Artificial Intelligence
Does Deeper Reasoning Compromise Alignment? Revealing and Mitigating of Alignment Collapse in Large Reasoning Models
Yu-Hang Wu, Yu-Jie Xiong, Henghua Zhang et al. (+3 more)
The emergence of Chain-of-Thought (CoT) has established a robust foundation for Large
Reasoning Models (LRMs). While deep
reasoning is widely believed to enhance safety
alignment, the stability of
alignment mechanisms under extended
reasoning remains underexplored. This paper challenges the prevailing view by revealing a critical vulnerability: Deep
Reasoning May Induce
Alignment Collapse. To rigorously quantify this phenomenon, we propose the
Alignment Loss Rate (ALR) metric. Our experiments demonstrate that as
reasoning depth increases, ALR rises significantly, indicating a severe degradation in model robustness against external perturbations. Capitalizing on this instability, a novel jailbreaking paradigm,
Reasoning Trap (RT), is proposed. RT induces the model into extended
reasoning to amplify the impact of
adversarial attacks, leading to a sharp decline in safety capabilities. To elucidate the mechanism behind this collapse, we identify
Attention Dilution as the root cause, arising from the competition for
attention between the extended
reasoning process and the original input. To mitigate this,
Reasoning Residual
Alignment (RRA), a lightweight defense strategy that dynamically re-emphasizes the input via residual connections integrated with the
reasoning process.
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All authors Yu-Hang Wu, Yu-Jie Xiong, Henghua Zhang, Bairui Zhang, Jia-Chen Zhang, Shaohua Li
Information Retrieval
Cassette: Case-to-Case Structural Distillation for Efficient Legal Case Retrieval
Yanran Tang, Ruihong Qiu, Hongzhi Yin et al. (+2 more)
Legal case
retrieval (LCR) is an essential tool for not only assisting legal practitioners to efficiently retrieve precedents but also enabling ordinary individuals to find valuable legal case information without relying on expensive professional legal services. Our previous work CaseLink demonstrated the effectiveness of using case to case graph structures to improve
retrieval accuracy. However, its high computational cost during inference on large-scale legal databases limits its practical use in real-world settings. The main inefficiency comes from constructing test time graphs and computing pairwise term frequency similarities of cases. This process has O(n^2) complexity for n legal cases, making the runtime prohibitive as the number of candidates grows. For example, the
retrieval time for one query on a database (COLIEE2022) with 1,563 candidate cases is more than 500 milliseconds, while the runtime would increase drastically to more than 3,500 seconds for a database (LeCaRDv2) with 55,192 candidate cases. To further enhance the
retrieval performance while achieving a significant speed-up, in this extension paper, Cassette framework is proposed with a
distillation strategy involving ranking objective and eigen-matching objective for an effective transfer of knowledge from a powerful and well-trained heavy teacher retriever to a lightweight and efficient hybrid student dual encoder. Specifically, the student query encoder is implemented as a multilayer perceptron model designed for fast online processing, whereas the student candidate encoder adopts a GNN architecture, suitable for an offline manner within the case database. Extensive experiments are conducted on three
benchmark datasets, and the results verify the effectiveness of the ranking
distillation while achieving high efficiency. The code has been released on https://github.com/yanran-tang/Cassette/.
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All authors Yanran Tang, Ruihong Qiu, Hongzhi Yin, Xue Li, Zi Huang
Databases
UnespDataLens-RM: A Reference Model for Analytical Data Engineering with Governance, Quality, Provenance, and Reproducibility
Ronaldo Celso Messias Correia, Douglas Francisquini Toledo and Camila Tolin Santos da Silva
The growing reliance on data in analytical processes and evidence-based decision-making has reinforced the importance of Data Engineering in building pipelines capable of integrating, transforming, validating, and delivering data from heterogeneous sources. However, the reliability of analytical assets depends not only on data processing capabilities but also on mechanisms for governance, quality assurance, provenance, traceability, versioning, and reproducibility throughout their lifecycle. These responsibilities are commonly addressed by different models, frameworks, and operational practices, resulting in methodological fragmentation across the analytical data lifecycle. To address this gap, this article proposes UnespDataLens-RM, a technology-independent reference model that integrates technical-operational processes and cross-cutting capabilities within a unified structure for Analytical Data Engineering. The model aims to support the specification, organization, and
evolution of analytical pipelines by incorporating governance, quality, provenance, traceability, and reproducibility from the design stage. Developed following the Design Science Research approach, UnespDataLens-RM comprises eight technical-operational modules, eight cross-cutting modules, complementary dimensions, and a formalized set of artifacts, metrics, and validation criteria. The resulting specification offers a conceptual and methodological framework for future instantiations and empirical evaluations of analytical pipelines designed to be more governable, documented, traceable, auditable, and reproducible.
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Computation and Language
NeoHorse-1: Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness
NeoHorse Team, Guoliang Cao, Guohao Dai et al. (+34 more)
Recursive self-improvement (RSI) requires a concrete mechanism through which an AI system observes its capabilities and converts that evidence into the next round of learning. We present NeoHorse-1, a family of
agent-native models developed to explore this path through agentic post-training. Our system combines a heterogeneous model pool with intelligent routing, recording the predicted capability demand, selected service tier, and subsequent interaction for each user turn. These records are converted into training examples that preserve interleaved
reasoning, tool calls, and harness context, and are admitted through structural validation, six-dimensional semantic evaluation, and subscene-level labeling. Routing signals organize supervised fine-tuning into a three-stage curriculum and extend to routing-guided on-policy
distillation, where a teacher supervises student-generated responses under the same progression. Capability-guided allocation then converts evaluation feedback into the next training mixture, closing an evaluation-selection-update loop in which what the system learns to do shapes what it learns from next. Across eleven
benchmarks covering harness-based
agents, tool use, coding, and instruction following, post-training raises the macro-average from 58.94 to 64.87 at 4B and from 65.60 to 69.04 at 9B, substantially narrowing the aggregate gap between the post-trained 4B model and the 9B base model. We further adapt NeoHorse-1-4B into NeoHorse-Jev-4B, a prefill-only model that returns structured decisions and probabilities for
agent workflows. NeoHorse-1 provides an initial prototype of this feedback-driven process and a path toward harness-mediated RSI across successive iterations.
