Robotics
Acting Through Registers
Yiwei Zhang, Yantai Yang, Jin Gao et al. (+7 more)
World Action Models (WAMs) use generative visual representations to condition
robot actions, yet how these representations organize action-relevant information remains poorly understood. We identify persistent high-norm spatial outliers in WAM representations and find that their action relevance persists even after normalization and K/V projection attenuate their extreme magnitude. Motivated by this observation, we introduce RegisterWAM, which adds learnable non-spatial registers to the world model and uses them directly for action conditioning. Registers alone do not fully eliminate spatial outliers; pairing them with observation flow-matching (FM) loss supervision suppresses spatial outliers while concentrating high-norm responses in registers. Beyond this reorganization, observation FM loss supervision also improves action learning without registers, motivating an additional noised-observation FM objective that learns current-scene recovery from corrupted inputs. The resulting model achieves 81.64% success on LIBERO-Plus without additional embodied pretraining data or foundation models, reaches 93.0% average success on RoboTwin 2.0, and improves real-world manipulation performance.
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All authors Yiwei Zhang, Yantai Yang, Jin Gao, Jin Lou, Andong Chen, Yufei Liu, Boyang Xing, Yuchen Zhu, Weiming Hu, Zhipeng Zhang
Machine Learning
Mixed Precision Scaling Laws for Quantized Pretraining
Jinuk Kim, Rajarshi Saha, Muhammed Fatih Balin et al. (+1 more)
Since scaling laws were introduced for modern LLMs (Kaplan et al., 2020), several works have extended them to account for quantization, tracking the emerging low-precision data types supported on AI accelerators. We develop a more accurate scaling law for quantized pretraining with mixed precision across the forward and backward passes, grounded in optimization theory and validated empirically. Our formulation separates the effects of quantization on model capacity and on optimization efficiency, and explicitly models the persistent optimization error from forward and backward quantization identified through convergence analysis. To select the regimes for fitting and evaluating our law, we first decompose existing 4-bit training recipes into their forward and backward components, isolating those that most affect loss. Our Qwen3 experiments under selected 4-bit regimes, together with published Llama-style experiments, show that our law predicts loss with up to $11\times$ lower MSE than existing laws as model size and
token count scale. Finally, we extend our law to layer-wise mixed precision and identify the optimal quantized-pretraining recipe, i.e., model size, training
tokens, and per-layer forward/backward precision, under a hardware-aware compute budget. Unlike prior precision scaling laws, our law predicts that the optimal precision increases with the compute budget.
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All authors Jinuk Kim, Rajarshi Saha, Muhammed Fatih Balin, Youngsuk Park
Machine Learning
The Tokens Strike Back: Safety-Aware Denoising with Directional Embedding-based Revision
Amman Yusuf, Jiwoo Shin, Ruizhi Deng et al. (+3 more)
The Safety-Aware Denoiser (SAD) is a recent, training-free, safety-guidance framework for text
diffusion models. During inference, SAD steers text samples toward safe regions of the
token probability space. In this paper, we propose $\mathit{SADDER}$ (Safety-Aware Denoising with Directional
Embedding-based Revision), which improves the safety and quality trade-offs of SAD by exploiting a safety-guidance signal in the
embedding (or activation) space for each
token position, then updating the
token positions using the SAD update rule multiplied by the guidance signal. This multiplication acts as a gating mechanism:
tokens with a high unsafe signal are more likely to be modified during inference following the SAD update rule. However, such an update does not automatically preserve the conditional mean, i.e., the denoiser of a fixed target distribution. We therefore theoretically derive new position-wise target distributions for which the modified denoiser is exact. Furthermore, we derive conditions under which the position-wise target distributions match a global target distribution. We test our $\mathit{SADDER}$ method on popular Masked
Diffusion Language Models (MDLMs) and Uniform-State
Diffusion Models (USDMs) across unsafe continuation and jailbreak settings, and show that $\mathit{SADDER}$ achieves a better balance between safety, generation quality, and inference cost than existing methods.
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All authors Amman Yusuf, Jiwoo Shin, Ruizhi Deng, Heitor Rodrigues Guimarães, Nikita Dvornik, Mijung Park
Machine Learning
Calibrated Disambiguation for Partial Multi-label Learning
Zhuoming Li, Yuheng Jia, Mi Yu et al. (+1 more)
Partial multi-label learning (PML) aims to train a classifier on
datasets whose instances are over-annotated with not only relevant labels but also irrelevant labels, which is common when
datasets are collected from crowd-sourcing platform. Existing works primarily approach it from a curriculum learning perspective, leveraging the memorization effect to disambiguate noisy labels and produce robust predictions. However, these methods are based on non-adaptive weighting functions and lack theoretical guidance for optimal weighting. To overcome these issues, a calibrated disambiguation model named PML-CD is proposed. We first formulate the optimal weighting function for curriculum-based disambiguation, which is equivalent to the calibration of the model's predicted confidences, thus providing guidance for curriculum designing. To obtain the optimal weighting function from PML
dataset during the training, a transferable calibrator is designed, which takes the histogram of positive samples' confidences as input, and outputs the optimal curriculum weighting for training. Prototype
alignment regularization is also proposed to promote the model's performance. Experiments conducted on Pascal VOC, MS-COCO, NUS-WIDE and CUB have verified that our method outperforms existing state-of-the-art PML methods.
