show that
topology-conditioned context improves failure diagnosis and supports lower-cost deployment.
Read the full abstract ▾
All authors Xinwen Liu, Zhuocheng Pan, Isabella Zhu, Jawei Zhang, Xudong Liu, Tianyu Wo
Robotics
Lifelong small-object navigation in changing object layouts: a benchmark and method
Jiagan Huang, Zikun Zhou, Zijian Ni et al. (+4 more)
Household
robots need to continually navigate to different objects in the same environment, many of which are small and portable, such as tools and toys. Their small visual footprint and frequent occlusion make reliable observation difficult, and they may be moved by people without the
robot observing the changes. We formulate this challenging task as Lifelong Small-object
Navigation in Changing Object Layouts (LiSoNav-COL).
Agents must seek suitable viewpoints for reliable observation, accumulate and reuse scene knowledge to efficiently locate subsequent targets, and update outdated
memory after object relocation. To eliminate the need for prior scene scanning, we also require
agents to start
navigation with empty scene
memory. Although practical, this task still lacks
benchmarks designed around its defining assumptions. To bridge this gap, we introduce LiSoNav-Eval, a dedicated
benchmark spanning 28 indoor scenes with 45 small-object categories. Its lifelong
navigation sequences include both unchanged and relocated targets to evaluate
memory reuse and adaptation to object relocation. To address this challenging task, we propose a
navigation method based on multi-view Inspection with Viewpoint-Anchored
Memory, dubbed IVAM-Nav. IVAM-Nav actively observes supporting surfaces from complementary viewpoints for reliable small-object perception and anchors the resulting
memory to their observation viewpoints, supporting relational
memory reuse and revalidation under similar viewing conditions. Extensive experiments on LiSoNav-Eval demonstrate favorable performance of IVAM-Nav against representative methods.
Benchmark analyses also show that smaller objects, larger environments, and longer relocation distances pose greater challenges. The
dataset and code are available here.
Read the full abstract ▾
All authors Jiagan Huang, Zikun Zhou, Zijian Ni, Hongpeng Wang, Guangming Lu, Jun Yu, Wenjie Pei
Information Retrieval
Training with Missed Targets in Generative Recommendation: Separating Supervision from Probability Competition
Xuesi Wang, Yangbin Shi and Xiaolin Zheng
Generative recommenders return a limited candidate set and may omit observed targets before reranking. A training strategy appends these missed targets to reranker training lists, although inference still ranks only original candidates. This operation simultaneously changes retrieved-target weight, adds supervision over appended targets, and makes the two groups compete for probability. An append/no-append comparison therefore cannot explain changes in returned-item rankings. We construct three matched losses that hold retrieved-target weight fixed while introducing appended-target supervision and group competition separately. The intermediate loss trains within both groups but normalizes them separately, preventing training-only targets from competing with inference candidates. Experiments with a released OneRec model and locally trained Amazon generators show that this competition can harm returned-item ranking. In four prespecified Amazon
Video Games comparisons, removing it improved full-target normalized discounted cumulative gain (FT-NDCG) by 7.8–22.2%; 95% intervals over users and three of four intervals over training runs excluded zero. A conservative development-set rule selected appended-target training for two of three generators in one held-out category and rejected it for all three in another, avoiding a 1.7% loss. Candidate completion should therefore be evaluated for each generator rather than applied automatically.
Read the full abstract ▾
Machine Learning
What Can a Gaussian Process Design Test
Ivan De Boi and Marnix Van Soom
A Gaussian process (GP) model can agree with the data for two reasons: its assumptions are right, or the chosen inputs could never have shown that they are wrong. The distinction can be checked from the design before any responses are observed. Every model implies relations that its noiseless responses must satisfy at the chosen inputs, such as the middle value lies on the line through its two neighbours. For GPs built from finitely many features, these relations are exactly the null space of the kernel
matrix. Gale duality gives them a
geometric interpretation, in which each observation has a vector and the smallest groups of observations that can expose an error are the
circuits. For other kernels the relations become soft: response patterns may be improbable under the prior rather than algebraically impossible. A standard test then combines two kinds of evidence. Structural evidence comes from a violated relation and grows without limit as the noise falls. Prior-based evidence only says that a departure is improbable under the prior. With all inputs at the two ends of an interval, for example, a GP can reject a straight line against a large
curvature, but only because the implied intercept is improbable, never because
curvature was seen. In simulations the predicted power matched the observed rejection rates. Choosing the next input by predicted power raised the power against a localised discrepancy from 0.48 to 0.72, against 0.51 when choosing by predictive variance, and a grid in two dimensions contained exact tests of additivity that a Latin hypercube lacked. The test itself is classical. The contribution is the prospective reading of that test: before observing the responses, the design already determines what kind of contradiction it can produce.
Read the full abstract ▾
Sound · Cross-listed from eess.AS
DuRe-ST: Dual-Relation Spectro-Temporal Modeling for Speech Deepfake Detection
Shaole Li, Siqing Qin, Youzhi Tu et al. (+1 more)
Previous
speech deepfake detectors can adaptively capture spectro-temporal dependencies through graph
attention, yet they largely overlook the co-variation between spectral and temporal representations. To address this gap, we construct a normalized affinity graph from their joint covariance and apply
polynomial graph filtering to capture higher-order covariance-induced dependencies. We first develop Cov-ST to isolate the contribution of covariance-based relational modeling. Although it improves detection performance, its sensitivity to the
polynomial order suggests limited robustness when covariance relations are modeled alone. We therefore propose DuRe-ST, which jointly exploits covariance-induced and graph-
attention-induced relations to capture complementary second-order co-variation and adaptive spectro-temporal dependencies. Experiments show that DuRe-ST achieves an average relative EER reduction of 25.9% over XLSR-AASIST on the ASVspoof
benchmarks and 28.4% across four cross-
dataset benchmarks with only 4-8k additional trainable back-end parameters. It further outperforms the strongest publicly available comparison models by 2.2-13.2% in relative EER on four
benchmarks, while remaining smaller than the publicly available models considered.
