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新论文 · v1arXiv:2611.01723

A sensor-driven physics-informed neural network for real-time occupant-centric space heating control

Yunfei Bai, Chenghao Li, Sunku Prasad Jenne, Hao Wang, Sizhe Zhang, Jihong Wang

原文摘要

Conventional space heating systems typically rely on single or sparse fixed-point temperature measurements, overlooking non-uniform indoor temperature distributions, transient thermal dynamics, and occupant-specific comfort requirements. This study explores an occupant-centric control strategy that aims to maintain the desired temperature around the occupant location. Spatially and temporally varying temperature fields, together with occupant mobility, make real-time local temperature estimation challenging. To address this challenge, we propose a control-oriented Physics-Informed Neural Network (PINN) model for indoor temperature-field prediction. Conventional PINNs rely on explicit time inputs and assume consistent temporal patterns across samples, which limits adaptability under variable initial conditions and heating sequences. The proposed model infers temporal dynamics implicitly from spatial coordinates and limited sensor measurements, improving robustness to changes in initial conditions and external disturbances. The method was validated across nine horizontal sensing planes in three controlled room-scale configurations. Against a validated COMSOL Multiphysics simulation, the PINN predicted the evaluated temperature fields with a maximum relative error below 1.85% across validation cases. These predictions supported an occupant-centric space heating control scheme. Compared with COMSOL-based offline control, the PINN-based system achieved an average prediction error of around 0.67%, and its rapid inference supports real-time occupant-centric control implementation. This work represents a move away from reliance on fixed-point temperature measurements in space heating control to an occupant-centric control strategy informed by real-time environmental feedback.

cs.LG · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01721

GDBack: A Group-Delay Backdoor Attack on Speaker Identification via Analytic All-Pass Filtering

Bicheng Xiong, Tao Li, Chenhui Cui, Zedong Xing, Qien Li, Weiping Wen, Rubing Huang, Tsong Yueh Chen

原文摘要

Existing phase-based backdoor attacks on speaker identification rely on shared absolute phase patterns for cross-sample trigger consistency, while other audio backdoor attacks have explored shared transformations that produce input-dependent triggers. Whether phase-based triggers can achieve similar consistency without imposing identical output phase patterns remains unclear. We propose GDBack, a poison-only group-delay backdoor attack using cascaded second-order analytic all-pass filters. Applying a shared transformation across utterances ideally preserves spectral magnitude while introducing a consistent frequency-dependent phase response and group-delay contribution, without forcing identical output phases. Experiments on LibriSpeech and VoxCeleb1 with ECAPA-TDNN, x-vector, and RawNet3 achieve attack success rates above 94% across all six settings (97.00% on average), while maintaining benign accuracy close to clean models. Phase and magnitude interventions, inverse compensation, and parameter-sensitivity analyses support the role of the induced phase transformation in backdoor activation. Speech quality and content evaluations indicate limited perceptual and linguistic distortion, while robustness experiments, including real over-the-air tests, reveal varying effectiveness across conditions and architectures. These findings demonstrate that cross-sample trigger consistency can arise from a shared phase transformation without requiring a common absolute phase pattern.

cs.SD · eess.AS

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.01525

Mean Field Reinforcement Learning: Theory and Algorithms for Mean Field Control

René Carmona, Mathieu Laurière

原文摘要

This monograph provides an introduction to mean field reinforcement learning through the lens of Markov decision processes arising from large-population stochastic control with mean field interactions and common noise. Starting from the connection between multi-agent reinforcement learning and mean field control, it develops the probabilistic, mathematical, and control-theoretic framework needed to formulate representative-agent learning problems, analyze their relationship with finite-population systems, and study both general and linear-quadratic models. The presentation includes dynamic programming principles, propagation-of-chaos limits, and theoretical analyses of tabular Q-learning and policy-gradient methods. It also discusses numerical implementations, including tabular schemes and deep reinforcement learning methods such as deep deterministic policy gradient. The goal is to give readers a coherent bridge between mean field control theory and reinforcement learning methodology, emphasizing the mathematical structure of the problems and the design of tractable learning approaches for large stochastic populations.

math.OC · cs.LG · cs.MA · math.PR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2512.14221

Mask-Conditional Conformal Prediction: Valid Uncertainty For All Missing Data Mechanisms

Jiarong Fan, Juhyun Park. Thi Phuong Thuy Vo, Nicolas Brunel

原文摘要

Conformal prediction (CP) offers a principled framework for uncertainty quantification, but it fails to guarantee mask-conditional coverage when faced with missing covariates. In addressing the heterogeneity induced by various missing patterns, Mask-Conditional Valid (MCV) Coverage has emerged as a more desirable property than Marginal Coverage. In this work, we adapt split CP to handle missing values by proposing a preimpute-mask-then-correct framework that can offer valid coverage. We show that our method provides guaranteed Marginal Coverage and Mask-Conditional Validity for general missing data mechanisms. A key component of our approach is a reweighted conformal prediction procedure that corrects the prediction sets after distributional imputation (multiple imputation) of the calibration dataset, making our method compatible with standard imputation pipelines. We derive two algorithms and prove that they achieve both marginal validity and MCV. We evaluate them on synthetic and real-world datasets. It reduces significantly the width of prediction intervals w.r.t standard MCV methods, while maintaining the target guarantees.