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All authors Zhuoming Li, Yuheng Jia, Mi Yu, Zicong Miao
Artificial Intelligence
Executable Prefix Values for Locating Agent Failures
Bingyu Shen, Jiaqi Tang, Lan Wei et al. (+3 more)
Work on debugging LLM
agents treats the "first mistake" marked by a human or LLM judge as ground truth for where a trajectory failed. We ask whether such labels point at causes or symptoms, and make the distinction executable: we restart the
agent from reconstructed intermediate states of a failed trajectory and estimate its prefix value V(h_t), the probability of success from each step, with a certified point of no return (PNR) for a failure. On 150 SWE-bench Verified tasks with two frozen open-weight
agents, we build such curves for 47 and 60 failed trajectories and probe every step labeled by four frontier judges under the Traverse rubric. We find that the labels do not mark what a controller needs. Judges of different lineages agree on the exact step in only 21/57 cases, and the newest judges flag mistakes in 8/10 and 10/10 successful runs. For 66% of the labels, the
agent's success probability does not drop at the labeled step; only 7/188 answers name the decisive step. Restarting at the measured restart point raises expected success from 0.44 to 0.57 offline, and real restarts confirm the advantage, whereas restarting at a judge's label does not (-0.08 to -0.03). We release our code, curves, and labels.
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All authors Bingyu Shen, Jiaqi Tang, Lan Wei, Yi Sheng, J. Jenny Li, Boyang Li
Robotics
From Pixels to Executable Physics: Behavior-Grounded World Models for Embodied Action Consequence Prediction
Ruo Chen, Jiahua Pang, Yingjie Niu et al. (+4 more)
In outdoor environments, varying terrain and contact conditions can cause identical action commands to produce different motion outcomes for quadruped
robots. Predicting these outcomes is essential for reliable navigation and informed path selection. Existing
video world models can generate visually plausible futures, yet often struggle to capture
robot dynamics and terrain interactions, leading to overly optimistic predictions of action outcomes. We present DR-WM, a physically consistent world model that incorporates
robot-terrain contact dynamics to predict the consequences of quadruped actions. Starting from an initial RGB image, our method reconstructs scene geometry and integrates dense, dimensionless priors over terrain contact properties, calibrated using real-world action-response data. The resulting terrain model supports the simulation of physics with spatially varying contact properties such as friction. Given the initial state of the
robot and candidate action sequences, we simulate its future motion and use the resulting camera trajectories and rendered geometry to guide the training of a
video world model. The generated future observations are then used to update the terrain reconstruction, enabling iterative model rollouts without additional real-world images. Extensive experiments demonstrate that DR-WM substantially outperforms geometry-only baselines and baselines with uniform physical parameters on key physical metrics characterizing action outcomes, while achieving the best performance among the evaluated baselines. These physically grounded predictions support more informed action selection: across 90 challenging scenarios, an 8B
vision-
language model augmented with DR-WM outperforms an unassisted 32B
vision-
language model on five of six evaluation metrics.
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All authors Ruo Chen, Jiahua Pang, Yingjie Niu, Hongyi Lin, Jun Tian, Yang Zhu, Chao Li
Information Retrieval
AiSAQ-in-Filtering for DRAM-free Filtered Information Retrieval
Kento Tatsuno, Daisuke Miyashita, Kazunari Sumiyoshi et al. (+3 more)
Filtering search is one of the crucial applications of approximate nearest neighbor search (ANNS). In filtering search, label information is used in conjunction with unstructured vector data. ANNS provides efficient similarity search in large-scale
datasets, however, the
memory footprint of storing both vector data and their associated label becomes a significant cost factor as
dataset sizes grow. To address this problem of increasing DRAM usage, we propose AiSAQ-in-Filtering, with a novel quantization method called LiPQ (Label-inclusive Product Quantization). LiPQ combines unstructured data and structured labels into a single representation, leading no growth of encoded data. Our proposal increased recall@1 of single-label searches by up to 55% and fewer disk I/Os compared with existing methods. This also retains the main advantages of AiSAQ, which can reduce DRAM footprint by up to 1/78 and index switching time comparing with existing methods. This proposal can also be applied to sparse-dense hybrid search beyond label filtering such as semantic and keyword search of documents.
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All authors Kento Tatsuno, Daisuke Miyashita, Kazunari Sumiyoshi, Hiroaki Yamanaka, Kosuke Nakata, Jun Deguchi
Computer Vision
4LC: 4D Latent Cache for Efficient Conditioning in Dynamic World Modeling
Jianhui Zhang, Zuyao Lin, Pengxin Xu et al. (+2 more)
For
video generation in dynamic world modeling, observed static and dynamic content should be efficiently reused to provide conditions for repeated generation at different times and viewpoints. We introduce 4LC, a 4D latent cache with composite visual representations and a flexible spatiotemporal access interface. As a latent cache, 4LC directly reuses visual representations of static and dynamic content to provide generation conditions, avoiding per-request RGB rendering and re-encoding. As a composite cache, 4LC binds visual representations from multiple visual encoders (e.g., VAE, DINO, V-JEPA, VGGT) to shared geometry and motion, allowing the composite representations to describe the dynamic world with rich information. Besides, we design a flexible spatiotemporal interface, which directly reads the cache at independently specified times and viewpoints and combines readouts across observations, providing a common conditioning format for diverse downstream tasks. Building on 4LC, we develop 4LC-
Video to address multiple
video generation tasks through different configurations of source observations, spatiotemporal requests, and historical reuse, including camera control, temporal control, novel-view synthesis, scene revisits, and long-term generation. Our experiments show more efficient condition acquisition, effective spatiotemporal controllability, and scene consistency across tasks using a single 4LC-
Video model without task-specific fine-tuning.
