Machine Learning
ABC-Align: Prediction-Powered Alignment with Adaptive Bias Control
Eric Frankel, Banghua Zhu, Sewoong Oh et al. (+1 more)
Language model post-training is often bottlenecked by the need for human-collected preference data, which is expensive and difficult to scale. Reinforcement learning from AI feedback (RLAIF) style approaches that leverage pseudo labels offer an abundant alternative but introduce systematic biases that degrade downstream
alignment. Recent general-purpose semi-supervised methods correct for teacher bias using a small set of human-labeled examples, but suffer from high variance especially when human annotations are scarce. To this end, we propose ABC-Align, leveraging abundant pseudo label signal to minimize variance and applying a lightweight, adaptive correction grounded in the human-labeled subset. The correction strength is tuned automatically during training using plug-in estimates of the relevant bias–variance quantities. On LLM
alignment with RLHF, DPO, and GRPO where human feedback is scarce, we empirically demonstrate that ABC-Align achieves superior performance over prior semi-supervised baselines in a series of experiments on an increasing scale. Our code is available at https://github.com/SewoongLab/abc-align .
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All authors Eric Frankel, Banghua Zhu, Sewoong Oh, Lillian J. Ratliff
Machine Learning
Emergence, Not Bandwidth: Physical Coupling and the Limits of Learned Multi-Agent Communication
Mihir Chauhan and Aniket Bera
Rate-limited multi-
agent teams raise three questions the emergent-communication literature has answered only empirically: what an optimal message should encode, what compression costs over a horizon, and when a learned protocol is unique enough for a teammate to read. We answer them for rate-limited Dec-POMDPs, then measure how far reinforcement learning falls short of the optimum. Our
theorems fix what is achievable independently of any learner, so a gap between an engineered and a learned sender at the same bit budget is an optimization fact, not an information-theoretic one. We instantiate this on three MuJoCo arenas spanning zero, partial and rigid physical coupling, charging every condition exactly 2 bits per decision, and create the discriminating regime by closing a physical side channel within one arena, holding bodies, task and reward fixed. Communication value is governed by coupling: under rigid coupling through a shared object, no channel beats silence (+0.001 +/- 0.001, p = 0.982, n = 25), since proprioception already carries that information; without coupling, every condition solves the task; under partial coupling, the engineered 2-bit sender reaches an interquartile mean of 1.000 but the learned one reaches 0.482, indistinguishable from silence (p = 0.400, n = 25). With a shared alphabet, bandwidth cannot explain the gap. Warm-starting from an engineered receiver localizes the failure: the same channel reaches 0.857 versus 0.562 cold-started (p < 0.001), so it is neither representational nor one of maintenance; reinforcement learning fails to discover the protocol. Cross-play shows learned protocols are individually meaningful but mutually unintelligible: self-play 0.980 collapses to 0.144 across seeds, and our best constructed
alignment leaves at least 77% of that gap. All headline results use 25 seeds per arena and seven published baselines at matched rate.
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Artificial Intelligence
PersMem: Internalizing Personality into Dual-Pathway Memory for LLM Agents
Hanzhong Zhang, Ziwei Xiang, Weicheng Xie et al. (+2 more)
The profile of a role-playing
agent usually depends on the pre-defined personality in a system
prompt, whereas its
memory processing pipeline, including prioritisation of stored
memories and subsequent
retrieval, remains independent of this personality. This separation causes the
agent's
memory processing to be inconsistent with the pre-defined personality, and makes it difficult to validate whether
agent behaviours follow this personality. In this paper, we propose Personality-Integrated
Memory (PersMem), which integrates personality into the
agent's
memory processing pipeline, making it consistently personality-dependent. PersMem processes
memory using four steps, where the personality is mapped to operation-specific parameters controlling: (i) affective appraisal annotating
emotion states of the user input; (ii) retention of previously stored
memories along with the current input; (iii) passive affect-driven
memory retrieval exploring
memories similar to user input in semantics and personality-guided
emotions; and (iv) active goal-driven
memory retrieval that refines and selects passively retrieved
memories for the reply. Consequently, consistency with the pre-defined personality can be examined by inspecting
memory-processing traces during human-
agent interactions. We evaluate these personality-dependent differences in attachment and Big Five settings. PersMem exceeds the chance baseline for four-way attachment classification by 23.1 percentage points. In Big Five dialogue comparisons, PersMem achieves 67.5% accuracy, 6.7 percentage points above a baseline using uniformly sampled
memories. On CoSER, PersMem achieves an average score of 66.13, with scores of 69.33 for Character Fidelity and 84.33 for Storyline Quality. Together, these results show that PersMem produces distinguishable personality-related
memory-processing patterns.
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All authors Hanzhong Zhang, Ziwei Xiang, Weicheng Xie, Shizhe Liu, Siyang Song
Computer Vision
From Static to Dynamic: On-Policy Distillation from Image to Video Diffusion Models
Bingqing Jiang, Li Luo, Zichao Yu et al. (+3 more)
On-policy
distillation (OPD) specializes pretrained
video diffusion models through teacher supervision along the student's own generation trajectory. Although large
video models are natural teachers, developing specialized
video experts can require costly
video data and training, while querying them incurs substantially higher latency than querying image experts. More readily available and cheaper to query, image experts offer a cost-effective alternative, particularly for largely temporal-agnostic capabilities such as aesthetics and OCR that admit frame-level supervision. However, heterogeneous image and
video latent spaces prevent direct supervision of intermediate student states, while image experts lack cross-frame motion supervision, making temporal consistency vulnerable to frame-level improvements. In this paper, we propose MILD, a Motion-Preserving Image-to-
Video Latent
Distillation framework that transfers specialized image expertise while preserving pretrained
video dynamics. MILD uses a learnable linear connector that aligns student latent states and predicted updates with those of image experts, enabling supervision transfer across heterogeneous latent spaces. We further constrain image-guided corrections around the pretrained student's predictions to preserve
video dynamics and incorporate an optical-flow-based motion reward to improve motion quality and temporal consistency. Across specialized image experts and multiple
video-student backbones, our method consistently outperforms
video-teacher OPD baselines, with further studies demonstrating effective transfer across connector designs and heterogeneous architectures. These results establish image-to-
video distillation as an effective route to improving
video generation by drawing on the diverse and evolving capabilities of the image-generation ecosystem.
