Computer Vision
REMEDY: How Far Is Video Generation from Medical Education World Models?
Lixing Tan, Yanghao Zhou, Qing Xia et al. (+3 more)
Recent
video generation models produce realistic
videos and show potential as a foundation for world models. These advances create opportunities for generating medical teaching demonstrations, which requires both convincing visual quality and precise procedural actions. However, whether current generators can meet these requirements has not been measured. To address this problem, we introduce Readiness Evaluation of Medical Education Demonstration sYnthesis (REMEDY), to our knowledge, the first
benchmark for AI-generated medical teaching demonstrations. REMEDY provides 900 first frames from real demonstration
videos, covering 12 tasks across four scenarios: operating room, imaging, clinic and bedside, and resuscitation. Five contemporary open-source
video generation models produce 4,500
videos from these frames. We combine task-specific clinical checklists with
video and motion quality metrics. Evaluation covers four dimensions: clinical action following, clinical profiles,
video quality, and motion quality. Our results show that realistic appearance and temporal consistency do not ensure correct clinical actions. Even the most advanced MiniMax-H3 achieves only 28.25% on strict clinical success rate, and fine-grained clinical actions remain challenging. These findings establish a foundation and roadmap for developing future medical education world models.
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All authors Lixing Tan, Yanghao Zhou, Qing Xia, Yuting Guo, Shuai Li, Aimin Hao
Software Engineering
Beyond the Model: Demystifying Harness Effects in Software Engineering Agents
Haichuan Hu, Quanjun Zhang, Shengcheng Yu et al. (+5 more)
Large
Language Model (LLM)-based
agents are increasingly used for software engineering tasks, yet their performance is not determined by the base model alone. The
agent harness substantially shapes how SE
agents interact with repositories, execute actions, and validate solutions. However, the role of harness design remains insufficiently understood, especially across different models, tasks, and harness components. In this paper, we present a systematic empirical study of harness effects in SE
agents. We first evaluate two representative harnesses, mini-SWE-
agent and OpenCode, with ten models from two prominent open-weight model families, Qwen and DeepSeek, on three
benchmarks: SWE-bench Pro, ProgramBench, and GitTaskBench. We then construct NanoHarness, a lightweight modular harness built on top of mini-SWE-
agent, and use it to analyze five representative harness components: tool registry, context compression, explicit planning, subagents, and lazy skills. Experimental results show that harness effectiveness depends jointly on model capability and task type. Complex harnesses provide diminishing marginal gains on SWE-style issue repair as model capability improves, but can benefit stronger models on more complex and open-ended repository-level tasks. Component-level analysis on ProgramBench further shows that structured tool use and task-specific subagents provide the most stable improvements, while context compression and general subagents can hurt repository-generation performance. When combined, NanoHarness improves over mini-SWE-
agent by 7.37 and 6.21 percentage points on Qwen3.7-Max and DeepSeek-V4-Pro, respectively, recovering most of the gains of product-level harnesses. These findings highlight harness design as a first-class factor in SE-
agent performance and provide insights for building more effective and efficient coding
agents.
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All authors Haichuan Hu, Quanjun Zhang, Shengcheng Yu, Zhifei Chen, Tianyu Luo, Chunrong Fang, Zhenyu Chen, Liang Xiao
Computation and Language
Streamlined Reflective Evolution for Task-Adaptive Self-Refinement Pipelines
Xiaofan Zhou and Lu Cheng
Reflective
prompt optimization improves large
language model (LLM) systems without updating model weights, but fixed architectures constrain how self-refinement is organized. We introduce Workflow-Designing
Agents (WDA), a framework for streamlined reflective
evolution of task-adaptive self-refinement pipelines. Starting from a minimal
prompt, WDA jointly evolves stage instructions and their sequential structure. During
evolution, we find that repeated revisions can accumulate redundant instructions in a single
prompt. In WDA, we propose to address this problem with SPLIT, which redistributes these instructions across specialized stages. Three-example reflection and local screening guide selective search, while calibration scores guide Pareto admission and rollback of unhelpful trailing updates. The resulting pipelines are task-adaptive: their instructions and depth are learned from task data, then fixed for all test inputs within that task. We evaluate WDA on five
benchmarks spanning knowledge, mathematical
reasoning, multi-hop question answering, and instruction following. On Qwen3.5-9B, WDA achieves an average score of 51.24%, improving over the initial solver by 8.63 percentage points and the variant without SPLIT by 2.60 points. On GPT-4.1-mini, it achieves 49.00%, with corresponding gains of 5.62 and 3.69 points. These results support task-adaptive self-refinement as a complementary direction to broader agentic workflow search.
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Machine Learning
Write Back the $\Delta$: Revisiting the Same Tokens with Fresh Representations
Wencheng Ye, Anning Hu, Xiangdong Zhang et al. (+5 more)
Transformers process information strictly forward through depth, preventing deeper computation from revisiting and refining earlier representations. To augment the standard forward pass, existing approaches either re-execute depth, incurring additional computation, or modify the residual stream using predefined directions, limiting their instance-level adaptation. Recently, inference-time feedback offers a direct mechanism for recycling endogenously produced computation by writing deeper residual states back to earlier layers, yet what should be fed back remains unclear. We argue that the depth increment Delta, capturing newly accumulated computation between two layers, provides a more effective, composable, and scalable feedback signal than the full state. Building on this observation, we introduce ReFlux, a learnable feedback graph that dynamically selects and composes increment-carrying routes. ReFlux supports synchronous feedback to the same
token and streaming feedback to subsequent
tokens. Extensive experiments across various models, corpora, and
benchmarks show that synchronous ReFlux consistently reduces perplexity across ten
language-modeling corpora, and improves accuracy by 2.1-2.3 points, with gains reaching 4.7 points on multi-hop
reasoning. Streaming ReFlux further retains most of these gains while preserving the base model's 1x theoretical backbone FLOPs. These results establish ReFlux as an efficient paradigm for unlocking the latent computational potential of LLMs, allowing them to revisit the same
tokens with fresh representations. Code implementation can be found at https://github.com/gooogleshanghai/reflux.