stat.ML · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01718

Delivered, Not Used: Evaluating Agent Memory at Its Point of Use

Changran Xu, Zeju Li, Qiang Xu

原文摘要

Task success, retrieval quality, and cost do not show whether an LLM agent used a delivered memory or what followed from that use. We introduce MemUse, which evaluates memory at its point of use in recorded trajectories: each memory delivery is compared with what the agent could already see and followed through the agent's later actions and their consequences. An agentic judge rates each delivered entry's relevance, accuracy, novelty, influence, and observed effect; programmatic checks enforce complete reading of cited evidence, and fixed rules aggregate the ratings into verdicts linked to memory histories and delivery cost. On 8,000 SWE-bench Verified episodes from three memory systems, resolve rates stay within 0.4 percentage points of a no-memory baseline, yet MemUse shows that most delivered entries were irrelevant, most relevant entries went unused, and in two systems most deliveries restated content the agent could still see. It also surfaces effects that outcomes hide: 23.2\% of episodes with beneficial memory use still failed, and 5 of 16 with harmful use still succeeded.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01715

EarlySelectKV: Anticipatory KV Selection via Cross-Layer Query Proxies

Yuhao Liu, Yuehao Li, Linghe Zhang, Gaobin Huang, Junran Li, Hongchang Wang, Haifang Jian

原文摘要

Query-conditioned sparse KV selection reduces long-context inference cost by attending to a subset of historical KV entries at each decoding step. Existing selectors must wait for the target-layer query before scoring candidates and generating indices, placing routing on the immediate pre-attention path. We introduce EarlySelectKV, a training-free transformation that uses proxy queries from earlier hidden states to precompute selected KV indices while retaining the real target-layer query for final attention. Tests with RocketKV, Quest, and Loki show no consistent pattern of quality degradation across the evaluated settings. In the primary RocketKV evaluation, ES-RocketKV differs by +0.018 points on LongBench, approximately -0.01 points on RULER, and +0.25 percentage points on representative NIAH/Passkey settings. In a TensorRT-LLM deployment with CPU-offloaded routing, ES-RocketKV improves end-to-end output throughput by up to 12.4% and reduces GPU-resident memory by 11.1% of the combined KV/K-summary pool. This makes routing timing an explicit optimization target alongside KV subset selection in sparse long-context decoding. Code is available at https://github.com/sea-with-sakura/EarlySelectKV.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01713

Route-Guided CBF-QP Repair for Safe Vision-Language-Action Models

Chan Park, Sunwoo Hong, Kirak Kim, Yoonjeong Park, Alexander W. Olson, Ali Pesaranghader

原文摘要

Vision-language-action (VLA) models can generate task-oriented robot actions, but they do not guarantee safe execution in obstacle-rich manipulation scenes. Although CBF-QP safety filters can locally project unsafe actions into a safe set, such correction may push the robot away from the learned task trajectory and leave the VLA policy without a reliable recovery direction. We propose Route-Guided CBF-QP Repair, a post-hoc repair module that uses the short-horizon action chunk predicted by the VLA to detect blocked nominal trajectories and generate entry-exit-rejoin guidance around obstacles. The selected route is added to the CBF-QP objective as a soft guidance term, while the CBF safety constraint remains hard. On SafeLIBERO, our method improves collision avoidance, task success, and SafeSuccess compared with AEGIS. Our project page is available at GitHub.

cs.AI · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01712

When Disagreement Labels Do Not Identify F1 Rankings

Roberto Fernández-Barrios, Iker Pastor-López, Asier González-Santocildes, Pablo García Bringas

原文摘要

Agreement labels cancel from an accuracy comparison but can change an F1 ranking. We specialize partial identification to an explicit audit after labeling disagreements: two agreement counts give exact bounds, certifying a ranking or a choice within a specified F1 tolerance under every completion. Across 3,200 frozen model pairs, shared completions tighten bounds and a 0.01 tolerance reduces labeling costs. A zero-loss (weak-winner) control attributes most savings to positive tolerance, not ties. Guarantees concern the evaluated batch, not future data.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.39567

Invariant Shape Analysis of Surfaces with Spherical Topology

Tanush Shaska

原文摘要

Every real spherical harmonic of even degree is congruent to its mirror image. We prove that a real spherical harmonic of odd degree $n\ge3$ with distinct Maxwell axes is congruent to its mirror image if and only if the hyperelliptic curve of genus $n-1$ defined by its binary form has a reduced automorphism of even order whose lift acts on the form by $-1$. We then determine the locus of these curves: it is the locus of curves with an extra involution if and only if $n-1$ is a power of two, and otherwise it also contains, for each even divisor $k$ of $n-1$ with odd cofactor $d\ge3$, an irreducible family $y^2=x K(x^{2k})$, $°K=d$, with generic reduced automorphism group $\Z/2k$. For $n=3$ the condition is the vanishing of the skew invariant of the binary sextic. As an application, the invariants of the truncated spherical harmonic expansion of a genus-zero surface, in a conformal parameterization normalized by its conformal barycenter, give a descriptor that is exactly invariant under parameterization, position and scale, records handedness, and is stable in the orbit distance.