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All authors Bingqing Jiang, Li Luo, Zichao Yu, Yujin Han, Zhaolong Su, Difan Zou
Machine Learning
Spexis: Speculative Lookahead Scheduling for LLM Inference
Hyungyu Jung, Jaehyeok Yu, Hoonseo Choi et al. (+3 more)
Spexis is a multi-GPU LLM inference framework that improves the efficiency of pipeline and tensor parallelism through speculative parallelism. Rather than using speculative decoding only to accelerate
token generation, Spexis runs speculation in parallel with normal execution, introducing a new parallelism axis without increasing KV-cache
memory usage. This improves
memory efficiency and helps mitigate the bottlenecks of multi-GPU inference. Spexis further uses lookahead scheduling to predict speculation quality and future
memory pressure, allowing it to reduce wasted speculation, KV-cache eviction, and recomputation. Built on top of vLLM, Spexis largely improves serving performance across a range of GPU configurations, achieving speedups of up to 34% over a baseline that uses the optimal combination of pipeline and tensor parallelism. Spexis's source code is publicly available at https://github.com/mlsys-seo/spexis.
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All authors Hyungyu Jung, Jaehyeok Yu, Hoonseo Choi, Sungkyun Kim, Jinho Lee, Jiwon Seo
Emerging Technologies
The Shape of Speed: Impacts of Partition Geometry and Rank Density in Distributed Quantum Circuit Simulations
Yikai Mao, Yuan He, Shaowen Li et al. (+1 more)
In distributed
quantum circuit simulation, a poorly shaped partition can halve performance before computation begins. Evaluation on Fugaku across 764 validated configurations (twelve
algorithms, thirteen
torus partition geometries, and six rank densities for 39-
qubit simulations on 1,024 nodes) shows that partition geometry dominates runtime. All twelve
algorithms run 1.73-2.31x slower on flat partitions than on near-cubic ones despite identical data transfer, proving the slowdown stems from network delivery rather than communication volume. This penalty scales with the 3D
torus partition aspect ratio (runtime $\propto a^{0.39}$, $r = 0.72$). Rank density is secondary, cutting runtime by 11% at 16 ranks per node only on compact geometries. Ultimately, requesting a near-cubic partition with 16 ranks per node roughly halves time-to-solution relative to flat partitions, which also consume 1.82x more energy. A simulator-free all-to-all microbenchmark confirms a similar geometry penalty for collective-dominated workloads.
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All authors Yikai Mao, Yuan He, Shaowen Li, Masaaki Kondo
Computer Vision
Rate-Distortion Adaptive Primitive Selection for Omnidirectional Gaussian Splatting
Yulong Cheng, Youneng Bao, Junfeng Zhou et al. (+2 more)
Learned image codecs (LICs) achieve high reconstruction quality, but their decoding speed is often insufficient for immersive virtual reality (VR). Gaussian splatting (GS) codecs render much faster, yet still lag in reconstruction quality and typically decide primitive allocation without considering the coding cost of each primitive. We introduce OIC-GS, an omnidirectional GS codec with a new hierarchical HEALPix primitive grid representation. Gaussian primitives are anchored at predefined spherical locations, eliminating explicit coordinate coding. Finer levels refine their coarser ancestors, naturally supporting coarse-to-fine reconstruction and layered transmission. The predefined grid also enables efficient viewport decoding by selecting only view-relevant primitives. We further introduce a lightweight
entropy model for quantized primitives and optimize the codec under a spherical rate-distortion objective. Primitives with insufficient rate-distortion benefit are automatically removed when their quantized opacity becomes zero, allowing OIC-GS to adapt both primitive density and level of detail without a fixed primitive budget. A single bitstream supports full-sphere, viewport-dependent, and progressive decoding. The first viewport reaches final quality after decoding only 52% of the bitstream, and is then rendered at 1,270 FPS. On a 100-image omnidirectional
benchmark, OIC-GS outperforms all evaluated GS codecs, reducing WS-PSNR BD-rate by 49.6% over GaussianImage++ and 68.6% over SGI, which uses a learned
entropy model.
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All authors Yulong Cheng, Youneng Bao, Junfeng Zhou, Mu Li, Jie Wen
Computation and Language
When Harness Beats Scale, and When Reading Beats Both
Ivan Bondarenko and Nikolay O. Nikitin
We describe our system for DocSem, the document-grounded quantitative
reasoning shared task at DocInsights 2026, and analyze why it succeeded on labeled data and failed on the test set. The pipeline pairs hybrid block
retrieval with Program-of-Thoughts (PoT) generation executed in a sandboxed interpreter, self-consistency sampling, and entity enrichment from chunk-level knowledge graphs. On our held-out split, application architecture moved the metrics far more than model scale did: PoT added 0.282 joint accuracy to a compact 7B model but at most 0.005 to a 72B model, and a 27B model with the full harness matched the 72B (0.884 vs.\ 0.873) at roughly 2.7$\times$ fewer parameters and a quarter of the CO$_2$. We read this through a distinction between world knowledge, which scales steeply with parameters, and
language knowledge, which scales gently, and show that structured-output training makes a compact model harness-ready rather than merely small. On the raster, watermarked test PDFs the same system collapsed to 13.58% joint (rank 149 of 163); a controlled re-rendering of the validation set reproduces the OCR half of the collapse while bounding what the simulation misses.