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All authors Wencheng Ye, Anning Hu, Xiangdong Zhang, Tianyi Wang, Yikang Li, Hengyu Jin, Bing Li, Junchi Yan
Computer Vision
Seeing Parts, Reasoning about Worlds: Visual Inference under Partial Observation
Wei Wang, Wenqiao Zhang, Yutong Lin et al. (+2 more)
World modeling under partial observation requires
reasoning about the complete worlds that remain compatible with limited visual evidence. Occluded objects and unseen regions can leave several world states possible; additional views can exclude alternatives and strengthen the conclusions supported by the observations. We introduce WorldScope to study this process through possible-world semantics, evidence-grounded data, and learned visual representations. WorldScope-1.2M provides 1.2 million English question-answer pairs spanning eight world properties, ten task interfaces, and three observation protocols. Its answers encode confirmed facts, supported bounds, and unresolved possibilities. Complementary supervision comprises 4,800 certified counterworld groups with equivalent base observations and different hidden object configurations and query answers. These groups provide physical witnesses of ambiguity and training-only labels for world compatibility and view-induced exclusions. We propose WorldFlow, which composes cross-view entity evidence and support-surface coverage into an image-subset evidence
lattice. Counterworld compatibility and transition objectives train subset representations to reflect how new observations constrain possible worlds. A shared answer generator uses these representations to predict the strongest supported conclusion. WorldScope-Bench evaluates claim judgments and evidence-dependent conclusions as views are selected, combined, removed, or ordered. On its 5,000-question test set, WorldFlow reaches 64.34% exact accuracy, improving over the same backbone trained on QA alone by 24.88 percentage points. It retains 50.43% accuracy on the 3,000 questions from structure-disjoint scenes.
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All authors Wei Wang, Wenqiao Zhang, Yutong Lin, Jun Xiao, Yueting Zhuang
Computer Vision
QuacamFM: Quaternion-Constrained Flow Matching for Camera Pose Estimation
Bao-Long Tran, Cuong Le, Tahereh Dehdarirad et al. (+2 more)
Camera pose estimation from multi-view images remains a challenge in computer
vision. Traditional methods often address this problem using Structure-from-Motion (SfM) with bundle adjustment. However, camera poses estimated from sparse views are inherently ambiguous due to insufficient
geometric constraints. Recent work leverages probabilistic models, such as
diffusion models, to generate multiple camera pose hypotheses and therefore capture this uncertainty better. Most of these methods represent camera rotations using unit quaternions, but treat them as unconstrained 4D vectors during the generative processes, thereby ignoring the unit-norm constraint of quaternions. Unconstrained quaternions create non-smooth and suboptimal generation trajectories. To this end, we propose *QuacamFM*, a quaternion-constrained flow matching framework for camera pose estimation that preserves unit quaternion representations throughout the entire flow trajectory. We design the optimal transport of the quaternion flows using smooth spherical linear interpolation. Experiments on CO3Dv2 demonstrate our method's advantage in camera pose accuracy over
diffusion-based methods and classical SfM approaches. We further show that our quaternion-constrained formulation outperforms the naive application of standard flow matching to 4D quaternion vectors on sparse-view camera pose estimation. Finally, it is observed that QuacamFM generalizes well across
datasets and in-the-wild examples.
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All authors Bao-Long Tran, Cuong Le, Tahereh Dehdarirad, Fredrik Viksten, Per-Erik Forssén
Computer Vision
De-biasing Skeleton-based Action Recognition with Convex Hull Adaptive Shift
Mengyuan Liu, Yuhang Wen, Yi Zhang et al. (+4 more)
Skeleton sequences can represent both individual actions and multi-entity interactions, encompassing human bodies, hands, objects, and
robots. Existing approaches to recognize skeleton-based actions and interactions usually adopt a late fusion strategy, which expects individuals are independent and identically distributed to train a robust weight-shared entity encoder. However, observed entity bias in various skeletal data violates this assumption, leading to suboptimal optimization of backbone models that might produce wrong recognition results. This bias arises from the world coordinate system's initial configuration, where the choice of origin often creates bias in representation. To this end, we propose a Convex Hull Adaptive Shift based normalization method to reduce Entity bias (CHASE), improving performance across a variety of skeleton-based action and interaction recognition tasks. To adaptively apply plausible shifts to the input skeletons, we formulate a plug-and-play parameterized network that ensures the relocated world origin lies within the skeleton convex hull, which avoids non-convergence by limiting the search space. To further minimize entity bias, we incorporate an auxiliary objective that leverages pair-wise distribution distances to guide network optimization. To support both single- and multi-entity actions, we propose a sub-entity strategy that offers a consistent formulation for both scenarios. Moreover, CHASE demonstrates compatibility with various intra-skeleton modalities, such as bones and velocities, highlighting its adaptability. Essentially, our method works as a normalization approach to reduce entity bias, enabling subsequent classifiers to achieve improved recognition performance across diverse settings. Extensive experiments on 7
datasets verify our approach by seamlessly integrating with various backbones and significantly boosting their performance.