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All authors NeoHorse Team, Guoliang Cao, Guohao Dai, Tianyu Guo, Kai Han, Hailin Hu, Zihan Jiang, Xiang Kuang, Boxun Li, Yulong Li, Zehua Pei, Yuchuan Tian, Jiamin Wang, Yu Wang, Yunhe Wang, Yihong Wu, Haiyang Xu, Shuo Zhang, Hang Zhou, Siyang Cheng, Jiayu Fan, Wei He, Qingrui Jiao, Hongguang Li, Zhiyuan Li, Runke Liu, Xi Liu, Xinchen Liu, Sinno Jialin Pan, Yi Ren, Liuyang Song, Chenyu Wang, Bei Yu, Quanlu Zhang, Xiangyu Zhang, Mengyu Zheng, Yingjie Zong
Computer Vision
WSPolypNet: Weakly Supervised Polyp Localization in Colonoscopy Videos
Giseong Hwang, Minjae Jo, Yeonghyeon Park et al. (+9 more)
Because dense frame-level annotation of colonoscopy
videos is costly, we propose WSPolypNet, a weakly supervised framework for polyp localization using only
video-level labels. WSPolypNet employs a 3D convolutional neural network trained with
video-level supervision to generate class activation maps (CAMs), which identify candidate polyp regions without requiring frame-level spatial annotations. The CAM-derived localization cues are further enhanced using a multi-view strategy and provided to MedSAM2 as point
prompts. MedSAM2 then propagates
segmentation masks across the
video, refining the coarse localization cues according to polyp boundaries. WSPolypNet achieved CorLoc scores of 47.80%, 43.68%, and 35.01% at IoU thresholds of 0.3, 0.5, and 0.7, respectively, compared with 36.87%, 33.72%, and 27.94% in the single-view setting. For small polyps, the multi-view strategy improved CorLoc@0.5 from 16.01% to 30.97%. The framework also achieved a recall of 94.51%. These results demonstrate the potential of weakly supervised spatiotemporal learning to substantially reduce spatial annotation requirements for polyp localization in colonoscopy
videos.
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All authors Giseong Hwang, Minjae Jo, Yeonghyeon Park, Kyeonghun Kim, Seoyeon Han, Donghoon Han, Haneul Kim, Yului Jeong, Insung Hwang, Pa Hong, Ken Ying-Kai Liao, Nam-Joon Kim
Artificial Intelligence
Less Is Personal: Learning Minimal Sufficient User Profiles for Personalized Language Models
Minghang Liu, Qiang Qiu, Yuanzhuo Wang et al. (+2 more)
Retrieval-augmented personalization enables large
language models to produce more accurate and preference-aligned outputs using relevant records retrieved from user histories. Personalized
language models typically prepend a fixed number of retrieved user records, even when additional history is redundant, harmful, or unrelated to a user's distinctive behavior. We study minimal sufficient personalization: constructing the least costly ordered profile for each input while preserving the utility achievable from a retrieved candidate pool. We introduce ENOUGH, a method that iteratively appends behavioral records or emits STOP to construct profiles with adaptive lengths. Offline, bounded counterfactual search evaluates profile prefixes by jointly considering downstream gains, user specificity, and
token costs. The resulting long-horizon targets are distilled into a multi-head value controller with explicit ranking and stopping supervision. At inference, the controller selects and orders records through lightweight decisions, and the frozen generator is invoked once after stopping. Extensive experiments on six personalized tasks demonstrate that ENOUGH consistently outperforms strong heuristic and
retrieval-augmented baselines in both effectiveness and efficiency, achieving minimal sufficient profiles that preserve personalization utility while reducing unnecessary context costs.
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All authors Minghang Liu, Qiang Qiu, Yuanzhuo Wang, Huawei Shen, Xueqi Cheng
Data Structures and Algorithms
Beyond Cut Balance: Spectral Sparsification of the Nonlinear Directed Laplacian
Yuichi Yoshida
Digraphs with constant cut balance admit nearly linear directed cut sparsifiers. This condition requires the total arc weights in the two directions of every cut to be within a constant factor of each other. We ask whether this condition also permits nearly linear spectral sparsification with respect to the energy of the nonlinear directed
Laplacian. For a weighted digraph $G=(V,E,w)$, let \[ Q_G^+(x)=\sum_{(u,v)\in E}w_{uv}(x_u-x_v)_+^2, \qquad (t)_+:=\max\{t,0\}. \] This energy agrees with the outgoing-cut function on binary vectors. A spectral sparsifier is a nonnegatively reweighted subgraph that preserves $Q_G^+(x)$ within a factor of $1\pm\varepsilon$ simultaneously for all $x\in\mathbb R^V$. We show that cut balance alone does not yield nearly linear spectral sparsifiers: for constant error, the worst-case support size for simple unweighted Eulerian digraphs is $\widetilde\Theta(n^{3/2})$, although Eulerian digraphs are perfectly cut-balanced and admit nearly linear directed cut sparsifiers. In contrast, we prove that every $n$-vertex tournament has a spectral sparsifier with $\widetilde O(n/\varepsilon^3)$ arcs, without any assumption on its cut balance. This includes the transitive tournament, whose cut balance is unbounded. Thus perfect balance does not guarantee nearly linear spectral sparsification, while unbounded imbalance does not preclude it. Finally, we use convex duality to show that preserving $Q_G^+$ also preserves, for every feasible demand vector, the optimum quadratic cost of a nonnegative flow. Hence the guarantee contains information beyond directed cut values.