cs.CV · math.AG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01710

CST: Lightweight Risk Triage with Source-Grounded Evidence for Web Discussions

Zhihua Wang, Yifeng Gao, Yunhao Feng, Rui Pan, Baiheng Zhao, Zixing Chen, Nan Li

原文摘要

Web communities rely on moderators to catch harmful messages such as insults, hate speech, and scams, but no one can read every message. Existing tools give each message a risk score, which tells moderators where to look but not what to read or how to check it. Yet meaning depends on context: the same reply, "I agree.", can support or oppose removing someone from a group. Moderators therefore need source-grounded evidence, not just scores: a few key messages kept with their original wording and replies. We present CST, a lightweight triage pipeline that first decides where to look, then what to show. It scores risk by pairing character patterns, which capture exact wording, with sentence meaning from a fixed multilingual model, so it handles several languages and runs on an ordinary CPU. It then groups related messages and picks from each group in turn, so a small reading budget covers different parts of a discussion, and links every pick back to the original thread. Across six tasks from five datasets in several languages, CST ranks harmful messages more accurately than a fully fine-tuned multilingual model and, under the same budget, finds more of them than reading by score alone. In a user study, reviewers needed at least 27% less time with CST's grouped evidence while keeping decision accuracy above 94%. CST turns risk scores into evidence that moderators can read quickly and check in context. Code and dataset reconstruction resources will be released at https://github.com/Kirito14IT/CST-MIL.

cs.CL · cs.AI · cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01709

Comparative Strategies for Handling Imbalanced QSAR Regression: A Case Study on hERG Activity Prediction

Ming-Yen Sung, Hsin Liu, Tung-Sheng Cho, Cheng-Wei Lee, Yufeng J. Tseng

原文摘要

Data imbalance frequently occurs in QSAR modeling, yet most studies focus on classification tasks, and few address regression tasks, as it is challenging to determine which regions of the data are sparse or dense. Data-level and algorithm-level imbalance-handling strategies can have different effects on model performance. In this study, we systematically compare various strategies across GCN and fingerprint-based machine learning models. To evaluate model performance more meaningfully, we partitioned the target region into several subregions based on both domain knowledge and data distribution. We focused on the region of greatest practical interest. Our results demonstrate that selecting models based on this interest-region metric leads to more reliable performance than using the total region metric. We also observed that different imbalance-handling strategies can have divergent effects depending on the model type. These findings highlight the importance of region-aware evaluation in regression QSAR modeling and provide practical guidance for selecting appropriate strategies for imbalanced datasets.

cs.LG · q-bio.QM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2512.20276

ActionFlow: A Pipelined Action Acceleration for Vision Language Models on Edge

Yuntao Dai, Hang Gu, Teng Wang, Qianyu Cheng, Yifei Zheng, Zhiyong Qiu, Lei Gong, Wenqi Lou 等 9 位作者

原文摘要

Vision-Language-Action (VLA) models have emerged as a unified paradigm for robotic perception and control, enabling emergent generalization and long-horizon task execution. However, their deployment in dynamic, real-world environments is severely hindered by high inference latency. While smooth robotic interaction requires control frequencies of 20-30 Hz, current VLA models typically operate at only 3-5 Hz on edge devices due to the memory-bound nature of autoregressive decoding. Existing optimizations often require extensive retraining or compromise model accuracy. To bridge this gap, we introduce ActionFlow, a system-level inference framework tailored for resource-constrained edge platforms. At the core of ActionFlow is a Cross-Request Pipelining strategy, a novel scheduler that redefines VLA inference as a macro-pipeline of micro-requests. The strategy intelligently batches memory-bound Decode phases with compute-bound Prefill phases across continuous time steps to maximize hardware utilization. Furthermore, to support this scheduling, we propose a Cross-Request State Packed Forward operator and a Unified KV Ring Buffer, which fuse fragmented memory operations into efficient dense computations. Experimental results demonstrate that ActionFlow achieves a 2.55x improvement in FPS on the OpenVLA-7B model without retraining, enabling real-time dynamic manipulation on edge hardware. Our work is available at https://github.com/Frankd35/Action-Flow.

cs.AI · cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.01770

Adaptive Auto-Harness: Sustained Self-Improvement for Agentic System Deployment on Open-Ended Task Streams

Zewen Liu, Zhan Shi, Yisi Sang, Bing He, Minhua Lin, Tianxin Wei, Dakuo Wang, Benoit Dumoulin 等 10 位作者