Auditing the physical nature of evaluation inputs precedes architecture, and the leaderboard's bimodality is consistent with reading quality, not
reasoning, having separated the field.
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Computer Vision
SyncRA: Learning Temporal Correspondence in Omni-Modal Models
Zelong Xu, Yan Li, Wenhe Hu et al. (+1 more)
Recent omni-modal models demonstrate strong perception of audio and visual inputs, yet often struggle to connect what they hear with what they see at the same moment. This weakness in temporal correspondence can cause models to associate spoken cues with the wrong visual scenes, producing plausible answers grounded in incorrect audio-visual pairings. We diagnose this problem through controlled temporal swaps, revealing that model answers do not reliably follow changes in these pairings. To address it, we propose Synchrony-Guided Representation
Alignment (SyncRA), a lightweight method for strengthening temporal correspondence between audio and
vision. Specifically, SyncRA contrasts intermediate audio-visual representations within each
video, aligning matching moments while separating mismatched ones to capture local temporal correspondence within a shared global context. The objective derives supervision directly from existing input timing, requiring no additional annotations and leaving inference unchanged. We evaluate SyncRA across four open omni-modal models spanning different sizes and architectures on five public
video benchmarks. SyncRA consistently outperforms answer-only fine-tuning across all model-
benchmark combinations, while substantially improving the ability to track changing audio-visual pairings in controlled evaluations. These results demonstrate that lightweight, targeted supervision can effectively strengthen temporal correspondence and translate into broad improvements in audio-visual question answering.
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All authors Zelong Xu, Yan Li, Wenhe Hu, Xiyang Hu
Robotics
FutureDuet: Decoupling Observation Access from Future Supervision in World Action Models
Jie Wu, Yuzhi Huang, Junqi Liu et al. (+5 more)
World Action Models (WAMs) augment
robot action generation with future visual supervision. Existing WAMs commonly fuse main and wrist observations into one visual stream and train both with the same future-
video objective, despite their different visual dynamics. A stable main camera reveals scene-level task
evolution, whereas wrist cameras move with the end effector, mixing local interaction changes with viewpoint shifts and self-occlusion. These contrasting predictive demands suggest that the two views may benefit from different future objectives. We introduce FutureDuet, which retains both views for control, while allowing each visual stream to receive a different future objective. For the main view, future RGB models task
evolution, while interaction masks and
robot skeletons focus supervision on task objects and
robot motion. For the wrist stream, future latent prediction models short-horizon interaction changes without requiring pixel-level reconstruction. ActionDiT jointly reads the resulting Task State and Interaction State, combining scene-level progress with close-range interaction evidence. All auxiliary prediction modules are training-only, adding no inference overhead. FutureDuet achieves 94.2% clean and 94.1% randomized success on RoboTwin50 and 99.2% average success on LIBERO. The improvements are most pronounced on six RoboTwin50 tasks that require precise interaction, averaging gains of 9.2% and 12.8% over Fast-WAM in clean and randomized settings. Controlled studies further show complementary gains from separating the wrist pathway and designing future supervision separately for the two views.
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All authors Jie Wu, Yuzhi Huang, Junqi Liu, Weichen Zhang, Haibin Huang, Yin Chen, Jingyan Jiang, Chi Zhang
Artificial Intelligence
CoeF-SFL: Preserving Collaborative Server-Client Learning with Enhanced Communication Efficiency
Junwoo Bae and Jin-Hyun Ahn
Split
Federated Learning (SFL) enables resource-constrained clients to participate in collaborative training, but vanilla SFL exchanges smashed data and gradients at every batch, which incurs significant communication overhead. Recent methods reduce this overhead with an auxiliary network at the client-side cut layer. However, we identify that this approach makes the client optimize a local objective that differs from the end-to-end objective, which fundamentally limits the collaborative training between the client and the server. We propose Compensated Feedback based SFL (CoeF-SFL), a communication-efficient framework that retains the end-to-end objective without any auxiliary network. In CoeF-SFL, the client and the server exchange the smashed data and the gradients once per round and reuse them during local training. Since this reuse makes the gradients stale on the client side, we compensate them with a
curvature-based correction in the activation space and develop two variants. CoeF-D approximates the Hessian with a diagonal gradient outer product, while CoeF-J exploits the tractable Jacobian-based Hessian of a surrogate loss that upper-bounds the true loss. We provide the theoretical background of each method, characterizing its compensation. Across
vision and
language tasks, model capacities, cut layers, and data distributions, CoeF-SFL significantly outperforms auxiliary-network-based methods under the same communication frequency, and the improvement is most substantial on
vision tasks. Code is available at https://anonymous.4open.science/r/CoeF-SFL-2686/README.md
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Artificial Intelligence
Improving Large Language Models for Code through Runtime Program-State Reasoning
Hongwei Li, Spandan Garg and Yufan Huang
Large
language models receive limited explicit training in
reasoning about runtime program states. We study whether training models to reason about runtime program states improves downstream software-engineering capabilities. We introduce two complementary program-state
reasoning tasks. Buggy input-output
reasoning requires a model to generate a concrete input that exposes a behavioral difference between a buggy program and a hidden correct implementation and to predict the resulting execution behavior. Precondition-postcondition
reasoning requires an
agent to symbolically characterize a bug-triggering precondition, predict the expected postcondition, explain their causal connection, and instantiate this
reasoning as an executable regression test. By incorporating these two tasks into a staged post-training pipeline, we develop Comet-9B, a 9B
language model based on Qwen3.5-9B Base. We evaluate the resulting checkpoints on repository-level patch generation, regression-test generation, and security PoC generation. Adding both program-state
reasoning tasks to supervised fine-tuning (SFT) on issue resolution improves success rates by 7.25 percentage points on SWE-bench Pro and 9.70 points on SWT-Bench Verified. Sequential reinforcement learning on the two tasks yields further gains of 7.25, 26.79, and 4.67 percentage points on SWE-bench Pro, SWT-Bench Verified, and CyberGym, respectively. Despite having only 9B parameters, Comet-9B achieves a score comparable to the reported GPT-5.2 result on SWE-bench Pro and matches the reported success rate of a GPT-4o-based
agent on SWT-Bench Verified.