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Artificial Intelligence
A Theory of Reliable Self-Evolution for Agent Harnesses
Qianshu Cai, Yonggang Zhang, Jun Nie et al. (+6 more)
In harness self-
evolution,
agents modify their own
prompts, code, tools, and orchestration while keeping the underlying
language model fixed. Recent work has shown that
agents can improve themselves in response to task failures and achieve substantial performance gains. However, gains on failed tasks do not automatically ensure that performance on previously successful tasks is preserved, raising concerns about reliable adoption. In this work, we provide a theoretically grounded condition under which a self-evolved harness can be reliably adopted. We then propose a validation rule to make the evolved system satisfy the reliable adoption condition with theoretical guarantees. Consequently, the system can achieve progressive improvement through
evolution. This leads to a natural question: Does reliable self-
evolution have a performance ceiling, and which factors govern this ceiling? Our theoretical results show that the performance ceiling is determined by the costs of verification and evaluation. On the other hand, self-
evolution may stall in practice. In this regard, we show that experimental evidence on
agent performance shifts can be used to identify the sources of stagnation. Our work thus establishes a theoretical framework for understanding and advancing reliable harness self-
evolution.
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All authors Qianshu Cai, Yonggang Zhang, Jun Nie, Maohao Ran, Huajiang Zheng, Jun Song, Xinmei Tian, Yike Guo, Wei Xue
Artificial Intelligence
OntologyBench: Can Dense Retrieval Satisfy Structured Biomedical Constraints?
Xiao Yu Cindy Zhang, Wyeth Wasserman and Jian Zhu
We introduce OntologyBench, a tiered biomedical
retrieval benchmark comprising 471,854 training and 125,744 evaluation query-document relevance pairs across concept grounding, relational
retrieval, and compositional phenotype-based
retrieval. Although these tasks can be tractable using ontology-aware reference methods, across task tiers,
embedding performance is generally lower on relational and compositional tasks than on concept-grounding tasks. Fine-tuning on ontology-derived supervision improves performance on several relational and compositional tasks, whereas the evaluated reranking and LLM-based candidate-scoring methods provide little or no end-to-end improvement. Errors frequently reflect diseases matching only subsets of the phenotype evidence. These findings indicate that the evaluated
embedding and reranking configurations do not reliably recover the compatibility encoded by the selected ontology relations and phenotype combinations and motivate
retrieval systems that better integrate learned representations with structured biomedical knowledge.
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Artificial Intelligence
Key Path Identification for Resolving Knowledge Conflicts via SAE-based Steering
Wenbo Zhang, Zhongxiang Sun, Zhiguang Han et al. (+1 more)
Sparse autoencoder (SAE)-based steering has been widely used to address knowledge conflicts by guiding LLMs to be more faithful to the contextual knowledge. Existing methods usually perform mass steering, which modifies a large batch of SAE features identified via correlation-based methods. However, due to the inaccurate correlation and the neglected feature interactions, mass steering methods fail to precisely identify the features that play the key roles in steering and introduce a large number of redundant ones, which add noise and weaken the steering effects. Our empirical studies reveal that steering only a small subset of the identified features can achieve comparable or even better performance. Motivated by this finding, we propose Key Path Identification (KPI), a novel method that identifies key steering features characterized by strong causal dependencies with both upstream and downstream features. From these features, KPI constructs key paths and steers through less feature modifications. In this way, KPI advances SAE-based steering from quantity-driven to quality-focused, offering a perspective for more precise and interpretable model editing. Experiments in RAG tasks with knowledge conflicts show that our method improves the accuracy by 18% on average compared to the best baseline of mass steering, effectively filtering redundant features, alleviating side effects and demonstrating the core role of key paths in steering.
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All authors Wenbo Zhang, Zhongxiang Sun, Zhiguang Han, Jun Xu
Machine Learning · Cross-listed from stat.CO
Optimal Slice-Adaptive Tuning of Hybrid Slice Sampling
Trevor Campbell
Slice sampling is a Markov chain Monte Carlo
algorithm that draws its next state uniformly from a "slice"—a super-level set of the target density function—at each iteration, thereby providing automatic local adaptivity to the scale of the target. In practice the exact slice is not known, so general-purpose implementations use an approximate slice that is grown from a starting interval of length $w>0$, with a computational cost that depends on $w$. This work presents an analysis of the average per-iteration number of target density evaluations, as a function of $w$, of hybrid slice sampling with various slice-finding schemes for targets with contiguous slices. The paper uses the results of the analysis to develop automated, slice-adaptive tuning schemes along with suboptimality bounds and asymptotic convergence guarantees. Simulations demonstrate that the tuning schemes reliably yield near-optimal slice-adaptive tuning with essentially no dependence on the initial setting of $w$.