原文摘要

Auto-harness systems improve LLM agents by adapting prompts, skills, tools, and supporting infrastructure from execution feedback. Sustaining these gains on open-ended task streams requires handling growing histories, heterogeneous tasks, and shifting distributions. We introduce Adaptive Auto-Harness, a unified framework that separates persistent evolution workspaces from deployable harnesses and supports interchangeable evolution procedures. We compare configurations adapted from five prior approaches on recorded streams of prediction markets, security challenges, and event forecasts. With the same Sonnet solver, our full system reaches 80.9\% market all-task accuracy and 50.2\% security Pass@1, versus 22.2\% and 37.2\% without evolution; market accuracy also exceeds a market-following control's 72.0\%. Our analyses show that artifact accumulation does not guarantee sustained gains, and knowledge that transfers across tasks can still lose value as the task distribution changes. Continued evolution can therefore help preserve earlier gains through revision of accumulated knowledge. Code: https://github.com/A-EVO-Lab/a-evolve.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01707

SMART-AIMC: Sensitivity-Aware Mixed ADC Resolution with Fine-Grained Tunability for Analog In-Memory Computing

Yung-Chin Chen, Ourania Stouraiti, Naveen Verma

原文摘要

Analog In-Memory Computing (IMC) is a promising paradigm for accelerating GEMM-based AI workloads by executing computations directly within the memory array in the analog domain. While IMC delivers substantial energy and throughput improvements, it can suffer from significant task accuracy degradation due to analog-to-digital converter (ADC) quantization noise, which occurs at the GEMM output and is thus different from weight/activation quantization, which occurs at the GEMM input. Mixed ADC resolution allows IMC modules to choose between high- and low-resolution ADC modes with different energy costs, offering a compelling accuracy-energy trade-off. However, selecting ADC resolutions across IMC units while respecting hardware granularity constraints remains challenging. In this paper, we propose SMART-AIMC, a Sensitivity-aware Mixed ADC Resolution framework with fine-grained Tunability for Analog In-Memory Computing. SMART-AIMC correlates the algorithmic task loss to hardware ADC quantization noise in the form of an ADC Channel Sensitivity (ACS) metric. Using this metric, SMART-AIMC systematically, and in a disciplined manner, allocates ADC resources to critical computations. Furthermore, we introduce a sensitivity-based channel reordering scheme that overcomes the hardware granularity constraints with no runtime overhead and approaches the optimal ADC quantization possible for the model. Evaluated across diverse workloads, SMART-AIMC unlocks a superior accuracy-energy Pareto frontier, providing (1) higher task accuracy at matched energy budgets and (2) fine-grained tuning across efficiency targets, from aggressive energy savings to near-digital inference accuracy. At the latter operating point, SMART-AIMC achieves $6.99\times$ higher modeled system-level energy efficiency for linear projections than the INT8 digital baseline with negligible accuracy degradation.

cs.AR · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01706

Agent-Based Disruption Simulation on an Edge-Level Product-Annotated Supply Network of the Automotive Industry

Benjamin Rolf, Hiroyasu Inoue, Sebastian Lang

原文摘要

Disruptions spread through supply networks when customers cannot replace the products they lose. We ask how portfolio-overlap and product-specific rules for selecting candidate suppliers change cascades when customers search for new suppliers and form new supply edges. From the MarkLines database, we construct a product-annotated network of about 77,000 firms and 197,000 supplier-customer edges in the global automotive industry. We extend an agent-based disruption model with edge-level products, a product-specific substitution rule based on the semantic similarity of product names, and calibrated capacities and inventories. Only 6% of the edges carry observed products. We impute the others and test the imputation against the annotated edges. We run 119,000 simulations. With all other mechanics fixed, the product-specific rule makes the cascades after a hub failure four to five times larger than the portfolio-overlap rule of the original model. Under alternative imputations and similarity thresholds, the factor ranges from 1.2 to 18. Within the simulations, substitutability also shapes which suppliers are critical. Network topology largely determines which firms a shock reaches, and substitutability determines how many of them fail. Of the 50 most damaging suppliers under random disruptions, topology alone identifies 20 and an index that adds product substitutability identifies 32. The index carries over without refitting to a regional scenario modelled on the 2011 Tohoku Earthquake. Supply network risk assessment should account for product-level substitutability and therefore needs product data on supply relationships.

cs.SI · econ.GN

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01705

Fréchet Oral Distance: A Coordinate-Free Metric and Benchmark for Speech-Driven Oral Animation

Jaehong Myung, Seungho Eum, Hyunjung Chung, Unsang Park

原文摘要

Recently, speech-driven facial and oral animation technologies have been used in various fields such as education, speech therapy, and healthcare. But methods to objectively evaluate the quality of generated results are still limited. Accordingly, we propose the Fréchet Oral Distance (FOD), which uses embeddings from an Oral Motion Encoder (OME) and a TIMIT-Vis reference distribution. In experiments, FOD reproduced model rankings similar to the coordinate-based metric, Lip Vertex Error (LVE), and showed trends consistent with user preference. Our framework provides objective and reproducible benchmarking without relying on expensive equipment or subjective evaluation, and it can be extended to compare a variety of 2D/3D models.