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All authors Mengyuan Liu, Yuhang Wen, Yi Zhang, Songtao Wu, Hong Liu, Junsong Yuan, Beichen Ding
Robotics
DRAM: Delta-rule Recurrent Associative Memory for Robot Manipulation Policies
Xinyu Zhao, Yixiang Shan, Tao Yang et al. (+5 more)
Robotic manipulation is inherently history-dependent, yet most pretrained robotic policies condition on only the current observation or a short temporal window. Equipping such policies with long-term
memory remains challenging: existing approaches either feed the backbone multi-frame observation windows, which substantially increase inference cost, or rely on pre-defined semantic features, which limit task generality and may also require the retraining of the backbone to adapt to the
memory. We introduce DRAM (Delta-rule Recurrent Associative
Memory), a plug-and-play
memory module that can be attached to a wide range of pretrained robotic policies, endowing them with long-horizon
memory without architectural modification or backbone retraining, requiring only task-specific post-training of the
memory module and action expert. DRAM maintains a fixed-size associative
memory using gated delta-rule linear
attention, with a modified update that incorporates all
tokens within each frame in parallel. An architecture-agnostic readout integrates historical context into action prediction across different policy architectures. Experiments show that DRAM consistently improves frozen pretrained policies over short-context baselines and alternative compact
memory designs, validating its effectiveness as a fixed-size, post-hoc
memory module trained with the backbone frozen.
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All authors Xinyu Zhao, Yixiang Shan, Tao Yang, Runyu Lei, Yiming Zhao, Jiaxin Fan, Zongbao Feng, Peng Jia
Computation and Language
What Should the Reflector See? An Empirical Study of Evidence in Reflective Prompt Optimization
Xiaofan Zhou and Lu Cheng
Reflective
prompt optimization revises instructions using examples of a model's behavior, but which evidence the reflector should receive remains unclear. We study evidence composition, visibility of examples, candidate selection and domain-knowledge policy within a single-parent Pareto-guided search. Using Qwen3.5-9B as both task model and reflector, we evaluate nine reflection strategies on five
datasets. From these experiments, we find three distinct patterns. For performance improvement, Failures-only produces the largest mean test gain (+8.0 percentage points), while Balanced-mix and No-examples+Val share the best mean performance rank. For reflection effectiveness, No-examples achieves the best rank for improving sampled parents, yet yields only a 1.4-point mean test gain: local reflection success does not necessarily produce a stronger final
prompt. For overfitting assessment, Failures-only and Balanced-mix share the lowest mean calibration-gap rank, while larger gaps on GPQA and IFBench show that calibration gains can overstate held-out improvement. This gap is a descriptive indicator, not a direct measure of overfitting. Together, these results show why reflection strategies should be assessed separately on final performance, parent improvement and calibration-to-test transfer.
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Computation and Language
Self-Reports Do Not Identify Self-Models: An Identifiability Test for Counterfactual Reports
Phongsakon Mark Konrad, Toygar Tanyel and Serkan Ayvaz
Language-model self-reports are evidence about behavior in a
prompt environment, not by themselves evidence of a self-model. We investigate counterfactual reports about affect-like states under activation interventions and ask whether the report remains bound to the named intervention when the demonstration environment changes. Across three open instruction models, wrong-source demonstrations move reports toward the source answer family, while explicit mechanism binding reduces this pull. Self-report
benchmarks should include environment-shift invariance tests under fixed intervention before treating accuracy as evidence for an autonomous report mechanism.
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Artificial Intelligence
ForkLeft: Entropy-First Rollouts for Prefix-Aligned Autoregressive-to-Diffusion Distillation
Junming Liu, Jicheng Wang, Yifeng He et al. (+2 more)
Autoregressive Next-
Token Prediction (NTP) has enabled strong
reasoning capabilities in
language models, while
Diffusion Language Models (DLMs) offer flexible
token orders and parallel generation. We ask whether DLMs can acquire NTP-style
reasoning through
distillation without giving up their native generation process. Direct
distillation, however, faces a fundamental mismatch: an autoregressive teacher predicts from a left prefix, whereas a DLM can condition on
tokens on both sides. We introduce ForkLeft, a
distillation framework that resolves this mismatch by separating the student's rollout from teacher supervision. During training, the student first performs
entropy-first rollouts that commit uncertain positions and expose potential forks. We then fix the resulting student prefix and distill an NTP teacher under the same context, with answer correctness determining the supervision source. At inference, the student returns to its native confidence-first parallel decoding. With Qwen3-30B-A3B-Base, ForkLeft improves Efficient-DLM-4B on all ten
benchmarks, raising MATH500 from 72.60% to 79.60% and consistently outperforming three alternative designs. The gains scale with teacher strength and generalize to SDAR-4B with only $500$ updates. At matched scale, the distilled 4B and 8B students exceed the published SDAR-Chat and OPDLM models on seven
benchmarks, showing that DLMs can learn NTP-style
reasoning without sacrificing native parallel generation. Code and
datasets will be released upon acceptance.