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Computation and Language
EviSI: An Evidence-Based Evaluation Agent for Simultaneous Interpreting
Ben Yan, Zongyao Li, Xiaoyu Chen et al. (+6 more)
Low-latency simultaneous
speech-to-
speech translation must keep pace with ongoing
speech while preserving key information. To meet these demands, systems use
segmentation, reformulation and condensation to reorganize and rephrase information. However, metrics developed for text translation, including BLEU and COMET, may not consistently distinguish faithful adaptations from semantic errors. We propose EviSI, a large
language model evaluation
agent combining Multidimensional Quality Metrics (MQM) with criteria developed with professional interpreters. Shared source evidence guides assessment across four dimensions: Anchor, Event, Logic and Fluency. Verified errors are deduplicated before deterministic scoring. On human-rated English to Chinese and Chinese to English data, EviSI recovers the aggregate English to Chinese human system ranking. Mean within-
dataset Kendall correlations for system rankings reach 0.707 and 0.467, respectively, exceeding evaluated BLEU and COMET baselines. A
multilingual extension to five directions without human ratings retains the dimensions and scoring rule, showing positive system ranking correlations with COMET throughout.
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All authors Ben Yan, Zongyao Li, Xiaoyu Chen, Daimeng Wei, Weidong Liu, Huan Zhao, Chong Li, Yaode Wang, Yuzhe Shang
Computer Vision
Boundary Voting Network for Ambiguity-Aware Timestamp-Supervised Action Segmentation
Runzhong Zhang, Yueqi Duan, Yang Chen et al. (+4 more)
Timestamp-supervised action
segmentation aims to segment and classify actions in untrimmed
videos with a random frame annotated per action. Precisely localizing action boundaries from timestamp annotations is crucial for this setting, as it enables generating framewise pseudo-labels and applying the well-explored fully-supervised training. However, prevailing methods struggle with intrinsic uncertainty in boundary localization due to less discriminative features in action-transiting regions. This imprecise boundary estimation significantly reduces the stability and reliability of the generated pseudo-labels in ambiguous action-transiting regions, consequently resulting in performance deterioration of the trained
segmentation models. In our paper, we introduce the boundary voting network that mitigates feature ambiguity by hierarchically propagating
video-level global prior knowledge into local action-transiting regions. By generating key action representations as votes throughout the
video and targeting action-transiting regions, all votes collaboratively contribute to action-transiting feature enhancement and boundary localization refinement. Extensive experiments demonstrate the effectiveness of our method on GTEA, 50Salads, and Breakfast
datasets.
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All authors Runzhong Zhang, Yueqi Duan, Yang Chen, Weipeng Hu, Chen Cai, Suchen Wang, Yap-Peng Tan
Artificial Intelligence · Cross-listed from q-fin.GN
AI for AI: Optimizing Additional Infrastructure Build-out to Power Artificial Intelligence Data Centers
Alexander Crosier, Kyle Onghai and Ronnie Sircar
The twenty-first century's transformative technology, artificial intelligence, is increasingly constrained by the twentieth century's transformative technology, the electricity grid. Rapid growth in electricity demand from data centers is leading to higher electricity prices, without a compensating supply-side response. We develop a framework linking data-center load growth, available generation capacity, and
market-clearing prices to understand this phenomenon. We first analyze a deterministic model to show how differing estimates of demand and supply growth rates affect prices. We then model the expansion of new data centers and their associated electricity demand, together with build-outs of new electricity supply, as
stochastic processes,resulting in probabilistic distributions of supply, demand, and prices rather than a single forecast. Finally, we formulate generation expansion as a
stochastic control problem in which a revenue-maximizing investor dynamically chooses the intensity of supply-side investments. The analysis highlights a central challenge of the data-center build-out: even when rapid demand growth increases the need for new generation, the uncertainties related to load forecasts, development execution risks, and value cannibalization from overbuilding capacity may weaken incentives to invest at the pace required to keep electricity prices stable.
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Machine Learning · Cross-listed from q-bio.GN
A Transformer-Based Delta Expression Encoder for Psilocybin Transcriptional Response: Architecture, Representations, and Biological Validation
Sai Jayakumar
Understanding why individuals respond differently to psilocybin requires modeling the drug's transcriptional perturbation signature at the
cell-type level. I present a
Transformer-based delta expression encoder that learns to classify differential gene expression status - upregulated, downregulated, or neutral - from single-
nucleus RNA-sequencing data, without supervision from pathway annotations or prior biological knowledge. The model is trained on pseudobulk profiles from 623 examples spanning 18
cell types, 2 drug conditions, and 6 timepoints derived from the Liao et al. 2025
dataset, and achieves 69.4% weighted classification accuracy. Three principal findings are reported, alongside one direct test of a published hypothesis that returned a result inconsistent with that hypothesis. First, per-
cell-type classification accuracy ranges from 28.3% (L2/3 IT, a primary HTR2A-expressing psilocybin target) to 99.6% (endothelial
cells), consistent with known psilocybin response biology. Second, psilocybin-induced transcriptional downregulation is significantly more stereotyped across individuals than upregulation (Mann-Whitney U=18615.0, p<0.0001), a novel finding with a cortical depth gradient across excitatory subtypes. Third,
attention-guided gene co-regulation analysis recovers drug-specific modules without pathway supervision. Separately, a direct test of whether baseline HTR2A expression predicts drug-response separability across
cell types found a significant negative correlation (Spearman r = -0.7088, p = 0.0021), the opposite of what a simple HTR2A-gating account would predict.
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Robotics
Dual-Layer Semantic-Spatial Belief Mapping for Aerial Object Goal Navigation
Jianqiang Xiao, Xiang Deng, Yuexuan Sun et al. (+3 more)
Aerial Object Goal
Navigation (ObjectNav) requires an unmanned aerial vehicle (
UAV) to locate a described target in an unknown outdoor environment using onboard visual observations.