cs.CV · cs.GR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01704

MatMoE: Matryoshka Mixture-of-Experts with Dynamic Mixed-Precision Quantization for Efficient Inference

Renjie Wei, Haochen Huang, Chuyu Qiu, Jinqi Wen, Dehao Xu, Meng Li

原文摘要

Mixture-of-Experts (MoE) architecture has been widely adopted in large language models (LLMs) since it reduces the computational cost through sparse expert activation. However, the massive parameter scale of MoE models hinders their deployment on resource-constrained edge devices. Although existing mixed-precision quantization reduces the model size, they are inherently static, fixing the bit-width of each expert across decoding steps. Such static schemes yield suboptimal performance as they overlook the dynamic variation in expert importance during decoding. Furthermore, they fail to adapt to the dynamic latency constraint driven by the fluctuating system utilization. To address these challenges, we propose MatMoE, a dynamic mixed-precision quantization framework for efficient MoE inference. MatMoE features: (1) a Matryoshka MoE PTQ algorithm that enables experts to operate flexibly at multiple precisions within a stringent memory budget; (2) a floor-and-compensation quantization method that eliminates redundant memory accesses in the Matryoshka representation; and (3) a dynamic bit-width allocation strategy that determines the precision for each expert based on their routing scores to satisfy the dynamic latency requirement. We implement our MatMoE system atop llama.cpp for both CPU and GPU platforms. Extensive evaluations across three modern MoE models and challenging benchmarks demonstrate that MatMoE achieves $4.0\% \sim 18.3\%$ higher accuracy than state-of-the-art static mixed-precision method. Compared to llama.cpp, MatMoE delivers $1.3\times \sim 3.7\times$ speedup on Raspberry Pi 5, and $1.2\times \sim 1.3\times$ speedup on NVIDIA RTX 4090 and Jetson AGX Orin.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.07797

BRASP: Hiding Where You Look in Encrypted Spatial Keyword Search

Jing Zhang, Ganxuan Yang, Yifei Yang, Siqi Wen, Baiyan Li, Zhengyang Qiu, Hasan Ahmed, Muhammad Bilal

原文摘要

Searchable Encryption (SE) enables users to query outsourced encrypted data while preserving data confidentiality. However, most efficient schemes still leak the search pattern and access pattern, which may allow an honest-but-curious cloud server to infer query contents, user interests, or returned records from repeated searches and observed results. Existing pattern-hiding solutions mainly target keyword queries and do not naturally support Boolean range queries over encrypted spatial data. This paper presents BRASP, a searchable encryption scheme for Boolean range queries over encrypted spatial data. BRASP combines Hilbert-curve-based prefix encoding with encrypted prefix--ID and keyword--ID inverted indexes to support efficient spatial range filtering and conjunctive keyword matching. To hide the search pattern and access pattern under a dual-server setting, BRASP integrates index shuffling for encrypted keyword and prefix entries with ID-field redistribution across two non-colluding cloud servers. BRASP also supports dynamic updates and achieves forward security. We formalize the security of BRASP through confidentiality, shuffle indistinguishability, query unforgeability, and forward-security analyses, and we evaluate its performance experimentally on a real-world dataset. The results show that BRASP effectively protects query privacy while incurring relatively low computation and communication overhead. To facilitate reproducibility and further research, the source code of BRASP is publicly available at https://github.com/Egbert-Lannister/BRASP

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01702

SparseLift: Automatically Optimizing Opaque Sparse Paths in Industrial-Scale Recommendation Model Training

Changqing Shi, Anan Liu, Genbao Chen, Junchao Yang

原文摘要

The sparse path, comprising sparse operations and collective communication, is a critical component of large-scale recommendation models. However, these operations are often represented as opaque nodes in the training computation graph, hindering whole-graph optimization. We present SparseLift, a low-overhead compiler for the sparse path of large-scale recommendation training. It exposes sparse computation and communication to whole-graph analysis to expand the optimization space and improve training efficiency. By analyzing model structure and runtime information, SparseLift reconstructs opaque nodes as analyzable subgraphs, recovering the resource semantics of sparse computation and communication within the graph. Building on the recovered semantics, we design incremental cost models and a constraint-aware planner to automatically select and organize subgraph optimizations into an execution plan that is reused during subsequent steady-state training. We evaluate SparseLift on an industrial video-platform recommendation task with 100B sparse parameters and the public DLRM benchmark. SparseLift improves steady-state throughput for ordinary industrial training steps by 16.5\% over the native baseline. On DLRM, it achieves $1.60\times$ and $1.26\times$ model-execution speedups over DLRM-Baseline and TorchRec, respectively.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01701

Just-in-Time Skill Retrieval via Long-Horizon State Tracking

Xin Lin, Ziheng Wang, Zerui Chen, Jiayao Ma, Zhimin Wei, Bingshuai Liu, Buchao Zhan, Qinggang Zhang 等 9 位作者