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All authors Jianhui Zhang, Zuyao Lin, Pengxin Xu, Jingdong Wang, Xingyu Chen
Machine Learning
RELAY: Selective Self-Distillation with Adaptive Hand-off for Rubric-Based Reinforcement Learning
Kunwang Zheng, Xinyu Sun, Yu Cao et al. (+7 more)
Rubric-based reinforcement learning grades a response against many written criteria but collapses them into one reward, which discards the detail the rubric carries and gives no GRPO reward gradient when a group's responses tie. A frozen teacher that reads the rubric as privileged context provides
token-level supervision, but we observe an early plateau in the gains over GRPO. RELAY uses advantages and rewards to select positive-advantage and low-scoring zero-advantage rollouts, and monitors the
distillation loss to decide when to retire the teacher. Stage 1 combines selective
distillation from a frozen teacher with GRPO; a plateau detector then removes the teacher, and Stage 2 continues with GRPO alone. Calibrated on one model-domain pair and reused unchanged in three others, the plateau detector fires between steps 122 and 171. RELAY scores highest in-domain among the compared methods in all four settings, ahead of GRPO on RaR-Medicine by 4.62 points with Qwen3-1.7B and 6.02 with Qwen3-4B. On Qwen3-1.7B/Medicine, removing the teacher from a shared checkpoint outperforms keeping, weakening or refreshing it. Distilling selected rollouts covering about half the response
tokens also outperforms distilling all rollouts at a matched average coefficient.
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All authors Kunwang Zheng, Xinyu Sun, Yu Cao, Shuocheng Li, Lei Cheng, Ying Liu, Qilong Zhang, Jinlong Chen, Hua Yang, Qianglong Chen
Machine Learning
FedLoRD: Personalized Federated Learning based on Low-Rank Structures of Model Parameter Differences
Tsutahiro Fukuhara, Junya Hara, Hiroshi Higashi et al. (+1 more)
We propose FedLoRD, a personalized
federated learning (PFL) method based on the low-rank structure of the differences between the global and personalized model parameters. PFL is a branch of
federated learning that aims to learn models tailored to the local data of each client under biased local data distributions. In its local training, each client learns a personalized model by regularizing the difference between its model parameters and the global ones sent from the server. The regularization function needs to be designed with priors on the model parameter difference; however, the existing PFL methods rely on empirical choices (e.g., $\ell_1$- and $\ell_2$-norms), which do not take into account the actual characteristics of the difference. The resulting mismatch of priors can lead to suboptimal personalized models. To address this issue, we propose a PFL method whose regularizer is built on an observed characteristic of the model parameter differences: they are approximately low-rank at every layer. In our method, we first formulate a local training problem with nuclear norm regularization on the layer-wise differences. We further rewrite this problem via the factorized representation of the nuclear norm so that it can be efficiently solved by a plain
stochastic gradient descent method. Image classification experiments on three
benchmark datasets demonstrate that FedLoRD outperforms several existing PFL methods.
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All authors Tsutahiro Fukuhara, Junya Hara, Hiroshi Higashi, Yuichi Tanaka
Computer Vision
Smartphone-Based, Field-Deployed AI Inspection System for Power Distribution Infrastructure
Sampath Balaji Masabathula, Srija Vanapalli, Gangadhar Kanchi et al. (+3 more)
Routine inspection of power distribution infrastructure remains challenging for utilities operating geographically dispersed networks, where conventional manual inspection can be difficult to perform consistently and at scale. This work presents a smartphone-based AI inspection system for assessing targeted distribution-infrastructure conditions from images captured by field personnel. The proposed workflow integrates image-quality and viewing-perspective validation with AI-assisted assessment of pole lean, crossarm and top-cleat tilt, voltage level, vegetation-clearance risk, and distribution-
transformer (DTR)-ground clearance. The system integrates deep-learning models with monocular depth estimation,
geometric analysis, and rule-based processing within a common multi-stage inspection workflow designed for practical field operation. The system was pilot deployed across five selected distribution feeders within the Eastern Power Distribution Company of Andhra Pradesh Ltd. (APEPDCL), where 1,222 defect instances were identified across the assessed categories and subsequently tracked through a rectification and verification workflow, with 84 defect instances recorded as rectified during the study period. Feeder-level interruption records were also examined over the study period; however, the available observations do not provide sufficient evidence to establish a reliability improvement attributable to the proposed system. A curated multi-module field-captured
dataset, PD-Defect, is also publicly released to support reproducible research in distribution-infrastructure inspection. The field deployment demonstrates the practical integration of smartphone-based AI-assisted inspection into field inspection and maintenance activities, providing a mechanism for systematic identification, documentation, rectification tracking, and verification of targeted distribution-infrastructure defects.