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Machine Learning
FORGE: Form-Optimal Routing of Grounded Evidence for Frozen LLM Agents
Xi Xiao, Yunbei Zhang, Chen Liu et al. (+7 more)
In agentic AI systems, frozen foundation models are increasingly deployed as closed-weight API endpoints, making downstream adaptation possible only through the inputs and inference procedures surrounding the model. As a result, for each input query, two coupled decisions largely determine both answer quality and
token cost: what evidence to provide and how much
reasoning budget to allocate. Fixed defaults along these axes are often suboptimal, misallocating support form or
reasoning depth on roughly 80% of queries in our analysis. To address this challenge, we propose FORGE, a unified framework for adapting frozen models through per-query routing over a joint action space that spans both support form and thinking depth. Under an
entropy-regularized, cost-aware utility objective, we derive a closed-form Boltzmann routing target and instantiate the policy as a lightweight 269K-parameter factorized router. The routing policy is trained around the frozen host, without any weight access, through a three-stage pipeline: offline arm enumeration, supervised Kullback-Leibler (KL)
distillation from the Boltzmann target, and Group Relative Policy Optimization (GRPO) refinement with host feedback. Across 5 knowledge-intensive
benchmarks and 8 frozen backbones ranging from 7B to 671B parameters, FORGE improves accuracy at 42-45% lower
token cost on both main hosts, transfers zero-shot across hosts at lower
token cost, and composes with intrinsic thinking budgets where available.
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All authors Xi Xiao, Yunbei Zhang, Chen Liu, Lin Zhao, Jialin Chen, Tianchen Zhao, Xiang Xu, Youngeun Kim, Tianyang Wang, Min Xu
Robotics
Predictive Semantic Safety: From Visual Physical Reasoning to Safety-Critical Control
Taekyung Kim, Salem Fradi, Yanning Dai et al. (+2 more)
Physical interactions can create future hazards that are not apparent from the
robot's current
geometric surroundings. We present a framework termed Predictive Semantic Safety (PSS), which connects visual physical
reasoning to backup-based safety filtering. A
vision-
language model (VLM) predicts physical events and their timing or directly predicts object displacements. An explicit motion model converts event hypotheses into object trajectories. Split conformal prediction calibrates position errors jointly across specified objects, observation times, and future times;
geometric shape bounds convert the resulting position regions into predicted object occupancy. PSS evaluates a prescribed backup maneuver against this occupancy and derives input-affine constraints for minimally modifying the nominal input while preserving backup feasibility under the
robot dynamics and input limits. MuJoCo experiments with a Unitree Go1 consider falling fixtures, impact-driven support loss, and contact propagation. PSS achieves a safe episode rate of 99.3%, compared with 43.3% for a Backup Control Barrier Function baseline that only uses current obstacle geometry.
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All authors Taekyung Kim, Salem Fradi, Yanning Dai, Mateusz Ostaszewski, Jürgen Schmidhuber
Machine Learning
FAST-Brain: A Flow-Aligned Spatio-Temporal Surrogate Brain Model
Shucheng Liu, Changchun Shi, Kai Zhang et al. (+1 more)
Modeling resting-state functional
magnetic resonance imaging (rs-fMRI) data is crucial for understanding
brain-wide neural activity. However, traditional methods struggle to capture complex temporal dynamics over long horizons, to account for the
brain's anatomical spatial structure, and to model high-dimensional ambient signals that lie on a low-dimensional intrinsic subspace. We propose FAST-
Brain, a unified flow-aligned spatio-temporal surrogate
brain model that addresses all three challenges. At its core is a flow-aligned generative framework that directly predicts the clean blood-oxygen-level-dependent (BOLD) signal, paired with a graph convolutional network that captures spatial structural constraints and a
Transformer that models long-range temporal dependencies. Theoretically, we show that under a low-dimensional subspace assumption, the approximation error of our model scales with the intrinsic dimension rather than the ambient dimension, which justifies our direct modeling of the BOLD signal. Extensive experiments on synthetic and Human Connectome Project
datasets demonstrate that FAST-
Brain achieves state-of-the-art performance in recovering functional connectivity, effective connectivity, and the implicit low-dimensional signal subspace.