原文摘要

Large language model (LLM) agents increasingly rely on external skill libraries to support complex task execution. However, determining which skills are needed remains challenging in long-horizon tasks. Existing methods typically select a fixed skill set before execution, implicitly assuming that a task's procedural requirements can be determined from its initial description. This assumption is restrictive for long-horizon interaction, where new requirements emerge from intermediate observations, execution outcomes, and newly revealed constraints. This motivates just-in-time skill retrieval, which dynamically selects skills as execution unfolds. Just-in-time skill retrieval is promising but challenging: the utility of a skill depends not only on the current context, but also on the evolving task state shaped by prior steps. To this end, we propose JitSkill, a state-grounded framework that addresses this challenge through continuous task-state tracking. By connecting emerging requirements with accumulated task progress, JitSkill provides procedural support throughout execution without treating each retrieval decision independently. Experiments across three agent benchmarks demonstrate its effectiveness in improving long-horizon task completion. Our code is available at https://github.com/XMUDeepLIT/JitSkill.git/.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01700

MemHarmBench: Evaluating Cross-Session Prompt Injection in Memory-Persistent AI Agents

Zhihua Wang, Yunhao Feng, Yifeng Gao, Rui Pan, Baiheng Zhao, Zixing Chen, Xingjun Ma

原文摘要

AI agents often rely on persistent memory to retain user preferences and operational rules across sessions. However, this capability introduces a new safety risk: malicious instructions from messages, documents, or Web content can be retained in memory and influence future tasks even after the original attack content is removed. Existing attack success metrics, however, fail to capture who is affected and how attacks translate into downstream harm during otherwise normal task execution. We introduce MemHarmBench, a victim-centric benchmark for evaluating cross-session prompt injection in memory-persistent AI agents. MemHarmBench considers three categories of potential victims: users, workspace/project owners, and external stakeholders. Our evaluation first exposes an agent to malicious instructions and tests whether they are retained in memory. We then remove the attack content and initiate a new session with a legitimate task while preserving the agent's memory. We jointly evaluate whether the agent follows the attacker's intent, completes the legitimate task, and exhibits observable execution anomalies. Across multiple agent systems, we find that cross-session attacks can remain behaviorally stealthy: agents often complete tasks normally and exhibit no detectable anomalies while still making critical decisions aligned with the attacker's intent. By connecting malicious memory retention to downstream decisions and affected parties, MemHarmBench provides a systematic framework for exposing safety risks that conventional attack- and task-success metrics may overlook. We publicly release MemHarmBench at https://github.com/Kirito14IT/MemHarmBench.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01696

Restoring Semantic Fidelity for Training-Free High-Resolution Image Generation in Diffusion Transformers

Junjie Li, Zengbin Wang, Gaoxiang Li, Muyi Sun, Yixin Zhang, Man Zhang

原文摘要

Diffusion Transformer (DiT)-based text-to-image models have recently achieved remarkable success in image synthesis. However, when directly applied beyond their training resolution, these models often suffer from severe semantic degradation, where meaningful semantic content disappears. Although existing training-free high-resolution generation methods can mitigate this issue, the inherent semantic degradation at high resolutions remains largely unresolved, leading to degraded fine-grained details. Furthermore, widely used structural guidance methods lack modeling of the structure and details in the intermediate trajectory, which degrades the quality of the generated results. In this paper, we propose a training-free framework for high-resolution image generation to address these challenges. First, we propose a text-visual scale-decoupled attention (TVSDA) module, which restores the model's ability to generate semantically meaningful content at high resolutions by preserving the global receptive field of text tokens while aligning the receptive field of image tokens with the training resolution. Second, we propose a structure-detail guidance (SDG) strategy that explicitly models structural information and fine details through scale-wise and temporal-wise guidance, overcoming the limitations of conventional low-resolution guidance. Extensive experiments demonstrate that our method significantly improves both structural coherence and detail fidelity over existing training-free DiT-based high-resolution generation approaches. The code is available at https://github.com/ljjcoder/Restoring-Semantic-Fidelity.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01695

SPAR: Experience-Driven Strategy Evolution in Multi-Session B2B Sales

Hangming Zhang, Yao Zhao, Haotian Kuang, C. L. Philip Chen, Tong Zhang, Tengyue Ma

原文摘要

B2B outbound sales involves high transaction values and relationship-based trust, so closing a deal typically requires multiple rounds of outreach building on prior context. Existing LLM-based sales agents operate within single sessions and cannot carry forward context or refine strategies from experience. Methods that do learn from experience store lessons as unstructured text and treat all outcomes as equally informative, with no mechanism to distinguish a flawed strategy from an external factor. We propose SPAR, a self-evolving framework that models selling strategies as testable hypotheses and refines them without parameter updates. A double-loop architecture lets an inner loop adapt to individual merchants through episodic memory while an outer loop evolves a shared strategy playbook through causal attribution and hypothesis-driven evolution. In a live A/B test on the 1688 platform with over 200,000 merchants, SPAR achieves +15.8% conversion lift and +35.8% high-intent merchant growth. Human evaluation by domain experts rates SPAR at 4.62 out of 5 on overall sales quality, the highest among all methods.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.23448