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All authors Sampath Balaji Masabathula, Srija Vanapalli, Gangadhar Kanchi, Sreenivasa Rao V.A.N, Vijaya Lalitha V, Sasank Chilamkurthy
Computation and Language
When Does an ASR Error Matter? Task-Role Diagnostics for Tool-Using Voice Agents
Xiaolong Wang, Shijie Su and Naqi Xiong
In cascaded voice
agents, ASR errors can have markedly different downstream consequences: a small transcription error may leave a tool call unchanged or alter its intent or arguments. Conventional word error rate (WER), however, does not capture these differences. We introduce SpeechEvalX, a paired diagnostic protocol that evaluates how task roles affected by ASR errors influence structured tool calls. Controlled edits are matched by source utterance, edit count, and operation, while task-role labels are compared with lexical severity, BERT cosine distance, Semantic-WER, and binary criticality. Across 594 source utterances, joint call accuracy falls from 82.3% with reference transcripts to 71.9-74.9% across three ASR front ends. Number and negation edits cause 21.9-25.7 percentage-point losses, versus 1.4-2.9 points for control edits. Detailed task-role labels improve source-grouped ROC-AUC from 0.73 to 0.78 over binary criticality, showing that downstream impact depends strongly on the task role affected.
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Sound
What Do Deepfake Audio Detectors Hear? From Self-Supervised Models to Audio LLMs
Wanying Ge, Xin Wang and Junichi Yamagishi
Real-world audio recordings for deepfake detection rarely consist solely of
speech; they often are mixtures of
speech and background events that themselves may also be synthetic. When processing these mixtures, it remains unclear whether detectors attend to the
speech or the background event, and where their spectral evidence lies. We study this on the CompSpoof V2 database, whose mixtures pair independently real-or-fake
speech and event components. Our results show that all systems detect fake events more accurately than fake
speech. However, this performance gap is largely driven by poor generalization to an unseen fake
speech domain, rather than a true difference in difficulty. Furthermore, sub-band probing reveals that the front-end encoder dictates where a countermeasure focuses its frequency evidence. To enable reliable deployment in real-world scenarios, future countermeasures should address out-of-domain generalization failures and carefully select front-end encoders aligned with their target acoustic evidence. Our source code and checkpoints are released at https://github.com/nii-yamagishilab/AntiDeepfake-AudioLLM.
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Software Engineering
StateHarness: Maintaining Evolving Project State for Long-Horizon Coding Agents
Chuan Zou, Yujun Zhou, Xiaonan Luo et al. (+1 more)
Long-horizon coding requires
agents to maintain an evolving view of the project they are changing: what problem is currently being solved, what has already been learned or modified, which other artifacts may be affected, and what work remains unresolved. When this information is scattered across a growing interaction history, local edits can become disconnected from their later consequences and progress can be difficult to carry forward. We introduce StateHarness, a runtime framework that maintains evolving project state without updating model parameters. The working
agent records its current problem and hypothesis, important findings and decisions, discovered repository relationships, and unfinished work. Runtime support
prompts record updates, searches for callers, tests, and documentation that may be affected by a change, and maintains a compact working brief for subsequent decisions. When behavioral signals accumulate, a separate diagnostic session revisits the recorded history and surfaces overlooked evidence. Across three repository-level evaluations, StateHarness improves task completion and several measures of change consistency. On RefactorBench, full-chain completion increases from 29.4% to 35.3% and cleanup-check accuracy from 87.0% to 95.4%. On five LHTB software-engineering tasks, mean reward improves from 0.427 to 0.576, exceeding LongHorizon-Harness at substantially lower recorded execution cost. On ChainSWE, bug-fix rate increases from 35.1% to 39.5% and residual debt decreases from 60 to 43 items, although pass-to-pass regressions increase. These results show that maintaining project state can improve long-horizon coding while introducing measurable execution and regression trade-offs.
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All authors Chuan Zou, Yujun Zhou, Xiaonan Luo, Xiangliang Zhang
Computer Vision · Cross-listed from eess.IV
Background2Background: a self-supervised background removal framework for high dimensional microscopy
Guangrui Ding, Haonan Lin, Rong Tang et al. (+8 more)
High-dimensional microscopy captures features across space, time, and spectrum, but complex backgrounds obscure signals. We present Background2Background (B2B), a self-supervised background removal framework leveraging the backgrounds larger correlation relative to the signal. Local pixel shuffling disrupts signal correlations while preserving background structure, transforming signals into effective noise for Noise2Noise-based background removal. Across fluorescence, stimulated Raman scattering and mid-infrared photothermal imaging, B2B achieves over 40-fold background suppression and reveals hidden signatures.