Vision-
language models (VLMs) can interpret open-ended target descriptions and visual observations, but their frame-level outputs are often noisy, sparse, and spatially transient. We propose AeroBelief, a dual-layer semantic-spatial belief mapping framework that transforms transient VLM observations into persistent spatial guidance. It separates broad contextual plausibility from target-specific evidence: an intuition layer accumulates scene-level semantic cues for exploration, while an evidence layer preserves qualified target-specific observations for approach and confirmation. Evidence-gated fusion combines the two layers into spatial belief hotspots. We further introduce object-conditioned visual
reasoning with conservative evidence qualification to improve observation reliability before spatial accumulation. In parallel,
egocentric regional guidance converts quadtree coverage into
UAV-centered, yaw-aligned directional proposals and stabilizes them through temporal commitment. Its regional scoring is independent of semantic belief values, maintaining exploration pressure and reducing repeated low-gain search. Experiments on the
UAV-ON
benchmark show that AeroBelief achieves the best reported overall SR, OSR, and SPL among the compared methods, reaching 21.61%, 35.57%, and 10.62, respectively. These results support the effectiveness of persistent semantic-spatial belief, conservative evidence qualification, and temporally stable regional guidance for aerial ObjectNav.
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All authors Jianqiang Xiao, Xiang Deng, Yuexuan Sun, Yanjin Wu, Wenbiao Yan, Liqiang Nie
Artificial Intelligence
WorldAgen: Unified State-Action Prediction with Test-Time World Model Training
Chi Wan, Kangrui Wang, Yuan Si et al. (+2 more)
How can
vision-
language-action (VLA) models adapt to new environments where world dynamics shift? While recent research has combined world modeling and action prediction to improve VLA performance, existing methods largely rely on pretraining on static
datasets, without mechanisms for active adaptation at deployment time. As a result, these models often fail to generalize when deployed in unseen scenarios with novel object configurations or dynamics. We present WorldAgen, a unified framework that jointly learns world modeling and action prediction while enabling Test-Time Training (TTT) to adapt to new environments. WorldAgen employs a shared
Transformer backbone with two heads: (1) a world model head that predicts future states from past state-action trajectories, and (2) an
agent model head that predicts actions conditioned on task instructions. We design a Mixed Unidirectional
Attention Mask to separate these two models. During test time, WorldAgen samples exploratory actions, collects ground-truth state transitions, and performs lightweight TTT updates to refine its world model. This adaptation improves the model's understanding of the environment and leads to more accurate action predictions. Experiments on the CALVIN and LIBERO
benchmarks demonstrate that our baseline model achieves comparable, and in some cases superior, performance to current state-of-the-art approaches. Moreover, with TTT on a small number of samples, our method surpasses existing state-of-the-art models, highlighting the effectiveness of adapting world models at inference time.
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All authors Chi Wan, Kangrui Wang, Yuan Si, Pingyue Zhang, Manling Li
Computation and Language
Snugi-AI-v2 @ eRisk 2026 Task 2: Early Depression Detection via a Learned Stopping Policy with Sustained Confidence Gate
Yuwen Chiu
We describe the Snugi-AI-v2 submission to eRisk 2026 Task 2, the second edition of contextualized early depression detection from Reddit discussions. Our central contribution is a learned MLP stopping policy trained to directly optimize ERDE50, replacing the fixed and tiered threshold strategies used in all prior eRisk Task 2 submissions. Combined with a sustained confidence gate that commits only after N=3 consecutive rounds of high policy confidence, the system reduces false positives caused by transient emotional posts without sacrificing recall. The pipeline encodes each discussion thread with a frozen MentalRoBERTa model, maps the accumulated representation to a depression probability via an MLP classifier, and delegates the timing decision to the learned policy. Our best run achieves F1 = 0.73 (Run 1) and F_latency = 0.70 (Runs 0 and 3), with a median alert round of 8 out of 500, completing the full evaluation in 1 hour 26 minutes, the fastest among all complete-submission teams. We report a systematic ablation across five runs spanning two encoder variants, four stopping strategies, and three gate values, along with negative results from GRPO policy training, BDI-II post filtering, MentalLongformer encoding, and DeBERTa ensembling. Code: https://github.com/chiuyuwen91/erisk-2026
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Databases
Generalized DBLog: A Verified Contract for Interleaving Copied Rows with a Change Log
Andreas Andreakis
Change-data capture (CDC) feeds downstream systems like caches, search indexes, and data warehouses from a database's log of committed row changes. When bootstrapping, adding a table, or repairing downstream data, a pipeline must also copy existing rows. Merging this copy with the active log introduces the copy-to-log handoff problem. Changes must not fall through a gap, and older copied state must not overwrite a newer logged update or resurrect a deleted row. DBLog, developed at Netflix, addressed this problem by reading tables in chunks and interleaving those reads with the live log. Watermarks identify the changes that overlap each read, and the log wins when a copied row is stale. Debezium and Flink CDC have since adapted this design. Earlier work proved that applying the original
algorithm's copied rows and logged changes in their emitted order reconstructs the source's rows, including the effect of every logged insert, update, and delete processed. Generalized DBLog asks when the same result holds for variants of that design. We state the conditions the source and capture implementation must satisfy. Once copying and reconciliation are complete, we prove that the result holds across all selected tables and key ranges even when their rows were read at different times. A single database snapshot is not required for the copy. Further logged changes advance the reconstructed state one event at a time. We establish these guarantees for classic watermarking, Debezium's signal-table and read-only modes, Flink CDC's parallel chunks, reads and dumps tied to exact log positions, and engine-consistent backups whose log position lies within known bounds. The complete theory is machine-checked in Isabelle/HOL, its core independently verified in Lean 4, and the protocols are also examined by bounded model checking in TLA+.