Certifiable inversion of resampling-induced spectral distortion in high-dimensional particle filters

Deming Song, Chi-Kin Lam, Kefeng Li, Hui Yu, Xilin Li, Huagang Lin

原文摘要

This paper formulates and solves the inverse problem of recovering the pre-resampling spectrum of a particle filter's ensemble covariance from the importance weights and the observed spectrum alone. Resampling controls weight degeneracy, but in high dimensions it also distorts that spectrum: the distortion is a free multiplicative convolution with a law computable from the weights, so the repair is a deconvolution. We first extend the forward spectral law to random importance weights by conditioning on the weights together with the realized offspring counts rather than on the weights alone, which makes the distortion law deterministic at the observed data and gives the zero-atom mass of the limiting law a direct reading as an effective-dimension proxy for the second-order information the filter has lost. Second, a regularized free deconvolution inverts the distortion with an explicit error budget at a fixed resolution scale, and a computable forward residual certifies when the model behind it is violated. Injectivity makes the inverse identifiable but not stable, so the budget rests on a resolution-restricted condition number whose positivity we prove, and is vacuous below that resolution rather than concealed in a hypothesis. Third, the repaired spectrum enters only as the preconditioner of a Metropolis-adjusted rejuvenation move, so spectral error changes the mixing constants and not the invariant target, and the full cycle obeys a geometrically summable error recursion whose abstention term is governed by when abstentions fall rather than how many occur. Pre-registered simulations confirm the forward law, an order-of-magnitude reduction of spectral error, and the abstention law. The repair recovers eigenvalue laws, not eigenvectors.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01694

Direct Covariance-to-Beamformer Mapping for Low-Latency Hybrid ISAC Beamforming with Subsequent DoA Estimation

Anjali K. S., Manoj Kumar Panda, Chandramani Singh

原文摘要

Integrated sensing and communication (ISAC) systems typically employ a two-stage beamforming pipeline, in which target directions are first estimated from the received covariance matrix and then used to compute the beamforming weights by solving non-convex optimization problems. Although effective, this approach introduces processing latency that can limit beam adaptation in dynamic environments. This letter proposes a beamforming deep neural network (BFDNN) for direct covariance-to-beamformer learning in ISAC systems. BFDNN is a compact regression network that maps covariance-matrix features directly to the hybrid beamforming weights produced by a hybrid ISAC beamformer, eliminating the direction-of-arrival (DoA) estimation stage during inference. Compared with MUSIC, classification-DNN, and regression-DNN based beamforming pipelines, BFDNN generates comparable beam patterns and higher effective spectral efficiencies while reducing the beamforming prediction latency by 25-30 times on the evaluated platform, demonstrating the potential of direct covariance-to-beamformer learning for low-latency ISAC beam updates. Moreover, the target directions can be inferred indirectly from the generated radiation pattern, yielding DoA accuracy competitive with that of dedicated learning-based methods in the evaluated scenario.

cs.LG · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.26822

HARN: Hierarchical Associative Resonance Network for Event-Driven Multi-Timeframe Forecasting

Nabeel Ahmad Saidd

原文摘要

Live financial forecasting must synchronize asynchronous bar completions across timeframes without using stale or unavailable information. We introduce HARN (Hierarchical Associative Resonance Network), an event-driven architecture that anchors each event to the finest configured timeframe and maintains persistent states and matrix-valued associative memories across temporal levels. Its timestamp-masked transition updates a level only when its completed-bar timestamp matches the current anchor, preserving inactive representations. HARN integrates causal multi-scale encoding, residual-driven gated associative writes, all-to-all cross-level resonance, and ordered bottom-up evidence aggregation to produce forecasts at each temporal level. Predictions are expressed as basis-point changes and reconstructed on the price scale for evaluation. We evaluate HARN on four assets spanning equities, foreign exchange, and gold (AAPL, EURUSD, USDCHF, and XAUUSD), using chronological 70/15/15 splits, 32-bar lookbacks, one-step horizons, and three random seeds. HARN obtains lower anchor-timeframe test mean absolute errors (MAEs) than the evaluated single-timeframe PatchTST and TimeXer baselines on all four assets. HARN's MAEs are 0.508, 0.000306, 0.000263, and 2.552, respectively; PatchTST records 2.074, 0.000759, 0.000638, and 9.563, while TimeXer records 2.882, 0.010838, 0.014144, and 15.297. These descriptive comparisons do not isolate architectural effects from differences in inputs, target formulations, or training procedures. Single-run component ablations on AAPL and EURUSD show small, non-monotonic differences. The results demonstrate HARN's feasibility for asynchronous multi-timeframe forecasting, while motivating controlled comparisons and broader ablations to establish its relative advantages and component contributions.

cs.LG · q-fin.CP · q-fin.GN

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2509.15035

Calibrated Generative AI as Meta-Reviewer: A Systemic Functional Linguistics Discourse Analysis of Reviews of Peer Reviews