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All authors Shucheng Liu, Changchun Shi, Kai Zhang, Hongtu Zhu
Artificial Intelligence
SemRD-V2X: Closure-Guided Communication with Bounded Inference for Cooperative Perception
Hu Xu, Chun Li, Siyuan Qiu et al. (+2 more)
Vehicle-to-Everything (V2X) cooperative perception improves 3-D detection by sharing intermediate features, but dense remote features may repeat context that the ego
agent can infer locally. Most communication-efficient designs optimize masks or codes empirically, leaving a more basic question open: which remote evidence is indispensable given the receiver's own observation? We introduce a closure-fidelity perspective on ego conditioned remote perception. Under a finite deductive abstraction and explicit conditions, its rate–distortion function decomposes over an irredundant core, and the exact zero-distortion rate becomes $P_A H(\pi_A)$. This analysis suggests a concrete design principle: transmit compact evidence and recover derivable context with bounded receiver-side inference. Guided by this principle, SemRD-V2X is an operational neural proxy that combines exact-budget BEV support selection, pointwise channel compression, and masked shared-weight reconstruction before standard fusion. Experiments on simulated V2XSet and real-world DAIR-V2X validate the resulting design. In a controlled five-run V2XSet comparison against a locally reproduced V2X-ViT-v1 baseline on one Tesla V100, SemRD-V2X reduces the analytical feature payload by $26.6\times$ while improving AP@0.5/AP@0.7 by 4.13/8.57 points, with 3.81% additional mean compute latency. These results position closure fidelity as both an analytical lens and an actionable design principle for communication-efficient cooperative perception.
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All authors Hu Xu, Chun Li, Siyuan Qiu, Zeyan Li, Jianfeng Xu
Hardware Architecture
PolyCIM: Improving Data Reuse in Digital CIM Accelerators with Polyhedral-Based Compilation
Yingjie Qi, Cenlin Duan, Yiou Wang et al. (+4 more)
Digital Compute-in-
Memory (CIM) presents a promising solution for accelerating deep neural networks (DNNs) through the integration of computational logic directly within
memory arrays. However, mapping modern DNN operators to CIM accelerators often results in severe array underutilization, due to the strict data reuse constraints imposed by the rigid CIM array structure. We observe that data reuse in modern DNNs forms hyperplane structures often oriented along non-axial directions, rendering them invisible to conventional mapping methods that only exploit axis-aligned reuse. In this work, we propose PolyCIM, a polyhedral-based compilation framework for CIM architectures that systematically exposes and realigns these hyperplanes through affine transformations. PolyCIM provides a unified abstraction capable of efficiently representing both diverse DNN workloads and digital CIM architectures. Through data reuse exposure, computation mapping, and data movement optimization, PolyCIM generates mappings for CIM architectures that achieve superior array utilization and performance. Experimental results show that PolyCIM delivers up to $4\times$ improvement in macro utilization and $3.2\times$ speedup, effectively bridging the gap between modern DNN operators and CIM architectures.
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All authors Yingjie Qi, Cenlin Duan, Yiou Wang, Yikun Wang, Xiaolin He, Weisheng Zhao, Jianlei Yang
Artificial Intelligence
Fuzzy Distribution Modeling for Synthetic Tabular Data Generation with Causality Preservation
Michael Vasilakakis and Dimitris K. Iakovidis
Synthetic tabular data generation provides an effective alternative for the training of machine learning models when real-world data is limited or inaccessible. However, the heterogeneous, non-smooth, and incomplete nature of tabular data poses fundamental challenges to conventional probabilistic and deep generative models, where their interpretability remains limited. This paper proposes a novel fuzzy distribution modeling methodology for synthetic tabular data generation based on fuzzy sets theory. Feature distributions are represented using fuzzy sets and feature dependencies are modeled through Fuzzy Cognitive Maps, resulting in a low-parameter, and an interpretable data representation. Synthetic samples are generated by sampling fuzzy concepts rather than raw values, enabling native support for mixed data types, missing values, and domain constraints. The methodology further supports linguistic queries and IF-THEN
reasoning, facilitating transparent simulation of decision-making processes. Experimental results on
benchmark datasets demonstrate competitive performance with respect to utility, fidelity and
privacy compared to state-of-the-art methods, while offering substantially improved interpretability. These results establish fuzzy distribution modeling as a principled and effective approach for synthetic tabular data generation in fuzzy systems and decision support applications.
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Machine Learning
Commutator Memory: Sparse, Path-Local Reading and Steering in Language Models
John Sweeney
Gradient updates on different data generally do not commute: training a
language model on two data sources in opposite orders gives different weights, even with the same data and total exposure. Loss or
benchmark deltas show that the models differ, not where. We ask whether this path dependence leaves a parametric training-history
memory: a weight component that flips sign when the two sources are swapped, is localized in output space, changes the held-out loss gap between the two orders under targeted interventions, and reveals which trained model came from which order. For one small SGD step of size $\eta$ on each of sources $A$ and $B$, the weight difference $\theta_{AB}-\theta_{BA}$ is, to leading order, $\eta^2 b_{AB}$, where $b_{AB}=H_Bg_A-H_Ag_B$ is the Lie bracket of the two gradient fields at the base model. We define commutator
memory by projecting the bracket through the logits into one score per vocabulary
token; the scores sum to the bracket's prediction of the gap. The scores are localized: on three models, the same readout of the measured $\theta_{AB}-\theta_{BA}$, or of a bracket from disjoint batches, shares 82-99% of the original top-20
tokens, versus 35-49% for norm-matched random directions. They are causally actionable: in Qwen-3-4B SFT, downweighting the ten
tokens with the largest predicted share of the gap closes a median 32% of the measured gap, while frequency-matched
tokens with near-zero scores have almost no effect. The weights themselves carry the component: projecting the difference between the two trained models onto $b_{AB}$ identifies which came from which order in 92% of cases across four LLMs (chance 50%). Controlled tests also cover matched-batch DPO, a frozen-rollout GRPO-style objective, and an AdamW endpoint check. The
memory is defined per source pair, not per example, and its projection on $b_{AB}$ decays with further training.