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All authors Junming Liu, Jicheng Wang, Yifeng He, Hao Chen, Jianzhong Qi
Machine Learning
Length-Independent State Tracking Under a Parallel Scan
Julien Brandoit, Arthur Fyon, Thomas Braipson et al. (+5 more)
Learning robust and scalable finite-state tracking is fundamental to sequence processing. While linear recurrent neural networks (RNNs), linear
attention, and state space models enable scalable parallel training through affine recurrences, their theoretical expressivity guarantees assume idealized arithmetic and do not extend to finite precision, where the parallel scan that makes them fast is itself a source of perturbation. We formalize finite-state tracking at finite precision and characterize length independence: tracking that stays correct at every sequence length, at a precision cost that does not grow with the length. We show that length-independent state tracking requires two competing dynamics within a single map: contraction to suppress numerical perturbations and separation to keep distinct states apart. We prove that affine recurrences, which offer a single rate at each step to serve both roles, realize at most definite automata at finite precision. Instead of treating scan compatibility as a restriction on the update map, we reinterpret it as a computational budget and introduce the Neural Finite-State Machine (NFSM): a nonaffine, scan-compatible RNN built for length-independent finite-state tracking. On synthetic
benchmarks spanning abelian and nonabelian groups, noninvertible monoids, and textual state-tracking tasks, affine baselines fail on every nondefinite task, most of them within a few hundred steps. A single NFSM layer instead learns the exact transition tables of every algebraic task, which certifies correctness beyond the tested lengths, and a stack of NFSMs keeps perfect accuracy on the textual tasks at every tested length.
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All authors Julien Brandoit, Arthur Fyon, Thomas Braipson, Tom Clara, Florent De Geeter, Pierre Sacré, Damien Ernst, Guillaume Drion
Computation and Language
Masking Frequent Tokens Sharpens Direct Preference Optimization
Harshvardhan Saini, Samyak Jha, Yiming Tang et al. (+1 more)
Direct Preference Optimization (DPO) aligns
language models by optimizing over sequence-level sums of
token-wise implicit reward differences. However, we identify a pervasive pathology in this formulation: a disproportionately small subset of high-frequency
token types dominates cumulative sequence scores while appearing symmetrically across both preferred and dispreferred responses. Specifically, under canonical Qwen tokenization on Anthropic HH-RLHF, merely 69
token types account for $55.1\%$ of all response
tokens and $85.9\%$ of within-pair shared
token mass, exhibiting substantially lower preference-side specificity than the remaining vocabulary. This symmetric ubiquity induces gradient
entanglement and dilutes the discriminative preference signal propagated through the objective. To resolve this issue, we introduce Anisotropic DPO (ADPO) and its canonical realization, Frequency-Hard DPO. Using a fixed, label-agnostic vocabulary mask, our method zeroes the implicit reward contribution of high-frequency response
tokens while assigning unit weight to informative positions, thereby suppressing gradient interference without modifying preference pairs, discarding context, or introducing learned parameters. Here, anisotropy designates non-uniform
token-level objective weighting rather than representational geometry. Extensive empirical evaluations on AlpacaEval, MT-Bench, and Arena-Hard demonstrate that Frequency-Hard DPO consistently outperforms standard DPO across Qwen-2.5-7B-Instruct and Llama-3-8B-Instruct, establishing that selectively masking shared high-frequency
tokens offers an effective, zero-overhead mechanism for robust preference
alignment.
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All authors Harshvardhan Saini, Samyak Jha, Yiming Tang, Dianbo Liu
Machine Learning
Rethinking Training-Inference Mismatch in LLM Reinforcement Learning: Where It Arises and How to Correct It
Tianrun Yu, Kaixiang Zhao, Shangzhe Li et al. (+4 more)
We study training-inference mismatch in reinforcement learning with verifiable rewards (RLVR) for large
language models, where rollouts are sampled by an inference engine while gradients are computed by a training engine, and the two engines assign different probabilities to the same
tokens. To account for this discrepancy in policy updates, we introduce calibrated importance sampling (CIS). CIS is motivated by an empirically supported logit-displacement characterization that expresses the mismatch as an additive displacement $\varepsilon_t$ in log-odds, determined by the per-logit perturbation before the softmax, whose distribution is approximately invariant to
token confidence. This characterization motivates a confidence-aware truncation: large positive displacements are truncated at a single constant threshold, which maps back to an importance-ratio cap that tightens as
token confidence increases. Theoretically, we show that CIS replaces the unbounded second moment that governs the error of exact importance sampling with a term bounded by a constant, at the cost of a bias controlled by the truncated excess. In evaluation across three mixture-of-experts models and five mathematical
reasoning benchmarks, CIS achieves the highest five-
benchmark average on all three models among the evaluated baselines. Diagnostic analyses show that CIS places less truncation bias on low-confidence
tokens than truncated importance sampling, while upward clipping of small importance weights reduces held-out accuracy.
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All authors Tianrun Yu, Kaixiang Zhao, Shangzhe Li, Yuxiao Yang, Porter Jenkins, Weitong Zhang, Taylor W. Killian
Artificial Intelligence
Controllable GNN Explanations via Multi-Metric Preference Selection
Rachit Verma, Yashraj J. Deshmukh and Anirban Dasgupta
Mechanisms for generating GNN explanations are crucial for building trust and mitigating biases in Graph Neural Networks (GNNs), especially in high-stakes scenarios. Most current methods optimize only for fidelity under the sparsity constraint. However, this discounts the need for interpretable explanations (those that consist of familiar motif patterns) and stable explanations (those that remain unchanged under structural perturbations). We propose a novel approach that optimizes GNN explanations across these metrics, exposing their relative weighing as a control. Experiments on various real-world
datasets, including MUTAG, BA-2Motif, BAMultiShapes, and
PROTEINS, suggest that our method produces higher-fidelity explanations than a state-of-the-art baseline on MUTAG and
PROTEINS across all evaluated budgets, and on BA-2Motif at larger budgets, while being faster in the regime of small explanation budgets. They also indicate that our method produces more stable explanations for the MUTAG and BA-2Motif
datasets. We also examine the relationship among different metrics via their induced trade-off surface and explore its dependence on the nature of the motif library.