Read the full abstract ▾
All authors Shaole Li, Siqing Qin, Youzhi Tu, Kong Aik Lee
Artificial Intelligence
RewardWeaver: Long-Horizon Interactive Learning for Language Agents via Self-Evolving Reward Adaptation
Hengbo Xiao, Boyao Zhang, Purui Liu et al. (+4 more)
Reinforcement learning with verifiable rewards (RLVR) has driven substantial progress in domains where task outcomes can be reliably evaluated, but long-horizon interaction remains challenging due to sparse terminal feedback and difficult credit assignment. Process rewards provide denser supervision, yet the capabilities most relevant for training can change as the policy evolves: a behavior that is easy to evaluate or frequently deficient need not be the bottleneck currently limiting task success. We introduce RewardWeaver, a self-evolving reward adaptation framework for
language agents in long-horizon interaction. RewardWeaver maintains a validated capability space in which the semantics of admitted Rubrics remain fixed, and closes the loop between policy optimization, task evaluation, failure attribution, and reward adaptation. After each training stage, it performs outcome-grounded backward attribution on low-outcome trajectories, aggregates recurrent and policy-controlled capability bottlenecks, and dynamically selects the corresponding process rewards for the next stage. Recurrent failures not covered by the existing capability space trigger a separate, controlled expansion procedure. We evaluate REWARDWEAVER on SOTOPIA, Amazon?HistoryPrice, and a newly constructed Sales
Benchmark. Across social interaction, bilateral bargaining, and domain-specific sales, REWARDWEAVER establishes new state-of-the-art (SOTA) results. Ablations further demonstrate the importance of dynamic reward allocation, failure-grounded attribution, and stable semantics for admitted capabilities.
Read the full abstract ▾
All authors Hengbo Xiao, Boyao Zhang, Purui Liu, Yuxuan Zheng, Haoran Yin, Haibo Liu, Fan Zhang
Machine Learning
Huishan Ji, Weiming Zhang, Qirui Ye et al. (+3 more)
We present EdgeRock, a family of
multimodal models that deliver strong
reasoning at constant
memory. Most models keep
reasoning history in a key-value (KV) cache that grows with every
token, consuming accelerator
memory, limiting concurrent requests, and competing with perception and control on edge devices. With fixed history,
memory is allocated once and never runs out, however long the model reasons. Constant
memory alone is not new: bounded-state models (recurrent, state-space, and sliding-window) already achieve it, but often at the cost of
reasoning, because they cannot keep the distant details that later
reasoning needs. We therefore introduce Rock
Memory Anchors (RMA): the model learns what is worth remembering and stores it as KV records in a fixed-size bank, directly readable however far back the source lies. EdgeRock-4B, our first open-source release, supports text and image
reasoning and reaches 82.9% mean pass@1 on AIME 2024-2026, while the bounded-state models we evaluate reach at most 18.8%, even at 14B scale. This result is on par with leading compact models whose
memory grows with
reasoning. Compared with its source model, EdgeRock-4B uses up to 99.1% less KV cache and delivers up to 6.42$\times$ higher end-to-end batch throughput.
Read the full abstract ▾
All authors Huishan Ji, Weiming Zhang, Qirui Ye, Jinchao Xu, Yike Guo, Hua Xu
Computer Vision
A Probabilistic Perspective on Wasserstein-Based Evidential Uncertainty for Out-of-Distribution Segmentation
Arnold Brosch, Abdelrahman Eldesokey, Michael Felsberg et al. (+1 more)
Semantic
segmentation networks operate on a fixed set of classes and therefore fail when out-of-distribution (OOD) objects appear during deployment, a critical limitation for safety-critical applications such as autonomous driving. Reliably identifying OOD objects requires well-calibrated epistemic uncertainty, yet common softmax-based confidence scores remain overconfident, while
Bayesian alternatives such as Monte Carlo dropout or deep ensembles require costly repeated forward passes. Evidential Deep Learning (EDL) offers an efficient alternative by modeling class probabilities as a Dirichlet distribution learned from a single deterministic forward pass. Existing EDL formulations rely on Euclidean objectives that push predictions towards the simplex vertices, encouraging overconfidence rather than preserving uncertainty for unfamiliar inputs. We instead employ Wasserstein-based objectives, which respect the geometry of the probability simplex, and study the influence of the Wasserstein order on
segmentation accuracy and OOD detection within a unified evidential framework. We evaluate this framework on a convolutional (DeepLabV3+) and a
transformer-based (SegFormer) architecture on the SegmentMeIfYouCan
benchmark, including LostAndFound, RoadObstacle21, RoadAnomaly21, and Fishyscapes. Our results show the optimal Wasserstein order is architecture-dependent: second-order objectives dominate on the convolutional backbone, third-order objectives on the
transformer backbone, and our framework surpasses comparable baselines on most metrics, with a single deterministic forward pass.
Read the full abstract ▾
All authors Arnold Brosch, Abdelrahman Eldesokey, Michael Felsberg, Kira Maag