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All authors Guangrui Ding, Haonan Lin, Rong Tang, Bethany Weinberg, Jianpeng Ao, Qing Xia, Guorong Hu, Dingcheng Sun, Jiaze Yin, Lei Tian, Ji-Xin Cheng
Cryptography and Security
Extending Layered Attestation into ARM Application Run-Times
Antonio Joia Neto, Bilel Sefsaf, Norrathep Rattanavipanon et al. (+4 more)
Modern mobile and edge platforms provide hardware-backed attestation of application and platform integrity at load-time. However, they offer little support once application execution begins. We present ARA, an Application-Runtime Anchor for ARM that extends this load-time chain of trust into execution. ARA bootstraps from the underlying ARM platform Root of Trust (RoT) through a measured loading chain and establishes an attested protected run-time within the application address space using recently introduced ARM Permission Overlay Extensions (POE). The resulting run-time domain can host programmable RoTs for Measurement and Storage (RTM/RTS), enabling application run-time integrity verification, attestation evidence generation, and protected use of secrets while retaining efficient access to application run-time state. As exemplary uses, we instantiate ARA for protected media decryption and application control flow measurement. Our prototype and evaluation show that ARA supports frequent run-time RoT-backed operations at near in-process cost, with roughly 10x and 50x lower overhead than out-of-process equivalents. This offers a practical bridge from layered attestation to application run-time trusted computing on ARM.
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All authors Antonio Joia Neto, Bilel Sefsaf, Norrathep Rattanavipanon, Carlos Chinea Perez, Hans Liljestrand, Jan-Erik Ekberg, Ivan De Oliveira Nunes
Artificial Intelligence
MAS-Gate: Unlocking Multi-Agent System Lifecycle via Node-Centric Quantification
Hehai Lin, Zixuan Wang, Ming Yan et al. (+6 more)
Large
language model (LLM)-based multi-
agent systems (MAS) can solve complex problems beyond the reach of a single
agent by decomposing tasks and coordinating complementary capabilities. Despite extensive research on MAS, existing work largely focuses on specific aspects or individual phases of their development and operation, resulting in fragmented perspectives with objectives and signals that are difficult to reconcile across these different settings. To bridge these disconnected perspectives, we conceptualize MAS development and operation as a three-phase lifecycle spanning design-time orchestration before execution, runtime intervention during execution, and post-hoc attribution after execution, and introduce MAS-Gate, a unified node-centric quantification applicable across these phases. Modeling each execution as a directed acyclic graph, MAS-Gate combines within-node utility, which estimates a node's contribution through changes in reference-answer likelihood, with inter-node strength, which approximates semantic uptake and potential downstream reach. We adapt this shared quantification to three objectives: providing a structure-aware dense reward for Orchestrator training, training an Editor to act on execution prefixes using paired rewards from completed edited and unedited trajectories, and supplying risk-ranked nodes as auxiliary evidence for LLM-based post-hoc attribution. Experiments spanning interaction environments, knowledge-intensive multi-hop question answering, general
reasoning, and post-hoc attribution demonstrate that MAS-Gate improves workflow construction, targeted runtime editing, and LLM-assisted error localization over corresponding phase-specific baselines. Together, these results establish MAS-Gate as a unified structural interface for improving MAS generation, dynamic editing, and error attribution throughout the lifecycle.
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All authors Hehai Lin, Zixuan Wang, Ming Yan, Peidong Wang, Shilei Cao, Minzhi Li, Bosheng Ding, Juepeng Zheng, Chengwei Qin
Artificial Intelligence
OpenDepth: Repurposing Image Editing Priors for Robust Open-World Monocular Depth Estimation
Mengke Zhang, Tianyi Yang, Xinzhuo Li et al. (+1 more)
Recently, the integration of generative priors has revolutionized Monocular Depth Estimation (MDE), achieving state-of-the-art (SOTA) performance with remarkably high data efficiency. However, although methods based on Text-to-Image (T2I) models excel in general scenes, their efficacy degrades sharply when confronted with open-world environmental perturbations, such as illumination variations and adverse weather. Since T2I priors are optimized for semantic synthesis rather than preserving pixel-level structure, they struggle to decouple geometry from appearance under severe visual shifts, easily triggering structural
hallucinations. Therefore, our core insight is to model open-world depth estimation as consistent
geometric inference across multi-style images, fully leveraging the powerful structure-preserving editing priors of Image-to-Image (I2I) models. Based on this, we propose OpenDepth, a robust open-world MDE framework that introduces "Intra-Domain Awareness" – the capability to map diverse stylistic inputs sharing identical geometry into a unified latent space. To achieve this, we first align shallow features within the
Diffusion Transformer (DiT) to anchor degraded observations to unified
geometric representations. Additionally, we introduce a joint RGB latent reconstruction task, requiring the model to recover clean RGB information from degraded inputs. This multi-stage strategy effectively decouples environmental style interference, compelling the model to focus strictly on underlying
geometric recovery. Extensive evaluations show that OpenDepth sets new SOTA records for robust zero-shot depth estimation across standard and extreme weather
benchmarks, demonstrating exceptional structural integrity and adaptability.