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Artificial Intelligence
From Latents to Wires: Surgical Post-Editing on Large Language Models
Jiankai Jin, Xiangzheng Zhang, Zhao Liu et al. (+4 more)
Given a large
language model (LLM), can whoever holds the weights name a semantic target (e.g., the model's identity), locate the model components that produce it, and edit them so that the target no longer appears while other capability is preserved? We call such an edit on a trained model a post-edit. We present L2W (latents to wires), a framework that performs
surgical post-edits for named semantic targets. For localization, L2W uses Jacobian lens (J-lens) attribution to score components against the semantic target. For
surgical removal, because LLM mechanisms are redundant (i.e., a semantic target may have multiple components producing it), L2W runs Counterexample-Guided Causal Cut (CGCC) until the target no longer appears. CGCC first cumulatively closes model components, treating each surviving expression of the target as a counterexample that exposes the next components to close, and then reopens some of them to preserve capability. In a controlled experiment with an implanted behavioural watermark, L2W removes the watermark, and its localization lands on the model region the implant changed. Across three model configurations, L2W removes model-metadata (e.g., identity) self-claims in all nine runs, and adult-content refusal in all three, with no held-out target residual. L2W further composes two post-edits on a text-to-image model: one removes the refusal of requested nudity, and a second removes the nude rendering the first exposes. The results support post-editing as a complement to post-training: post-training installs preferred behaviours, and post-editing removes named unwanted ones.
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All authors Jiankai Jin, Xiangzheng Zhang, Zhao Liu, Wenzhuo Xu, Dongdong Yang, Deyue Zhang, Quanchen Zou
Machine Learning
Beyond the Manifold Hypothesis: Hybrid Spectral Parameterizations for Flow Matching
Ségolène Martin, Anne Gagneux, Quentin Bertrand et al. (+2 more)
Flow matching and
diffusion can be trained to predict different quantities, most commonly the data $x_1$, the source noise $x_0$, or the velocity~$v$. Although theoretically equivalent, these can lead to substantially different performances. We identify two main drivers for these differences: the source–data signal-to-noise ratio, and the information bottleneck induced by the neural architecture. We show that, beyond intrinsic data dimension, the factor affecting the optimal parametrization the most is a certain signal-to-noise ratio in each data covariance direction. From this analysis, we introduce new spectral hybrid parameterizations that adapt across time and data covariance directions; we show that these are optimal for Gaussian data. We also show that architecture-induced compression changes which parameterization is easier to learn, with $v$-prediction being more sensitive to discarded directions than $x_1$-prediction. Experiments across architectures and source scales show that our spectral parameterizations are robust across regimes, can substantially accelerate optimization, while incurring essentially no additional training cost compared with standard parameterizations.
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All authors Ségolène Martin, Anne Gagneux, Quentin Bertrand, Rémi Emonet, Mathurin Massias
Computation and Language
DualGuard: Dual-Mode Quality Control for Logic-Preserving Data Augmentation
Shenghao Li and Lin Zhao
Large
language models provide a practical way to generate augmented data for logical
reasoning at scale, but a larger generation volume does not guarantee semantic, label, or logical reliability. Existing work has improved generation quality through generation constraints, candidate validation, filtering, and feedback-based revision; however, once a quality judgment is available, deciding whether a candidate should be retained, filtered, or repaired remains an important control problem. We propose DualGuard, a dual-mode quality-control framework for logic-preserving data augmentation. The first mode uses the current instance and candidate batch for selective retention, filtering, attribution, and targeted feedback. The second mode accumulates cross-instance execution records of augmentation actions on top of per-sample diagnosis and attribution, compares new executions against each action's own historical behavior, and supports retrospective anomaly inspection, targeted rollback, and bounded repair. Both modes share semantic verification and additionally use symbolic verification when a reliable logical form is available. Across seven downstream tasks in the Two-Stage Transfer setting, DualGuard achieves the highest Accuracy on five tasks and outperforms the no-augmentation BERT baseline on all seven. Controlled ablations further show complementary roles for
Memory, Z3, and history-aware anomaly control.
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Artificial Intelligence
Multi-Agent System Search via Active Substructure-aware Policy Optimization
Beicheng Xu, Bowen Fan, Weitong Qian et al. (+2 more)
LLMs enable multi-
agent systems (MAS) to tackle complex tasks, but manually designing
agent roles,
prompts, and communication structures requires substantial expertise and effort. This motivates learning policies that construct query-specific MAS from execution reward. Existing approaches typically train these policies by repeatedly traversing a fixed set of training queries and assigning rewards at the workflow level. However, this overlooks differences in queries' evolving learning potential and obscures which substructures improve solution quality. In this paper, we propose Active Substructure-aware Policy Optimization (ASPO), a RL framework for query-level MAS search. ASPO introduces an Adaptive Query-Selection Mechanism (AQSM) that focuses training on queries at the policy's competence boundary: those it can solve but not yet reliably. A complementary discovery mechanism widens architectural exploration for hard queries, helping distinguish insufficient exploration from operator capability limits. Beyond query selection, ASPO introduces substructure-level rewards that measure output-quality gains within each action's descendant subgraph. These rewards guide proximal policy optimization to reinforce useful architectural refinements and discourage redundant or harmful computation. Together, these mechanisms prioritize learnable queries and provide fine-grained feedback for learning effective MASs. Across six
benchmarks spanning mathematical
reasoning, general question answering, and code generation, ASPO ranks first on every
benchmark against twelve baselines.