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Sound
SAIL: Spatial Audio Intelligence with Large Language Models via Disentangled Acoustic-Spatial Encoding and Dual-Stream Q-Former
Zhengding Luo, Jinyang Wu, Haozhe Ma et al. (+3 more)
Spatial audio large
language models (LLMs) enable
embodied agents, wearable assistants, and immersive systems to recognize sound events, localize sources, and reason about their spatial relationships. However, existing spatial audio LLMs often rely on early fusion of acoustic and spatial features and source-agnostic
token representations. These designs make it difficult to preserve the correspondence between individual sound events and their spatial attributes, particularly in multi-source scenes. To address this limitation, we propose SAIL, a Spatial Audio Intelligence framework with LLMs that preserves acoustic-spatial structure and source-level correspondence from audio encoding to LLM
alignment. SAIL introduces a Disentangled Spatial Audio
Transformer that represents Mel-spectrogram and interaural phase difference features as separate acoustic and spatial streams. Source-discriminative task queries further learn event, direction, and distance information for each source. A Dual-Stream Q-Former then aligns the two streams with the LLM using acoustic and spatial queries organized by source slots. Compared with the early-fusion baseline, SAIL achieves consistent improvements in dual-source sound event detection, direction and distance estimation, and spatial
reasoning. These results demonstrate the importance of structured, source-discriminative audio representations for multi-source spatial understanding and
reasoning.
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All authors Zhengding Luo, Jinyang Wu, Haozhe Ma, Yanghao Zhou, Woon-Seng Gan, Wenwu Wang
Computer Vision
E-WAVE: Event-based Continuous Optical Flow via Warping-Aligned Visual Encoding
Jiale Wu, Xiaoyang Bai, Haoming Yu et al. (+3 more)
Temporally dense optical flow is essential for dynamic perception in immersive VR/AR systems, where rapid head, hand, and object motion must be continuously captured and tracked. Existing frame-based optical flow estimation methods are constrained by the tradeoff between temporal resolution and computational cost; while event cameras, with their high temporal resolution and energy efficiency, serve as a natural solution to the dilemma. However, event-based approaches commonly rely on correlation volumes to capture pairwise voxel correspondences, which incur substantial
memory and computation overhead. We present E-WAVE, a correlation-free framework for high-temporal-resolution (HTR) optical flow estimation from event streams. Instead of constructing all-pairs correlation volumes, E-WAVE employs global
attention mechanism to model long-range feature dependencies and performs trajectory guided feature warping using Bézier curve. Through iterative updates, it predicts trajectories that allow for querying at arbitrary timestamps without repeated inference. Experiments on MultiFlow and DSEC-Flow demonstrate a 25% lower trajectory error and comparable endpoint flow estimation accuracy relative to state-of-the art baselines. Additional evaluations on self-captured data using a head-mounted prototype validate that E-WAVE remains robust under challenging real-world conditions.
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All authors Jiale Wu, Xiaoyang Bai, Haoming Yu, Yiwei Chen, Yifan Peng, Weiwei Xu
Computation and Language
CRISP: Cultural Reward Modeling for Implicit Situated Propriety
Zekun Yuan, Yangfan Ye, Baohang Li et al. (+6 more)
As large
language models (LLMs) are increasingly deployed across countries and regions, the ability to recognize and respond appropriately to diverse cultural contexts becomes increasingly important. However, existing research has largely focused on cultural knowledge or tasks with predefined response spaces, while open-ended culturally situated behavior remains comparatively underexplored. In this work, we introduce CRISP-RM, a culturally situated reward model that assigns rewards according to cultural appropriateness in open-ended social scenarios. During policy optimization, we further introduce Norm Grounding Supervision (NGS), providing guidance that enhances the policy's sensitivity to relevant cultural norms. To construct culturally situated data, we employ a collaborative multi-
agent framework that instantiates implicit cultural norms into diverse social scenarios and further curate NormCompass as a dedicated testbed. We conduct comprehensive experiments to evaluate the effectiveness of CRISP-RM in both reward modeling and policy optimization. Best-of-\(N\) experiments show that CRISP-RM consistently outperforms strong general reward models. During GRPO policy optimization, CRISP-RM generally improves culturally situated behavior, while incorporating NGS yields further gains. Further analyses demonstrate the advantages of CRISP-RM in distinguishing culturally appropriate behavior beyond superficial fluency and politeness, while NGS provides complementary gains during policy optimization by improving norm grounding.