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All authors Mengke Zhang, Tianyi Yang, Xinzhuo Li, Anlong Ming
Artificial Intelligence
Verifier-Harness Co-Evolution for Training-Free Self-Improvement on Open-Ended Tasks
Tianyi Xu, Yaolun Zhang, Yiran Wu et al. (+6 more)
Training-free self-improvement lets an
agent use feedback to revise the response it is about to return and to update the harness state that shapes how it handles later tasks. On open-ended tasks that feedback rarely comes from reference answers or executable tests, so the
agent has to rely on a model-based verifier. When the verifier is wrong, the current response can get worse, and misleading experience can persist across the task stream. A verifier that never changes also grows stale as the
agent's responses improve and task demands shift. We introduce Unified Co-
Evolution (UCoE), a training-free method that evolves an executable verifier program together with the
agent's harness state while all model parameters stay fixed. UCoE synthesizes candidate verifier programs and tests them on response contrasts, pairs of responses that differ in one declared property and have a known preferred side. It selects the candidate whose discrimination on its weakest contrast family is highest, a quantity we call driving capacity. The selected verifier diagnoses one weakness in each draft, guides a single repair, decides which response is served, and determines whether the accepted repair may enter the harness state. As tasks complete, their diagnostics seed new candidate verifiers, which must beat the current verifier on the same contrasts before adoption. Across six open-ended
benchmarks, UCoE averages 59.00% preference against matched direct generation with Qwen3-8B and 57.19% with Qwen3.8-27B, margins of 9.00 and 7.19 percentage points. On three verifiable
benchmarks, the same method raises the mean from 36.06% for direct generation to 38.48%. Code is available at https://github.com/tianyi0216/Unified-Co-
Evolution.
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All authors Tianyi Xu, Yaolun Zhang, Yiran Wu, Joe Nguyen, Shengyu Dai, Saba Moeinizade, Zhenwen Shao, Qingyun Wu, Huazheng Wang
Computation and Language
Facts Don't Speak Louder than Words: The Behavioral Essence of Long-CoT Distillation
Yongcan Yu, Jian Liang, Lingxiao He et al. (+5 more)
While long chain-of-thought (CoT)
distillation significantly improves the
reasoning abilities of
language models, its underlying mechanism remains poorly understood. Current pipelines generally rely on rejection sampling to gather
reasoning traces across diverse
prompts, assuming that factual correctness and broad problem coverage are indispensable. Surprisingly, we find that training exclusively on flawed
reasoning trajectories or a restricted
prompt set yields nearly comparable performance. Driven by this counterintuitive finding, we propose the Behavioral Hypothesis: the essence of Long-CoT
distillation is behavioral
alignment with structured
reasoning patterns, rather than factual knowledge memorization. We then validate this by demonstrating substantial
reasoning gains even when models are fine-tuned exclusively on previously mastered problems-effectively isolating behavioral
alignment from novel knowledge injection. Having established that
reasoning patterns are the true bottleneck, we investigate how to learn them across varying levels of complexity optimally. We reveal a critical interplay: while the recent probability-based loss excels at learning simple patterns, the standard cross-
entropy loss is essential for scaling to complex CoTs. We believe these insights demystify CoT
distillation and provide a principled foundation for the future development of
reasoning models. Code is available on https://github.com/yuyongcan/Long-CoT-
Distillation.
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All authors Yongcan Yu, Jian Liang, Lingxiao He, Kuangpu Guo, Yanbo Wang, Shuo Lu, Meng Wang, Ran He
Systems and Control
Constrained Grid-Forming Control for Photovoltaic Systems: A Control Barrier Function Approach
Amir Afshari and Dominic Groß
This paper leverages control barrier functions (CBFs) to systematically account for constraints of voltage source converters (VSCs) and photovoltaic (PV) resources in grid-forming (GFM) control. The proposed control combines (unconstrained) dual-port GFM control with control barrier functions (CBFs) to ensure reliable and safe operation of a GFM PV system under grid faults, sudden changes in irradiance, and load perturbations. First, CBFs are used to encode physical and operational limits of the PV system, namely current and voltage limits at its ac and dc terminals. Then, CBFs are used to restrict the dynamics of dual-port GFM control to within the VSC and PV limits during both transient and steady states. To this end, a cost function is defined that encodes trade-offs between forming the ac voltage phase angle/frequency and voltage magnitude under constraints. Then, real-time optimization is used to make the least modification, as measured by the cost function, to the (unconstrained) dual-port GFM control state to remain within the safe set encoded by the CBFs. Finally, the performance of the developed constrained GFM control is evaluated through EMT simulations in MATLAB/Simulink.
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Computer Vision
PLEDGE: Progressive Lossy Error-Bounded Compression of 3D Gaussian Splatting
Mingze Xia, Yuxiao Li, Baixi Sun et al. (+6 more)
Existing 3D Gaussian Splatting (3DGS) codecs report reconstruction quality after compression, and give users little control over the distortion of individual primitives. We present PLEDGE, a training-free, progressive, error-bounded codec that translates four user-declared tolerances into per-primitive quantization bounds: screen-space position, footprint deformation due to rotation and scale, view-dependent color, and opacity. The bounds apply throughout a declared camera domain, including previously unseen viewpoints. Position and opacity are bounded directly; footprint deformation is bounded under the rasterizer's affine projection model, and color is bounded over all viewing directions. PLEDGE organizes quantized residuals into a progressive bitstream with explicit error bounds for every valid prefix. Experiments across thirteen scenes verify all declared per-mechanism bounds and demonstrate competitive rate-distortion performance. The four tolerances incur substantially different rate costs, so users can spend rate where their application needs it.