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All authors Beicheng Xu, Bowen Fan, Weitong Qian, Lingching Tung, Bin Cui
Artificial Intelligence
PrismQuant: Optimal Null-Space Rotations for Grouped Quantizers
Yanlong Chen, Yining Chen, Song Zhang et al. (+2 more)
Smaller activation outliers do not necessarily imply better low-bit quantization: their
alignment with the quantizer matters. We introduce PrismQuant, a quantizer-aware rotation framework that aligns the leading activation eigenspace with the constant group subspace of asymmetric grouped INT4. The affine offsets represent the energy in this subspace without widening the range within the group. We formulate rotation design as a Ky Fan trace maximization and derive a closed-form solution that is provably optimal for this
alignment objective. Compact Householder transformations and their compact-WY representation enable gradient-free construction and efficient application at both foldable and online sites. A predictive range law further connects unaligned activation energy and group size to quantization-relevant variation. Experiments on Llama, Qwen, and Mistral span dense models up to 70B parameters and a 30B mixture-of-experts model. Under W4A4KV4, PrismQuant sets the state of the art on Llama-3.2-3B among the compared methods in both perplexity and accuracy. On Llama-3.1-70B, it attains 3.85 perplexity and 72.46% average zero-shot accuracy, only 0.22 percentage points below full precision. In the deployment study on Llama-3.1-8B, our optimized implementation achieves 1.51x prefill and 1.22x CUDA Graph decode speedups over matched FP16 baselines, with 56.34% lower decode peak
memory and only 2.35% additional Graph decode latency over Hadamard. Code and models are available at https://github.com/ForeverBlue816/PrismQuant and https://huggingface.co/collections/ForeverBlue/prsimquant.
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All authors Yanlong Chen, Yining Chen, Song Zhang, Amirhossein Habibian, Yawei Li
Artificial Intelligence
Authorization Closure Graph: Minimal Repair for LLM Agents with Evolving User Instructions
Qingzhuo Wang, CaiYi Wang, Jinglu Meng et al. (+4 more)
Tool-using large
language model (LLM)
agents increasingly perform state-changing actions that require user authorization. Yet existing approaches do not provide a principled mechanism for selectively updating prior authorization when only part of an instruction changes. To this end, we propose an Authorization-Closure-Graph (ACG)-based framework that represents authorization and its dependencies as an evolving, versioned state. ACG selectively invalidates authority affected by a revision while preserving unaffected portions of the authorization state, and computes a minimal repair that identifies only the missing evidence or authority required for execution. This enables
agents to adapt to revised instructions while avoiding stale authority and unnecessary authorization requests. We evaluate ACG across three advanced LLMs in two natural tasks, and ACG consistently improves action safety rate and task success rate. Code is available at https://github.com/weiliang822/ACG.
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All authors Qingzhuo Wang, CaiYi Wang, Jinglu Meng, Ruiyang Qin, Kunyu Peng, Zhihua Wei, Wen Shen
Computer Vision
CityToolVQA: Tool-Augmented Visual Question Answering for 3D Spatial Cognition in Urban Low-Altitude Environments
Boao Yu, Yingzhen Nie, Yue Hu et al. (+2 more)
CityToolVQA addresses the weak performance of
Vision-
Language Models (VLMs) on quantitative tasks in urban low-altitude visual question answering. We divide the seven tasks into qualitative and quantitative groups: qualitative questions are answered directly by the VLM, whereas quantitative questions are handled by an external visual-
geometric toolchain that performs object grounding,
segmentation, depth back-projection, and spatial computation. The toolchain can be attached to different VLMs in a zero-shot manner; a Depth-Assisted
Prompt Inference (DAPI) fallback is triggered when the main-chain detection is invalid or unreliable, and CityToolVQA-SFT adapts the 8B backbone to tool-conditioned inputs. On the 73,324-question Open3D-VQA-v2 test set, CityToolVQA-SFT (Qwen3-VL-8B) reaches 67.6% overall accuracy, and attaching the toolchain to ten open-source VLMs improves quantitative-task accuracy by 12.7-36.9 percentage points. These results indicate that externalizing explicit 3D
geometric computation effectively complements the limited ability of RGB-only VLMs to estimate metric distances and object sizes.
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All authors Boao Yu, Yingzhen Nie, Yue Hu, Zhengqiu Zhu, Rusheng Ju
Machine Learning
HoTS: Homophily-Aware Temperature Scaling for Graph Neural Network Calibration
Inwoo Tae, Yoontae Hwang and Yongjae Lee
For graph node classification, calibrated class probabilities are needed when confidence scores, usually the maximum predicted class probability, are used to rank predictions, defer uncertain nodes to human review, or control risk. Existing post-hoc calibrators either apply one global temperature or use graph-aware modules without a principled structural form. We study how local graph structure should enter node-level calibration. Our first results show that a logit-only temperature rule is insufficient when nodes with identical logits but different local homophily require different optimal temperatures. We then analyze a population-concentration contextual
stochastic block model with Gaussian features and a one-layer linear GCN. Under equidistant class means, the Bayes posterior over class-template scores is a temperature-scaled softmax whose inverse-temperature is governed by a homophily-dependent signal strength. In the positive-signal homophilic regime, the resulting temperature decreases approximately inversely with normalized local homophily. This law motivates Homophily-aware Temperature Scaling (HoTS), a simple post-hoc calibrator that assigns each node a positive scalar temperature from
entropy-based logit concentration and estimated local homophily. HoTS has three temperature parameters, preserves the predicted class, and learns the strength of the structural correction from calibration data. Across 18 node-classification
benchmarks, two GNN backbones, and eight calibration baselines, HoTS achieves the best mean Expected Calibration Error (ECE) of 4.79%, the best average rank, and the most reliable confidence ranking in selective classification. Code is available at https://github.com/inu0104/HoTS.