Gabriela C. Zapata, Bill Cope, Mary Kalantzis, Duane Searsmith

原文摘要

This study investigates the use of generative AI to support formative assessment through machine-generated reviews of peer reviews in graduate online courses at a public university in the United States. The analysis draws on Systemic Functional Linguistics and Appraisal Theory to examine how 120 metareviews construct meaning across ideational and interpersonal dimensions as well as the textual dimension. The findings suggest that generative AI can approximate key rhetorical and relational features of effective human feedback by providing clear direction while maintaining a supportive stance. The reviews demonstrated a balance between praise and constructive critique alongside alignment with rubric expectations and structured staging that foregrounded student agency. By modeling these qualities AI metafeedback has the potential to scaffold feedback literacy and enhance learner engagement with peer review. We conclude that carefully calibrated AI can contribute to hybrid human AI feedback ecosystems when embedded in thoughtfully designed workflows. However its effective integration into educational practice requires critical attention to its limitations and adaptation to specific educational contexts.

cs.AI · cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01692

Phase-wise MLLM Tuning for Multi-framework WebUI Code Generation

Haoran Ma, Linxiao Li, Chenyue Wang, Haochen Sui, Jiechao Gao

原文摘要

Multimodal large language models (MLLMs) achieve strong performance in translating WebUI screenshots into HTML/CSS. However, when they faced multiple frameworks (React/Vue/Angular), they often suffer from negative transfer that leads to compilation failures. These failures commonly stem from mixing framework specific syntax and violating framework constraints. This paper studies supervised fine-tuning for multi-framework WebUI code generation and proposes a phase-wise MLLM tuning method driven by compatibility and heterogeneity signals across frameworks. We first apply a normalization mapping that preserves each framework's native syntax while suppressing noise from variable naming and literal content. We then estimate cross framework heterogeneity and compatibility offline, and automatically derive training groups and their ordering by maximizing a phased objective. Finally, we perform phase-wise adapter tuning to reduce cross framework interference. Experiments on multiple benchmarks show that our proposed method improves both generation quality and compilation success rate compared with baselines.

cs.SE · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.34356

RynnVLA-Latent: Scaling Robotic Manipulation Foundation Models with 3D-Grounded Latent Actions

Haoyu Zhao, Xingyue Zhao, Zhian Su, Bohan Hou, Yinzhou Tang, Fengyi Wang, Kehan Li, Yuming Jiang 等 14 位作者

原文摘要

Scaling vision-language-action (VLA) models is constrained by scarce action-labeled demonstrations and incompatible control spaces across robot embodiments. We present RynnVLA-Latent, a framework that turns heterogeneous interaction videos into scalable supervision for robot policy pretraining. At its core, RynnLAM learns continuous, 3D-grounded latent actions from visual transitions without requiring native robot controls. By combining residual future-feature prediction, camera-compensated 3D-flow supervision, and camera-motion decoupling, RynnLAM encourages representations that capture control-relevant motion rather than appearance changes alone. We apply RynnLAM to 30.1K hours of human, robot, and simulation videos, producing 745.52M latent-action descriptors. RynnVLA-Latent then learns to predict latent-action chunks from visual observations and language through flow matching, before adapting its pretrained backbone and action expert to embodiment-specific robot actions. The resulting 4B policy achieves 98.8% success on LIBERO, 84.7% on LIBERO-Plus without benchmark-specific adaptation, 51.9% average success across five VLABench tracks, and 88.6% average success on the RoboTwin 2.0 bimanual benchmark. It also achieves the highest observed success rates among the compared methods on six real-world manipulation tasks across dexterous-hand and mobile-gripper platforms. Together with representation ablations and data-scaling results, these findings demonstrate the potential of latent-action supervision for scaling robot policy learning beyond native action-labeled data.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01688

SEALECT: Scene-Conditioned Pose Priors for Plausible Maritime Vessel Retrieval

Faisal Mohammad, Mohamed Younis

原文摘要

We address scene-conditioned object retrieval for compositional augmentation in maritime imagery, where strong geometric constraints make appearance-only retrieval produce physically implausible vessel insertions. We propose SEALECT, a retrieval framework that jointly models visual compatibility, vessel pose, scale, and scene geometry through a unified score combining neural similarity, scene-conditioned pose priors, and observed-pose consistency. We construct a maritime scene-pose library and model the pose and scale priors nonparametrically, as a kernel that conditions each prior continuously on the scene with no learned parameters. Across seven maritime benchmarks, our trained scene-conditioned retriever exceeds the strongest appearance encoder (SigLIP 2) in recall (+0.14), and the training-free prior raises geometric plausibility further at negligible recall cost, more than halving the constraint violations of appearance retrieval and outperforming learned foreground-search baselines (GALA-style, DiscoFOS). In a blind study of end-to-end composites (BootPlace placement, DiffHarmony harmonization), SEALECT ranks first among six methods, ahead of random and GALA-style selection and statistically tied with CLIP, DiscoFOS, and DINO.

cs.CV · cs.LG

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