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All authors Zekun Yuan, Yangfan Ye, Baohang Li, Shuaibo Zhao, Zekun Zhou, Ziming Li, Qichen Hong, Kun Chen, Xiaocheng Feng
Machine Learning
Learning to Steer, Steering to See: Unveiling the Geometry of RLVR in Large Language Models via Trainable Vectors
Yuchen Cai, Ding Cao, Qixiang Yin et al. (+10 more)
Reinforcement learning (RL) has become a key paradigm for enhancing the
reasoning of large
language models, yet the high dimensionality of parameter updates makes its training dynamics hard to analyze. We study reinforcement learning with verifiable rewards (RLVR) and use vector steering to identify a low-dimensional effective
manifold in activation space associated with RL-induced gains. We uncover two
geometric properties. (1) Effective
Manifold Capacity: the capacity needed to reproduce RL gains can be very small but is not infinitely compressible; at extremely low capacity, intervention dimensionality and input-dependent expressiveness become key constraints, and this requirement varies with injection depth. (2) Control
Manifold Separation: effective control directions lie mainly in the low-variance complement of the activation principal subspace. Within a task and base model, the learned geometry stays largely consistent across training configurations, and across tasks
geometric alignment correlates with capability transfer. Experiments on 5 LLMs and 6 verifiable-reward tasks support these findings. We then propose Alpha-Stabler, a plug-and-play framework with a Predictor that monitors principal-subspace intrusion for early collapse warnings, and a Controller that removes the principal-subspace component of activation gradients during backpropagation while preserving the orthogonal complement. Alpha-Stabler stabilizes training for 2,000 steps and consistently improves RL gains, offering practical insights for robust post-training. Code: https://github.com/caiyuchen-ustc/On_Policy_Vector_Training
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All authors Yuchen Cai, Ding Cao, Qixiang Yin, Xin Xu, Kai Yang, Siye Wu, Pengyuan Wang, Jiaxuan Wang, Weijie Liu, Saiyong Yang, Guangzhong Sun, Guiquan Liu, Junfeng Fang
Machine Learning
Guang Li, Takahiro Ogawa and Miki Haseyama
Dataset distillation compresses a training set into a small synthetic set, usually evaluated one at a time. In
federated and data-governance settings, several parties distill their own data and a user trains on their union. We ask whether the union of separately distilled sets reproduces training on the union of the real data composability and show that it can fail even when every source is distilled exactly and the total budget admits an exact joint distillate. Compressing a training trajectory into fewer steps transforms the source statistics nonlinearly, so averaging compressed sources differs from compressing their average. For quadratic objectives we derive the exact composition error for two-to-one step compression in terms of the source-Hessian variance and the linear terms of the losses, and on a smooth network at small step sizes this prediction captures the local endpoint discrepancy in magnitude and direction. For learned synthetic sets, however, the composed error decomposes exactly into this local discrepancy and an aggregate source residual. Under endpoint matching the residual exceeds the structural term by more than an order of magnitude, and under distribution matching the two terms partly cancel. Joint
distillation also retains an accuracy advantage when both sets are distilled from the same
dataset, where the local discrepancy is exactly zero. Training fidelity and downstream accuracy are therefore distinct requirements, neither established by evaluating each set on its own.
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Artificial Intelligence
SAGE: Structured Strategic Reasoning for Efficient LLM Game Playing
Zhiwei Chen, Tianchun Wang, Zhongtao Rao et al. (+3 more)
A strong LLM strategic
agent should reason prospectively over uncertain futures, adapt its strategy to opponents' behavioral tendencies, and continuously recalibrate its decision process from interaction experience. However, incorporating these sources in free-form
reasoning could lead to unsupported strategic assumptions, inconsistent opponent estimates, and harmful interference from irrelevant historical interactions. To address these issues, we propose SAGE, a training-free inference-time framework that structures LLM strategic
reasoning around three coordinated operations: anchor, adapt, and recalibrate. SAGE first anchors
reasoning to an
equilibrium policy that provides a strategically valid prior. It then conditions deviations from this anchor on a soft belief over opponent behavioral tendencies, enabling opponent-specific exploitation. Finally, SAGE distills strategically related interactions into counterfactual hypotheses about previously missing considerations, allowing past experience to recalibrate the model's
reasoning. We evaluate SAGE on three repeated imperfect-information games: Leduc Hold'em, Liar's Dice, and Goofspiel, against various opponent types in each game. Compared with
reasoning-intensive LLM
agents, including Suspicion-
Agent, ReTA,
Agent-Pro, EMO, and Hypothetical Minds, SAGE achieves up to a 127.6% payoff improvement in Liar's Dice while reducing input and output
token usage by up to 80% and 90%, respectively. In direct match-up play, it attains non-negative mean payoff against 5/10, 8/10, and 8/10 evaluated opponents in Leduc Hold'em, Liar's Dice, and Goofspiel, respectively, while using relatively fewer
tokens. Code is available at https://github.com/chenzhwsysu57/SAGE.
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All authors Zhiwei Chen, Tianchun Wang, Zhongtao Rao, Haiming Zhu, Ding Cao, Tianxiang Zhao
Computational Engineering
A Comparative Study on Robust Topology Optimization of Design-Dependent Pressure-Actuated Compliant Mechanisms with Quadrilateral Elements
Swagatam Islam Sarkar and Prabhat Kumar
This paper presents a comparative study of compliant mechanisms generated using a robust
topology optimization technique involving design-dependent pressure loads. Design domains are parameterized using standard and higher-order quadrilateral elements. Both eroded and blueprint configurations are considered. A min-max optimization model combined with an output-spring method is employed to extremize the mechanisms' output displacements. A volume and a strain energy constraint are applied to the blueprint and the eroded designs, respectively. The optimization process is executed using the method of moving asymptotes. Numerical experiments are performed to optimize the pressure-actuated inverter and gripper mechanisms using Q4, Q8, and Q9 elements, and the results are compared. The research highlights how quadrilateral element selection influences both the resulting
topologies and performance characteristics.