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Cryptography and Security
CyberClear: A Benchmark for LLM Agent Systems on APT Attack Chain Provenance
Qi Chen, Fushuo Huo, Hangli Shen et al. (+3 more)
Large
language model
agents have demonstrated promising capabilities in cybersecurity tasks, yet their ability to reconstruct complete Advanced Persistent Threat attack campaigns from complex security logs remains largely unexplored. Existing cybersecurity
benchmarks for
agents mainly focus on vulnerability discovery, exploitation, and security analysis tasks, leaving the evaluation of attack chain provenance under realistic security logs insufficiently studied. To address this gap, we introduce CyberClear, a
benchmark for evaluating LLM
agents and advanced
agent systems on APT attack chain provenance from long-context security logs. CyberClear covers both single-step attacks and multi-stage attack chains, requiring
agents to identify attack evidence, infer attack progression, and generate provenance graphs containing entities, causal relationships, MITRE ATT&CK techniques, and forensic evidence. To enable comprehensive evaluation, we develop an evaluation method tailored to APT attack chain provenance. Unlike conventional text similarity metrics that focus on surface-level matching, our evaluation examines whether reconstructed graphs preserve the semantics of attack chains across single-step behavior correctness, multi-step behavior identification, temporal and causal consistency, entity and relationship fidelity, and overall attack narrative consistency. Advanced multi-
agent systems powered by state-of-the-art LLMs still struggle on CyberClear, motivating us to propose CyberProvenance, an
agent cyber harness designed for multi-
agents that augments LLM
agents with evidence accumulation, execution-based validation, and feedback-guided refinement mechanisms for reliable attack-chain provenance. Extensive evaluations on CyberClear demonstrate the effectiveness of CyberProvenance in improving evidence
reasoning, execution-grounded validation, and complete APT attack chain reconstruction.
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All authors Qi Chen, Fushuo Huo, Hangli Shen, Jingcai Guo, Shuhao Li, Guang Cheng
Artificial Intelligence
PluginRSI: Recursive Improvement of Agent Harnesses with Reusable Plugins
Yaorui Shi, Yuchun Miao, Yuxin Chen et al. (+5 more)
The harness surrounding a
language model is a central determinant of
agent performance. Recent methods optimize harnesses by searching over complete programs, where individual mechanisms are difficult to isolate and reuse. We introduce PluginRSI, which represents a harness as a composition of atomized plugins and organizes harness
evolution around these plugins. Individual plugins are improved independently and accumulated in a shared library, then recombined into new harnesses at each iteration. PluginRSI improves over existing harness optimization methods across software engineering, command-line interaction, and question-answering tasks. The resulting harnesses retain their advantage when transferred to other solver models without further optimization. The evolved plugin library accelerates subsequent optimization from the initial harness, which helps faster and higher convergence on unseen tasks. These results show that accumulating reusable mechanisms provides an effective basis for continued harness improvement.
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All authors Yaorui Shi, Yuchun Miao, Yuxin Chen, Jiayuan Zhang, Yueqing Sun, Xierui Song, Xiang Wang, An Zhang
Artificial Intelligence
MergeHEIR: Mitigating Multimodal Hallucinations as the Tax of Model Merging
Jinyu Li, Hao Fang, Zhiming Zhang et al. (+3 more)
Model merging consolidates task-specialized experts into a single deployable model. However, we show that such capability consolidation incurs a merging tax of increased
hallucination: across 8 model-merging methods, every merged checkpoint exhibits a higher
hallucination rate than the average of its constituent experts. An intuitive approach is to adapt existing
hallucination-mitigation methods to the post-merge model, yet this unconstrained adaptation disrupts inherited capabilities, creating a tension between
hallucination mitigation and expertise retention. To tackle this challenge, we introduce MergeHEIR, a post-merge adaptation framework designed to reduce this merging tax while preserving expertise inherited from initial experts. Using small expert-task calibration sets, MergeHEIR constructs layer-wise null-space projectors via SVD from task-specific activations collected from the merged checkpoint, and periodically projects the accumulated post-merge displacement onto the resulting null spaces to preserve inherited expertise. Theoretically, we establish minimum-distortion and maximum-dimensionality guarantees, characterize the threshold-controlled adaptation-retention trade-off, and extend perturbation guarantees beyond finite calibration data. Across 24 paired comparisons spanning three MLLM configurations and 8 model-merging methods, MergeHEIR consistently mitigates
hallucination while largely preserving inherited expertise, demonstrating a more favorable
hallucination-retention trade-off.