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Robotics
OmniNav: Robust Long-Horizon Target Navigation in Dynamic Environments
Yujie Tang, Meiling Wang, Jinhao Jiang et al. (+4 more)
Long-horizon target
navigation requires a
robot to sustain task execution across evolving observations, decisions, and physical interactions. This requires three coupled capabilities: maintaining valid scene
memory, revising target beliefs under partial observability, and selecting interaction-feasible
navigation endpoints. However, the state underlying each capability is only conditionally valid: scene representations become stale when objects move or disappear, unsuccessful searches alter beliefs over target locations, and geometrically convenient endpoints may still be infeasible for manipulation. To address these challenges, we present OmniNav, which formulates long-horizon
navigation as continual inference over a factorized task state posterior coupling scene validity, target belief, and interaction feasibility. For representation, OmniNav incrementally constructs an updatable 3D object scene
memory, preventing stale scene evidence from propagating to subsequent decisions. For exploration, it introduces an evidence-aware
Bayesian belief-revision mechanism that derives dependency-aware region priors from semantic context, incorporates unsuccessful searches as negative evidence, and updates them for posterior-guided frontier selection. For interaction, OmniNav incorporates manipulation reachability and collision constraints into
navigation-endpoint selection and propagates execution feedback through hierarchical closed-loop recovery. Extensive experiments demonstrate that OmniNav achieves the highest success rates among the compared methods on semantic ObjectNav and fine-grained instance
navigation benchmarks, remains robust to target relocation, and improves real-world pick-and-place success from 53.3% to 71.7% over an adapted open-loop baseline. The project page of OmniNav is available at https://omni-nav.github.io/.
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All authors Yujie Tang, Meiling Wang, Jinhao Jiang, Sibo Zuo, Yinan Deng, Xinyu Zhang, Yufeng Yue
Computation and Language
When Metrics Reward the Worst Translations: Internalizing Cultural Reasoning for Social Media Translation Evaluation
Yiwen Qiu, Linjuan Wu, Dingming Li et al. (+7 more)
Automatic translation quality metrics trained on general-domain corpora systematically fail on social media content, where communicative intent is encoded in culturally loaded expressions (internet slang, homophonic ciphers, and platform-specific idioms) rather than surface
token patterns. We conduct a systematic empirical analysis demonstrating that standard metrics including COMET, XCOMET, and BERTScore exhibit near-zero or negative correlation with human cultural judgments, and even display a severity inversion in which scores increase as translation quality deteriorates. We further show that this failure extends to large
language model judges: Qwen3-235B achieves Cohen's kappa of only 0.162, revealing that the bottleneck is not
reasoning capacity but cultural grounding: models lack the domain-specific cultural knowledge needed to identify which aspects of a translation require scrutiny. To address this, we propose CuRIL, a reinforcement learning framework that internalizes cultural
reasoning: cultural annotations are prepended inside the model's
reasoning, excluded from policy gradients via a
token-level loss mask, and injected with a probability that decays to zero over training, progressively forcing autonomous cultural judgment. On a 1,444-sample human-annotated social media translation
benchmark, Qwen3-8B trained with CuRIL achieves Cohen's kappa 0.370 and Exact Match accuracy of 45.22%, approaching Gemini-3.1-Pro with 30x fewer parameters and surpassing models up to 235B in scale. We further demonstrate that our judge produces reliable reward signals for downstream translation optimization, reducing the low-quality translation rate by over 20 percentage points under independent human evaluation.
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All authors Yiwen Qiu, Linjuan Wu, Dingming Li, Yizhou Liu, Zixuan Wang, Haolei Xu, Ye Guo, Daoxin Zhang, Weiming Lu, Yongliang Shen
Computer Vision
SciFigure2Code: An AI-Reconstructed Benchmark for Scientific Figure-to-Code
Wentao Li, Yibo Wu, Yizhe Chen et al. (+5 more)
Scientific figures are the interface through which research claims are inspected and reused, but final published panels rarely expose the data or plotting code that produced them. Recovering this hidden provenance from pixels is therefore underdetermined. We introduce SciFigure2Code, an AI-reconstructed
benchmark that evaluates presentation recovery: generating editable Python programs that preserve how a scientific panel is arranged and read. Role-specialized Codex
agents generate, execute, visually refine, and audit silver-standard presentation programs that capture geometry, visual hierarchy, encodings, annotations, and typography. This reconstruction-and-audit protocol turns final published panels into auditable reference packages; the resulting resource contains 6,740 reviewed panels and SciFigureBench, a balanced 337-panel test set across 31 chart subtypes, five domains, and three complexity levels. Across 14 zero-shot models, execution and structural fidelity remain weak; a 328-panel full-figure study further exposes failures to locate and isolate requested panels. Claude Opus 4.7 leads image-only reconstruction, Claude Opus 4.6 leads caption-assisted reconstruction, and plan-then-code prompting improves all four tested models. SciFigure2Code provides an auditable testbed for constructing editable, visually faithful scientific figure presentations.