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Distributed Computing
VarioPath: Workload-Aware All-to-All Communication for PCIe GPU Clusters
Yao Fei, Jin Fang, Size Zheng et al. (+4 more)
AlltoAllv communication is a critical primitive in distributed large-model inference, particularly for mixture-of-experts (MoE) models. The growing adoption of PCIe GPU systems for cost-efficient inference makes AlltoAllv performance on these systems increasingly important. Without a dedicated scale-up interconnect (e.g., NVLink or Infinity Fabric), PCIe GPU systems carry both intra-node and inter-node traffic through the PCIe hierarchy, where concurrent transfers can contend for PCIe link bandwidth. This link contention, compounded by skewed traffic distributions and dynamic traffic demand, makes efficient AlltoAllv scheduling challenging. Existing approaches are either poorly suited to PCIe GPU systems or incur substantial schedule synthesis overhead that reduces their practicality in real-world deployments. We present VarioPath, an efficient AlltoAllv scheduling framework for PCIe GPU systems. It combines an offline
topology-aware analyzer with an online demand-aware scheduler. The analyzer records contention-free transfer patterns as AlltoAllv channels and exploits
topology symmetry to build a compact catalog for efficient search. The online scheduler decomposes each AlltoAllv invocation's demand across a sequence of channels, adapting to rapidly changing and skewed traffic while incurring low planning overhead. Evaluation on four platforms (up to 256 GPUs) shows average AlltoAllv speedups of 5.88x over FAST and 1.72x over DeepEP. End-to-end experiments show that VarioPath reduces Qwen3 inference latency by up to 27.2% and Wan2.1 generation latency by 6.1%.
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All authors Yao Fei, Jin Fang, Size Zheng, Gongming Zhao, Hongli Xu, Jiacheng Zhu, Zhijing Xin
Data Structures and Algorithms
The directed temporal exploration problem
Marcelo Garlet Milani, Lucas Picasarri-Arrieta, Chaoliang Tang et al. (+1 more)
We study the temporal exploration problem on temporal digraphs. We prove that a lifetime of $O(n^2)$ suffices to guarantee the existence of a temporal exploration on always-unilateral temporal digraphs. We complement this with a $\Omega(n^2)$ lower bound, even in the case where each snapshot has maximum undirected degree 2; for always-strong temporal digraphs, the lower bound still holds even if the maximum undirected degree is 3. This stands in stark contrast with the undirected setting. For the large minimum degree setting, we show that a lifetime of $4n/3 - 1$ is sufficient and necessary for guaranteeing the existence of a temporal exploration on temporal digraphs where each snapshot is semicomplete. For always-strong temporal digraphs where each snapshot has minimum undirected degree at least $n - c - 1$, we prove that a lifetime of $O(cn)$ guarantees the existence of a temporal exploration, and we also prove that this is asymptotically tight. From a computational perspective, our results for temporal semicomplete digraphs also yield a
polynomial-time, factor-$4/3$
algorithm for deciding if a temporal semicomplete digraph admits a temporal exploration within the first $\ell$ snapshots. We complement this showing that no
polynomial-time, factor-$(4/3 - \epsilon)$ approximation
algorithm exists, even if every snapshot is a tournament, unless P$=$NP.
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All authors Marcelo Garlet Milani, Lucas Picasarri-Arrieta, Chaoliang Tang, Hehui Wu
Computer Vision
SkillPE: Creativity-Oriented Cinematic Skill Evolution for Text-to-Video Prompt Engineering
Yanwei Huang, Mingxuan Zhu, Shujie Li et al. (+3 more)
Achieving high-quality, cinematic results in text-to-
video generation remains challenging for non-experts, whose
prompts often lack professional narrative and creative design. We propose SkillPE, a
prompt engineering (PE) framework that evolves reusable cinematic skills from expert-authored seeds. SkillPE represents shot logic, composition, lighting, sound design, and other filmmaking cues in a fine-grained format, and retrieves movie references categorized as resonators (good matches), dissonants (weak matches), and divergents (creatively useful near-misses). The first two refine when and how a skill should be applied, while divergents inspire alternative cinematic realizations at different degrees of modification while preserving the user intent. Candidate skills are assessed through generated
videos along
prompt fidelity, cinematic quality, narrative appeal, and creativity to construct the final skill libraries. Experiments on StoryEval and VBench show improvements of up to 1.40 points over the strongest external baseline and 0.51 points over seed skills on 7-point four-dimensional evaluation, while remaining competitive on
benchmark-native metrics. Overall, SkillPE offers a practical approach to balancing fidelity and creativity in cinematic text-to-
video generation. Code is available at https://github.com/Ais0n/SkillPE .
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All authors Yanwei Huang, Mingxuan Zhu, Shujie Li, Shiyuan Liu, Yuanxing Zhang, Arpit Narechania
Networking
HOCCL: Offloading Collective Communication from GPU Cores to Accelerate Distributed Training
Yao Fei, Gongming Zhao, Hongli Xu et al. (+5 more)
Large
language model training involves massive computation on GPU streaming multiprocessors (SMs), the primary compute units of GPUs. Since SMs host specialized accelerators such as Tensor Cores, their efficient utilization is critical to training efficiency. Unfortunately, existing collective communication systems compete with computation for SMs, as they consume SMs for communication-related data movement and synchronization operations. We observe that communication can, in principle, be driven by DMA engines, thereby eliminating SM involvement in communication. Based on this insight, we propose HOCCL, a zero-SM collective communication framework consisting of three components: a stream manager, a point-to-point (P2P) executor, and a collective scheduler. The stream manager preserves operator-level temporal ordering with other GPU kernels. The P2P executor enables zero-SM point-to-point communication, while the collective scheduler orchestrates P2P transfers to maximize bandwidth. Experiments show that HOCCL preserves near-peak communication performance, achieving within 3% of the state of the art on average, while eliminating communication occupancy on nearly 10% of total GPU SMs. By freeing SM resources for computation, HOCCL improves end-to-end training throughput by up to 5%.
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All authors Yao Fei, Gongming Zhao, Hongli Xu, Jin Fang, Jiacheng Zhu, Shuo Xu, Kun Huang, Zhuolong Yu