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All authors Jinyu Li, Hao Fang, Zhiming Zhang, Jiawei Kong, Bin Chen, Shu-Tao Xia
Artificial Intelligence
Carnator: Fast Text-to-Video Generation with Generation-Native Compatibility-Guided Cross-Request Reuse
Xingkun Yin, Xuebin Tang, Mingkun Xu et al. (+1 more)
Video diffusion transformers produce high-quality
videos, yet iterative denoising incurs substantial inference latency, limiting interactive and large-scale serving. Most existing acceleration methods focus on individual requests, thereby restricting efficiency gains to redundancy within a single generation trajectory. Recent cross-request reuse offers an additional source of savings, but existing approaches often infer reusability from coarse semantic similarity. This conflates semantic relatedness with generation-level computational compatibility, so aggressive reuse may accept incompatible historical computation while conservative reuse leaves substantial acceleration unrealized. We present Carnator, a cross-request acceleration framework that addresses this challenge by extracting and using generation-native compatibility evidence directly from the model's evolving internal states. Specifically, Carnator performs a lightweight early probe to construct an Early Signature from internal
diffusion states, assessing reuse validity through risk-aware compatibility decisions. The same evidence characterizes reuse scope by localizing target-specific computation and guiding joint reuse of historical latent trajectories and sparse
attention connectivity. Across three text-to-
video backbones, Carnator consistently achieves higher cache-hit end-to-end acceleration than the evaluated cross-request baselines despite more selective cache acceptance, reaching up to 2.17$\times$ speedup while maintaining competitive generation quality.
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All authors Xingkun Yin, Xuebin Tang, Mingkun Xu, Hongyang Du
Robotics
RE-0: Verified Recursive Improvement of Embodied Code-as-Policy Agents through Local On-Policy Distillation
Jiawei Zhang, Xiangrong Zhang, Rui Song et al. (+3 more)
Code-as-Policy
agents accomplish long-horizon
embodied tasks by generating and executing code, yet continually improving them with teachers that are stronger but not globally reliable remains a key challenge. Existing
distillation methods typically treat the teacher's complete behavior as the supervision target and thus misassign training credit on states where the teacher fails. We propose RE-0, a recursively verified policy improvement framework: rather than assuming that the teacher globally outperforms the student, RE-0 requests local corrections from the teacher on the student's own failure histories and checks in the environment whether each correction is genuinely beneficial; verified corrections yield immediate improvement. Building on this, we propose RE-OPD, which turns verified interventions into supervision for on-policy
distillation. Only counterfactually verified teacher interventions provide distribution-level supervision, weighted by their measured local benefit, and the improvement they induce is projected back into the standalone student, so both where supervision is applied and how much credit the teacher receives co-evolve with the student policy. We further prove that the student's per-round gain is lower-bounded by its verified intervention gain up to verification and projection error terms. Experiments across multiple Code-as-Policy
embodied tasks show that RE-0 improves both teacher-assisted execution and the standalone student, and generalizes to novel
robots and scenes.
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All authors Jiawei Zhang, Xiangrong Zhang, Rui Song, Huanbin Zhou, Chengye Song, Hongzhou Wang
Computer Vision
Jian Tang, Jiawei Fan, Qiannan Zhou et al. (+3 more)
Diffusion Transformers (DiTs) have become the standard backbone for high-quality generative modeling, yet deploying them in conditional generation tasks remains computationally prohibitive because bidirectional joint
attention repeatedly processes large reference streams. While existing optimization schemes mitigate generic temporal redundancy, they typically rely on coarse-grained static reuse and overlook the distinct dynamics of references and targets. Specifically, we observe that reference representations often evolve slowly along the generation trajectory, while the target often assigns little
attention mass to them; reference drift and this target-to-reference exposure jointly shape how strongly stale reference states affect the target. To exploit these patterns, we introduce \RefAdapt, a training-free framework for adaptive control of joint
attention between references and targets. Instead of rigid static strategies, \RefAdapt combines consecutive target-Q change with previously observed target-to-reference
attention mass to control reference computation adaptively at block granularity. Under ultra-few-step settings, \RefAdapt enables speedups of up to $2.097\times$ on 4-step MiniMax H3 and $3.54\times$ on 8-step Qwen Image Edit, while maintaining comparable visual quality.
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All authors Jian Tang, Jiawei Fan, Qiannan Zhou, Qingbin Liu, Jiang Bian, Zang Li
Software Engineering
How AI Changes DevOps Performance: A Mechanism-Based Simulation
Mamdouh Alenezi
Context. AI tools for software development have progressed from code completion to autonomous
agents, yet evidence on their effects on DORA performance is mixed, mostly cross-sectional, and often conflates AI-generated code (AGC) with agentic AI (AGT). Objective. We examine how AGC and AGT, separately and jointly, affect deployment frequency, lead time, change failure rate, recovery time, and deployment rework rate. Method. We audit the evidence, formalise a two-construct model with four capability moderators, and implement a discrete-event simulation of a delivery pipeline. Six experiments include a factorial design, Shapley decompositions, sensitivity analysis over 600 parameter draws, a fixed-demand robustness test, and a staggered-adoption panel of 5,000 simulated teams with counterfactuals. Results. AGC increased failure rate, rework, and recovery time in both capability profiles. When AI increased change volume, AGC shortened lead time only where review capacity was spare (-13%) and lengthened it once review saturated; in the low-capability team, its deployment-frequency gain came entirely from unplanned rework. AGT improved deployment frequency (+31% to +34%), lead time (-8% to -22%), and recovery time (-29% to -38%), but its stability effect depended on autonomous CI repair masking defects, and escaped defects increased in the high-capability team. Capability reduced AGC's absolute failure-rate penalty (7.9 vs. 1.5 points) but not its relative penalty (+34% vs. +84%). Two-way fixed effects underestimated adoption effects by 8-16%; detecting the modelled stability effect required about 50 teams, and its capability moderation about 400. Conclusions. Within the model, DORA metrics respond to AI through queueing, batching, and oversight, not only code quality. We derive measurement rules, guardrails for agentic remediation, and a sample-size-informed field-validation protocol.
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