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All authors Mingze Xia, Yuxiao Li, Baixi Sun, Wenbo Li, Pu Jiao, Sheng Di, Franck Cappello, Hanqi Guo, Xin Liang
Systems and Control
Stability Transfer for Autonomous Systems via Higher-Order Taylor-Truncated Lyapunov Certificates
Jose D. Hoyos, Zehui Lu and Shaoshuai Mou
We develop analytical sufficient conditions for certifying the local asymptotic stability of nonlinear autonomous systems based on Lyapunov functions derived from their finite-order Taylor
polynomial approximations. This approach translates the stability analysis problem from the original nonlinear dynamics to a more computationally tractable
polynomial domain. The derived conditions allow for the explicit calculation of a guaranteed inner approximation of the region of attraction ($\mathrm{ROA}$). We show how sum-of-squares (SOS) programming and grid search can be used to systematically search for
polynomial Lyapunov functions that yield a large certified threshold radius and extract a certified invariant set inner approximation of the $\mathrm{ROA}$ of the nonpolynomial original system. The framework is then applied to a control tuning problem: finding reduced-effort Linear Quadratic Regulator (LQR) controllers that ensure that a given initial state of interest belongs to the certified $\mathrm{ROA}$ of the nonpolynomial closed loop system. The effectiveness of the overall methodology is demonstrated using a pendulum with nonlinear damping and a nonlinear control system.
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Computer Vision
Morphology-aware deep learning predicts colorectal cancer survival from whole-slide images
Hikmat Khan, Metin N. Gurcan, Anil Parwani et al. (+3 more)
Background: Five-year survival prediction from hematoxylin-and-eosin whole-slide images could support risk stratification, but the task is weakly supervised, and the tissue morphologies carrying prognostic information remain incompletely characterized. Methods: We conducted a retrospective secondary analysis using TCGA-COAD/READ (n=562) for morphology-specific analysis, Alliance/ CALGB 89803 (n=424) for model development, and SurGen (n=218) for external evaluation. We propose MorphContrast, a morphology-aware contrastive multiple-instance learning framework that uses tissue labels as structural supervision for five-year survival prediction. MorphContrast combines gated-
attention pooling with three auxiliary objectives: patch-level morphology labels define within-patient contrasts, outcome concordance or discordance defines cross-patient contrasts within matched morphologies, and diversity regularization discourages representational collapse. Morphology-matched patches from patients with discordant outcomes serve as hard negatives, encouraging prognostic discrimination within comparable histologic contexts. Results: Morphology-specific models show weak but distributed prognostic discrimination: adipose tissue achieves an area under the receiver operating characteristic curve (AUC) of 0.622, stroma achieves 0.604, and both perform comparably to tumor epithelium (0.601-0.605). On Alliance/CALGB 89803, the single model achieves an AUC of 0.642, while the post hoc ensemble reaches a pooled out-of-fold AUC of 0.70 and a risk-group hazard ratio of 3.79 (95% confidence interval: 2.55-5.63). On the external SurGen colon cancer cohort (n=218), the single model and ensemble achieve AUCs of 0.669 and 0.678, respectively. Conclusions: These findings support tissue morphology as structural supervision for outcome-aware representation learning in colorectal cancer prognosis.
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All authors Hikmat Khan, Metin N. Gurcan, Anil Parwani, Wendy L. Frankel, Wei Chen, Muhammad Khalid Khan Niazi
Numerical Analysis
Strong convergence rate of the implicit midpoint method for stochastic KdV equations
Junjie Li, Xinyan Niu and Chun Li
We establish a strong convergence rate for the implicit midpoint method applied to the periodic
stochastic Korteweg-de Vries equation with additive noise, addressing a question that has remained open since the work of Debussche and Printems. To control nonlinear error growth, we obtain exponential integrability estimates for the exact and comparison solutions using the coercivity of a classical KdV invariant. We then derive local error bounds from estimates for the comparison solutions and control error accumulation through an $L^2$ error propagation identity that isolates a martingale increment. Under $H_0^{9/2}$ regularity assumptions on the noise and the deterministic initial data, we prove that, on every fixed finite time interval, the maximum $L^2(\mathbb{T})$ error at the time points is $O(h)$ in $L^p(\Omega)$ for every $1\le p<\infty$. Notably, the exponential integrability and the convergence rate hold for every damping coefficient $\lambda\ge0$, including the undamped case $\lambda=0$. Numerical experiments confirm the predicted first-order convergence.
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Computation and Language
TDR: Tagalog Diacritic Restoration Dataset
Ivan Yuri De Leon, Lanz Railey Fermin, Zachary Aldridge Africa et al. (+1 more)
Homographs are words that share identical spelling but differ in meaning and pronunciation. In Tagalog, these distinctions are disambiguated in writing through tuldik (diacritical marks), which are rarely preserved in contemporary Tagalog text. This scarcity of diacritics limits diacritic restoration, which is useful for downstream tasks such as prosody selection for text-to-
speech synthesis. To address this gap, this study introduces TDR, an annotated corpus of 40 frequent Tagalog homographs constructed from news text. It also introduces a collocation-driven
retrieval method to expand the
dataset and mitigate severe class imbalance among minor variants. Finally, we show that TDR supplies a capability absent from pretrained models, and that this capability can be learned by small, inexpensive models as well as by fine-tuned LLMs.