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All authors Wentao Li, Yibo Wu, Yizhe Chen, Ruixuan Chen, Jiangjie Qiu, Yijun Li, Zhao Leyi, Xiaonan Wang
Information Theory
Function Tables for Secure Distributed Matrix Multiplication
Rafael G. L. D'Oliveira, Giulia Gaggero, Arturo Jaramillo Gil et al. (+3 more)
We introduce function tables, an entrywise representation of the coefficient functions that appear in the worker responses of a secure distributed
matrix multiplication (SDMM) scheme. We work under the outer-product partition, with $K$ row blocks, $L$ column blocks, and
privacy against any $T$ colluding workers, in the general model of linear encoding and linear decoding. In this representation,
privacy is a rank condition on the data and mask coefficients, and decodability is linear independence of the desired entries modulo the nuisance space. Degree tables, cyclic-addition tables, and algebraic-geometry constructions are the special cases obtained by restricting the coefficient functions to a structured family; we impose no such restriction, so our converses bind every linear scheme. For $T=1$, we determine the exact optimum over every finite field $\mathbb{F}_q$: it is $KL+K+L$ when $q\geq3$, and $KL+K+L+1$ over $\mathbb{F}_2$, where the identity $z^2=z$ forces one more worker. For arbitrary $T$, we prove $N\geq KL+K+L$ and $N\geq\max\{K,L\}+T$ with no MDS hypothesis on the masks; the first is stronger than the previously known bound $KL+\max\{K,L\}+2T-1$ whenever $\min\{K,L\}\geq2T$. We then reduce field feasibility exactly to MDS existence: a scheme exists over $\mathbb{F}_q$ if and only if an $[\max\{K,L\}+T,T]$ linear MDS code does, and whenever it does, a Cartesian construction attains $N=(K+T)(L+T)$ over that same field. For $T=2$ this makes $q\geq\max\{K,L\}+1$ necessary and sufficient, and we give a projective-line construction with $N=KL+K+L+2$ whenever $KL+K+L$ divides $q-1$; for $K,L\geq2$ it matches the best known worker count while requiring only an element of order $KL+K+L$.
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All authors Rafael G. L. D'Oliveira, Giulia Gaggero, Arturo Jaramillo Gil, Hiram H. López, Cecilia Martínez-Reyes, Divyesh Vaghasiya
Machine Learning
Geodesic-informed Generative Diffusion Model For Topology-preserved Image Video Generation
Nian Wu, Nivetha Jayakumar, Jiarui Xing et al. (+1 more)
Generative
diffusion models have emerged as a class of powerful techniques for various imaging applications, including but not limited to synthesis, reconstruction, and
segmentation. Despite their success, current generative models pose two key limitations. First, they primarily rely on image intensity and texture information, with limited
attention to underlying object geometry. As a result, they do not guarantee
geometric or topological consistency during the generation process, which is a crucial requirement for high-stakes domains such as computational anatomy, biology, and robotics, where preserving object structure is critical. Second, existing models fail to explicitly learn or represent shape changes in the generative process. Such deformation dynamics remain occluded within network parameters; hence leaving the transformation process uninterpretable and physically uninformed. To address these challenges, we introduce IGG (Image Generation informed by Geodesic dynamics), a novel framework that integrates
topology-preserving geodesic principles into the
diffusion-based generative process. In contrast to conventional methods that operate in image intensity space, IGG learns and synthesizes diverse samples within geodesic deformation spaces, where
geometric object changes are learned as smooth and invertible smooth mappings from a given template/source image. Our code is publicly available at https://github.com/nellie689/IGG.
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All authors Nian Wu, Nivetha Jayakumar, Jiarui Xing, Miaomiao Zhang
Machine Learning
Topology-induced Operators Reveal Complementary Graph Representations without Training
Meng Qin, Jinqiang Cui, Hongwei Zheng et al. (+2 more)
Graph representation learning has largely focused on designing increasingly sophisticated models to transform graph
topology into vector representations, or
embeddings. However, the extent to which
embedding quality depends on model learning, rather than on the underlying topological transformations, remains unclear. Here, we show that informative
embeddings can be derived without complicated model design and gradient-based training. Propagating random features through implicit hierarchical structures induced by random walks and anonymous walks yields
embeddings that capture node proximity and structural role, respectively. These two training-free
embeddings preserve complementary aspects of graph organization and perform competitively with classic and recent methods across various node-, edge-, and graph-level tasks. They often require substantially less computation, resulting in a favorable quality-efficiency trade-off. Combining the two types of
embeddings further improves inference quality of some tasks compared with using either
embedding type alone. Our results suggest that informative graph
embeddings can arise from carefully chosen topological transformations before any learning operation is applied.
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All authors Meng Qin, Jinqiang Cui, Hongwei Zheng, Weihua Li, Sen Pei
Artificial Intelligence
SWE-Bench Pro Verified: A Reliable Benchmark for Software Engineering Agents
Pujun Zheng, Zixin Shang, Shufan Jiang et al. (+5 more)
SWE-Bench Pro has emerged as a standard
benchmark for evaluating software engineering
agents on challenging repository-level tasks. However, our analysis work show that its evaluation is undermined by two sources of unreliability: reward hacking, enabled by leakage of gold solutions or hidden evaluation information, and task quality issues, including misleading problem statements and improperly scoped tests. These issues can inflate
benchmark performance and obscure
agents' true coding ability. We present SWE-Bench Pro Verified, a verified version of SWE-Bench Pro that addresses both problems. Our approach combines anti-hacking safeguards that eliminate major leakage channels without disrupting normal
agent functionality, with task refinement that minimally corrects inconsistencies within flawed instances. Evaluations on SWE-Bench Pro Verified reveal that some models perform substantially worse than previously evaluated, suggesting that existing results on SWE-Bench Pro may overestimate real software engineering capability. SWE-Bench Pro Verified offers a more trustworthy
benchmark for assessing software engineering
agents.
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All authors Pujun Zheng, Zixin Shang, Shufan Jiang, Wenhui Tian, Dongsheng Zhu, Zerun Ma, Dingbo Yuan, Qi Zhang
Computer Vision
MRI-Guided Reslice-Refined Cross-Slice SDF Reconstruction of the Left Ventricle from Cardiac MRI with Sparse Axial Supervision
Quanxin Zheng and Shuai Zhao
Reconstructing a three-dimensional left-ventricular (LV) endocardial surface from cardiac
magnetic resonance (CMR) data is challenging when supervision is available on only a small number of axial slices. Through-plane geometry is weakly constrained, and automatically generated two-dimensional masks can propagate
segmentation errors into the recovered shape. We present MR-RS-SDFR, a per-case implicit signed distance field (SDF) framework that reconstructs a continuous LV surface from a CMR volume and sparse axial weak masks. The method first builds a cross-slice SDF initialization from axial and longitudinal
geometric cues and then refines the field using two complementary signals: MRI edge-field normal
alignment, which provides an image-derived boundary cue independent of the weak masks, and differentiable reslice Dice and contour consistency, which preserve agreement with the observed planes. We evaluate three weak-mask generators – LOO TransUNet, LOO nnU-Net, and an off-the-shelf Medical SAM3 model used without MM-WHS-specific training or fine-tuning – and five sparsity levels from 4 to 64 axial planes. In the sparse-16 setting, final MR-RS-SDFR reconstruction reaches 0.928 Dice and 3.80mm HD95 with Medical SAM3 masks. The upstream generators do not exhibit a single common ranking across 2D and dense 3D
segmentation, and nnU-Net- and Medical-SAM3-driven sparse reconstruction achieve the same mean final Dice despite different upstream error profiles. Across all three sparse-16 mask sources, MR-RS-SDFR is numerically better than protocol-matched full GHD+DVS in both Dice and HD95. Final Dice improves markedly from sparse-4 to sparse-16 and then saturates at the reported precision through sparse-64. These results support MRI-guided per-case SDF refinement as a reconstruction strategy that remains effective across weak-mask generators and supervision densities.
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Computation and Language
ConversationalVoice: Full-Duplex Speech Data from Real Conversations through Source-Faithful Reconstruction and Conversation-Grounded Expansion
Richard Yucheng He, Baodong Cao, Chen Xu et al. (+2 more)
Full-duplex
speech models require training data that preserves turn-taking, overlap, interruption, and backchannel behavior, yet these signals are entangled across speakers in noisy real-world recordings. We present Conversational Voice, a pipeline that converts real two-speaker excerpts into three complementary training-data artifacts. (1) Separation recovers speaker-specific tracks with stable speaker assignments, a canonical transcript, and naturally observed interaction timing. (2) Reconstruction generates
speech in matched voices from a fixed source transcript, reconstructs the source turn order, pauses, and overlaps, and adds word-level
alignment and delivery instructions. (3) Expansion generates new dialogue constrained by the source context, speakers, and observed interaction pattern. Automatic speaker-verification metrics remain strong across stages, with same-speaker similarity of 0.983-0.991 and positive discrimination margins of 0.199-0.209. Predicted
speech quality (NISQA MOS) is 3.56 for separation, 4.41 for reconstruction, and 4.61 for expansion. A Gemini-based automatic evaluator assigns expansion mean scores of 4.94/5 for contextual coherence and 4.80/5 for dialogue naturalness. Expansion and reconstruction exhibit broadly similar interaction profiles; expansion's turn, overlap-event, backchannel, and interruption rates are 4.6%, 8.0%, 13.2%, and 16.0% lower, respectively. We evaluate data properties only; downstream gains in full-duplex model training remain for future work.
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All authors Richard Yucheng He, Baodong Cao, Chen Xu, Yihang Liu, Tairan Chen
Networking
Information-Entropy-Driven Fault Propagation Modeling for Probabilistic Network Performance Prediction
Lusha Mo, Fengxiao Tang, Xiaonan Wang et al. (+1 more)
Network faults can trigger cascading effects that cause abrupt and nonstationary performance degradation. Existing learning-based performance predictors mainly focus on normal operation or treat fault-induced
topology and routing changes as static inputs, and typically produce deterministic point estimates. They overlook fault-propagation dynamics and uncertainty in performance
evolution. The predefined-rule and purely data-driven propagation models lack a unified representation of fault definition, propagation mechanism, and impact quantification. Additionally, generic denoisers in conditional
diffusion models fail to incorporate fault propagation into uncertainty modeling. To address these limitations, we propose an information-
entropy-driven fault propagation paradigm (IEFP) that characterizes fault propagation via relative
entropy, mutual information and transfer
entropy. We then design a fault-aware graph message-passing mechanism that propagation contexts modulate network representation learning. We further develop FEMNet, which employs this mechanism as a tailored denoiser within a conditional
diffusion model to enable probabilistic network performance prediction under complex fault scenarios. Compared with the strongest baselines, IEFP improves fault-prediction performance, while FEMNet reduces errors in both point and probabilistic KPI prediction.
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All authors Lusha Mo, Fengxiao Tang, Xiaonan Wang, Ming Zhao
Computation and Language
IGT @ FinMMEval 2026 Task 2: Question-Type Prompting with Targeted Extraction for Multilingual Financial QA
Yuwen Chiu
We present the IGT system for PolyFiQA Task 2 of the FinMMEval Lab at CLEF 2026, a
multilingual financial question answering task over English SEC filings and
multilingual news articles (English, Chinese, Japanese, Spanish, Greek) for four companies. Our central observation is that the 344 development questions divide into two families requiring fundamentally different approaches: structured numeric types (R&D ratio, cash flow, capital expenditure) are best answered by direct keyword extraction on filing text, while synthesis types (investment strategy, capital allocation, top-three revenue focuses) require rule-based
multilingual news passage selection. A
dataset analysis reveals that 17-18 of 19 ground-truth reference answers per synthesis type share an exact evidence label prefix, whose unigram
tokens contribute directly to ROUGE-1 overlap. The final system achieves development ROUGE-1 approximately 0.395, a 60% relative improvement over a generic RAG baseline (approximately 0.247), and ranks 3rd of 12 teams on the official test set with ROUGE-1 = 0.3071, Precision = 0.2821, and Recall = 0.4044.
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