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All authors Ivan Yuri De Leon, Lanz Railey Fermin, Zachary Aldridge Africa, Richell Isaiah Flores
Computer Vision
Tanawpamana: Benchmarking Vision-Language Models on Philippine Cultural Heritage Sites
Ivan Yuri De Leon, Hiroki Ouchi and Sakriani Sakti
Vision-
language models are increasingly used to identify and describe places, yet their recognition and knowledge of specific cultures remain poorly understood, particularly in underrepresented regions such as the Philippines. This study introduces the Tanawpamana
Dataset, a repository of Philippine heritage site images with metadata based on the Talapamana registry of the National Commission for Culture and the Arts of the Philippines. We also introduce the Tanawpamana
Benchmark, comprising 4,046 tasks designed to evaluate models' recognition and knowledge of heritage sites. Evaluating seven
vision-
language models, we find that they struggle more with fine-grained visual discrimination among similar heritage sites than with coarse recognition, and that their geographic knowledge is weaker when inferred from image inputs than from text.
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Robotics
Training Generalist Navigation Policies via Action Self-Distillation
Bowen Feng, Keqin Wang, Yulong Yang et al. (+4 more)
Language provides a natural interface for humans to specify and reason about goals, but training
language-conditioned robotic policies and
reasoning typically requires expensive manual annotations. We propose Action Self-
Distillation (ASD), a method for learning
language-conditioned
reasoning for robotic navigation without human-annotated
language or
reasoning labels. ASD uses an open-source VLM, which contains broad world knowledge and
reasoning capabilities, as both a teacher network and a student policy. During training, the self-teacher receives privileged trajectory feedback and infers the underlying navigation goal together with goal-oriented
reasoning. The student is then trained to condition on the inferred navigation goal, predict future ego-frame trajectory, and provide a description of the scene semantics, plan, and
reasoning, from onboard observations alone. Across 13 challenging indoor and outdoor real-world navigation tasks, our method improves success rate over the strongest baseline by $30\%$ in indoor settings, $30\%$ on multi-stage tasks, and achieves $95\%$ success rate on outdoor tasks, validating that privileged trajectory feedback can enable self-
distillation for scalable generalist navigation policies without human annotations.
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All authors Bowen Feng, Keqin Wang, Yulong Yang, Amogh Joshi, Christine Allen-Blanchette, Dhruv Shah, Felix Heide
Computer Vision
Physical-Field-Bridged Material Parameter Inference from Multi-View Deformation Videos
Linrui Shou, Jie Li, Yu Liu et al. (+1 more)
Estimating simulation-ready material parameters from visual observations is crucial for deformable-object manipulation but challenging because material properties are only indirectly revealed through physical responses. We introduce physical-field-bridged material inference, a single-pass framework that uses dense physical responses to connect multi-view RGB deformation
videos with constitutive-type classification and continuous parameter regression. Unlike inverse-physics methods requiring per-scene reconstruction, online tuning, or simulation-in-the-loop optimization, our model predicts force/contact masks, projected motion-flow fields, and projected stress-response maps, then conditions material inference on these fields to reduce reliance on appearance or category shortcuts. We construct DeformFieldBench, a 3DGS MPM learning environment comprising 5,000 simulations across 100 object shapes and five mechanically informative loading modes, with dense pixel-aligned physical supervision. Our method achieves the best median errors on all four continuous parameters, improves stress-hotspot recall from 0.0762 to 0.5121, and attains 0.8676 SSIM/36.82 dB PSNR in same-simulator replay on unseen objects. Under a protocol-matched GSO evaluation, inference and future-state prediction take approximately 30 seconds, compared with 15-120 minutes for per-scene optimization. The method also yields measurable replay agreement on five real-world sequences. These results support dense physical fields as an effective bridge for fast material inference, while cross-simulator and real-world tests reveal remaining domain and identifiability gaps.
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All authors Linrui Shou, Jie Li, Yu Liu, Tao Huang
Machine Learning
Revisiting the 16x Pretraining Compute Gap between Diffusion and Autoregressive Language Models
Guanghan Wang, Bing He, Zhan Shi et al. (+3 more)
Diffusion language models (DLMs) have attracted wide
attention for their fast, parallel decoding, but they are believed to be far less efficient to train than autoregressive
language models (ARMs): a widely cited study reports that a DLM needs roughly 16x the training compute of an ARM to match its likelihood, a figure that has made pretraining DLMs from scratch look prohibitive at scale. We find that the methodology behind this figure has fallen out of step with current DLM practice: it scores the model with its evidence lower bound (ELBO) and trains a full-sequence, decoder-only model. We therefore revisit the scaling laws of DLMs, adopting the exact likelihood under the model's own decoding order, block
diffusion training, an encoder-decoder architecture, and a sweep over the block sizes used in practice. Under this new protocol, we study across ten model variants, about 1,700 models in total, and find that pretraining DLMs is far cheaper than previously thought. Concretely, scoring with the exact likelihood reduces the compute multiple to 12.8x; block
diffusion training and an encoder-decoder architecture further reduce it to 7.8x at block size 32, and smaller block sizes narrow it to 4.6x at block size 8 and 3.0x at block size 4. We complement these scaling analyses with an empirical rule for the optimal decoder depth of encoder-decoder block
diffusion, a held-out compute budget beyond the fitting range at which the gap between variants persists, and downstream
benchmark results where likelihood improvements carry over to accuracy. Overall, the 16x figure substantially overstates the pretraining cost gap between DLMs and ARMs, and pretraining DLMs from scratch is far more viable than previously assumed.
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All authors Guanghan Wang, Bing He, Zhan Shi, Benoit Dumoulin, Hanqing Lu, Volodymyr Kuleshov